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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.
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.
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.
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
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.
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.
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.
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.
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.