Remote Sensing Image-based Assessment of Land Dynamics Transformation into Tea Plantations using Support Vector Machine

Abstract

Precisely monitoring land use dynamics and spatial distributions is essential for sustainable development and long-term land management. Tea is one of the leading beverage crops cultivated in Bangladesh, expanding rapidly in northern districts and forcing land use change. This study aims to decipher the expansion of tea cultivation and land dynamics transformation to tea land areas in the northern region of Bangladesh by using Landsat 5 TM and Landsat 8 OLI/TIRS surface reflectance images. The supervised support vector machine (SVM) method was used for classification purposes, resulting in three classified maps for the years 2004, 2013 and 2022, having overall accuracies of 91.43%, 98.67%, and 98.48% and kappa coefficients of 89.51, 98.37, and 98.13, respectively. The images were classified into six land use classes: agriculture, tea cultivation area, settlement, waterbody, bare land, and forest. Land transformation results reveal that overall tea land increased by 41.08% from 2004 to 2022, experiencing a downward trend during 2004-2013, while from 2013-2022, a dramatic rise of 70.01% (equal to 4683.60 ha) tea cultivation area was found in Panchagarh district. The bare land was among the most highly transformed land classes into tea plantations, followed by the forest area. This study underlines the advantages of cutting-edge technologies in land transformation detection for sustainable land management and policy implications for the sustainable development of small landholding livelihoods and the tea industry.