Forest fires are disasters that are common around the world. They pose an ongoing challenge in scientific and forest management. Predicting forest fires improves the levels of forest-fire prevention and risk avoidance. This study aimed to construct a forest risk map for China. We base our map on Visible Infrared Imaging Radiometer Suite data from 17,330 active fires for the period 2012–2019, and combined terrain, meteorology, social economy, vegetation, and other factors closely related to the generation of forest-fire disasters for modeling and predicting forest fires. Four machine learning models for predicting forest fires were compared (i.e., random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP), and gradient-boosting decision tree (GBDT) algorithm), and the RF model was chosen (its accuracy, precision, recall, F1, AUC values were 87.99%, 85.94%, 91.51%, 88.64% and 95.11% respectively). The Chinese seasonal fire zoning map was drawn with the municipal administrative unit as the spatial scale for the first time. The results show evident seasonal and regional differences in the Chinese forest-fire risks; forest-fire risks are relativity high in the spring and winter, but low in fall and summer, and the areas with high regional fire risk are mainly in the provinces of Yunnan (including the cities of Qujing, Lijiang, and Yuxi), Guangdong (including the cities of Shaoguan, Huizhou, and Qingyuan), and Fujian (including the cities of Nanping and Sanming). The major contributions of this study are to (i) provide a framework for large-scale forest-fire risk prediction having a low cost, high precision, and ease of operation, and (ii) improve the understanding of forest-fire risks in China.

Net ecosystem productivity (NEP) is an important indicator for estimating regional carbon sources/sinks. The study focuses on a comprehensive computational simulation and spatiotemporal variation study of the NEP in the Yellow River basin from 2000 to 2020 using NPP data products from MODIS combined with a quantitative NEP estimation model followed by a comprehensive analysis of the spatiotemporal variation characteristics and dynamic procession persistence analysis based on meteorological data and land use data. The results show that: (1) The total NEP in the Yellow River basin had an overall increasing trend from 2000 to 2020, with a Theil–Sen trend from −23.37 to 43.66 gCm⁻²a⁻¹ and a mean increase of 4.64 gCm⁻²a⁻¹ (p < 0.01, 2-tailed). (2) Most areas of the Yellow River basin are carbon sink areas, and the annual average NEP per unit area was 208.56 gCm⁻²a⁻¹ from 2000 to 2020. There were, however, substantial spatial and temporal variations in the NEP. Most of the carbon source area was located in the Kubuqi Desert and its surroundings. (3) Changes in land use patterns were the main cause of changes in regional NEP. During the 2000–2020 period, 1154.24 t of NEP were added, mainly due to changes in land use, e.g., the conversion of farmland to forests and grasslands. (4) The future development in 83.43% of the area is uncertain according to the Hurst index dynamic persistence analysis. In conclusion, although the carbon−sink capacity of the terrestrial ecosystem in the Yellow River basin is increasing and the regional carbon sink potential is increasing in the future, the future development of new energy resources has regional uncertainties, and the stability of the basin ecosystem needs to be enhanced.

Assessing and predicting forest fires has long been an arduous task. Nowadays, the rapid advancement of artificial intelligence and machine learning technologies have provided a novel solution to forest fire occurrence assessment and prediction. In this research, we developed a novel hybrid machine-learning-technique algorithm to improve forest fire prediction based on random forest (RF), gradient-boosting decision tree (GBDT), support vector machine (SVM), and other machine learning models. The dataset we employed was satellite fire point data from 2010 to 2018 from the Chinese Department of Fire Prevention. The efficacy and performance of our methods were examined by validating the model fit and predictive capability. The results showed that the ensemble model LR (logistic regression)-RF-SVM-GBDT outperformed the single RFSVMGBDT model and the LR-RF-GBDT integrated framework, displaying higher accuracy and greater robustness. We believe that our newly developed hybrid machine-learning algorithm has the potential to improve the accuracy of predicting forest fire occurrences, thus enabling more efficient firefighting efforts and saving time and resources.

The alterations in vegetation cover in the Three North Protection Forest Project area influence its ecological and environmental management. It helps to study soil erosion, environmental change, and ecosystem protection to provide basic data support. Based on the Google Earth Engine cloud platform, this paper selects MODIS 3Q1 data from 2000–2020 and uses the image element dichotomous model to estimate the fractional vegetation cover (FVC) of the Three North Protection Forest Project area, evaluates the stability and temporal and spatial variation of FVC, investigates the coupling relationship between the FVC and temperature and rainfall through evaluation indexes such as the slope of inter-annual variation and partial correlation coefficient, and then analyzes the influence of land use changes on the FVC. The results show that the fractional vegetation cover of the Three North Protection Forest Project area as a whole has increased significantly over the past 20 years, the medium and high vegetation coverage areas have reached 36.4%, the high vegetation FVC has increased from 20.66% in 2000 to 21.59% in 2020, and the vegetation activity is increasing. The vegetation cover is significantly affected by the topographic effect, with the vegetation cover improving relatively well at slopes between 5–13° and elevations ranging from 2000–2500 m. The overall positive correlation between vegetation and temperature and vegetation and precipitation in the study area was 54.08% and 70.04%, respectively, and precipitation was the key factor influencing vegetation growth in the research region. Human activities have a stronger effect on vegetation construction than destruction, and this study contributes to the evaluation of the benefits of the Three North Protection Forest Project and the understanding of human influence on environmental change.