TY - JOUR A1 - Näschen, Kristian A1 - Diekkrüger, Bernd A1 - Evers, Mariele A1 - Höllermann, Britta A1 - Steinbach, Stefanie A1 - Thonfeld, Frank T1 - The impact of land use/land cover change (LULCC) on water resources in a tropical catchment in Tanzania under different climate change scenarios JF - Sustainability N2 - Many parts of sub-Saharan Africa (SSA) are prone to land use and land cover change (LULCC). In many cases, natural systems are converted into agricultural land to feed the growing population. However, despite climate change being a major focus nowadays, the impacts of these conversions on water resources, which are essential for agricultural production, is still often neglected, jeopardizing the sustainability of the socio-ecological system. This study investigates historic land use/land cover (LULC) patterns as well as potential future LULCC and its effect on water quantities in a complex tropical catchment in Tanzania. It then compares the results using two climate change scenarios. The Land Change Modeler (LCM) is used to analyze and to project LULC patterns until 2030 and the Soil and Water Assessment Tool (SWAT) is utilized to simulate the water balance under various LULC conditions. Results show decreasing low flows by 6–8% for the LULC scenarios, whereas high flows increase by up to 84% for the combined LULC and climate change scenarios. The effect of climate change is stronger compared to the effect of LULCC, but also contains higher uncertainties. The effects of LULCC are more distinct, although crop specific effects show diverging effects on water balance components. This study develops a methodology for quantifying the impact of land use and climate change and therefore contributes to the sustainable management of the investigated catchment, as it shows the impact of environmental change on hydrological extremes (low flow and floods) and determines hot spots, which are critical for environmental development. KW - SWAT model KW - Land Change Modeler KW - Scenario analysis KW - Extreme flows KW - Tanzania KW - Kilombero Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193825 SN - 2071-1050 VL - 11 IS - 24 ER - TY - JOUR A1 - Latifi, Hooman A1 - Valbuena, Ruben T1 - Current trends in forest ecological applications of three-dimensional remote sensing: Transition from experimental to operational solutions? JF - Forests N2 - The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue “3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function”, which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide. KW - 3D remote sensing KW - composition KW - forest ecology KW - function KW - structure Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193282 SN - 1999-4907 VL - 10 IS - 10 ER - TY - JOUR A1 - Ghazaryan, Gohar A1 - Rienow, Andreas A1 - Oldenburg, Carsten A1 - Thonfeld, Frank A1 - Trampnau, Birte A1 - Sticksel, Sarah A1 - Jürgens, Carsten T1 - Monitoring of urban sprawl and densification processes in Western Germany in the light of SDG indicator 11.3.1 based on an automated retrospective classification approach JF - Remote Sensing N2 - By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning. KW - impervious surface KW - Landsat time series KW - change detection KW - SDG 11.3.1 KW - population change Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-236671 SN - 2072-4292 VL - 13 IS - 9 ER - TY - JOUR A1 - Fekri, Erfan A1 - Latifi, Hooman A1 - Amani, Meisam A1 - Zobeidinezhad, Abdolkarim T1 - A training sample migration method for wetland mapping and monitoring using Sentinel data in Google Earth Engine JF - Remote Sensing N2 - Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year. KW - wetland KW - Google Earth Engine (GEE) KW - training sample migration KW - sentinel Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-248542 SN - 2072-4292 VL - 13 IS - 20 ER - TY - JOUR A1 - Hagg, Wilfried A1 - Mayr, Elisabeth A1 - Mannig, Birgit A1 - Reyers, Mark A1 - Schubert, David A1 - Pinto, Joaquim G. A1 - Peters, Juliane A1 - Pieczonka, Tino A1 - Juen, Martin A1 - Bolch, Tobias A1 - Paeth, Heiko A1 - Mayer, Christoph T1 - Future climate change and its impact on runoff generation from the debris-covered Inylchek glaciers, Central Tian Shan, Kyrgyzstan JF - Water N2 - The heavily debris-covered Inylchek glaciers in the central Tian Shan are the largest glacier system in the Tarim catchment. It is assumed that almost 50% of the discharge of Tarim River are provided by glaciers. For this reason, climatic changes, and thus changes in glacier mass balance and glacier discharge are of high impact for the whole region. In this study, a conceptual hydrological model able to incorporate discharge from debris-covered glacier areas is presented. To simulate glacier melt and subsequent runoff in the past (1970/1971–1999/2000) and future (2070/2071–2099/2100), meteorological input data were generated based on ECHAM5/MPI-OM1 global climate model projections. The hydrological model HBV-LMU was calibrated by an automatic calibration algorithm using runoff and snow cover information as objective functions. Manual fine-tuning was performed to avoid unrealistic results for glacier mass balance. The simulations show that annual runoff sums will increase significantly under future climate conditions. A sensitivity analysis revealed that total runoff does not decrease until the glacier area is reduced by 43%. Ice melt is the major runoff source in the recent past, and its contribution will even increase in the coming decades. Seasonal changes reveal a trend towards enhanced melt in spring, but a change from a glacial-nival to a nival-pluvial runoff regime will not be reached until the end of this century. KW - glaciers KW - debris-covered glaciers KW - hydrological modelling KW - climate scenarios KW - Tian Shan Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197592 SN - 2073-4441 VL - 10 IS - 11 ER - TY - JOUR A1 - Mayr, Stefan A1 - Klein, Igor A1 - Rutzinger, Martin A1 - Kuenzer, Claudia T1 - Systematic water fraction estimation for a global and daily surface water time-series JF - Remote Sensing N2 - Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product's performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series. KW - earth observation KW - landsat KW - MODIS KW - remote sensing KW - probability KW - Sentinel-2 KW - subpixel KW - water Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-242586 SN - 2072-4292 VL - 13 IS - 14 ER - TY - JOUR A1 - Stereńczak, Krzysztof A1 - Laurin, Gaia Vaglio A1 - Chirici, Gherardo A1 - Coomes, David A. A1 - Dalponte, Michele A1 - Latifi, Hooman A1 - Puletti, Nicola T1 - Global Airborne Laser Scanning Data Providers Database (GlobALS) — a new tool for monitoring ecosystems and biodiversity JF - Remote Sensing N2 - Protection and recovery of natural resource and biodiversity requires accurate monitoring at multiple scales. Airborne Laser Scanning (ALS) provides high-resolution imagery that is valuable for monitoring structural changes to vegetation, providing a reliable reference for ecological analyses and comparison purposes, especially if used in conjunction with other remote-sensing and field products. However, the potential of ALS data has not been fully exploited, due to limits in data availability and validation. To bridge this gap, the global network for airborne laser scanner data (GlobALS) has been established as a worldwide network of ALS data providers that aims at linking those interested in research and applications related to natural resources and biodiversity monitoring. The network does not collect data itself but collects metadata and facilitates networking and collaborative research amongst the end-users and data providers. This letter describes this facility, with the aim of broadening participation in GlobALS. KW - LiDAR KW - forest KW - database KW - networking KW - GlobALS Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207819 SN - 2072-4292 VL - 12 IS - 11 ER - TY - JOUR A1 - Huth, Juliane A1 - Gessner, Ursula A1 - Klein, Igor A1 - Yesou, Hervé A1 - Lai, Xijun A1 - Oppelt, Natascha A1 - Kuenzer, Claudia T1 - Analyzing water dynamics based on Sentinel-1 time series — a study for Dongting Lake wetlands in China JF - Remote Sensing N2 - In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China's second largest freshwater lake in the middle reaches of the Yangtze River — the Dongting Lake — and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA's Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake's wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km\(^2\), indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km\(^2\). Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km\(^2\), which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers. KW - Earth observation KW - SAR KW - Sentinel–1 KW - time series KW - Dongting Lake KW - water dynamics KW - floodpath lake KW - Ramsar Convention on Wetlands Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205977 SN - 2072-4292 VL - 12 IS - 11 ER - TY - JOUR A1 - Forkuor, Gerald A1 - Ullmann, Tobias A1 - Griesbeck, Mario T1 - Mapping and monitoring small-scale mining activities in Ghana using Sentinel-1 time series (2015−2019) JF - Remote Sensing N2 - Illegal small-scale mining (galamsey) in South-Western Ghana has grown tremendously in the last decade and caused significant environmental degradation. Excessive cloud cover in the area has limited the use of optical remote sensing data to map and monitor the extent of these activities. This study investigated the use of annual time-series Sentinel-1 data to map and monitor illegal mining activities along major rivers in South-Western Ghana between 2015 and 2019. A change detection approach, based on three time-series features — minimum, mean, maximum — was used to compute a backscatter threshold value suitable to identify/detect mining-induced land cover changes in the study area. Compared to the mean and maximum, the minimum time-series feature (in both VH and VV polarization) was found to be more sensitive to changes in backscattering within the period of investigation. Our approach permitted the detection of new illegal mining areas on an annual basis. A backscatter threshold value of +1.65 dB was found suitable for detecting illegal mining activities in the study area. Application of this threshold revealed illegal mining area extents of 102 km\(^2\), 60 km\(^2\) and 33 km\(^2\) for periods 2015/2016–2016/2017, 2016/2017–2017/2018 and 2017/2018–2018/2019, respectively. The observed decreasing trend in new illegal mining areas suggests that efforts at stopping illegal mining yielded positive results in the period investigated. Despite the advantages of Synthetic Aperture Radar data in monitoring phenomena in cloud-prone areas, our analysis revealed that about 25% of the Sentinel-1 data, mostly acquired in March and October (beginning and end of rainy season respectively), were unusable due to atmospheric effects from high intensity rainfall events. Further investigation in other geographies and climatic regions is needed to ascertain the susceptibility of Sentinel-1 data to atmospheric conditions. KW - Sentine-1 KW - mining KW - image artifacts KW - time-series features KW - galamsey KW - Ghana Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203204 SN - 2072-4292 VL - 12 IS - 6 ER - TY - JOUR A1 - Heinemann, Sascha A1 - Siegmann, Bastian A1 - Thonfeld, Frank A1 - Muro, Javier A1 - Jedmowski, Christoph A1 - Kemna, Andreas A1 - Kraska, Thorsten A1 - Muller, Onno A1 - Schultz, Johannes A1 - Udelhoven, Thomas A1 - Wilke, Norman A1 - Rascher, Uwe T1 - Land surface temperature retrieval for agricultural areas using a novel UAV platform equipped with a thermal infrared and multispectral sensor JF - Remote Sensing N2 - Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data. KW - UAV KW - thermal infrared KW - multispectral VNIR KW - LST KW - emissivity KW - NDVI thresholds KW - atmospheric correction KW - agricultural mapping KW - low-cost applications Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203557 SN - 2072-4292 VL - 12 IS - 7 ER -