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 (R 2 = 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 R 2 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 (SM) is an important indicator of climate and environmental drought, the basis of sustainable and healthy operation of global ecosystems [1], and an important parameter for crop growth monitoring, yield estimation and drought monitoring [2]. Large-scale soil moisture monitoring is an important component of agricultural water management and crop drought monitoring [3]. The region of the Tropic of Cancer in Yunnan Province is characterized by unique geographical conditions, high vegetation coverage, outstanding biodiversity, abundant products (such as Panax notoginseng, purple rice, Punica granatum and Yunnan large leaf tea), and "living fossil" plants (such as Alsophila spinulosa and Manglietiastrum sinicum). The lack of soil moisture has a significant impact on the leaf area and yield of these crops [4]. The quality of crops can be improved and the efficiency of water resource utilization can be effectively increased by regulation
Habitat quality is an important spatial dynamic factor for evaluating the effectiveness of biodiversity conservation. This study aimed to understand the characteristic variation of habitat quality in Shangri-La City, which can provide a basis for decision-making by relevant departments to protect the biodiversity of the area. The spatiotemporal variation of habitat quality of Shangri-La City in 1989-2015 was estimated based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The results showed that: (1) The overall habitat quality presented a decreasing trend from 1989 to 2015, with Jiantang, Naxi, Xiaozhongdian, the northern part of Hutiaoxia and the southern part of Sanba the most significant. (2) The highest value of the average habitat quality of Shangri-La City was located in the middle-elevation regions, while the lowest value was located in the low-elevation areas, and the average habitat quality of the two regions showed a decreasing trend from 1989 to 2015. (3) The average habitat quality of the steep and extremely steep slopes in Shangri-La was higher than that of the slight slopes. This study was useful for biodiversity conservation policy-making for ecological fragile region in China.
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