Phragmites australis (P. australis) is one of the most important plant species found in wetland ecosystems, and its aboveground biomass (AGB) is a key indicator for assessing the quality or health of a wetland site. In this study, we combined Sentinel-1/2 images and field observation data collected in 2020, to delineate the distribution of P. australis in the Momoge Ramsar Wetland site by using a random forest method, and further, to estimate AGB by comparing multiple linear regression models. The results showed that the overall classification accuracy of P. australis using the random forest method was 89.13% and the P. australis area in the site was 135.74 km² in 2020. Among various remote sensing variables, the largest correlation coefficient was observed between dry weight of AGB of P. australis and Sentinel-2 red edge B₇, and between fresh weight of P. australis AGB and red edge B₅. The optimal models for estimating dry and fresh weight of P. australis AGB were multiple linear regression models, with an accuracy of 75.4% and 69.2%, respectively. In 2020, it was estimated that the total fresh weight of P. australis AGB in this Ramsar site was 21.2 × 10⁷ kg and the total dry weight was 7.2 × 10⁷ kg. The larger weight of P. australis AGB was identified mainly at central and western sites. The application of Sentinel-2 red-edge band for AGB estimation can significantly improve the model estimation accuracy. The findings of this study will provide a scientific basis for the management and protection of wetland ecosystems and sustainable utilization of P. australis resources.

Daxing’anling Mountain, in the northeastern part of China, contains a large amount of soil organic carbon (SOC). Using data including topography, climate, and vegetation, the spatial distribution of SOC content was modeled using classical and geography-based statistics, as well as a geographically weighted kriging model. The study findings include: (1) SOC content generally ranges 40–70 g/kg, with high SOC content in the southwest and low SOC content in the southeast; (2) Results of principal component analysis suggested the normalized difference vegetation index is the best predictor of patterns in SOC; and (3) The geo-weighted regression Kriging model well reflects factors influencing spatial distribution of SOC content. This study provides important baseline information for environmental protection in the Daxing’anling Mountain area, as well as general information as to important factors that mediate this important reservoir of soil carbon.

The negative air ion (NAI) concentration is an essential indicator of air quality and atmospheric pollution. The NAI concentration can be used to monitor air quality on a regional scale and is commonly determined using field measurements. However, obtaining these measurements is time-consuming. In this paper, the relationship between remotely sensed surface parameters (such as land surface temperature, normalized difference vegetation index (NDVI), and leaf area index) obtained from MODIS data products and the measured NAI concentration using a stepwise regression method was analyzed to estimate the spatial distribution of the NAI concentration and verify the precision. The results indicated that the NAI concentration had a negative correlation with temperature, leaf area index (LAI), and gross primary production while it exhibited a positive correlation with the NDVI. The relationship between land surface temperature and the NAI concentration in the Daxing’anling region is expressed by the regression equation of y = -35.51x₁ + 11206.813 (R² = 0.6123). Additionally, the NAI concentration in northwest regions with high forest coverage was higher than that in southeast regions with low forest coverage, suggesting that forests influence the air quality and reduce the impact of environmental pollution. The proposed inversion model is suitable for evaluating the air quality in Daxing’anling and provides a reference for air quality evaluation in other areas. In the future, we will expand the quantity and distribution range of sampling points, conduct continuous observations of NAI concentrations and environmental parameters in the research areas with different land-use types, and further improve the accuracy of inversion results to analyze the spatiotemporal dynamic changes in NAI concentration and explore the possibility of expanding the application areas of NAI monitoring.

Global warming significantly affects forest ecosystems in the Northern Hemisphere’s mid-to-high latitudes, altering tree growth, productivity, and spatial distribution. Additionally, spatial and temporal heterogeneity exists in the responses of different tree species to climate change. This research focuses on two key species in China’s Greater Khingan Range: Larix gmelinii (Rupr.) Kuzen. (Pinaceae) and Quercus mongolica Fisch. ex Ledeb. (Fagaceae). We utilized a Maxent model optimized by the kuenm R package to predict the species’ potential habitats under various future climate scenarios (2050s and 2070s) considering three distinct Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, and SSP5-8.5. We analyzed 313 distribution records and 15 environmental variables and employed geospatial analysis to assess habitat requirements and migration strategies. The Maxent model demonstrated high predictive accuracy, with Area Under the Curve (AUC) values of 0.921 for Quercus mongolica and 0.985 for Larix gmelinii. The high accuracy was achieved by adjusting the regularization multipliers and feature combinations. Key factors influencing the habitat of Larix gmelinii included the mean temperature of the coldest season (BIO11), mean temperature of the warmest season (BIO10), and precipitation of the driest quarter (BIO17). Conversely, Quercus mongolica’s habitat suitability was largely affected by annual mean temperature (BIO1), elevation, and annual precipitation (BIO12). These results indicate divergent adaptive responses to climate change. Quercus mongolica’s habitable area generally increased in all scenarios, especially under SSP5-8.5, whereas Larix gmelinii experienced more complex habitat changes. Both species’ distribution centroids are expected to shift northwestward. Our study provides insights into the divergent responses of coniferous and broadleaf species in the Greater Khingan Range to climate change, contributing scientific information vital to conserving and managing the area’s forest ecosystems.

Wildfires, a recurring and persistent natural disaster, present direct threats to both ecological balance and human safety. Despite the northeastern region of China boasting abundant forest resources, it grapples with a significant wildfire issue. This study, focused on China’s northeastern region, employs sophisticated methodologies, including the Mann–Kendall (MK) mutation test, sliding t-test, and geographical heat maps, to unveil the spatial distribution and temporal trends of wildfires. Furthermore, a random forest model is utilized to develop a wildfire susceptibility map, enabling an in-depth analysis of the relationships between various potential factors and wildfires, along with an assessment of the significance of these driving factors. The research findings indicate that wildfires in the northeastern region exhibit distinct seasonality, with the highest occurrences in the autumn and spring and fewer incidents in the summer and winter. Apart from the spring season, historical wildfires show a decreasing trend during other seasons. Geographically, wildfires tend to cluster, with over half of the high-risk areas concentrated at the junction of the Greater Khingan Mountains and Lesser Khingan Mountains in the northeastern region. The random forest model assumes a pivotal role in the analysis, accurately identifying both natural and human-induced factors, including topography, climate, vegetation, and anthropogenic elements. This research further discloses that climate factors predominantly influence wildfires in the northeastern region, with sunshine duration being the most influential factor. In summary, this study highlights the variation in various wildfire-driving factors, providing the basis for tailored management strategies and region-specific fire prevention. Through a comprehensive analysis of the spatiotemporal patterns of wildfires and associated risk factors, this research offers valuable insights for mitigating wildfire risks and preserving the region’s ecological integrity.

Introduction: To investigate the differences in the response of radial growth of larch (Larix gmelinii) at varying altitude gradients to climatic factors, this study established tree-ring width chronologies of Larix gmelinii at altitudes of 900 m, 1,120 m and 1,300 m based dendroclimatology.Methods: We also employed R packages including “dplR,” “static” and “moving” to determine the correlation between larch at different altitudes and climatic factors and to explore the sensitivity and stability between radial growth and the climate of larch at different altitudes in the northern part of the Greater Khingan Mountains Range in Northeast China.Results: The results revealed the radial growth of larch in high altitude areas to exhibit a downward trend due to climate warming, while the growth of trees in intermediate and low altitude areas showed an upward trend. The growth–climate relationship exhibited a significant negative correlation between radial growth in low altitude larch (900 m) and temperatures in the current winter (February and March) and growing season (July, October of the current year, and October of the previous year) (p < 0.05). Moreover, high altitude larch (1,300 m) showed a significant negative correlation with temperature in January, March, and October of the current year (p < 0.05). However, in intermediate altitude areas (1,120 m), the radial growth of trees was significantly positively correlated with the temperature of the growing season (May, June and August). Precipitation in April was observed to promote the radial growth of low-altitude larch. Moving correlation analysis revealed that the inhibitory effect of low temperatures in winter and high temperatures in the growing season on the radial growth of larch at three altitudes gradually strengthened, transitioning from significant negative/positive correlations to significant positive/negative correlations along the altitudinal gradient.Discussion: Larch trees in high and low altitudes exhibited an increased sensitivity to winter and spring precipitation, while in the intermediate region, the inhibitory effect of growing season precipitation on larch radial growth continued to intensify. The results of the study have an important reference value for the in-depth understanding of the growth dynamics of Larix gmelinii natural forests in the northern part of the Greater Khingan Mountains under climate change.