The utilisation of cloud computing and remote sensing approach to assess environmental sustainability in Malaysia

Abstract

Monitoring of the environment over a large area will require huge amount of data. The implementation of conventional methods will be time consuming and very costly. Furthermore, one way to assess the environmental sustainability is by analysing the changes of land cover over two different periods. Therefore, this study implemented a cloud computing approach utilising an open source Remote Ecosystem Monitoring Assessment Pipeline (REMAP) to map the changes of Land use land cover (LULC) over Peninsular Malaysia utilising Landsat data obtained from two different periods (2003 and 2017). This approach has utilised a powerful inbuilt machine learning algorithm, Random Forest (RF) to test the performance of cloud computing using REMAP to produce LULC maps over Peninsular Malaysia. The results showed an acceptable LULC maps and the changes between two periods were analysed. Therefore, the utilisation of machine learning algorithm with the integration of cloud computing using REMAP can reduce the cost, lessen the processing time, produce LULC maps, and perform change analysis over large area.