@article{MahmoudDukerConradetal.2016, author = {Mahmoud, Mahmoud Ibrahim and Duker, Alfred and Conrad, Christopher and Thiel, Michael and Ahmad, Halilu Shaba}, title = {Analysis of Settlement Expansion and Urban Growth Modelling Using Geoinformation for Assessing Potential Impacts of Urbanization on Climate in Abuja City, Nigeria}, series = {Remote Sensing}, volume = {8}, journal = {Remote Sensing}, number = {3}, doi = {10.3390/rs8030220}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-146644}, pages = {220}, year = {2016}, abstract = {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.}, language = {en} } @article{ConradSchoenbrodtStittLoewetal.2016, author = {Conrad, Christopher and Sch{\"o}nbrodt-Stitt, Sarah and L{\"o}w, Fabian and Sorokin, Denis and Paeth, Heiko}, title = {Cropping Intensity in the Aral Sea Basin and Its Dependency from the Runoff Formation 2000-2012}, series = {Remote Sensing}, volume = {8}, journal = {Remote Sensing}, number = {630}, doi = {10.3390/rs8080630}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147701}, year = {2016}, abstract = {This study is aimed at a better understanding of how upstream runoff formation affected the cropping intensity (CI: number of harvests) in the Aral Sea Basin (ASB) between 2000 and 2012. MODIS 250 m NDVI time series and knowledge-based pixel masking that included settlement layers and topography features enabled to map the irrigated cropland extent (iCE). Random forest models supported the classification of cropland vegetation phenology (CVP: winter/summer crops, double cropping, etc.). CI and the percentage of fallow cropland (PF) were derived from CVP. Spearman's rho was selected for assessing the statistical relation of CI and PF to runoff formation in the Amu Darya and Syr Darya catchments per hydrological year. Validation in 12 reference sites using multi-annual Landsat-7 ETM+ images revealed an average overall accuracy of 0.85 for the iCE maps. MODIS maps overestimated that based on Landsat by an average factor of ~1.15 (MODIS iCE/Landsat iCE). Exceptional overestimations occurred in case of inaccurate settlement layers. The CVP and CI maps achieved overall accuracies of 0.91 and 0.96, respectively. The Amu Darya catchment disclosed significant positive (negative) relations between upstream runoff with CI (PF) and a high pressure on the river water resources in 2000-2012. Along the Syr Darya, reduced dependencies could be observed, which is potentially linked to the high number of water constructions in that catchment. Intensified double cropping after drought years occurred in Uzbekistan. However, a 10 km × 10 km grid of Spearman's rho (CI and PF vs. upstream runoff) emphasized locations at different CI levels that are directly affected by runoff fluctuations in both river systems. The resulting maps may thus be supportive on the way to achieve long-term sustainability of crop production and to simultaneously protect the severely threatened environment in the ASB. The gained knowledge can be further used for investigating climatic impacts of irrigation in the region.}, language = {en} } @article{UllmannBuedelBaumhaueretal.2016, author = {Ullmann, Tobias and B{\"u}del, Christian and Baumhauer, Roland and Padashi, Majid}, title = {Sentinel-1 SAR Data Revealing Fluvial Morphodynamics in Damghan (Iran): Amplitude and Coherence Change Detection}, series = {International Journal of Earth Science and Geophysics}, volume = {2}, journal = {International Journal of Earth Science and Geophysics}, number = {1}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147863}, pages = {007}, year = {2016}, abstract = {The Sentinel-1 Satellite (S-1) of ESA's Copernicus Mission delivers freely available C-Band Synthetic Aperture Radar (SAR) data that are suited for interferometric applications (InSAR). The high geometric resolution of less than fifteen meter and the large coverage offered by the Interferometric Wide Swath mode (IW) point to new perspectives on the comprehension and understanding of surface changes, the quantification and monitoring of dynamic processes, especially in arid regions. The contribution shows the application of S-1 intensities and InSAR coherences in time series analysis for the delineation of changes related to fluvial morphodynamics in Damghan, Iran. The investigations were carried out for the period from April to October 2015 and exhibit the potential of the S-1 data for the identification of surface disturbances, mass movements and fluvial channel activity in the surroundings of the Damghan Playa. The Amplitude Change Detection highlighted extensive material movement and accumulation - up to sizes of more than 4,000 m in width - in the east of the Playa via changes in intensity. Further, the Coherence Change Detection technique was capable to indicate small-scale channel activity of the drainage system that was neither recognizable in the S-1 intensity nor the multispectral Landsat-8 data. The run off caused a decorrelation of the SAR signals and a drop in coherence. Seen from a morphodynamic point of view, the results indicated a highly dynamic system and complex tempo-spatial patterns were observed that will be subject of future analysis. Additionally, the study revealed the necessity to collect independent reference data on fluvial activity in order to train and adjust the change detector.}, language = {en} } @article{UllmannSchmittJagdhuber2016, author = {Ullmann, Tobias and Schmitt, Andreas and Jagdhuber, Thomas}, title = {Two Component Decomposition of Dual Polarimetric HH/VV SAR Data: Case Study for the Tundra Environment of the Mackenzie Delta Region, Canada}, series = {Remote Sensing}, volume = {8}, journal = {Remote Sensing}, number = {12}, doi = {10.3390/rs8121027}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147879}, pages = {1027}, year = {2016}, abstract = {This study investigates a two component decomposition technique for HH/VV-polarized PolSAR (Polarimetric Synthetic Aperture Radar) data. The approach is a straight forward adaption of the Yamaguchi decomposition and decomposes the data into two scattering contributions: surface and double bounce under the assumption of a negligible vegetation scattering component in Tundra environments. The dependencies between the features of this two and the classical three component Yamaguchi decomposition were investigated for Radarsat-2 (quad) and TerraSAR-X (HH/VV) data for the Mackenzie Delta Region, Canada. In situ data on land cover were used to derive the scattering characteristics and to analyze the correlation among the PolSAR features. The double bounce and surface scattering features of the two and three component scattering model (derived from pseudo-HH/VV- and quad-polarized data) showed similar scattering characteristics and positively correlated-R2 values of 0.60 (double bounce) and 0.88 (surface scattering) were observed. The presence of volume scattering led to differences between the features and these were minimized for land cover classes of low vegetation height that showed little volume scattering contribution. In terms of separability, the quad-polarized Radarsat-2 data offered the best separation of the examined tundra land cover types and will be best suited for the classification. This is anticipated as it represents the largest feature space of all tested ones. However; the classes "wetland" and "bare ground" showed clear positions in the feature spaces of the C- and X-Band HH/VV-polarized data and an accurate classification of these land cover types is promising. Among the possible dual-polarization modes of Radarsat-2 the HH/VV was found to be the favorable mode for the characterization of the aforementioned tundra land cover classes due to the coherent acquisition and the preserved co-pol. phase. Contrary, HH/HV-polarized and VV/VH-polarized data were found to be best suited for the characterization of mixed and shrub dominated tundra.}, language = {en} } @article{KnauerGessnerFensholtetal.2016, author = {Knauer, Kim and Gessner, Ursula and Fensholt, Rasmus and Kuenzer, Claudia}, title = {An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes}, series = {Remote Sensing}, volume = {8}, journal = {Remote Sensing}, number = {5}, doi = {10.3390/rs8050425}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180712}, pages = {425}, year = {2016}, abstract = {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}, language = {en} } @article{EmmertKneisel2017, author = {Emmert, Adrian and Kneisel, Christof}, title = {Internal structure of two alpine rock glaciers investigated by quasi-3-D electrical resistivity imaging}, series = {The Cryosphere}, volume = {11}, journal = {The Cryosphere}, doi = {10.5194/tc-11-841-2017}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-157569}, pages = {841-855}, year = {2017}, abstract = {Interactions between different formative processes are reflected in the internal structure of rock glaciers. Therefore, the detection of subsurface conditions can help to enhance our understanding of landform development. For an assessment of subsurface conditions, we present an analysis of the spatial variability of active layer thickness, ground ice content and frost table topography for two different rock glaciers in the Eastern Swiss Alps by means of quasi-3-D electrical resistivity imaging (ERI). This approach enables an extensive mapping of subsurface structures and a spatial overlay between site-specific surface and subsurface characteristics. At Nair rock glacier, we discovered a gradual descent of the frost table in a downslope direction and a constant decrease of ice content which follows the observed surface topography. This is attributed to ice formation by refreezing meltwater from an embedded snow bank or from a subsurface ice patch which reshapes the permafrost layer. The heterogeneous ground ice distribution at Uertsch rock glacier indicates that multiple processes on different time domains were involved in the development. Resistivity values which represent frozen conditions vary within a wide range and indicate a successive formation which includes several advances, past glacial overrides and creep processes on the rock glacier surface. In combination with the observed topography, quasi-3-D ERI enables us to delimit areas of extensive and compressive flow in close proximity. Excellent data quality was provided by a good coupling of electrodes to the ground in the pebbly material of the investigated rock glaciers. Results show the value of the quasi-3-D ERI approach but advise the application of complementary geophysical methods for interpreting the results.}, language = {en} } @article{KnauerGessnerFensholtetal.2017, author = {Knauer, Kim and Gessner, Ursula and Fensholt, Rasmus and Forkuor, Gerald and Kuenzer, Claudia}, title = {Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the environment}, series = {Remote Sensing}, volume = {9}, journal = {Remote Sensing}, number = {2}, doi = {10.3390/rs9020132}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-171905}, year = {2017}, abstract = {Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92\% (2001), 91\% (2007), and 91\% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91\% between 2001 and 2014 to a total of 116,900 km\(^2\). While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future.}, language = {en} } @article{ForkuorHounkpatinWelpetal.2017, author = {Forkuor, Gerald and Hounkpatin, Ozias K.L. and Welp, Gerhard and Thiel, Michael}, title = {High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models}, series = {PLOS One}, volume = {12}, journal = {PLOS One}, number = {1}, doi = {10.1371/journal.pone.0170478}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180978}, pages = {21}, year = {2017}, abstract = {Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.}, language = {en} } @article{HaunertWolff2017, author = {Haunert, Jan-Henrik and Wolff, Alexander}, title = {Beyond maximum independent set: an extended integer programming formulation for point labeling}, series = {ISPRS International Journal of Geo-Information}, volume = {6}, journal = {ISPRS International Journal of Geo-Information}, number = {11}, doi = {10.3390/ijgi6110342}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158960}, pages = {342}, year = {2017}, abstract = {Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in a map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label-label intersections) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the weight of the labeling, the influences of labels on other labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate both for randomly generated point sets and an existing data set of cities. Moreover, a relaxation of our ILP allows for a simple and efficient heuristic, which yielded near-optimal solutions for our instances.}, language = {en} } @article{BanksMillardBehnamianetal.2017, author = {Banks, Sarah and Millard, Koreen and Behnamian, Amir and White, Lori and Ullmann, Tobias and Charbonneau, Francois and Chen, Zhaohua and Wang, Huili and Pasher, Jon and Duffe, Jason}, title = {Contributions of actual and simulated satellite SAR data for substrate type differentiation and shoreline mapping in the Canadian Arctic}, series = {Remote Sensing}, volume = {9}, journal = {Remote Sensing}, number = {12}, doi = {10.3390/rs9121206}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172630}, year = {2017}, abstract = {Detailed information on the land cover types present and the horizontal position of the land-water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response operations in the event of environmental emergencies such as oil spills. Previous work has demonstrated potential for accurate classification via fusion of optical and SAR data, though what contribution either makes to model accuracy is not well established, nor is it clear what shorelines can be classified using optical or SAR data alone. In this research, we evaluate the relative value of quad pol RADARSAT-2 and Landsat 5 data for shoreline mapping by individually excluding both datasets from Random Forest models used to classify images acquired over Nunavut, Canada. In anticipation of the RADARSAT Constellation Mission (RCM), we also simulate and evaluate dual and compact polarimetric imagery for shoreline mapping. Results show that SAR data is needed for accurate discrimination of substrates as user's and producer's accuracies were 5-24\% higher for models constructed with quad pol RADARSAT-2 and DEM data than models constructed with Landsat 5 and DEM data. Models based on simulated RCM and DEM data achieved significantly lower overall accuracies (71-77\%) than models based on quad pol RADARSAT-2 and DEM data (80\%), with Wetland and Tundra being most adversely affected. When classified together with Landsat 5 and DEM data, however, model accuracy was less affected by the SAR data type, with multiple polarizations and modes achieving independent overall accuracies within a range acceptable for operational mapping, at 89-91\%. RCM is expected to contribute positively to ongoing efforts to monitor change and improve emergency preparedness throughout the Arctic.}, language = {en} } @article{RichardAbdelRahmanSubramanianetal.2017, author = {Richard, Kyalo and Abdel-Rahman, Elfatih M. and Subramanian, Sevgan and Nyasani, Johnson O. and Thiel, Michael and Jozani, Hosein and Borgemeister, Christian and Landmann, Tobias}, title = {Maize cropping systems mapping using RapidEye observations in agro-ecological landscapes in Kenya}, series = {Sensors}, volume = {17}, journal = {Sensors}, number = {11}, doi = {10.3390/s17112537}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-173285}, year = {2017}, abstract = {Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93\% was attained for the LULC classification, while the class accuracies (PA: producer's accuracy and UA: user's accuracy) for the two cropping systems were consistently above 85\%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10-20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.}, language = {en} } @article{UllmannBanksSchmittetal.2017, author = {Ullmann, Tobias and Banks, Sarah N. and Schmitt, Andreas and Jagdhuber, Thomas}, title = {Scattering characteristics of X-, C- and L-Band PolSAR data examined for the tundra environment of the Tuktoyaktuk Peninsula, Canada}, series = {Applied Sciences}, volume = {7}, journal = {Applied Sciences}, number = {6}, doi = {10.3390/app7060595}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158362}, pages = {595}, year = {2017}, abstract = {In this study, polarimetric Synthetic Aperture Radar (PolSAR) data at X-, C- and L-Bands, acquired by the satellites: TerraSAR-X (2011), Radarsat-2 (2011), ALOS (2010) and ALOS-2 (2016), were used to characterize the tundra land cover of a test site located close to the town of Tuktoyaktuk, NWT, Canada. Using available in situ ground data collected in 2010 and 2012, we investigate PolSAR scattering characteristics of common tundra land cover classes at X-, C- and L-Bands. Several decomposition features of quad-, co-, and cross-polarized data were compared, the correlation between them was investigated, and the class separability offered by their different feature spaces was analyzed. Certain PolSAR features at each wavelength were sensitive to the land cover and exhibited distinct scattering characteristics. Use of shorter wavelength imagery (X and C) was beneficial for the characterization of wetland and tundra vegetation, while L-Band data highlighted differences of the bare ground classes better. The Kennaugh Matrix decomposition applied in this study provided a unified framework to store, process, and analyze all data consistently, and the matrix offered a favorable feature space for class separation. Of all elements of the quad-polarized Kennaugh Matrix, the intensity based elements K0, K1, K2, K3 and K4 were found to be most valuable for class discrimination. These elements contributed to better class separation as indicated by an increase of the separability metrics squared Jefferys Matusita Distance and Transformed Divergence. The increase in separability was up to 57\% for Radarsat-2 and up to 18\% for ALOS-2 data.}, language = {en} } @phdthesis{Knoefel2018, author = {Kn{\"o}fel, Patrick}, title = {Energiebilanzmodellierung zur Ableitung der Evapotranspiration - Beispielregion Khorezm}, edition = {1. Auflage}, publisher = {W{\"u}rzburg University Press}, address = {W{\"u}rzburg}, isbn = {978-3-95826-042-9 (Print)}, issn = {0510-9833}, doi = {10.25972/WUP-978-3-95826-043-6}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-135669}, school = {W{\"u}rzburg University Press}, pages = {276}, year = {2018}, abstract = {Zum Verst{\"a}ndnis der komplexen Wechselwirkungen innerhalb des Klimasystems der Erde sind Kenntnisse {\"u}ber den hydrologischen Zyklus und den Energiekreislauf essentiell. Eine besondere Rolle obliegt hierbei der Evapotranspiration (ET), da sie eine wesentliche Teilkomponente beider oben erw{\"a}hnter Kreisl{\"a}ufe ist. Die exakte Quantifizierung der regionalen, tats{\"a}chlichen Evapotranspiration innerhalb der Wasser- und Energiekreisl{\"a}ufe der Erdoberfl{\"a}che auf unterschiedlichen zeitlichen und r{\"a}umlichen Skalen ist f{\"u}r hydrologische, klimatologische und agronomische Fragestellungen von großer Bedeutung. Dabei ist eine realistische Absch{\"a}tzung der regionalen tats{\"a}chlichen Evapotranspiration die wichtigste Herausforderung der hydrologischen Modellierung. Besonders die unterschiedlichen r{\"a}umlichen und zeitlichen Aufl{\"o}sungen von Satelliteninformationen machen die Fernerkundung sowohl f{\"u}r globale als auch regionale hydrologischen Fragestellungen interessant. Zus{\"a}tzlich zur Notwendigkeit des Prozessverst{\"a}ndnisses des Wasserkreislaufs auf globaler Ebene kommt dessen regionale Bedeutung f{\"u}r die Landwirtschaft, insbesondere in Bew{\"a}sserungssystemen arider Regionen. In ariden Klimazonen {\"u}bersteigt die Menge der Verdunstung oft bei weitem die Niederschlagsmengen. Aufgrund der geringen Niederschlagsmenge muss in ariden agrarischen Regionen das zum Pflanzenwachstum ben{\"o}tigte Wasser mit Hilfe k{\"u}nstlicher Bew{\"a}sserung aufgebracht werden. Der jeweilige lokale Bew{\"a}sserungsbedarf h{\"a}ngt von der Feldfrucht und deren Wachstumsphase, den Klimabedingungen, den Bodeneigenschaften und der Ausdehnung der Wurzelzone ab. Die Evapotranspiration ist als Komponente der regionalen Wasserbilanz eine wichtige Steuerungsgr{\"o}ße und Effizienzindikator f{\"u}r das lokale Bew{\"a}sserungsmanagement. Die Bew{\"a}sse-rungslandwirtschaft verbraucht weltweit etwa 70 \% der verf{\"u}gbaren S{\"u}ßwasservorkom-men. Dies wird als einer der Hauptgr{\"u}nde f{\"u}r die weltweit steigende Wasserknappheit identifiziert. Dabei liegt die Wasserentnahme des landwirtschaftlichen Sektors in den OECD Staaten im Mittel bei etwa 44 \%, in den Staaten Mittelasiens bei {\"u}ber 90 \%. Bei der Erstellung der vorliegenden Arbeit kam die Methode der residualen Bestimmung der Energiebilanz zum Einsatz. Eines der weltweit am h{\"a}ufigsten eingesetzten und vali-dierten fernerkundlichen Residualmodelle zur ET Ableitung ist das SEBAL-Modell (Surface Energy Balance Algorithm for Land, mit {\"u}ber 40 ver{\"o}ffentlichten Studien. SEBAL eignet sich zur Quantifizierung der Verdunstung großfl{\"a}chiger Gebiete und wurde bisher {\"u}ber-wiegend in der Bew{\"a}sserungslandwirtschaft eingesetzt. Aus diesen Gr{\"u}nden wurde es f{\"u}r die Bearbeitung der Fragestellungen in dieser Arbeit ausgew{\"a}hlt. SEBAL verwendet physikalische und empirische Beziehungen zur Berechnung der Energiebilanzkomponenten basierend auf Fernerkundungsdaten, bei gleichzeitig minimalem Einsatz bodengest{\"u}tzter Daten. Als Eingangsdaten werden u.a. Informationen {\"u}ber Strahlung, Bodenoberfl{\"a}chentemperatur, NDVI, LAI und Albedo verwendet. Zus{\"a}tzlich zu SEBAL wurden einige Komponenten der SEBAL Weiterentwicklung METRIC (Mapping Evapotranspiration with Internalized Calibration) verwendet, um die Modellierung der ET vorzunehmen. METRIC {\"u}berwindet einige Limitierungen des SEBAL Verfahrens und kann beispielsweise auch in st{\"a}rker reliefierten Regionen angewendet werden. Außerdem erm{\"o}glicht die Integration einer gebietsspezifischen Referenz-ET sowie einer Landnutzungsklassifikation eine bessere regionale Anpassung des Residualverfahrens. Unter der Annahme der Bedingungen zum Zeitpunkt der Fernerkundungsaufnahme ergibt sich die Energiebilanz an der Erdoberfl{\"a}che RN = LvE + H + G. Demnach teilt sich die verf{\"u}gbare Strahlungsenergie RN in die Komponenten latenter W{\"a}rme (LVE), f{\"u}hlbarer W{\"a}rme (H) und Bodenw{\"a}rme (G) auf. Durch Umstellen der Gleichung kann auf die latente W{\"a}rme geschlossen werden. Das wesentliche Ziel der vorliegenden Arbeit ist die Optimierung, Erweiterung und Validierung des ausgew{\"a}hlten SEBAL Verfahrens zur regionalen Modellierung der Energiebilanzkomponenten und der daraus abgeleiteten tats{\"a}chlichen Evapotranspiration. Die validierten Modellergebnisse der Gebietsverdunstung der Jahre 2009-2011 sollen anschließend als Grundlage dienen, das Gesamtverst{\"a}ndnis der regionalen Prozesse des Wasserkreislaufs zu verbessern. Die Arbeit basiert auf der Datengrundlage von MODIS Daten mit 1 km r{\"a}umlicher Aufl{\"o}sung. W{\"a}hrend die Komponenten verf{\"u}gbare Strahlungsenergie und f{\"u}hlbarer W{\"a}rmestrom physikalisch basiert ermittelt werden, beruht die Berechnung des Bodenw{\"a}rmestroms ausschließlich auf empirischen Absch{\"a}tzungen. Ein großer Nachteil des empirischen Ansatzes ist die Vernachl{\"a}ssigung des zeitlichen Versatzes zwischen Strahlungsbilanz und Bodenw{\"a}rmestrom in Abh{\"a}ngigkeit der aktuellen Bodenfeuchtesituation. Ein besonderer Schwerpunkt der vorliegenden Arbeit liegt auf der Bewertung und Verbesserung der Modellg{\"u}te des Bodenw{\"a}rmestroms durch Verwendung eines neuen Ansatzes zur Integration von Bodenfeuchteinformationen. Daher wird in der Arbeit ein physikalischer Ansatz entwickelt der auf dem Ansatz der periodischen Temperaturver{\"a}nderung basiert. Hierbei wurde neben dem ENVISAT ASAR SSM Produkt der TU Wien das operationelle Oberfl{\"a}chenbodenfeuchteprodukt ASCAT SSM als Fernerkundungseingangsdaten ausgew{\"a}hlt. Die mit SEBAL modellierten Energiebilanzkomponenten werden durch eine intensive Validierung mit bodengest{\"u}tzten Messungen bewertet, die Messungen stammen von Bodensensoren und Daten einer Eddy-Kovarianz-Station aus den Jahren 2009 bis 2011. Die Region Khorezm gilt als charakteristisch f{\"u}r die wasserbezogene Problematik der Bew{\"a}sserungslandwirtschaft Mittelasiens und wurde als Untersuchungsgebiet f{\"u}r diese Arbeit ausgew{\"a}hlt. Die wesentlichen Probleme dieser Region entstehen durch die nach wie vor nicht nachhaltige Land- und Wassernutzung, das marode Bew{\"a}sserungsnetz mit einer Verlustrate von bis zu 40 \% und der Bodenversalzung aufgrund hoher Grundwasserspiegel. Im Untersuchungsgebiet wurden in den Jahren 2010 und 2011 umfangreiche Feldarbeiten zur Erhebung lokaler bodengest{\"u}tzter Informationen durchgef{\"u}hrt. Bei der Evaluierung der modellierten Einzelkomponenten ergab sich f{\"u}r die Strahlungsbi-lanz eine hohe Modellg{\"u}te (R² > 0,9; rRMSE < 0,2 und NSE > 0,5). Diese Komponente bildet die Grundlage bei der Bezifferung der f{\"u}r die Prozesse an der Erdoberfl{\"a}che zur Verf{\"u}gung stehenden Energie. F{\"u}r die f{\"u}hlbaren W{\"a}rmestr{\"o}me wurden ebenfalls gute Ergebnisse erzielt, mit NSE von 0,31 und rRMSE von ca. 0,21. F{\"u}r die residual bestimmte Gr{\"o}ße der latenten W{\"a}rmestr{\"o}mung konnte eine insgesamt gute Modellg{\"u}te festgestellt werden (R² > 0,6; rRMSE < 0,2 und NSE > 0,5). Dementsprechend gut wurde die t{\"a}gliche Evapotranspiration modelliert. Hier ergab sich, nach der Interpolation t{\"a}glicher Werte, eine insgesamt ausreichend gute Modellg{\"u}te (R² > 0,5; rRMSE < 0,2 und NSE > 0,4). Dies best{\"a}tigt die Ergebnisse vieler Energiebilanzstudien, die lediglich den f{\"u}r die Ableitung der Evapotranspiration maßgebenden W{\"a}rmestrom untersuchten. Die Modellergebnisse f{\"u}r den Bodenw{\"a}rmestrom konnten durch die Entwicklung und Verwendung des neu entwickelten physikalischen Ansatzes von NSE < 0 und rRMSE von ca. 0,57 auf NSE von 0,19 und rRMSE von 0,35 verbessert werden. Dies f{\"u}hrt zu einer insgesamt positiven Einsch{\"a}tzung des Verbesserungspotenzials des neu entwickelten Bodenw{\"a}rmestromansatzes bei der Berechnung der Energiebilanz mit Hilfe von Fernerkundung.}, subject = {Evapotranspiration}, language = {de} } @article{LauschBorgBumbergeretal.2018, author = {Lausch, Angela and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Heurich, Marco and Huth, Andreas and Jung, Andr{\´a}s and Klenke, Reinhard and Knapp, Sonja and Mollenhauer, Hannes and Paasche, Hendrik and Paulheim, Heiko and Pause, Marion and Schweitzer, Christian and Schmulius, Christiane and Settele, Josef and Skidmore, Andrew K. and Wegmann, Martin and Zacharias, Steffen and Kirsten, Toralf and Schaepman, Michael E.}, title = {Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches}, series = {Remote Sensing}, volume = {10}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs10071120}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197691}, pages = {1120}, year = {2018}, abstract = {Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.}, language = {en} } @article{NyamekyeThielSchoenbrodtStittetal.2018, author = {Nyamekye, Clement and Thiel, Michael and Sch{\"o}nbrodt-Stitt, Sarah and Zoungrana, Benewinde J.-B. and Amekudzi, Leonard K.}, title = {Soil and water conservation in Burkina Faso, West Africa}, series = {Sustainability}, volume = {10}, journal = {Sustainability}, number = {9}, issn = {2071-1050}, doi = {10.3390/su10093182}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197653}, pages = {3182}, year = {2018}, abstract = {Inadequate land management and agricultural activities have largely resulted in land degradation in Burkina Faso. The nationwide governmental and institutional driven implementation and adoption of soil and water conservation measures (SWCM) since the early 1960s, however, is expected to successively slow down the degradation process and to increase the agricultural output. Even though relevant measures have been taken, only a few studies have been conducted to quantify their effect, for instance, on soil erosion and environmental restoration. In addition, a comprehensive summary of initiatives, implementation strategies, and eventually region-specific requirements for adopting different SWCM is missing. The present study therefore aims to review the different SWCM in Burkina Faso and implementation programs, as well as to provide information on their effects on environmental restoration and agricultural productivity. This was achieved by considering over 143 studies focusing on Burkina Faso's experience and research progress in areas of SWCM and soil erosion. SWCM in Burkina Faso have largely resulted in an increase in agricultural productivity and improvement in food security. Finally, this study aims at supporting the country's informed decision-making for extending already existing SWCM and for deriving further implementation strategies.}, language = {en} } @article{ReichmuthHenningPinneletal.2018, author = {Reichmuth, Anne and Henning, Lea and Pinnel, Nicole and Bachmann, Martin and Rogge, Derek}, title = {Early detection of vitality changes of multi-temporal Norway spruce laboratory needle measurements—the ring-barking experiment}, series = {Remote Sensing}, volume = {10}, journal = {Remote Sensing}, number = {1}, doi = {10.3390/rs10010057}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-159253}, pages = {57}, year = {2018}, abstract = {The focus of this analysis is on the early detection of forest health changes, specifically that of Norway spruce (Picea abies L. Karst.). In this analysis, we planned to examine the time (degree of early detection), spectral wavelengths and appropriate method for detecting vitality changes. To accomplish this, a ring-barking experiment with seven subsequent laboratory needle measurements was carried out in 2013 and 2014 in an area in southeastern Germany near Alt{\"o}tting. The experiment was also accompanied by visual crown condition assessment. In total, 140 spruce trees in groups of five were ring-barked with the same number of control trees in groups of five that were selected as reference trees in order to compare their development. The laboratory measurements were analysed regarding the separability of ring-barked and control samples using spectral reflectance, vegetation indices and derivative analysis. Subsequently, a random forest classifier for determining important spectral wavelength regions was applied. Results from the methods are consistent and showed a high importance of the visible (VIS) spectral region, very low importance of the near-infrared (NIR) and minor importance of the shortwave infrared (SWIR) spectral region. Using spectral reflectance data as well as indices, the earliest separation time was found to be 292 days after ring-barking. The derivative analysis showed that a significant separation was observed 152 days after ring-barking for six spectral features spread through VIS and SWIR. A significant separation was detected using a random forest classifier 292 days after ring-barking with 58\% separability. The visual crown condition assessment was analysed regarding obvious changes of vitality and the first indication was observed 302 days after ring-barking as bark beetle infestation and yellowing of foliage in the ring-barked trees only. This experiment shows that an early detection, compared with visual crown assessment, is possible using the proposed methods for this specific data set. This study will contribute to ongoing research for early detection of vitality changes that will support foresters and decision makers.}, language = {en} } @article{ZielewskaBuettnerHeurichMuelleretal.2018, author = {Zielewska-B{\"u}ttner, Katarzyna and Heurich, Marco and M{\"u}ller, J{\"o}rg and Braunisch, Veronika}, title = {Remotely Sensed Single Tree Data Enable the Determination of Habitat Thresholds for the Three-Toed Woodpecker (Picoides tridactylus)}, series = {Remote Sensing}, volume = {10}, journal = {Remote Sensing}, number = {12}, issn = {2072-4292}, doi = {10.3390/rs10121972}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197565}, year = {2018}, abstract = {Forest biodiversity conservation requires precise, area-wide information on the abundance and distribution of key habitat structures at multiple spatial scales. We combined airborne laser scanning (ALS) data with color-infrared (CIR) aerial imagery for identifying individual tree characteristics and quantifying multi-scale habitat requirements using the example of the three-toed woodpecker (Picoides tridactylus) (TTW) in the Bavarian Forest National Park (Germany). This bird, a keystone species of boreal and mountainous forests, is highly reliant on bark beetles dwelling in dead or dying trees. While previous studies showed a positive relationship between the TTW presence and the amount of deadwood as a limiting resource, we hypothesized a unimodal response with a negative effect of very high deadwood amounts and tested for effects of substrate quality. Based on 104 woodpecker presence or absence locations, habitat selection was modelled at four spatial scales reflecting different woodpecker home range sizes. The abundance of standing dead trees was the most important predictor, with an increase in the probability of TTW occurrence up to a threshold of 44-50 dead trees per hectare, followed by a decrease in the probability of occurrence. A positive relationship with the deadwood crown size indicated the importance of fresh deadwood. Remote sensing data allowed both an area-wide prediction of species occurrence and the derivation of ecological threshold values for deadwood quality and quantity for more informed conservation management.}, language = {en} } @article{WeiBlaschke2018, author = {Wei, Chunzhu and Blaschke, Thomas}, title = {Pixel-wise vs. object-based impervious surface analysis from remote sensing: correlations with land surface temperature and population density}, series = {Urban Science}, volume = {2}, journal = {Urban Science}, number = {1}, issn = {2413-8851}, doi = {10.3390/urbansci2010002}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197829}, pages = {2}, year = {2018}, abstract = {Impervious surface areas (ISA) are heavily influenced by urban structure and related structural features. We examined the effects of object-based impervious surface spatial pattern analysis on land surface temperature and population density in Guangzhou, China, in comparison to classic per-pixel analyses. An object-based support vector machine (SVM) and a linear spectral mixture analysis (LSMA) were integrated to estimate ISA fraction using images from the Chinese HJ-1B satellite for 2009 to 2011. The results revealed that the integrated object-based SVM-LSMA algorithm outperformed the traditional pixel-wise LSMA algorithm in classifying ISA fraction. More specifically, the object-based ISA spatial patterns extracted were more suitable than pixel-wise patterns for urban heat island (UHI) studies, in which the UHI areas (landscape surface temperature >37 °C) generally feature high ISA fraction values (ISA fraction >50\%). In addition, the object-based spatial patterns enable us to quantify the relationship of ISA with population density (correlation coefficient >0.2 in general), with global human settlement density (correlation coefficient >0.2), and with night-time light map (correlation coefficient >0.4), and, whereas pixel-wise ISA did not yield significant correlations. These results indicate that object-based spatial patterns have a high potential for UHI detection and urbanization monitoring. Planning measures that aim to reduce the urbanization impacts and UHI intensities can be better supported.}, language = {en} } @article{MayrKuenzerGessneretal.2019, author = {Mayr, Stefan and Kuenzer, Claudia and Gessner, Ursula and Klein, Igor and Rutzinger, Martin}, title = {Validation of earth observation time-series: a review for large-area and temporally dense land surface products}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {22}, issn = {2072-4292}, doi = {10.3390/rs11222616}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193202}, year = {2019}, abstract = {Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.}, language = {en} } @article{AbdullahiWesselHuberetal.2019, author = {Abdullahi, Sahra and Wessel, Birgit and Huber, Martin and Wendleder, Anna and Roth, Achim and Kuenzer, Claudia}, title = {Estimating penetration-related X-band InSAR elevation bias: a study over the Greenland ice sheet}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {24}, issn = {2072-4292}, doi = {10.3390/rs11242903}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193902}, year = {2019}, abstract = {Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R\(^2\) = 68\% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection.}, language = {en} } @article{NaeschenDiekkruegerEversetal.2019, author = {N{\"a}schen, Kristian and Diekkr{\"u}ger, Bernd and Evers, Mariele and H{\"o}llermann, Britta and Steinbach, Stefanie and Thonfeld, Frank}, title = {The impact of land use/land cover change (LULCC) on water resources in a tropical catchment in Tanzania under different climate change scenarios}, series = {Sustainability}, volume = {11}, journal = {Sustainability}, number = {24}, issn = {2071-1050}, doi = {10.3390/su11247083}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193825}, year = {2019}, abstract = {Many parts of sub-Saharan Africa (SSA) are prone to land use and land cover change (LULCC). In many cases, natural systems are converted into agricultural land to feed the growing population. However, despite climate change being a major focus nowadays, the impacts of these conversions on water resources, which are essential for agricultural production, is still often neglected, jeopardizing the sustainability of the socio-ecological system. This study investigates historic land use/land cover (LULC) patterns as well as potential future LULCC and its effect on water quantities in a complex tropical catchment in Tanzania. It then compares the results using two climate change scenarios. The Land Change Modeler (LCM) is used to analyze and to project LULC patterns until 2030 and the Soil and Water Assessment Tool (SWAT) is utilized to simulate the water balance under various LULC conditions. Results show decreasing low flows by 6-8\% for the LULC scenarios, whereas high flows increase by up to 84\% for the combined LULC and climate change scenarios. The effect of climate change is stronger compared to the effect of LULCC, but also contains higher uncertainties. The effects of LULCC are more distinct, although crop specific effects show diverging effects on water balance components. This study develops a methodology for quantifying the impact of land use and climate change and therefore contributes to the sustainable management of the investigated catchment, as it shows the impact of environmental change on hydrological extremes (low flow and floods) and determines hot spots, which are critical for environmental development.}, language = {en} } @article{LatifiHeurich2019, author = {Latifi, Hooman and Heurich, Marco}, title = {Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {11}, issn = {2072-4292}, doi = {10.3390/rs11111260}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197358}, year = {2019}, abstract = {Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue "Remote Sensing-Based Forest Inventories from Landscape to Global Scale", which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide.}, language = {en} } @article{UereyenKuenzer2019, author = {Uereyen, Soner and Kuenzer, Claudia}, title = {A review of earth observation-based analyses for major river basins}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {24}, issn = {2072-4292}, doi = {10.3390/rs11242951}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193849}, pages = {2951}, year = {2019}, abstract = {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.}, language = {en} } @article{NillUllmannKneiseletal.2019, author = {Nill, Leon and Ullmann, Tobias and Kneisel, Christof and Sobiech-Wolf, Jennifer and Baumhauer, Roland}, title = {Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {19}, issn = {2072-4292}, doi = {10.3390/rs11192329}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193301}, year = {2019}, abstract = {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.}, language = {en} } @article{UllmannSauerbreyHoffmeisteretal.2019, author = {Ullmann, Tobias and Sauerbrey, Julia and Hoffmeister, Dirk and May, Simon Matthias and Baumhauer, Roland and Bubenzer, Olaf}, title = {Assessing Spatiotemporal Variations of Sentinel-1 InSAR Coherence at Different Time Scales over the Atacama Desert (Chile) between 2015 and 2018}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {24}, issn = {2072-4292}, doi = {10.3390/rs11242960}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193836}, pages = {2960}, year = {2019}, abstract = {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.}, language = {en} } @article{BaumhoerDietzKneiseletal.2019, author = {Baumhoer, Celia A. and Dietz, Andreas J. and Kneisel, C. and Kuenzer, C.}, title = {Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {21}, issn = {2072-4292}, doi = {10.3390/rs11212529}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193150}, pages = {2529}, year = {2019}, abstract = {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.}, language = {en} } @article{PhilippLevick2019, author = {Philipp, Marius B. and Levick, Shaun R.}, title = {Exploring the potential of C-Band SAR in contributing to burn severity mapping in tropical savanna}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {1}, issn = {2072-4292}, doi = {10.3390/rs12010049}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193789}, pages = {49}, year = {2019}, abstract = {The ability to map burn severity and to understand how it varies as a function of time of year and return frequency is an important tool for landscape management and carbon accounting in tropical savannas. Different indices based on optical satellite imagery are typically used for mapping fire scars and for estimating burn severity. However, cloud cover is a major limitation for analyses using optical data over tropical landscapes. To address this pitfall, we explored the suitability of C-band Synthetic Aperture Radar (SAR) data for detecting vegetation response to fire, using experimental fires in northern Australia. Pre- and post-fire results from Sentinel-1 C-band backscatter intensity data were compared to those of optical satellite imagery and were corroborated against structural changes on the ground that we documented through terrestrial laser scanning (TLS). Sentinel-1 C-band backscatter (VH) proved sensitive to the structural changes imparted by fire and was correlated with the Normalised Burn Ratio (NBR) derived from Sentinel-2 optical data. Our results suggest that C-band SAR holds potential to inform the mapping of burn severity in savannas, but further research is required over larger spatial scales and across a broader spectrum of fire regime conditions before automated products can be developed. Combining both Sentinel-1 SAR and Sentinel-2 multi-spectral data will likely yield the best results for mapping burn severity under a range of weather conditions.}, language = {en} } @article{KoehlerKuenzer2020, author = {Koehler, Jonas and Kuenzer, Claudia}, title = {Forecasting spatio-temporal dynamics on the land surface using Earth Observation data — a review}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {21}, issn = {2072-4292}, doi = {10.3390/rs12213513}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216285}, year = {2020}, abstract = {Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.}, language = {en} } @article{LiGuanGaoetal.2020, author = {Li, Ningbo and Guan, Lianwu and Gao, Yanbin and Du, Shitong and Wu, Menghao and Guang, Xingxing and Cong, Xiaodan}, title = {Indoor and outdoor low-cost seamless integrated navigation system based on the integration of INS/GNSS/LIDAR system}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {19}, issn = {2072-4292}, doi = {10.3390/rs12193271}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216229}, year = {2020}, abstract = {Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50\% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80\% success rate in navigation mode switching.}, language = {en} } @article{ThonfeldSteinbachMuroetal.2020, author = {Thonfeld, Frank and Steinbach, Stefanie and Muro, Javier and Kirimi, Fridah}, title = {Long-term land use/land cover change assessment of the Kilombero catchment in Tanzania using random forest classification and robust change vector analysis}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs12071057}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203513}, year = {2020}, abstract = {Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment.}, language = {en} } @article{UlloaTorrealbaStahlmannWegmannetal.2020, author = {Ulloa-Torrealba, Yrneh and Stahlmann, Reinhold and Wegmann, Martin and Koellner, Thomas}, title = {Over 150 years of change: object-oriented analysis of historical land cover in the Main river catchment, Bavaria/Germany}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {24}, issn = {2072-4292}, doi = {10.3390/rs12244048}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220029}, year = {2020}, abstract = {The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600\%, a severe loss of cropland (-24\%), a moderate reduction in meadows (-4\%), and a small gain in forests (+4\%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82\% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65\% for historical maps of poor quality and the maximum was 91\% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.}, language = {en} } @article{DirscherlDietzKneiseletal.2020, author = {Dirscherl, Mariel and Dietz, Andreas J. and Kneisel, Christof and Kuenzer, Claudia}, title = {Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs12071203}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203735}, year = {2020}, abstract = {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.}, language = {en} } @article{StereńczakLaurinChiricietal.2020, author = {Stereńczak, Krzysztof and Laurin, Gaia Vaglio and Chirici, Gherardo and Coomes, David A. and Dalponte, Michele and Latifi, Hooman and Puletti, Nicola}, title = {Global Airborne Laser Scanning Data Providers Database (GlobALS) — a new tool for monitoring ecosystems and biodiversity}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {11}, issn = {2072-4292}, doi = {10.3390/rs12111877}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207819}, year = {2020}, abstract = {Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS.}, language = {en} } @article{HuthGessnerKleinetal.2020, author = {Huth, Juliane and Gessner, Ursula and Klein, Igor and Yesou, Herv{\´e} and Lai, Xijun and Oppelt, Natascha and Kuenzer, Claudia}, title = {Analyzing water dynamics based on Sentinel-1 time series — a study for Dongting Lake wetlands in China}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {11}, issn = {2072-4292}, doi = {10.3390/rs12111761}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205977}, year = {2020}, abstract = {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.}, language = {en} } @article{ForkuorUllmannGriesbeck2020, author = {Forkuor, Gerald and Ullmann, Tobias and Griesbeck, Mario}, title = {Mapping and monitoring small-scale mining activities in Ghana using Sentinel-1 time series (2015-2019)}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {6}, issn = {2072-4292}, doi = {10.3390/rs12060911}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203204}, year = {2020}, abstract = {Illegal small-scale mining (galamsey) in South-Western Ghana has grown tremendously in the last decade and caused significant environmental degradation. Excessive cloud cover in the area has limited the use of optical remote sensing data to map and monitor the extent of these activities. This study investigated the use of annual time-series Sentinel-1 data to map and monitor illegal mining activities along major rivers in South-Western Ghana between 2015 and 2019. A change detection approach, based on three time-series features — minimum, mean, maximum — was used to compute a backscatter threshold value suitable to identify/detect mining-induced land cover changes in the study area. Compared to the mean and maximum, the minimum time-series feature (in both VH and VV polarization) was found to be more sensitive to changes in backscattering within the period of investigation. Our approach permitted the detection of new illegal mining areas on an annual basis. A backscatter threshold value of +1.65 dB was found suitable for detecting illegal mining activities in the study area. Application of this threshold revealed illegal mining area extents of 102 km\(^2\), 60 km\(^2\) and 33 km\(^2\) for periods 2015/2016-2016/2017, 2016/2017-2017/2018 and 2017/2018-2018/2019, respectively. The observed decreasing trend in new illegal mining areas suggests that efforts at stopping illegal mining yielded positive results in the period investigated. Despite the advantages of Synthetic Aperture Radar data in monitoring phenomena in cloud-prone areas, our analysis revealed that about 25\% of the Sentinel-1 data, mostly acquired in March and October (beginning and end of rainy season respectively), were unusable due to atmospheric effects from high intensity rainfall events. Further investigation in other geographies and climatic regions is needed to ascertain the susceptibility of Sentinel-1 data to atmospheric conditions.}, language = {en} } @article{HoeserBachoferKuenzer2020, author = {Hoeser, Thorsten and Bachofer, Felix and Kuenzer, Claudia}, title = {Object detection and image segmentation with deep learning on Earth Observation data: a review — part II: applications}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {18}, issn = {2072-4292}, doi = {10.3390/rs12183053}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213152}, year = {2020}, abstract = {In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.}, language = {en} } @article{HoeserKuenzer2020, author = {Hoeser, Thorsten and Kuenzer, Claudia}, title = {Object detection and image segmentation with deep learning on Earth observation data: a review-part I: evolution and recent trends}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {10}, issn = {2072-4292}, doi = {10.3390/rs12101667}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205918}, year = {2020}, abstract = {Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and rapidly developing field mainly driven by advances in computer vision (CV). To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019. Thereby, we highlight the connections between the most important CNN architectures and cornerstones coming from CV in order to alleviate the evaluation of modern DL models. Furthermore, we briefly outline the evolution of the most popular DL frameworks and provide a summary of datasets in EO. By discussing well performing DL architectures on these datasets as well as reflecting on advances made in CV and their impact on future research in EO, we narrow the gap between the reviewed, theoretical concepts from CV and practical application in EO.}, language = {en} } @article{HeinemannSiegmannThonfeldetal.2020, author = {Heinemann, Sascha and Siegmann, Bastian and Thonfeld, Frank and Muro, Javier and Jedmowski, Christoph and Kemna, Andreas and Kraska, Thorsten and Muller, Onno and Schultz, Johannes and Udelhoven, Thomas and Wilke, Norman and Rascher, Uwe}, title = {Land surface temperature retrieval for agricultural areas using a novel UAV platform equipped with a thermal infrared and multispectral sensor}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs12071075}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203557}, year = {2020}, abstract = {Land surface temperature (LST) is a fundamental parameter within the system of the Earth's surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data.}, language = {en} } @article{HolzwarthThonfeldAbdullahietal.2020, author = {Holzwarth, Stefanie and Thonfeld, Frank and Abdullahi, Sahra and Asam, Sarah and Da Ponte Canova, Emmanuel and Gessner, Ursula and Huth, Juliane and Kraus, Tanja and Leutner, Benjamin and Kuenzer, Claudia}, title = {Earth Observation based monitoring of forests in Germany: a review}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {21}, issn = {2072-4292}, doi = {10.3390/rs12213570}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216334}, year = {2020}, abstract = {Forests in Germany cover around 11.4 million hectares and, thus, a share of 32\% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany's forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.}, language = {en} } @article{SognoTraidlHoffmannKuenzer2020, author = {Sogno, Patrick and Traidl-Hoffmann, Claudia and Kuenzer, Claudia}, title = {Earth Observation data supporting non-communicable disease research: a review}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {16}, issn = {2072-4292}, doi = {10.3390/rs12162541}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-211113}, year = {2020}, abstract = {A disease is non-communicable when it is not transferred from one person to another. Typical examples include all types of cancer, diabetes, stroke, or allergies, as well as mental diseases. Non-communicable diseases have at least two things in common — environmental impact and chronicity. These diseases are often associated with reduced quality of life, a higher rate of premature deaths, and negative impacts on a countries' economy due to healthcare costs and missing work force. Additionally, they affect the individual's immune system, which increases susceptibility toward communicable diseases, such as the flu or other viral and bacterial infections. Thus, mitigating the effects of non-communicable diseases is one of the most pressing issues of modern medicine, healthcare, and governments in general. Apart from the predisposition toward such diseases (the genome), their occurrence is associated with environmental parameters that people are exposed to (the exposome). Exposure to stressors such as bad air or water quality, noise, extreme heat, or an overall unnatural surrounding all impact the susceptibility to non-communicable diseases. In the identification of such environmental parameters, geoinformation products derived from Earth Observation data acquired by satellites play an increasingly important role. In this paper, we present a review on the joint use of Earth Observation data and public health data for research on non-communicable diseases. We analyzed 146 articles from peer-reviewed journals (Impact Factor ≥ 2) from all over the world that included Earth Observation data and public health data for their assessments. Our results show that this field of synergistic geohealth analyses is still relatively young, with most studies published within the last five years and within national boundaries. While the contribution of Earth Observation, and especially remote sensing-derived geoinformation products on land surface dynamics is on the rise, there is still a huge potential for transdisciplinary integration into studies. We see the necessity for future research and advocate for the increased incorporation of thematically profound remote sensing products with high spatial and temporal resolution into the mapping of exposomes and thus the vulnerability and resilience assessment of a population regarding non-communicable diseases.}, language = {en} } @article{ReinermannAsamKuenzer2020, author = {Reinermann, Sophie and Asam, Sarah and Kuenzer, Claudia}, title = {Remote Sensing of Grassland Production and Management - A Review}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {12}, issn = {2072-4292}, doi = {10.3390/rs12121949}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207799}, year = {2020}, abstract = {Grasslands cover one third of the earth's terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.}, language = {en} } @article{DhillonDahmsKuebertFlocketal.2020, author = {Dhillon, Maninder Singh and Dahms, Thorsten and Kuebert-Flock, Carina and Borg, Erik and Conrad, Christopher and Ullmann, Tobias}, title = {Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {11}, issn = {2072-4292}, doi = {10.3390/rs12111819}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207845}, year = {2020}, abstract = {This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m\(^2\)) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m\(^2\)). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).}, language = {en} } @article{ReinersAsamFreyetal.2021, author = {Reiners, Philipp and Asam, Sarah and Frey, Corinne and Holzwarth, Stefanie and Bachmann, Martin and Sobrino, Jose and G{\"o}ttsche, Frank-M. and Bendix, J{\"o}rg and Kuenzer, Claudia}, title = {Validation of AVHRR Land Surface Temperature with MODIS and in situ LST — a TIMELINE thematic processor}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {17}, issn = {2072-4292}, doi = {10.3390/rs13173473}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246051}, year = {2021}, abstract = {Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed.}, language = {en} } @article{DechHolzwarthAsametal.2021, author = {Dech, Stefan and Holzwarth, Stefanie and Asam, Sarah and Andresen, Thorsten and Bachmann, Martin and Boettcher, Martin and Dietz, Andreas and Eisfelder, Christina and Frey, Corinne and Gesell, Gerhard and Gessner, Ursula and Hirner, Andreas and Hofmann, Matthias and Kirches, Grit and Klein, Doris and Klein, Igor and Kraus, Tanja and Krause, Detmar and Plank, Simon and Popp, Thomas and Reinermann, Sophie and Reiners, Philipp and Roessler, Sebastian and Ruppert, Thomas and Scherbachenko, Alexander and Vignesh, Ranjitha and Wolfmueller, Meinhard and Zwenzner, Hendrik and Kuenzer, Claudia}, title = {Potential and challenges of harmonizing 40 years of AVHRR data: the TIMELINE experience}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {18}, issn = {2072-4292}, doi = {10.3390/rs13183618}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246134}, year = {2021}, abstract = {Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.}, language = {en} } @article{KhareDeslauriersMorinetal.2021, author = {Khare, Siddhartha and Deslauriers, Annie and Morin, Hubert and Latifi, Hooman and Rossi, Sergio}, title = {Comparing time-lapse PhenoCams with satellite observations across the boreal forest of Quebec, Canada}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {1}, issn = {2072-4292}, doi = {10.3390/rs14010100}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252213}, year = {2021}, abstract = {Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R\(^2\) from 0.66 to 0.85) than NDVI (R\(^2\) from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather.}, language = {en} } @article{MeisterLangeAthinodorouUllmann2021, author = {Meister, Julia and Lange-Athinodorou, Eva and Ullmann, Tobias}, title = {Preface: Special Issue "Geoarchaeology of the Nile Delta"}, series = {E\&G Quarternary Science Journal}, volume = {70}, journal = {E\&G Quarternary Science Journal}, doi = {10.5194/egqsj-70-187-2021}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-261195}, pages = {187-190}, year = {2021}, abstract = {No abstract available.}, language = {en} } @article{RiyasSyedKumaretal.2021, author = {Riyas, Moidu Jameela and Syed, Tajdarul Hassan and Kumar, Hrishikesh and Kuenzer, Claudia}, title = {Detecting and analyzing the evolution of subsidence due to coal fires in Jharia coalfield, India using Sentinel-1 SAR data}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {8}, issn = {2072-4292}, doi = {10.3390/rs13081521}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-236703}, year = {2021}, abstract = {Public safety and socio-economic development of the Jharia coalfield (JCF) in India is critically dependent on precise monitoring and comprehensive understanding of coal fires, which have been burning underneath for more than a century. This study utilizes New-Small BAseline Subset (N-SBAS) technique to compute surface deformation time series for 2017-2020 to characterize the spatiotemporal dynamics of coal fires in JCF. The line-of-sight (LOS) surface deformation estimated from ascending and descending Sentinel-1 SAR data are subsequently decomposed to derive precise vertical subsidence estimates. The most prominent subsidence (~22 cm) is observed in Kusunda colliery. The subsidence regions also correspond well with the Landsat-8 based thermal anomaly map and field evidence. Subsequently, the vertical surface deformation time-series is analyzed to characterize temporal variations within the 9.5 km\(^2\) area of coal fires. Results reveal that nearly 10\% of the coal fire area is newly formed, while 73\% persisted throughout the study period. Vulnerability analyses performed in terms of the susceptibility of the population to land surface collapse demonstrate that Tisra, Chhatatanr, and Sijua are the most vulnerable towns. Our results provide critical information for developing early warning systems and remediation strategies.}, language = {en} } @article{KhareLatifiKhare2021, author = {Khare, Suyash and Latifi, Hooman and Khare, Siddhartha}, title = {Vegetation growth analysis of UNESCO World Heritage Hyrcanian forests using multi-sensor optical remote sensing data}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {19}, issn = {2072-4292}, doi = {10.3390/rs13193965}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-248398}, year = {2021}, abstract = {Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility.}, language = {en} } @article{UphusLuepkeYuanetal.2021, author = {Uphus, Lars and L{\"u}pke, Marvin and Yuan, Ye and Benjamin, Caryl and Englmeier, Jana and Fricke, Ute and Ganuza, Cristina and Schwindl, Michael and Uhler, Johannes and Menzel, Annette}, title = {Climate effects on vertical forest phenology of Fagus sylvatica L., sensed by Sentinel-2, time lapse camera, and visual ground observations}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {19}, issn = {2072-4292}, doi = {10.3390/rs13193982}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-248419}, year = {2021}, abstract = {Contemporary climate change leads to earlier spring phenological events in Europe. In forests, in which overstory strongly regulates the microclimate beneath, it is not clear if further change equally shifts the timing of leaf unfolding for the over- and understory of main deciduous forest species, such as Fagus sylvatica L. (European beech). Furthermore, it is not known yet how this vertical phenological (mis)match — the phenological difference between overstory and understory — affects the remotely sensed satellite signal. To investigate this, we disentangled the start of season (SOS) of overstory F.sylvatica foliage from understory F. sylvatica foliage in forests, within nine quadrants of 5.8 × 5.8 km, stratified over a temperature gradient of 2.5 °C in Bavaria, southeast Germany, in the spring seasons of 2019 and 2020 using time lapse cameras and visual ground observations. We explained SOS dates and vertical phenological (mis)match by canopy temperature and compared these to Sentinel-2 derived SOS in response to canopy temperature. We found that overstory SOS advanced with higher mean April canopy temperature (visual ground observations: -2.86 days per °C; cameras: -2.57 days per °C). However, understory SOS was not significantly affected by canopy temperature. This led to an increase of vertical phenological mismatch with increased canopy temperature (visual ground observations: +3.90 days per °C; cameras: +2.52 days per °C). These results matched Sentinel-2-derived SOS responses, as pixels of higher canopy height advanced more by increased canopy temperature than pixels of lower canopy height. The results may indicate that, with further climate change, spring phenology of F. sylvatica overstory will advance more than F. sylvatica understory, leading to increased vertical phenological mismatch in temperate deciduous forests. This may have major ecological effects, but also methodological consequences for the field of remote sensing, as what the signal senses highly depends on the pixel mean canopy height and the vertical (mis)match.}, language = {en} } @article{GhazaryanRienowOldenburgetal.2021, author = {Ghazaryan, Gohar and Rienow, Andreas and Oldenburg, Carsten and Thonfeld, Frank and Trampnau, Birte and Sticksel, Sarah and J{\"u}rgens, Carsten}, title = {Monitoring of urban sprawl and densification processes in Western Germany in the light of SDG indicator 11.3.1 based on an automated retrospective classification approach}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {9}, issn = {2072-4292}, doi = {10.3390/rs13091694}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-236671}, year = {2021}, abstract = {By 2050, two-third of the world's population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75\%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30\% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning.}, language = {en} }