Acta Prataculturae Sinica ›› 2022, Vol. 31 ›› Issue (6): 23-34.DOI: 10.11686/cyxb2021180
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Yu-zhuo ZHANG1(), Zhi-gui YANG1, Hong-yan YU2, Qiang ZHANG3, Shu-xia YANG3, Ting ZHAO1, Hua-hua XU1, Bao-ping MENG1(), Yan-yan LV1
Received:
2021-05-07
Revised:
2021-06-21
Online:
2022-06-20
Published:
2022-05-11
Contact:
Bao-ping MENG
Yu-zhuo ZHANG, Zhi-gui YANG, Hong-yan YU, Qiang ZHANG, Shu-xia YANG, Ting ZHAO, Hua-hua XU, Bao-ping MENG, Yan-yan LV. Estimating grassland above ground biomass based on the STARFM algorithm and remote sensing data——A case study in the Sangke grassland in Xiahe County, Gansu Province[J]. Acta Prataculturae Sinica, 2022, 31(6): 23-34.
MODIS影像日期 Date of MODIS image | Landsat影像日期 Date of Landsat image | 采样时间 Sampling time | 样地数 Number of plots | 样方数 Number of samples |
---|---|---|---|---|
2013-08-13 | 2013-08-08 | 2013-08-08 | 11 | 55 |
2014-07-28 | 2014-07-26 | 2014-07-28 | 13 | 65 |
2015-07-12 | 2015-07-13 | 2015-07-13 | 13 | 65 |
2016-07-27 | 2016-07-31 | 2016-07-26 | 11 | 55 |
Table 1 Date remote sensing image and field survey
MODIS影像日期 Date of MODIS image | Landsat影像日期 Date of Landsat image | 采样时间 Sampling time | 样地数 Number of plots | 样方数 Number of samples |
---|---|---|---|---|
2013-08-13 | 2013-08-08 | 2013-08-08 | 11 | 55 |
2014-07-28 | 2014-07-26 | 2014-07-28 | 13 | 65 |
2015-07-12 | 2015-07-13 | 2015-07-13 | 13 | 65 |
2016-07-27 | 2016-07-31 | 2016-07-26 | 11 | 55 |
植被指数和生物量Vegetation index and biomass | 最大值Max | 最小值Min | 平均值Mean | 标准偏差STD | 变异系数CV |
---|---|---|---|---|---|
NDVIMODIS | 0.84 | 0.58 | 0.74 | 0.08 | 0.15 |
NDVILandsat | 0.91 | 0.55 | 0.77 | 0.10 | 0.13 |
NDVISTARFM | 0.95 | 0.47 | 0.72 | 0.11 | 0.17 |
生物量Biomass (kg·hm-2) | 3997.33 | 745.52 | 2299.30 | 972.60 | 0.42 |
Table 2 Statistical description of vegetation index and grassland biomass in the study area
植被指数和生物量Vegetation index and biomass | 最大值Max | 最小值Min | 平均值Mean | 标准偏差STD | 变异系数CV |
---|---|---|---|---|---|
NDVIMODIS | 0.84 | 0.58 | 0.74 | 0.08 | 0.15 |
NDVILandsat | 0.91 | 0.55 | 0.77 | 0.10 | 0.13 |
NDVISTARFM | 0.95 | 0.47 | 0.72 | 0.11 | 0.17 |
生物量Biomass (kg·hm-2) | 3997.33 | 745.52 | 2299.30 | 972.60 | 0.42 |
植被指数 Vegetation index | 线性Linear | 对数Logarithmic | 乘幂Power | 指数Exponential | ||||
---|---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | R2 | RMSE | |
NDVIMODIS | 0.17 | 917.21 | 0.17 | 916.80 | 0.24 | 937.79 | 0.24 | 940.72 |
NDVILandsat | 0.65 | 586.72 | 0.66 | 576.76 | 0.76 | 634.83 | 0.75 | 665.32 |
NDVISTARFM | 0.49 | 714.93 | 0.52 | 690.97 | 0.58 | 795.62 | 0.54 | 847.99 |
Table 3 Accuracy validation of biomass regression models in study area
植被指数 Vegetation index | 线性Linear | 对数Logarithmic | 乘幂Power | 指数Exponential | ||||
---|---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | R2 | RMSE | |
NDVIMODIS | 0.17 | 917.21 | 0.17 | 916.80 | 0.24 | 937.79 | 0.24 | 940.72 |
NDVILandsat | 0.65 | 586.72 | 0.66 | 576.76 | 0.76 | 634.83 | 0.75 | 665.32 |
NDVISTARFM | 0.49 | 714.93 | 0.52 | 690.97 | 0.58 | 795.62 | 0.54 | 847.99 |
植被指数 Vegetation index | 参数估计和T检验Parameter estimation and T test | 回归显著性检验Regression significance test | |||
---|---|---|---|---|---|
参数Parameter | 估计值Estimated value | T | R2 | F | |
NDVIMODIS | A B | 2.64 4561.14 | 3.81 4.55 | 0.24 | 14.52** |
NDVILandsat | A B | 3.37 5031.89 | 12.13 12.09 | 0.76 | 147.19** |
NDVISTARFM | A B | 2.49 4812.23 | 7.92 8.44 | 0.58 | 62.75** |
Table 4 T test and F test for optimal inversion model
植被指数 Vegetation index | 参数估计和T检验Parameter estimation and T test | 回归显著性检验Regression significance test | |||
---|---|---|---|---|---|
参数Parameter | 估计值Estimated value | T | R2 | F | |
NDVIMODIS | A B | 2.64 4561.14 | 3.81 4.55 | 0.24 | 14.52** |
NDVILandsat | A B | 3.37 5031.89 | 12.13 12.09 | 0.76 | 147.19** |
NDVISTARFM | A B | 2.49 4812.23 | 7.92 8.44 | 0.58 | 62.75** |
植被指数 Vegetation index | 模型 Model | 模型公式 Formulas | R2 | RMSE (kg·hm-2) |
---|---|---|---|---|
NDVIMODIS | 乘幂Power | y=114.03x2.6373 | 0.24 | 937.79 |
NDVILandsat | 乘幂Power | y=125.80x3.3683 | 0.76 | 634.83 |
NDVISTARFM | 乘幂Power | y=120.31x2.4920 | 0.58 | 795.62 |
Table 5 The optimal AGB estimation model based on NDVILandsat, NDVIMODIS and NDVISTARFM
植被指数 Vegetation index | 模型 Model | 模型公式 Formulas | R2 | RMSE (kg·hm-2) |
---|---|---|---|---|
NDVIMODIS | 乘幂Power | y=114.03x2.6373 | 0.24 | 937.79 |
NDVILandsat | 乘幂Power | y=125.80x3.3683 | 0.76 | 634.83 |
NDVISTARFM | 乘幂Power | y=120.31x2.4920 | 0.58 | 795.62 |
样区 Plot | 指标 Norm | 植被指数 Vegetation index | ||
---|---|---|---|---|
NDVIMODIS | NDVILandsat | NDVISTARFM | ||
1号样区Plot No.1 | 绝对误差Absolute error (×104 kg) | 2.99 | 2.25 | 1.91 |
相对误差Relative error (%) | 45.96 | 34.67 | 29.37 | |
2号样区Plot No.2 | 绝对误差Absolute error (×104 kg) | 1.55 | 1.06 | 0.43 |
相对误差Relative error (%) | 39.78 | 27.28 | 11.04 | |
3号样区Plot No.3 | 绝对误差Absolute error (×104 kg) | 0.75 | 0.51 | 0.20 |
相对误差Relative error (%) | 36.85 | 25.29 | 11.04 | |
4号样区Plot No.4 | 绝对误差Absolute error (×104 kg) | 1.35 | 0.65 | 0.77 |
相对误差Relative error (%) | 24.88 | 12.01 | 9.95 | |
5号样区Plot No.5 | 绝对误差Absolute error (×104 kg) | 3.26 | 2.61 | 4.25 |
相对误差Relative error (%) | 12.21 | 9.80 | 14.23 | |
研究区All study area | 绝对误差Absolute error (×104 kg) | 9.89 | 3.45 | 5.75 |
相对误差Relative error (%) | 22.22 | 7.75 | 15.95 |
Table 6 Accuracy evaluation of the inversion biomass based on NDVILandsat, NDVIMODIS and NDVISTARFM
样区 Plot | 指标 Norm | 植被指数 Vegetation index | ||
---|---|---|---|---|
NDVIMODIS | NDVILandsat | NDVISTARFM | ||
1号样区Plot No.1 | 绝对误差Absolute error (×104 kg) | 2.99 | 2.25 | 1.91 |
相对误差Relative error (%) | 45.96 | 34.67 | 29.37 | |
2号样区Plot No.2 | 绝对误差Absolute error (×104 kg) | 1.55 | 1.06 | 0.43 |
相对误差Relative error (%) | 39.78 | 27.28 | 11.04 | |
3号样区Plot No.3 | 绝对误差Absolute error (×104 kg) | 0.75 | 0.51 | 0.20 |
相对误差Relative error (%) | 36.85 | 25.29 | 11.04 | |
4号样区Plot No.4 | 绝对误差Absolute error (×104 kg) | 1.35 | 0.65 | 0.77 |
相对误差Relative error (%) | 24.88 | 12.01 | 9.95 | |
5号样区Plot No.5 | 绝对误差Absolute error (×104 kg) | 3.26 | 2.61 | 4.25 |
相对误差Relative error (%) | 12.21 | 9.80 | 14.23 | |
研究区All study area | 绝对误差Absolute error (×104 kg) | 9.89 | 3.45 | 5.75 |
相对误差Relative error (%) | 22.22 | 7.75 | 15.95 |
年份 Year | 研究区The study area | ||||||
---|---|---|---|---|---|---|---|
指标Index | 1 | 2 | 3 | 4 | 5 | 整体All | |
2013 | MEAN | 3508.00 | 2062.67 | 1876.00 | 3249.78 | 2762.93 | 2876.56 |
STD | 750.24 | 371.12 | 332.65 | 573.77 | 675.20 | 777.49 | |
CV | 0.21 | 0.18 | 0.18 | 0.18 | 0.24 | 0.27 | |
2014 | MEAN | 3318.10 | 2415.50 | 2034.30 | 2123.85 | 2498.04 | 2524.62 |
STD | 1279.95 | 771.33 | 383.13 | 494.95 | 576.57 | 1398.16 | |
CV | 0.39 | 0.32 | 0.19 | 0.23 | 0.23 | 0.55 | |
2015 | MEAN | 2096.56 | 2236.48 | 2262.96 | 2370.00 | 2285.45 | 2263.90 |
STD | 168.35 | 146.23 | 39.13 | 205.07 | 300.85 | 268.03 | |
CV | 0.08 | 0.07 | 0.02 | 0.09 | 0.13 | 0.12 | |
2016 | MEAN | 3187.20 | 3436.00 | 3326.40 | 3158.40 | 2874.80 | 3049.45 |
STD | 1058.61 | 430.28 | 780.75 | 709.26 | 703.17 | 768.73 | |
CV | 0.33 | 0.13 | 0.23 | 0.22 | 0.24 | 0.25 |
Table 7 Statistical analysis of measured data in each year
年份 Year | 研究区The study area | ||||||
---|---|---|---|---|---|---|---|
指标Index | 1 | 2 | 3 | 4 | 5 | 整体All | |
2013 | MEAN | 3508.00 | 2062.67 | 1876.00 | 3249.78 | 2762.93 | 2876.56 |
STD | 750.24 | 371.12 | 332.65 | 573.77 | 675.20 | 777.49 | |
CV | 0.21 | 0.18 | 0.18 | 0.18 | 0.24 | 0.27 | |
2014 | MEAN | 3318.10 | 2415.50 | 2034.30 | 2123.85 | 2498.04 | 2524.62 |
STD | 1279.95 | 771.33 | 383.13 | 494.95 | 576.57 | 1398.16 | |
CV | 0.39 | 0.32 | 0.19 | 0.23 | 0.23 | 0.55 | |
2015 | MEAN | 2096.56 | 2236.48 | 2262.96 | 2370.00 | 2285.45 | 2263.90 |
STD | 168.35 | 146.23 | 39.13 | 205.07 | 300.85 | 268.03 | |
CV | 0.08 | 0.07 | 0.02 | 0.09 | 0.13 | 0.12 | |
2016 | MEAN | 3187.20 | 3436.00 | 3326.40 | 3158.40 | 2874.80 | 3049.45 |
STD | 1058.61 | 430.28 | 780.75 | 709.26 | 703.17 | 768.73 | |
CV | 0.33 | 0.13 | 0.23 | 0.22 | 0.24 | 0.25 |
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