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 - Dech, Stefan A1 - Holzwarth, Stefanie A1 - Asam, Sarah A1 - Andresen, Thorsten A1 - Bachmann, Martin A1 - Boettcher, Martin A1 - Dietz, Andreas A1 - Eisfelder, Christina A1 - Frey, Corinne A1 - Gesell, Gerhard A1 - Gessner, Ursula A1 - Hirner, Andreas A1 - Hofmann, Matthias A1 - Kirches, Grit A1 - Klein, Doris A1 - Klein, Igor A1 - Kraus, Tanja A1 - Krause, Detmar A1 - Plank, Simon A1 - Popp, Thomas A1 - Reinermann, Sophie A1 - Reiners, Philipp A1 - Roessler, Sebastian A1 - Ruppert, Thomas A1 - Scherbachenko, Alexander A1 - Vignesh, Ranjitha A1 - Wolfmueller, Meinhard A1 - Zwenzner, Hendrik A1 - Kuenzer, Claudia T1 - Potential and challenges of harmonizing 40 years of AVHRR data: the TIMELINE experience JF - Remote Sensing N2 - 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. KW - AVHRR KW - Earth Observation KW - harmonization KW - time series analysis KW - climate related trends KW - automatic processing KW - Europe KW - TIMELINE Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-246134 SN - 2072-4292 VL - 13 IS - 18 ER - TY - JOUR A1 - Reinermann, Sophie A1 - Gessner, Ursula A1 - Asam, Sarah A1 - Kuenzer, Claudia A1 - Dech, Stefan T1 - The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics JF - Remote Sensing N2 - 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. KW - drought KW - time series KW - heat wave KW - agriculture KW - climate extremes KW - climate change KW - crop statistics KW - MODIS KW - Germany Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-225165 VL - 11 IS - 15 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 - Holzwarth, Stefanie A1 - Thonfeld, Frank A1 - Abdullahi, Sahra A1 - Asam, Sarah A1 - Da Ponte Canova, Emmanuel A1 - Gessner, Ursula A1 - Huth, Juliane A1 - Kraus, Tanja A1 - Leutner, Benjamin A1 - Kuenzer, Claudia T1 - Earth Observation based monitoring of forests in Germany: a review JF - Remote Sensing N2 - Forests in Germany cover around 11.4 million hectares and, thus, a share of 32% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany’s forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps. KW - remote sensing KW - earth observation KW - forest KW - forest monitoring KW - forest disturbances KW - Germany KW - review Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-216334 SN - 2072-4292 VL - 12 IS - 21 ER - TY - JOUR A1 - Kacic, Patrick A1 - Thonfeld, Frank A1 - Gessner, Ursula A1 - Kuenzer, Claudia T1 - Forest structure characterization in Germany: novel products and analysis based on GEDI, Sentinel-1 and Sentinel-2 data JF - Remote Sensing N2 - 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. KW - forest KW - forest structure Germany KW - canopy height KW - Global Ecosystem Dynamics Investigation KW - GEDI KW - Sentinel-1 KW - Sentinel-2 KW - random forest regression Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-313727 SN - 2072-4292 VL - 15 IS - 8 ER -