Journal of Geographical Sciences ›› 2022, Vol. 32 ›› Issue (6): 1076-1102.doi: 10.1007/s11442-022-1986-5
• Research article • Previous Articles Next Articles
SONG Xiaolong1(), MI Nan1,*(
), MI Wenbao1,2, LI Longtang2
Received:
2021-11-17
Accepted:
2022-03-09
Online:
2022-06-25
Published:
2022-08-25
Contact:
MI Nan
E-mail:songxl@stu.nxu.edu.cn;minan@nxu.edu.cn
About author:
Song Xiaolong (1991‒), PhD Candidate, specialized in remote sensing monitoring of grassland resources and research on forage-livestock balance. E-mail: songxl@stu.nxu.edu.cn
Supported by:
SONG Xiaolong, MI Nan, MI Wenbao, LI Longtang. Spatial non-stationary characteristics between grass yield and its influencing factors in the Ningxia temperate grasslands based on a mixed geographically weighted regression model[J].Journal of Geographical Sciences, 2022, 32(6): 1076-1102.
Table 1
Influencing factors and descriptions included in the analysis of grass yield
Variable | Abbreviation | Unit | Variable description |
---|---|---|---|
Intercept | Intercept | g | Intercept term of the model |
Average rainfall in July | AJR | mm | Average July rainfall |
Relative humidity | RH | % | Percentage of water vapor pressure in the air vs. saturated water vapor pressure at the same temperature |
NPP | NPP | gC·m‒2·a‒1 | Net primary production capacity of vegetation, refers to the total amount of organic matter accumulated by photosynthesis in unit area and unit time of green plants, minus the remaining part after autotrophic respiration |
Ratio resident-area index | PRI | - | Proportion of impervious surface in the surface area per unit area: |
Elevation | DEM | m | Altitude of sample point |
Daily temperature range | DTR | ℃ | Difference between maximum and minimum daily temperatures |
Distance from gully | DS | m | Distance from sample point to valley |
Distance from road | DP | m | Distance from sample point to road |
Distance from river | DR | m | Distance from sample point to river |
Night light | NL | - | Night light distribution in the study area |
Figure 3
Spatial distribution of influencing factors across Yuanzhou District (NPP: Net primary production; PRI: Ratio resident-area index; DEM: Elevation; AJR: Average rainfall in July; NL: Night light; DP: Distance from road; DS: Distance from gully; DR: Distance from river; DTR: Daily temperature range; RH: Relative humidity)
Table 3
Comparison of statistical parameters of different linear regression models: ordinary least squares (OLS), geographically weighted regression (GWR), and mixed GWR (MGWR)
Parameter | OLS | GWR | MGWR |
---|---|---|---|
Residual sum of squares | 959.816 | 236.960 | 23.701 |
-2 log-likelihood | 3697.432 | 3138.206 | 2558.904 |
Classic AIC | 3731.432 | 3270.442 | 2159.397 |
AICc | 3731.660 | 3273.816 | 2951.878 |
BIC/MDL | 3831.737 | 3660.555 | 2331.959 |
CV | 5863.375 | 4370.185 | - |
R2 | 0.643 | 0.801 | 0.891 |
Adjusted R2 | 0.642 | 0.797 | 0.889 |
Table 4
Estimates of OLS model parameters
Variable | Coefficient | T-test | Significance (p) |
---|---|---|---|
Intercept | 366.649 | 8.482 | 0.000 |
AJR | 20.505 | 3.867 | 0.000 |
RH | -14.883 | -2.593 | 0.010 |
NPP | -7.321 | -2.305 | 0.021 |
PRI | -6.363 | -2.580 | 0.010 |
DEM | 74.194 | 27.200 | 0.000 |
DTR | -40.112 | -7.649 | 0.000 |
DS | -17.548 | -5.498 | 0.000 |
DP | 3.870 | 1.490 | 0.004 |
DR | 9.427 | 4.379 | 0.000 |
NL | -7.397 | -1.181 | 0.004 |
Table 5
Estimates of GWR model parameters
Variable | Min | Max | Lwr Quartile | Median | Upr Quartile |
---|---|---|---|---|---|
Intercept | 664.366 | 165.834 | 373.563 | 451.646 | |
AJR | -49.684 | 92.181 | -12.087 | 6.980 | 35.114 |
RH | -39.418 | 144.282 | -11.053 | 8.416 | 27.694 |
NPP | -6.232 | 32.852 | 10.403 | 15.679 | 23.962 |
PRI | -7.563 | 2.873 | -3.637 | -2.698 | -1.007 |
DEM | -13.540 | 99.800 | -3.165 | 33.316 | 59.446 |
DTR | -95.718 | 54.511 | -24.421 | -3.836 | 12.769 |
DS | -32.859 | -0.367 | -20.809 | -10.443 | -4.266 |
DP | -38.467 | 41.975 | -7.914 | 0.182 | 8.727 |
DR | -56.344 | 59.046 | -18.597 | -2.029 | 18.442 |
NL | -53.406 | 56.678 | -10.603 | -1.480 | 10.847 |
Table 6
Estimates of MGWR model parameters
Variable | Mean | STD | Min | Median | Max |
---|---|---|---|---|---|
Intercept | 0.827 | 1.170 | -1.119 | 0.817 | 2.834 |
DEM | -0.040 | 0.165 | -0.340 | 0.001 | 0.188 |
NL | 0.456 | 2.361 | -4.255 | 0.002 | 8.176 |
PRI | 0.003 | 0.003 | -0.001 | 0.003 | 0.012 |
NPP | 0.077 | 0.112 | -0.213 | 0.042 | 0.585 |
DS | -0.011 | 0.054 | -0.245 | -0.013 | 0.237 |
DP | -0.073 | 0.156 | -0.415 | -0.054 | 0.301 |
DR | 0.180 | 0.300 | -0.757 | 0.232 | 0.859 |
DTR | -1.227 | 1.316 | -4.145 | -0.715 | 0.561 |
RH | -3.013 | 1.329 | -4.979 | -2.750 | -1.274 |
AJR | 1.687 | 1.313 | -0.267 | 1.498 | 3.938 |
Figure 5
Spatial patterns of regression coefficients for influencing factors of grass yield based on the mixed geographically weighted regression (MGWR) model (a. AJR: Average rainfall in July; b. PRI: Ratio resident-area index; c. DEM: Elevation; d. RH: Relative humidity; e. NL: Night light; f. DP: Distance from road; g. DS: Distance from gully; h. DR: Distance from river; i. DTR: Daily temperature range; j. NPP: Net primary production)
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