植物生态学报 ›› 2013, Vol. 37 ›› Issue (1): 18-25.DOI: 10.3724/SP.J.1258.2013.00002
收稿日期:
2012-08-27
接受日期:
2012-11-13
出版日期:
2013-08-27
发布日期:
2013-01-15
通讯作者:
吴见
作者简介:
*E-mail:xiangfeidewujian@126.com基金资助:
WU Jian*(), LIU Min-Shi, LI Wei-Tao
Received:
2012-08-27
Accepted:
2012-11-13
Online:
2013-08-27
Published:
2013-01-15
Contact:
WU Jian
摘要:
为系统地研究特定区域的植被盖度信息提取技术, 在不同的地形条件下, 比较了目前流行的多种高光谱遥感植被盖度提取方法。结果表明: 最优高光谱归一化植被指数(NDVI1)的建模和验证精度均高于其他两种归一化植被指数(NDVI), 直接采用NDVI建立的回归模型对研究区植被盖度的估测能力低于像元二分模型; 阴坡的最佳模型为基于一阶微分的偏最小二乘回归模型(PLSR模型), 其建模决定系数(R2)为0.810, 均方根误差(RMSE)为6.29, 验证R2为0.773, RMSE为8.85; 阳坡的最佳模型为基于二阶微分的PLSR模型, 其建模R2为0.823, RMSE为6.04, 验证R2为0.801, RMSE为7.35; 平原的最佳模型为全受限的线性光谱混合分解模型(FCLS), 其验证R2为0.852, RMSE为5.86。
吴见, 刘民士, 李伟涛. 不同地形条件下植被盖度信息提取技术研究. 植物生态学报, 2013, 37(1): 18-25. DOI: 10.3724/SP.J.1258.2013.00002
WU Jian, LIU Min-Shi, LI Wei-Tao. Research on vegetation cover information extraction technologies under different terrain conditions. Chinese Journal of Plant Ecology, 2013, 37(1): 18-25. DOI: 10.3724/SP.J.1258.2013.00002
NIR (nm) | Red (nm) | 建模 Modeling | 验证 Verification | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | ||||
NDVI1 | 823.65 | 701.55 | 0.656 | 10.73 | 0.638 | 11.74 | |
NDVI2 | 803.3 | 671.02 | 0.508 | 14.65 | 0.497 | 14.40 | |
NDVI3 | TM4 | TM3 | 0.483 | 15.01 | 0.476 | 15.29 |
表1 不同归一化植被指数(NDVI)估测阴坡植被盖度的建模和验证结果
Table 1 Modeling and verification results of shady slope vegetation coverage estimation of different normalized differential vegetation index (NDVI)
NIR (nm) | Red (nm) | 建模 Modeling | 验证 Verification | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | ||||
NDVI1 | 823.65 | 701.55 | 0.656 | 10.73 | 0.638 | 11.74 | |
NDVI2 | 803.3 | 671.02 | 0.508 | 14.65 | 0.497 | 14.40 | |
NDVI3 | TM4 | TM3 | 0.483 | 15.01 | 0.476 | 15.29 |
NIR (nm) | Red (nm) | 建模 Modeling | 验证 Verification | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | ||||
NDVI1 | 823.65 | 701.55 | 0.731 | 8.85 | 0.711 | 9.94 | |
NDVI2 | 803.3 | 671.02 | 0.712 | 9.87 | 0.682 | 10.73 | |
NDVI3 | TM4 | TM3 | 0.713 | 9.96 | 0.708 | 10.69 |
表2 不同归一化植被指数(NDVI)估测阳坡植被盖度的建模和验证结果
Table 2 Modeling and verification results of sunny slope vegetation coverage estimation of different normalized differential vegetation index (NDVI)
NIR (nm) | Red (nm) | 建模 Modeling | 验证 Verification | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | ||||
NDVI1 | 823.65 | 701.55 | 0.731 | 8.85 | 0.711 | 9.94 | |
NDVI2 | 803.3 | 671.02 | 0.712 | 9.87 | 0.682 | 10.73 | |
NDVI3 | TM4 | TM3 | 0.713 | 9.96 | 0.708 | 10.69 |
NIR (nm) | Red (nm) | 建模 Modeling | 验证 Verification | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | ||||
NDVI1 | 823.65 | 701.55 | 0.746 | 7.09 | 0.732 | 8.95 | |
NDVI2 | 803.3 | 671.02 | 0.723 | 7.84 | 0.706 | 10.47 | |
NDVI3 | TM4 | TM3 | 0.705 | 9.12 | 0.691 | 10.70 |
表3 不同归一化植被指数(NDVI)估测平原植被盖度的建模和验证结果
Table 3 Modeling and verification results of plain vegetation coverage estimation of each normalized differential vegetation index (NDVI)
NIR (nm) | Red (nm) | 建模 Modeling | 验证 Verification | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | ||||
NDVI1 | 823.65 | 701.55 | 0.746 | 7.09 | 0.732 | 8.95 | |
NDVI2 | 803.3 | 671.02 | 0.723 | 7.84 | 0.706 | 10.47 | |
NDVI3 | TM4 | TM3 | 0.705 | 9.12 | 0.691 | 10.70 |
图1 最优高光谱归一化植被指数(NDVI1)的阴坡植被盖度预测值验证结果。RMSE, 均方根误差。
Fig. 1 Verification results of the prediction value of shady slope vegetation coverage by the optimal hyper spectral normalized differential vegetation index (NDVI1). RMSE, root mean square error.
NDVIsoil | NDVIveg | 阴坡 Shady slope | 阳坡 Sunny slope | 平原 Plain | ||||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | |||||
NDVI1 | -0.10 | 0.64 | 0.656 | 10.38 | 0.730 | 9.15 | 0.758 | 8.06 | ||
NDVI2 | -0.06 | 0.62 | 0.537 | 12.69 | 0.717 | 10.06 | 0.725 | 9.90 | ||
NDVI3 | -0.02 | 0.55 | 0.504 | 14.17 | 0.684 | 11.23 | 0.712 | 10.34 |
表4 不同地形条件下不同归一化植被指数(NDVI)的像元二分模型验证结果
Table 4 Verification results of pixel binary model of different normalized differential vegetation index (NDVI) under different terrain conditions
NDVIsoil | NDVIveg | 阴坡 Shady slope | 阳坡 Sunny slope | 平原 Plain | ||||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | |||||
NDVI1 | -0.10 | 0.64 | 0.656 | 10.38 | 0.730 | 9.15 | 0.758 | 8.06 | ||
NDVI2 | -0.06 | 0.62 | 0.537 | 12.69 | 0.717 | 10.06 | 0.725 | 9.90 | ||
NDVI3 | -0.02 | 0.55 | 0.504 | 14.17 | 0.684 | 11.23 | 0.712 | 10.34 |
图2 基于最优高光谱NDVI(NDVI1)的像元二分模型阴坡植被盖度预测值验证结果。RMSE, 均方根误差。
Fig. 2 Verification results of the prediction value of shady slope vegetation coverage of pixel binary model based on the optimal high spectral NDVI (NDVI1). RMSE, root mean square error.
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 8 | 0.693 | 11.28 | 0.667 | 11.95 | |
一阶微分 FD | 7 | 0.748 | 9.55 | 0.714 | 11.03 | |
二阶微分 SD | 9 | 0.732 | 10.17 | 0.708 | 11.15 | |
包络线去除 CR | 9 | 0.794 | 8.49 | 0.753 | 9.06 |
表5 不同光谱处理方法主成分回归(PCR)模型阴坡植被盖度建模和验证结果
Table 5 Modeling and verification results of shady slope vegetation coverage by principal component regression (PCR) models based on different spectral processing methods
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 8 | 0.693 | 11.28 | 0.667 | 11.95 | |
一阶微分 FD | 7 | 0.748 | 9.55 | 0.714 | 11.03 | |
二阶微分 SD | 9 | 0.732 | 10.17 | 0.708 | 11.15 | |
包络线去除 CR | 9 | 0.794 | 8.49 | 0.753 | 9.06 |
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 10 | 0.739 | 10.26 | 0.641 | 12.76 | |
一阶微分 FD | 12 | 0.766 | 9.14 | 0.685 | 11.57 | |
二阶微分 SD | 8 | 0.775 | 8.90 | 0.639 | 12.88 | |
包络线去除 CR | 10 | 0.753 | 9.67 | 0.650 | 12.34 |
表6 不同光谱处理方法主成分回归(PCR)模型阳坡植被盖度建模和验证结果
Table 6 Modeling and verification results of sunny slope vegetation coverage by principal component regression (PCR) model based on different spectral processing methods
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 10 | 0.739 | 10.26 | 0.641 | 12.76 | |
一阶微分 FD | 12 | 0.766 | 9.14 | 0.685 | 11.57 | |
二阶微分 SD | 8 | 0.775 | 8.90 | 0.639 | 12.88 | |
包络线去除 CR | 10 | 0.753 | 9.67 | 0.650 | 12.34 |
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 7 | 0.745 | 10.06 | 0.703 | 11.68 | |
一阶微分 FD | 10 | 0.759 | 9.71 | 0.726 | 10.04 | |
二阶微分 SD | 11 | 0.681 | 11.93 | 0.659 | 12.48 | |
包络线去除 CR | 9 | 0.730 | 10.51 | 0.717 | 11.45 |
表7 不同光谱处理方法主成分回归(PCR)模型平原植被盖度建模和验证结果
Table 7 Modeling and verification results of plain vegetation coverage by principal component regression (PCR) model based on different spectral processing methods
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 7 | 0.745 | 10.06 | 0.703 | 11.68 | |
一阶微分 FD | 10 | 0.759 | 9.71 | 0.726 | 10.04 | |
二阶微分 SD | 11 | 0.681 | 11.93 | 0.659 | 12.48 | |
包络线去除 CR | 9 | 0.730 | 10.51 | 0.717 | 11.45 |
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 9 | 0.742 | 10.64 | 0.718 | 11.34 | |
一阶微分 FD | 13 | 0.810 | 6.29 | 0.773 | 8.85 | |
二阶微分 SD | 10 | 0.794 | 7.78 | 0.761 | 9.16 | |
包络线去除 CR | 8 | 0.726 | 10.15 | 0.692 | 11.54 |
表8 不同光谱处理方法偏最小二乘回归(PLSR)模型阴坡植被盖度建模和验证结果
Table 8 Modeling and verification results of shady slope vegetation coverage by partial least squares regression (PLSR) model based on different spectral processing methods
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 9 | 0.742 | 10.64 | 0.718 | 11.34 | |
一阶微分 FD | 13 | 0.810 | 6.29 | 0.773 | 8.85 | |
二阶微分 SD | 10 | 0.794 | 7.78 | 0.761 | 9.16 | |
包络线去除 CR | 8 | 0.726 | 10.15 | 0.692 | 11.54 |
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 11 | 0.783 | 8.18 | 0.756 | 9.84 | |
一阶微分 FD | 7 | 0.790 | 7.96 | 0.758 | 9.23 | |
二阶微分 SD | 9 | 0.823 | 6.04 | 0.801 | 7.35 | |
包络线去除 CR | 8 | 0.731 | 10.27 | 0.692 | 11.49 |
表9 不同光谱处理方法偏最小二乘回归(PLSR)模型阳坡植被盖度建模和验证结果
Table 9 Modeling and verification results of sunny slope vegetation coverage by partial least squares regression (PLSR) model based on different spectral processing methods
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 11 | 0.783 | 8.18 | 0.756 | 9.84 | |
一阶微分 FD | 7 | 0.790 | 7.96 | 0.758 | 9.23 | |
二阶微分 SD | 9 | 0.823 | 6.04 | 0.801 | 7.35 | |
包络线去除 CR | 8 | 0.731 | 10.27 | 0.692 | 11.49 |
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 10 | 0.765 | 8.90 | 0.743 | 10.21 | |
一阶微分 FD | 8 | 0.804 | 7.22 | 0.809 | 6.95 | |
二阶微分 SD | 7 | 0.836 | 6.13 | 0.760 | 8.57 | |
包络线去除 CR | 11 | 0.773 | 9.15 | 0.739 | 10.38 |
表10 不同光谱处理方法偏最小二乘回归(PLSR)模型平原植被盖度建模和验证结果
Table 10 Modeling and verification results of plain vegetation coverage by partial least squares regression (PLSR) model based on different spectral processing methods
光谱处理方法 Spectrum processing method | 变量个数 Variable number | 建模 Modeling | 验证 Verification | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |||
无变换数据 NO | 10 | 0.765 | 8.90 | 0.743 | 10.21 | |
一阶微分 FD | 8 | 0.804 | 7.22 | 0.809 | 6.95 | |
二阶微分 SD | 7 | 0.836 | 6.13 | 0.760 | 8.57 | |
包络线去除 CR | 11 | 0.773 | 9.15 | 0.739 | 10.38 |
分解模型 Decomposition model | 验证 Verification | |||||||
---|---|---|---|---|---|---|---|---|
阴坡 Shady slope | 阳坡 Sunny slope | 平原 Plain | ||||||
R2 | RMSE | R2 | RMSE | R2 | RMSE | |||
LS | 0.618 | 14.59 | 0.709 | 11.92 | 0.746 | 10.37 | ||
FCLS | 0.653 | 12.87 | 0.761 | 8.45 | 0.852 | 5.86 |
表11 不同地形条件下全受限的LSMM (FCLS)和非受限的LSMM (LS)模型分解的植被分量精度验证结果
Table 11 Accuracy verification results of vegetation component unmixed by full confining linear spectral mixture model (FCLS) and linear spectral mixture model (LS) under different terrain conditions
分解模型 Decomposition model | 验证 Verification | |||||||
---|---|---|---|---|---|---|---|---|
阴坡 Shady slope | 阳坡 Sunny slope | 平原 Plain | ||||||
R2 | RMSE | R2 | RMSE | R2 | RMSE | |||
LS | 0.618 | 14.59 | 0.709 | 11.92 | 0.746 | 10.37 | ||
FCLS | 0.653 | 12.87 | 0.761 | 8.45 | 0.852 | 5.86 |
[1] | Asner GP, Lobell DB (2000). A biogeophysical approach for automated SWIR unmixing of soils and vegetation. Remote Sensing of Environment, 74, 99-112. |
[2] | Blackburn GA (1998). Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66, 273-285. |
[3] | Cheng HF, Zhang WB, Chen F (2008). Advances in researches on application of remote sensing method to estimating vegetation coverage. Remote Sensing for Land & Resources, 20(1), 13-18. (in Chinese with English abstract) |
[ 程红芳, 章文波, 陈锋 (2008). 植被覆盖度遥感估算方法研究进展. 国土资源遥感, 20(1), 13-18.] | |
[4] | Drake NA, Mackin S, Settle JJ (1999). Mapping vegetation, soils, and geology in semi-arid shrublands using spectral matching and mixture modeling of SWIR AVIRIS imagery. Remote Sensing of Environment, 68, 12-25. |
[5] | Du MY, Wu WB, Guo ZD (2002). Research on multi-source geographic information based classification of desertification. Journal of Image and Graphics, 7, 740-743. (in Chinese with English abstract) |
[ 杜明义, 武文波, 郭达志 (2002). 多源地学信息在土地荒漠化遥感分类中的应用研究. 中国图形图像学报, 7, 740-743.] | |
[6] | Elvidge CD, Chen ZK (1995). Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment, 54, 38-48. |
[7] | Hansen MC, Defries RS, Townshend JRG, Sohlberg R, Dimiceli C, Carroll M (2002). Towards an operational MODIS continuous field of percent tree cover agorithm: examples using AVHRR and MODIS data. Remote Sensing of Environment, 83, 303-319. (in Chinese with English abstract) |
[8] | Kremer RG, Running SW (1993). Community type differentiation using NOAA/AVHRR data within a sagebrush-steppe ecosystem. Remote Sensing of Environment, 46, 311-318. |
[9] | Li XS, Gao ZH, Li ZY, Bai LN, Wang BY (2010). Estimation of sparse vegetation coverage in arid region based on hy- perspectral mixed pixel decomposition. Chinese Journal of Applied Ecology, 21, 152-158. (in Chinese with English abstract) |
[ 李晓松, 高志海, 李增元, 白黎娜, 王琫瑜 (2010). 基于高光谱混合像元分解的干旱地区稀疏植被覆盖度优测. 应用生态学报, 21, 152-158.] | |
[10] | Li XS, Li ZY, Gao ZH, Bai LN, Wang BY (2011). Estimation of vegetation cover in desertified regions from hyperion imageries using NDVI and partial least squares regression. Journal of Desert Research, 31, 162-167. (in Chinese with English abstract) |
[ 李晓松, 李增元, 高志海, 白黎娜, 王琫瑜 (2011). 基于NDVI与偏最小二乘回归的荒漠化地区植被覆盖度高光谱遥感优测. 中国沙漠, 31, 162-167.] | |
[11] | Thenkabail PS, Smith RB, de Pauw E (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing Environment, 71, 158-182. |
[12] | Townshend JRG, Justice CO (1986). Analysis of dynamics of African vegetation using the normalized difference vegetation index. International Journal of Remote Sensing, 12, 1224-1242. |
[13] | Yang JH, Ci LJ (2006). Progress on remote sensing-based desertification assessment. World Forestry Research, 19(6), 11-17. (in Chinese with English abstract) |
[ 杨晓晖, 慈龙骏 (2006). 基于遥感技术的荒漠化评价研究进展. 世界林业研究, 19(6), 11-17.] |
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