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Mapping threatened dry deciduous dipterocarp forest in South-east Asia for conservation management
(2014)
Habitat loss is the primary reason for species extinction, making habitat conservation a critical strategy for maintaining global biodiversity. Major habitat types, such as lowland tropical evergreen forests or mangrove forests, are already well represented in many conservation priorities, while others are underrepresented. This is particularly true for dry deciduous dipterocarp forests (DDF), a key forest type in Asia that extends from the tropical to the subtropical regions in South-east Asia (SE Asia), where high temperatures and pronounced seasonal precipitation patterns are predominant. DDF are a unique forest ecosystem type harboring a wide range of important and endemic species and need to be adequately represented in global biodiversity conservation strategies. One of the greatest challenges in DDF conservation is the lack of detailed and accurate maps of their distribution due to inaccurate open-canopy seasonal forest mapping methods. Conventional land cover maps therefore tend to perform inadequately with DDF. Our study accurately delineates DDF on a continental scale based on remote sensing approaches by integrating the strong, characteristic seasonality of DDF. We also determine the current conservation status of DDF throughout SE Asia. We chose SE Asia for our research because its remaining DDF are extensive in some areas but are currently degrading and under increasing pressure from significant socio-economic changes throughout the region. Phenological indices, derived from MODIS vegetation index time series, served as input variables for a Random Forest classifier and were used to predict the spatial distribution of DDF. The resulting continuous fields maps of DDF had accuracies ranging from R-2 = 0.56 to 0.78. We identified three hotspots in SE Asia with a total area of 156,000 km(2), and found Myanmar to have more remaining DDF than the countries in SE Asia. Our approach proved to be a reliable method for mapping DDF and other seasonally influenced ecosystems on continental and regional scales, and is very valuable for conservation management in this region.
The Northern Bald Ibis (Geronticus eremita, NBI) is an endangered migratory species, which went extinct in Europe in the 17th century. Currently, a translocation project in the frame of the European LIFE program is carried out, to reintroduce a migratory population with breeding colonies in the northern and southern Alpine foothills and a common wintering area in southern Tuscany. The population meanwhile consists of about 200 individuals, with about 90% of them carrying a GPS device on their back. We used biologging data from 2021 to model the habitat suitability for the species in the northern Alpine foothills. To set up a species distribution model, indices describing environmental conditions were calculated from satellite images of Landsat-8, and in addition to the well-proven use of optical remote sensing data, we also included Sentinel-1 actively sensed observation data, as well as climate and urbanization data. A random forest model was fitted on NBI GPS positions, which we used to identify regions with high predicted foraging suitability within the northern Alpine foothills. The model resulted in 84.5% overall accuracy. Elevation and slope had the highest predictive power, followed by grass cover and VV intensity of Sentinel-1 radar data. The map resulting from the model predicts the highest foraging suitability for valley floors, especially of Inn, Rhine, and Salzach-Valley as well as flatlands, like the Swiss Plateau and the agricultural areas surrounding Lake Constance. Areas with a high suitability index largely overlap with known historic breeding sites. This is particularly noteworthy because the model only refers to foraging habitats without considering the availability of suitable breeding cliffs. Detailed analyses identify the transition zone from extensive grassland management to intensive arable farming as the northern range limit. The modeling outcome allows for defining suitable areas for further translocation and management measures in the frame of the European NBI reintroduction program. Although required in the international IUCN translocation guidelines, the use of models in the context of translocation projects is still not common and in the case of the Northern Bald Ibis not considered in the present Single Species Action Plan of the African-Eurasian Migratory Water bird Agreement. Our species distribution model represents a contemporary snapshot, but sustainability is essential for conservation planning, especially in times of climate change. In this regard, a further model could be optimized by investigating sustainable land use, temporal dynamics, and climate change scenarios.
In den letzten Jahrzehnten ist eine verstärkte Veränderung der Landoberfläche beobachtet worden. Diese Prozesse sind direkten und indirekten anthropogenen Einflüssen zuzuschreiben, wie Deforestation oder Klimawandel. Mit dieser Entwicklung geht der Verlust und die Fragmentation von naturnahen Flächen einher. Für das Fortbestehen von Populationen verschiedenster Organismen in einer derartig geformten Landschaft ist entscheidend, inwieweit die Migration zwischen bestehenden Fragmenten gewährleistet ist. Diese wird von der Eignung der umgebenden Landschaft beeinflusst. Im Kontext einer klimatischen Veränderung und verstärkter anthropogener Landnutzung ist die Analyse der räumlichen Anordnung von Habitatfragmenten und der Qualität der umgebenden Landschaft besonders für die globale Aufrechterhaltung der Biodiversität wichtig. Großräumige Muster der Landschaftsveränderung können mit Hilfe von Satellitendaten analysiert werden, da es nur diese ermöglichen die Landbedeckung flächendeckend, reproduzierbar und auf einer adäquaten räumlichen Auflösung zu kartieren. Besonders zeitlich hochaufgelöste Daten liefern wertvolle Informationen bezüglich der Dynamik der Landbedeckung. Diese Arbeit beschäftigt sich mit der Analyse der Fragmentation in Westafrika und der potentiellen Bedeutung von singulären Fragmenten und deren potentiellen Auswirkungen auf die Biodiversität. Dafür wurden zeitlich hoch- und räumlich mittelaufgelöste Daten des Aufnahmesystems MODIS verwendet, mit denen für das Untersuchungsgebiet Westafrika die Landbedeckung klassifziert wurde. Für die darauf folgenden Analysen der räumlichen Konfiguration der Fragmente wurde der Fokus auf Regenwaldgebiete gelegt. Die Analyse von räumlichen Mustern der Regenwaldfragmente liefert weiterführende qualitative Informationen der individuellen Teilbereiche. Die räumliche Anordnung wurde sowohl mit etablierten Maßen als auch mittels in dieser Arbeit erstellter robuster und übertragbarer Indizes quantifiziert. Es konnte gezeigt werden, dass die Verwendung von aussagekräftigen Indizes, besonders, wenn sie alle benachbarten Fragmente und die Qualität der umgebenden Matrix berücksichtigen, die räumliche Differenzierung von Fragmenten verbessert. Jedoch ist die Anwendung dieser Maße abhängig von den Ansprüchen einer Art. Daher muss die artspezifische Perzeptionen der Landschaft auf der Basis der Indizes implementiert werden, da die Übertragung der Ergebnisse einzelner Indizes auf andere räumliche Auflösungen und andere Regionen nur begrenzt möglich war. Des Weiteren wurden potentielle Einflussfaktoren auf die räumlichen Muster mittels Neutraler Landschaftsmodelle untersucht. Hierbei ergaben sich je nach Region und Index unterschiedliche Ergebnisse, allerdings konnte der Einfluss anthropogen induzierter Veränderungen auf die Landbedeckung postuliert werden. Die große Bedeutung der räumlichen Attribution von Landbedeckungsklassen konnte in dieser Arbeit aufgezeigt werden. Der alleinige Fokus auf die Kartierung von z. B. Waldfragmenten ohne deren räumliche Anordnung zu berücksichtigen, kann zu falschen Schlüssen bezüglich deren ökologischen, hydrologischen und klimatologischen Bedeutung führen.
Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in Côte d’Ivoire using high- and moderateresolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixelbased modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.
Background
Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health.
Methodology
We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d’Ivoire and validated against readily available survey data from school-aged children.
Principal Findings
Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d’Ivoire.
Conclusions/Significance
A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data.
Background:
Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions.
Methods:
We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised.
Results:
We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from.
Conclusions:
Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.
Remote sensing for disease risk profiling: a spatial analysis of schistosomiasis in West Africa
(2014)
Global environmental change leads to the emergence of new human health risks. As a consequence, transmission opportunities of environment-related diseases are transformed and human infection with new emerging pathogens increase. The main motivation for this study is the considerable demand for disease surveillance and monitoring in relation to dynamic environmental drivers. Remote sensing (RS) data belong to the key data sources for environmental modelling due to their capabilities to deliver spatially continuous information repeatedly for large areas with an ecologically adequate spatial resolution.
A major research gap as identified by this study is the disregard of the spatial mismatch inherent in current modelling approaches of profiling disease risk using remote sensing data. Typically, epidemiological data are aggregated at school or village level. However, these point data do neither represent the spatial distribution of habitats, where disease-related species find their suitable environmental conditions, nor the place, where infection has occurred. As a consequence, the prevalence data and remotely sensed environmental variables, which aim to characterise the habitat of disease-related species, are spatially disjunct.
The main objective of this study is to improve RS-based disease risk models by incorporating the ecological and spatial context of disease transmission. Exemplified by the analysis of the human schistosomiasis disease in West Africa, this objective includes the quantification of the impact of scales and ecological regions on model performance.
In this study, the conditions that modify the transmission of schistosomiasis are reviewed in detail. A conceptual underpinning of the linkages between geographical RS measures, disease transmission ecology, and epidemiological survey data is developed. During a field-based analysis, environmental suitability for schistosomiasis transmission was assessed on the ground, which is then quantified by a habitat suitability index (HSI) and applied to RS data. This conceptual model of environmental suitability is refined by the development of a hierarchical model approach that statistically links school-based disease prevalence with the ecologically relevant measurements of RS data. The statistical models of schistosomiasis risk are derived from two different algorithms; the Random Forest and the partial least squares regression (PLSR). Scale impact is analysed based on different spatial resolutions of RS data. Furthermore, varying buffer extents are analysed around school-based measurements. Three distinctive sites of Burkina Faso and Côte d’Ivoire are specifically modelled to represent a gradient of ecozones from dry savannah to tropical rainforest including flat and mountainous regions.
The model results reveal the applicability of RS data to spatially delineate and quantitatively evaluate environmental suitability for the transmission of schistosomiasis. In specific, the multi-temporal derivation of water bodies and the assessment of their riparian vegetation coverage based on high-resolution RapidEye and Landsat data proofed relevant. In contrast, elevation data and water surface temperature are constraint in their ability to characterise habitat conditions for disease-related parasites and freshwater snail species. With increasing buffer extent observed around the school location, the performance of statistical models increases, improving the prediction of transmission risk. The most important RS variables identified to model schistosomiasis risk are the measure of distance to water bodies, topographic variables, and land surface temperature (LST). However, each ecological region requires a different set of RS variables to optimise the modelling of schistosomiasis risk. A key result of the hierarchical model approach is its superior performance to explain the spatial risk of schistosomiasis.
Overall, this study stresses the key importance of considering the ecological and spatial context for disease risk profiling and demonstrates the potential of RS data. The methodological approach of this study contributes substantially to provide more accurate and relevant geoinformation, which supports an efficient planning and decision-making within the public health sector.
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.
A disease is non-communicable when it is not transferred from one person to another. Typical examples include all types of cancer, diabetes, stroke, or allergies, as well as mental diseases. Non-communicable diseases have at least two things in common — environmental impact and chronicity. These diseases are often associated with reduced quality of life, a higher rate of premature deaths, and negative impacts on a countries' economy due to healthcare costs and missing work force. Additionally, they affect the individual's immune system, which increases susceptibility toward communicable diseases, such as the flu or other viral and bacterial infections. Thus, mitigating the effects of non-communicable diseases is one of the most pressing issues of modern medicine, healthcare, and governments in general. Apart from the predisposition toward such diseases (the genome), their occurrence is associated with environmental parameters that people are exposed to (the exposome). Exposure to stressors such as bad air or water quality, noise, extreme heat, or an overall unnatural surrounding all impact the susceptibility to non-communicable diseases. In the identification of such environmental parameters, geoinformation products derived from Earth Observation data acquired by satellites play an increasingly important role. In this paper, we present a review on the joint use of Earth Observation data and public health data for research on non-communicable diseases. We analyzed 146 articles from peer-reviewed journals (Impact Factor ≥ 2) from all over the world that included Earth Observation data and public health data for their assessments. Our results show that this field of synergistic geohealth analyses is still relatively young, with most studies published within the last five years and within national boundaries. While the contribution of Earth Observation, and especially remote sensing-derived geoinformation products on land surface dynamics is on the rise, there is still a huge potential for transdisciplinary integration into studies. We see the necessity for future research and advocate for the increased incorporation of thematically profound remote sensing products with high spatial and temporal resolution into the mapping of exposomes and thus the vulnerability and resilience assessment of a population regarding non-communicable diseases.
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.
The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR's Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).
Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.
Public safety and socio-economic development of the Jharia coalfield (JCF) in India is critically dependent on precise monitoring and comprehensive understanding of coal fires, which have been burning underneath for more than a century. This study utilizes New-Small BAseline Subset (N-SBAS) technique to compute surface deformation time series for 2017–2020 to characterize the spatiotemporal dynamics of coal fires in JCF. The line-of-sight (LOS) surface deformation estimated from ascending and descending Sentinel-1 SAR data are subsequently decomposed to derive precise vertical subsidence estimates. The most prominent subsidence (~22 cm) is observed in Kusunda colliery. The subsidence regions also correspond well with the Landsat-8 based thermal anomaly map and field evidence. Subsequently, the vertical surface deformation time-series is analyzed to characterize temporal variations within the 9.5 km\(^2\) area of coal fires. Results reveal that nearly 10% of the coal fire area is newly formed, while 73% persisted throughout the study period. Vulnerability analyses performed in terms of the susceptibility of the population to land surface collapse demonstrate that Tisra, Chhatatanr, and Sijua are the most vulnerable towns. Our results provide critical information for developing early warning systems and remediation strategies.
Maize cropping systems mapping using RapidEye observations in agro-ecological landscapes in Kenya
(2017)
Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer’s accuracy and UA: user’s accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10–20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.
Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence-absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high-resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1-scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non-irrigated land. When both options occurred, the animals disregarded non-irrigated land leading to its misclassification as non-agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non-irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture - with other land-use dependencies and observation times - can contribute to better results in similar applications.
Satellite-derived land surface temperature dynamics in the context of global change — a review
(2023)
Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.
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.
Introduction: Grasslands cover one third of the agricultural area in Germany and are mainly used for fodder production. However, grasslands fulfill many other ecosystem functions, like carbon storage, water filtration and the provision of habitats. In Germany, grasslands are mown and/or grazed multiple times during the year. The type and timing of management activities and the use intensity vary strongly, however co-determine grassland functions. Large-scale spatial information on grassland activities and use intensity in Germany is limited and not openly provided. In addition, the cause for patterns of varying mowing intensity are usually not known on a spatial scale as data on the incentives of farmers behind grassland management decisions is not available.
Methods: We applied an algorithm based on a thresholding approach utilizing Sentinel-2 time series to detect grassland mowing events to investigate mowing dynamics in Germany in 2018–2021. The detected mowing events were validated with an independent dataset based on the examination of public webcam images. We analyzed spatial and temporal patterns of the mowing dynamics and relationships to climatic, topographic, soil or socio-political conditions.
Results: We found that most intensively used grasslands can be found in southern/south-eastern Germany, followed by areas in northern Germany. This pattern stays the same among the investigated years, but we found variations on smaller scales. The mowing event detection shows higher accuracies in 2019 and 2020 (F1 = 0.64 and 0.63) compared to 2018 and 2021 (F1 = 0.52 and 0.50). We found a significant but weak (R2 of 0–0.13) relationship for a spatial correlation of mowing frequency and climate as well as topographic variables for the grassland areas in Germany. Further results indicate a clear value range of topographic and climatic conditions, characteristic for intensive grassland use. Extensive grassland use takes place everywhere in Germany and on the entire spectrum of topographic and climatic conditions in Germany. Natura 2000 grasslands are used less intensive but this pattern is not consistent among all sites.
Discussion: Our findings on mowing dynamics and relationships to abiotic and socio-political conditions in Germany reveal important aspects of grassland management, including incentives of farmers.
Mapping Bushfire Distribution and Burn Severity in West Africa Using Remote Sensing Observations
(2010)
Fire has long been considered to be the main ecological factor explaining the origin and maintenance of West African savannas. It has a very high occurrence in these savannas due to high human pressure caused by strong demographic growth and, concomitantly, is used to transform natural savannas into farmland and is also used as a provider of energy. This study was carried out with the support of the BIOTA project funded by the German ministry for Research and Education. The objective of this study is to establish the spatial and temporal distribution of bushfires during a long observation period from 2000 to 2009 as well as to assess fire impact on vegetation through mapping of the burn severity; based on remote sensing and field data collections. Remote sensing was used for this study because of the advantages that it offers in collecting data for long time periods and on different scales. In this case, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument at 1km resolution is used to assess active fires, and understand the seasonality of fire, its occurrence and its frequency within the vegetation types on a regional scale. Landsat ETM+ imagery at 30 m and field data collections were used to define the characteristics of burn severity related to the biomass loss on a local scale. At a regional scale, the occurrence of fires and rainfall per month correlated very well (R2 = 0.951, r = -0.878, P < 0.01), which shows that the lower the amount of rainfall, the higher the fire occurrence and vice versa. In the dry season, four fire seasons were determined on a regional scale, namely very early fires, which announce the beginning of the fires, early and late fires making up the peak of fire in December/January and very late fires showing the end of the fire season and the beginning of the rainy season. Considerable fire activity was shown to take place in the vegetation zones between the Forest and the Sahel areas. Within these zones, parts of the Sudano-Guinean and the Guinean zones showed a high pixel frequency, i.e. fires occurred in the same place in many years. This high pixel frequency was also found in most protected areas in these zones. As to the kinds of land cover affected by fire, the highest fire occurrence is observed within the Deciduous woodlands and Deciduous shrublands. Concerning the burn severity, which was observed at a local scale, field data correlated closely with the ΔNBR derived from Landsat scenes of Pendjari National Park (R2 = 0.76). The correlation coefficient according to Pearson is r = 0.84 and according to Spearman-Rho, the correlation coefficient is r = 0.86. Very low and low burn severity (with ΔNBR value from 0 to 0.40) affected the vegetation weakly (0-35 percent of biomass loss) whereas moderate and high burn severity greatly affected the vegetation, leading to up to 100 percent of biomass loss, with the ΔNBR value ranging from 0.41 to 0.99. It can be seen from these results that remotely sensed images offer a tool to determine the fire distribution over large regions in savannas and that the Normalised Burn Ratio index can be applied to West Africa savannas. The outcomes of this thesis will hopefully contribute to understanding and, eventually, improving fire regimes in West Africa and their response to climate change and changes in vegetation diversity.
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.