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Jilin Province is located in the northeast of China, and has fragile ecosystems, and a vulnerable environment. Large-scale, long time series, high-precision land-use/cover change (LU/CC) data are important for spatial planning and environmental protection in areas with high surface heterogeneity. In this paper, based on the high temporal and spatial fusion data of Landsat and MODIS and the Google Earth Engine (GEE), long time series LU/CC mapping and spatio-temporal analysis for the period 2000–2023 were realized using the random forest remote sensing image classification method, which integrates remote sensing indices. The prediction results using the OL-STARFM method were very close to the real images and better contained the spatial image information, allowing its application to the subsequent classification. The average overall accuracy and kappa coefficient of the random forest classification products obtained using the fused remote sensing index were 95.11% and 0.9394, respectively. During the study period, the area of cultivated land and unused land decreased as a whole. The area of grassland, forest, and water fluctuated, while building land increased to 13,442.27 km2 in 2023. In terms of land transfer, cultivated land was the most important source of transfers, and the total area share decreased from 42.98% to 38.39%. Cultivated land was mainly transferred to grassland, forest land, and building land, with transfer areas of 7682.48 km2, 8374.11 km2, and 7244.52 km2, respectively. Grassland was the largest source of land transfer into cultivated land, and the land transfer among other feature types was relatively small, at less than 3300 km2. This study provides data support for the scientific management of land resources in Jilin Province, and the resulting LU/CC dataset is of great significance for regional sustainable development.
Jilin Province is located in the northeast of China, and has fragile ecosystems, and a vulnerable environment. Large-scale, long time series, high-precision land-use/cover change (LU/CC) data are important for spatial planning and environmental protection in areas with high surface heterogeneity. In this paper, based on the high temporal and spatial fusion data of Landsat and MODIS and the Google Earth Engine (GEE), long time series LU/CC mapping and spatio-temporal analysis for the period 2000–2023 were realized using the random forest remote sensing image classification method, which integrates remote sensing indices. The prediction results using the OL-STARFM method were very close to the real images and better contained the spatial image information, allowing its application to the subsequent classification. The average overall accuracy and kappa coefficient of the random forest classification products obtained using the fused remote sensing index were 95.11% and 0.9394, respectively. During the study period, the area of cultivated land and unused land decreased as a whole. The area of grassland, forest, and water fluctuated, while building land increased to 13,442.27 km2 in 2023. In terms of land transfer, cultivated land was the most important source of transfers, and the total area share decreased from 42.98% to 38.39%. Cultivated land was mainly transferred to grassland, forest land, and building land, with transfer areas of 7682.48 km2, 8374.11 km2, and 7244.52 km2, respectively. Grassland was the largest source of land transfer into cultivated land, and the land transfer among other feature types was relatively small, at less than 3300 km2. This study provides data support for the scientific management of land resources in Jilin Province, and the resulting LU/CC dataset is of great significance for regional sustainable development.
In the Bamen Bay area of the Qinglan Harbor Mangrove Provincial Nature Reserve in Wenchang, Hainan Province, China, mangrove aboveground biomass (AGB) was estimated using high-resolution UAV ortho-imagery and UAV LiDAR data. The spatial distribution characteristics of AGB were studied using global Moran’s I index and hotspot analysis. Optimal geographic detectors and regression models were employed to analyze the relationship between AGB and key environmental factors. The results indicate that (1) the average AGB in the study area was 141.22 Mg/ha, with significant spatial variation. High AGB values were concentrated in the southwestern and northeastern regions, while low values were mainly found in the central and southeastern regions. (2) Plant species, water pH, soil total potassium, salinity, dissolved oxygen, elevation, soil organic matter, soil total phosphorus, and soil total nitrogen were identified as major factors influencing the spatial distribution of AGB. The interaction results indicate either bifactor enhancement or nonlinear enhancement, showing a significantly higher impact compared with single factors. (3) Comprehensive regression model results reveal that soil total nitrogen was the primary factor affecting AGB, followed by soil total potassium, with water pH having the least impact. Factors positively correlated with AGB promoted biomass growth, while elevation negatively affected AGB, inhibiting biomass accumulation. The findings provide critical insights that can guide targeted conservation efforts and management strategies aimed at enhancing mangrove ecosystem health and resilience, particularly by focusing on key areas identified for potential improvement and by addressing the complex interactions among environmental factors.
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