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Sonstige beteiligte Institutionen
- The Italian Federation of Parks and Nature Reserves (3)
- ALPARC - The Alpine Network of Protected Areas (2)
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- Salzburg Institute for Regional Planning and Housing (2)
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- 243964 (1)
- 776019 (1)
- 818182 (1)
- 834709 (1)
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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.