Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

The accurate mapping and monitoring of forests is essential for the sustainable management of forest ecosystems. Advancements in the Landsat satellite series have been very useful for various forest mapping applications. However, the topographic shadows of irregular mountains are major obstacles to accurate forest classification. In this paper we test five topographic correction methods: improved cosine correction, Minnaert, C-correction, Statistical Empirical Correction (SEC) and Variable Empirical Coefficient Algorithm (VECA), with multisource digital elevation models (DEM) to reduce the topographic relief effect in mountainous terrain produced by the Landsat Thematic Mapper (TM)-5 and Operational Land Imager (OLI)-8 sensors. The effectiveness of topographic correction methods is assessed by visual interpretation and the reduction in standard deviation (SD), by means of the coefficient of variation (CV). Results show that the SEC performs best with the Shuttle Radar Topographic Mission (SRTM) 30 m x 30 m DEM. The random forest (RF) classifier is used for forest classification, and the overall accuracy of forest classification is evaluated to compare the performances of topographic corrections. Our results show that the C-correction, SEC, and VECA corrected imagery were able to improve the accuracy of Landsat TM-5 and OLI-8 forest classifications. The highest accuracy of forest type classification is obtained with the newly available high-resolution SRTM DEM and SEC method.

PIMPLE Uday;  SITTHI Asamaporn;  SIMONETTI Dario;  PUNGKUL Sukan;  LEADPRATHOM Kumron;  CHIDTHAISONG Amnat; 

2017-02-28

MDPI AG

JRC105838

2071-1050,   

http://www.mdpi.com/2071-1050/9/2/258,    https://publications.jrc.ec.europa.eu/repository/handle/JRC105838,   

10.3390/su9020258,