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This study evaluates uncertainty factors in using multitemporal Landsat images for subtle change detection, including atmosphere, topography, phenology, and sun and view angles. The study is based on monitoring forest succession with a set of multiple Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images spanning 15 years over the H.J. Andrews Experimental Forest in the Western Cascades of Oregon. The algorithms for removing atmospheric effects from remotely sensed images evaluated include a new version of the dark object subtraction (DOS3) method, the dense dark vegetation (DDV) method, the path radiance (PARA) approach, and the 6S radiative transfer codes. We found that the DOS3 approach undercorrects the image, and the recently developed DDV and PARA approaches can produce surface reflectance values closely matching those produced by 6S using in situ measurements of atmospheric aerosol optical depth. Atmospheric effects reduce normalized difference vegetation index (NDVI) and greenness, and increase brightness and wetness. Topography modifies brightness and greenness, but has minimal effects on NDVI and wetness, and it interacts with sun angle. Forest stands at late successional stages are more sensitive to topography than younger stands. Though the study areas are covered predominantly by evergreen needleleaf forests, phenological effect is significant. Sun angle effects are confounded with phenology, and reflectance values for stands at different successional stages are related to sun angles nonlinearly. Though Landsat has a small field of view angle, the view angle effects from overlapping Landsat scenes for a mountainous forested landscape may not be ignored when monitoring forest succession with multitemporal images.
Geoscience and Remote Sensing, IEEE Transactions on (Volume:41 , Issue: 11 )
Date of Publication: Nov. 2003