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Reduction of atmospheric and topographic effect on Landsat TM data for forest classification
Authors:H. Huang  N. Clinton  F. Hui
Affiliation:1. State Key Laboratory of Remote Sensing Science , jointly sponsored by the Institute of Remote Sensing Applications of the Chinese Academy of Sciences and Beijing Normal University , Beijing, 100101, China;2. Graduate School of the Chinese Academy of Sciences , Beijing, 100049, China;3. Division of Ecosystem Science , University of California , Berkeley, CA 94720‐3114, USA;4. State Key Laboratory of Remote Sensing Science , jointly sponsored by the Institute of Remote Sensing Applications of the Chinese Academy of Sciences and Beijing Normal University , Beijing, 100101, China
Abstract:The incident radiance in forested areas with rugged terrain varies greatly with the changes in solar elevation and azimuth, slope and aspect of the terrain, and the relative position of trees. The geotropic nature must be considered in the course of topographic correction. The Sun‐Canopy‐Sensor (SCS) model is introduced to substitute the cosine correction in a physical model. We used an atmospheric simulation code, MODTRAN, and a digital elevation model (DEM) to calculate the path radiance, downwards diffuse radiance and two‐way transmittance of direct and diffuse light at different altitudes. Based on the atmospheric parameters derived above and the Lambertian assumption, surface reflectance in a forested area was retrieved from Landsat Thematic Mapper (TM) imagery using a revised physical model. Meanwhile, a smoothed DEM was used to assess the effect of noise on the DEM and misregistration between the DEM and the satellite imagery. Correlation analysis, spectral comparison between sunlit and shaded slopes and a support vector machine (SVM) classification were performed to assess the effect of the revised radiometric correction algorithm. Results indicate that the revised physical model with smoothed DEM is more adequate for forested terrain and more consistent spectra for similar vegetation under different illuminations can be obtained. Finally, higher classification accuracy of forested land can be achieved with the revised correction algorithm compared with the SCS correction and the original physical correction model.
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