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.

This study aimed to accurately map burned forest areas and analyze the spatial distribution of forest fires under complex terrain conditions. This study integrates Landsat 8, Sentinel-2, and MODIS data to map burned forest areas in the complex terrain of western Yunnan. A machine learning workflow was developed on Google Earth Engine by combining Dynamic World land cover data with official fire records, utilizing a logistic regression-based feature selection strategy and an enhanced SNIC segmentation GEOBIA framework. The performance of four classifiers (RF, SVM, KNN, CART) in burn detection was evaluated through a comparative analysis of their spectral–spatial discrimination capabilities. The results indicated that the RF classifier achieved the highest performance, with an overall accuracy of 96.32% and a Kappa coefficient of 0.951. Spatial analysis further revealed that regions at medium altitudes (800–1600 m) and moderate slopes (15–25°) are more prone to forest fires. This study demonstrates a robust approach for generating accurate large-scale forest fire maps and provides valuable insights for effective fire management in complex terrain areas.

Olea europaea L. is an economically and ecologically significant species, for which accurate biomass estimation provides critical insights for artificial propagation, yield forecasting, and carbon sequestration assessments. Currently, research on biomass estimation for Olea europaea L. remains scarce, and there is a lack of efficient, accurate, and scalable technical solutions. To address this gap, this study achieved, for the first time, non-destructive estimation of Olea europaea L. biomass across individual tree to plot scales by integrating UAV-RGB (Unmanned Aerial Vehicle-Red-Green-Blue) imagery with the U²-Net model. This study initially developed allometric models for W-D-H, CA-D, and CA-H in Olea europaea L. (where W = biomass, D = ground diameter, H = tree height, and CA = canopy area). A single-parameter CA-based whole-plant biomass model was subsequently developed utilizing the optimal models. An innovative whole-plant biomass estimation model (UAV-RGB, U²-Net Total Biomass, UUTB) that combines UAV-RGB imagery with U²-Net at the sample-plot level was developed and assessed. The results revealed the following: (1) The model for Olea europaea L. aboveground biomass (AGB) was W_A = 0.0025D^1.943H^0.690 (R² = 0.912), the model for belowground biomass (BGB) was W_B = 0.012D^1.231H^0.525 (R² = 0.693), the model for CA-D was D = 4.31427C^0.513 (R² = 0.751), CA-H model was H = 226.51939C^0.268 (R² = 0.500). (2) The optimal AGB model for CA single-parameter was W_A = 1.80901C^1.181 (R² = 0.845), and the model for BGB was W_B = 1.25043C^0.772 (R² = 0.741). (3) The R² of Olea europaea L. biomass, as estimated by CA derived from the U²-Net and UUTB models, was 0.855. This study presents the first integration of UAV-RGB imagery and the U²-Net model for biomass estimation in Olea europaea L., which not only addresses the research gap in species-specific allometric modeling but also overcomes the limitations of traditional manual measurement methods. The proposed approach provides a reliable technical foundation for accurate assessment of both economic yield and ecological carbon sequestration capacity.

Carbon stock (CS) is an important indicator of the structure and function of forest ecosystems, and plays an important role in mitigating climate change, maintaining ecological system balance, promoting carbon trading, and other socioeconomic and ecological values. Olea europaea L. is a species of high economic and ecological value, and its excellent nutritional composition, strong drought tolerance, sustainable production characteristics, and promotion of agrodiversity make it important in guaranteeing food security. Accurately estimating the CS of Olea europaea L. offers a reliable reference for its artificial breeding and yield prediction. Firstly, an independent estimation model of Olea europaea L. CS was constructed, while a compatibility model of Olea europaea L. unitary and binary CS was constructed using nonlinear metric error. Secondly, in the CS compatibility model system, the total CS model of Olea europaea L. was constructed by the Logarithmic Nonlinear Seemingly Unrelated Regression (LNSUR) method with D and D²H as independent variables. The results show: (1) The independent model of Aboveground CS (AGCS) was C = 0.0014D^1.92876H^0.67174 (R² = 0.909), and the independent model of Belowground CS (BGCS) was C = 0.00723D^1.23578H^0.48553 (R² = 0.686). The AGCS compatibility model effectively addresses the issue of component sums not equaling the total, while maintaining a low RMSE (1.918); (2) The LNSUR model improved the accuracy of the BGCS model more significantly (R² = 0.787), and the estimated total CS also had a smaller RMSE (0.241~0.418); (3) Whole-plant CS of Olea europaea L. in 15 sample plots was estimated using the CS independent model and the LNSUR model with an R² of 0.964. This study is the first attempt to construct a CS estimation model for Olea europaea L., which provides a scientific and technological basis for the monitoring of its economic and ecological value indicators, such as yield and carbon sink capacity.