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  - Remelgado, Ruben
A1  - Leutner, Benjamin
A1  - Safi, Kamran
A1  - Sonnenschein, Ruth
A1  - Kuebert, Carina
A1  - Wegmann, Martin
T1  - Linking animal movement and remote sensing - mapping resource suitability from a remote sensing perspective
JF  - Remote Sensing in Ecology and Conservation
N2  - Optical remote sensing is an important tool in the study of animal behavior providing ecologists with the means to understand species-environment interactions in combination with animal movement data. However, differences in spatial and temporal resolution between movement and remote sensing data limit their direct assimilation. In this context, we built a data-driven framework to map resource suitability that addresses these differences as well as the limitations of satellite imagery. It combines seasonal composites of multiyear surface reflectances and optimized presence and absence samples acquired with animal movement data within a cross-validation modeling scheme. Moreover, it responds to dynamic, site-specific environmental conditions making it applicable to contrasting landscapes. We tested this framework using five populations of White Storks (Ciconia ciconia) to model resource suitability related to foraging achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for absences. These results were influenced by the temporal composition of the seasonal reflectances indicated by the lower accuracies associated with higher day differences in relation to the target dates. Additionally, population differences in resource selection influenced our results marked by the negative relationship between the model accuracies and the variability of the surface reflectances associated with the presence samples. Our modeling approach spatially splits presences between training and validation. As a result, when these represent different and unique resources, we face a negative bias during validation. Despite these inaccuracies, our framework offers an important basis to analyze species-environment interactions. As it standardizes site-dependent behavioral and environmental characteristics, it can be used in the comparison of intra- and interspecies environmental requirements and improves the analysis of resource selection along migratory paths. Moreover, due to its sensitivity to differences in resource selection, our approach can contribute toward a better understanding of species requirements.
KW  - Landsat
KW  - movement ecology
KW  - optical remote sensing
KW  - resource mapping
KW  - resource suitability
KW  - surface reflectances
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-225199
VL  - 4
IS  - 3
ER  -