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

Uday Pimple, Asamaporn Sitthi, Dario Simonetti, Sukan Pungkul, Kumron Leadprathom and Amnat Chidthaisong
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Uday Pimple: The Joint Graduate School of Energy and Environment (JGSEE) and Centre of Excellence on Energy Technology and Environment, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
Asamaporn Sitthi: Department of Geography, Faculty of Social Sciences, Kasetsart University, Bangkok 10900, Thailand
Dario Simonetti: European Commission, Joint Research Centre, Directorate D-Sustainable Resources-Bio-Economy Unit, 21027 Ispra (VA), Italy
Sukan Pungkul: Royal Forest Department, 61 Phaholyothin Road, Chatuchak, Bangkok 10900, Thailand
Kumron Leadprathom: Royal Forest Department, 61 Phaholyothin Road, Chatuchak, Bangkok 10900, Thailand
Amnat Chidthaisong: The Joint Graduate School of Energy and Environment (JGSEE) and Centre of Excellence on Energy Technology and Environment, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand

Sustainability, 2017, vol. 9, issue 2, 1-26

Abstract: 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 the topographic correction methods are 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 × 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 the topographic corrections. Our results show that the C-correction, SEC and VECA corrected imagery were able to improve the forest classification accuracy of Landsat TM-5 from 78.41% to 81.50%, 82.38%, and 81.50%, respectively, and OLI-8 from 81.06% to 81.50%, 82.38%, and 81.94%, respectively. The highest accuracy of forest type classification is obtained with the newly available high-resolution SRTM DEM and SEC method.

Keywords: topographic effect; topographic correction; DEM; improved cosine correction; Minnaert; C-correction; SEC; VECA; Landsat TM-5 and OLI-8; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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