Yan Yan

Karst ecosystems, 15% of Earth’s land, are critical for soil erosion research due to their unique geology and hydrology. This study investigated the patterns and trends of soil erosion in global karst regions from 2000 to 2020 using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model combined with robust trend methods (Theil-Sen and Mann-Kendall). Correlation analysis and scenario analysis were employed to quantify the driving factors and evaluate the contributions of various factors. A global decline in erosion was found, with intense erosion in southwest Asia, southern Europe, and northwestern North America. Asia had the highest soil erosion rate, followed by North America, Europe, and other regions. Among them, the soil erosion rate in North America showed an upward trend. Russia, China, and Europe were key in erosion reduction (29.98%, 28.01%, 18.68%). Rainfall strongly correlated with erosion; vegetation’s link varied by region. Temperature negatively correlated with erosion in some areas. Scenario analysis quantified contributions of human activities (47.06%) and climate (26.68%). These findings highlight the joint role of natural and human factors in soil erosion management, important for crafting effective conservation strategies in karst areas globally.

This study assessed how spatiotemporal changes in land use dynamics affect ecosystem services responses using land use data (1990–2020) from the Guangxi Xijiang River Basin. The results indicate that cropland, forest, water, barren, and impervious areas increased 0.18%, 1.28%, 14.9%, 636.54%, and 208.03%, respectively, while shrubland and grassland decreased by 43.02% and 80.61%. Spatially, vegetation cover was higher in the eastern, northern, and western sections, whereas the central and southern regions were dominated by cropland and impervious surfaces. Water yield, habitat quality, carbon storage and soil conservation decreased by 13.38%, 9.75%, 7.43% and 10.77%, respectively, with notable decreases in the northeastern, eastern, and northwestern areas. The total amounts of these services were 15.06 × 10¹⁰ m³, 0.45, 17335TgC and 9.42 × 10¹⁰ t, respectively. Land use changes affected ecosystem services as follows: cropland and impervious areas enhanced water yield but reduced habitat quality, carbon storage and soil conservation; forests, shrublands, and grasslands promoted the regulation and support services related to carbon storage, habitat quality and soil conservation; wetlands improved habitat quality and soil conservation; and water and barren land had limited impacts relative to other land types. This study addresses a methodological gap in dynamic ecosystem service assessment in the Guangxi Xijiang River Basin and offers insights into integrated land and ecosystem management.

Eucalyptus is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of Eucalyptus biomass is important for plantation forest management and the prediction of carbon stock in terrestrial ecosystems. In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. The results of the study concluded that the prediction model accuracy of the natural transformed regression equations (R² = 0.873, RMSE = 0.312 t/ha, RRMSE = 0.0091) outperformed that of the machine learning algorithms at the individual tree scale. Among the machine learning models, the SVR prediction model accuracy was the best (R² = 0.868, RMSE = 7.932 t/ha, RRMSE = 0.231). In this study, UAV-LiDAR-based data had great potential in predicting the AGB of Eucalyptus trees, and the tree height parameter had the strongest correlation with AGB. In summary, the combination of UAV LiDAR data and machine learning algorithms to construct a predictive forest AGB model has high accuracy and provides a solution for carbon stock assessment and forest ecosystem assessment.

As an emerging powerful tool for forest resource surveys, the unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) sensors provide an efficient way to detect individual trees. Therefore, it is necessary to explore the most suitable individual tree segmentation algorithm and analyze the sensitivity of the parameter setting to determine the optimal parameters, especially for the Eucalyptus spp. forest, which is one of the most important hardwood plantations in the world. In the study, four methods were employed to segment individual Eucalyptus spp. plantations from normalized point cloud data and canopy height model generated from the original UAV-LiDAR data. And the parameter sensitivity of each segmentation method was analyzed to obtain the optimal parameter setting according to the extraction accuracy. The performance of the segmentation result was assessed by three indices including detection rate, precision, and overall correctness. The results indicated that the watershed algorithm performed better than other methods as the highest overall correctness (F = 0.761) was generated from this method. And the segmentation methods based on the canopy height model performed better than those based on normalized point cloud data. The detection rate and overall correctness of low-density plots were better than high-density plots, while the precision was reversed. Forest structures and individual wood characteristics are important factors influencing the parameter sensitivity. The performance of segmentation was improved by optimizing the key parameters of the different algorithms. With optimal parameters, different segmentation methods can be used for different types of Eucalyptus plots to achieve a satisfying performance. This study can be applied to accurate measurement and monitoring of Eucalyptus plantation.

Quantitative analysis of the spatiotemporal pattern of urban expansion and forecasting of the progress towards SDG11.3.1 are of great significance for the promotion of sustainable urban development. This study employed the spatiotemporal normalized threshold method to extract urban built-up areas in the Pearl River–Xijiang Economic Belt based on night-time light data and investigated the intricate patterns of urban expansion from 2000 to 2020. Then, the historical trends of the SDG11.3.1 indicators within the economic belt were evaluated, and future urban built-up areas were predicted based on the SSP1 scenario. The results indicate the following: (1) Built-up area extraction has an overall accuracy that exceeds 97% and G-mean values that all surpass 82%, indicating the high accuracy of the method. (2) The Pearl River–Xijiang Economic Belt demonstrates evident urban expansion trends, albeit with uneven development. The urban area of the economic belt has expanded from 1020.29 km² to 3826.87 km², the expansion direction of each city is different, and the center of gravity of the economic belt has moved to the southeast. (3) During the period from 2008 to 2020, the entire economic belt experienced a situation where the urban expansion rate was lower than the population growth rate, and there was an imbalance in urban development (LCRPGR = 0.33). However, looking ahead to the period from 2020 to 2030, the average LCRPGR for the entire economic belt shows a significant upward trend, approaching the ideal state of sustainable development (LCRPGR ≈ 1).

Plant water use is an important function reflecting vegetation physiological status and affects plant growth, productivity, and crop/fruit quality. Although hyperspectral vegetation indices have recently been proposed to assess plant water use, limited sample sizes for established models greatly astricts their wide applications. In this study, we have managed to gather a large volume of continuous measurements of canopy spectra through proximally set spectroradiometers over the canopy, enabling us to investigate the feasibility of using continuous narrow-band indices to trace canopy-scale water use indicated by the stem sap flux density measured with sap flow sensors. The results proved that the newly developed D (520, 560) index was optimal to capture the variation of sap flux density under clear sky conditions (R² = 0.53), while the best index identified for non-clear sky conditions was the D (530, 575) (R² = 0.32). Furthermore, the bands used in these indices agreed with the reported sensitive bands for estimating leaf stomatal conductance which has a critical role in transpiration rate regulation over a short time period. Our results should point a way towards using proximal hyperspectral indices to trace tree water use directly.