Scientia Silvae Sinicae ›› 2020, Vol. 56 ›› Issue (3): 38-47.doi: 10.11707/j.1001-7488.20200305
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Lü Zhou,Guanglong Ou,Junfeng Wang,Hui Xu*
Received:
2019-08-22
Online:
2020-03-25
Published:
2020-04-08
Contact:
Hui Xu
CLC Number:
Lü Zhou,Guanglong Ou,Junfeng Wang,Hui Xu. Light Saturation Point Determination and Biomass Remote Sensing Estimation of Pinus kesiya var. langbianensis Forest Based on Spatial Regression Models[J]. Scientia Silvae Sinicae, 2020, 56(3): 38-47.
Table 1
Remote sensing variables used in the study"
变量Variables | 公式或说明A formula or description |
植被指数Vegetation index | |
NDVI | (B5-B4)/(B5+B4) |
SRI | B5/B4 |
SAVI | 1.5×(B5-B4)/(B5+B4+0.5) |
PVI | 0.939B5-0.344B4+0.9 |
BVI | 0.272 8B2+0.249 3B3+0.480 6B4+0.556 8B5+0.443 8B6+0.170 6B7 |
GVI | -0.272 8B2-0.217 4B3-0.550 8B4+0.722 1B5+0.073 3B6-0.164 8B7 |
WVI | 0.144 6B2+0.176 1B3+0.332 2B4+0.339 6B5-0.621 0B6-0.418 6B7 |
IIVI | (B5-B6)/(B5+B6) |
DVI | B5-B4 |
ARVI | (B4-2B3+B1)/(B4+2B3-B1) |
TVI | |
MVI5 | (B5+B4-B2)/(B5+B4+B2) |
MVI7 | (B5-B7)/(B5+B7) |
Albedo | 0.356B1+0.130B3+0.373B4+0.85B5+0.072B7-0.0018 |
信息增强因子Information enhancement factor | |
KT1 | 0.304B2+0.279B3+0.474B4+0.559B5+0.508B6+0.186B7 |
KT2 | 0.285B2-0.244B3-0.212B4+0.787B5-0.421B6-0.372B7 |
KT3 | 0.151B2+0.197B3+0.328B4+0.341B5-0.711B6-0.457B7 |
PCA1 | 0.135B1-0.043B2+0.356B3-0.014B4+0.561B5+0.287B6+0.677B7 |
PCA2 | 0.163B1-0.062B2+0.392B3-0.019B4+0.470B5+0.237B6-0.733B7 |
PCA3 | 0.217B1-0.048B2+0.448B3-0.015B4+0.048B5-0.863B6+0.045B7 |
PCA4 | 0.290B1-0.149B2+0.563B3-0.024B4-0.677B5+0.339B6+0.055B7 |
PCA5 | 0.032B1+0.934B2+0.025B3+0.143B4-0.032B5+0.038B6-0.004B7 |
PCA6 | 0.667B1-0.122B2-0.361B3-0.639B4+0.035B5-0.001B6+0.006B7 |
PCA7 | 0.523B1-0.288B2-0.268B3+0.755B4+0.037B5-0.003B6+0.002B7 |
Table 2
Correlation analysis on remote sensing variables"
序号 Number | 变量 Variables | 相关性 Correlation |
1 | B1 | -0.470** |
2 | B2 | -0.470** |
3 | B3 | -0.461** |
4 | B4 | -0.424** |
5 | B5 | -0.223** |
6 | B6 | -0.417** |
7 | B7 | -0.395** |
8 | SRI | 0.347** |
9 | GVI | -0.350** |
10 | BVI | -0.321** |
11 | WVI | 0.278** |
12 | PCA1 | -0.432** |
13 | PCA2 | -0.145** |
14 | PCA3 | 0.327** |
15 | PCA4 | -0.173** |
16 | PCA5 | -0.466** |
17 | PCA6 | 0.327** |
18 | PCA7 | -0.459** |
19 | KT1 | -0.447** |
20 | KT2 | 0.416** |
21 | KT3 | 0.247** |
22 | IIVI | 0.307** |
23 | MVI5 | 0.307** |
24 | SAVI | 0.311** |
25 | TVI | 0.351** |
26 | NDVI | 0.351** |
27 | MVI7 | -0.198** |
28 | DVI | 0.027 |
29 | PVI | -0.113** |
30 | Albedo | -0.456** |
31 | ARVI | -0.386** |
Table 4
Model comparison among OLS, SLM and SEM"
参数 Parameter | OLS | SLM | SEM | GWR |
P-LM | — | 0.000 0 | 0.000 0 | — |
LM | — | 50.342 0 | 63.400 9 | — |
P-RLM | — | 0.073 1 | 0.000 0 | — |
RLM | — | 3.212 5 | 16.271 4 | — |
AIC | 4 628.7 | 4 599.7 | 4 596.3 | 4 577.8 |
Moran’s I | 0.094 | 0.007 2 | -0.015 9 | 0.020 |
P | 0.000 | 0.206 | 0.107 | 0.037 |
R2 | 0.236 | 0.288 | 0.294 | 0.373 |
Table 7
Descriptive statistics of the local regression parameters of GWR model"
变量 Variables | 最小值 Min. | 上四分位数 Upper quartile | 中位数 Median | 下四分位数 Lower quartile | 最大值 Max. |
截距Intercept | -45.919 | 110.198 | 129.785 | 140.332 | 163.636 |
SAVI | -0.000 007 | 0.000 002 | 0.000 003 | 0.000 004 | 0.000 008 |
PCA2 | -0.007 04 | 0.005 55 | 0.012 9 | 0.022 0 | 0.052 5 |
B1 | -0.123 | -0.085 3 | -0.067 8 | -0.056 6 | 0.037 5 |
Table 9
Jackknife method test"
模型Models | < 50 t·hm-2 | 50~100 t·hm-2 | >100 t·hm-2 | All | |||||||
ME | MRE | ME | MRE | ME | MRE | ME | MRE | ||||
OLS | -21.515 | -0.394 | 22.734 | 0.314 | 65.653 | 1.467 | 14.002 | 0.247 | |||
SLM | -24.388 | -0.402 | 7.247 | 0.124 | 40.787 | 0.578 | 2.116 | 0.025 | |||
SEM | -24.920 | -0.412 | 7.098 | 0.117 | 42.840 | 0.617 | 2.070 | 0.022 | |||
GWR | -19.755 | -0.335 | 5.275 | 0.105 | 39.862 | 0.556 | 2.172 | 0.031 |
Table 10
Model reversion results for OLS, SLM, SEM and GWR models"
模型 Models | 总生物量 Total biomass/t | 单位面积生物量 Biomass per unit area/(t·hm-2) | 与实测值偏差 Deviation from measured values(%) |
OLS | 8 054 406.752 | 68.521 | 27.272 |
SLM | 8 136 806.883 | 69.222 | 28.575 |
SEM | 7 847 717.995 | 66.763 | 24.007 |
GWR | 7 816 332.771 | 66.496 | 23.511 |
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