%0 Journal Article %1 KLEIN2022102672 %A Klein, Igor %A van der Woude, Sietse %A Schwarzenbacher, Frederic %A Muratova, Nadiya %A Slagter, Bart %A Malakhov, Dmitry %A Oppelt, Natascha %A Kuenzer, Claudia %D 2022 %J International Journal of Applied Earth Observation and Geoinformation %K Kuenzer LSFE article %P 102672 %R https://doi.org/10.1016/j.jag.2021.102672 %T Predicting suitable breeding areas for different locust species – A multi-scale approach accounting for environmental conditions and current land cover situation %U https://www.sciencedirect.com/science/article/pii/S0303243421003792 %V 107 %X In this study, we present a fused multi-scale approach to model habitat suitability index (HSI) maps for three different locust species. The presented methodology was applied for the Italian locust (Calliptamus italicus, CIT) in Pavlodar oblast, Northern Kazakhstan, for the Moroccan locust (Dociostaurus maroccanus, DMA) in Turkistan oblast, South Kazakhstan and for the desert locust (Schistocerca gregaria) in Awash river basin, Ethiopia, Djibouti, Somalia. The main novelty is based on implementing results from ecological niche modelling (ENM) with time-series analyses of high spatial resolution remote sensing data (Sentinel-2) and further auxiliary datasets in a fused HSI model. Within the ENM important climatic variables (e.g. temperature, rainfall) and edaphic variables (e.g. sand and moisture contents) are included at a coarse spatial resolution. The analyses of Sentinel-2 time-series data enables mapping locust breeding habitats based on recent remotely sensed land observation at high spatial resolution and mirror the actual vegetation state, land use, land cover and in this way identify areas with favorable conditions for egg survival and breeding. The fused HSI results for year 2019 were validated based on ground field observation and reach area under curve (AUC) performance of 0.747% for CIT, 0.850% for DMA and 0.801% for desert locust. The innovation of this study is a multi-scale approach which accounts not only for climatic and environmental conditions but also for current vegetation and land management situation. This kind of up-to-date spatial detailed information on breeding suitability could enable area prioritization for risk assessment, monitoring and early intervention of locust pests.

@article{KLEIN2022102672, abstract = {In this study, we present a fused multi-scale approach to model habitat suitability index (HSI) maps for three different locust species. The presented methodology was applied for the Italian locust (Calliptamus italicus, CIT) in Pavlodar oblast, Northern Kazakhstan, for the Moroccan locust (Dociostaurus maroccanus, DMA) in Turkistan oblast, South Kazakhstan and for the desert locust (Schistocerca gregaria) in Awash river basin, Ethiopia, Djibouti, Somalia. The main novelty is based on implementing results from ecological niche modelling (ENM) with time-series analyses of high spatial resolution remote sensing data (Sentinel-2) and further auxiliary datasets in a fused HSI model. Within the ENM important climatic variables (e.g. temperature, rainfall) and edaphic variables (e.g. sand and moisture contents) are included at a coarse spatial resolution. The analyses of Sentinel-2 time-series data enables mapping locust breeding habitats based on recent remotely sensed land observation at high spatial resolution and mirror the actual vegetation state, land use, land cover and in this way identify areas with favorable conditions for egg survival and breeding. The fused HSI results for year 2019 were validated based on ground field observation and reach area under curve (AUC) performance of 0.747% for CIT, 0.850% for DMA and 0.801% for desert locust. The innovation of this study is a multi-scale approach which accounts not only for climatic and environmental conditions but also for current vegetation and land management situation. This kind of up-to-date spatial detailed information on breeding suitability could enable area prioritization for risk assessment, monitoring and early intervention of locust pests.}, added-at = {2022-01-21T19:24:32.000+0100}, author = {Klein, Igor and {van der Woude}, Sietse and Schwarzenbacher, Frederic and Muratova, Nadiya and Slagter, Bart and Malakhov, Dmitry and Oppelt, Natascha and Kuenzer, Claudia}, biburl = {https://www.bibsonomy.org/bibtex/2fafb9e43fb368b770261b78efdd89048/earthobs_uniwue}, description = {Predicting suitable breeding areas for different locust species – A multi-scale approach accounting for environmental conditions and current land cover situation - ScienceDirect}, doi = {https://doi.org/10.1016/j.jag.2021.102672}, interhash = {ed75ce25b623abc0d7fb9e5539e8ba33}, intrahash = {fafb9e43fb368b770261b78efdd89048}, issn = {0303-2434}, journal = {International Journal of Applied Earth Observation and Geoinformation}, keywords = {Kuenzer LSFE article}, pages = 102672, timestamp = {2022-01-21T19:24:32.000+0100}, title = {Predicting suitable breeding areas for different locust species – A multi-scale approach accounting for environmental conditions and current land cover situation}, url = {https://www.sciencedirect.com/science/article/pii/S0303243421003792}, volume = 107, year = 2022 }