Utilization of Google Earth Engine (GEE) for land cover monitoring over Klang Valley, Malaysia

Abstract

Abstract Geospatial Big Data is currently received overwhelming attention and are on highlight globally and Google Earth Engine (GEE) is currently the hot pot platform to cater big data processing for Remote Sensing and GIS. Currently few or no study regarding the usage of this platform to study land use/cover changes over years in Malaysia. The objective is to evaluate the feasibility of GEE as a free cloud-based platform by performing classification of Klang Valley area from Landsat composites of three different years (1988-2003-2018) using multiple Machine Learning Algorithms (MLA). The best classification results were then imported and further processed to quantify the changes over the years using commercial software. Although, the classification results are of high accuracy but CART shows the best accuracy with 94.71%, 97.72% and 96.57% in 1988, 2003 and 2018 in comparison with RF and SVM. Some misclassified pixels were encountered because the annual composited images were compiled without taken into considerations of crops phenological stages (paddy) which resulted to the misclassified agricultural land into urban and bare land. Hence, the selection and composition of data initially had to be structured and strategized prior to processing as they can affect the classification result and further analysis. Regardless, GEE has performed quite well and fast in term of time and processing complexity of multiple datasets with minimal human interaction and intervention. Generally, GEE has proven to be reliable in fulfilling the objectives of this study to evaluate the GEE feasibility by performing classification and quantifying the land use/cover of studied area and provide good base for further analysis using different platform.