Unintended consequences: Factors influencing oil palm plantation expansion

Abstract

We investigated the landscape variables affecting the current dramatic expansion of oil palm plantations in Lam Thap district Krabi Province Thailand. THEOS satellite data was used to map land use classifications using the support vector machine method. Seven land use classes were consolidated into oil palm plantations and other rural uses. A logistic regression model was then applied to search for the relationship between two land use classes and the landscape variables of slope (three classes) and soil drainage (four classes). Overall, slope and drainage were statistically significant in explaining oil palm plantation expansion. Flat areas with poor drainage were the strongest factors. This contradicts the known optimal agronomic requirements for oil palms of well drainage. We suggest that what is observed is an unintended consequence of government oil palm support policies.

A Review: Progression of Remote Sensing (RS) and Geographical Information System (GIS) Applications in Oil Palm Management and Sustainability

“…In different studies by Mfondoum et al ( 2019), GIS-based multi-criteria decision making through Weighted Linear Combination (WLC) and Fuzzy Analytic Hierarchy Process (FAHP) analysis can help them in determine the best potential area to develop new oil palm plantation while considering most of the environmental factors (rainfall, temperature, sunlight, slope, elevation, soil fertility, soil moisture, and forest cover area). Moreover, research conducted by Wongsai et al (2020) in Thailand, revealed how the landscape variable affects the expansion of oil palm plantations, especially from slope and drainage factors. The research was performed by using THEOS satellite image with the support of a vector machine classifier and logistic regression model for their relationship analysis.…”