Rapid method for yearly LULC classification using Random Forest and incorporating time-series NDVI and topography: a case study of Thanh Hoa province, Vietnam. Issue 27 (13th December 2022)

• Record Type: Journal Article
• Authors: Le, Trong Dieu Hien; Pham, Luan Hong; Dinh, Quang Toan; Hang, Nguyen Thi Thuy; Tran, Thi Anh Thu
• Is Part Of: Geocarto international. Volume 37:Issue 27(2023)
• Page Start: 17200
• Page End: 17215
• Publication Date: 2022-12-13
• Subjects: MODIS -- LULC classification -- random forest -- remote sensing; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2022.2123959
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Land-use and land-cover (LULC) mapping in the complex area is a challenging task due to the mixed vegetation patterns, and rough mountains with fast-flowing rivers. In Vietnam, LULC update is not frequently. In this study, we applied a supervised machine learning (Random forest—RF) approach to mapping LULC in Thanh Hoa province, Vietnam from 2011 to 2015 utilizing multi-temporal Normalized Difference Vegetation Index (NDVI) data from MODIS, combined with topographic features. Random forest classification (RFC) reached a total prediction accuracy of 91% and Kappa coefficient (K) of 0.89 across eight LULCs. Besides, the results showed that the features extracted from time-series NDVI comprising the mean of yearly NDVI, the sum of NDVI, and the topography were the important variables controlling the LULC classification. For similar studies on the distribution of LULC, the method proposed in this study could be helpful.