Remote Sensing · 2018 · 20 citations · 9 references
Natural ResourcesGeographyGeoinformaticsLandscape Change DetectionLandscape Pattern AnalysisRemote SensingChange DetectionLand Cover Change DetectionLand Cover ClassificationLandscape ChangeLandscape ClassificationRemote Sensing Change DetectionAerial Image Change DetectionChange Detection From Remote SensingField Data CollectionLand Change DetectionLand Use Change MappingLandscape Change MonitoringLandscape MonitoringSatellite Image Change Detection
We evaluated the feasibility of using aerial photo-based office methods rather than field-collected data to validate Landsat-based change detection products in national parks in Washington State. Landscape change was performed using LandTrendr algorithm. The resulting change patches were labeled in the office using aerial imagery and a random sample of patches was visited in the field by experienced analysts. Comparison of the two labels and associated confidence shows that the magnitude or severity of the change is a strong indicator of whether field assessment is warranted, and that confusion about patches with lower magnitude changes is not always resolved with a field visit. Our work demonstrates that validation of Landsat-derived landscape change patches can be done using office based tools such as aerial imagery, and that such methods provide an adequate validation for most change types, thus reducing the need for expensive field visits.
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Leo Breiman · Machine Learning · 2001 · 126.2K citations · Full text
High-Resolution Global Maps of 21st-Century Forest Cover Change
Matthew C. Hansen, Peter Potapov, Rebecca Moore et al. · Science · 2013 · 11.6K citations