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.

Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D) Terrain model, which was created using an image fusion digital elevation model (DEM), to select training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications.

Located northeast of Yuxi City, Yunnan Province, China, the Fuxian Lake Basin is not only a famous scenic area, but also an ecological and environmental protection zone. The basin is a plateau mountainous lake region that has key ecosystem vulnerability and sensitivity. A dynamic evaluation of ecological vulnerability is important for assessing the ecological environment of a region, which is significant for the protection of ecological environments and providing ecological security early warnings. Based on remote sensing (RS) and geographic information system (GIS) technology, remote sensing data, meteorological data, and economic statistics were used to perform a vulnerability analysis for the Fuxian Lake Watershed. Using the “cause and effect” evaluation model that relies on the remote sensing image pixel as the basic evaluation unit, the ecological vulnerability and the dynamic changes of the basin were evaluated using the analytic hierarchy process (AHP) method. Data collected in 1974, 1977, 1987, 1990, 1996, 2000, 2006, 2012, and 2015 were analyzed to determine the temporal and spatial distribution characteristics and the dynamic changes over longer time-spans for ecological vulnerability. Afterward, the effects of land use and the consequential changes to ecological vulnerability were studied. The results indicated that the ecological vulnerability showed a trend of “overall stability but local strengthening” in the Fuxian Lake Basin from 1974 to 2015. The annual ecological vulnerability integrated index for the basin was 2.55±0.16, which indicates a transitional state between micro-vulnerability and mild vulnerability. On the basis of maintaining the degree of original vulnerability, the vulnerability of each grade was most commonly converted to the adjacent grade. The degree of ecological vulnerability was more commonly converted toward a more severe grade, and regions were less likely to recover from the severe vulnerability grade. The ecological vulnerability integrated index of the bare land was the highest (3.28), and the ecological environment of bare land was most vulnerable in the Fuxian Lake Basin.

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.

Soil moisture is a direct source of water for terrestrial plants. Measurement of soil moisture can help to characterize plant growth and portray regional soil moisture information in a timely manner, which is of great significance for water and soil regulation. This study uses the Tropic of Cancer in Yunnan Province as a research area, and based on MODIS product data, the apparent thermal inertia (ATI), vegetation supply water index (VSWI) and surface water capacity index (SWCI) are calculated. The measured data are used in regression analysis modeling to retrieve the soil moisture in the study area. The results show that the three indices of ATI, SWCI and VSWI have good correlation with soil moisture, among which VSWI has the best fitting effect, and ATI and SWCI can retrieve soil moisture more effectively. The soil moisture contents are mostly concentrated in the range of 0.2 to 0.4 m3/m3, among which the soil moisture of Jianshui County, Mengzi City, Wenshan County, Yanshan County, Gejiu City and Shiping County are the smallest, and the soil moisture in the western portion of Gengma County and Cangyuan County and the central portion of Jinggu County are the largest. As the fractional vegetation cover increases, the soil moisture content also gradually increases, and the mean value is greater than 0.2 m3/m3.