Forest canopy height is defined as the distance between the highest point of the tree canopy and the ground, which is considered to be a key factor in calculating above-ground biomass, leaf area index, and carbon stock. Large-scale forest canopy height monitoring can provide scientific information on deforestation and forest degradation to policymakers. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in 2018, with the Advanced Topographic Laser Altimeter System (ATLAS) instrument taking on the task of mapping and transmitting data as a photon-counting LiDAR, which offers an opportunity to obtain global forest canopy height. To generate a high-resolution forest canopy height map of Jiangxi Province, we integrated ICESat-2 and multi-source remote sensing imagery, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission, and forest age data of Jiangxi Province. Meanwhile, we develop four canopy height extrapolation models by random forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Gradient Boosting Decision Tree (GBDT) to link canopy height in ICESat-2, and spatial feature information in multi-source remote sensing imagery. The results show that: (1) Forest canopy height is moderately correlated with forest age, making it a potential predictor for forest canopy height mapping. (2) Compared with GBDT, SVM, and KNN, RF showed the best predictive performance with a coefficient of determination (R²) of 0.61 and a root mean square error (RMSE) of 5.29 m. (3) Elevation, slope, and the red-edge band (band 5) derived from Sentinel-2 were significantly dependent variables in the canopy height extrapolation model. Apart from that, Forest age was one of the variables that the RF moderately relied on. In contrast, backscatter coefficients and texture features derived from Sentinel-1 were not sensitive to canopy height. (4) There is a significant correlation between forest canopy height predicted by RF and forest canopy height measured by field measurements (R² = 0.69, RMSE = 4.02 m). In a nutshell, the results indicate that the method utilized in this work can reliably map the spatial distribution of forest canopy height at high resolution.

As a unique phenomenon of soil erosion in the granite-red-soil hilly area of southern China, Benggang has seriously affected agricultural development and regional sustainable development. However, few studies have focused on the driving factors and their interactions with Benggang erosion at the regional scale. The primary driving forces of Benggang erosion were identified by the factor detector of the geographical detector, and the interaction between factors was determined by the interaction detector of the geographical detector. The 10 conditioning driving factors included terrain, hydrology, vegetation, soil, geomorphology, and land use. Benggang erosion in Ganzhou City principally occurred in the granite-red-soil forest hill, characterized by an elevation below 400 m above sea level, slope below 25° of concavity, a distance to the gully less than 500 m, a vegetation coverage of 40–60%, and an average rainfall erosivity of 6400–7000 MJ·mm/(hm²·h·a). The key driving factors for Benggang erosion were rainfall erosivity, elevation, and land use. Moreover, the interaction of any two factors was stronger than that of a single factor, and the nonlinear enhancement factors had a stronger synergistic effect on erosion. Therefore, the comprehensive influence of many factors must be considered when predicting and preventing Benggang erosion.

Landscape patterns are a result of the combined action of natural and social factors. Quantifying the relationships between landscape pattern changes, soil erosion, and sediment yield in river basins can provide regulators with a foundation for decision-making. Many studies have investigated how land-use changes and the resulting landscape patterns affect soil erosion in river basins. However, studies examining the effects of terrain, rainfall, soil erodibility, and vegetation cover factors on soil erosion and sediment yield from a landscape pattern perspective remain limited. In this paper, the upper Ganjiang Basin was used as the study area, and the amount of soil erosion and the amount of sediment yield in this basin were first simulated using a hydrological model. The simulated values were then validated. On this basis, new landscape metrics were established through the addition of factors from the revised universal soil loss equation to the land-use pattern. Five combinations of landscape metrics were chosen, and the interactions between the landscape metrics in each combination and their effects on soil erosion and sediment yield in the river basin were examined. The results showed that there were highly similar correlations between the area metrics, between the fragmentation metrics, between the spatial structure metrics, and between the evenness metrics across all the combinations, while the correlations between the shape metrics in Combination 1 (only land use in each year) differed notably from those in the other combinations. The new landscape indicator established based on Combination 4, which integrated the land-use pattern and the terrain, soil erodibility, and rainfall erosivity factors, were the most significantly correlated with the soil erosion and sediment yield of the river basin. Finally, partial least-squares regression models for the soil erosion and sediment yield of the river basin were established based on the five landscape metrics with the highest variable importance in projection scores selected from Combination 4. The results of this study provide a simple approach for quantitatively assessing soil erosion in other river basins for which detailed observation data are lacking.

Poyang Lake is not only a globally important stopover for migratory birds and a habitat for fish, but also one of the main sand mining areas in China. However, sand mining activities significantly affect the suspended sediment concentration in Poyang Lake, thereby impacting water quality, altering lake bed topography, and potentially disturbing the living environment of plants and animals. Therefore, understanding the suspended sediment concentration and the spatiotemporal patterns of sand mining activities in Poyang Lake holds vital significance for effective lake management and biodiversity conservation. Utilizing surface reflectance (SR) data derived from Landsat satellite imagery sourced via Google Earth Engine spanning from 1989 to 2018, coupled with SSC data recorded by daily observation at Hukou hydrological stations, we conducted a comparative analysis of five distinct methods: Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART), and Back Propagation Neural Network (BPNN). Moreover, the applicability of machine learning methods was analyzed and compared across four scenarios (#01 Landsat 5, #02 Landsat 7, #03 Landsat 8, and #04, Landsat 5, 7, and 8). Using the optimal SSC estimation model, we investigated the spatial and temporal trends of sand mining activities in Poyang Lake from 1989 to 2021, along with their potential impact on suspended sediment levels in the water body. The results showed that the prediction accuracy of machine learning is better than that of linear regression models, and the RF model has the best performance. The RF model generated R $^{2} \gt 0.9$ for four scenarios (#01 Landsat 5, #02 Landsat 7, #03 Landsat 8, and #04 Landsat 5, 7, and 8) and showed little to no overfitting. The generated SSC map can clearly show the distribution of SSC in Poyang Lake, unveiling the impact of sand mining activities on suspended sediment, especially around sand dredgers where SSC can exceed 0.15 g/L. Sand mining activities in Poyang Lake emerged after 2000 and gradually shifted southward and expanded, reaching a peak in 2016. Fortunately, under government regulation, illegal sand mining has been effectively controlled and is currently concentrated near Songmenshan Island. Despite the fact that sand mining has been brought under control, there remains a necessity for heightened oversight to prevent any impact on national nature reserves. The Random Forest (RF) model demonstrates significant potential in utilizing Landsat satellite data to predict SSC in Poyang Lake, as well as to monitor sand mining activities in the area.

Quantifying secondary forest age (SFA) is essential to evaluate the carbon processes of forest ecosystems at regional and global scales. However, the successional stages of secondary forests remain poorly understood due to low-frequency thematic maps. This study aimed to estimate SFA with higher frequency and more accuracy by using dense Landsat archives. The performances of four time-series change detection algorithms—moving average change detection (MACD), Continuous Change Detection and Classification (CCDC), LandTrendr (LT), and Vegetation Change Tracker (VCT)—for detecting forest regrowth were first evaluated. An ensemble model was then developed to determine more accurate timings for forest regrowth based on the evaluation results. Finally, after converting the forest regrowth year to the SFA, the spatiotemporal and topographical distributions of the SFA were analyzed. The proposed ensemble model was validated in Jiangxi province, China, which is located in a subtropical region and has experienced drastic forest disturbances, artificial afforestation, and natural regeneration. The results showed that: (1) the developed ensemble model effectively determined forest regrowth time with significantly decreased omission and commission rates compared to the direct use of the four single algorithms; (2) the optimal ensemble model combining the independent algorithms obtained the final SFA for Jiangxi province with the lowest omission and commission rates in the spatial domain (14.06% and 24.71%) and the highest accuracy in the temporal domain (R² = 0.87 and root mean square error (RMSE) = 3.17 years); (3) the spatiotemporal and topographic distribution from 1 to 34 years in the 2021 SFA map was analyzed. This study demonstrated the feasibility of using change detection algorithms for estimating SFA at regional to national scales and provides a data foundation for forest ecosystem research.

Vegetation is a fundamental component of terrestrial ecosystems, and accurately assessing the effects of seasonal climate variations, extreme weather events, and land use changes on vegetation dynamics is crucial. The Ganjiang River Basin (GRB), a key region for water conservation and recharge in southeastern China, has experienced significant land use changes and variable climate in the past. However, comprehensive evaluations of how these changes have impacted vegetation remain limited. To address this gap, we used machine learning models (random forest and XGBoost) to assess the impact of seasonal and extreme climate variables, land cover, topography, soil properties, atmospheric CO₂, and night-time light intensity on vegetation dynamics. We found that the annual mean NDVI showed a slight increase from 1990 to 1999 but has decreased significantly over the last 8 years. XGBoost was better than the RF model in simulating the NDVI when using all five types of data source (R² = 0.85; RMSE = 0.04). The most critical factors influencing the NDVI were forest and cropland ratio, followed by soil organic carbon content, elevation, cation exchange capacity, night-time light intensity, and CO₂ concentration. Spring minimum temperature was the most important seasonal climate variable. Both linear and nonlinear relationships were identified between these variables and the NDVI, with most variables exhibiting threshold effects. These findings underscore the need to develop and implement effective land management strategies to enhance vegetation health and promote ecological balance in the region.