Forest fires are an important disturbance that affects ecosystem stability and pose a serious threat to the ecosystem. However, the recovery process of forest ecological quality (EQ) after a fire in plateau mountain areas is not well understood. This study utilizes the Google Earth Engine (GEE) and Landsat data to generate difference indices, including NDVI, NBR, EVI, NDMI, NDWI, SAVI, and BSI. After segmentation using the Simple Non-Iterative Clustering (SNIC) method, the data were input into a random forest (RF) model to accurately extract the burned area. A 2005–2020 remote sensing ecological index (RSEI) time series was constructed, and the recovery of post-fire forest EQ was evaluated through Theil–Sen slope estimation, Mann–Kendall (MK) trend test, stability analysis, and integration with topographic information systems. The study shows that (1) from 2006 to 2020, the post-fire forest EQ improved year by year, with an average annual increase rate of 0.014/a. The recovery process exhibited an overall trend of “decline initially-fluctuating increase-stabilization”, indicating that RSEI can be used to evaluate the post-fire forest EQ in complex plateau mountainous regions. (2) Between 2006 and 2020, the EQ of forests exhibited a significant increasing trend spatially, with 84.32% of the areas showing notable growth in RSEI, while 1.80% of the regions experienced a declining trend. (3) The coefficient of variation (CV) of RSEI in the study area was 0.16 during the period 2006–2020, indicating good overall stability in the process of post-fire forest EQ recovery. (4) Fire has a significant impact on the EQ of forests in low-altitude areas, steep slopes, and sun-facing slopes, and recovery is slow. This study offers scientific evidence for monitoring and assessing the recovery of post-fire forest EQ in plateau mountainous regions and can also inform ecological restoration and management efforts in similar areas.

Accurate mapping of burned forest areas in mountainous regions is essential for wildfire assessment and post-fire ecological management. This study develops an FS-SNIC-ML workflow that integrates multi-source optical fusion, semi-automatic sample generation, feature selection, and object-based machine-learning classification to support reliable burned-area mapping under complex terrain conditions. A pseudo-invariant feature (PIFS)-based fusion of Sentinel-2 and Landsat 8 imagery was employed to generate cloud-free, gap-free, and spectrally consistent pre- and post-fire reflectance datasets. Burned and unburned samples were constructed using a semi-automatic SAM–GLCM–PCA–Otsu procedure and county-level stratified sampling to ensure spatial representa-tiveness. Feature selection using LR, RF, and Boruta identified dNBR, dNDVI, and dEVI as the most discriminative variables. Within the SNIC-supported GEOBIA framework, four classifiers were evaluated; RF performed best, achieving overall accuracies of 92.02% for burned areas and 94.04% for unburned areas, outperforming SVM, CART, and KNN. K-means clustering of dNBR revealed spatial variation in fire conditions, while geographical detector analysis showed that NDVI, temperature, soil moisture, and their pairwise interactions were the dominant drivers of wildfire hotspot density. The proposed workflow provides an effective and transferable approach for high-precision burned-area extraction and quantification of wildfire-driving factors in mountainous forest regions.

In the ecological context of global climate change, ensuring the stable carbon sequestration capacity of forest ecosystems, which is among the most important components of terrestrial ecosystems, is crucial. Forest fires are disasters that often burn vegetation and damage forest ecosystems. Accurate recognition of firegrounds is essential to analyze global carbon emissions and carbon flux, as well as to discover the contribution of climate change to the succession of forest ecosystems. The common recognition of firegrounds relies on remote sensing data, such as optical data, which have difficulty describing the characteristics of vertical structural damage to post-fire vegetation, whereas airborne LiDAR is incapable of large-scale observations and has high costs. The new generation of satellite-based photon counting radar ICESat-2/ATLAS (Advanced Topographic Laser Altimeter System, ATLAS) data has the advantages of large-scale observations and low cost. The ATLAS data were used in this study to extract three significant parameters, namely general, canopy, and topographical parameters, to construct a recognition index system for firegrounds based on vertical structure parameters, such as the essential canopy, based on machine learning of the random forest (RF) and extreme gradient boosting (XGBoost) classifiers. Furthermore, the spatio-temporal parameters are more accurate, and widespread use scalability was explored. The results show that the canopy type contributed 79% and 69% of the RF and XGBoost classifiers, respectively, which indicates the feasibility of using ICESat-2/ATLAS vertical structure parameters to identify firegrounds. The overall accuracy of the XGBoost classifier was slightly greater than that of the RF classifier according to 10-fold cross-validation, and all the evaluation metrics were greater than 0.8 after the independent sample test under different spatial and temporal conditions, implying the potential of ICESat-2/ATLAS for accurate fireground recognition. This study demonstrates the feasibility of ATLAS vertical structure parameters in identifying firegrounds and provides a novel and effective way to recognize firegrounds based on different spatial–temporal vertical structure information. This research reveals the feasibility of accurately identifying fireground based on parameters of ATLAS vertical structure by systematic analysis and comparison. It is also of practical significance for economical and effective precise recognition of large-scale firegrounds and contributes guidance for forest ecological restoration.

Fire is a crucial ecological factor, however, the organ-level effects of wildfire on plant non-structural carbohydrates (NSC) and stoichiometric characteristics are poorly understood. We studied the effects of fire on sugar, starch, carbon (C), nitrogen (N), and phosphorus (P) content in the needles, branches, stems, and fine roots of fire-damaged Pinus yunnanensis trees (F-trees). Starch, sugar, and NSC content were higher in the roots and needles of F-trees than in that of undamaged individuals (H-trees). C, N, and P content and stoichiometry characteristics of F-trees also differed significantly from H-trees. The N/P content of both F-tree and H-tree needles were <14 prior to forest fire disturbance and increased thereafter, indicating that the N limitation increased after fire disturbance. In addition, F-tree physiology did not return to control levels nine months after fire disturbance, indicating that more time is needed to recover. These findings suggest that forest management practices that increase soil N content will likely accelerate post-fire recovery. Because fire frequency and intensity will continue to increase in response to climate change, our results are of great value in improving our understanding of post-fire ecosystem dynamics.

Understanding post-fire visible greenness change is important for assessing spatial heterogeneity in mountainous burned landscapes, but satellite observations often cannot capture local variation. This study developed a workflow using Unmanned Aerial Vehicle (UAV) Red–Green–Blue (RGB) imagery for RGB-interpreted burn severity classification and Green Leaf Index (GLI)-derived visible greenness change analysis three years after fire. The workflow integrated object-based Random Forest (RF) classification, bi-temporal GLI difference ($\Delta$GLI) detection, and terrain-stratified analysis under RGB-only conditions. Object-based multi-feature representation, including a 41-dimensional (41D) feature set of color, texture, and gradient metrics, supported local burn severity mapping, although performance gain over the 23-dimensional (23D) set was modest and not statistically significant. The burned area was dominated by high and moderate severity classes. GLI-derived analysis showed limited visible greenness increase (mean $\Delta$GLI = 0.0058), with slightly more than half of pixels being positive; high severity areas had higher $\Delta$GLI, while low severity areas showed limited or negative values. $\Delta$GLI also varied across terrain, being higher on steeper slopes, mid-to-upper elevations, and east-facing aspects. The workflow provides a practical local-scale approach for post-fire analysis using high-resolution UAV RGB imagery, with results interpreted as case-specific visible greenness patterns rather than comprehensive ecological recovery.

Global warming has increased the frequency of droughts, further complicating wildfire susceptibility assessment in topographically complex mountainous regions. However, the factors associated with wildfire occurrence and their nonlinear spatial responses under contrasting drought conditions remain poorly understood. To address this knowledge gap, we conducted a case study in a fire-prone mountainous region of southwestern China. Drought and non-drought periods were identified using the Standardized Precipitation Evapotranspiration Index (SPEI) and run theory. Using historical wildfire records from 2006 to 2020 and 16 wildfire drivers, we developed three machine-learning models and applied GeoShapley to quantify the contributions of key predictors and characterize their spatial dynamics under different drought conditions. The results showed that the Extreme Gradient Boosting (XGB) model achieved the best predictive performance (AUC = 0.85–0.91) and effectively captured the spatial patterns of wildfire susceptibility. Meteorological variables consistently emerged as the dominant controls on wildfire occurrence, although their relative importance and functional effects differed substantially between drought and non-drought conditions. GeoShapley analysis further revealed pronounced spatial heterogeneity in the effects of the major drivers, with these spatial patterns further modulated by drought conditions. In particular, during drought periods, the local geographical context amplified the spatial interaction effect of precipitation, resulting in a stronger risk-enhancing effect than during non-drought periods. By decomposing variable contributions into non-spatial main effects and spatially explicit interaction effects, this study reveals how the effects of wildfire drivers vary spatially under contrasting drought conditions and provides a robust framework for targeted wildfire mitigation in complex mountainous landscapes.