A study on Singapore's vegetation cover and land use change using remote sensing

Abstract

While the benefits of trees are well-known, there are few studies on the vegetation cover in Singapore as traditional data acquisition is inefficient. In this study, we put together an efficient land use classification pipeline for the highly urbanized country using Sentinel-2 (S2) images. We adopted an object-based (OB) approach which uses Simple Non-iterative Clustering (SNIC) for clustering and Grey Level Co-occurrence Matrix (GLCM) for textural indices. Random Forest (RF) classifier was used for classification. We produced maps with 85.8% accuracy for the years 2016 to 2021. We then analysed the vegetation cover changes using change detection methods, and identified areas with significant vegetation loss (24.4km2 or 3.14% of our study area) or gain (40.4km2 or 5.20% of our study area). We also determined the type of land use conversions in these areas. This study contributes to tree management, environmental impact assessments (EIA) and policy-making. It also lays the groundwork for future studies on city livability.