草业学报 ›› 2022, Vol. 31 ›› Issue (6): 23-34.DOI: 10.11686/cyxb2021180
张玉琢1(), 杨志贵1, 于红妍2, 张强3, 杨淑霞3, 赵婷1, 许画画1, 孟宝平1(), 吕燕燕1
收稿日期:
2021-05-07
修回日期:
2021-06-21
出版日期:
2022-06-20
发布日期:
2022-05-11
通讯作者:
孟宝平
作者简介:
E-mail: mengbp09@lzu.edu.cn基金资助:
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
摘要:
遥感数据具有实时、动态、大范围等特点,在草地资源监测与管理研究中获得了广泛应用。然而,单一的遥感植被指数无法同时满足草地地上生物量观测中时空分辨率的需求。因此,本研究基于时间序列Landsat NDVI和MODIS NDVI数据,结合时空融合算法(spatial and temporal adaptive reflectance fusion model, STARFM),生成了2000-2016年高时空分辨率的植被指数数据集(NDVISTARFM,时间分辨率为16 d,空间分辨率为30 m),并基于2013-2016年地面实测草地地上生物量数据,构建了夏河县桑科草原高寒草地地上生物量遥感反演模型,分析了2000-2016年研究区草地地上生物量生长状况和变化趋势。结果表明:1)基于NDVISTARFM的最优估测模型为乘幂模型,其R2为0.58,均方根误差(root mean square error, RMSE)为795.62 kg·hm-2,模型的表现能力次于Landsat NDVI最优估测模型(R2=0.76,RMSE=634.83 kg·hm-2),而优于MODIS NDVI最优估测模型(R2=0.24,RMSE=937.79 kg·hm-2);2)基于NDVISTARFM最优估测模型对各样区草地地上生物量总产的估测精度优于MODIS NDVI而次于Landsat NDVI,总体精度达84.05%;3)2000-2016年来,夏河县研究区草地地上生物量总体呈现增加趋势,其中90%左右的区域年增量大于30 kg·hm-2,草地地上生物量呈现减少趋势的区域仅占2.30%。
张玉琢, 杨志贵, 于红妍, 张强, 杨淑霞, 赵婷, 许画画, 孟宝平, 吕燕燕. 基于STARFM的草地地上生物量遥感估测研究——以甘肃省夏河县桑科草原为例[J]. 草业学报, 2022, 31(6): 23-34.
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.
图1 研究区概况a为研究区位置;b为草地AGB采样点分布;c、d和e分别为研究区2013年8月8日Landsat 8 OLI真彩色合成图像以及Landsat 8 OLI和MOD13Q1 NDVI植被指数。a is the location of the study area; b is the distribution of AGB sampling sites in grassland; c, d and e are the real-color composite images of Landsat 8 OLI, NDVI of Landsat 8 OLI and MOD13Q1 in the study area on August 8, 2013.
Fig.1 Overview of the study area
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 |
表1 研究区遥感影像及外业调查时间
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 |
表2 研究区植被指数和草地生物量统计性描述
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 |
表3 研究区草地生物量回归模型精度检验
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** |
表4 最优反演模型参数T检验和回归显著性F检验
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 |
表5 基于NDVILandsat、NDVIMODIS和NDVISTARFM最优AGB估测模型
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 |
表6 基于NDVILandsat、NDVIMODIS和NDVISTARFM最优估测模型的研究区草地产草量的精度评价
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 |
图4 基于NDVISTARFM和NDVIMODIS最优估测模型反演的草地AGB年最大值slope动态变化a和b分别为基于NDVIMODIS和NDVISTARFM最优估测模型反演的草地AGB年最大值slope动态变化;AGB年变化量单位为kg·hm-2·year-1。a and b are the slope dynamic variation of annual maximum grassland AGB based on the optimal estimation models of NDVIMODIS and NDVISTARFM,respectively; The unit of annual variation of aboveground biomass is kg·hm-2·year-1.
Fig.4 The slope dynamic variation of annual maximum grassland AGB based on the optimal estimation model of NDVISTARFM and NDVIMODIS
图5 3种NDVI对比(a)为MODIS NDVI, (b)为Landsat NDVI,(c)为融合后NDVI。(a) is MODIS NDVI, (b) is Landsat NDVI, (c) is NDVI after fusion.
Fig.5 Three types of NDVI comparison
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 |
表7 各年份的实测数据统计性分析
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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