Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities.

Tea is an economically important crop. Evaluating the suitability of tea can better optimize the regional layout of the tea industry and provide a scientific basis for tea planting plans, which is also conducive to the sustainable development of the tea industry in the long run. Driving force analysis can be carried out to better understand the main influencing factors of tea growth. The main purpose of this study was to evaluate the suitability of tea planting in the study area, determine the prioritization of tea industry development in this area, and provide support for the government’s planning and decision making. This study used Sentinel image data to obtain the current land use data of the study area. The results show that the accuracy of tea plantation classification based on Sentinel images reached 86%, and the total accuracy reached 92%. Then, we selected 14 factors, including climate, soil, terrain, and human-related factors, using the analytic hierarchy process and spatial analysis technology to evaluate the suitability of tea cultivation in the study area and obtain a comprehensive potential distribution map of tea cultivation. The results show that the moderately suitable area (36.81%) accounted for the largest proportion of the tea plantation suitability evaluation, followed by the generally suitable area (31.40%), the highly suitable area (16.91%), and the unsuitable area (16.23%). Among these areas, the highly suitable area is in line with the distribution of tea cultivation at the Yingde municipal level. Finally, to better analyze the contribution of each factor to the suitability of tea, the factors were quantitatively evaluated by the Geodetector model. The most important factors affecting the tea cultivation suitability evaluation were temperature (0.492), precipitation (0.367), slope (0.302), and elevation (0.255). Natural factors influence the evaluation of the suitability of tea cultivation, and the influence of human factors is relatively minor. This study provides an important scientific basis for tea yield policy formulation, tea plantation site selection, and adaptation measures.

Global climate change has raised concerns about the relationship between ecosystems and forests, which is a core component of the carbon cycle and a critical factor in understanding and mitigating the effects of climate change. Forest models and sufficient information for predictions are important for ensuring efficient afforestation activities and sustainable forest development. Based on the theory of difference equations and the general rules of tree growth, this study established a difference equation for the relationship between the ratio of tree diameter at breast height (DBH) to the tree height and age of age of China’s main arbor species. A comparison with equations that represent the traditional tree growth models, i.e., Logistic and Richards equations, showed that the difference equations exhibited higher precision for both fitting and verification data. Moreover, the biomass carbon stocks (BCS) of Chinese forests from 2013 to 2050 were predicted by combining the 8th Chinese Ministry of Forestry and partial continuous forest inventory (CFI) data sets. The results showed that the BCS of Chinese forests would increase from 7342 to 11,030 terra grams of carbon (Tg C) in 2013–2050, with an annual biomass C (carbon) sink of 99.68 Tg C year⁻¹, and they indicated that the Chinese land-surface forest vegetation has an important carbon sequestration capability.

Forests are indispensable materials and spiritual foundations for promoting ecosystem circulation and human survival. Exploring the environmental impact mechanism on individual-tree growth is of great significance. In this study, the effects of biogeoclimate, competition, and topography on the growth of Betula spp. and Cunninghamia lanceolata (Lamb.) Hook., two tree species with high importance value in China, were explored by gradient boosting regression tree (GBRT), k-nearest neighbor (KNN), and random forest (RF) machine learning (ML) algorithms. The results showed that the accuracy of RF was better than KNN, which was better than GBRT. All ML algorithms performed well for future diameter at breast height (DBH) predictions; the Willmott’s indexes of agreement (WIA) of each ML algorithm in predicting the future DBH were all higher than 0.97, and the R² was higher than 0.98 and 0.90, respectively. The individual tree annual growth rate is mainly affected by the single-tree size, and the external environment can promote or inhibit tree growth. Climate and stand structure variables were relatively more important for tree growth than the topographic factors. Lower temperature and precipitation, higher stand density, and canopy closure were more unfavorable for their growth. In afforestation, the following factors should be considered in order: geographic location, meteorological climate, stand structure, and topography.

The Relief Degree of Land Surface (RDLS) is an important index to evaluate regional environment. It has a significant effect on the local climate, geologic hazards, the path and speed of fire spreading, the migrations of wild animals, and the runoff path and speed of precipitation. The forest-steppe ecotone in northern China is one of ecological fragile zones. In-depth study of the RDLS of the forest-steppe ecotone in northern China will help to implement ecological projects scientifically and promote the construction of the national ecological security barrier. The Shuttle Radar Topography Mission (SRTM-GL1 30 m) data were used to determine the optimal analysis window for RDLS based on the mean change-point method, and the elevation difference was extracted based on the window analysis method. The RDLS model was used to extract RDLS of the forest-steppe ecotone and analyzed with the help of a spatial auto-correlation model. The correlation between mean elevation, relative elevation difference, and RDLS was also analyzed. The results show that the optimal analysis window size for RDLS was 29 × 29, corresponding to an area of 0.76 km². The RDLS under the optimal analysis window extracted from SRTM-GL1 (30 m) ranged from 0.084 to 3.516. The RDLS had significant spatial clustering, with high RDLS mainly distributed in the mountainous areas and low RDLS mainly distributed in mountain-to-plain transition zone; the RDLS between different administrative units and different watersheds had obvious variability. Overall, the RDLS was characterized as decreasing, increasing, and then decreasing from the south to north, while it was high in the west and low in the east. And the RDLS was linearly positively correlated with mean elevation and relative elevation difference. In the future, the implementation of major ecological projects in the forest-steppe ecotone in northern China, such as soil and water conservation, afforestation tree species selection, ecological corridor design, ecological management, geological disaster prevention, and forest fire prevention, should fully consider the local topographic conditions. These research results can provide topographic references for the implementation of ecological planning and engineering in this area and similar areas. It contributes to sustainable development and maximization of ecological benefits and promotes the establishment of a national ecological security barrier.

Fractional Vegetation Cover (FVC) is an important parameter to reflect vegetation growth and describe plant canopy structure. This study integrates both active and passive remote sensing, capitalizing on the complementary strengths of optical and radar data, and applies various machine learning algorithms to retrieve FVC. The results demonstrate that, for FVC retrieval, the optimal combination of optical remote sensing bands includes B2 (490 nm), B5 (705 nm), B8 (833 nm), B8A (865 nm), and B12 (2190 nm) from Sentinel-2, achieving an Optimal Index Factor (OIF) of 522.50. The LiDAR data of ICESat-2 imagery is more suitable for extracting FVC than that of GEDI imagery, especially at a height of 1.5 m, and the correlation coefficient with the measured FVC is 0.763. The optimal feature variable combinations for FVC retrieval vary among different vegetation types, including synthetic aperture radar, optical remote sensing, and terrain data. Among the three models tested—multiple linear regression, random forest, and support vector machine—the random forest model outperformed the others, with fitting correlation coefficients all exceeding 0.974 and root mean square errors below 0.084. Adding LiDAR data on the basis of optical remote sensing combined with machine learning can effectively improve the accuracy of remote sensing retrieval of vegetation coverage.