Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

Abstract

In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naïve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster–Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.

A review on advancements in lithological mapping utilizing machine learning algorithms and remote sensing data

“…It achieved the highest overall classification accuracy (OA) of approximately 93%. [ 83 ] RF/SVM/classification and regression tree (CART)/minimum distance (MD)/naïve Bayes (NB) Sentinel 2 A The comparison of individual classifiers, SVM exhibits the highest accuracy, reaching almost 88%, which is 12% higher than the RF MLA. [ 84 ] SVM/NB/K-NN/RF ASTER/Landsat 8 OLI/Sentinel-1/Sentinel-2A.…”

Lithological discrimination and mineralogical mapping using Landsat-8 OLI and ASTER remote sensing data: Igoudrane region, jbel saghro, Anti Atlas, Morocco

“…The resulted components provide accurate lithological discrimination and hydrothermal alteration detection [ 53 , 54 ]. The first three components involve the maximum of geological information and provide the highest variance ( Table 3 , Table 4 ), this is why MNF1, MNF2 and MNF3 were visualized as RGB composite in attempt to differentiate the lithological limits [ 50 , 55 ].…”