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

Journal article

Authors/Editors

Strategic Research Themes

No matching items found.

Publication Details

Author list: Pimple U., Sitthi A., Simonetti D., Pungkul S., Leadprathom K., Chidthaisong A.

Publisher: MDPI

Publication year: 2017

Journal: Sustainability (2071-1050)

Volume number: 9

Issue number: 2

ISSN: 2071-1050

eISSN: 2071-1050

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013484236&doi=10.3390%2fsu9020258&partnerID=40&md5=82e883e49d8e26f6ca97d36c81cacfc5

Languages: English-Great Britain (EN-GB)

View in Web of Science | View on publisher site | View citing articles in Web of Science

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. ฉ 2017 by the author.

Keywords

C-correction, DEM, Improved cosine correction, Landsat TM-5 and OLI-8, Minnaert, SEC, Topographic correction, Topographic effect, VECA