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 - THES A1 - Geßner, Ursula T1 - Räumliche und zeitliche Muster der Vegetationsstruktur in Savannen des südlichen Afrika T1 - Spatial and temporal patterns of vegetation structure in southern African savannas N2 - Die Veränderung der terrestrischen Ökosysteme, ist ein grundlegendes Element des Globalen Wandels. In diesem Kontext unterliegt auch eines der größten Biome der Erde, die tropische und subtropische Savanne, immer stärkeren Veränderungen. Dieses Biom in sozioökonomischer und ökologischer Hinsicht von besonderer Bedeutung. Für einen rasch wachsenden Teil der Weltbevölkerung bildet es die Grundlage für das Betreiben von Weidewirtschaft, Ackerbau und Tourismus. In nationalen und internationalen Forschungsprogrammen zum Globalen Wandel hat die Analyse von Landnutzungs- und Landbedeckungsänderungen in den vergangenen Jahrzehnten zunehmend an Bedeutung gewonnen. Die Landbedeckungsdynamik von Savannenökosystemen ist jedoch noch nicht hinreichend verstanden, so dass diese Ökosysteme in globalen Studien nur ansatzweise berücksichtigt werden können. Besondere Herausforderungen bei der Erfassung der Landbedeckung und ihrer Dynamik liegen im Falle der Savannen in der heterogenen räumlichen Verteilung der Wuchsformen, in den graduellen Übergängen zwischen Landbedeckungsklassen und in der hohen inner- und interannuellen Variabilität der Vegetationsdecke. Vor diesem Hintergrund beschäftigt sich diese Dissertation mit der fernerkundungsbasierten Erfassung und Interpretation der Vegetationsstruktur und der Vegetationsdynamik von Savannen am Beispiel ausgewählter afrikanischer Untersuchungsregionen. Die Vegetationsstruktur wird in dieser Dissertation in Form von Bedeckungsgraden holziger Vegetation, krautiger Vegetation und vegetationsloser Fläche erfasst. Es kommt ein mehrskaliges Verfahren zum Einsatz, in dem höchstaufgelöste IKONOS- und QuickBird-Daten, Landsat-Daten und annuelle MODIS-Zeitreihen ausgewertet werden. Der Ansatz basiert auf der Methodik der Ensemble-Regeressionbäume und stellt eine Erweiterung und Optimierung der Herangehensweise des MODIS-Standardproduktes Vegetation Continuous Fields (VCF) nach Hansen et al. (2002) dar. Beim Vergleich mit unabhängigen Validierungsdaten der nächst höheren Auflösungsebene zeigt sich das Potenzial der vorgestellten Methodik. Die räumliche Übertragbarkeit der Regressionsbäume wird am Beispiel von zwei Vegetationstypen innerhalb der Zentralnamibischen Savanne dargestellt. In diesem Zusammenhang zeigt sich der hohe Stellenwert einer optimalen Auswahl an Trainingsdaten mit einer repräsentativen Abdeckung der Wertespanne aller existierenden Bedeckungsgrade. Die erarbeiteten Resultate unterstreichen, die optimale Eignung der Subpixel-Bedeckungsgrade, gerade zur Beschreibung von Savannenlandschaften. In der Kombination von herkömmlichen, diskreten Landbedeckungs- oder Vegetationskarten mit Informationen zu Bedeckungsgraden wird ein besonderer Mehrwert für weiterführende Analysen gesehen. Die Dynamik der Savannenvegetation wird in dieser Arbeit sowohl auf biannueller als auch auf mehrjähriger Skala charakterisiert. Bei der biannuellen Analyse werden die Veränderungen der holzigen Vegetationsbedeckung zwischen den Jahren 2003/04 und 2006/07 erfasst. Hierfür findet eine zeitliche Übertragung des zuvor vorgestellten Verfahrens zur Ableitung von Bedeckungsanteilen statt. Im Rahmen der biannuellen Untersuchungen können Veränderungsflächen identifiziert werden, ohne Einschränkung auf Übergänge zwischen fest definierten Klassengrenzen. In Ergänzung der biannuellen Analysen werden aus MODIS-EVI- und Niederschlagszeitreihen Maßzahlen abgeleitet, die den Zusammenhang zwischen Niederschlag und Vegetationsentwicklung, die Variabilität und die Trends der Vegetation über einen Zeitraum von acht Jahren beschreiben. Hierbei kommen beispielsweise Korrelationsanalysen zwischen Vegetationsindex- und Niederschlagszeitreihen zum Einsatz. Zudem werden Trendanalysen der Vegetationsindex-Zeitreihen durchgeführt. Die Trends werden einerseits allein aus den Zeitreihen der Vegetationsindizes ermittelt, andererseits wird bei der Berechnung von Restrends (Residual Trends) der Einfluss des Niederschlags berücksichtigt. Neben den Korrelations- und Trendanalysen werden unterschiedliche Variabilitätsmaße der Vegetationsindex-Zeitreihen genutzt, um die mehrjährige Vegetationsdynamik zu beschreiben. Durch die Kombination von Fernerkundungsdaten unterschiedlicher räumlicher und zeitlicher Auflösungen wird in dieser Dissertation die heterogene Vegetationsstruktur und die komplexe Vegetationsdynamik ausgewählter afrikanischer Savannenökosysteme beschreiben. N2 - The change of terrestrial ecosystems is a fundamnental element of Global Change. In this context, also one of the largest terrestrial biomes, the tropical and subtropical savanna, is subject to increasing changes. This biome is of high socioeconomic and ecologic relevance both globally and for the vulnerable African continent. Savannas provide fundamental natural resources for cropping, grazing and tourism for a rapidly growing part of the global population. Furthermore, savannas are characterised by an extraordinary biodiversity. With regard to carbon storage and carbon fluxes they occupy the second rank after the global forest biome. During the last few decades, national and international research programmes have increasingly focused on the analysis of land cover and land use change. However, the land surface dynamics of savanna ecosystems are not yet fully understood. The consequence is, that savanna ecosystems can not yet be adequately included in global studies. When analysing the land surface and land surface dynamics of savannas, the major challenge is to be seen in the heterogeneous spatial patterns of vegetation growth forms, in gradual transitions between land cover classes and in the high innerand interannual variability of vegetation. In this framework, this thesis deals with the remote sensing based assessment and interpretation of vegetation structure and vegetation dynamics in selected African savanna regions. Vegetation structure is delineated as per pixel fractional cover of woody vegetation, herbaceous vegetation and bare surface. A multiscale approach is used which includes the analysis of very high resolution IKONOS and QuickBird data, high resolution Landsat data, and medium resolution MODIS time-series. The approach is based on ensemble regression trees and can be seen as an extension and regional optimisation of the approach of the MODIS standard product Vegetation Continuous Fields (VCF, Hansen et al. 2002). The spatial transferability of the regression tree ensembles is demonstrated for two vegetation types of the central Namibian savanna. In this context, the high relevance of an optimal selection of training data is demonstrated. This means first of all that training data should cover the complete range of existing cover types of the area under investigation. The presented results underline the high potential of analysing sub-pixel fractional cover in savanna landscapes as the typical heterogeneous structure and the gradual transitions of vegetation cover are captured adequately. Added value is seen in the combination of the delineated fractional vegetation cover with conventional discrete classifications. The dynamics of the savanna vegetation of the northern Namibian study region Kalahari Woodland is examined both at biannual and multiannual scale. In the biannual analysis, changes of woody vegetation cover between the years 2003/04 and 2006/07 are delineated. For this purpose, the presented multiscale approach for the delineation of fractional vegetation cover is adapted in order to also analyse the situation of a past year. This temporal transfer is conceived in a way that does not require very high resolution satellite data for the past. Based on the biannual analyses, areas of change can be identified without any constraint to predefined transitions of class boundaries. In addition to the biannual analyses, time-series of MODIS-EVI and rainfall are analysed. Metrics are calculated from these time-series which describe the connection between precipitation and vegetation development as well as gradual changes (trends) and variability of the vegetation for a period of 8 years. Here, correlation analyses between time-series of rainfall and vegetation indices are used. Furthermore, trend analyses of vegetation indices are performed as well as Restrend (Residual trend) analyses accounting for the influence of precipitation on vegetation. In addition to these correlation and trend analyses different measures of variability are calculated. In this thesis, the heterogeneous vegetation structure and the complex vegetation dynamics of selected African savannas are characterised by the combined analysis of remote sensing data of different spatial and temporal resolutions. The presented analyses reveal possibilities to enhance regional studies of the state and dynamics of savanna ecosystems. KW - Savanne KW - Optische Fernerkundung KW - Vegetation KW - Südafrika KW - Bedeckungsgrade KW - Landbedeckung KW - Landbedeckungsveränderungen KW - Fernerkundung KW - fractional cover KW - land cover KW - land cover change Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-55128 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 - Knauer, Kim A1 - Gessner, Ursula A1 - Fensholt, Rasmus A1 - Forkuor, Gerald A1 - Kuenzer, Claudia T1 - Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the environment JF - Remote Sensing N2 - Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92% (2001), 91% (2007), and 91% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91% between 2001 and 2014 to a total of 116,900 km\(^2\). While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future. KW - remote sensing KW - Africa KW - agriculture KW - Burkina Faso KW - data fusion KW - ESTARFM framework KW - irrigation KW - land surface phenology KW - Landsat KW - MODIS KW - plantation KW - protected areas KW - TIMESAT Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-171905 VL - 9 IS - 2 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 - Asam, Sarah A1 - Gessner, Ursula A1 - Almengor González, Roger A1 - Wenzl, Martina A1 - Kriese, Jennifer A1 - Kuenzer, Claudia T1 - Mapping crop types of Germany by combining temporal statistical metrics of Sentinel-1 and Sentinel-2 time series with LPIS data JF - Remote Sensing N2 - Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping. KW - agriculture KW - random forest classification KW - multispectral data KW - radar data KW - spectral statistics KW - temporal statistics KW - IACS Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-278969 SN - 2072-4292 VL - 14 IS - 13 ER - TY - JOUR A1 - Knauer, Kim A1 - Gessner, Ursula A1 - Fensholt, Rasmus A1 - Kuenzer, Claudia T1 - An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes JF - Remote Sensing N2 - Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km\(^{2}\) in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture KW - vegetation dynamics KW - ESTARFM KW - MODIS KW - Landsat KW - phenology KW - West Africa KW - cloud gap filling KW - time series analysis Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-180712 VL - 8 IS - 5 ER - TY - JOUR A1 - Thonfeld, Frank A1 - Gessner, Ursula A1 - Holzwarth, Stefanie A1 - Kriese, Jennifer A1 - da Ponte, Emmanuel A1 - Huth, Juliane A1 - Kuenzer, Claudia T1 - A first assessment of canopy cover loss in Germany's forests after the 2018–2020 drought years JF - Remote Sensing N2 - 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. KW - forest KW - canopy cover loss KW - drought KW - Sentinel-2 KW - Landsat-8 KW - disturbance index KW - time series Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-255306 SN - 2072-4292 VL - 14 IS - 3 ER - TY - JOUR A1 - Reinermann, Sophie A1 - Gessner, Ursula A1 - Asam, Sarah A1 - Ullmann, Tobias A1 - Schucknecht, Anne A1 - Kuenzer, Claudia T1 - Detection of grassland mowing events for Germany by combining Sentinel-1 and Sentinel-2 time series JF - Remote Sensing N2 - 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. KW - earth observation KW - remote sensing KW - harvests KW - cutting events KW - grazing KW - pasture KW - meadow KW - optical KW - SAR KW - PolSAR KW - InSAR Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-267164 SN - 2072-4292 VL - 14 IS - 7 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 -