Karst rocky desertification (KRD) has become one of the most serious ecological and environmental problems in karst areas. At present, mapping KRD with a high accuracy and on a large scale is still a difficult problem in the control of KRD. In this study, a random forest (RF) based on maximum information coefficient and correlation coefficient feature selection is proposed to predict KRD. Nine predictors stood out as feature factors to estimate KRD. Rock exposure was the most important predictor, followed by fractional vegetation cover for the prediction of KRD processes. The kappa and classification accuracy indexes were to evaluate the performance of the model. We recorded overall accuracy rate and kappa index values of 94.7% and 0.92 for the testing datasets respectively. The RF model was then used to predict the KRD in 2001, 2011, 2016, and 2020, and it was found that the KRD in the study area has exhibited a positive trend of improvement. Therefore, the use of multisource remote sensing data combined with the RF model can obtain better prediction results of KRD, thereby providing a new idea for large-scale estimation of the KRD in peak-cluster depression.

In recent decades, China has exhibited the fastest and most remarkable social-economic development in the world. As a result of such development, the forest cover of the country has undergone radical changes. This paper aims to develop a method for analyzing long-term and spatial changes in forest cover based on historical maps and remote sensing images. Moreover, we will focus on the reduction or restoration of forests distributed at different altitudes, slopes, soils, and lithologic types in different periods, to reveal the problems that should be paid attention to in forest restoration in karst areas. A typical county of China was selected as the study area. A historical military operation map was considered the principal source of basic data. These data were then combined with Landsat satellite images to conduct quantitative analysis on changes in the spatial area and location of forest cover with a long time series. The findings are as follows: in terms of time series, the forest area in the study area showed a trend of decreasing at first and then increasing, with the year 1986 as the turning point. In terms of spatial patterns, a considerable difference is observed in regions with changes in forest cover under different historical periods. Changes are obvious in limestone areas, rock soil areas, and areas with an elevation of 2000–2500 m and a slope gradient of 6°–15°. Spatial–temporal changes in forest cover reflect the effects of the war, national policy, and economic development to some extent. All these results indicate that, despite its limitations, a historical map is a valuable document for studying an ecological environment.

Introduction: Suspended particulate matter (SPM) is a critical indicator of water quality and has a significant impact on the nearshore ecological environment. Consequently, the quantitative evaluation of SPM concentrations is essential for managing nearshore environments and planning marine resources.Methods: This study utilized Sentinel-2’s single band and water index variables to develop a remote sensing inversion model for oceanic SPM in the estuary of the Pinglu Canal in China. Six machine learning algorithms were employed: K-nearest neighbor regression (KNNR), AdaBoost regression (ABR), random forest (RF), gradient boosting regression (GBR), extreme gradient boosting regression (XGBR), and light generalized boosted regression (LGBM). The model with the optimal performance was then selected for further analysis. This research applied the established model to investigate the spatial-temporal dynamics of SPM from 2021 to 2023.Results: The findings indicated that (1) the XGBR algorithm exhibited superior performance (R² = 0.9042, RMSE = 3.0258 mg/L), with LGBM (R² =0.8258, RMSE = 4.0813 mg/L) and GBR (R² = 0.823, RMSE = 4.3477 mg/L) also demonstrating effective fitting. However, the ABR, RF, and KNNR algorithms produced less satisfactory fitting results. (2) Additionally, the study revealed that the combination of input variables in the XGBR algorithm was more accurate than single-variable inputs. (3) The contribution of single-band variables to the XGBR algorithm surpassed that of water index variables, with B12, B4, and B11 emerging as the top three influential variables in the model. (4) The annual SPM concentration in the study area exhibited an overall increasing trend, while its spatial distribution generally decreased from the estuary toward the Maowei Sea and Qinzhou Bay.Discussion: The combination of Sentinel-2 data and XGBR model has shown good performance in retrieving SPM concentration, providing a new method and approach for large-scale estimation of SPM concentration.

By studying the structural characteristics and carbon storage of the mangrove island ecosystem in the Beibu Gulf, this study provides a scientific basis for mangrove ecological compensation in the coastal areas of Guangxi, South China Sea. On the basis of the unmanned aerial vehicle remote sensing images and a sample plot survey, the object-oriented multi-scale segmentation algorithm is used to extract the mangrove community type information, and one-way analysis of variance is conducted to analyse the structural characteristics of the mangrove community. The carbon storage and carbon density of different mangrove ecosystems were obtained based on the allometric growth equation of mangrove plants. The analysis yielded four main results. (1) The island group covers about 27.10 ha, 41.32% (11.20 ha) of which represents mangrove areas. The mangrove forest is widely distributed in the tidal flats around the islands. (2) The main mangrove types were Aegiceras corniculatum, Kandelia obovata + Aegiceras corniculatum, Avicennia marina + Aegiceras corniculatum and Avicennia marina communities. (3) Amongst the mangrove plants, Avicennia marina had the highest biomass (18.52 kg plant–1), followed by Kandelia obovata (7.84 kg plant–1) and Aegiceras corniculatum (3.85 kg plant–1). (4) The mangrove carbon density difference was significant. Kandelia obovata had the highest carbon density (148.03 t ha–1), followed by Avicennia marina (104.79 t ha–1) and Aegiceras corniculatum (99.24 t ha–1). The carbon storage of the mangrove island ecosystem was 1194.70 t, which was higher than in other areas with the same latitude. The carbon sequestration capacity of the mangrove was relatively strong.