Journal of Resources and Ecology ›› 2019, Vol. 10 ›› Issue (6): 676-684.DOI: 10.5814/j.issn.1674-764X.2019.06.013
• Resources and Environment • Previous Articles Next Articles
LI Ge1,2, WANG Juanle1,4,*(), WANG Yanjie1,3, WEI Haishuo1,2
Received:
2019-06-03
Accepted:
2019-07-24
Online:
2019-11-30
Published:
2019-12-09
Contact:
WANG Juanle
Supported by:
LI Ge, WANG Juanle, WANG Yanjie, WEI Haishuo. Estimation of Grassland Production in Central and Eastern Mongolia from 2006 to 2015 via Remote Sensing[J]. Journal of Resources and Ecology, 2019, 10(6): 676-684.
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URL: http://www.jorae.cn/EN/10.5814/j.issn.1674-764X.2019.06.013
No. | Latitude (N) | Longitude (E) | Fresh weight of grass (g) |
---|---|---|---|
1 | 47.9721° | 106.5823° | 56.7 |
2 | 47.6294° | 106.9654° | 45.0 |
3 | 47.4334° | 106.9728° | 75.0 |
4 | 47.8030° | 107.5299° | 45.0 |
5 | 47.6933° | 108.5277° | 41.7 |
6 | 47.4661° | 109.5361° | 53.3 |
7 | 47.2799° | 110.8333° | 123.3 |
8 | 47.3462° | 111.5222° | 100.0 |
9 | 46.3005° | 108.4715° | 33.3 |
10 | 46.6880° | 108.0976° | 38.3 |
11 | 47.7360° | 106.8914° | 48.0 |
12 | 47.6222° | 106.8917° | 29.3 |
13 | 47.3096° | 106.6711° | 32.7 |
14 | 46.9239° | 106.6290° | 52.7 |
15 | 46.5641° | 106.5309° | 27.3 |
16 | 46.2120° | 106.4173° | 26.7 |
17 | 45.8025° | 106.3062° | 10.0 |
18 | 45.4868° | 106.7585° | 8.7 |
19 | 45.1543° | 105.5664° | 7.3 |
20 | 44.7918° | 105.5927° | 4.0 |
21 | 45.4658° | 101.1738° | 15.3 |
22 | 45.7006° | 101.3769° | 21.3 |
23 | 45.8073° | 101.7643° | 59.3 |
24 | 45.9798° | 102.1687° | 34.0 |
25 | 46.1417° | 102.6169° | 38.0 |
26 | 46.5678° | 103.0513° | 71.3 |
27 | 46.8410° | 103.3977° | 21.3 |
28 | 47.1718° | 103.6255° | 22.0 |
29 | 47.5747° | 104.7149° | 37.3 |
No. | Latitude (N) | Longitude (E) | Fresh weight of grass (g) |
---|---|---|---|
1 | 47.9721° | 106.5823° | 56.7 |
2 | 47.6294° | 106.9654° | 45.0 |
3 | 47.4334° | 106.9728° | 75.0 |
4 | 47.8030° | 107.5299° | 45.0 |
5 | 47.6933° | 108.5277° | 41.7 |
6 | 47.4661° | 109.5361° | 53.3 |
7 | 47.2799° | 110.8333° | 123.3 |
8 | 47.3462° | 111.5222° | 100.0 |
9 | 46.3005° | 108.4715° | 33.3 |
10 | 46.6880° | 108.0976° | 38.3 |
11 | 47.7360° | 106.8914° | 48.0 |
12 | 47.6222° | 106.8917° | 29.3 |
13 | 47.3096° | 106.6711° | 32.7 |
14 | 46.9239° | 106.6290° | 52.7 |
15 | 46.5641° | 106.5309° | 27.3 |
16 | 46.2120° | 106.4173° | 26.7 |
17 | 45.8025° | 106.3062° | 10.0 |
18 | 45.4868° | 106.7585° | 8.7 |
19 | 45.1543° | 105.5664° | 7.3 |
20 | 44.7918° | 105.5927° | 4.0 |
21 | 45.4658° | 101.1738° | 15.3 |
22 | 45.7006° | 101.3769° | 21.3 |
23 | 45.8073° | 101.7643° | 59.3 |
24 | 45.9798° | 102.1687° | 34.0 |
25 | 46.1417° | 102.6169° | 38.0 |
26 | 46.5678° | 103.0513° | 71.3 |
27 | 46.8410° | 103.3977° | 21.3 |
28 | 47.1718° | 103.6255° | 22.0 |
29 | 47.5747° | 104.7149° | 37.3 |
Element | Precipitation | Temperature | DEM | EVI | MSAVI | NDVI | PsnNet |
---|---|---|---|---|---|---|---|
Correlation coefficient | 0.645** | -0.477* | 0.108 | 0.762** | 0.804** | 0.798** | 0.856** |
P value | 0.001 | 0.022 | 0.625 | 0.000 | 0.000 | 0.000 | 0.000 |
Element | Precipitation | Temperature | DEM | EVI | MSAVI | NDVI | PsnNet |
---|---|---|---|---|---|---|---|
Correlation coefficient | 0.645** | -0.477* | 0.108 | 0.762** | 0.804** | 0.798** | 0.856** |
P value | 0.001 | 0.022 | 0.625 | 0.000 | 0.000 | 0.000 | 0.000 |
Parameter | Model type | Inversion model | R2 | Sig. | RMSE (kg ha-1) | Accuracy (%) |
---|---|---|---|---|---|---|
EVI | Linear model | Y = -19.490+384.791X1 | 0.46 | 0.000 | 423.55 | 66 |
Exponential model | Y = 4.257exp(12.647X1) | 0.63 | 0.000 | 466.53 | 63 | |
Multivariate model | Y = -16.655+436.870X1 - 0.049X | 0.47 | 0.002 | 423.91 | 66 | |
MSAVI | Linear model | Y = -27.370+165.323X2 | 0.57 | 0.000 | 370.00 | 71 |
Exponential model | Y = 3.594exp(5.224X2) | 0.72 | 0.000 | 279.09 | 78 | |
Multivariate model | Y = -18.070+239.539X2 - 0.177X | 0.61 | 0.000 | 383.40 | 70 | |
NDVI | Linear model | Y = -13.940+204.158X3 | 0.56 | 0.000 | 335.37 | 73 |
Exponential model | Y = 5.728exp(6.300X3) | 0.68 | 0.000 | 306.68 | 76 | |
Multivariate model | Y = -3.192+264.166X3 - 0.119X | 0.59 | 0.000 | 308.76 | 75 | |
MOD17A2H PsnNet | Linear model | Y = 0.482+0.403X4 | 0.68 | 0.000 | 389.21 | 69 |
Exponential model | Y = 12.701exp(0.010X4) | 0.66 | 0.000 | 322.82 | 74 | |
Multivariate model | Y = 16.944+0.481X4 - 0.106X | 0.70 | 0.000 | 375.42 | 70 |
Parameter | Model type | Inversion model | R2 | Sig. | RMSE (kg ha-1) | Accuracy (%) |
---|---|---|---|---|---|---|
EVI | Linear model | Y = -19.490+384.791X1 | 0.46 | 0.000 | 423.55 | 66 |
Exponential model | Y = 4.257exp(12.647X1) | 0.63 | 0.000 | 466.53 | 63 | |
Multivariate model | Y = -16.655+436.870X1 - 0.049X | 0.47 | 0.002 | 423.91 | 66 | |
MSAVI | Linear model | Y = -27.370+165.323X2 | 0.57 | 0.000 | 370.00 | 71 |
Exponential model | Y = 3.594exp(5.224X2) | 0.72 | 0.000 | 279.09 | 78 | |
Multivariate model | Y = -18.070+239.539X2 - 0.177X | 0.61 | 0.000 | 383.40 | 70 | |
NDVI | Linear model | Y = -13.940+204.158X3 | 0.56 | 0.000 | 335.37 | 73 |
Exponential model | Y = 5.728exp(6.300X3) | 0.68 | 0.000 | 306.68 | 76 | |
Multivariate model | Y = -3.192+264.166X3 - 0.119X | 0.59 | 0.000 | 308.76 | 75 | |
MOD17A2H PsnNet | Linear model | Y = 0.482+0.403X4 | 0.68 | 0.000 | 389.21 | 69 |
Exponential model | Y = 12.701exp(0.010X4) | 0.66 | 0.000 | 322.82 | 74 | |
Multivariate model | Y = 16.944+0.481X4 - 0.106X | 0.70 | 0.000 | 375.42 | 70 |
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