Extracting Land Cover Data Using GEE: A Review of the Classification Indices

Land Use/Land Cover (LU/LC) data includes most of the information suitable for tackling many environmental issues. Remote sensing is largely recognized as the most significant method to extract them through the application of various techniques. They can be extracted through the application of many techniques. Among the several classification approaches, the index-based method has been recognized as the best one to gather LU/LC information from different images sources. The present work is intended to assess its performance exploiting the great potentialities of Google Earth Engine (GEE), a cloud-processing environment introduced by Google to storage and handle a large number of information. Twelve atmospherically corrected Landsat satellite images were collected on the experimental site of Siponto, in Southern Italy. Once the clouds masking procedure was completed, a large number of indices were implemented and compared in GEE platform to detect sparse and dense vegetation, water, bare soils and built-up areas. Among the tested algorithms, only NDBaI2, CVI, WI2015, SwiRed and STRed indices showed satisfying performance. Although NDBaI2 was able to extract all the main LU/LC categories with a high Overall Accuracy (OA) (82.59%), the other mentioned indices presented a higher accuracy than the first one but are able to identify just few classes. An interesting performance is shown by the STRed index since it has a very high OA and can extract mining areas, water and green zones. GEE appeared the best solution to manage the geospatial big data.