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 3 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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Section: Mapping Lulcmentioning

confidence: 99%

Section: Mapping Lulcmentioning

confidence: 99%

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

SELLAMI,

Rhinane

2024

Geosciences

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Aquatic vegetation mapping with UAS-cameras considering phenotypes

Szabó,

Bertalan,

Szabó

et al. 2024

Ecological Informatics

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Land-Use Transitions and Its Driving Mechanism Analysis in Putian City, China, during 2000–2020

Peng,

Wu,

Lin

et al. 2024

Sustainability

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Investigating the spatial-temporal evolution of land use and its driving forces provides a scientific basis for policy formulation, land-use structure adjustment, and ecological civilization development. Using the Google Earth Engine (GEE) platform, this study analyzed remote sensing images from 2000, 2010, and 2020 to derive basic land-use data for Putian City and its five districts and counties. These data were then systematically analyzed using methodologies such as Single Land-use Dynamics and Geo-informatic Tupu to reveal the characteristics of land-use transitions (LUTs), and the spatial-temporal evolution pattern over the past two decades in Putian City, China. Subsequently, socioeconomic conditions and macro policies were identified as driving factors to further explore the mechanisms behind land-use evolution in the study area through canonical correspondence analysis (CCA). The findings revealed that: (1) The predominant land-use structure in Putian City consisted mainly of cultivated land and forest land, with other land types interspersed within them, while built-up land exhibited continual outward expansion. (2) Various regions within Putian City exhibited varying degrees of abandoned farmland, ultimately transforming into wasteland (grassland) with weed growth, presenting significant challenges for ensuring food security and mitigating the conversion of cultivated land to non-agricultural and non-grain uses. (3) Specific macro-economic development objectives during distinct periods, particularly urban expansion and the growth of the secondary industry resulting from municipal and county mergers, emerged as pivotal factors driving the spatial and temporal evolution of land use and influenced the differential distribution pattern across Putian City. Consequently, this study suggests bolstering scientific planning and implementing effective regulations concerning land use, and it advocates for the efficient utilization of space-time resources pertaining to cultivated land, integrating them with agriculture, culture, and tourism endeavors. Such measures are proposed to ensure the harmonized and sustainable development of the regional ecological economy.

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

Cited by 3 publications

References 113 publications

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

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

Aquatic vegetation mapping with UAS-cameras considering phenotypes

Land-Use Transitions and Its Driving Mechanism Analysis in Putian City, China, during 2000–2020

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