Abundant and refined structural information under forest canopy can be obtained by using terrestrial laser scanning (TLS) technology. This study explores the methods of using TLS to obtain point cloud data and estimate individual tree height and diameter at breast height (DBH) at plot level in regions with complex terrain. Octree segmentation, connected component labeling and random Hough transform (RHT) are comprehensively used to identify trunks and extract DBH of trees in sample plots, and tree height is extracted based on the growth direction of the trees. The results show that the topography, undergrowth shrubs, and forest density influence the scanning range of the plots and the accuracy of feature extraction. There are differences in the accuracy of the results for different morphological forest species. The extraction accuracy of Yunnan pine forest is the highest (DBH: Root Mean Square Error (RMSE) = 1.17 cm, Tree Height: RMSE = 0.54 m), and that of Quercus semecarpifolia Sm. forest is the lowest (DBH: RMSE = 1.22 cm, Tree Height: RMSE = 1.23 m). At plot scale, with the increase of the mean DBH or tree height in plots, the estimation errors show slight increases, and both DBH and height tend to be underestimated.

Grassland resources are important land resources. However, grassland degradation has become evident in recent years, which has reduced the function of soil and water conservation and restricted the development of animal husbandry. Timely and accurate monitoring of grassland changes and understanding the degree of degradation are the foundation for the scientific use of grasslands. The grassland degradation index of ground comprehensive evaluation (grassland degradation index, GDI_g) is a digital expression of grassland growth that can accurately indicate the degradation of grasslands. In this research, the accuracy of GDI_g in evaluating grassland degradation is discussed; the typical areas of grassland degradation in Shangri-La City, i.e., the towns of Jiantang and Xiaozhongdian, are selected as the research area. Through a field survey and spectroscopy combined with Huanjing-1 (HJ-1) satellite image data, grassland degradation was monitored in the study area from 2008 to 2017. The results show that: (1) GDI_g based on six indicators, namely, above-ground biomass, cover level, height, biomass of edible herbage, biomass of toxic weeds, and species richness, can effectively indicate grassland degradation, with the accuracy of the degradation grade assessment reaching 98.6%. (2) The correlation between the GDI_g and the grey values of 4 wavebands and 7 types of vegetation indexes derived from the HJ-1 is analysed, and the degraded grassland inversion model was built and revised based on HJ-1 data. The grassland degradation evaluation index of remote sensing (GDI_rs) model indicates that grassland degradation is proportional to the ratio vegetation index (RVI). (3) The grassland area was 405.40 km² in the initial monitoring period, accounting for 17.26% of the study area, while at the end of the monitoring period, the area was 338.87 km², with a loss of 66.53 km². From 2008 to 2017, the area of non-degraded and slightly degraded grassland in the study area presented a downward trend, with decreases of 59.87 km² and 49.93 km², respectively. In contrast, the area of moderately degraded grassland increased by 41.17 km² from 91.58 km² in 2008 to 132.74 km² in 2017, accounting for 39.17% of the grassland. The area of severely degraded grassland was 78.32 km², accounting for 23.11% of the grassland in 2017. (4) The degraded grasslands in the study area mainly transformed into the degradation-enhanced (deterioration) type. As the transformation rate gradually slows down, the current situation of grassland degradation is not hopeful.

Soil organic matter (SOM) is an important index to evaluate soil fertility and soil quality, while playing an important role in the terrestrial carbon cycle. The technology of hyperspectral remote sensing is an important method to estimate SOM content efficiently and accurately. This study researched the best hyperspectral estimation model for SOM content in Shangri-La forest soil. The spectral reflectance of soils with sizes of 2 mm, 1 mm, 0.50 mm, and 0.25 mm were measured indoors. After smoothing and de-noising, the reciprocal reflectance (RR), logarithmic reflectance (LR), first-derivative reflectance (FR), reciprocal first-derivative reflectance (RFR), logarithmic first-derivative reflectance (LFR), and mathematical transformations of the original spectral reflectance (REF) were carried out to analyze the relevance of spectral reflectance and SOM content and extract the characteristic bands. Finally the simple linear regression (SLR), multiple stepwise linear regression (SMLR), and partial least squares regression (PLSR) models for SOM content estimation were established. The results showed that: (1) With the decrease of soil particle size, the spectral reflectance increased. The smaller the soil particle sizes, the more obvious was the increase in spectral reflectance. (2) The sensitive bands of SOM were mainly in the 580–690 nm range (correlation coefficient (R) > 0.6, p-value (p) < 0.01), and the spectral information of SOM could be significantly enhanced by first-order differential transformation. (3) Comparing the three models, PLSR had better estimation ability than SMLR and SLR. The precision of the 0.25 mm soil particle size and the LFR index in the PLSR estimation model of SOM content was the best (coefficient of determination of validation (R_v²) = 0.91, root mean square error of validation (RMSE_v) = 13.41, the ratio of percent deviation (RPD) = 3.33). The results provide a basis for monitoring SOM content rapidly in the forests of Northwest Yunnan, and provide a reference for forest SOM estimation in other areas.

Leaf Area Index (LAI) is an important index that reflects the growth status of forest vegetation and land surface processes. It is of important practical significance to quantitatively and accurately estimate Leaf Area Index. We used the Landsat-8 operational land imager single-band images, and 15 vegetation indices that were extracted from the multi-band were combined with the LAI data measured from the CI-110 canopy digital imager to establish the LAI estimation model. Through the leave-one-out cross-validation method, the accuracy of various model estimation results was verified and compared, and the optimal estimation model was obtained to generate the LAI distribution map of Shangri-La City. The results show that: (1) the multivariable model method is better than the single-variable model method when estimating LAI, and its determination coefficient is the highest (R² = 0.7903). (2) The full-sample dataset is divided into Alpine Pine forest, Oak forest, Spruce–fir forest, and Yunnan Pine forest for analysis. The coefficient of determination of the model simulation is improved to varying degrees, and the highest R² increased by 0.1652, 0.1040, 0.1264, and 0.0079, respectively, over the full-sample. The corresponding best models are LAI–DVI (Difference Vegetation Index), LAI–NNIR (normalized near-infrared), LAI–NMDI (Normalized Multi-band Drought Index), and LAI–RVI (Ratio Vegetation Index). (3) The LAI values in Shangri-La City ranged from 0.9654 to 5.5145 and are mainly concentrated in high vegetation coverage areas; and the higher the vegetation coverage level, the higher the LAI value.

Cropland slope structure is a key factor influencing agricultural sustainability and ecological risk, especially in topographically complex regions. This study proposes a novel framework that integrates slope spectrum analysis with H3 hexagonal grid partitioning to examine the spatiotemporal dynamics of cropland slope across China from 1990 to 2023. Using 30 m CLCD land cover data, we derived key indicators, including the T-value, upper slope limit (ULS), peak area proportion (PaP), slope at maximum area (SMA), and cropland slope change index (CSCI). This grid-based, multi-indicator approach enables the fine-scale detection of slope structure transitions. Results show that the average slope of cropland fluctuated at around 4.12°, peaking at 4.18° in 2003, while the ULS remained stable at 17°, with 95% of cropland below this threshold. Regionally, cropland in southwest and northwest China was concentrated on steeper slopes (ULS > 26°, PaP < 10%), whereas flatter areas in north and south China had cropland mainly below 15°. From 1990 to 2023, upslope expansion was evident in south China (CSCI > 10), while downslope shifts aligned with high-slope cropland in the western regions. Geographically weighted regression revealed significant positive correlations between increasing ULS and CSCI and elevated cropland fragmentation and soil erosion in hilly areas. These findings highlight the ecological risks of cropland expansion into steep terrain. The proposed framework offers a spatially explicit perspective of cropland slope evolution and supports targeted strategies for land management and ecological restoration.

Rapid urbanization and stringent ecological protection policies in China have reshaped spatial competition among urban, agricultural, and ecological spaces. However, existing studies often overlook how this competition evolves across different slope structures. To address this, this study establishes a fine-scale analytical framework using H3 hexagonal grids and slope spectrum analysis to investigate slope structure evolution and spatial competition patterns from 1990 to 2023. The results reveal a distinct topographic stratification: urban space dominates low-slope regions (15°) but faces encroachment in transition zones. Furthermore, cluster analysis identifies significant regional heterogeneity aligned with China’s macro-topography, including “low-slope agglomeration” in the Eastern Plains, “interwoven upslope” patterns in the Southern Hilly Regions, and ecological dominance in the Western Highlands. Association analysis using GeoDetector and Multiscale Geographically Weighted Regression (MGWR) indicates that competition intensity is most strongly associated with human activity factors, especially human footprint and nighttime lights ($q>0.29$), which show the highest explanatory power among the examined factor groups. The interaction between human activity and elevation further shows relatively high explanatory power ($q=0.41$), suggesting that spatial competition is more pronounced where intensive human activities overlap with topographic constraints. Crucially, this study challenges the traditional flat-projection planning model. We propose a transition to “three-dimensional topographic regulation,” advocating differentiated management strategies—such as strict “slope redlines” for urban-agricultural transition zones—to mitigate intensifying spatial conflicts in complex terrains and safeguard agricultural sustainability.