@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{ClaussYanKuenzer2016, author = {Clauss, Kersten and Yan, Huimin and Kuenzer, Claudia}, title = {Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series}, series = {Remote Sensing}, volume = {8}, journal = {Remote Sensing}, number = {5}, doi = {10.3390/rs8050434}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180557}, year = {2016}, abstract = {Rice is an important food crop and a large producer of green-house relevant methane. Accurate and timely maps of paddy fields are most important in the context of food security and greenhouse gas emission modelling. During their life-cycle, rice plants undergo a phenological development that influences their interaction with waves in the visible light and infrared spectrum. Rice growth has a distinctive signature in time series of remotely-sensed data. We used time series of MODIS (Moderate Resolution Imaging Spectroradiometer) products MOD13Q1 and MYD13Q1 and a one-class support vector machine to detect these signatures and classify paddy rice areas in continental China. Based on these classifications, we present a novel product for continental China that shows rice areas for the years 2002, 2005, 2010 and 2014 at 250-m resolution. Our classification has an overall accuracy of 0.90 and a kappa coefficient of 0.77 compared to our own reference dataset for 2014 and correlates highly with rice area statistics from China's Statistical Yearbooks (R2 of 0.92 for 2010, 0.92 for 2005 and 0.90 for 2002). Moderate resolution time series analysis allows accurate and timely mapping of rice paddies over large areas with diverse cropping schemes.}, language = {en} } @article{AyanuConradJentschetal.2015, author = {Ayanu, Yohannes and Conrad, Christopher and Jentsch, Anke and Koellner, Thomas}, title = {Unveiling undercover cropland inside forests using landscape variables: a supplement to remote sensing image classification}, series = {PLoS ONE}, volume = {10}, journal = {PLoS ONE}, number = {6}, doi = {10.1371/journal.pone.0130079}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151686}, pages = {e0130079}, year = {2015}, abstract = {The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential.}, 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{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{LauterbachBorrmannHessetal.2015, author = {Lauterbach, Helge A. and Borrmann, Dorit and Heß, Robin and Eck, Daniel and Schilling, Klaus and N{\"u}chter, Andreas}, title = {Evaluation of a Backpack-Mounted 3D Mobile Scanning System}, series = {Remote Sensing}, volume = {7}, journal = {Remote Sensing}, number = {10}, doi = {10.3390/rs71013753}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-126247}, pages = {13753-13781}, year = {2015}, abstract = {Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario.}, language = {en} } @article{WalzWegmannLeutneretal.2015, author = {Walz, Yvonne and Wegmann, Martin and Leutner, Benjamin and Dech, Stefan and Vounatsou, Penelope and N'Goran, Eli{\´e}zer K. and Raso, Giovanna and Utzinger, J{\"u}rg}, title = {Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling}, series = {Geospatial Health}, volume = {10}, journal = {Geospatial Health}, number = {2}, doi = {10.4081/gh.2015.398}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-126148}, pages = {398}, year = {2015}, abstract = {Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in C{\^o}te d'Ivoire using high- and moderateresolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70\% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixelbased modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.}, language = {en} } @article{WalzWegmannDechetal.2015, author = {Walz, Yvonne and Wegmann, Martin and Dech, Stefan and Vounastou, Penelope and Poda, Jean-Noel and N'Goran, Eli{\´e}zer K. and Raso, Giovanna and Utzinger, J{\"u}rg}, title = {Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing}, series = {PLoS Neglected Tropical Diseases}, volume = {9}, journal = {PLoS Neglected Tropical Diseases}, number = {11}, doi = {10.1371/journal.pntd.0004217}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125845}, pages = {e0004217}, year = {2015}, abstract = {Background Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. Methodology We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in C{\^o}te d'Ivoire and validated against readily available survey data from school-aged children. Principal Findings Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of C{\^o}te d'Ivoire. Conclusions/Significance A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data.}, language = {en} } @article{ZoungranaConradAmekudzietal.2015, author = {Zoungrana, Benewinde Jean-Bosco and Conrad, Christopher and Amekudzi, Leonard K. and Thiel, Michael and Dapola Da, Evariste and Forkuor, Gerald and L{\"o}w, Fabian}, title = {Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa}, series = {Remote Sensing}, volume = {7}, journal = {Remote Sensing}, number = {9}, doi = {10.3390/rs70912076}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125866}, pages = {12076-12102}, year = {2015}, abstract = {Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92\%. Natural vegetation loss was estimated to be 17.9\% ± 2.5\% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors.}, 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{TimmermansvanderTolTimmermansetal.2015, author = {Timmermans, Wim J. and van der Tol, Christiaan and Timmermans, Joris and Ucer, Murat and Chen, Xuelong and Alonso, Luis and Moreno, Jose and Carrara, Arnaud and Lopez, Ramon and Fernando de la Cruz, Tercero and Corcoles, Horacio L. and de Miguel, Eduardo and Sanchez, Jose A. G. and Perez, Irene and Belen, Perez and Munoz, Juan-Carlos J. and Skokovic, Drazen and Sobrino, Jose and Soria, Guillem and MacArthur, Alasdair and Vescovo, Loris and Reusen, Ils and Andreu, Ana and Burkart, Andreas and Cilia, Chiara and Contreras, Sergio and Corbari, Chiara and Calleja, Javier F. and Guzinski, Radoslaw and Hellmann, Christine and Herrmann, Ittai and Kerr, Gregoire and Lazar, Adina-Laura and Leutner, Benjamin and Mendiguren, Gorka and Nasilowska, Sylwia and Nieto, Hector and Pachego-Labrador, Javier and Pulanekar, Survana and Raj, Rahul and Schikling, Anke and Siegmann, Bastian and von Bueren, Stefanie and Su, Zhongbo (Bob)}, title = {An Overview of the Regional Experiments for Land-atmosphere Exchanges 2012 (REFLEX 2012) Campaign}, series = {Acta Geophysica}, volume = {63}, journal = {Acta Geophysica}, number = {6}, doi = {10.2478/s11600-014-0254-1}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-136491}, pages = {1465-1484}, year = {2015}, abstract = {The REFLEX 2012 campaign was initiated as part of a training course on the organization of an airborne campaign to support advancement of the understanding of land-atmosphere interaction processes. This article describes the campaign, its objectives and observations, remote as well as in situ. The observations took place at the experimental Las Tiesas farm in an agricultural area in the south of Spain. During the period of ten days, measurements were made to capture the main processes controlling the local and regional land-atmosphere exchanges. Apart from multi-temporal, multi-directional and multi-spatial space-borne and airborne observations, measurements of the local meteorology, energy fluxes, soil temperature profiles, soil moisture profiles, surface temperature, canopy structure as well as leaf-level measurements were carried out. Additional thermo-dynamical monitoring took place at selected sites. After presenting the different types of measurements, some examples are given to illustrate the potential of the observations made.}, language = {en} } @article{NguyenKerstenSenmaoetal.2015, author = {Nguyen, Duy Ba and Kersten, Clauss and Senmao, Cao and Vahid, Naeimi and Kuenzer, Claudia and Wagner, Wolfgang}, title = {Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data}, series = {Remote Sensing}, volume = {7}, journal = {Remote Sensing}, number = {12}, doi = {10.3390/rs71215808}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-137554}, pages = {15868-15893}, year = {2015}, abstract = {Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double- and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3\% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R-2 of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions.}, language = {en} } @article{SchwindtKneisel2011, author = {Schwindt, Daniel and Kneisel, Christof}, title = {Optimisation of quasi-3D electrical resistivity imaging - application and inversion for investigating heterogeneous mountain permafrost}, series = {The Cryosphere Discuss}, volume = {5}, journal = {The Cryosphere Discuss}, doi = {10.5194/tcd-5-3383-2011}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-138017}, pages = {3383-3421}, year = {2011}, abstract = {This study aimed to optimise the application, efficiency and interpretability of quasi-3D resistivity imaging for investigating the heterogeneous permafrost distribution at mountain sites by a systematic forward modelling approach. A three dimensional geocryologic model, representative for most mountain permafrost settings, was developed. Based on this geocryologic model quasi-3D models were generated by collating synthetic orthogonal 2D arrays, demonstrating the effects of array types and electrode spacing on resolution and interpretability of the inversion results. The effects of minimising the number of 2D arrays per quasi-3D grid were tested by enlarging the spacing between adjacent lines and by reducing the number of perpendicular tie lines with regard to model resolution and loss of information value. Synthetic and measured quasi-3D models were investigated with regard to the lateral and vertical resolution, reliability of inverted resistivity values, the possibility of a quantitative interpretation of resistivities and the response of the inversion process on the validity of quasi-3D models. Results show that setups using orthogonal 2D arrays with electrode spacings of 2 m and 3 m are capable of delineating lateral heterogeneity with high accuracy and also deliver reliable data on active layer thickness. Detection of permafrost thickness, especially if the permafrost base is close to the penetration depth of the setups, and the reliability of absolute resistivity values emerged to be a weakness of the method. Quasi-3D imaging has proven to be a promising tool for investigating permafrost in mountain environments especially for delineating the often small-scale permafrost heterogeneity, and therefore provides an enhanced possibility for aligning permafrost distribution with site specific surface properties and morphological settings.}, language = {en} } @article{NaidooDuPreezStuartHilletal.2012, author = {Naidoo, Robin and Du Preez, Pierre and Stuart-Hill, Greg and Jago, Mark and Wegmann, Martin}, title = {Home on the Range: Factors Explaining Partial Migration of African Buffalo in a Tropical Environment}, series = {PLoS One}, volume = {7}, journal = {PLoS One}, number = {5}, doi = {10.1371/journal.pone.0036527}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-134935}, pages = {e36527}, year = {2012}, abstract = {Partial migration (when only some individuals in a population undertake seasonal migrations) is common in many species and geographical contexts. Despite the development of modern statistical methods for analyzing partial migration, there have been no studies on what influences partial migration in tropical environments. We present research on factors affecting partial migration in African buffalo (Syncerus caffer) in northeastern Namibia. Our dataset is derived from 32 satellite tracking collars, spans 4 years and contains over 35,000 locations. We used remotely sensed data to quantify various factors that buffalo experience in the dry season when making decisions on whether and how far to migrate, including potential man-made and natural barriers, as well as spatial and temporal heterogeneity in environmental conditions. Using an information-theoretic, non-linear regression approach, our analyses showed that buffalo in this area can be divided into 4 migratory classes: migrants, non-migrants, dispersers, and a new class that we call "expanders". Multimodel inference from least-squares regressions of wet season movements showed that environmental conditions (rainfall, fires, woodland cover, vegetation biomass), distance to the nearest barrier (river, fence, cultivated area) and social factors (age, size of herd at capture) were all important in explaining variation in migratory behaviour. The relative contributions of these variables to partial migration have not previously been assessed for ungulates in the tropics. Understanding the factors driving migratory decisions of wildlife will lead to better-informed conservation and land-use decisions in this area.}, language = {en} } @article{KotteLoewHuberetal.2012, author = {Kotte, K. and L{\"o}w, F. and Huber, S. G. and Krause, T. and Mulder, I. and Sch{\"o}ler, H. F.}, title = {Organohalogen emissions from saline environments - spatial extrapolation using remote sensing as most promising tool}, series = {Biogeosciences}, volume = {9}, journal = {Biogeosciences}, number = {3}, doi = {10.5194/bg-9-1225-2012}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-134265}, pages = {1225-1235}, year = {2012}, abstract = {Due to their negative water budget most recent semi-/arid regions are characterized by vast evaporates (salt lakes and salty soils). We recently identified those hyper-saline environments as additional sources for a multitude of volatile halogenated organohalogens (VOX). These compounds can affect the ozone layer of the stratosphere and play a key role in the production of aerosols. A remote sensing based analysis was performed in the Southern Aral Sea basin, providing information of major soil types as well as their extent and spatial and temporal evolution. VOX production has been determined in dry and moist soil samples after 24 h. Several C1- and C2 organohalogens have been found in hyper-saline topsoil profiles, including CH3Cl, CH3Br, CHBr3 and CHCl3. The range of organohalogens also includes trans-1,2-dichloroethene (DCE), which is reported here to be produced naturally for the first time. Using MODIS time series and supervised image classification a daily production rate for DCE has been calculated for the 15 000 km\(^2\) ranging research area in the southern Aralkum. The applied laboratory setup simulates a short-term change in climatic conditions, starting from dried-out saline soil that is instantly humidified during rain events or flooding. It describes the general VOX production potential, but allows only for a rough estimation of resulting emission loads. VOX emissions are expected to increase in the future since the area of salt affected soils is expanding due to the regressing Aral Sea. Opportunities, limits and requirements of satellite based rapid change detection and salt classification are discussed.}, language = {en} } @article{ElsebergBorrmannNuechter2013, author = {Elseberg, Jan and Borrmann, Dorit and N{\"u}chter, Andreas}, title = {Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms}, series = {Remote Sensing}, volume = {5}, journal = {Remote Sensing}, number = {11}, doi = {10.3390/rs5115871}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-130478}, pages = {5871-5906}, year = {2013}, abstract = {Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets.}, language = {en} } @article{NaeimiLeinenkugelSabeletal.2013, author = {Naeimi, Vahid and Leinenkugel, Patrick and Sabel, Daniel and Wagner, Wolfgang and Apel, Heiko and Kuenzer, Claudia}, title = {Evaluation of Soil Moisture Retrieval from the ERS and Metop Scatterometers in the Lower Mekong Basin}, series = {Remote Sensing}, volume = {5}, journal = {Remote Sensing}, number = {4}, doi = {10.3390/rs5041603}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-130480}, pages = {1603-1623}, year = {2013}, abstract = {The natural environment and livelihoods in the Lower Mekong Basin (LMB) are significantly affected by the annual hydrological cycle. Monitoring of soil moisture as a key variable in the hydrological cycle is of great interest in a number of Hydrological and agricultural applications. In this study we evaluated the quality and spatiotemporal variability of the soil moisture product retrieved from C-band scatterometers data across the LMB sub-catchments. The soil moisture retrieval algorithm showed reasonable performance in most areas of the LMB with the exception of a few sub-catchments in the eastern parts of Laos, where the land cover is characterized by dense vegetation. The best performance of the retrieval algorithm was obtained in agricultural regions. Comparison of the available in situ evaporation data in the LMB and the Basin Water Index (BWI), an indicator of the basin soil moisture condition, showed significant negative correlations up to R = -0.85. The inter-annual variation of the calculated BWI was also found corresponding to the reported extreme hydro-meteorological events in the Mekong region. The retrieved soil moisture data show high correlation (up to R = 0.92) with monthly anomalies of precipitation in non-irrigated regions. In general, the seasonal variability of soil moisture in the LMB was well captured by the retrieval method. The results of analysis also showed significant correlation between El Ni{\~n}o events and the monthly BWI anomaly measurements particularly for the month May with the maximum correlation of R = 0.88.}, language = {en} } @article{DubovykMenzConradetal.2012, author = {Dubovyk, Olena and Menz, Gunter and Conrad, Christopher and Kann, Elena and Machwitz, Miriam and Khamzina, Asia}, title = {Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling}, series = {Environmental Monitoring and Assessment}, volume = {185}, journal = {Environmental Monitoring and Assessment}, number = {6}, doi = {10.1007/s10661-012-2904-6}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-129912}, pages = {4775-4790}, year = {2012}, abstract = {Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000-2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region's area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds = 330 \%), land-use intensity (odds = 103 \%), low soil quality (odds = 49 \%), slope (odds = 29 \%), and salinity of the groundwater (odds = 26 \%). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.}, 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{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{UereyenBachoferKuenzer2022, author = {Uereyen, Soner and Bachofer, Felix and Kuenzer, Claudia}, title = {A framework for multivariate analysis of land surface dynamics and driving variables — a case study for Indo-Gangetic river basins}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {1}, issn = {2072-4292}, doi = {10.3390/rs14010197}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-255295}, year = {2022}, abstract = {The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.}, 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{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{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{FaOliveroRealetal.2015, author = {Fa, John E. and Olivero, Jes{\´u}s and Real, Raimundo and Farf{\´a}n, Miguel A. and M{\´a}rquez, Ana L. and Vargas, J. Mario and Ziegler, Stefan and Wegmann, Martin and Brown, David and Margetts, Barrie and Nasi, Robert}, title = {Disentangling the relative effects of bushmeat availability on human nutrition in central Africa}, series = {Scientific Reports}, volume = {5}, journal = {Scientific Reports}, number = {8168}, doi = {10.1038/srep08168}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144110}, year = {2015}, abstract = {We studied links between human malnutrition and wild meat availability within the Rainforest Biotic Zone in central Africa. We distinguished two distinct hunted mammalian diversity distributions, one in the rainforest areas (Deep Rainforest Diversity, DRD) containing taxa of lower hunting sustainability, the other in the northern rainforest-savanna mosaic, with species of greater hunting potential (Marginal Rainforest Diversity, MRD). Wild meat availability, assessed by standing crop mammalian biomass, was greater in MRD than in DRD areas. Predicted bushmeat extraction was also higher in MRD areas. Despite this, stunting of children, a measure of human malnutrition, was greater in MRD areas. Structural equation modeling identified that, in MRD areas, mammal diversity fell away from urban areas, but proximity to these positively influenced higher stunting incidence. In DRD areas, remoteness and distance from dense human settlements and infrastructures explained lower stunting levels. Moreover, stunting was higher away from protected areas. Our results suggest that in MRD areas, forest wildlife rational use for better human nutrition is possible. By contrast, the relatively low human populations in DRD areas currently offer abundant opportunities for the continued protection of more vulnerable mammals and allow dietary needs of local populations to be met.}, 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{IbebuchiSchoenbeinPaeth2022, author = {Ibebuchi, Chibuike Chiedozie and Sch{\"o}nbein, Daniel and Paeth, Heiko}, title = {On the added value of statistical post-processing of regional climate models to identify homogeneous patterns of summer rainfall anomalies in Germany}, series = {Climate Dynamics}, volume = {59}, journal = {Climate Dynamics}, number = {9-10}, issn = {0930-7575}, doi = {10.1007/s00382-022-06258-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324122}, pages = {2769-2783}, year = {2022}, abstract = {A fuzzy classification scheme that results in physically interpretable meteorological patterns associated with rainfall generation is applied to classify homogeneous regions of boreal summer rainfall anomalies in Germany. Four leading homogeneous regions are classified, representing the western, southeastern, eastern, and northern/northwestern parts of Germany with some overlap in the central parts of Germany. Variations of the sea level pressure gradient across Europe, e.g., between the continental and maritime regions, is the major phenomenon that triggers the time development of the rainfall regions by modulating wind patterns and moisture advection. Two regional climate models (REMO and CCLM4) were used to investigate the capability of climate models to reproduce the observed summer rainfall regions. Both regional climate models (RCMs) were once driven by the ERA-Interim reanalysis and once by the MPI-ESM general circulation model (GCM). Overall, the RCMs exhibit good performance in terms of the regionalization of summer rainfall in Germany; though the goodness-of-match with the rainfall regions/patterns from observational data is low in some cases and the REMO model driven by MPI-ESM fails to reproduce the western homogeneous rainfall region. Under future climate change, virtually the same leading modes of summer rainfall occur, suggesting that the basic synoptic processes associated with the regional patterns remain the same over Germany. We have also assessed the added value of bias-correcting the MPI-ESM driven RCMs using a simple linear scaling approach. The bias correction does not significantly alter the identification of homogeneous rainfall regions and, hence, does not improve their goodness-of-match compared to the observed patterns, except for the one case where the original RCM output completely fails to reproduce the observed pattern. While the linear scaling method improves the basic statistics of precipitation, it does not improve the simulated meteorological patterns represented by the precipitation regimes.}, language = {en} } @article{GeyerLandingMeieretal.2023, author = {Geyer, Gerd and Landing, Ed and Meier, Stefan and H{\"o}hn, Stefan}, title = {Oldest known West Gondwanan graptolite: Ovetograptus? sp. (lower Agdzian/lowest Wuliuan; basal Middle Cambrian) of the Franconian Forest, Germany, and review of pre-Furongian graptolithoids}, series = {Pal{\"a}ontologische Zeitschrift}, volume = {97}, journal = {Pal{\"a}ontologische Zeitschrift}, number = {4}, issn = {0031-0220}, doi = {10.1007/s12542-022-00627-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324099}, pages = {677-686}, year = {2023}, abstract = {The occurrence of a likely graptolite in lowest Wuliuan strata of the Franconian Forest almost certainly records the oldest known graptolithoid hemichordate in West Gondwana and possibly the oldest graptolite presently known. The fossil is a delicate, erect, apparently unbranched rhabdosome with narrow thecae tentatively assigned to the poorly known genus Ovetograptus of the Dithecodendridae. This report includes an overview of pre-Furongian graptolithoids with slight corrections on the stratigraphic position of earlier reported species.}, language = {en} } @article{RaiZieglerAbeletal.2022, author = {Rai, P. and Ziegler, K. and Abel, D. and Pollinger, F. and Paeth, H.}, title = {Performance of a regional climate model with interactive vegetation (REMO-iMOVE) over Central Asia}, series = {Theoretical and Applied Climatology}, volume = {150}, journal = {Theoretical and Applied Climatology}, number = {3-4}, issn = {0177-798X}, doi = {10.1007/s00704-022-04233-y}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324155}, pages = {1385-1405}, year = {2022}, abstract = {The current study evaluates the regional climate model REMO (v2015) and its new version REMO-iMOVE, including interactive vegetation and plant functional types (PFTs), over two Central Asian domains for the period of 2000-2015 at two different horizontal resolutions (0.44° and 0.11°). Various statistical metrices along with mean bias patterns for precipitation, temperature, and leaf area index have been used for the model evaluation. A better representation of the spatial pattern of precipitation is found at 0.11° resolution over most of Central Asia. Regarding the mean temperature, both model versions show a high level of agreement with the validation data, especially at the higher resolution. This also reduces the biases in maximum and minimum temperature. Generally, REMO-iMOVE shows an improvement regarding the temperature bias but produces a larger precipitation bias compared to the REMO conventional version with interannually static vegetation. Since the coupled version is capable to simulate the mean climate of Central Asia like its parent version, both can be used for impact studies and future projections. However, regarding the new vegetation scheme and its spatiotemporal representation exemplified by the leaf area index, REMO-iMOVE shows a clear advantage over REMO. This better simulation is caused by the implementation of more realistic and interactive vegetation and related atmospheric processes which consequently add value to the regional climate model.}, language = {en} } @article{Libanda2023, author = {Libanda, Brigadier}, title = {Performance assessment of CORDEX regional climate models in wind speed simulations over Zambia}, series = {Modeling Earth Systems and Environment}, volume = {9}, journal = {Modeling Earth Systems and Environment}, number = {1}, issn = {2363-6203}, doi = {10.1007/s40808-022-01504-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324147}, pages = {253-262}, year = {2023}, abstract = {There is no single solution to cutting emissions, however, renewable energy projects that are backed by rigorous ex-ante assessments play an important role in these efforts. An inspection of literature reveals critical knowledge gaps in the understanding of future wind speed variability across Zambia, thus leading to major uncertainties in the understanding of renewable wind energy potential over the country. Several model performance metrics, both statistical and graphical were used in this study to examine the performance of CORDEX Africa Regional Climate Models (RCMs) in simulating wind speed across Zambia. Results indicate that wind speed is increasing at the rate of 0.006 m s\(^{-1}\) per year. RCA4-GFDL-ESM2M, RCA4-HadGEM2-ES, RCA4-IPSL-CM5A-MR, and RCA4-CSIRO-MK3.6.0 were found to correctly simulate wind speed increase with varying magnitudes on the Sen's estimator of slope. All the models sufficiently reproduce the annual cycle of wind speed with a steady increase being observed from April reaching its peak around August/September and beginning to drop in October. Apart from RegCM4-MPI-ESM and RegCM4-HadGEM2, the performance of RCMs in simulating spatial wind speed patterns is generally good although they overestimate it by ~ 1 m s\(^{-1}\) in the western and southern provinces of the country. Model performance metrics indicate that with a correlation coefficient of 0.5, a root mean square error of 0.4 m s\(^{-1}\), an RSR value of 7.7 and a bias of 19.9\%, RCA4-GFDL-ESM2M outperforms all other models followed by RCA4-HadGEM2, and RCA4-CM5A-MR respectively. These results, therefore, suggest that studies that use an ensemble of RCA4-GFDL-ESM2M, RCA4-HadGEM2, and RCA4-CM5A-MR would yield useful results for informing future renewable wind energy potential in Zambia.}, language = {en} } @article{KanmegneTamgaLatifiUllmannetal.2023, author = {Kanmegne Tamga, Dan and Latifi, Hooman and Ullmann, Tobias and Baumhauer, Roland and Thiel, Michael and Bayala, Jules}, title = {Modelling the spatial distribution of the classification error of remote sensing data in cocoa agroforestry systems}, series = {Agroforestry Systems}, volume = {97}, journal = {Agroforestry Systems}, number = {1}, issn = {0167-4366}, doi = {10.1007/s10457-022-00791-2}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324139}, pages = {109-119}, year = {2023}, abstract = {Cocoa growing is one of the main activities in humid West Africa, which is mainly grown in pure stands. It is the main driver of deforestation and encroachment in protected areas. Cocoa agroforestry systems which have been promoted to mitigate deforestation, needs to be accurately delineated to support a valid monitoring system. Therefore, the aim of this research is to model the spatial distribution of uncertainties in the classification cocoa agroforestry. The study was carried out in C{\^o}te d'Ivoire, close to the Ta{\"i} National Park. The analysis followed three steps (i) image classification based on texture parameters and vegetation indices from Sentinel-1 and -2 data respectively, to train a random forest algorithm. A classified map with the associated probability maps was generated. (ii) Shannon entropy was calculated from the probability maps, to get the error maps at different thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the generated error maps were analysed using a Geographically Weighted Regression model to check for spatial autocorrelation. From the results, a producer accuracy (0.88) and a user's accuracy (0.91) were obtained. A small threshold value overestimates the classification error, while a larger threshold will underestimate it. The optimal value was found to be between 0.3 and 0.4. There was no evidence of spatial autocorrelation except for a smaller threshold (0.2). The approach differentiated cocoa from other landcover and detected encroachment in forest. Even though some information was lost in the process, the method is effective for mapping cocoa plantations in C{\^o}te d'Ivoire.}, language = {en} } @article{FrimmelChakravartiBasei2022, author = {Frimmel, Hartwig E. and Chakravarti, Rajarshi and Basei, Miguel A. S.}, title = {Detrital zircon ages from Archaean conglomerates in the Singhbhum Craton, eastern India: implications on economic Au-U potential}, series = {Mineralium Deposita}, volume = {57}, journal = {Mineralium Deposita}, number = {8}, issn = {0026-4598}, doi = {10.1007/s00126-022-01121-3}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324084}, pages = {1499-1514}, year = {2022}, abstract = {New U-Pb age and Hf isotope data obtained on detrital zircon grains from Au- and U-bearing Archaean quartz-pebble conglomerates in the Singhbhum Craton, eastern India, specifically the Upper Iron Ore Group in the Badampahar Greenstone Belt and the Phuljhari Formation below the Dhanjori Group provide insights into the zircon provenance and maximum age of sediment deposition. The most concordant, least disturbed \(^{207}\)Pb/\(^{206}\)Pb ages cover the entire range of known magmatic and higher grade metamorphic events in the craton from 3.48 to 3.06 Ga and show a broad maximum between 3.38 and 3.18 Ga. This overlap is also mimicked by Lu-Hf isotope analyses, which returned a wide range in \(_{εHf}\)(t) values from + 6 to - 5, in agreement with the range known from zircon grains in igneous and metamorphic rocks in the Singhbhum Craton. A smaller but distinct age peak centred at 3.06 Ga corresponds to the age of the last major magmatic intrusive event, the emplacement of the Mayurbhanj Granite and associated gabbro, picrite and anorthosite. Thus, these intrusive rocks must form a basement rather than being intrusive into the studied conglomerates as previously interpreted. The corresponding detrital zircon grains all have a subchondritic Hf isotopic composition. The youngest reliable zircon ages of 3.03 Ga in the case of the basal Upper Iron Ore Group in the east of the craton and 3.00 Ga for the Phuljhari Formation set an upper limit on the age of conglomerate sedimentation. Previously published detrital zircon age data from similarly Au-bearing conglomerates in the Mahagiri Quartzite in the Upper Iron Ore Group in the south of the craton gave a somewhat younger maximum age of sedimentation of 2.91 Ga. There, the lower limit on sedimentation is given by an intrusive relationship with a c. 2.8 Ga granite. The time window thus defined for conglomerate deposition on the Singhbhum Craton is almost identical to the age span established for the, in places, Au- and U-rich conglomerates in the Kaapvaal Craton of South Africa: the 2.98-2.78 Ga Dominion Group and Witwatersrand Supergroup in South Africa. Since the recognition of first major concentration of gold on Earth's surface by microbial activity having taken place at around 2.9 Ga, independent of the nature of the hinterland, the above similarity in age substantially increases the potential for discovering Witwatersrand-type gold and/or uranium deposits on the Singhbhum Craton. Further age constraints are needed there, however, to distinguish between supposedly less fertile (with respect to Au) > 2.9 Ga and more fertile < 2.9 Ga successions.}, language = {en} } @article{Ibebuchi2023, author = {Ibebuchi, Chibuike Chiedozie}, title = {Circulation patterns linked to the positive sub-tropical Indian Ocean dipole}, series = {Advances in Atmospheric Sciences}, volume = {40}, journal = {Advances in Atmospheric Sciences}, number = {1}, issn = {0256-1530}, doi = {10.1007/s00376-022-2017-2}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324119}, pages = {110-128}, year = {2023}, abstract = {The positive phase of the subtropical Indian Ocean dipole (SIOD) is one of the climatic modes in the subtropical southern Indian Ocean that influences the austral summer inter-annual rainfall variability in parts of southern Africa. This paper examines austral summer rain-bearing circulation types (CTs) in Africa south of the equator that are related to the positive SIOD and the dynamics through which specific rainfall regions in southern Africa can be influenced by this relationship. Four austral summer rain-bearing CTs were obtained. Among the four CTs, the CT that featured (i) enhanced cyclonic activity in the southwest Indian Ocean; (ii) positive widespread rainfall anomaly in the southwest Indian Ocean; and (iii) low-level convergence of moisture fluxes from the tropical South Atlantic Ocean, tropical Indian Ocean, and the southwest Indian Ocean, over the south-central landmass of Africa, was found to be related to the positive SIOD climatic mode. The relationship also implies that positive SIOD can be expected to increase the amplitude and frequency of occurrence of the aforementioned CT. The linkage between the CT related to the positive SIOD and austral summer homogeneous regions of rainfall anomalies in Africa south of the equator showed that it is the principal CT that is related to the inter-annual rainfall variability of the south-central regions of Africa, where the SIOD is already known to significantly influence its rainfall variability. Hence, through the large-scale patterns of atmospheric circulation associated with the CT, the SIOD can influence the spatial distribution and intensity of rainfall over the preferred landmass through enhanced moisture convergence.}, language = {en} } @article{FaethKunzKneisel2022, author = {F{\"a}th, Julian and Kunz, Julius and Kneisel, Christof}, title = {Monitoring spatiotemporal soil moisture changes in the subsurface of forest sites using electrical resistivity tomography (ERT)}, series = {Journal of Forestry Research}, volume = {33}, journal = {Journal of Forestry Research}, number = {5}, issn = {1007-662X}, doi = {10.1007/s11676-022-01498-x}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324073}, pages = {1649-1662}, year = {2022}, abstract = {The effects of drought on tree mortality at forest stands are not completely understood. For assessing their water supply, knowledge of the small-scale distribution of soil moisture as well as its temporal changes is a key issue in an era of climate change. However, traditional methods like taking soil samples or installing data loggers solely collect parameters of a single point or of a small soil volume. Electrical resistivity tomography (ERT) is a suitable method for monitoring soil moisture changes and has rarely been used in forests. This method was applied at two forest sites in Bavaria, Germany to obtain high-resolution data of temporal soil moisture variations. Geoelectrical measurements (2D and 3D) were conducted at both sites over several years (2015-2018/2020) and compared with soil moisture data (matric potential or volumetric water content) for the monitoring plots. The greatest variations in resistivity values that highly correlate with soil moisture data were found in the main rooting zone. Using the ERT data, temporal trends could be tracked in several dimensions, such as the interannual increase in the depth of influence from drought events and their duration, as well as rising resistivity values going along with decreasing soil moisture. The results reveal that resistivity changes are a good proxy for seasonal and interannual soil moisture variations. Therefore, 2D- and 3D-ERT are recommended as comparatively non-laborious methods for small-spatial scale monitoring of soil moisture changes in the main rooting zone and the underlying subsurface of forested sites. Higher spatial and temporal resolution allows a better understanding of the water supply for trees, especially in times of drought.}, language = {en} } @article{Ibebuchi2022, author = {Ibebuchi, Chibuike Chiedozie}, title = {Patterns of atmospheric circulation in Western Europe linked to heavy rainfall in Germany: preliminary analysis into the 2021 heavy rainfall episode}, series = {Theoretical and Applied Climatology}, volume = {148}, journal = {Theoretical and Applied Climatology}, number = {1-2}, issn = {0177-798X}, doi = {10.1007/s00704-022-03945-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324100}, pages = {269-283}, year = {2022}, abstract = {The July 2021 heavy rainfall episode in parts of Western Europe caused devastating floods, specifically in Germany. This study examines circulation types (CTs) linked to extreme precipitation in Germany. It was investigated if the classified CTs can highlight the anomaly in synoptic patterns that contributed to the unusual July 2021 heavy rainfall in Germany. The North Atlantic Oscillation was found to be the major climatic mode related to the seasonal and inter-annual variations of most of the classified CTs. On average, wet (dry) conditions in large parts of Germany can be linked to westerly (northerly) moisture fluxes. During spring and summer seasons, the mid-latitude cyclone when located over the North Sea disrupts onshore moisture transport from the North Atlantic Ocean by westerlies driven by the North Atlantic subtropical anticyclone. The CT found to have the highest probability of being associated with above-average rainfall in large part of Germany features (i) enhancement and northward track of the cyclonic system over the Mediterranean; (ii) northward track of the North Atlantic anticyclone, further displacing poleward, the mid-latitude cyclone over the North Sea, enabling band of westerly moisture fluxes to penetrate Germany; (iii) cyclonic system over the Baltic Sea coupled with northeast fluxes of moisture to Germany; (iv) and unstable atmospheric conditions over Germany. In 2021, a spike was detected in the amplitude and frequency of occurrence of the aforementioned wet CT suggesting that in addition to the nearly stationary cut-off low over central Europe, during the July flood episode, anomalies in the CT contributed to the heavy rainfall event.}, language = {en} } @article{PhilippDietzUllmannetal.2023, author = {Philipp, Marius and Dietz, Andreas and Ullmann, Tobias and Kuenzer, Claudia}, title = {A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {3}, issn = {2072-4292}, doi = {10.3390/rs15030818}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304447}, year = {2023}, abstract = {This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June-September for the years 2017-2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments.}, language = {en} } @article{KacicThonfeldGessneretal.2023, author = {Kacic, Patrick and Thonfeld, Frank and Gessner, Ursula and Kuenzer, Claudia}, title = {Forest structure characterization in Germany: novel products and analysis based on GEDI, Sentinel-1 and Sentinel-2 data}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {8}, issn = {2072-4292}, doi = {10.3390/rs15081969}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313727}, year = {2023}, abstract = {Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience.}, language = {en} } @article{ReinersSobrinoKuenzer2023, author = {Reiners, Philipp and Sobrino, Jos{\´e} and Kuenzer, Claudia}, title = {Satellite-derived land surface temperature dynamics in the context of global change — a review}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs15071857}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311120}, year = {2023}, abstract = {Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.}, language = {en} } @article{DhillonKuebertFlockDahmsetal.2023, author = {Dhillon, Maninder Singh and K{\"u}bert-Flock, Carina and Dahms, Thorsten and Rummler, Thomas and Arnault, Joel and Steffan-Dewenter, Ingolf and Ullmann, Tobias}, title = {Evaluation of MODIS, Landsat 8 and Sentinel-2 data for accurate crop yield predictions: a case study using STARFM NDVI in Bavaria, Germany}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs15071830}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311132}, year = {2023}, abstract = {The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km\(^2\)) for the year 2019. For WW and OSR, the synthetic products' high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R\(^2\) = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R\(^2\) = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R\(^2\) = 0.77 and relative RMSE (RRMSE) = 8.17\%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R\(^2\) = 0.66 and RRMSE = 11.35\%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world.}, language = {en} } @article{KacicKuenzer2022, author = {Kacic, Patrick and Kuenzer, Claudia}, title = {Forest biodiversity monitoring based on remotely sensed spectral diversity — a review}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {21}, issn = {2072-4292}, doi = {10.3390/rs14215363}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290535}, year = {2022}, abstract = {Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on α, β, and γ scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts "vegetation indices", "spectral information content", and "spectral species" for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future.}, language = {en} } @article{DhillonDahmsKuebertFlocketal.2022, author = {Dhillon, Maninder Singh and Dahms, Thorsten and K{\"u}bert-Flock, Carina and Steffan-Dewenter, Ingolf and Zhang, Jie and Ullmann, Tobias}, title = {Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {3}, issn = {2072-4292}, doi = {10.3390/rs14030677}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323471}, year = {2022}, abstract = {The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region's cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5-6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R\(^2\) = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R\(^2\) = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R\(^2\) = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R\(^2\) = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R\(^2\) = 0.60, RMSE = 0.05) and S-MOD13Q1 (R\(^2\) = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution.}, language = {en} } @article{GhasemiLatifiPourhashemi2022, author = {Ghasemi, Marziye and Latifi, Hooman and Pourhashemi, Mehdi}, title = {A novel method for detecting and delineating coppice trees in UAV images to monitor tree decline}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {23}, issn = {2072-4292}, doi = {10.3390/rs14235910}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297258}, year = {2022}, abstract = {Monitoring tree decline in arid and semi-arid zones requires methods that can provide up-to-date and accurate information on the health status of the trees at single-tree and sample plot levels. Unmanned Aerial Vehicles (UAVs) are considered as cost-effective and efficient tools to study tree structure and health at small scale, on which detecting and delineating tree crowns is the first step to extracting varied subsequent information. However, one of the major challenges in broadleaved tree cover is still detecting and delineating tree crowns in images. The frequent dominance of coppice structure in degraded semi-arid vegetation exacerbates this problem. Here, we present a new method based on edge detection for delineating tree crowns based on the features of oak trees in semi-arid coppice structures. The decline severity in individual stands can be analyzed by extracting relevant information such as texture from the crown area. Although the method presented in this study is not fully automated, it returned high performances including an F-score = 0.91. Associating the texture indices calculated in the canopy area with the phenotypic decline index suggested higher correlations of the GLCM texture indices with tree decline at the tree level and hence a high potential to be used for subsequent remote-sensing-assisted tree decline studies.}, language = {en} } @article{OuedraogoHackmanThieletal.2023, author = {Ouedraogo, Valentin and Hackman, Kwame Oppong and Thiel, Michael and Dukiya, Jaiye}, title = {Intensity analysis for urban Land Use/Land Cover dynamics characterization of Ouagadougou and Bobo-Dioulasso in Burkina Faso}, series = {Land}, volume = {12}, journal = {Land}, number = {5}, issn = {2073-445X}, doi = {10.3390/land12051063}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-319397}, year = {2023}, abstract = {Ouagadougou and Bobo-Dioulasso remain the two major urban centers in Burkina Faso with an increasing trend in human footprint. The research aimed at analyzing the Land Use/Land Cover (LULC) dynamics in the two cities between 2003 and 2021 using intensity analysis, which decomposes LULC changes into interval, category and transition levels. The satellite data used for this research were composed of surface reflectance imagery from Landsat 5, Landsat 7 and Landsat 8 acquired from the Google Earth Engine Data Catalogue. The Random Forest, Support Vector Machine and Gradient Tree Boost algorithms were employed to run supervised image classifications for four selected years including 2003, 2009, 2015 and 2021. The results showed that the landscape is changing in both cities due to rapid urbanization. Ouagadougou experienced more rapid changes than Bobo-Dioulasso, with a maximum annual change intensity of 3.61\% recorded between 2015 and 2021 against 2.22\% in Bobo-Dioulasso for the period 2009-2015. The transition of change was mainly towards built-up areas, which gain targeted bare and agricultural lands in both cities. This situation has led to a 78.12\% increase of built-up surfaces in Ouagadougou, while 42.24\% of agricultural land area was lost. However, in Bobo-Dioulasso, the built class has increased far more by 140.67\%, and the agricultural land areas experienced a gain of 1.38\% compared with the 2003 baseline. The study demonstrates that the human footprint is increasing in both cities making the inhabitants vulnerable to environmental threats such as flooding and the effect of an Urban Heat Island, which is information that could serve as guide for sustainable urban land use planning.}, language = {en} } @article{KleinCoccoUereyenetal.2022, author = {Klein, Igor and Cocco, Arturo and Uereyen, Soner and Mannu, Roberto and Floris, Ignazio and Oppelt, Natascha and Kuenzer, Claudia}, title = {Outbreak of Moroccan locust in Sardinia (Italy): a remote sensing perspective}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {23}, issn = {2072-4292}, doi = {10.3390/rs14236050}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297232}, year = {2022}, abstract = {The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43\% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23\% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97\%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats.}, language = {en} }