Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India

Abstract

The growing human population accelerates alterations in land use and land cover (LULC) over time, putting tremendous strain on natural resources. Monitoring and assessing LULC change over large areas is critical in a variety of fields, including natural resource management and climate change research. LULC change has emerged as a critical concern for policymakers and environmentalists. As the need for the reliable estimation of LULC maps from remote sensing data grows, it is critical to comprehend how different machine learning classifiers perform. The primary goal of the present study was to classify LULC on the Google Earth Engine platform using three different machine learning algorithms—namely, support vector machine (SVM), random forest (RF), and classification and regression trees (CART)—and to compare their performance using accuracy assessments. The LULC of the study area was classified via supervised classification. For improved classification accuracy, NDVI (normalized difference vegetation index) and NDWI (normalized difference water index) indices were also derived and included. For the years 2016, 2018, and 2020, multitemporal Sentinel-2 and Landsat-8 data with spatial resolutions of 10 m and 30 m were used for the LULC classification. ‘Water bodies’, ‘forest’, ‘barren land’, ‘vegetation’, and ‘built-up’ were the major land use classes. The average overall accuracy of SVM, RF, and CART classifiers for Landsat-8 images was 90.88%, 94.85%, and 82.88%, respectively, and 93.8%, 95.8%, and 86.4% for Sentinel-2 images. These results indicate that RF classifiers outperform both SVM and CART classifiers in terms of accuracy.

Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

“…Figure S1 depicts the RCM grid points and sub-basins in the study area. This basin has been chosen as the study area as part of the ongoing research project IWMM-BIS, titled "Integrated Water Management Model for Brazil, India and South Africa under climate change scenarios" [43].…”

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

“…We obtained that optical satellite images are beneficial for land and land-cover maps. Some approaches that use the same technologies and tools for the land-cover map are [48,50,64].…”

“…Some studies in the literature used Sentinel-2 and the three classification algorithms mentioned. Praticó et al [48] and Loukika et al [64] used Sentinel-2 and the RF, SVM, and CART algorithms. The best results obtained were with RF and SVM.…”

A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine

“…Today, with the development of cloud-based platforms such as Google Earth Engine (GEE), it has been possible to process remote sensing data online without downloading [20]. Various studies showed the effectiveness of GEE in different remote sensing applications such as landcover classification [21,22], wetland detection [23], water quality monitoring [24], flood mapping [19], and impact analysis of drought and floods on croplands [25].…”