Forest fires lead to permafrost degradation and localized drought, and regional droughts increase the probability of forest fires, leading to a positive feedback loop between climate change and fires. However, the relationship between fire occurrence and climatic factors change is unclear for boreal forests, which represent the largest land-based biome and stock of carbon. Here, we analyzed the relationship between lightning fire occurrence and meteorological and topographic factors based on the fire frequency, burned area, and meteorological data from the primeval forest region of the northern Daxing’an Mountains in China. We found that lightning fires occurred most frequently at an altitude of 600 to 700 m. From 1999 to 2019, the frequency of lightning fires showed an overall upward trend, whereas the affected area had no obvious change. It can be attributed to fire suppression efforts and greatly increased investment in fire prevention in China. Snow cover had a strong regulatory effect on the start and end dates of lightning fires for seasonal cycle. The frequency of lightning fires was positively correlated with the average temperature, maximum temperature, and surface evaporation and negatively correlated with precipitation and surface soil moisture (0–10 cm). The result will be useful in the spatially assessment of fire risk, the planning and coordination of regional efforts to identify areas at greatest risk, and in designing long-term lightning fires management strategies.

The desert steppe serves as a transitional zone between grasslands and deserts, and long-term monitoring of aboveground biomass (AGB) in the desert steppe is essential for understanding grassland changes. While AGB observation techniques based on multisource remote-sensing data and machine-learning algorithms have been widely applied, research on monitoring methods specifically for the desert steppe remains limited. In this study, we focused on the desert steppe of Inner Mongolia, China, as the study area and used field sampling data, MODIS data, MODIS-based vegetation indices (VI), and environmental factors (topography, climate, and soil) to compare the performance of four commonly used machine-learning algorithms: multiple linear regression (MLR), partial least-squares regression (PLS), random forest (RF), and support vector machine (SVM) in AGB estimation. Based on the optimal model, the spatial–temporal characteristics of AGB from 2000 to 2020 were calculated, and the driving forces of climate change and human activities on AGB changes were quantitatively analyzed using the random forest algorithm. The results are as follows: (1) RF demonstrated outstanding performance in terms of prediction accuracy and model robustness, making it suitable for AGB estimation in the desert steppe of Inner Mongolia; (2) VI contributed the most to the model, and no significant difference was found between soil-adjusted VIs and traditional VIs. Elevation, slope, precipitation, and temperature all had positive effects on the model; (3) from 2000 to 2020, the multiyear average AGB in the study area was 58.34 g/m², exhibiting a gradually increasing distribution pattern from the inner region to the outer region (from north to south); (4) from 2000 to 2020, the proportions of grassland with AGB slightly and significantly increasing trend in the study area were 87.08% and 5.13%, respectively, while the proportions of grassland with AGB slightly and significantly decreasing trend were 7.76% and 0.05%, respectively; and (5) over the past 20 years, climate change, particularly precipitation, has been the primary driving force behind AGB changes of the study area. This research holds reference value for improving desert steppe monitoring capabilities and the rational planning of grassland resources.

Crown fires are a major disturbance in boreal and cold–temperate forests worldwide, threatening both ecosystems and human activities. The Da Xing’anling Mountains of Northeast China exemplify these dangers due to their complex vegetation and high crown fire potential. Crown fire occurrence depends on vertical fuel continuity, fuel load, heating value, surface fire spread rate, and critical fireline intensity. However, many assessments rely on single-factor metrics or low-adaptability simulations. This study developed a Potential Canopy Fire Danger Index (PCDI) that integrates five parameters—fuel vertical distribution continuity index, fuel loading, heating value, surface fire rate of spread, and critical fireline intensity—based on field surveys and combustion tests. Pinus pumila (Regel, 1861), with its dense shrub layer, showed the highest PCDI values (0.502, 0.583 and 0.527), whereas other forest types generally fell in the low to low–moderate range (0.350–0.450), with ≈75% of plots within these classes. Surface fire spread rate correlated most strongly with PCDI, followed by vertical fuel continuity, heating value, and fuel load; critical fireline intensity had minimal influence. The elevated hazard in P. pumila reflects its structural and fuel characteristics, while other forest types present comparatively lower dangers. Model checks indicated high stability and agreement with BehavePlus 6.0 scenarios, with the PCDI showing the lowest sample SD. The PCDI provides a quantitative framework for assessing crown fire danger in cold–temperate forests and supports targeted mitigation—prioritizing P. pumila while employing cost-effective maintenance in low-danger forest types.