Random Forest Analysis of Land Use and Land Cover Change Using Sentinel-2 Data in Van Yen, Yen Bai Province, Vietnam

Truong, Xuan Quang; Dang, Nguyen Hien Duong; Do, Thi Hang; Tran, Nhat Duong; Nga, Thi Thu; Anh, Tran Van; Yordanov, V.; Brovelli, M. A.; Khuc, Thanh Dong

doi:10.1007/978-3-031-20463-0_27

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…In evaluating the performance of a predictive model, combining the ROC curve and AUC with other metrics such as accuracy, sensitivity, and specificity will provide a more comprehensive overview of the model's performance [22]. The formula for calculating sensitivity is as follows: Sensitivity = TP / (TP + FN) (5) Where TP (True Positive) is the number of true positive instances correctly identified, and FN (False Negative) is the number of false negative instances incorrectly identified.…”

Section: Evaluate Model Performancementioning

confidence: 99%

Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam Using Boosting Models

Tran,

Brovelli,

et al. 2024

Preprint

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The Ca Mau Peninsula situated in the Mekong Delta of Vietnam, features a low-lying terrain. Over recent years, the region has encountered the adverse impacts of climate change, leading to both land subsidence and rising sea levels. In this study, we assessed the land subsidence susceptibility in the Ca Mau peninsula utilizing three Boosting machine learning models: AdaBoost, Gradient Boosting, and Extremely Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), Groundwater levels, distance to roads, Digital Terrain Model (DTM), normalized vegetation index (NDVI), geology, soil composition, and proximity to rivers and streams. A dataset comprising 1950 subsidence sample points was employed for the model, with 1910 points obtained from the PSInSAR Radar method, and the remaining points derived from leveling measurements. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the ROC curve, Area under the curve (AUC), Specificity, Sensitivity, and Overall Accuracy. The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC above 0.9 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau peninsula.

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Section: Evaluate Model Performancementioning

confidence: 99%

Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam Using Boosting Models

Tran,

Brovelli,

et al. 2024

Preprint

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“…Truong et al [68] used random forest in machine learning and the maximum likelihood algorithm, respectively, to classify and extract land cover in the Pham Ngan District, An Phuy Province, Vietnam. Their results showed that the OA and Kappa coefficients of the random forest algorithm were significantly higher than those of the maximum likelihood method, and, in particular, the results were more accurate for all land use types except the building class.…”

Section: Exploration Of Methods and Results Of Land-use Type Classifi...mentioning

confidence: 99%

Landscape Analysis and Ecological Risk Assessment during 1995–2020 Based on Land Utilization/Land Coverage (LULC) and Random Forest: A Case Study of the Fushun Open-Pit Coal Area in Liaoning, China

Xu,

Cheng

2024

Sustainability

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China’s Fushun open-pit mine is the largest century-old coal mine in Asia. Large-scale mining and the use of coal has caused dramatic changes in the regional urban landscape pattern, seriously affecting the ecological function and ecosystem stability of its surrounding landscape. Evaluating the ecological risks of the landscape in the urban areas of open-pit mines contributes to the risk management of regional ecosystems and the sustainable development of society. This study selected six-phase Landsat ETM/OLI remote sensing images from 1995 to 2020 and combined them with the random forest model to carry out an LULC classification of the open-pit mine and its surrounding areas and, on this basis, discusses the evolution of its landscape pattern and evaluates the ecological risks. It fills the gap in the research on the evolution of regional landscape patterns and ecological risks in the study area and improves the automatic classification efficiency of LULC for use in open-pit mines. The results show that the classification accuracy of LULC regarding open-pit mines based on image pixels and the random forest model can reach 30 m, and the rate of accuracy can reach 92–97%. From 1995 to 2020, the coverage area of forest land and building land in the study area has increased and is mainly composed of grassland and undeveloped land. The use of land was transferred to the mining area, the water body area maintained a relative dynamic balance, and the overall vegetation coverage of the mining area was greatly improved; the forest land began to expand from the surrounding area to the mining area in 2010, and the construction land began to move from the areas surrounding the mining area to the surrounding valleys in 2015. The landforms have extended radially, and the landscape sprawl index has increased, indicating the optimization of the ecological environment; the high- and medium-risk areas decreased by 75.51 km2, the low-risk areas expanded by 461.48 km2, and the overall ecological risk index decreased. From this, it is possible to conclude that the landscape restoration project adopted in the study area has achieved great results, and the improvement of the ecological environment also directly affects the increase of construction land. These research results can provide scientific guidance for the rational utilization and sustainable development of land resources in urban areas of open-pit mines.

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Section: Evaluation Of Model Performancementioning

confidence: 99%

Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models

Tran,

Brovelli,

et al. 2024

IJGI

Self Cite

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The Ca Mau Peninsula, situated in the Mekong Delta of Vietnam, features low-lying terrain. In addition to the challenges posed by climate change, land subsidence in the area is exacerbated by the overexploitation of groundwater and intensive agricultural practices. In this study, we assessed the land subsidence susceptibility in the Ca Mau Peninsula utilizing three boosting machine learning models: AdaBoost, Gradient Boosting, and Extreme Gradient Boosting (XGB). Eight key factors were identified as the most influential in land subsidence within Ca Mau: land cover (LULC), groundwater depth, digital terrain model (DTM), normalized vegetation index (NDVI), geology, soil composition, distance to roads, and distance to rivers and streams. The dataset includes 2011 points referenced from the Persistent Scattering SAR Interferometry (PSI) method, of which 1011 points are subsidence points and the remaining are non-subsidence points. The sample points were split, with 70% allocated to the training set and 30% to the testing set. Following computation and execution, the three models underwent evaluation for accuracy using statistical metrics such as the receiver operating characteristic (ROC) curve, area under the curve (AUC), specificity, sensitivity, and overall accuracy (ACC). The research findings revealed that the XGB model exhibited the highest accuracy, achieving an AUC and ACC above 0.88 for both the training and test sets. Consequently, XGB was chosen to construct a land subsidence susceptibility map for the Ca Mau Peninsula. In addition, 31 subsidence points measured by leveling surveys between 2005 and 2020, provided by the Department of Survey, Mapping and Geographic Information Vietnam, were used for validating the land subsidence susceptibility from the XGB method. The findings indicate a 70.9% accuracy rate in predicting subsidence susceptibility compared to the leveling measurement points.

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Random Forest Analysis of Land Use and Land Cover Change Using Sentinel-2 Data in Van Yen, Yen Bai Province, Vietnam

Cited by 8 publications

References 16 publications

Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam Using Boosting Models

Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam Using Boosting Models

Landscape Analysis and Ecological Risk Assessment during 1995–2020 Based on Land Utilization/Land Coverage (LULC) and Random Forest: A Case Study of the Fushun Open-Pit Coal Area in Liaoning, China

Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models

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