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Institute
- Institut für Geographie und Geologie (360) (remove)
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Sonstige beteiligte Institutionen
- The Italian Federation of Parks and Nature Reserves (3)
- ALPARC - The Alpine Network of Protected Areas (2)
- Eurac research (2)
- Salzburg Institute for Regional Planning and Housing (2)
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- 308377 (2)
- 20-3044-2-11 (1)
- 227159 (1)
- 243964 (1)
- 776019 (1)
- 818182 (1)
- 834709 (1)
- LIFE12 BIO/AT/000143 (1)
- LIFE20 NAT/AT/000049 (1)
In the 1960s, when most African nations gained their independence after the age of colonialism, several theories and strategies emerged with the goal of "developing" these apparently "underdeveloped" territories. One of the most influential approaches for this task was represented in Julius K. Nyerere´s idea of Ujamaa, the Tanzanian version of African socialism.
Even before the Arusha Declaration established Ujamaa as a national development strategy in 1967, several groups of politicized young farmers took to the empty countryside of Tanzania to implement their own version of cooperative development. From one of these attempts emerged the Ruvuma Development Association (RDA), which organized up to 18 villages in southwestern Tanzania. The RDA became the inspiration for Nyerere´s concretization of Ujamaa and its implementation on national level. Yet, the central state could not replicate the success of the peasants, which was based on voluntariness and intrinsic motivation.
In 2015, this exploratory study has revisited the Region of Ruvuma. Through a case study approach, relying mostly on qualitative methods, new insights into the local history of Ujamaa and its perception have been gathered. In particular, narrative interviews with contemporary witnesses and group interviews with the present-day farmers’ groups have been conducted. Furthermore, NGOs active within the region, as well as regional and local government institutions were among the key stakeholders identified to concretize the local narrative of Ujamaa development. All interviews were analyzed according to the principles of qualitative content analysis. Additionally, individual villager questionnaires were used to achieve a more holistic picture of the local perception of development, challenges and the Ujamaa era.
None of the original Ujamaa groups of the times of the RDA was still operational at the time of research and no case of village-wide organization of collective agriculture could be observed. Nevertheless, in all of the three case study villages, several farmers’ groups (vikundi) were active in organizing development activities for their members. Furthermore, the perception of the Ujamaa era was generally positive throughout all of the case study sites. Yet, there have been significant differences in this perception, based on the village, age, gender and field size of the recipients. Overall, the period of Ujamaa was seen as an inspiration for present-day group activities, and the idea of such activities as a remedy for the developmental challenges of these villages was common among all stakeholders.
This thesis concludes that the positive perception of group activities as a vehicle for village development and the perception of Ujamaa history as a positive asset for the inception and organization of farmers’ groups would be highly beneficial to further attempts to support such development activities. However, the limitations in market access and capital availability for these highly-motivated group members have to be addressed by public and private development institutions. Otherwise, "the smell of Ujamaa" will be of little use for the progress of these villages.
A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts
(2023)
This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June–September for the years 2017–2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments.
Semi-arid tree covers, in both high and coppice growth forms, play an essential role in protecting water and soil resources and provides multiple ecosystem services across fragile ecosystems. Thus, they require continuous inventories. Quantification of forest structure in these tree covers provides important measures for their management and biodiversity conservation. We present a framework, based on consumer-grade UAV photogrammetry, to separately estimate primary variables of tree height (H) and crown area (A) across diverse coppice and high stands dominated by Quercus brantii Lindl. along the latitudinal gradient of Zagros mountains of western Iran. Then, multivariate linear regressions were parametrized with H and A to estimate the diameter at breast height (DBH) of high trees because of its importance to accelerate the existing practical DBH inventories across Zagros Forests. The estimated variables were finally applied to a model tree aboveground biomass (AGB) for both vegetative growth forms by local allometric equations and Random Forest models. In each step, the estimated variables were evaluated against the field reference values, indicating practically high accuracies reaching root mean square error (RMSE) of 0.68 m and 4.74 cm for H and DBH, as well as relative RMSE < 10% for AGB estimates. The results generally suggest an effective framework for single tree-based attribute estimation over mountainous, semi-arid coppice, and high stands.
In Germany, as in many Western societies, demographic change will lead to a higher number of senior visitors to natural recreational areas and national parks. Given the high physiological requirements of many outdoor recreation activities, especially in mountain areas, it seems likely that demographic change will affect the spatial behaviour of national park visitors, which may pose a challenge to the management of these areas. With the help of GPS tracking and a standardized questionnaire (n=481), this study empirically investigates the spatial behaviour of demographic age brackets in Berchtesgaden National Park (NP) and the potential effects of demographic change on the use of the area. Cluster analysis revealed four activity types in the study area. More than half of the groups with visitors aged 60 and older belong to the activity type of Walker.
A first assessment of canopy cover loss in Germany's forests after the 2018–2020 drought years
(2022)
Central Europe was hit by several unusually strong periods of drought and heat between 2018 and 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast forest areas in Germany. We present the first assessment of canopy cover loss in Germany for the period of January 2018–April 2021. Our approach makes use of dense Sentinel-2 and Landsat-8 time-series data. We computed the disturbance index (DI) from the tasseled cap components brightness, greenness, and wetness. Using quantiles, we generated monthly DI composites and calculated anomalies in a reference period (2017). From the resulting map, we calculated the canopy cover loss statistics for administrative entities. Our results show a canopy cover loss of 501,000 ha for Germany, with large regional differences. The losses were largest in central Germany and reached up to two-thirds of coniferous forest loss in some districts. Our map has high spatial (10 m) and temporal (monthly) resolution and can be updated at any time.
The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.
Effects of climate change‐induced events on forest ecosystem dynamics of composition, function and structure call for increased long‐term, interdisciplinary and integrated research on biodiversity indicators, in particular within strictly protected areas with extensive non‐intervention zones. The long‐established concept of forest supersites generally relies on long‐term funds from national agencies and goes beyond the logistic and financial capabilities of state‐ or region‐wide protected area administrations, universities and research institutes.
We introduce the concept of data pools as a smaller‐scale, user‐driven and reasonable alternative to co‐develop remote sensing and forest ecosystem science to validated products, biodiversity indicators and management plans. We demonstrate this concept with the Bohemian Forest Ecosystem Data Pool, which has been established as an interdisciplinary, international data pool within the strictly protected Bavarian Forest and Šumava National Parks and currently comprises 10 active partners. We demonstrate how the structure and impact of the data pool differs from comparable cases.
We assessed the international influence and visibility of the data pool with the help of a systematic literature search and a brief analysis of the results. Results primarily suggest an increase in the impact and visibility of published material during the life span of the data pool, with highest visibilities achieved by research conducted on leaf traits, vegetation phenology and 3D‐based forest inventory.
We conclude that the data pool results in an efficient contribution to the concept of global biodiversity observatory by evolving towards a training platform, functioning as a pool of data and algorithms, directly communicating with management for implementation and providing test fields for feasibility studies on earth observation missions.
Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km\(^2\)) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a F\(_1\)-score of 93.0% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter.
A novel method for detecting and delineating coppice trees in UAV images to monitor tree decline
(2022)
Monitoring tree decline in arid and semi-arid zones requires methods that can provide up-to-date and accurate information on the health status of the trees at single-tree and sample plot levels. Unmanned Aerial Vehicles (UAVs) are considered as cost-effective and efficient tools to study tree structure and health at small scale, on which detecting and delineating tree crowns is the first step to extracting varied subsequent information. However, one of the major challenges in broadleaved tree cover is still detecting and delineating tree crowns in images. The frequent dominance of coppice structure in degraded semi-arid vegetation exacerbates this problem. Here, we present a new method based on edge detection for delineating tree crowns based on the features of oak trees in semi-arid coppice structures. The decline severity in individual stands can be analyzed by extracting relevant information such as texture from the crown area. Although the method presented in this study is not fully automated, it returned high performances including an F-score = 0.91. Associating the texture indices calculated in the canopy area with the phenotypic decline index suggested higher correlations of the GLCM texture indices with tree decline at the tree level and hence a high potential to be used for subsequent remote-sensing-assisted tree decline studies.
This study investigates the surroundings of Munigua (municipium Flavium Muniguense), a small Roman town in the ancient province of Hispania Baetica (SW Spain). The city's economy was based primarily on copper and iron mining, which brought financial prosperity to its citizens. Local production of agricultural goods is thought to have been of little importance, as the regional soil conditions do not seem to be suitable for extensive agriculture.
To evaluate the recent soil agro-potential and to find evidence for prehistoric and historic land use in the surroundings of Munigua, we applied a pedo-geomorphological approach based on the physico-chemical analysis of 14 representative soil and sediment exposures. Selected samples were analyzed for bulk chemistry, texture and phytoliths. The chronostratigraphy of the sequences was based on radiocarbon dating of charcoal samples. The site evaluation of the present-day soil agro-potential was carried out according to standard procedures and included evaluation of potential rootability, available water-storage capacity and nutrient budget within the uppermost 1 m.
The results show that moderate to very good soil agro-potential prevails in the granitic and floodplain areas surrounding Munigua. Clearly, recent soil agro-potential in these areas allows the production of basic agricultural goods, and similar limited agricultural use should also have been possible in ancient times. In contrast, weak to very weak present-day soil agro-potential prevails in the metamorphic landscape due to the occurrence of shallow and sandy to stony soils.
In addition, the study provides pedo-geomorphological evidence for prehistoric and historic land use in pre-Roman, Roman and post-Roman times. Catenary soil mapping in the vicinity of a Roman house complex reveals multi-layered colluvial deposits. They document phases of hillslope erosion mainly triggered by human land use between 4063 ± 82 and 3796 ± 76 cal BP, around 2601 ± 115 cal BP, and between 1424 ± 96 and 421 ± 88 cal BP. Moreover, geochemical and phytolith analyses of a Roman hortic Anthrosol indicate the local cultivation of agricultural products that contributed to the food supply of Munigua.
Overall, the evidence of Roman agricultural use in the Munigua area indicates that the city's economy was by no means focused solely on mining. The production of basic agricultural products was also part of Munigua's economic portfolio. Our geoarcheological study thus supports the archeological concept of economically diversified Roman cities in the province of Baetica and in Hispania.
Regardless of political boundaries, river basins are a functional unit of the Earth’s land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream–downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins.
Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region.
An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3% of the number of patches and a decrease of the mean patch area of 86.1% for the selected time period, resulting in altered habitats for flora and fauna.
Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year.
While the place of birth plays a crucial role for women’s birth experiences, the interest in out-of-hospital births has increased during the Covid-19 pandemic. Related to this, various international policies recommend enabling women to choose where to give birth. We aimed to analyze Swiss women’s choice between birth hospitals and birth centers. Employing spatial accessibility analysis, we incorporated four data types: highly disaggregated population data, administrative data, street network data, addresses of birth hospitals and birth centers. 99.8% of Swiss women of childbearing age were included in the analysis (N = 1.896.669). We modelled car travel times from a woman’s residence to the nearest birth hospital and birth center. If both birth settings were available within 30 minutes, a woman was considered to have a true choice. Only 58.2% of women had a true choice. This proportion varied considerably across Swiss federal states. The main barrier to a true choice was limited accessibility of birth centers. Median travel time to birth hospitals was 9.8 (M = 12.5), to birth centers 23.9 minutes (M = 28.5). Swiss women are insufficiently empowered to exercise their reproductive autonomy as their choice of place of birth is significantly limited by geographical constraints. It is an ethical and medical imperative to provide women with a true choice. We provide high-resolution insights into the accessibility of birth settings and strong arguments to (re-)examine the need for further birth centers (and birth hospitals) in specific geographical areas. Policy-makers are obligated to improve the accessibility of birth centers to advance women’s autonomy and enhance maternal health outcomes after childbirth. The Covid-19 pandemic offers an opportunity to shift policy.
Climate change is likely to decrease surface water availability in Central Asia, thereby necessitating land use adaptations in irrigated regions. The introduction of trees to marginally productive croplands with shallow groundwater was suggested for irrigation water-saving and improving the land’s productivity. Considering the possible trade-offs with water availability in large-scale afforestation, our study predicted the impacts on water balance components in the lower reaches of the Amudarya River to facilitate afforestation planning using the Soil and Water Assessment Tool (SWAT). The land-use scenarios used for modeling analysis considered the afforestation of 62% and 100% of marginally productive croplands under average and low irrigation water supply identified from historical land-use maps. The results indicate a dramatic decrease in the examined water balance components in all afforestation scenarios based largely on the reduced irrigation demand of trees compared to the main crops. Specifically, replacing current crops (mostly cotton) with trees on all marginal land (approximately 663 km\(^2\)) in the study region with an average water availability would save 1037 mln m\(^3\) of gross irrigation input within the study region and lower the annual drainage discharge by 504 mln m\(^3\). These effects have a considerable potential to support irrigation water management and enhance drainage functions in adapting to future water supply limitations.
Agriculture is mankind’s primary source of food production and plays the key role for cereal supply to humanity. One of the future challenges will be to feed a constantly growing population, which is expected to reach more than nine billion by 2050. The potential to expand cropland is limited, and enhancing agricultural production efficiency is one important means to meet the future food demand. Hence, there is an increasing demand for dependable, accurate and comprehensive agricultural intelligence on crop production. The value of satellite earth observation (EO) data for agricultural monitoring is well recognized. One fundamental requirement for agricultural monitoring is routinely updated information on crop acreage and the spatial distribution of crops. With the technical advancement of satellite sensor systems, imagery with higher temporal and finer spatial resolution became available. The classification of such multi-temporal data sets is an effective and accurate means to produce crop maps, but methods must be developed that can handle such large and complex data sets. Furthermore, to properly use satellite EO for agricultural production monitoring a high temporal revisit frequency over vast geographic areas is often necessary. However, this often limits the spatial resolution that can be used. The challenge of discriminating pixels that correspond to a particular crop type, a prerequisite for crop specific agricultural monitoring, remains daunting when the signal encoded in pixels stems from several land uses (mixed pixels), e.g. over heterogeneous landscapes where individual fields are often smaller than individual pixels.
The main purposes of the presented study were (i) to assess the influence of input dimensionality and feature selection on classification accuracy and uncertainty in object-based crop classification, (ii) to evaluate if combining classifier algorithms can improve the quality of crop maps (e.g. classification accuracy), (iii) to assess the spatial resolution requirements for crop identification via image classification.
Reporting on the map quality is traditionally done with measures that stem from the confusion matrix based on the hard classification result. Yet, these measures do not consider the spatial variation of errors in maps. Measures of classification uncertainty can be used for this purpose, but they have attained only little attention in remote sensing studies. Classifier algorithms like the support vector machine (SVM) can estimate class memberships (the so called soft output) for each classified pixel or object. Based on these estimations, measures of classification uncertainty can be calculated, but it has not been analysed in detail, yet, if these are reliable in predicting the spatial distribution of errors in maps. In this study, SVM was applied for the classification of agricultural crops in irrigated landscapes in Middle Asia at the object-level. Five different categories of features were calculated from RapidEye time series data as classification input. The reliability of classification uncertainty measures like entropy, derived from the soft output of SVM, with regard to predicting the spatial distribution of error was evaluated. Further, the impact of the type and dimensionality of the input data on classification uncertainty was analysed. The results revealed that SMVs applied to the five feature categories separately performed different in classifying different types of crops. Incorporating all five categories of features by concatenating them into one stacked vector did not lead to an increase in accuracy, and partly reduced the model performance most obviously because of the Hughes phenomena. Yet, applying the random forest (RF) algorithm to select a subset of features led to an increase of classification accuracy of the SVM. The feature group with red edge-based indices was the most important for general crop classification, and the red edge NDVI had an outstanding importance for classifying crops. Two measures of uncertainty were calculated based on the soft output from SVM: maximum a-posteriori probability and alpha quadratic entropy. Irrespective of the measure used, the results indicate a decline in classification uncertainty when a dimensionality reduction was performed. The two uncertainty measures were found to be reliable indicators to predict errors in maps. Correctly classified test cases were associated with low uncertainty, whilst incorrectly test cases tended to be associated with higher uncertainty.
The issue of combining the results of different classifier algorithms in order to increase classification accuracy was addressed. First, the SVM was compared with two other non-parametric classifier algorithms: multilayer perceptron neural network (MLP) and RF. Despite their comparatively high classification performance, each of the tested classifier algorithms tended to make errors in different parts of the input space, e.g. performed different in classifying crops. Hence, a combination of the complementary outputs was envisaged. To this end, a classifier combination scheme was proposed, which is based on existing algebraic operators. It combines the outputs of different classifier algorithms at the per-case (e.g. pixel or object) basis. The per-case class membership estimations of each classifier algorithm were compared, and the reliability of each classifier algorithm with respect to classifying a specific crop class was assessed based on the confusion matrix. In doing so, less reliable classifier algorithms were excluded at the per-class basis before the final combination. Emphasis was put on evaluating the selected classification algorithms under limiting conditions by applying them to small input datasets and to reduced training sample sets, respectively. Further, the applicability to datasets from another year was demonstrated to assess temporal transferability. Although the single classifier algorithms performed well in all test sites, the classifier combination scheme provided consistently higher classification accuracies over all test sites and in different years, respectively. This makes this approach distinct from the single classifier algorithms, which performed different and showed a higher variability in class-wise accuracies. Further, the proposed classifier combination scheme performed better when using small training set sizes or when applied to small input datasets, respectively.
A framework was proposed to quantitatively define pixel size requirements for crop identification via image classification. That framework is based on simulating how agricultural landscapes, and more specifically the fields covered by one crop of interest, are seen by instruments with increasingly coarser resolving power. The concept of crop specific pixel purity, defined as the degree of homogeneity of the signal encoded in a pixel with respect to the target crop type, is used to analyse how mixed the pixels can be (as they become coarser) without undermining their capacity to describe the desired surface properties (e.g. to distinguish crop classes via supervised or unsupervised image classification). This tool can be modulated using different parameterizations to explore trade-offs between pixel size and pixel purity when addressing the question of crop identification. Inputs to the experiments were eight multi-temporal images from the RapidEye sensor. Simulated pixel sizes ranged from 13 m to 747.5 m, in increments of 6.5 m. Constraining parameters for crop identification were defined by setting thresholds for classification accuracy and uncertainty. Results over irrigated agricultural landscapes in Middle Asia demonstrate that the task of finding the optimum pixel size did not have a “one-size-fits-all” solution. The resulting values for pixel size and purity that were suitable for crop identification proved to be specific to a given landscape, and for each crop they differed across different landscapes. Over the same time series, different crops were not identifiable simultaneously in the season and these requirements further changed over the years, reflecting the different agro-ecological conditions the investigated crops were growing in. Results further indicate that map quality (e.g. classification accuracy) was not homogeneously distributed in a landscape, but that it depended on the spatial structures and the pixel size, respectively. The proposed framework is generic and can be applied to any agricultural landscape, thereby potentially serving to guide recommendations for designing dedicated EO missions that can satisfy the requirements in terms of pixel size to identify and discriminate crop types.
Regarding the operationalization of EO-based techniques for agricultural monitoring and its application to a broader range of agricultural landscapes, it can be noted that, despite the high performance of existing methods (e.g. classifier algorithms), transferability and stability of such methods remain one important research issue. This means that methods developed and tested in one place might not necessarily be portable to another place or over several years, respectively. Specifically in Middle Asia, which was selected as study region in this thesis, classifier combination makes sense due to its easy implementation and because it enhanced classification accuracy for classes with insufficient training samples. This observation makes it interesting for operational contexts and when field reference data availability is limited. Similar to the transferability of methods, the application of only one certain kind of EO data (e.g. with one specific pixel size) over different landscapes needs to be revisited and the synergistic use of multi-scale data, e.g. combining remote sensing imagery of both fine and coarse spatial resolution, should be fostered. The necessity to predict and control the effects of spatial and temporal scale on crop classification is recognized here as a major goal to achieve in EO-based agricultural monitoring.
Rapid population growth in West Africa has led to expansion in croplands due to the need to grow more food to meet the rising food demand of the burgeoning population. These expansions negatively impact the sub-region's ecosystem, with implications for water and soil quality, biodiversity and climate. In order to appropriately monitor the changes in croplands and assess its impact on the ecosystem and other environmental processes, accurate and up-to-date information on agricultural land use is required. But agricultural land use mapping (i.e. mapping the spatial distribution of crops and croplands) in West Africa has been challenging due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. This study, therefore, investigated the possibilities of improving agricultural land use mapping by utilizing optical satellite images with higher spatial and temporal resolution as well as images from Synthetic Aperture Radar (SAR) systems which are near-independent of weather conditions. The study was conducted at both watershed and regional scales.
At watershed scale, classification of different crop types in three watersheds in Ghana, Burkina Faso and Benin was conducted using multi-temporal: (1) only optical images (RapidEye) and (2) optical plus dual polarimetric (VV/VH) SAR images (TerraSAR-X). In addition, inter-annual or short term (2-3 years) changes in cropland area in the past ten years were investigated using historical Landsat images. Results obtained indicate that the use of only optical images to map different crop types in West Africa can achieve moderate classification accuracies (57% to 71%). Overlaps between the cropping calendars of most crops types and certain inter-croppings pose a challenge to optical images in achieving an adequate separation between those crop classes. Integration of SAR images, however, can improve classification accuracies by between 8 and 15%, depending on the number of available images and their acquisition dates. The sensitivity of SAR systems to different crop canopy architectures and land surface characteristics improved the separation between certain crop types. The VV polarization of TerraSAR-X was found to better discrimination between crop types than the VH. Images acquired between August and October were found to be very useful for crop mapping in the sub-region due to structural differences in some crop types during this period.
At the regional scale, inter-annual or short term changes in cropland area in the Sudanian Savanna agro-ecological zone in West Africa were assessed by upscaling historical cropland information derived at the watershed scale (using Landsat imagery) unto a coarse spatial resolution, but geographically large, satellite imagery (MODIS) using regression based modeling. The possibility of using such regional scale cropland information to improve government-derived agricultural statistics was investigated by comparing extracted cropland area from the fractional cover maps with district-level agricultural statistics from Ghana The accuracy of the fractional cover maps (MAE between 14.2% and 19.1%) indicate that the heterogeneous agricultural landscape of West Africa can be suitably represented at the regional or continental scales by estimating fractional cropland cover on low resolution Analysis of the results revealed that cropland area in the Sudanian Savanna zone has experienced inter-annual or short term fluctuations in the past ten years due to a variety of factors including climate factors (e.g. floods and droughts), declining soil fertility, population increases and agricultural policies such as fertilizer subsidies. Comparison of extracted cropland area from the fractional cover maps with government's agricultural statistics (MoFA) for seventeen districts (second administrative units) in Ghana revealed high inconsistencies in the government statistics, and highlighted the potential of satellite derived cropland information at regional scales to improve national/sub-national agricultural statistics in West Africa.
The results obtained in this study is promising for West Africa, considering the recent launch of optical (Landsat 8) and SAR sensors (Sentinel-1) that will provide free data for crop mapping in the sub-region. This will improve chances of obtaining adequate satellite images acquired during the cropping season for agricultural land use mapping and bolster opportunities of operationalizing agricultural land use mapping in West Africa. This can benefit a wide range of biophysical and economic models and improve decision making based on their results.
Nationalparks sind das älteste und bekannteste flächenbezogene Naturschutzinstrument weltweit. Für den Erhalt einer nachhaltigen Lebensgrundlage und die Entwicklung der Biodiversität sowie für mehr Naturdynamik in der Landschaft haben sie eine sehr große Bedeutung, auch in unseren Breiten. Dennoch ist die Einstellung zu Nationalparks von Seiten der unmittelbaren Anwohner nicht immer unproblematisch. Entsprechend versucht die vorliegende wissenschaftliche Analyse neue Erkenntnisse bezüglich der Akzeptanz der Nationalparks Bayerischer Wald und Berchtesgaden, den ältesten Deutschlands, aufzuzeigen. Empirische Grundlagen für diese Studie sind eine bayernweite Online-Befragung, qualitative Experteninterviews und aufwändige repräsentative schriftliche Befragungen in den Nationalpark-Landkreisen Regen und Freyung-Grafenau bzw. Berchtesgadener Land im Jahr 2018. Auch die zeitliche Entwicklung der Akzeptanz wird auf Basis der Ergebnisse von Vorgängerstudien, soweit möglich, berücksichtigt. Dabei sind es ökonomische, emotionale, interpersonelle, soziokulturelle und nicht zuletzt für Geographen besonders interessante raumzeitliche Prädiktoren der Akzeptanz beider Nationalparks, die im Fokus der Untersuchungen stehen.
Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km\(^{2}\) in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture
An Overview of the Regional Experiments for Land-atmosphere Exchanges 2012 (REFLEX 2012) Campaign
(2015)
The REFLEX 2012 campaign was initiated as part of a training course on the organization of an airborne campaign to support advancement of the understanding of land-atmosphere interaction processes. This article describes the campaign, its objectives and observations, remote as well as in situ. The observations took place at the experimental Las Tiesas farm in an agricultural area in the south of Spain. During the period of ten days, measurements were made to capture the main processes controlling the local and regional land-atmosphere exchanges. Apart from multi-temporal, multi-directional and multi-spatial space-borne and airborne observations, measurements of the local meteorology, energy fluxes, soil temperature profiles, soil moisture profiles, surface temperature, canopy structure as well as leaf-level measurements were carried out. Additional thermo-dynamical monitoring took place at selected sites. After presenting the different types of measurements, some examples are given to illustrate the potential of the observations made.
Die städtische Umwelt ist in steter Veränderung, vor allem durch den Bau, aber auch durch die Zerstörung von städtischen Elementen. Die formelle Entwicklung ist ein Prozess mit langen Planungszeiträumen und die bebaute Landschaft wirkt daher statisch. Dagegen unterliegen informelle oder spontane Siedlungen aufgrund ihrer stets unvollendeten städtischen Form einer hohen Dynamik – so wird in der Literatur berichtet. Allerdings sind Dynamik und die morphologischen Merkmale der physischen Transformation in solchen Siedlungen, die städtische Armut morphologisch repräsentieren, auf globaler Ebene bisher kaum mit einer konsistenten Datengrundlage empirisch untersucht worden. Hier setzt die vorliegende Arbeit an. Unter der Annahme, dass die erforschte zeitliche Dynamik in Europa geringer ausfällt, stellt sich die generelle Frage nach einer katalogisierten Erfassung physischer Wohnformen von Armut speziell in Europa. Denn Wohnformen der Armut werden oft ausschließlich mit dem ‚Globalen Süden‘ assoziiert, insbesondere durch die Darstellung von Slums. Tatsächlich ist Europa sogar die Wiege der Begriffe ‚Slum‘ und ‚Ghetto‘, die vor Jahrhunderten zur Beschreibung von Missständen und Unterdrückung auftauchten. Bis heute weist dieser facettenreiche Kontinent eine enorme Vielfalt an physischen Wohnformen der Armut auf, die ihre Wurzeln in unterschiedlichen Politiken, Kulturen, Geschichten und Lebensstilen haben. Um über diese genannten Aspekte Aufschluss zu erlangen, bedarf es u.a. der Bildanalyse durch Satellitenbilder. Diese Arbeit wird daher mittels Fernerkundung bzw. Erdbeobachtung (EO) sowie zusätzlicher Literaturrecherchen und einer empirischen Erhebung erstellt. Um Unsicherheiten konzeptionell und in der Erfassung offenzulegen, ist die Methode der manuellen Bildinterpretation von Armutsgebieten kritisch zu hinterfragen.
Das übergeordnete Ziel dieser Arbeit ist eine bessere Wissensbasis über Armut zu schaffen, um Maßnahmen zur Reduzierung von Armut entwickeln zu können. Die Arbeit dient dabei als eine Antwort auf die Nachhaltigkeitsziele der Vereinten Nationen. Es wird Grundlagenforschung betrieben, indem Wissenslücken in der Erdbeobachtung zu physisch-baulichen bzw. morphologischen Erscheinungen von Armut auf Gebäude-Ebene explorativ analysiert werden. Die Arbeit wird in drei Forschungsthemen bzw. Studienteile untergliedert:
Ziel des ersten Studienteils ist die globale raumzeitliche Erfassung von Dynamiken durch Anknüpfung an bisherige Kategorisierungen von Armutsgebieten. Die bisherige Wissenslücke soll gefüllt werden, indem über einen Zeitraum von etwa sieben Jahren in 16 dokumentierten Manifestationen städtischer Armut anhand von Erdbeobachtungsdaten eine zeitliche Analyse der bebauten Umwelt durchgeführt wird. Neben einer global verteilten Gebietsauswahl wird die visuelle Bildinterpretation (MVII) unter Verwendung von hochauflösenden optischen Satellitendaten genutzt. Dies geschieht in Kombination mit in-situ- und Google Street View-Bildern zur Ableitung von 3D-Stadtmodellen. Es werden physische Raumstrukturen anhand von sechs räumlichen morphologischen Variablen gemessen: Anzahl, Größe, Höhe, Ausrichtung und Dichte der Gebäude sowie Heterogenität der Bebauung. Diese ‚temporale Analyse‘ zeigt zunächst sowohl inter- als auch intra-urbane Unterschiede. Es lassen sich unterschiedliche, aber generell hohe morphologische Dynamiken zwischen den Untersuchungsgebieten finden. Dies drückt sich in vielfältiger Weise aus: von abgerissenen und rekonstruierten Gebieten bis hin zu solchen, wo Veränderungen innerhalb der gegebenen Strukturen auftreten. Geographisch gesehen resultiert in der Stichprobe eine fortgeschrittene Dynamik, insbesondere in Gebieten des Globalen Südens. Gleichzeitig lässt sich eine hohe räumliche Variabilität der morphologischen Transformationen innerhalb der untersuchten Gebiete beobachten. Trotz dieser teilweise hohen morphologischen Dynamik sind die räumlichen Muster von Gebäudefluchten, Straßen und Freiflächen überwiegend konstant. Diese ersten Ergebnisse deuten auf einen geringen Wandel in Europa hin, weshalb diese europäischen Armutsgebiete im folgenden Studienteil von Grund auf erhoben und kategorisiert werden.
Ziel des zweiten Studienteils ist die Erschaffung einer neuen Kategorisierung, speziell für das in der Wissenschaft unterrepräsentierte Europa. Die verschiedenen Formen nicht indizierter Wohnungsmorphologien werden erforscht und kategorisiert, um das bisherige globale wissenschaftliche ontologische Portfolio für Europa zu erweitern. Hinsichtlich dieses zweiten Studienteils bietet eine Literaturrecherche mit mehr als 1.000 gesichteten Artikeln die weitere Grundlage für den folgenden Fokus auf Europa. Auf der Recherche basierend werden mittels der manuellen visuellen Bildinterpretation (engl.: MVII) erneut Satellitendaten zur Erfassung der physischen Morphologien von Wohnformen genutzt. Weiterhin kommen selbst definierte geographische Indikatoren zu Lage, Struktur und formellem Status zum Einsatz. Darüber hinaus werden gesellschaftliche Hintergründe, die durch Begriffe wie ‚Ghetto‘, ‚Wohnwagenpark‘, ‚ethnische Enklave‘ oder ‚Flüchtlingslager‘ beschrieben werden, recherchiert und implementiert. Sie sollen als Erklärungsansatz für Armutsviertel in Europa dienen. Die Stichprobe der europäischen, insgesamt aber unbekannten Grundgesamtheit verdeutlicht eine große Vielfalt an physischen Formen: Es wird für Europa eine neue Kategorisierung von sechs Hauptklassen entwickelt, die von ‚einfachsten Wohnstätten‘ (z. B. Zelten) über ‚behelfsmäßige Unterkünfte ‘ (z. B. Baracken, Container) bis hin zu ‚mehrstöckigen Bauten‘ - als allgemeine Taxonomie der Wohnungsnot in Europa - reicht. Die Untersuchung zeigt verschiedene Wohnformen wie z. B. unterirdische oder mobile Typen, verfallene Wohnungen oder große Wohnsiedlungen, die die Armut im Europa des 21. Jahrhunderts widerspiegeln. Über die Wohnungsmorphologie hinaus werden diese Klassen durch die Struktur und ihren rechtlichen Status beschrieben - entweder als geplante oder als organisch-gewachsene bzw. weiterhin als formelle, informelle oder hybride (halblegale) Formen. Geographisch lassen sich diese ärmlichen Wohnformen sowohl in städtischen als auch in ländlichen Gebieten finden, mit einer Konzentration in Südeuropa. Der Hintergrund bei der Mehrheit der Morphologien betrifft Flüchtlinge, ethnische Minderheiten und sozioökonomisch benachteiligte Menschen - die ‚Unterprivilegierten‘.
Ziel des dritten Studienteils ist eine kritische Analyse der Methode. Zur Erfassung all dieser Siedlungen werden heutzutage Satellitenbilder aufgrund der Fortschritte bei den Bildklassifizierungsmethoden meist automatisch ausgewertet. Dennoch spielt die MVII noch immer eine wichtige Rolle, z.B. um Trainingsdaten für Machine-Learning-Algorithmen zu generieren oder für Validierungszwecke. In bestimmten städtischen Umgebungen jedoch, z.B. solchen mit höchster Dichte und struktureller Komplexität, fordern spektrale und textur-basierte Verflechtungen von überlappenden Dachstrukturen den menschlichen Interpreten immer noch heraus, wenn es darum geht einzelne Gebäudestrukturen zu erfassen. Die kognitive Wahrnehmung und die Erfahrung aus der realen Welt sind nach wie vor unumgänglich. Vor diesem Hintergrund zielt die Arbeit methodisch darauf ab, Unsicherheiten speziell bei der Kartierung zu quantifizieren und zu interpretieren. Kartiert werden Dachflächen als ‚Fußabdrücke‘ solcher Gebiete. Der Fokus liegt dabei auf der Übereinstimmung zwischen mehreren Bildinterpreten und welche Aspekte der Wahrnehmung und Elemente der Bildinterpretation die Kartierung beeinflussen. Um letztlich die Methode der MVII als drittes Ziel selbstkritisch zu reflektieren, werden Experimente als sogenannte ‚Unsicherheitsanalyse‘ geschaffen. Dabei digitalisieren zehn Testpersonen bzw. Probanden/Interpreten sechs komplexe Gebiete. Hierdurch werden quantitative Informationen über räumliche Variablen von Gebäuden erzielt, um systematisch die Konsistenz und Kongruenz der Ergebnisse zu überprüfen. Ein zusätzlicher Fragebogen liefert subjektive qualitative Informationen über weitere Schwierigkeiten. Da die Grundlage der hierfür bisher genutzten Kategorisierungen auf der subjektiven Bildinterpretation durch den Menschen beruht, müssen etwaige Unsicherheiten und damit Fehleranfälligkeiten offengelegt werden. Die Experimente zu dieser Unsicherheitsanalyse erfolgen quantifiziert und qualifiziert. Es lassen sich generell große Unterschiede zwischen den Kartierungsergebnissen der Probanden, aber eine hohe Konsistenz der Ergebnisse bei ein und demselben Probanden feststellen. Steigende Abweichungen korrelieren mit einer steigenden baustrukturellen (morphologischen) Komplexität. Ein hoher Grad an Individualität bei den Probanden äußert sich in Aspekten wie z.B. Zeitaufwand beim Kartieren, in-situ Vorkenntnissen oder Vorkenntnissen beim Umgang mit Geographischen Informationssystemen (GIS). Nennenswert ist hierbei, dass die jeweilige Datenquelle das Kartierungsverfahren meist beeinflusst. Mit dieser Studie soll also auch an der Stelle der angewandten Methodik eine weitere Wissenslücke gefüllt werden. Die bisherige Forschung komplexer urbaner Areale unter Nutzung der manuellen Bildinterpretation implementiert oftmals keine Unsicherheitsanalyse oder Quantifizierung von Kartierungsfehlern. Fernerkundungsstudien sollten künftig zur Validierung nicht nur zweifelsfrei auf MVII zurückgreifen können, sondern vielmehr sind Daten und Methoden notwendig, um Unsicherheiten auszuschließen.
Zusammenfassend trägt diese Arbeit zur bisher wenig erforschten morphologischen Dynamik von Armutsgebieten bei. Es werden inter- wie auch intra-urbane Unterschiede auf globaler Ebene präsentiert. Dabei sind allgemein hohe morphologische Transformationen zwischen den selektierten Gebieten festzustellen. Die Ergebnisse deuten auf einen grundlegenden Kenntnismangel in Europa hin, weshalb an dieser Stelle angeknüpft wird. Eine über Europa verteilte Stichprobe erlaubt eine neue morphologische Kategorisierung der großen Vielfalt an gefundenen physischen Formen. Die Menge an Gebieten erschließt sich in einer unbekannten Grundgesamtheit. Zur Datenaufbereitung bisheriger Analysen müssen Satellitenbilder manuell interpretiert werden. Das Verfahren birgt Unsicherheiten. Als kritische Selbstreflexion zeigt eine Reihe von Experimenten signifikante Unterschiede zwischen den Ergebnissen der Probanden auf, verdeutlicht jedoch bei ein und derselben Person Beständigkeit.
In den letzten Jahrzehnten ist eine verstärkte Veränderung der Landoberfläche beobachtet worden. Diese Prozesse sind direkten und indirekten anthropogenen Einflüssen zuzuschreiben, wie Deforestation oder Klimawandel. Mit dieser Entwicklung geht der Verlust und die Fragmentation von naturnahen Flächen einher. Für das Fortbestehen von Populationen verschiedenster Organismen in einer derartig geformten Landschaft ist entscheidend, inwieweit die Migration zwischen bestehenden Fragmenten gewährleistet ist. Diese wird von der Eignung der umgebenden Landschaft beeinflusst. Im Kontext einer klimatischen Veränderung und verstärkter anthropogener Landnutzung ist die Analyse der räumlichen Anordnung von Habitatfragmenten und der Qualität der umgebenden Landschaft besonders für die globale Aufrechterhaltung der Biodiversität wichtig. Großräumige Muster der Landschaftsveränderung können mit Hilfe von Satellitendaten analysiert werden, da es nur diese ermöglichen die Landbedeckung flächendeckend, reproduzierbar und auf einer adäquaten räumlichen Auflösung zu kartieren. Besonders zeitlich hochaufgelöste Daten liefern wertvolle Informationen bezüglich der Dynamik der Landbedeckung. Diese Arbeit beschäftigt sich mit der Analyse der Fragmentation in Westafrika und der potentiellen Bedeutung von singulären Fragmenten und deren potentiellen Auswirkungen auf die Biodiversität. Dafür wurden zeitlich hoch- und räumlich mittelaufgelöste Daten des Aufnahmesystems MODIS verwendet, mit denen für das Untersuchungsgebiet Westafrika die Landbedeckung klassifziert wurde. Für die darauf folgenden Analysen der räumlichen Konfiguration der Fragmente wurde der Fokus auf Regenwaldgebiete gelegt. Die Analyse von räumlichen Mustern der Regenwaldfragmente liefert weiterführende qualitative Informationen der individuellen Teilbereiche. Die räumliche Anordnung wurde sowohl mit etablierten Maßen als auch mittels in dieser Arbeit erstellter robuster und übertragbarer Indizes quantifiziert. Es konnte gezeigt werden, dass die Verwendung von aussagekräftigen Indizes, besonders, wenn sie alle benachbarten Fragmente und die Qualität der umgebenden Matrix berücksichtigen, die räumliche Differenzierung von Fragmenten verbessert. Jedoch ist die Anwendung dieser Maße abhängig von den Ansprüchen einer Art. Daher muss die artspezifische Perzeptionen der Landschaft auf der Basis der Indizes implementiert werden, da die Übertragung der Ergebnisse einzelner Indizes auf andere räumliche Auflösungen und andere Regionen nur begrenzt möglich war. Des Weiteren wurden potentielle Einflussfaktoren auf die räumlichen Muster mittels Neutraler Landschaftsmodelle untersucht. Hierbei ergaben sich je nach Region und Index unterschiedliche Ergebnisse, allerdings konnte der Einfluss anthropogen induzierter Veränderungen auf die Landbedeckung postuliert werden. Die große Bedeutung der räumlichen Attribution von Landbedeckungsklassen konnte in dieser Arbeit aufgezeigt werden. Der alleinige Fokus auf die Kartierung von z. B. Waldfragmenten ohne deren räumliche Anordnung zu berücksichtigen, kann zu falschen Schlüssen bezüglich deren ökologischen, hydrologischen und klimatologischen Bedeutung führen.
Methoden und Techniken sind in der geographischen Handelsforschung gleichermaßen in der Grundlagenforschung, in der universitärenAusbildung, in der praktischen Anwendung und der Fortbildung von hoher Bedeutung. Der vorliegende Band vertieft einige bekannte methodische Aspekte, setzt aber auch neue Akzente hinsichtlich Analysemethodik und Modellierung.
Die Beiträge in dem vorliegenden Band zeigen weitergehende Möglichkeiten auf, in der geographischen Handelsforschung und insbesondere der Praxis bedeutsame Fragestellungen methodisch fassen und behandeln zu können. Die Reihenfolge der Beiträge ist thematisch gegliedert. Die Thematik wird zunächst eher allgemein orientiert vorgestellt und dann mittels einer bestimmten Fragestellung oder Untersuchung konkretisiert. So wird der umfassende Beitrag von K. E. Klein zum Einsatz geographischer Informationssysteme im Einzelhandel durch die Studie von J. Scharfenberger zu mikrogeographischen Routing- und Marktpotenzialanalysen ergänzt. Die Modellierung und Prognose von Marktgebieten im Einzelhandel wird von R. Klein zunächst allgemein diskutiert und durch die Untersuchungen von C. Kanhäusser vertieft. Die Beiträge von R. Hesse / A. Schmid sowie J. Rauh / T. Schenk / M. Fehler / F. Klügl / F. Puppe zeigen mit Simulationsmodellen und der räumlichen Optimierung neue methodische Anwendungsmöglichkeiten auf, die geeignet sind, die in der Regel vorhandene Trennung zwischen der individualistischen und der strukturellen Perspektive aufzulösen. Erstgenannte wird in der Regel z.B. bei der Untersuchung des Konsumentenverhaltens eingenommen, die Letztgenannte bei der (Standort-)Analyse der Angebotsseite.
Individual mobility and human patterns analyses is receiving increasing attention in numerous interdisciplinary studies and publications using the concept of time-geography but is largely unknown to the subdiscipline of sports geography. Meanwhile the visualization and evaluation of large data of individual patterns are still a major challenge. While a qualitative, microscale view on spatial-temporal topics is more common in today's pattern research using mostly 24h time intervals, this work examines a quantitative approach focusing on an extended period of life. This paper presents a combination of time-geographic approaches with 3D-geoinformation systems and demonstrates their value for analysing individual mobility by implementing a path-homogeneity factor (HPA). Using the example of professional athletes, it is shown which groups display greater similarities in their career paths. While a high homogeneity suggests that groups make similar decisions through socially influenced processes, low values allow the assumption that external processes provide stronger, independent individual structures.
11 Conclusion
11.1 Glaze compositions
Glazes from tiles of imposing Islamic buildings and some tableware glazes of the medieval epoch in Central Asia, the Middle East, Asia Minor, and North Africa are analysed regarding their main composition and colouring agents. Three major production recipes can be distinguished, i.e. alkali glazes, alkali lead glazes, and lead glazes. In the work of Tite (2011), Islamic glazes from Egypt, Iran, Iraq, and Syria are subdivided into four groups of composition, being partly consistent with those of this work. The alkali lime glazes with <2 wt% PbO correspond to the alkali glazes, but with higher content of CaO. The second and third group of low lead alkali and lead alkali glazes (2-10 wt% PbO and 10-35 wt% PbO) can be subsumed to the alkali lead group described here. Tite´s high lead group has PbO contents >35 wt% and is comparable to the lead glazes (>30 wt% PbO) of this study. The lead and the alkali oxides serve as a flux for the lowering the melting point.
In the interaction of ceramic body and glaze, primarily an influence from Si, Al, and K is observed in the line scans from the cross section of ceramic and glaze. However, the input of ceramic material doesn’t seem to be critical for the classification of glazes according to their alkali and alkali lead compositions.
In every epoch and locality, except of the Ilkhanate dynasty in Iran, lead glaze samples can be verified. This is also observed in previous investigations e.g. from medieval Iraq, Jordan and Iran (McCarthy, 1996; Al-Saad, 2002; Holakooei et al., 2014). In the Moroccan and Bulgarian glazes, lead seems to be the only important flux. In part, the lead flux is supplemented by additional alkali contents. The lack of alkali and alkali lead glazes in Bulgarian and Moroccan glazes (assuming that the Ottoman alkali lead glazes are imported tableware) seems to affect the regions with Roman-influenced history and with geographical distance to the Near East alkali flux tradition.
For the alkali lead glazes and alkali glazes, the overall characteristic is sodium dominated, although the absolute soda values are in part surprisingly low. Samples from Bukhara, Takht-i-Suleiman and the Turkish localities have the highest, but still moderate Na2O values up to 15 wt%, compared to other analyses from e.g. India (Gill & Rehren, 2011).
The source of the alkali flux is either mineral natron or plant ash. The source can be determined regarding the MgO values, limited to 1.3 wt% in mineral natron and exceeding 2.0 wt% in the case of plant ashes. In the samples of the present study, the K2O component is not suitable for the indication of the flux-relevant alkali source due to its broad scattering. The P2O5 contents are also enhanced in the plant ash compositions but the data set is not sufficient for statistical evaluation. An influence of the ceramic body on the glaze composition is observed only for SiO2, Al2O3, and K2O in quartz frit ceramics with slight K-feldspar content.
The earliest Uzbek tableware glazes from the 10th-11th century (Seljuq period) were generally produced using a lead flux. The same applies to part of the Uzbek tile glazes which were produced between the 13th and 16th century. In Iran, glazes from the 12th century (Khwarezmid period) are lead glazes, but also alkali-fluxed glazes with mineral natron characteristics can be found. Although the production of lead-rich glazes was established from the 8th-9th century on in Iraq, Syria, and Egypt (Henshaw, 2010; Tite et al., 2011), alkali glazes are found in almost all regions except of Bulgaria and Morocco.
Plant ash-fluxed alkali glazes are found in 13th century glazes from Takht-i-Suleiman. The plant ash flux technology is assumed to be continuously used in Mesopotamia, Iran, and Central Asia (Sayre & Smith, 1974; Henderson, 2009), but it could be shown that a parallel use of mineral natron parallel existed in the alkali glaze production from the 12th-15th century from Uzbekistan to Afghanistan. Mineral natron characteristics are also reported by Mason (2004) for Syrian and Iranian alkali glazes on lustre ware of the 8th-14th century. Tile glazes with partly mineral natron compositions are found in the Mughal architectural glazes from the 14th- 17th century from India (Gill et al., 2014).
Alkali and alkali lead tile glazes from Samarkand from the 13th century (Mongolian period) have mineral natron flux characteristics, but samples from the 15th century (Timurid period) show plant ash signature. Alkali fluxed Uzbek glazes from Bukhara from the 16th century (Sheibanid dynasty) are also made by plant ash flux and are subdivided into two groups with high and low sodium oxide content. The Afghan alkali glazes have sodium oxide contents similar to the sodium-poor Uzbek subgroup, which points to a possible exchange of glaze makers or glaze making technology from Uzbekistan and Afghanistan in the 15th-17th century. Regarding the extensive exchange of Timurid craftsmen in Central Asia, this option seems to be even more likely (Golombek, 1996). One sample from the 15th century from Afghanistan with mineral natron reveals that this material was parallel used in these centuries.
Concerning the colouring of the glazes, it has to be distinguished between pigments and colouring ions which are incorporated in the glassy matrix. The colouring agents for translucent glazes are cations of various transition metals. As ions, Co2+ (blue), Cu2+ (green in a lead rich matrix), Fe3+ (brown/black), Mn4+ (brown/black) and Mn3+ (violet) are determined by EPMA. For opaque yellow, white, and turquoise glazes, different pigments were used. The crystalline pigments are investigated by a µ XRD2 device with the result of SnO2, SiO2, and PbSiO4 as whitening agents. PbSiO4 and Pb2Sn2O6 are found in the yellow glaze, from which only the lead tin oxide causes the yellow colour. In the black glazes, different Cr-rich pigments, Cu-Cr-Mn-oxides and iron containing clinopyroxenes are found, even in samples of the same period and region. Cr-rich particles are also detected in two turquoise Afghan glazes from the 15th and 16th century. The use of the ions of Fe, Cu, Co, Cr, and Mn seems to be widely common in the Islamic glazes and corresponds to the described colouring agents in e.g. the study of Tite (2011). The use of opacifying SnO2 particles is widespread as it is reported from different Islamic glazes from Iraq, Iran, Egypt, and Syria (Henshaw, 2010; O´Kane, 2011; Tite, 2011). The colouring agents are known already from former, e.g. Egyptian, Roman and pre-islamic periods, but especially SnO2 pigments became increasingly widespread in the Islamic glazing tradition. The use of yellow and black pigments instead varies already within the buildings from Bukhara from Cr crystals and clino-pyroxenes in the mosque Khoja Zainuddin to a Cu-Cr-Mn-oxide in the madrassa Mir-i Arab of the same epoch.
Regarding the matrix compositions connected with the colouring, a certain assignment within the different locations and epochs can be seen. It is noticeable that e.g. the content of lead in turquoise glazes in Uzbekistan is in the range of 0.0-9.2 wt% Pb, whereas blue glazes are mostly alkali ones with PbO contents <2.0 wt%. The turquoise glazes show, that this restriction is not influenced by any defaults of availability and processability. The assumption of common addition of lead and tin to the glaze, which is already described for Iranian glazes of the 13th century (Allan et al., 1973) cannot be confirmed by correlations of tin and lead oxide in the compositions.
11.2 Portable XRF measurement
With the p-XRF, semi-quantitative information about the major element compositions is generated. The depth of the detectable signals depends on the analysed sample setup. The p-XRF data are collected with the XL3 Hybrid device of the company Analyticon Instruments. In the comparison of p-XRF results of the “mining” program from Uzbek glazes with EPMA results, the same major composition groups can be distinguished. The Moroccan glazes, all lead rich, are measured with the “mining” as well as with the “soil” program, revealing a better performance in the “mining” measurements. The deviations are nevertheless high, because of the high lead contents, which make the calculation of matrix correction difficult.
The measurement of the colouring oxides MnO2, CoO, and CuO is satisfying with the internal calibration of the device and even improved with the “mining” program measurement, if compared to the results of the “soil” program. The measurements of glaze imitations lead to better results than that of bulk glass. This can be attributed to the smoother surface texture.
In spite of the accuracy limits in the measurements of particular elements in glazes, the classification of flux composition into three groups could be confirmed with the p XRF analysis. The measurement precision is therefore sufficient for the semi-quantitative analysis of the flux characteristic of glazes. Especially for the on-site measurement of large sample quantities on historical buildings, the device is a suitable tool.
11.3 Restoration material
The ORMOCER® fulfils the requirements of stability, reversibility, and transparency, which are imposed to a modern restoration material. As pigments, historically coloured glass, cobalt blue, Egyptian blue, lead tin yellow, manganese violet, iron oxide, copper oxide, and cassiterite were used. The metal compounds have higher colour intensities than the pigments of coloured glass. It has to be considered that the proportion of ORMOCER® in the batch must be high enough (70 vol%) to guarantee the ORMOCER® properties of weathering and mechanical stability. The adhesion properties of the ORMOCER® and the homogeneity of the mixture are the best in a fraction of max. 30 vol% particles per ORMOCER®.
With integrated particles, the ORMOCER® G materials show homogeneous coatings, whereas the particles in the ORMCOER® E show more agglomeration. In the sedimentation and weathering experiments, the use of an ultrasonic finger in combination with a roller mill is favourable compared to the treatment with bead grinding mill. The treatments with ultrasonic finger and roller mill result in less sedimentation and better adhesion of the dispersions. The treatment of the dispersions in the bead grinding mill does not result in sufficient adhesion, certainly due to the sedimentation behaviour and a congregation of particles on the bottom of the coating.
The modification of dispersed nano-particles by 3-methacryl-oxypropyltrimethoxysilan leads to a further homogenization in the sedimentation tests. It is therefore approved for the use in coloured glaze supplements. In weathered coatings of nano-particle compounds, the surface modification shows certainly no enhancement of stability.
The treatment of pigmented coatings with an additional layer of pure ORMOCER® results in a bright and transparent appearing, which is closer to the original optical appearance of the glaze. A long-time test application on a historical building will be the next step to validate the suitability of the restoration material.
This study analyzed the spatiotemporal pattern of settlement expansion in Abuja, Nigeria, one of West Africa’s fastest developing cities, using geoinformation and ancillary datasets. Three epochs of Land-use Land-cover (LULC) maps for 1986, 2001 and 2014 were derived from Landsat images using support vector machines (SVM). Accuracy assessment (AA) of the LULC maps based on the pixel count resulted in overall accuracy of 82%, 92% and 92%, while the AA derived from the error adjusted area (EAA) method stood at 69%, 91% and 91% for 1986, 2001 and 2014, respectively. Two major techniques for detecting changes in the LULC epochs involved the use of binary maps as well as a post-classification comparison approach. Quantitative spatiotemporal analysis was conducted to detect LULC changes with specific focus on the settlement development pattern of Abuja, the federal capital city (FCC) of Nigeria. Logical transitions to the urban category were modelled for predicting future scenarios for the year 2050 using the embedded land change modeler (LCM) in the IDRISI package. Based on the EAA, the result showed that urban areas increased by more than 11% between 1986 and 2001. In contrast, this value rose to 17% between 2001 and 2014. The LCM model projected LULC changes that showed a growing trend in settlement expansion, which might take over allotted spaces for green areas and agricultural land if stringent development policies and enforcement measures are not implemented. In conclusion, integrating geospatial technologies with ancillary datasets offered improved understanding of how urbanization processes such as increased imperviousness of such a magnitude could influence the urban microclimate through the alteration of natural land surface temperature. Urban expansion could also lead to increased surface runoff as well as changes in drainage geography leading to urban floods.
In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China's second largest freshwater lake in the middle reaches of the Yangtze River — the Dongting Lake — and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA's Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake's wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km\(^2\), indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km\(^2\). Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km\(^2\), which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers.
Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species — the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera) — and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management.
Air temperatures in the Arctic have increased substantially over the last decades, which has extensively altered the properties of the land surface. Capturing the state and dynamics of Land Surface Temperatures (LSTs) at high spatial detail is of high interest as LST is dependent on a variety of surficial properties and characterizes the land–atmosphere exchange of energy. Accordingly, this study analyses the influence of different physical surface properties on the long-term mean of the summer LST in the Arctic Mackenzie Delta Region (MDR) using Landsat 30 m-resolution imagery between 1985 and 2018 by taking advantage of the cloud computing capabilities of the Google Earth Engine. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Tasseled Cap greenness (TCG), brightness (TCB), and wetness (TCW) as well as topographic features derived from the TanDEM-X digital elevation model are used in correlation and multiple linear regression analyses to reveal their influence on the LST. Furthermore, surface alteration trends of the LST, NDVI, and NDWI are revealed using the Theil-Sen (T-S) regression method. The results indicate that the mean summer LST appears to be mostly influenced by the topographic exposition as well as the prevalent moisture regime where higher evapotranspiration rates increase the latent heat flux and cause a cooling of the surface, as the variance is best explained by the TCW and northness of the terrain. However, fairly diverse model outcomes for different regions of the MDR (R2 from 0.31 to 0.74 and RMSE from 0.51 °C to 1.73 °C) highlight the heterogeneity of the landscape in terms of influential factors and suggests accounting for a broad spectrum of different factors when modeling mean LSTs. The T-S analysis revealed large-scale wetting and greening trends with a mean decadal increase of the NDVI/NDWI of approximately +0.03 between 1985 and 2018, which was mostly accompanied by a cooling of the land surface given the inverse relationship between mean LSTs and vegetation and moisture conditions. Disturbance through wildfires intensifies the surface alterations locally and lead to significantly cooler LSTs in the long-term compared to the undisturbed surroundings.
This study investigates synthetic aperture radar (SAR) time series of the Sentinel-1 mission acquired over the Atacama Desert, Chile, between March 2015 and December 2018. The contribution analyzes temporal and spatial variations of Sentinel-1 interferometric SAR (InSAR) coherence and exemplarily illustrates factors that are responsible for observed signal differences. The analyses are based on long temporal baselines (365–1090 days) and temporally dense time series constructed with short temporal baselines (12–24 days). Results are compared to multispectral data of Sentinel-2, morphometric features of the digital elevation model (DEM) TanDEM-X WorldDEM™, and to a detailed governmental geographic information system (GIS) dataset of the local hydrography. Sentinel-1 datasets are suited for generating extensive, nearly seamless InSAR coherence mosaics covering the entire Atacama Desert (>450 × 1100 km) at a spatial resolution of 20 × 20 meter per pixel. Temporal baselines over several years lead only to very minor decorrelation, indicating a very high signal stability of C-Band in this region, especially in the hyperarid uplands between the Coastal Cordillera and the Central Depression. Signal decorrelation was associated with certain types of surface cover (e.g., water or aeolian deposits) or with actual surface dynamics (e.g., anthropogenic disturbance (mining) or fluvial activity and overland flow). Strong rainfall events and fluvial activity in the periods 2015 to 2016 and 2017 to 2018 caused spatial patterns with significant signal decorrelation; observed linear coherence anomalies matched the reference channel network and indicated actual episodic and sporadic discharge events. In the period 2015–2016, area-wide loss of coherence appeared as strip-like patterns of more than 80 km length that matched the prevailing wind direction. These anomalies, and others observed in that period and in the period 2017–2018, were interpreted to be caused by overland flow of high magnitude, as their spatial location matched well with documented heavy rainfall events that showed cumulative precipitation amounts of more than 20 mm.
Die mit dem Klimawandel einhergehenden Umweltveränderungen, wie steigende Temperaturen, Abnahme der Sommer- und Zunahme der Winterniederschläge, häufigere und längere Trockenperioden, zunehmende Starkniederschläge, Stürme und Hitzewellen betreffen besonders den Bodenwasserhaushalt in seiner zentralen Regelungsfunktion für den Landschaftswasserhaushalt. Von der Wasserverfügbarkeit im Boden hängen zu einem sehr hohen Grad auch die Erträge der Land- und Forstwirtschaft ab. Eine besonders große Bedeutung kommt dabei der Wasserspeicherkapazität der Böden zu, da während einer Trockenphase die effektiven Niederschläge den Wasserbedarf der Pflanzen nicht decken können und das bereits gespeicherte Bodenwasser das Überleben der Pflanzen sicherstellen kann. Für die land- und forstwirtschaftlichen Akteure sind in diesem Kontext quantitative und qualitative Aussagen zu den Auswirkungen des Klimawandels auf den Boden essenziell, um die notwendigen Anpassungsmaßnahmen für ihre Betriebe treffen zu können.
Zielsetzungen der vorliegenden Arbeit bestehen darin, die Dynamik der Bodenfeuchte in unterfränkischen Böden besser zu verstehen, die Datenlage zum Verlauf der Bodenfeuchte zu verbessern und die Auswirkungen von prognostizierten klimatischen Parametern abschätzen zu können. Hierzu wurden an sechs für ihre jeweiligen Naturräume und hinsichtlich ihrer anthropogenen Nutzung charakteristischen Standorten meteorologisch-bodenhydrologische Messstationen installiert. Die Messstationen befinden sich in einem Rigosol auf Buntsandstein in einem Weinberg bei Bürgstadt sowie auf einer Parabraunerde im Lössgebiet bei Herchsheim unter Ackernutzung. Am Übergang von Muschelkalk in Keuper befinden sich die Stationen in Obbach, wo eine Braunerde unter Ackernutzung vorliegt und im Forst des Universitätswalds Sailershausen werden die Untersuchungen in einer Braunerde-Terra fusca durchgeführt. Im Forst befinden sich auch die Stationen in Oberrimbach mit Braunerden aus Sandsteinkeuper und in Willmars mit Braunerden aus Buntsandstein. Der Beobachtungszeitraum dieser Arbeit reicht von Juli 2018 bis November 2022. In diesen Zeitraum fiel die dreijährige Dürre von 2018 bis 2020, das Jahr 2021 mit einem durchschnittlichen Witterungsverlauf und das Dürrejahr 2022.
Das Langzeitmonitoring wurde von umfangreichen Gelände- und Laboranalysen der grundlegenden bodenkundlichen Parameter der Bodenprofile und der Standorte begleitet. Die bodengeographischen-geomorphologischen Standortanalysen bilden zusammen mit den qualitativen Auswertungen der Bodenfeuchtezeitreihen die Grundlage für Einschätzungen zu den Auswirkungen des Klimawandels auf den Bodenwasserhaushalt. Verlässliche Aussagen zum Bodenwasserhaushalt können nur auf Grundlage von zeitlich und räumlich hoch aufgelösten Daten getroffen werden. Bodenfeuchtezeitreihen zusammen mit den bodenphysikalischen Daten lagen in dieser Datenqualität für Unterfranken bisher nur sehr vereinzelt vor.
Die vorliegenden Ergebnisse zeigen, dass die untersuchten Böden entsprechend den jeweiligen naturräumlichen Gegebenheiten sehr unterschiedliche bodenhydrologische Eigenschaften aufweisen. Während langer Trockenphasen können beispielsweise die Parabraunerden am Standort Herchsheim wegen ihrer höheren Wasserspeicherkapazität die Pflanzen länger mit Wasser versorgen als die sandigen Braunerden am Standort Oberrimbach. Die Bodenfeuchteregime im Beobachtungszeitraum waren sehr stark vom Witterungsverlauf einzelner Jahre abhängig. Das Bodenfeuchteregime bei einem durchschnittlichen Witterungsverlauf wie in 2021 zeichnet sich durch eine langsame Abnahme der Bodenfeuchte ab Beginn der Vegetationsperiode im Frühjahr aus. Regelmäßige Niederschläge im Frühjahr füllen den oberflächennahen Bodenwasserspeicher immer wieder auf und sichern den Bodenwasservorrat in der Tiefe bis in den Hochsommer. Im Hochsommer können Pflanzen dann während der Trockenphasen ihren Wasserbedarf aus den tieferen Horizonten decken. Im Gegensatz dazu nimmt die Bodenfeuchte in Dürrejahren wie 2018 bis 2020 oder 2022 bereits im Frühjahr bis in die untersten Horizonte stark ab. Die nutzbare Feldkapazität ist zum Teil schon im Juni weitgehend ausgeschöpft, womit für spätere Trockenphasen kein Bodenwasser mehr zur Verfügung steht. Die Herbst- und Winterniederschläge sättigen den Bodenwasservorrat wieder bis zur Feldkapazität auf. Bei tiefreichender Erschöpfung des Bodenwassers wurde die Feldkapazität erst im Januar oder Februar erreicht.
Im Zuge der land- und forstwirtschaftlichen Nutzung ist eine gute Datenlage zu den bodenkundlichen und standörtlichen Gegebenheiten für klimaadaptierte Anpassungsstrategien essentiell. Wichtige Zielsetzungen bestehen grundsätzlich in der Erhaltung der Bodenfunktionen, in der Verbesserung der Infiltrationskapazität und Wasserspeicherkapazität. Hier kommt dem Boden als interaktive Austauschfläche zwischen den Sphären und damit dem Bodenschutz eine zentrale Bedeutung zu. Die in Zukunft erwarteten klimatischen Bedingungen stellen an jeden Boden andere Herausforderungen, welchen mit standörtlich abgestimmten Bodenschutzmaßnahmen begegnet werden kann.
Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments.
Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr.
Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions.
Weltweit sind Trockengebiete in ständiger Veränderung, verursacht durch natürliche klimatische Schwankungen und oftmals durch Prozesse der Landdegradation. Auch weisen die meisten semi-ariden Naturräume eine große räumliche Heterogenität auf, hervorgerufen durch ein kleinräumiges Mosaik aus Gräsern, kleineren Sträuchern und Bereichen offenliegenden Bodens. Die Dichte der Vegetation wird primär vom pflanzenverfügbaren Wasser bestimmt, aber auch der Entwicklungs- und Degradationszustand der Böden sowie anthropogen bedingte Faktoren spielen hierbei eine Rolle. Zur Charakterisierung und Kartierung der Vegetation sowie zur Bewertung des Bodenerosionsrisikos und des Degradationszustands hat sich die Erhebung der Bedeckungsgrade von vitaler, photosynthetisch aktiver Vegetation (PV), von abgestorbener oder zeitweise vertrockneter und somit nicht photosynthetisch aktiver Vegetation (NPV) sowie von offenliegendem Boden als zweckmäßig herausgestellt. Die Nutzung der Fernerkundung für diese Aufgabe erfolgt zumeist nur für kleinmaßstäbige Kartierungen und – im Falle von Multispektralsensoren – unter Vernachlässigung nicht-photosynthetisch aktiver Vegetation. Die räumliche Variabilität der Vegetation-Boden-Mosaike liegt oftmals in der Größenordnung von wenigen Metern und somit unterhalb des räumlichen Auflösungsvermögens von Fernerkundungssystemen. Um dennoch die verschiedenen Anteile innerhalb eines Pixels identifizieren und quantifizieren zu können, sind Methoden der Subpixel-Klassifikation notwendig. In dieser Arbeit wird eine Methodik zur verbesserten und automatisierbaren Ableitung von Bodenbedeckungsgraden in semi-ariden Naturräumen vorgestellt. Hierzu wurde ein Verfahren zur linearen spektralen Entmischung in Form einer Multiple Endmember Spectral Mixture Analysis (MESMA) entwickelt und umgesetzt. Durch diese Methodik kann explizit die spektrale Variabilität von Vegetation und Boden in das Mischungsmodellmiteinbezogen werden, und quantitative Anteile für die funktionalen Klassen PV, NPV und Boden innerhalb eines Pixels erfasst werden. Durch die räumliche Kartierung der verwendeten EM wird weiterhin eine thematische Klassifikation erreicht. Die hierfür benötigten Informationen können – wie im Falle der Spektren reiner Materialien (EM-Spektren) – aus den Bilddaten selbst abgeleitet werden, oder können – wie ein Geländemodell und die Information über den Scanwinkel – im Zuge der Vorprozessierung aus weiteren Datenquellen erzeugt werden. Hinsichtlich der automatisierten EM-Ableitung wird eine zweistufige Methodik eingesetzt, welche auf einer angepassten Version des Sequential Maximum Angle Convex Cone (SMACC)-Verfahrens sowie der Analyse einer ersten Entmischungsiteration basiert. Die Klassifikation der gefundenen potentiellen EM erfolgt durch ein merkmalsbasiertes Verfahren. Weiterhin weisen nicht-photosynthetisch aktive Vegetation und Boden eine hohe spektrale Ähnlichkeit auf. Zur sicheren Trennung kann die Identifikation schmaler Absorptionsbanden dienen. Zu diesen zählen beispielsweise die Absorptionsbanden von Holozellulose und – je nach Bodentyp – Absorptionsbanden von Bodenmineralen. Auch die spektrale Variabilität der Klassen PV und NPV erfordert zur sicheren Unterscheidung die Verwendung biophysikalisch erklärbarer Merkmale im Spektrum. Hierzu zählen unter anderem die Stärke der Chlorophyll-Absorption, die Form und Lage der ’RedEdge’ und das Auftreten von Holozellulosebanden. Da diese spektrale Information bei herkömmlichen Entmischungsansätzen nicht berücksichtigt wird, erfolgt überwiegend eine Optimierung der Gesamtalbedo, was zu einer schlechten Trennung der Klassen führen kann. Aus diesem Grund wird in der vorliegenden Arbeit der MESMA-Ansatz dahingehend erweitert, dass spektrale Information in Form von identifizierten und parametrisierten Absorptionsbanden in den Entmischungsprozess mit einfließt und hierdurch das Potential hyperspektraler Datensätze besser genutzt werden kann. Auch wird in einer zusätzlichen Entmischungsiteration die räumliche Nachbarschaft betrachtet, um insbesondere die Verwendung des sinnvollsten Boden-EMs zu gewährleisten. Ein zusätzliches Problemfeld stellt die numerische Lösung des überbestimmten und oftmals schlecht konditionierten linearen Mischungsmodells dar. Hierzu kann durch die Verwendung des BVLS-Algorithmus und des Ausschlusses kritischer EM-Kombinationen eine numerisch stabile Lösung gefunden werden. Um die oftmals immense Rechenzeit von MESMA-Verfahren zu verkürzen, besteht die Möglichkeit einer iterativen EM-Auswahl und somit die Vermeidung einer Lösung des Mischungssystems durch Berechnung aller EM-Kombinationen (’Brute-Force’-Ansatz). Ein weiterer wichtiger Punkt ist die explizite pixelweise Angabe zur Zuverlässigkeit der Entmischungsergebnisse. Dies erfolgt auf Basis des Mischungsmodells selbst, durch den Vergleich zu empirischen Regressionsmodellen, durch die Berücksichtigung des lokalen Einfallswinkels sowie durch die Integration von Qualitätsangaben der Ausgangsdaten. Um das Verfahren systematisch und unter kontrollierten Bedingungen zu verifizieren und um den Einfluss verschiedener externer Parameter sowie die typischen Genauigkeiten auf einer breiten Datenbasis zu ermitteln, wird eine Simulationskette zur Erzeugung synthetischer Mischungen erstellt. In diese Simulationen fließen Feldspektren von Böden und Pflanzen verschiedener semi-arider Gebiete mit ein, um möglichst viele Fälle abdecken zu können. Die eigentliche Validierung erfolgt auf HyMap-Datensätzen des Naturparks ’Cabo de Gata’ in der andalusischen Provinz Almería sowie auf Messungen, die begleitend im Feld durchgeführt wurden. Hiermit konnte die Methodik auf ihre Genauigkeit unter den konkreten Anforderungen des Anwendungsbeispiels überprüft werden. Die erzielbare Genauigkeit dieser automatisierten Methodik liegt mit einem mittleren Fehler um rund 10% Abundanz absolut im selben Wertebereich oder nur geringfügig höher als die Ergebnisse publizierter manueller MESMA-Ansätze. Weiterhin konnten die typischen Genauigkeiten der Verifikation im Zuge der Validierung bestätigt werden. Den limitierenden Faktor des Ansatzes stellen in der Praxis fehlerhafte oder unvollständige EM-Modelle dar. Mit der vorgestellten Methodik ist somit die Möglichkeit gegeben, die Bedeckungsgrade quantitativ und automatisiert im Subpixelbereich zu erfassen.
Informationen über die Landbedeckung und die mit der anthropogenen Komponente verbundenen Landnutzung sind elementare Bestandteile für viele Bereiche der Politik, Wirtschaft und Wissenschaft. Darunter fallen beispielsweise die Strukurentwicklungsprogramme der EU, die Schadensregulierung im Versicherungswesen und die Modellierung von Stoffkreisläufen. CORINE Land Cover (CLC) wurde infolge eines erweiterten Bedarfs an einem europaweit harmonisierten Datensatz der Landoberfläche erstellt. Das CORINE Projekt weist für diese Arbeit eine hohe Relevanz durch die regelmäßigen Aktualisierungen von 10 Jahren, dem Einsatz der Daten in vielen europäischen und nationalen Institutionen und der guten Dokumentation der CORINE Nomenklatur auf. Die Erstellung der Daten basiert auf der computergestützten manuellen Interpretation, da automatische Verfahren durch die Komplexität der Aufgabenstellung und Thematik nicht in der Lage waren, den menschlichen Interpreten zu ersetzen. Diese Arbeit stellt eine Methodik vor, um CORINE Land Cover aus optischen Fernerkundungsdaten für eine kommende Aktualisierung abzuleiten. Hierzu dienen die Daten von CLC 1990 und der Fernerkundungsdatensatz Image 2000 als Grundlage, sowie die CLC 2000 Klassifikation als Referenz. Die entwickelte und in dem Softwarepaket gnosis implementierte Methodik wendet die objektorientierte Klassifikation in Kombination mit Theorien aus der menschlichen Bildwahrnehmung an. In diesen Theorien wird die Bildwahrnehmung als informationstechnischer Prozess gesehen, der den Klassifikationsprozess in die drei folgenden Subprozesse unterteilt: Bildsegmentierung, Merkmalsgenerierung und Klassenzuweisung. Die Bildsegmentierung generiert aus den untersten Bildprimitiven (Pixeln) bedeutungsvolle Bildsegmente. Diesen Bildsegmenten wird eine Anzahl von bildinvarianten Merkmalen aus den Fernerkundungsdaten für die Bestimmung der CLC Klasse zugewiesen. Dabei liegt die wichtigste Information in der Ableitung der Landbedeckung durch den überwachten Stützvektor-Klassifikator. Die Landoberfläche wird hierzu in zehn Basisklassen untergliedert, um weiteren Merkmalen einen semantischen Unterbau zu geben. Zur Bestimmung der anthropogenen Komponente von ausgewählten Landnutzungsklassen, wie beispielsweise Ackerland und Grünland, wird der phänologische Verlauf der Vegetation durch die Parameter temporale Variabilität und temporale Intensität beschrieben. Neben dem jahreszeitlichen Verlauf der Vegetation können Nachbarschaftsbeziehungen untersucht werden, um weitere anthropogene Klassen und heterogen aufgebaute Sammelklassen beschreiben zu können. Der Versiegelungsgrad als Beispiel für eine Reihe von unscharfen Merkmalen dient der weiteren Differenzierung der verschiedenen Siedlungsklassen aus CORINE LC. Mit Hilfe dieser Merkmale werden die CLC Klassen in abstrakter Form im Klassenkatalog (a-priori Wissensbasis) als Protoklassen beschrieben. Die eigentliche Objekterkennung basiert auf der Repräsentation der CORINE Objekte durch ihre einzelnen Bestandteile und vergleicht die gefundenen Strukturen mit der Wissensbasis. Semantisch homogen aufgebaute Klassen, wie Wälder und Siedlungen oder Protoklassen mit eindeutigen Merkmalen, beispielsweise zur Bestimmung von Grünland durch die Phänologie, können durch den bottom–up Ansatz identifiziert werden. Das übergeordnete CLC Objekt kann direkt aus den Bestandteilen zusammengebaut und einer Klasse zugewiesen werden. Semantisch heterogene Klassen, wie zum Beispiel bestimmte Sammelklassen (Komplexe Parzellenstrukur), können durch ihre Bestandteile validiert werden, indem die Bestandteile eines existierenden CLC Objektes mit der Wissensbasis auf Konsistenz untersucht werden (top–down Ansatz). Eine a-priori Datengrundlage ist für die Erkennung dieser Klassen essentiell. Die Untersuchung der drei Testgebiete (Frankfurt, Berlin, Oldenburg) zeigte, dass von der CORINE LC Nomenklatur 13 Klassen identifiziert und weiteren 14 Klassen validiert werden können. Zehn Klassen können durch diese Methodik aufgrund fehlender Merkmale oder Zusatzdaten nicht klassifiziert werden. Die Gesamtgenauigkeit der automatisierten Klassifikation für die Testgebiete beträgt zwischen 70% und 80% für die umgesetzten Klassen. Betrachtet man davon einzelne Klassen, wie Siedlungs-, Wald- oder Wasserklassen, wird aufgrund der verwendeten Merkmale eine Klassifikationsgenauigkeit von über 90% erreicht. Ein möglicher Einsatz der entwickelten Software gnosis liegt in der Unterstützung einer kommenden CORINE Aktualisierung durch die Prozessierung der identifizierbaren Klassen. Diese CLC Klassen müssen vom Interpreten nicht mehr überprüft werden. Für bestimmte CLC Klassen aus dem Top-down Ansatz wird der Interpret die letzte Entscheidung aus einer Auswahl von Klassen treffen müssen. Weiterhin können die berechneten Merkmale, wie die temporalen Eigenschaften und der Versiegelungsgrad dem Bearbeiter als Entscheidungsgrundlage zur Verfügung gestellt werden. Der Einsatz dieser neu entwickelten Methode führt zu einer Optimierung des bestehenden Aufnahmeverfahrens durch die Integration von semi-automatisierten Prozessen.
Beobachtung des Hydroxyl (OH*)-Airglow: Untersuchung von Klimasignalen und atmosphärischen Wellen
(2009)
Die obere Mesosphäre ist die Atmosphärenschicht, die von etwa 80-100 km Höhe reicht. Aufgrund der geringen Luftdichte – sie ist fünf bis sechs Größenordnungen geringer als an der Erdoberfläche – und der effektiven Abstrahlung von Wärme in den Weltraum („Strahlungskühlung“) wird generell angenommen, dass Klimasignale in diesem Höhenbereich sehr viel ausgeprägter sein sollten als in den unteren Atmosphärenschichten. Es wird daher erwartet, dass Beobachtungen in dieser Region der Atmosphäre eine frühzeitige Erkennung von Klimatrends mit guter statistischer Signifikanz erlauben sollten. Daten, die von diesen Messungen bereitgestellt werden, sind wichtig für die Weiterentwicklung und Verbesserung numerischer Klimamodelle, die die mittlere Atmosphäre abdecken. Dieser Höhenbereich der Atmosphäre ist messtechnisch jedoch nur schwer zugänglich. Die Dichte der Messnetze ist keinesfalls vergleichbar mit denen für die Beobachtung etwa der Stratosphäre oder der Troposphäre; Routinemessungen gibt es kaum. Direkte Messungen werden mit raketengestützten Instrumenten, indirekte Messungen über satellitengestützte und bodengebundene Techniken, wie z.B. Lidar, Radar und Spektroskopie, vorgenommen. Die vorliegende Arbeit basiert auf Daten des „GRound-based Infrared P-branch Spectrometer (GRIPS)“, das Infrarot-Emissionen aus der sogenannten OH*-Airglow-Schicht misst, aus denen die Temperatur in ~87 km Höhe abgeleitet werden kann. Neben anthropogenen Einflüssen auf das Klima gibt es natürliche Effekte, die Temperaturschwankungen in der oberen Mesosphäre verursachen können. Für die Interpretation experimenteller Daten ist das Verständnis dieser natürlichen Quellen der Variabilität wichtig. Daher wird mithilfe einer 25-jährigen Zeitreihe der über Wuppertal (51,3°N, 7,2°O) gemessenen OH*-Temperaturen die potentielle Wechselwirkung der Dynamik der oberen Mesosphäre mit der Sonnenaktivität untersucht. Eine Korrelation der Aktivität planetarer Wellen mit dem solaren Magnetfeld (22-jähriger solarer Hale-Zyklus) konnte festgestellt werden. Als möglicher physikalischer Mechanismus wird vorgeschlagen, dass der Ringstrom im Erdinnern und damit das interne Magnetfeld der Erde durch das solare Magnetfeld moduliert wird, was wiederum zu Modulationen des totalen Magnetfeldes im Erdinnern über die Kopplung elektromagnetischer Drehmomente zwischen dem Erdkern und dem Erdmantel führt. Als Folge sollte die Rotationsperiode der Erde – und damit die Aktivität planetarer Wellen – durch die solare Magnetfeldstärke moduliert sein. Der Aktivität planetarer Wellen ist zudem eine quasi-zweijährige Schwingung überlagert. Zumeist ist die Wellenaktivität verstärkt, wenn sich die Windrichtung des mittleren zonalen Windes der äquatorialen Quasi-Biennalen Oszillation (QBO) von einem Westwind zu einem Ostwind umkehrt. Darüber hinaus konnte festgestellt werden, dass die unregelmäßige Verteilung der Sonnenflecken auf der Sonnenscheibe aufgrund der Rotation der Sonne zu Fluktuationen der OH*-Temperatur führt. Häufig beobachtet werden ausgeprägte spektrale Komponenten in den OH*-Temperaturfluktuationen im Periodenbereich von 27 bis 31 Tagen. Diese Signaturen werden vorläufig auf die differentielle Rotation der Sonne zurückgeführt. Dynamische Prozesse wie z.B. atmosphärische Schwerewellen sind von großer Bedeutung für den Energiehaushalt der oberen Mesosphäre / unteren Thermosphäre (MLT-Region). Daher müssen sie in Klimamodellen berücksichtigt werden, was derzeit jedoch nur durch einfache Parametrisierungen bewerkstelligt werden kann. Um eine möglichst realistische Modellierung der großräumigen Zirkulationssysteme zu ermöglichen, ist die Kenntnis der Strukturfunktionen der Schwerewellen sowie ihre Quell- und Senkenstärken in Raum und Zeit erforderlich. Messungen von Schwerewellen sind daher unabdingbar. In der vorliegenden Arbeit werden im Rahmen von Fallstudien Temperatursignaturen untersucht, wie sie von Schwerewellen erzeugt werden. Verwendet werden hierfür zeitlich hoch aufgelöste OH*-Temperaturzeitreihen aufgenommen am Hohenpeißenberg (47,8°N, 11,0°O) und an der Zugspitze (47,5°N, 11,0°O). Durch den Alpenkamm induzierte Schwerewellen können identifiziert und Schwerewellenparameter wie beispielsweise die Ausbreitungsrichtung oder die Phasengeschwindigkeit quantifiziert werden. Messungen, aufgenommen von Bord des deutschen Forschungsschiffes „Polarstern“ im Golf von Biskaya (um 48°N, 6°O), werden mit satellitenbasierten Beobachtungen kombiniert. Es wird gezeigt, dass Schwerewellen, die von einem atlantischen Zyklon erzeugt werden, die Temperatur in der Mesopausenregion beeinflussen können. Das GRIPS-System ist ferner prinzipiell zur schnellen Erkennung von Naturgefahren wie z.B. Tsunamis, Erdbeben oder Vulkanaktivität geeignet, da solche Ereignisse Infraschall erzeugen, der wiederum erkennbare Temperaturfluktuationen in der OH*-Airglow-Schicht verursacht. Am Beispiel des Sumatra-Tsunamis von 2004 wird diese Möglichkeit quantitativ diskutiert.
The recently observed consistent loss of β-diversity across ecosystems indicates increasingly homogeneous communities in patches of landscapes, mainly caused by increasing land-use intensity. Biodiversity is related to numerous ecosystem functions and stability. Therefore, decreasing β-diversity is also expected to reduce multifunctionality. To assess the impact of homogenization and to develop guidelines to reverse its potentially negative effects, we combine expertise from forest science, ecology, remote sensing, chemical ecology and statistics in a collaborative and experimental β-diversity approach. Specifically, we will address the question whether the Enhancement of Structural Beta Complexity (ESBC) in forests by silviculture or natural disturbances will increase biodiversity and multifunctionality in formerly homogeneously structured production forests. Our approach will identify potential mechanisms behind observed homogenization-diversity-relationships and show how these translate into effects on multifunctionality. At eleven forest sites throughout Germany, we selected two districts as two types of small ‘forest landscapes’. In one of these two districts, we established ESBC treatments (nine differently treated 50x50 m patches with a focus on canopy cover and deadwood features). In the second, the control district, we will establish nine patches without ESBC. By a comprehensive sampling, we will monitor 18 taxonomic groups and measure 21 ecosystem functions, including key functions in temperate forests, on all patches. The statistical framework will allow a comprehensive biodiversity assessment by quantifying the different aspects of multitrophic biodiversity (taxonomical, functional and phylogenetic diversity) on different levels of biodiversity (α-, β-, γ-diversity). To combine overall diversity, we will apply the concept of multidiversity across the 18 taxa. We will use and develop new approaches for quantification and partitioning of multifunctionality at α- and β- scales. Overall, our study will herald a new research avenue, namely by experimentally describing the link between β-diversity and multifunctionality. Furthermore, we will help to develop guidelines for improved silvicultural concepts and concepts for management of natural disturbances in temperate forests reversing past homogenization effects.
Bewertung und Auswirkungen der Simulationsgüte führender Klimamoden in einem Multi-Modell Ensemble
(2013)
Der rezente und zukünftige Anstieg der atmosphärischen Treibhausgaskonzentration bedeutet für das terrestrische Klimasystem einen grundlegenden Wandel, der für die globale Gesellschaft schwer zu bewältigende Aufgaben und Herausforderungen bereit hält. Eine effektive, rühzeitige Anpassung an diesen Klimawandel profitiert dabei enorm von möglichst genauen Abschätzungen künftiger Klimaänderungen.
Das geeignete Werkzeug hierfür sind Gekoppelte Atmosphäre Ozean Modelle (AOGCMs). Für solche Fragestellungen müssen allerdings weitreichende Annahmen über die zukünftigen klimarelevanten Randbedingungen getroffen werden. Individuelle Fehler dieser Klimamodelle, die aus der nicht perfekten Abbildung der realen Verhältnisse und Prozesse resultieren, erhöhen die Unsicherheit langfristiger Klimaprojektionen. So unterscheiden sich die Aussagen verschiedener AOGCMs im Hinblick auf den zukünftigen Klimawandel insbesondere bei regionaler Betrachtung, deutlich. Als Absicherung gegen Modellfehler werden üblicherweise die Ergebnisse mehrerer AOGCMs, eines Ensembles an Modellen, kombiniert. Um die Abschätzung des Klimawandels zu präzisieren, wird in der vorliegenden Arbeit der Versuch unternommen, eine Bewertung der Modellperformance der 24 AOGCMs, die an der dritten Phase des Vergleichsprojekts für gekoppelte Modelle (CMIP3) teilgenommen haben, zu erstellen. Auf dieser Basis wird dann eine nummerische Gewichtung für die Kombination des Ensembles erstellt. Zunächst werden die von den AOGCMs simulierten Klimatologien für einige
grundlegende Klimaelemente mit den betreffenden klimatologien verschiedener Beobachtungsdatensätze quantitativ abgeglichen. Ein wichtiger methodischer Aspekt
hierbei ist, dass auch die Unsicherheit der Beobachtungen, konkret Unterschiede zwischen verschiedenen Datensätzen, berücksichtigt werden. So zeigt sich, dass die Aussagen, die aus solchen Ansätzen resultieren, von zu vielen Unsicherheiten in den Referenzdaten beeinträchtigt werden, um generelle Aussagen zur Qualität von AOGCMs zu treffen. Die Nutzung der Köppen-Geiger Klassifikation offenbart jedoch, dass die prinzipielle Verteilung der bekannten Klimatypen im kompletten CMIP3 in vergleichbar guter Qualität reproduziert wird. Als Bewertungskriterium wird daher hier die Fähigkeit der AOGCMs die großskalige natürliche Klimavariabilität, konkret die hochkomplexe gekoppelte
El Niño-Southern Oscillation (ENSO), realistisch abzubilden herangezogen. Es kann anhand verschiedener Aspekte des ENSO-Phänomens gezeigt werden, dass nicht alle AOGCMs hierzu mit gleicher Realitätsnähe in der Lage sind. Dies steht im Gegensatz zu den dominierenden Klimamoden der Außertropen, die modellübergreifend überzeugend repräsentiert werden. Die wichtigsten Moden werden, in globaler Betrachtung, in verschiedenen Beobachtungsdaten über einen neuen Ansatz identifiziert. So können für einige bekannte Zirkulationsmuster neue Indexdefinitionen gewonnen werden, die sich sowohl als äquivalent zu den Standardverfahren erweisen und im Vergleich zu diesen zudem eine deutliche Reduzierung
des Rechenaufwandes bedeuten. Andere bekannte Moden werden dagegen als weniger bedeutsame, regionale Zirkulationsmuster eingestuft. Die hier vorgestellte
Methode zur Beurteilung der Simulation von ENSO ist in guter Übereinstimmung mit anderen Ansätzen, ebenso die daraus folgende Bewertung der gesamten Performance
der AOGCMs. Das Spektrum des Southern Oscillation-Index (SOI) stellt somit eine aussagekräftige Kenngröße der Modellqualität dar.
Die Unterschiede in der Fähigkeit, das ENSO-System abzubilden, erweisen sich als signifikante Unsicherheitsquelle im Hinblick auf die zukünftige Entwicklung einiger fundamentaler und bedeutsamer Klimagrößen, konkret der globalen Mitteltemperatur,
des SOIs selbst, sowie des indischen Monsuns. Ebenso zeigen sich signifikante Unterschiede für regionale Klimaänderungen zwischen zwei Teilensembles des CMIP3, die auf Grundlage der entwickelten Bewertungsfunktion eingeteilt werden. Jedoch sind diese Effekte im Allgemeinen nicht mit den Auswirkungen der
anthropogenen Klimaänderungssignale im Multi-Modell Ensemble vergleichbar, die für die meisten Klimagrößen in einem robusten multivariaten Ansatz detektiert und
quantifiziert werden können. Entsprechend sind die effektiven Klimaänderungen, die sich bei der Kombination aller Simulationen als grundlegende Aussage des
CMIP3 unter den speziellen Randbedingungen ergeben nahezu unabhängig davon, ob alle Läufe mit dem gleichen Einfluss berücksichtigt werden, oder ob die erstellte nummerische Gewichtung verwendet wird. Als eine wesentliche Begründung hierfür kann die Spannbreite der Entwicklung des ENSO-Systems identifiziert werden. Dies
bedeutet größere Schwankungen in den Ergebnissen der Modelle mit funktionierendem ENSO, was den Stellenwert der natürlichen Variabilität als Unsicherheitsquelle
in Fragen des Klimawandels unterstreicht. Sowohl bei Betrachtung der Teilensembles als auch der Gewichtung wirken sich dadurch gegenläufige Trends im SOI
ausgleichend auf die Entwicklung anderer Klimagrößen aus, was insbesondere bei letzterem Vorgehen signifikante mittlere Effekte des Ansatzes, verglichen mit der
Verwendung des üblichen arithmetischen Multi-Modell Mittelwert, verhindert.
Regional climate models (RCMs) are tools used to project future climate change at a regional scale. Despite their high horizontal resolution, RCMs are characterized by systematic biases relative to observations, which can result in unrealistic interpretations of future climate change signals. On the other hand, bias correction (BC) is a popular statistical post-processing technique applied to improve the usability of output from climate models. Like every other statistical technique, BC has its strengths and weaknesses. Hence, within the regional context of Germany, and for temperature and precipitation, this study is dedicated to the assessment of the impact of different BC techniques on the RCM output. The focuses are on the impact of BC on the RCM’s statistical characterization, and physical consistency defined as the spatiotemporal consistency between the bias-corrected variable and the simulated physical mechanisms governing the variable, as well as the correlations between the bias-corrected variable and other (simulated) climate variables. Five BC techniques were applied in adjusting the systematic biases in temperature and precipitation RCM outputs. The BC techniques are linear scaling, empirical quantile mapping, univariate quantile delta mapping, multivariate quantile delta mapping that considers inter-site dependencies, and multivariate quantile delta mapping that considers inter-variable dependencies (MBCn). The results show that each BC technique adds value in reducing the biases in the statistics of the RCM output, though the added value depends on several factors such as the temporal resolution of the data, choice of RCM, climate variable, region, and the metric used in evaluating the BC technique. Further, the raw RCMs reproduced portions of the observed modes of atmospheric circulation in Western Europe, and the observed temperature, and precipitation meteorological patterns in Germany. After the BC, generally, the spatiotemporal configurations of the simulated meteorological patterns as well as the governing large-scale mechanisms were reproduced.
However, at a more localized spatial scale for the individual meteorological patterns, the BC changed the simulated co-variability of some grids, especially for precipitation. Concerning the co-variability among the variables, a physically interpretable positive correlation was found between temperature and precipitation during boreal winter in both models and observations. For most grid boxes in the study domain and on average, the BC techniques that do not adjust inter-variable dependency did not notably change the simulated correlations between the climate variables. However, depending on the grid box, the (univariate) BC techniques tend to degrade the simulated temporal correlations between temperature and precipitation. Further, MBCn which adjusts biases in inter-variable dependency has the skill to improve the correlations between the simulated variables towards observations.
Verbleibende Unsicherheiten im Kohlenstoffhaushalt in Ökosystemen der hohen nördlichen Breiten können teilweise auf die Schwierigkeiten bei der Erfassung der räumlich und zeitlich hoch variablen Methanemissionsraten von Permafrostböden zurückgeführt werden. Methan ist ein global abundantes atmosphärisches Spurengas, welches signifikant zur Erwärmung der Atmosphäre beiträgt. Aufgrund der hohen Sensibilität des arktischen Bodenkohlenstoffreservoirs sowie der großen von Permafrost unterlagerten Landflächen sind arktische Gebiete am kritischsten von einem globalen Klimawandel betroffen. Diese Dissertation adressiert den Bedarf an Modellierungsansätzen für die Bestimmung der Quellstärke nordsibirischer permafrostbeeinflusster Ökosysteme der nassen polygonalen Tundra mit Hinblick auf die Methanemissionen auf regionalem Maßstab. Die Arbeit präsentiert eine methodische Struktur in welcher zwei prozessbasierte Modelle herangezogen werden, um die komplexen Wechselwirkungen zwischen den Kompartimenten Pedosphäre, Biosphäre und Atmosphäre, welche zu Methanemissionen aus Permafrostböden führen, zu erfassen. Es wird ein Upscaling der Gesamtmethanflüsse auf ein größeres, von Permafrost unterlagertes Untersuchungsgebiet auf Basis eines prozessbasierten Modells durchgeführt. Das prozessbasierte Vegetationsmodell Biosphere Energy Hydrology Transfer Model (BETHY/DLR) wird für die Berechnung der Nettoprimärproduktion (NPP) arktischer Tundravegetation herangezogen. Die NPP ist ein Maß für die Substratverfügbarkeit der Methanproduktion und daher ein wichtiger Eingangsparameter für das zweite Modell: Das prozessbasierte Methanemissionsmodell wird anschließend verwendet, um die Methanflüsse einer gegebenen Bodensäule explizit zu berechnen. Dabei werden die Prozesse der Methanogenese, Methanotrophie sowie drei verschiedene Transportmechanismen – molekulare Diffusion, Gasblasenbildung und pflanzengebundener Transport durch vaskuläre Pflanzen – berücksichtigt. Das Methanemissionsmodell ist für Permafrostbedingungen modifiziert, indem das tägliche Auftauen des Permafrostbodens in der kurzen arktischen Vegetationsperiode berücksichtigt wird. Der Modellantrieb besteht aus meteorologischen Datensätzen des European Center for Medium-Range Weather Forecasts (ECMWF). Die Eingangsdatensätze werden mit Hilfe von in situ Messdaten validiert. Zusätzliche Eingangsdaten für beide Modelle werden aus Fernerkundungsdaten abgeleitet, welche mit Feldspektralmessungen validiert werden. Eine modifizierte Landklassifikation auf der Basis von Landsat-7 Enhanced Thematic Mapper Plus (ETM+) Daten wird für die Ableitung von Informationen zu Feuchtgebietsverteilung und Vegetationsbedeckung herangezogen. Zeitserien der Auftautiefe werden zur Beschreibung des Auftauens bzw. Rückfrierens des Bodens verwendet. Diese Faktoren sind die Haupteinflussgrößen für die Modellierung von Methanemissionen aus permafrostbeeinflussten Tundraökosystemen. Die vorgestellten Modellergebnisse werden mittels Eddy-Kovarianz-Messungen der Methanflüsse validiert, welche während der Vegetationsperioden der Jahre 2003-2006 im südlichen Teil des Lena Deltas (72°N, 126°E) vom Alfred Wegener Institut für Polar- und Meeresforschung (AWI) durchgeführt wurden. Das Untersuchungsgebiet Lena Delta liegt an der Laptewsee in Nordostsibirien und ist durch Ökosysteme der arktischen nassen polygonalen Tundra sowie kalten kontinuierlichen Permafrost charakterisiert. Zeitlich integrierte Werte der modellierten Methanflüsse sowie der in situ Messungen zeigen gute Übereinstimmungen und weisen auf eine leichte Modellunterschätzung von etwa 10%.
Die hier vorgelegte geographisch-historische Abhandlung basiert auf dem Vergleich von zwei im zeitlichen Abstand von ca 60 Jahren (1958/59 = Dissertation und 2016/17 = wiederholendes Geländeprojekt) erfolgten Untersuchungen zum Verlauf und zum morphologischen Ergebnis von Bodenerosion nach akuten Starkregen sowie infolge schleichend-langfristiger Abspülung von Feinboden in verschiedenen Relieftypen des Taubertalgebietes. Alle Vorgänge der Bodenabtragung erfuhren erhebliche Differenzierung durch die unterschiedlichen Verfahren der landwirtschaftlichen Nutzung (z.B. Weinbau, Ackerbau,Viehhaltung). In zeitlichem Vergleich der einzelnen Lokalitäten und Fallstudien (Kartierung, Fotografie, Datenerfassung)konnte einerseits Abschwächung, andererseits Verstärkung der Bodenabspülung festgesetllt werden. Um längerfristig rückblickend die Wirkungsweise der flächen- u. linienhaften Bodenabtragung einzubeziehen, wurden historisch-archivalische Berichte über Folgen von Witterungsereignissen einbezogen und als Auswahl entsprechend der verschiedenen Bodennutzungsarten zusammengestellt. Diese Belege geben Aufschluss über historische Methoden und Techniken zur Verminderung erosionsbedingter Bodenverluste und damit zur Vermeidung existenzmindernder Ernteschäden. Mit diesem Rückblick ergaben sich auch Hinweise auf Phasen historisch-klimatisch veränderter Niederschlagsregime. Im Hinblick auf die durch den Klimawandel zu erwartende Zunahme der Starkregenanteile ergibt sich die Notwendigkeit, den Oberflächenabfluss von Regenmengen und damit deren Erosionskraft durch bodenschonende Nutzungsweisen zu verlangsamen.
In dieser Arbeit wird ein Verfahren zur Modellierung der Bodenerosion auf Ackerflächen in einem Untersuchungsgebiet im UNESCO-Biosphärenreservat Rhön vorgestellt. Als Grundlage dienen flächendeckend verfügbare, hochauflösende Datensätzen zu allen relevanten Faktoren. Ziel ist es die Sensitivität des Modells gegenüber verschiedenen Faktoren sowie die Übertragbarkeit des Verfahrens auf größere Untersuchungsgebiete zu testen. Die Modellierung findet dabei in ArcView 3.2 über die Extension AVErosion von SCHÄUBLE (2005) statt, während die Vorprozessierung in ArcMap von ESRI durchgeführt wird. Zunächst werden grundlegende Begriffe zu den Prozessen, Einflussfaktoren und Messmethoden von Bodenerosion erläutert. Die von Bodenerosion verursachten Schäden und mögliche Schutzmaßnahmen werden aufgrund ihrer Relevanz, unter anderem für die betroffenen Landwirte, geschildert. Nach dem Überblick über die wichtigsten Erosionsmodelle werden die hier verwendete Allgemeine Bodenabtragsgleichung (ABAG) und ihre einzelnen Berechnungsschritte vorgestellt. Das Modellierungstool AVErosion verwendet zusätzlich Elemente der Modified Universal Soil Loss Equation (MUSLE87). Zur Bodenerosionsmodellierung stehen hochauflösende Datensätze aus dem Untersuchungsgebiet zur Verfügung, aus denen in der Vorprozessierung die Raster der Faktoren errechnet werden. Insgesamt werden zehn Szenarien mit verschiedenen C-Faktoren und zwei Szenarien mit variierendem R-Faktor modelliert. Daraufhin wird das Untersuchungsgebiet nach physisch-geographischen Gesichtspunkten beschrieben und die landwirtschaftliche Nutzung in der Region charakterisiert. Die Ergebnisse der Modellierung zeigen, dass neben den Reliefeigenschaften die Bodenbewirtschaftung auf den Ackerflächen den größten Einfluss auf den Bodenabtrag hat. Die Variationen der Niederschlagssumme in den R-Faktor-Szenarien hat hingegen vergleichsweise wenig Auswirkungen auf das Modellierungsergebnis. Zwar konnte durch das Fehlen von aktuellen Bewirtschaftungsdaten keine Modellierung der tatsächlichen Bodenerosion erzielt werden, jedoch zeigen die verschiedenen C-Faktor-Szenarien den potentiellen Bodenabtrag bei unterschiedlicher Bewirtschaftung. Es wird deutlich, dass auf erosionsgefährdeten Flächen durch eine angepasste Form der landwirtschaftlichen Nutzung geringere Abtragswerte in der Modellierung erreicht werden können. Die Methode lässt sich gut auf das Untersuchungsgebiet im Biosphärenreservat Rhön anwenden und zeigt Potential zur Übertragung auf größere Untersuchungsgebiete
Business lmprovement Districts (BID) gelten als vielversprechendes Instrument der Quartiersentwicklung und Modell zur Attraktivitätssteigerung von Innenstädten und Stadtteilzentren. In BID bezahlen Grundeigentümer oder Gewerbetreibende eine Abgabe für eine private Organisation, die Programme zur Attraktivitätssteigerung des Gebiets durchführt. Die Gründung eines BID bedarf anfänglich der Zustimmung eines Mindestteils der Betroffenen, die Abgabe ist aber nach der Implementierung für alle verbindlich.
Nachdem sich BID in Nordamerika weit verbreitet haben, wird das Modell nun weltweit implementiert. In Deutschland haben einige Bundesländer, darunter als erste Hamburg, Hessen, Bremen und Schleswig-Holstein, bereits entsprechende Gesetze eingeführt, andere Bundesländer arbeiten daran.
Die Autoren des Bandes ziehen ein Zwischenfazit der Implementierung von BID in Deutschland und analysieren die Erfolge und Probleme sowie Konflikte, die bei der Etablierung von BID auftauchen. Sie thematisieren die Wesentlichen Forschungsstränge, die sich mit BID auseinandersetzen, die paradigmatisch für den Wandel von government zu governance in der Stadtentwicklung stehen, und diskutieren an konkreten Fallstudien grundsätzliche Fragen der Quartiersentwicklung,
der Partizipation und die Vor- und Nachteile verpflichtender und freiwilliger Modelle der Zentrenentwicklung. Der Blick auf internationale Erfahrungen zeigt alternative Entwicklungspfade von BID auf.
We analyze the processing of cereals and its role at Early Neolithic Göbekli Tepe, southeastern Anatolia (10th / 9th millennium BC), a site that has aroused much debate in archaeological discourse. To date, only zooarchaeological evidence has been discussed in regard to the subsistence of its builders. Göbekli Tepe consists of monumental round to oval buildings, erected in an earlier phase, and smaller rectangular buildings, built around them in a partially contemporaneous and later phase. The monumental buildings are best known as they were in the focus of research. They are around 20 m in diameter and have stone pillars that are up to 5.5 m high and often richly decorated. The rectangular buildings are smaller and–in some cases–have up to 2 m high, mostly undecorated, pillars. Especially striking is the number of tools related to food processing, including grinding slabs/bowls, handstones, pestles, and mortars, which have not been studied before. We analyzed more than 7000 artifacts for the present contribution. The high frequency of artifacts is unusual for contemporary sites in the region. Using an integrated approach of formal, experimental, and macro- / microscopical use-wear analyses we show that Neolithic people at Göbekli Tepe have produced standardized and efficient grinding tools, most of which have been used for the processing of cereals. Additional phytolith analysis confirms the massive presence of cereals at the site, filling the gap left by the weakly preserved charred macro-rests. The organization of work and food supply has always been a central question of research into Göbekli Tepe, as the construction and maintenance of the monumental architecture would have necessitated a considerable work force. Contextual analyses of the distribution of the elements of the grinding kit on site highlight a clear link between plant food preparation and the rectangular buildings and indicate clear delimitations of working areas for food production on the terraces the structures lie on, surrounding the circular buildings. There is evidence for extensive plant food processing and archaeozoological data hint at large-scale hunting of gazelle between midsummer and autumn. As no large storage facilities have been identified, we argue for a production of food for immediate use and interpret these seasonal peaks in activity at the site as evidence for the organization of large work feasts.
In recent years, the midlatitudes are characterized by more intense heatwaves in summer and sometimes severe cold spells in winter that might emanate from changes in atmospheric circulation, including synoptic‐scale and planetary wave activity in the midlatitudes. In this study, we investigate the heat and momentum exchange between the mean flow and atmospheric waves in the North Atlantic sector and adjacent continents by means of the physically consistent Eliassen–Palm flux diagnostics applied to reanalysis and forced climate model data. In the long‐term mean, momentum is transferred from the mean flow to atmospheric waves in the northwest Atlantic region, where cyclogenesis prevails. Further downstream over Europe, eddy fluxes return momentum to the mean flow, sustaining the jet stream against friction. A global climate model is able to reproduce this pattern with high accuracy. Atmospheric variability related to atmospheric wave activity is much more expressed at the intraseasonal rather than the interannual time‐scale. Over the last 40 years, reanalyses reveal a northward shift of the jet stream and a weakening of intraseasonal weather variability related to synoptic‐scale and planetary wave activity. This pertains to the winter and summer seasons, especially over central Europe, and correlates with changes in the North Atlantic Oscillation as well as regional temperature and precipitation. A very similar phenomenon is found in a climate model simulation with business‐as‐usual scenario, suggesting an anthropogenic trigger in the weakening of intraseasonal weather variability in the midlatitudes.
Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R\(^2\) > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg\(^{−1}\) in 1990 to 9.23 gC·kg\(^{−1}\) in 2010 and then declined to 5.31 gC·Kg\(^{−1}\) in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.