Forest is the largest vegetation carbon pool in the global terrestrial ecosystem. The spatial distribution and change of forest biomass are of importance to reveal the surface spatial variation and driving factors, to analyze and evaluate forest productivity, and to evaluate ecological function of forest. In this study, broad-leaved forests located in a typical state nature reserve in northern subtropics were selected as the study area. Based on ground survey data and high-resolution remote sensing images, three machine learning models were used to identify the best remote sensing quantitative inversion model of forest biomass. The biomass of broad-leaved forest with 30-m resolution in the study area from 1998 to 2016 was estimated by using the best model about every two years. With the estimated biomass, multiple leading factors to cause biomass temporal change were then identified from dozens of remote sensing factors by investigating their nonlinear correlations. Our results showed that the artificial neural network (ANN) model was the best (R² = 0.8742) among the three, and its accuracy was also much higher than that of the traditional linear or nonlinear models. The mean biomass of the broad-leaved forest in the study area from 1998 to 2016 ranged from 90 to 145 Mg ha⁻¹, showing an obvious temporal variation. Instead of biomass, biomass change (BC) was studied further in this research. Significant correlations were found between BC in broad-leaved forest and three climate factors, including average daily maximum surface temperature, maximum precipitation, and maximum mean temperature. It was also found that BC has a strong correlation with the biomass at the previous time (i.e., two years ago). Those quantitative correlations were used to construct a linear model of BC with high accuracy (R² = 0.8873), providing a new way to estimate the biomass change of two years later based on the observations of current biomass and the three climate factors.

As tourism development and human disturbance increase, they have been affecting the functional structure distribution, sustainable utilization of resources, and ecological benefits of nature reserves. At the same time, ecological risk has increased significantly. This study proposes ecotourism development patterns for risk areas at each level, to provide a basis for stabilizing and promoting the ecological sustainable development of nature reserves. Yaoluoping National Nature Reserve in China is used as a study case. Based on GaoFen-2 satellite data within the reserve area in 2017, ENVI, Fragstats and ArcGIS10.2 are used for land use classification as well as evaluation indicators selection and analysis. The ecotourism risk assessment model of the reserve is constructed, which is also imbedded with analytic hierarchy process and Ecological Risk Index. The model consists of landscape ecology, topographic hydrology, land use and human activities factors, and it can produce the distribution map at the ecotourism risk level of the reserve. Results show that the high ecotourism risk areas in the reserve are mainly located around rivers and roads, and they are distributed in strips. Overall, the ecotourism risk level in the northern part of the reserve is higher than that in the southern part. Nearly 90% of the regional ecotourism risk is at the middle or low level, implying the reserve in a healthy level overall. However, except for low-risk areas, the proportion of risk areas at all levels in the core zone is higher than that in the buffer zone.

As an important forest type, deciduous broad-leaved forest is crucial for estimating forest carbon sequestration capacity and evaluating forest carbon balance. This study focuses on the natural deciduous broad-leaved forest of Mazongling Nature Reserve in Jinzhai County of China. WorldView-2 images were selected as data source. 36 candidate factors including vegetation indices, texture features, and topographic factors were used for modelling. Three machine learning algorithms (i.e., random forest, k-nearest neighbor, and artificial neural network) were used to establish the optimal quantitative retrieval model for natural deciduous broad-leaved biomass. Results showed that the ANN model was the best predictor with R² = 0.69 and RMSE = 31.53 (Mg·ha⁻¹). Combining the ANN model with the complete spatial coverage of remote sensing data, we developed a distribution map of natural deciduous broad-leaved biomass in the Mazongling forest farm. The estimated average biomass of the study area was 90.34 ± 47.96 Mg·ha⁻¹. In addition, the influence of light saturation on model accuracy is also discussed. This study confirms that remote sensing data in temporal and spatial space can improve the model estimation accuracy.