• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 2, 530 (2022)
Teng NIU1、1; 3;, Jie LU1、1; 2; *;, Jia-xin YU4、4;, Ying-da WU5、5;, Qian-qian LONG3、3;, and Qiang YU3、3;
Author Affiliations
  • 11. Institute of Tibet Plateau Ecology, Tibet Agriculture & Animal Husbandry University, Linzhi 860000, China
  • 33. Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China
  • 44. Hutuohe State-Owned Forest Farm of Shijiazhuang Forestry Bureau, Shijiazhuang 050000, China
  • 55. Forest and Grassland Fire Fighting Research of China Fire and Rescue Institute, Beijing 102202, China
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    DOI: 10.3964/j.issn.1000-0593(2022)02-0530-07 Cite this Article
    Teng NIU, Jie LU, Jia-xin YU, Ying-da WU, Qian-qian LONG, Qiang YU. Research on Inversion of Water Conservation Distribution of Forest Ecosystem in Alpine Mountain Based on Spectral Features[J]. Spectroscopy and Spectral Analysis, 2022, 42(2): 530 Copy Citation Text show less
    Vegetation type and distribution location of sampling points
    Fig. 1. Vegetation type and distribution location of sampling points
    Schematic diagram of spectrometer measuring leaf spectrum
    Fig. 2. Schematic diagram of spectrometer measuring leaf spectrum
    Reflectance spectrum curve of different vegetation leaves
    Fig. 3. Reflectance spectrum curve of different vegetation leaves
    First derivative spectral curve of different vegetation types(a): First derivative spectral curve;(b): Reflectance spectrum trilateral parameters
    Fig. 4. First derivative spectral curve of different vegetation types
    (a): First derivative spectral curve;(b): Reflectance spectrum trilateral parameters
    Distribution of water conservation of forest ecosystem in Bayi district
    Fig. 5. Distribution of water conservation of forest ecosystem in Bayi district
    Accuracy verification of water conservation forecast model(a): Verification of picea likiangensis var. linzhiensis inversion accuracy; (b): Verification of Pimus densata inversion accuracy;(c): Verification of Quercus aquifolioides inversion accuracy; (d): Verification of Rhododendron nivale inversion accuracy
    Fig. 6. Accuracy verification of water conservation forecast model
    (a): Verification of picea likiangensis var. linzhiensis inversion accuracy; (b): Verification of Pimus densata inversion accuracy;(c): Verification of Quercus aquifolioides inversion accuracy; (d): Verification of Rhododendron nivale inversion accuracy
    植被类型林冠层
    截流量
    枯落物
    持水量
    土壤
    含水量
    水源涵
    养总量
    雪层杜鹃13.54±1.3513.51±2.39158.69±10.21185.74±12.21
    林芝云杉54.72±3.2115.36±1.22309.33±28.97379.41±21.65
    高山松49.86±2.5317.74±2.31287.61±15.88355.21±25.36
    高山栎30.33±5.6823.69±1.11450.97±31.74504.99±42.11
    Table 1. Vegetation water conservation(t·hm-2)
    参数描述雪层
    杜鹃
    林芝
    云杉
    高山松高山栎
    R400紫光波段0.3590.2460.3550.329
    R440蓝光波段0.2430.2370.2980.267
    R480青光波段0.3410.3390.3560.374
    R540绿光波段, 可见光绿峰0.3570.457*0.442*0.449*
    R580黄光波段0.3890.3910.3610.312
    R600橙光波段0.2770.3240.3390.341
    R700红光波段0.1670.2580.220.303
    SDr红边0.2930.2870.2580.212
    SDy黄边0.3330.3680.3050.357
    SDb蓝边0.3160.2890.2260.303
    R1 450近红外波段水吸收带-0.387-0.355-0.213-0.372
    R1 950中红外波段水吸收带-0.454*-0.488*-0.571*-0.488*
    R3 450中红外波段水吸收带-0.332-0.371-0.225-0.301
    NDWI归一化水体指数0.567*0.49*0.46*0.467*
    NDVI归一化植被指数0.487*0.471*0.459*0.466*
    LWI叶面水含量指数0.3120.3650.3390.353
    Table 2. Correlation between vegetation reflectance spectrum and water conservation
    植被类型拟合模型
    林芝云杉W=981.24R540-1 457.66R1 950+792.29NDWI+506.25NDVI-502.38
    高山松W=803.77R540-1 287.65R1 950+578.92NDWI+743.11NDVI-556.67
    高山栎W=1 245.75R540-530.45R1 950+638.21NDWI+744.21NDVI-634.77
    雪层杜鹃W=-878.66R1 950+754.37NDWI+375.92NDVI-366.86
    Table 3. Prediction model for water conservation capacity of different vegetation
    Teng NIU, Jie LU, Jia-xin YU, Ying-da WU, Qian-qian LONG, Qiang YU. Research on Inversion of Water Conservation Distribution of Forest Ecosystem in Alpine Mountain Based on Spectral Features[J]. Spectroscopy and Spectral Analysis, 2022, 42(2): 530
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