Institut für Geographie und Geologie
Refine
Has Fulltext
- yes (43)
Is part of the Bibliography
- yes (43)
Year of publication
Document Type
- Journal article (43)
Language
- English (43)
Keywords
- remote sensing (17)
- time series (10)
- MODIS (8)
- earth observation (7)
- machine learning (7)
- Earth observation (6)
- SAR (5)
- Sentinel-2 (5)
- deep learning (5)
- Earth Observation (4)
- agriculture (4)
- forest (4)
- review (4)
- AVHRR (3)
- Sentinel-1 (3)
- climate change (3)
- drought (3)
- dynamics (3)
- pasture (3)
- permafrost (3)
- time series analysis (3)
- validation (3)
- AI (2)
- Antarctic ice sheet (2)
- Antarctica (2)
- CNN (2)
- Europe (2)
- Germany (2)
- Google Earth Engine (2)
- InSAR (2)
- Landsat (2)
- Mekong (2)
- TIMELINE (2)
- artificial intelligence (2)
- change vector analysis (2)
- coastal erosion (2)
- convolutional neural networks (2)
- forecast (2)
- global change (2)
- grazing (2)
- hydrology (2)
- image segmentation (2)
- land surface (2)
- locust outbreak (2)
- meadow (2)
- neural networks (2)
- object detection (2)
- optical (2)
- probability (2)
- rice (2)
- satellite data (2)
- supraglacial lakes (2)
- surface water (2)
- synthetic aperture RADAR (2)
- synthetic aperture radar (2)
- ASAR (1)
- ASCAT (1)
- AVHRR data (1)
- Africa (1)
- Alps (1)
- Amu Darya (1)
- Asia (1)
- Burkina Faso (1)
- Central Asia (1)
- China (1)
- DEM (1)
- Dociostaurus maroccanus (1)
- Dongting Lake (1)
- ENVISAT ASAR WSM (1)
- EO data (1)
- ESTARFM (1)
- ESTARFM framework (1)
- Envisat (1)
- GEDI (1)
- Global Ecosystem Dynamics Investigation (1)
- Greenland ice sheet (1)
- Himalaya Karakoram (1)
- IACS (1)
- InSAR height (1)
- Indus-Ganges-Brahmaputra-Meghna (1)
- Land Surface Temperature (1)
- Landsat-8 (1)
- MODIS image (1)
- Markov chains (1)
- Mekong-Delta (1)
- NDVI (1)
- Northern Xinjiang (1)
- OCSVM (1)
- Pamir (1)
- PolSAR (1)
- Ramsar Convention on Wetlands (1)
- SAR imagery (1)
- SBAS (1)
- SVM (1)
- Sentinel–1 (1)
- Snow Line Elevation (1)
- Southeast Asia (1)
- Southeast China (1)
- Syr Darya (1)
- TIMESAT (1)
- TanDEM-X (1)
- TerraSAR-X (1)
- Tian Shan (1)
- Vietnam (1)
- WSM (1)
- West Africa (1)
- abandoned land (1)
- accuracy (1)
- agricultural drought (1)
- agricultural pests (1)
- algorithm (1)
- alpha diversity (1)
- anthroposphere (1)
- aquaculture (1)
- atmosphere (1)
- automatic processing (1)
- band SAR data (1)
- beta diversity (1)
- biodiversity (1)
- biomass (1)
- biosphere (1)
- canopy cover loss (1)
- canopy height (1)
- catchment (1)
- causal networks (1)
- central asia (1)
- change detection (1)
- circum-Arctic (1)
- climate extremes (1)
- climate related trends (1)
- cloud (1)
- cloud gap filling (1)
- coal (1)
- coal fire (1)
- coastal zone (1)
- convolutional neural network (1)
- crop statistics (1)
- cropping systems (1)
- cryosphere (1)
- cutting (1)
- cutting events (1)
- data fusion (1)
- degradation (1)
- difference water index (1)
- disturbance index (1)
- drought impact (1)
- environmental modeling (1)
- error estimation (1)
- exposure (1)
- flood detection (1)
- floodpath lake (1)
- food security (1)
- forest disturbances (1)
- forest monitoring (1)
- forest structure Germany (1)
- future prediction (1)
- gamma diversity (1)
- geoanalysis (1)
- global (1)
- global warming (1)
- harmonization (1)
- harvests (1)
- heat wave (1)
- hydrological drought (1)
- ice sheet dynamics (1)
- ice sheet hydrology (1)
- intercomparison (1)
- interferometry (1)
- interpolation (1)
- irrigation (1)
- land cover (1)
- land surface dynamics (1)
- land surface phenology (1)
- land surface temperature (1)
- land use (1)
- landsat (1)
- locust habitat (1)
- locust monitoring (1)
- locust pest (1)
- major river basins (1)
- management (1)
- mangrove ecosystems (1)
- mekong delta (1)
- meteorological drought (1)
- modeling (1)
- mountains (1)
- mowing (1)
- multispectral data (1)
- multitemporal ALOS/PALSAR imagery (1)
- non-communicable disease (1)
- optical diversity (1)
- optical remote sensing (1)
- paddy (1)
- paddy rice (1)
- partial correlation (1)
- penetration bias (1)
- phenology (1)
- plantation (1)
- polarimetric SAR (1)
- ponds (1)
- productivity (1)
- products (1)
- protected areas (1)
- public health (1)
- radar (1)
- radar data (1)
- random forest (1)
- random forest classification (1)
- random forest regression (1)
- reliability (1)
- rice mapping (1)
- satellite (1)
- satellite remote sensing (1)
- scatterometer (1)
- seasonality (1)
- semantic segmentation (1)
- sentinel-2 (1)
- settlement growth (1)
- snow (1)
- snow cover (1)
- snow cover area (1)
- snow cover duration (1)
- soil moisture (1)
- soil moisture retrieval (1)
- spatial analyses (1)
- spatio-temporal analysis (1)
- spectral diversity (1)
- spectral statistics (1)
- spectral variation hypothesis (1)
- subpixel (1)
- subsidence (1)
- surface melt (1)
- surface water area (1)
- temperature (1)
- temporal statistics (1)
- thaw (1)
- thermokarst (1)
- time-series (1)
- trends (1)
- uncertainty (1)
- urban modelling (1)
- use intensity (1)
- variability (1)
- vegetation dynamics (1)
- water (1)
- water dynamics (1)
- watershed (1)
- yield (1)
Institute
Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource — if not overexploited — sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.
Detection of grassland mowing events for Germany by combining Sentinel-1 and Sentinel-2 time series
(2022)
Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany.
A first assessment of canopy cover loss in Germany's forests after the 2018–2020 drought years
(2022)
Central Europe was hit by several unusually strong periods of drought and heat between 2018 and 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast forest areas in Germany. We present the first assessment of canopy cover loss in Germany for the period of January 2018–April 2021. Our approach makes use of dense Sentinel-2 and Landsat-8 time-series data. We computed the disturbance index (DI) from the tasseled cap components brightness, greenness, and wetness. Using quantiles, we generated monthly DI composites and calculated anomalies in a reference period (2017). From the resulting map, we calculated the canopy cover loss statistics for administrative entities. Our results show a canopy cover loss of 501,000 ha for Germany, with large regional differences. The losses were largest in central Germany and reached up to two-thirds of coniferous forest loss in some districts. Our map has high spatial (10 m) and temporal (monthly) resolution and can be updated at any time.
The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.
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.
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.
Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments.
Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter- and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000-2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ.
Mapping aquaculture ponds for the coastal zone of Asia with Sentinel-1 and Sentinel-2 time series
(2021)
Asia dominates the world's aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture has expanded rapidly in coastal regions in Asia since the early 1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income and employment, contributes to food security, and has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental scale. We present a multi-sensor approach that utilizes Earth observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as the onshore area up to 200 km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at a single-pond level based on temporal features derived from high-spatial-resolution SAR and optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units at regional, national, and sub-national scale.
Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product's quality with respect to its use for modeling and the geoscientific community.