TY - JOUR A1 - Mayr, Stefan A1 - Kuenzer, Claudia A1 - Gessner, Ursula A1 - Klein, Igor A1 - Rutzinger, Martin T1 - Validation of earth observation time-series: a review for large-area and temporally dense land surface products JF - Remote Sensing N2 - 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. KW - accuracy KW - error estimation KW - global KW - intercomparison KW - remote sensing KW - uncertainty Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193202 SN - 2072-4292 VL - 11 IS - 22 ER - TY - JOUR A1 - Abdullahi, Sahra A1 - Wessel, Birgit A1 - Huber, Martin A1 - Wendleder, Anna A1 - Roth, Achim A1 - Kuenzer, Claudia T1 - Estimating penetration-related X-band InSAR elevation bias: a study over the Greenland ice sheet JF - Remote Sensing N2 - 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. KW - InSAR height KW - penetration bias KW - cryosphere KW - TanDEM-X KW - Greenland ice sheet KW - DEM Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193902 SN - 2072-4292 VL - 11 IS - 24 ER - 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 - Heurich, Marco T1 - Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects JF - Remote Sensing N2 - Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue “Remote Sensing-Based Forest Inventories from Landscape to Global Scale”, which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide. KW - remote sensing KW - forest resources inventory KW - spatial scale Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197358 SN - 2072-4292 VL - 11 IS - 11 ER - TY - JOUR A1 - Uereyen, Soner A1 - Kuenzer, Claudia T1 - A review of earth observation-based analyses for major river basins JF - Remote Sensing N2 - Regardless of political boundaries, river basins are a functional unit of the Earth’s land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream–downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins. KW - major river basins KW - catchment KW - watershed KW - Earth observation KW - remote sensing KW - spatial analyses KW - land surface KW - surface water Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193849 SN - 2072-4292 VL - 11 IS - 24 ER - TY - JOUR A1 - Nill, Leon A1 - Ullmann, Tobias A1 - Kneisel, Christof A1 - Sobiech-Wolf, Jennifer A1 - Baumhauer, Roland T1 - Assessing Spatiotemporal Variations of Landsat Land Surface Temperature and Multispectral Indices in the Arctic Mackenzie Delta Region between 1985 and 2018 JF - Remote Sensing N2 - Air temperatures in the Arctic have increased substantially over the last decades, which has extensively altered the properties of the land surface. Capturing the state and dynamics of Land Surface Temperatures (LSTs) at high spatial detail is of high interest as LST is dependent on a variety of surficial properties and characterizes the land–atmosphere exchange of energy. Accordingly, this study analyses the influence of different physical surface properties on the long-term mean of the summer LST in the Arctic Mackenzie Delta Region (MDR) using Landsat 30 m-resolution imagery between 1985 and 2018 by taking advantage of the cloud computing capabilities of the Google Earth Engine. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Tasseled Cap greenness (TCG), brightness (TCB), and wetness (TCW) as well as topographic features derived from the TanDEM-X digital elevation model are used in correlation and multiple linear regression analyses to reveal their influence on the LST. Furthermore, surface alteration trends of the LST, NDVI, and NDWI are revealed using the Theil-Sen (T-S) regression method. The results indicate that the mean summer LST appears to be mostly influenced by the topographic exposition as well as the prevalent moisture regime where higher evapotranspiration rates increase the latent heat flux and cause a cooling of the surface, as the variance is best explained by the TCW and northness of the terrain. However, fairly diverse model outcomes for different regions of the MDR (R2 from 0.31 to 0.74 and RMSE from 0.51 °C to 1.73 °C) highlight the heterogeneity of the landscape in terms of influential factors and suggests accounting for a broad spectrum of different factors when modeling mean LSTs. The T-S analysis revealed large-scale wetting and greening trends with a mean decadal increase of the NDVI/NDWI of approximately +0.03 between 1985 and 2018, which was mostly accompanied by a cooling of the land surface given the inverse relationship between mean LSTs and vegetation and moisture conditions. Disturbance through wildfires intensifies the surface alterations locally and lead to significantly cooler LSTs in the long-term compared to the undisturbed surroundings. KW - LST KW - thermal remote sensing KW - Landsat time series KW - arctic greening KW - Google Earth Engine Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193301 SN - 2072-4292 VL - 11 IS - 19 ER - TY - JOUR A1 - Ullmann, Tobias A1 - Sauerbrey, Julia A1 - Hoffmeister, Dirk A1 - May, Simon Matthias A1 - Baumhauer, Roland A1 - Bubenzer, Olaf T1 - Assessing Spatiotemporal Variations of Sentinel-1 InSAR Coherence at Different Time Scales over the Atacama Desert (Chile) between 2015 and 2018 JF - Remote Sensing N2 - This study investigates synthetic aperture radar (SAR) time series of the Sentinel-1 mission acquired over the Atacama Desert, Chile, between March 2015 and December 2018. The contribution analyzes temporal and spatial variations of Sentinel-1 interferometric SAR (InSAR) coherence and exemplarily illustrates factors that are responsible for observed signal differences. The analyses are based on long temporal baselines (365–1090 days) and temporally dense time series constructed with short temporal baselines (12–24 days). Results are compared to multispectral data of Sentinel-2, morphometric features of the digital elevation model (DEM) TanDEM-X WorldDEM™, and to a detailed governmental geographic information system (GIS) dataset of the local hydrography. Sentinel-1 datasets are suited for generating extensive, nearly seamless InSAR coherence mosaics covering the entire Atacama Desert (>450 × 1100 km) at a spatial resolution of 20 × 20 meter per pixel. Temporal baselines over several years lead only to very minor decorrelation, indicating a very high signal stability of C-Band in this region, especially in the hyperarid uplands between the Coastal Cordillera and the Central Depression. Signal decorrelation was associated with certain types of surface cover (e.g., water or aeolian deposits) or with actual surface dynamics (e.g., anthropogenic disturbance (mining) or fluvial activity and overland flow). Strong rainfall events and fluvial activity in the periods 2015 to 2016 and 2017 to 2018 caused spatial patterns with significant signal decorrelation; observed linear coherence anomalies matched the reference channel network and indicated actual episodic and sporadic discharge events. In the period 2015–2016, area-wide loss of coherence appeared as strip-like patterns of more than 80 km length that matched the prevailing wind direction. These anomalies, and others observed in that period and in the period 2017–2018, were interpreted to be caused by overland flow of high magnitude, as their spatial location matched well with documented heavy rainfall events that showed cumulative precipitation amounts of more than 20 mm. KW - Chile KW - Atacama KW - Sentinel-1 KW - InSAR KW - coherence KW - geomorphology Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193836 SN - 2072-4292 VL - 11 IS - 24 ER - TY - JOUR A1 - Baumhoer, Celia A. A1 - Dietz, Andreas J. A1 - Kneisel, C. A1 - Kuenzer, C. T1 - Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning JF - Remote Sensing N2 - Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr. KW - Antarctica KW - coastline KW - deep learning KW - semantic segmentation KW - Getz Ice Shelf KW - calving front KW - glacier front KW - U-Net KW - convolutional neural network KW - glacier terminus Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193150 SN - 2072-4292 VL - 11 IS - 21 ER - TY - JOUR A1 - Philipp, Marius B. A1 - Levick, Shaun R. T1 - Exploring the potential of C-Band SAR in contributing to burn severity mapping in tropical savanna JF - Remote Sensing N2 - The ability to map burn severity and to understand how it varies as a function of time of year and return frequency is an important tool for landscape management and carbon accounting in tropical savannas. Different indices based on optical satellite imagery are typically used for mapping fire scars and for estimating burn severity. However, cloud cover is a major limitation for analyses using optical data over tropical landscapes. To address this pitfall, we explored the suitability of C-band Synthetic Aperture Radar (SAR) data for detecting vegetation response to fire, using experimental fires in northern Australia. Pre- and post-fire results from Sentinel-1 C-band backscatter intensity data were compared to those of optical satellite imagery and were corroborated against structural changes on the ground that we documented through terrestrial laser scanning (TLS). Sentinel-1 C-band backscatter (VH) proved sensitive to the structural changes imparted by fire and was correlated with the Normalised Burn Ratio (NBR) derived from Sentinel-2 optical data. Our results suggest that C-band SAR holds potential to inform the mapping of burn severity in savannas, but further research is required over larger spatial scales and across a broader spectrum of fire regime conditions before automated products can be developed. Combining both Sentinel-1 SAR and Sentinel-2 multi-spectral data will likely yield the best results for mapping burn severity under a range of weather conditions. KW - burn severity KW - Sentinel-1 KW - Sentinel-2 KW - terrestrial LiDAR Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193789 SN - 2072-4292 VL - 12 IS - 1 ER -