TY - THES A1 - Walz, Yvonne T1 - Remote sensing for disease risk profiling: a spatial analysis of schistosomiasis in West Africa T1 - Fernerkundung für die Risikoabschätzung von Krankheiten: Eine räumliche Analyse der Krankheit Schistosomiasis in West Afrika N2 - 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. N2 - Globale Umweltveränderungen rufen neue Gesundheitsrisiken hervor. Eine Konsequenz sind veränderte Bedingungen für die Übertragung von umweltbezogenen Krankheiten und ansteigende Infektionen mit neu auftauchenden Erregern. Die Motivation für diese Arbeit basiert auf der steigenden Nachfrage, dynamische Veränderungen der Umwelt und deren Beziehung zu Veränderungen von umweltbedingten Krankheiten zu überwachen. Fernerkundungsdaten gehören zu den wichtigsten Datenquellen für die Umweltmodellierung, da diese es ermöglichen, die Landbedeckung flächendeckend, reproduzierbar und in einer adäquaten räumlichen Auflösung zu kartieren. Ein Forschungsbedarf, der in dieser Studie identifiziert wird, ist die fehlende Berücksichtigung einer räumlichen Diskrepanz innerhalb der bisherigen Vorgehensweise der Modellierung von Krankheitsrisiken mit Fernerkundungsdaten. Typischerweise werden epidemiologische Daten als Prävalenz einer Krankheit aggregiert erhoben, beispielsweise auf Schul- oder Dorfebene. Jedoch repräsentieren diese Punktmessungen weder die räumliche Verteilung von Habitaten, in welchen krankheitsrelevante Arten ihre geeigneten Umweltbedingungen vorfinden, noch den Ort, an dem sich die Menschen infiziert haben. Die Konsequenz ist, dass Messpunkte der Krankheitprävalenz und fernerkundungsbasierte Umweltvariablen, welche das Habitat von krankheitsrelevanten Arten charakterisieren sollen, räumlich nicht übereinstimmen. Das Hauptziel dieser Studie ist, ein Verfahren für die Anwendung von Fernerkundungsdaten bei der Modellierung von Krankheitsrisiken zu entwickeln, welches sowohl den ökologischen als auch den räumlichen Kontext der Krankheitsübertragung widerspiegelt. Am Beispiel der Krankheit Schistosomiasis werden weitere mögliche Einflussgrößen auf die Modellgüte quantitativ bewertet. Dies sind unter anderem die verschiedenen Skalenniveaus und die Heterogenität von Ökozonen. In dieser Arbeit werden die Bedingungen, die auf die Übertragung von Schistosomiasis einen Einfluss haben, aus der bestehenden Literatur im Detail ermittelt. Es wird eine konzeptionelle Grundlage entwickelt, die bestehende Zusammenhänge zwischen satellitengestützten Messungen, der Ökologie der Krankheitsübertragung sowie zu den Ergebnissen der epidemiologischen Studien ermittelt. Während eines Aufenthaltes im Untersuchungsgebiet wurde die Eignung der Umwelt für die Übertragung der Schistosomiasis analysiert. Diese Umwelteignung wird durch die Entwicklung eines Habitat-Eignungs-Index (habitat suitability index, HSI) quantifiziert und mit relevanten Fernerkundungsvariablen verknüpft. Im nächsten Schritt werden Inhalte dieses konzeptionellen Modells gezielt für die Entwicklung eines hierarchischen Modellansatzes verwendet, welcher die gemessene Prävalenz in einen statistischen Zusammenhang mit ökologisch relevanten Messungen von Fernerkundungsdaten bringt. Die statistischen Modelle des Risikos, sich mit Schistosomiasis zu infizieren, basieren auf zwei verschiedenen Modellalgorithmen, dem sogenannten Zufalls-Wald Algorithmus (Random Forest) und der Regression der partiellen, kleinsten Quadrate (Partial Least Squares Regression, PLSR). Der Einfluss von räumlichen Skalen auf die Risikomodellierung wird anhand verschiedener räumlicher Auflösungen der Fernerkundungsdaten ermittelt. Darüber hinaus werden unterschiedlich große Einzugsgebiete mit Hilfe eines Pufferverfahrens (Buffer) anhand der Schulen mit Prävalenzmessungen analysiert. Risikomodelle der Schistosomiasis werden für drei ausgewählte Untersuchungsgebiete in Burkina Faso und der Elfenbeinküste erstellt, welche einen ökologischen Gradienten von der Trockensavanne zum tropischen Regenwald sowie von flachen und bergigen Regionen darstellt. Diese Studie zeigt, dass Fernerkundungsdaten für die räumliche Abgrenzung und eine quantitative Bewertung der Umwelteignung für die Übertragung der Schistosomiasis geeignet sind. Besonders relevante Informationen sind zeitlich dynamische Veränderungen der Wasserbedeckung sowie die Erfassung des Grades der Ufervegetationsbedeckung auf Basis von hochaufgelösten RapidEye und Landsat Daten. Hingegen sind topographische Daten und die satellitengestützten Messungen der Temperatur nur eingeschränkt geeignet um Habitate der Parasiten und Frischwasserschnecken als wesentlichen Bestandteil der Krankheitsübertragung zu charakterisieren. Bei zunehmender Größe des Einzugsgebietes der Schulen verbessern sich die statistischen Modelle und können somit das Übertragungsrisiko besser erfassen. Die wichtigsten Fernerkundungsvariablen für die Modellierung des Schistosomiasis Risikos sind die Distanz zum nächstgelegenen Gewässer, topographische Variablen sowie die Landoberflächentemperatur (land surface temperature, LST). Für jede Ökozone muss jedoch eine geeignete Zusammenstellung von Fernerkundungsvariablen getroffen werden. Ein ganz wesentliches Ergebnis der hierarchischen statistischen Modellierung ist eine verbesserte Erklärung des räumlichen Risikos von Schistosomiasis. Insgesamt unterstreicht diese Studie die Bedeutsamkeit des ökologischen und räumlichen Kontexts für die Abschätzung des Krankheitsrisikos und demonstriert das Potential von Fernerkundungsdaten. Der methodische Ansatz dieser Arbeit kann wesentlich dazu beitragen, genaue und relevante Geoinformationen bereitzustellen. Damit wird eine effizientere Planung und Entscheidungsfindung innerhalb des Gesundheitssektors ermöglicht. N2 - Le changement environnemental global conduit à l'émergence de nouveaux risques pour la santé humaine. En conséquence, les voies de transmission des maladies liées à l'environnement, sont modifies de meme que l'infection humaine avec l´accroissement des nouveaux agents pathogènes émergents. La motivation principale de cette étude est la demande considérable pour la surveillance et le suivi des maladie en relation avec la dynamique des facteurs environnementaux. Les données de la télédétection sont les sources principales utilisees pour la modélisation de l'environnement en raison de leurs capacités à fournir une information de maniere spatiale, repetitive et continue pour les grandes surfaces avec une résolution spatiale écologique adéquate. L´importante lacune de la recherche scientifique identifiée par cette étude est la non considération de la disparité spatiale inhérente dans les approches actuelles de modélisation des risques de la maladie en utilisant des données de la télédétection. Généralement, les données épidémiologiques sont regrouper à l'école ou au niveau du village. Toutefois, ces données ne peuvent pas représenter la distribution spatiale des habitats et definir les conditions environnementales favorable a la proliferation des agents pathogenes de la maladie, ni le lieu, où l'infection s´est produite. En conséquence, les données sur la prévalence et les variables environnementales de la télédétection, qui visent à caractériser l'habitat des agents liés à la maladie, sont spatialement disjointes. L'objectif principal de cette étude est d'améliorer en utilisant la télédétection les modèles de risque de maladie en incorporant l´aspect écologique et spatiale de la transmission de la maladie. Illustré par l'étude des personnes infectées de la schistosomiase en Afrique de l'Ouest, cet objectif comprend la quantification du niveau d'impact des régions écologiques sur les performances du modèle. Dans cette étude, les conditions qui modifient la transmission de la schistosomiase sont examinées en détail. Une approche conceptuelle reliant les données mesurées issues de la télédétection, la transmission de la maladie, l'écologie et des données de l'enquête épidémiologique a été développé. A partir d'une étude sur le terrain, les facteurs environnementaux à la transmission de la schistosomiase ont été évalués, ensuite quantifiés par l´indice de qualité de l'habitat (habitat suitability index, HSI) et combiné aux données de la télédétection. Le modèle conceptuel de la pertinence environnemental a été affiné par le développement d'une approche de modèle hiérarchique qui relie statistiquement la prévalence de la maladie en milieu scolaire avec les mesures écologiques pertinentes de données de la télédétection. Les modèles statistiques de risque de schistosomiase proviennent de deux différents algorithmes; la forêt aléatoire (Random Forest) et la régression des moindres carrés partiels (Partial Least Squares Regression, PLSR). Le niveau d'impact a été analysé sur la base de différentes résolutions spatiales de données de la télédétection. En outre, des divers degrés carre des bassin de réception ont été analysés autour de mesures en milieu scolaire. Trois sites distinctifs du Burkina Faso et de la Côte d'Ivoire sont spécifiquement modélisés pour représenter un gradient de écozones de savane sèche a forêt tropicale y compris les régions plates et montagneuses. Les résultats du modèle révèlent l'applicabilité des données de la télédétection pour la délimitation spatiale et l’évaluation quantitative de la pertinence de l'environnement pour la transmission de la schistosomiase. Precisement, la dérivation multi-temporelle des course d'eau et l'évaluation de leur couverture riveraine de végétation a partir des images à haute résolution RapidEye et Landsat jugées adequate. En revanche, les données d'altitude et de température de la surface de l'eau ont montré certaines limites dans leur capacité à caractériser les conditions de l'habitat des parasites et des escargots en tant que composantes essentielles de la transmission de la maladie. Avec l'augmentation des degrés carres des bassins de réception observés autour de l'emplacement de l'école, la performance des modèles statistiques augmente, améliorant ainsi la prédiction du risque de transmission. Les plus importantes variables des données de la télédétection identifiées pour modéliser le risque de schistosomiase sont la mesure de la distance des plans d'eau, les variables topographiques, et la température de surface de la terre (land surface temperature, LST). Cependant, chaque région écologique nécessite une serie différente de variables de données de télédétection afin d´optimiser la modélisation du risque de schistosomiase. Le résultat primordial de l'approche du modèle hiérarchique est sa supérieure performance à expliquer le risque spatiale de la schistosomiase. Dans l'ensemble, cette étude souligne l'importance cruciale de tenir compte du contexte écologique et spatiale pour le profilage du risque de maladie et démontre le potentiel des données de télédétection. L'approche méthodologique de cette étude contribue de manière substantielle à fournir avec plus de précision et de pertinence l'information géographique, prenant en charge une planification efficace et la prise de décision dans le secteur de la santé publique. KW - Westafrika KW - Fernerkundung KW - schistosomiasis KW - risk profiling KW - Umweltveränderung KW - Gesundheitsgefährdung KW - Bilharziose KW - remote sensing KW - diseases Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-108845 ER - TY - JOUR A1 - Walz, Yvonne A1 - Wegmann, Martin A1 - Dech, Stefan A1 - Raso, Giovanna A1 - Utzinger, Jürg T1 - Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook JF - Parasites & Vectors N2 - 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. KW - ecology KW - scale KW - remote sensing KW - risk profiling KW - spatial modelling KW - schistosomiasis KW - geographical information system KW - intermediate host snail KW - epidemology Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-148778 VL - 8 IS - 163 ER - TY - JOUR A1 - Walz, Yvonne A1 - Wegmann, Martin A1 - Dech, Stefan A1 - Vounastou, Penelope A1 - Poda, Jean-Noel A1 - N'Goran, Eliézer K. A1 - Raso, Giovanna A1 - Utzinger, Jürg T1 - Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing JF - PLoS Neglected Tropical Diseases N2 - 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. KW - schistosomiasis KW - Burkina Faso KW - remote sensing KW - surface water KW - habitats KW - agricultural irrigation KW - rivers KW - snails Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-125845 VL - 9 IS - 11 ER - TY - JOUR A1 - Walz, Yvonne A1 - Wegmann, Martin A1 - Leutner, Benjamin A1 - Dech, Stefan A1 - Vounatsou, Penelope A1 - N'Goran, Eliézer K. A1 - Raso, Giovanna A1 - Utzinger, Jürg T1 - Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling JF - Geospatial Health N2 - 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. KW - Côte d’Ivoire KW - schistosomiasis KW - spatial risk profiling KW - remote sensing KW - ecological relevant model Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-126148 VL - 10 IS - 2 ER - TY - JOUR A1 - Wehner, Helena A1 - Huchler, Katharina A1 - Fritz, Johannes T1 - Quantification of foraging areas for the Northern Bald Ibis (Geronticus eremita) in the northern Alpine foothills: a random forest model fitted with optical and actively sensed earth observation data JF - Remote Sensing N2 - 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. KW - Northern Bald Ibis KW - conservation KW - species distribution modeling KW - random forest modeling KW - remote sensing KW - reintroduction Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262245 SN - 2072-4292 VL - 14 IS - 4 ER - TY - JOUR A1 - Wei, Chunzhu A1 - Blaschke, Thomas T1 - Pixel-wise vs. object-based impervious surface analysis from remote sensing: correlations with land surface temperature and population density JF - Urban Science N2 - Impervious surface areas (ISA) are heavily influenced by urban structure and related structural features. We examined the effects of object-based impervious surface spatial pattern analysis on land surface temperature and population density in Guangzhou, China, in comparison to classic per-pixel analyses. An object-based support vector machine (SVM) and a linear spectral mixture analysis (LSMA) were integrated to estimate ISA fraction using images from the Chinese HJ-1B satellite for 2009 to 2011. The results revealed that the integrated object-based SVM-LSMA algorithm outperformed the traditional pixel-wise LSMA algorithm in classifying ISA fraction. More specifically, the object-based ISA spatial patterns extracted were more suitable than pixel-wise patterns for urban heat island (UHI) studies, in which the UHI areas (landscape surface temperature >37 °C) generally feature high ISA fraction values (ISA fraction >50%). In addition, the object-based spatial patterns enable us to quantify the relationship of ISA with population density (correlation coefficient >0.2 in general), with global human settlement density (correlation coefficient >0.2), and with night-time light map (correlation coefficient >0.4), and, whereas pixel-wise ISA did not yield significant correlations. These results indicate that object-based spatial patterns have a high potential for UHI detection and urbanization monitoring. Planning measures that aim to reduce the urbanization impacts and UHI intensities can be better supported. KW - impervious surface areas KW - object-based image analysis KW - land surface temperature KW - population density Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197829 SN - 2413-8851 VL - 2 IS - 1 ER - TY - JOUR A1 - Yang, Xuting A1 - Yao, Wanqiang A1 - Li, Pengfei A1 - Hu, Jinfei A1 - Latifi, Hooman A1 - Kang, Li A1 - Wang, Ningjing A1 - Zhang, Dingming T1 - Changes of SOC content in China's Shendong coal mining area during 1990–2020 investigated using remote sensing techniques JF - Sustainability N2 - Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R\(^2\) > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg\(^{−1}\) in 1990 to 9.23 gC·kg\(^{−1}\) in 2010 and then declined to 5.31 gC·Kg\(^{−1}\) in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone. KW - loess plateau KW - coal mining area KW - SOC content prediction KW - human disturbance KW - vegetation restoration KW - climate change Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-278939 SN - 2071-1050 VL - 14 IS - 12 ER - TY - JOUR A1 - Ziegler, Alice A1 - Meyer, Hanna A1 - Otte, Insa A1 - Peters, Marcell K. A1 - Appelhans, Tim A1 - Behler, Christina A1 - Böhning-Gaese, Katrin A1 - Classen, Alice A1 - Detsch, Florian A1 - Deckert, Jürgen A1 - Eardley, Connal D. A1 - Ferger, Stefan W. A1 - Fischer, Markus A1 - Gebert, Friederike A1 - Haas, Michael A1 - Helbig-Bonitz, Maria A1 - Hemp, Andreas A1 - Hemp, Claudia A1 - Kakengi, Victor A1 - Mayr, Antonia V. A1 - Ngereza, Christine A1 - Reudenbach, Christoph A1 - Röder, Juliane A1 - Rutten, Gemma A1 - Schellenberger Costa, David A1 - Schleuning, Matthias A1 - Ssymank, Axel A1 - Steffan-Dewenter, Ingolf A1 - Tardanico, Joseph A1 - Tschapka, Marco A1 - Vollstädt, Maximilian G. R. A1 - Wöllauer, Stephan A1 - Zhang, Jie A1 - Brandl, Roland A1 - Nauss, Thomas T1 - Potential of airborne LiDAR derived vegetation structure for the prediction of animal species richness at Mount Kilimanjaro JF - Remote Sensing N2 - The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results. KW - biodiversity KW - species richness KW - LiDAR KW - elevation KW - partial least square regression KW - arthropods KW - birds KW - bats KW - predictive modeling Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262251 SN - 2072-4292 VL - 14 IS - 3 ER - TY - JOUR A1 - Zielewska-Büttner, Katarzyna A1 - Heurich, Marco A1 - Müller, Jörg A1 - Braunisch, Veronika T1 - Remotely Sensed Single Tree Data Enable the Determination of Habitat Thresholds for the Three-Toed Woodpecker (Picoides tridactylus) JF - Remote Sensing N2 - Forest biodiversity conservation requires precise, area-wide information on the abundance and distribution of key habitat structures at multiple spatial scales. We combined airborne laser scanning (ALS) data with color-infrared (CIR) aerial imagery for identifying individual tree characteristics and quantifying multi-scale habitat requirements using the example of the three-toed woodpecker (Picoides tridactylus) (TTW) in the Bavarian Forest National Park (Germany). This bird, a keystone species of boreal and mountainous forests, is highly reliant on bark beetles dwelling in dead or dying trees. While previous studies showed a positive relationship between the TTW presence and the amount of deadwood as a limiting resource, we hypothesized a unimodal response with a negative effect of very high deadwood amounts and tested for effects of substrate quality. Based on 104 woodpecker presence or absence locations, habitat selection was modelled at four spatial scales reflecting different woodpecker home range sizes. The abundance of standing dead trees was the most important predictor, with an increase in the probability of TTW occurrence up to a threshold of 44–50 dead trees per hectare, followed by a decrease in the probability of occurrence. A positive relationship with the deadwood crown size indicated the importance of fresh deadwood. Remote sensing data allowed both an area-wide prediction of species occurrence and the derivation of ecological threshold values for deadwood quality and quantity for more informed conservation management. KW - deadwood KW - standing deadwood KW - dead tree KW - snags KW - three-toed woodpecker (Picoides tridactylus) KW - habitat suitability model (HSM) KW - habitat requirements KW - airborne laser scanning (ALS) KW - CIR aerial imagery Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197565 SN - 2072-4292 VL - 10 IS - 12 ER - TY - JOUR A1 - Zoungrana, Benewinde Jean-Bosco A1 - Conrad, Christopher A1 - Amekudzi, Leonard K. A1 - Thiel, Michael A1 - Dapola Da, Evariste A1 - Forkuor, Gerald A1 - Löw, Fabian T1 - Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa JF - Remote Sensing N2 - Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92%. Natural vegetation loss was estimated to be 17.9% ± 2.5% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors. KW - Burkina Faso KW - West Africa KW - multi-temporal images KW - mono-temporal image KW - ancillary data KW - LULCC Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-125866 VL - 7 IS - 9 ER -