Identification of winter land use in temperate agricultural landscapes based on Sentinel-1 and 2 times-series
Land cover and land use monitoring, particularly during winter season, is still a major environmental and scientific issue in agricultural areas. From an environmental point of view, the presence and type of vegetation cover in winter have an impact on pollutant transport to water bodies. From a methodological point of view, characterizing spatio-temporal dynamics of winter land cover and land use at a field scale remains a challenge due to the diversity of farming strategies and practices. The objective of this study was to evaluate the potential of optical and SAR time-series to improve the monitoring of winter land use in an area of 130 km². For that purpose, Sentinel-1 and 2 time-series were classified using SVM and RF algorithms. Winter land use was identified with an overall accuracy of 81% and a kappa index of 0.77 from a combination of Sentinel-1 and 2 images.
| Main Authors: | , , , , |
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| Format: | conference_item biblioteca |
| Language: | eng |
| Published: |
IEEE
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| Online Access: | http://agritrop.cirad.fr/599351/ http://agritrop.cirad.fr/599351/1/IGARSS.2018.8517673.pdf |
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| Summary: | Land cover and land use monitoring, particularly during winter season, is still a major environmental and scientific issue in agricultural areas. From an environmental point of view, the presence and type of vegetation cover in winter have an impact on pollutant transport to water bodies. From a methodological point of view, characterizing spatio-temporal dynamics of winter land cover and land use at a field scale remains a challenge due to the diversity of farming strategies and practices. The objective of this study was to evaluate the potential of optical and SAR time-series to improve the monitoring of winter land use in an area of 130 km². For that purpose, Sentinel-1 and 2 time-series were classified using SVM and RF algorithms. Winter land use was identified with an overall accuracy of 81% and a kappa index of 0.77 from a combination of Sentinel-1 and 2 images. |
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