Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery

Ganjirad, Mohammad; Bagheri, Hossein

doi:10.1016/j.ecoinf.2024.102498

Cited by 8 publications

(2 citation statements)

References 113 publications

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“…A new approach was developed by comparing the results of five GEE-based composition methods to ultimately adopt the best one. Based on the literature, research employing GEE for LULC mapping and monitoring has grown significantly in recent years [32][33][34], with most of these studies comparing classifier performance while a few have compared findings from the same dataset using various compositions, including [23,35,36]. However, the methodology of our approach, which is completely built on the GEE cloud computing platform, began by selecting a collection of images of the S2 L2 sentinel (23 images) between March 1 and November 30 of 2021 (summer, autumn, and spring).…”

Section: Mapping Lulcmentioning

confidence: 99%

“…For example, the mean composite dataset contains B2_mean, B3_mean, B4_mean, B8_mean, NDVI_mean, NDWI_mean, NDBI_mean, and BDI_mean. Numerous studies have shown that the integration of spectral indices can improve the accuracy of classification and the ability to differentiate between different forms of land cover [23,33,37]. By using specific features of the land surface, such as the amount of vegetation, built-up areas, water areas, and bare soil, these indices provide a more complete and accurate description of land cover.…”

Section: Mapping Lulcmentioning

confidence: 99%

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Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco

SELLAMI,

Rhinane

2024

Geosciences

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Recently, the earth’s climate has changed considerably, leading to several hazards, including flash floods (FFs). This study aims to introduce an innovative approach to mapping and identifying FF exposure in the city of Tetouan, Morocco. To address this problem, the study uses different machine learning methods applied to remote sensing imagery within the Google Earth Engine (GEE) platform. To achieve this, the first phase of this study was to map land use and land cover (LULC) using Random Forest (RF), a Support Vector Machine (SVM), and Classification and Regression Trees (CART). By comparing the results of five composite methods (mode, maximum, minimum, mean, and median) based on Sentinel images, LULC was generated for each method. In the second phase, the precise LULC was used as a related factor to others (Stream Power Index (SPI), Topographic Position Index (TPI), Slope, Profile Curvature, Plan Curvature, Aspect, Elevation, and Topographic Wetness Index (TWI)). In addition to 2024 non-flood and flood points to predict and detect FF susceptibility, 70% of the dataset was used to train the model by comparing different algorithms (RF, SVM, Logistic Regression (LR), Multilayer Perceptron (MLP), and Naive Bayes (NB)); the rest of the dataset (30%) was used for evaluation. Model performance was evaluated by five-fold cross-validation to assess the model’s ability on new data using metrics such as precision, score, kappa index, recall, and the receiver operating characteristic (ROC) curve. In the third phase, the high FF susceptibility areas were analyzed for two-way validation with inundated areas generated from Sentinel-1 SAR imagery with coherent change detection (CDD). Finally, the validated inundation map was intersected with the LULC areas and population density for FF exposure and assessment. The initial results of this study in terms of LULC mapping showed that the most appropriate method in this research region is the use of an SVM trained on a mean composite. Similarly, the results of the FF susceptibility assessment showed that the RF algorithm performed best with an accuracy of 96%. In the final analysis, the FF exposure map showed that 2465 hectares were affected and 198,913 inhabitants were at risk. In conclusion, the proposed approach not only allows us to assess the impact of FF in this study area but also provides a versatile approach that can be applied in different regions around the world and can help decision-makers plan FF mitigation strategies.

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