Mountainous vegetation type classification plays a fundamental role in resource investigation in forested areas, making it necessary to accurately identify mountain vegetation types. However, Mountainous vegetation growth is readily affected by terrain and climate, which often makes interpretation difficult. This study utilizes Sentinel-2A images and object-oriented machine learning methods to map vegetation types in the complex mountainous region of Jiuzhaigou County, China, incorporating multiple auxiliary features. The results showed that the inclusion of different features improved the accuracy of mountain vegetation type classification, with terrain features, vegetation indices, and spectral features providing significant benefits. After feature selection, the accuracy of mountain vegetation type classification was further improved. The random forest recursive feature elimination (RF_RFE) algorithm outperformed the RliefF algorithm in recognizing mountain vegetation types. Extreme learning machine (ELM), random forest (RF), rotation forest (ROF), and ROF_ELM algorithms all achieved good classification performance, with an overall accuracy greater than 84.62%. Comparing the mountain vegetation type distribution maps obtained using different classifiers, we found that classification algorithms with the same base classifier ensemble exhibited similar performance. Overall, the ROF algorithm performed the best, achieving an overall accuracy of 89.68%, an average accuracy of 88.48%, and a Kappa coefficient of 0.879.

The complexity of forest ecosystems leads to differences in the distribution patterns of different vegetation types along elevation gradients. This study aimed to explore the characteristics of AGB variations along elevation gradients for different forest types and tree species components in the Qinling–Daba Mountains. Based on 329 field vegetation survey plots, including four sampling transects and four representative mountains, individual tree AGB was calculated using allometric biomass equations. Further, generalized additive models (GAMs) were used to investigate the relationships between AGB and elevation for four forest types (broadleaf forests, coniferous forests, mixed coniferousbroadleaf forests, and shrublands) and three AGB components (total AGB (tAGB), broadleaf species AGB (bAGB), and coniferous species AGB (cAGB)) across eight vegetation survey regions. The results showed that the AGB of different forest types is significantly related to elevation (p < 0.05), with broadleaf forest AGB showing a unimodal pattern with elevation, coniferous forest and mixed forest AGB increasing with elevation, and shrubland AGB exhibiting a noticeable rise at higher elevations. The AGB components across different vegetation survey regions also showed significant relationships with elevation (p < 0.05), with broadleaf species AGB displaying a monotonically increasing trend in regions with a small elevation range and exhibiting a unimodal or bimodal distribution in regions with a large elevation range, while coniferous species AGB generally increased with elevation. Although elevation significantly influenced forest AGB, the variation in R² values indicated that elevation is not the sole determinant of AGB variation. This study improves the understanding of spatial patterns of forest biomass along elevation gradients.

Timely and accurate acquisition of crop planting areas and spatial distribution are deemed essential for grasping food configurations and guiding agricultural production. Despite the increasing research on crop mapping and changes with the development of remote sensing technology, most studies have focused on large-scale regions, with limited research being conducted in fragmented and ecologically vulnerable valley areas. To this end, this study utilized Landsat ETM+/OLI images as the data source to extract additional features, including vegetation index, terrain, and texture. We employed the Random Forest Recursive Feature Elimination (RF_RFE) algorithm for feature selection and evaluated the effectiveness of three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Rotation Forest (ROF)—for crop extraction. Then, based on the optimal classifiers, the main crops in the Huangshui basin for the years of 2002, 2014, and 2022 were extracted. Finally, the transfer matrix, the gravity center model, and the Standard Deviation Ellipse (SDE) model were used to analyze the spatio—temporal changes of crops over the past 20 years in the Huangshui basin. The results showed that the spectral, vegetation index, and terrain features played a crucial role in crop extraction. Comparing the performance of the classifiers, the ROF algorithm displayed superior effectiveness in crop identification. The overall accuracy of crop extraction was above 86.97%, and the kappa coefficient was above 0.824. Notably, between 2002 and 2022, significant shifts in crop distribution within the Huangshui basin were observed. The highland barley experienced a net increase in planting area at a rate of 8.34 km²/year, while the spring wheat and oilseed rape demonstrated net decreases at rates of 16.02 km²/year and 14.28 km²/year, respectively. Furthermore, the study revealed that highland barley exhibited the most substantial movement, primarily expanding towards the southeast direction.