Google Earth Engine for Advanced Land Cover Analysis from Landsat-8 Data with Spectral and Topographic Insights

Abstract

The primary goal of this research is to see how effective cloud-based computing services such as Google Earth Engine (GEE) platform are at classifying multitemporal satellite images and producing high-quality land cover maps for the target year of 2020, with the possibility of using it on a larger-scale area such as metropolitan Melbourne as a test site. To create high-quality land cover maps, the GEE is utilized to analyze a total of 80 Landsat-8 images. The support vector machine (SVM) approach is used to classify the images. Moreover, we use spectral bands, spectral indices, and topographic parameters to improve classification and address the limitations of existing approaches for classification with restricted input variables. Furthermore, we apply a postprocessing strategy to increase the model’s performance by removing the salt-and-pepper noise created by misclassified pixels in supervised classification results. The results demonstrate that given all parameters, the SVM approach achieves an overall accuracy (OA) and kappa accuracy of 88.47% and 85.34%, respectively. Following the implementation of the postprocessing technique, the OA and kappa improve to 92.90% and 90.99%, respectively. The results indicate that Landsat-8 multitemporal data, spectral indices, topographic components, and postprocessing techniques are all important in land cover mapping. Therefore, the use of freely accessible GEE technology and multitemporal Landsat-8 data ensures that decision makers have the resources they need to track land cover throughout the year.