植物生态学报 ›› 2012, Vol. 36 ›› Issue (10): 1106-1119.DOI: 10.3724/SP.J.1258.2012.01106
收稿日期:
2011-07-14
接受日期:
2012-05-08
出版日期:
2012-07-14
发布日期:
2012-09-26
通讯作者:
宋创业
作者简介:
E-mail: songcy@ibcas.ac.cnSONG Chuang-Ye1,*(), LIU Hui-Ming2, LIU Gao-Huan3, HUANG Chong3
Received:
2011-07-14
Accepted:
2012-05-08
Online:
2012-07-14
Published:
2012-09-26
Contact:
SONG Chuang-Ye
摘要:
为了采用广义加法模型整合数字高程模型和遥感数据进行植被分布的预测, 并探索耦合环境变量和遥感数据作为预测变量是否能够有效地提高植被分布预测的精度, 选择海拔、坡度、至黄河最近距离、至海岸线最近距离, 以及从SPOT5遥感影像中提取的光谱变量作为预测变量, 采用广义加法模型整合环境变量和光谱变量, 建立植被分布预测模型。研究设置3种建模情景(以环境变量作为预测变量, 以光谱变量作为预测变量, 综合使用环境变量与光谱变量作为预测变量)对黄河三角洲的优势植被类型的分布进行了预测, 并对预测结果采用偏差分析、受试者工作特征曲线和野外采样点对比等3种方法进行了验证。结果表明: (1)基于广义加法模型的植被分布预测方法具有一定的实用性, 可以较为准确地预测植被的分布; 盖度较高的植被类型预测精度较高, 盖度较低的植被类型预测精度较低, 植物群落结构的特点是出现这些差异的主要原因; 综合使用环境变量和光谱变量作为预测变量的模型, 预测精度高于单独以环境变量或者光谱变量作为预测变量的模型。(2)环境变量、光谱变量大多被选入模型, 二者均对植被分布预测有重要的作用; 同一预测变量在不同植被类型的预测模型中的贡献不同, 这与植被的光谱、环境特征差异有关; 同一预测变量在不同的建模情景下对模型的贡献不同, 环境变量与光谱变量的耦合效应可能是导致预测变量对模型的贡献出现变化的原因。
宋创业, 刘慧明, 刘高焕, 黄翀. 采用广义加法模型整合数字高程模型和遥感数据进行植被分布预测. 植物生态学报, 2012, 36(10): 1106-1119. DOI: 10.3724/SP.J.1258.2012.01106
SONG Chuang-Ye, LIU Hui-Ming, LIU Gao-Huan, HUANG Chong. Applying generalized additive model to integrate digital elevation model and remotely sensed data to predict the vegetation distribution. Chinese Journal of Plant Ecology, 2012, 36(10): 1106-1119. DOI: 10.3724/SP.J.1258.2012.01106
图4 植被类型识别和空间分布预测流程。GRASP, 广义回归分析与空间预测。
Fig. 4 Procedure of vegetation type identification and spatial distribution prediction. GRASP, generalized regression analysis and spatial prediction.
变量 Variable | 柽柳灌丛 Tamarix chinensis shrub | 芦苇草甸 Phragmites australis meadow | 翅碱蓬群落 Suaeda heteroptera community |
---|---|---|---|
海拔 Altitude (m) | 2.31 ± 0.91a | 1.42 ± 0.50b | 1.72 ± 0.77c |
坡度 Slope (°) | 0.06 ± 0.06b | 0.04 ± 0.04a | 0.04 ± 0.04a |
至海岸线最近距离 Nearest distance to coastline (km) | 11.62 ± 6.73a | 11.97 ± 4.07a | 10.55 ± 6.39b |
至黄河最近距离 Nearest distance to the Yellow River (km) | 28.00 ± 9.03a | 24.76 ± 6.06b | 29.40 ± 6.00c |
短波红外波段灰度值 Digital number of short infrared band | 77.63 ± 9.77a | 67.66 ± 11.43b | 79.41 ± 14.65c |
红波段灰度值 Digital number of visible red band | 69.60 ± 9.49a | 59.95 ± 7.17b | 75.31 ± 13.88c |
近红外波段灰度值 Digital number of near infrared band | 66.37 ± 8.52a | 71.80 ± 9.10b | 70.31 ± 9.87c |
绿波段灰度值 Digital number of visible green band | 76.47 ± 5.66a | 71.62 ± 4.50b | 79.24 ± 7.03c |
归一化植被指数 Normalized difference vegetation index | 124.54 ± 12.09a | 138.78 ± 11.87b | 123.65 ± 15.46c |
表1 3种植被类型的光谱特征及其分布区的环境特征(平均值±标准偏差)
Table 1 Spectral attributes of three vegetation types and environmental characteristics of their distribution range (mean ± SD)
变量 Variable | 柽柳灌丛 Tamarix chinensis shrub | 芦苇草甸 Phragmites australis meadow | 翅碱蓬群落 Suaeda heteroptera community |
---|---|---|---|
海拔 Altitude (m) | 2.31 ± 0.91a | 1.42 ± 0.50b | 1.72 ± 0.77c |
坡度 Slope (°) | 0.06 ± 0.06b | 0.04 ± 0.04a | 0.04 ± 0.04a |
至海岸线最近距离 Nearest distance to coastline (km) | 11.62 ± 6.73a | 11.97 ± 4.07a | 10.55 ± 6.39b |
至黄河最近距离 Nearest distance to the Yellow River (km) | 28.00 ± 9.03a | 24.76 ± 6.06b | 29.40 ± 6.00c |
短波红外波段灰度值 Digital number of short infrared band | 77.63 ± 9.77a | 67.66 ± 11.43b | 79.41 ± 14.65c |
红波段灰度值 Digital number of visible red band | 69.60 ± 9.49a | 59.95 ± 7.17b | 75.31 ± 13.88c |
近红外波段灰度值 Digital number of near infrared band | 66.37 ± 8.52a | 71.80 ± 9.10b | 70.31 ± 9.87c |
绿波段灰度值 Digital number of visible green band | 76.47 ± 5.66a | 71.62 ± 4.50b | 79.24 ± 7.03c |
归一化植被指数 Normalized difference vegetation index | 124.54 ± 12.09a | 138.78 ± 11.87b | 123.65 ± 15.46c |
预测变量 Predictive variables | 植被类型 Vegetation type | 样本数 Number of samples | 模型 Model | AUC | D2 |
---|---|---|---|---|---|
环境变量 Environmental variable | 柽柳灌丛 Tamarix chinensis shrub | 56 | s (SP, 4) + s (ALT, 4) + s (DR, 4) | 0.863 | 0.36 |
芦苇草甸 Phragmites australis meadow | 48 | s (SP, 4) + s (ALT, 4) + s (DR, 4) + s (DC, 4) | 0.900 | 0.44 | |
翅碱蓬群落 Suaeda heteroptera community | 20 | s (SP, 4) + s (ALT, 4) + s (DR, 4) + s (DC, 4) | 0.795 | 0.20 | |
光谱变量 Spectral variable | 柽柳灌丛 Tamarix chinensis shrub | 56 | s (DNSIR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) + s (DNVR, 4) | 0.842 | 0.291 |
芦苇草甸 Phragmites australis meadow | 48 | s (DNSIR, 4) + s (DNVR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) | 0.855 | 0.335 | |
翅碱蓬群落 Suaeda heteroptera community | 20 | s (DNSIR, 4) + s (DNVR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) | 0.797 | 0.268 | |
环境变量+光谱变量 Environmental variable + spectral variable | 柽柳灌丛 Tamarix chinensis shrub | 56 | s (SP, 4) + s (ALT, 4) + s (DNSIR, 4) + s (DNVR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) + s (DR, 4) + s (DC, 4) | 0.938 | 0.561 |
芦苇草甸 Phragmites australis meadow | 48 | s (SP, 4) + s (ALT, 4) + s (DNSIR, 4) + s (DNVR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) + s (DR, 4) + s (DC, 4) | 0.941 | 0.554 | |
翅碱蓬群落 Suaeda heteroptera community | 20 | s (SP, 4) + s (ALT, 4) + s (DNSIR, 4) + s (DNVR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) + s (DR, 4) + s (DC, 4) | 0.884 | 0.412 |
表2 3种植被类型的广义加法模型
Table 2 Generalized additive models of three vegetation types
预测变量 Predictive variables | 植被类型 Vegetation type | 样本数 Number of samples | 模型 Model | AUC | D2 |
---|---|---|---|---|---|
环境变量 Environmental variable | 柽柳灌丛 Tamarix chinensis shrub | 56 | s (SP, 4) + s (ALT, 4) + s (DR, 4) | 0.863 | 0.36 |
芦苇草甸 Phragmites australis meadow | 48 | s (SP, 4) + s (ALT, 4) + s (DR, 4) + s (DC, 4) | 0.900 | 0.44 | |
翅碱蓬群落 Suaeda heteroptera community | 20 | s (SP, 4) + s (ALT, 4) + s (DR, 4) + s (DC, 4) | 0.795 | 0.20 | |
光谱变量 Spectral variable | 柽柳灌丛 Tamarix chinensis shrub | 56 | s (DNSIR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) + s (DNVR, 4) | 0.842 | 0.291 |
芦苇草甸 Phragmites australis meadow | 48 | s (DNSIR, 4) + s (DNVR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) | 0.855 | 0.335 | |
翅碱蓬群落 Suaeda heteroptera community | 20 | s (DNSIR, 4) + s (DNVR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) | 0.797 | 0.268 | |
环境变量+光谱变量 Environmental variable + spectral variable | 柽柳灌丛 Tamarix chinensis shrub | 56 | s (SP, 4) + s (ALT, 4) + s (DNSIR, 4) + s (DNVR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) + s (DR, 4) + s (DC, 4) | 0.938 | 0.561 |
芦苇草甸 Phragmites australis meadow | 48 | s (SP, 4) + s (ALT, 4) + s (DNSIR, 4) + s (DNVR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) + s (DR, 4) + s (DC, 4) | 0.941 | 0.554 | |
翅碱蓬群落 Suaeda heteroptera community | 20 | s (SP, 4) + s (ALT, 4) + s (DNSIR, 4) + s (DNVR, 4) + s (DNNIR, 4) + s (DNVG, 4) + s (NDVI, 4) + s (DR, 4) + s (DC, 4) | 0.884 | 0.412 |
预测变量 Predictive variables | 坡度 Slope | 海拔 Altitude | 至海岸线 最近距离 Nearest distance to coastline | 至黄河 最近距离 Nearest distance to the Yellow River | 短波红 外波段 灰度值 Digital number of short infrared band | 红波段灰度值 Digital number of visible red band | 近红外 波段灰 度值 Digital number near infrared band | 绿波段灰度值 Digital number of visible green band | 归一化 植被指数 Normalized difference vegetation index | |
---|---|---|---|---|---|---|---|---|---|---|
环境变量 Environmental variable | 柽柳灌丛 Tamarix chinensis shrub | 49.5 | 248.1 | - | 171.6 | - | - | - | - | - |
芦苇草甸 Phragmites australis meadow | 130.1 | 83.5 | 96.9 | 228.3 | - | - | - | - | - | |
翅碱蓬群落 Suaeda heteroptera community | 36.4 | 116.8 | 74.9 | 175.5 | - | - | - | - | - | |
光谱变量 Spectral variable | 柽柳灌丛 Tamarix chinensis shrub | - | - | - | - | 91.1 | 70.9 | 16.2 | 59.2 | 23.4 |
芦苇草甸 Phragmites australis meadow | - | - | - | - | 38.2 | 23.5 | 39.8 | 33.9 | 33.4 | |
翅碱蓬群落 Suaeda heteroptera community | - | - | - | - | 20.6 | 79.3 | 45.9 | 54.5 | 43.8 | |
环境变量+ 光谱变量 Environmental variable + spectral variable | 柽柳灌丛 Tamarix chinensis shrub | 40.2 | 130.3 | 7.6 | 37.4 | 20.7 | 61.2 | 26.2 | 42.5 | 36.6 |
芦苇草甸 Phragmites australis meadow | 84.8 | 57.8 | 95.1 | 107.7 | 17.3 | 22.5 | 7.1 | 40.7 | 29.5 | |
翅碱蓬群落 Suaeda heteroptera community | 34.9 | 96.5 | 57.5 | 97.1 | 15.4 | 64.7 | 40.1 | 32.8 | 77.7 |
表3 预测变量对广义加法模型的贡献
Table 3 Contribution of predictive variables to the generalized additive models
预测变量 Predictive variables | 坡度 Slope | 海拔 Altitude | 至海岸线 最近距离 Nearest distance to coastline | 至黄河 最近距离 Nearest distance to the Yellow River | 短波红 外波段 灰度值 Digital number of short infrared band | 红波段灰度值 Digital number of visible red band | 近红外 波段灰 度值 Digital number near infrared band | 绿波段灰度值 Digital number of visible green band | 归一化 植被指数 Normalized difference vegetation index | |
---|---|---|---|---|---|---|---|---|---|---|
环境变量 Environmental variable | 柽柳灌丛 Tamarix chinensis shrub | 49.5 | 248.1 | - | 171.6 | - | - | - | - | - |
芦苇草甸 Phragmites australis meadow | 130.1 | 83.5 | 96.9 | 228.3 | - | - | - | - | - | |
翅碱蓬群落 Suaeda heteroptera community | 36.4 | 116.8 | 74.9 | 175.5 | - | - | - | - | - | |
光谱变量 Spectral variable | 柽柳灌丛 Tamarix chinensis shrub | - | - | - | - | 91.1 | 70.9 | 16.2 | 59.2 | 23.4 |
芦苇草甸 Phragmites australis meadow | - | - | - | - | 38.2 | 23.5 | 39.8 | 33.9 | 33.4 | |
翅碱蓬群落 Suaeda heteroptera community | - | - | - | - | 20.6 | 79.3 | 45.9 | 54.5 | 43.8 | |
环境变量+ 光谱变量 Environmental variable + spectral variable | 柽柳灌丛 Tamarix chinensis shrub | 40.2 | 130.3 | 7.6 | 37.4 | 20.7 | 61.2 | 26.2 | 42.5 | 36.6 |
芦苇草甸 Phragmites australis meadow | 84.8 | 57.8 | 95.1 | 107.7 | 17.3 | 22.5 | 7.1 | 40.7 | 29.5 | |
翅碱蓬群落 Suaeda heteroptera community | 34.9 | 96.5 | 57.5 | 97.1 | 15.4 | 64.7 | 40.1 | 32.8 | 77.7 |
图5 基于广义加法模型预测的3种植被类型分布概率。At、Ap、As分别表示柽柳灌丛、芦苇草甸和翅碱蓬群落在情景A中预测的分布概率; Bt、Bp、Bs分别表示柽柳灌丛、芦苇草甸和翅碱蓬群落在情景B中预测的分布概率; Ct、Cp、Cs分别表示柽柳灌丛、芦苇草甸和翅碱蓬群落在情景C中预测的分布概率。情景A是以环境变量作为预测变量; 情景B是以光谱变量作为预测变量; 情景C是以环境变量与光谱变量作为预测变量。
Fig. 5 Predicted distribution probability of three vegetation types based on generalized additive model. At, Ap and As is respectively the predicted distribution probability of Tamarix chinensis shrub, Phragmites australis meadow, Suaeda heteroptera community under scene A; Bt, Bp and Bs is respectively the predicted distribution probability of Tamarix chinensis shrub, Phragmites australis meadow, Suaeda heteroptera community under scene B; Ct, Cp and Cs is respectively the predicted distribution probability of Tamarix chinensis shrub, Phragmites australis meadow, Suaeda heteroptera community under scene C. Scene A, using environmental variables as predictive variables; Scene B, using spectral variables as predictive variables; Scene C, using both environmental variables and spectral variables as predictive variables.
图6 基于广义加法模型预测的3种植被类型分布图。 A, 以环境变量作为预测变量。B, 以光谱变量作为预测变量。C, 以环境变量和光谱变量作为预测变量。
Fig. 6 Predicted distribution map of three vegetation types based on generalized additive models. A, using environmental variables as predictive variables. B, using spectral variables as predictive variables. C, using both environmental variables and spectral variables as predictive variables.
预测变量 Predictive variables | 验证样方的植被类型 Vegetation type of the quadrat for validation | 广义加法模型预测的植被类型 Predicted vegetation type by generalized additive model | ||
---|---|---|---|---|
柽柳灌丛 Tamarix chinensis shrub | 芦苇草甸 Phragmites australis meadow | 翅碱蓬群落 Suaeda heteroptera community | ||
光谱变量 Spectral variable | 柽柳灌丛 Tamarix chinensis shrub | 36.7% | 43.4% | 19.9% |
芦苇草甸 Phragmites australis meadow | 14.7% | 58.9% | 26.4% | |
翅碱蓬群落 Suaeda heteroptera community | 18.4% | 30.4% | 51.2% | |
环境变量 Environmental variable | 柽柳灌丛 Tamarix chinensis shrub | 34.1% | 37.2% | 28.7% |
芦苇草甸 Phragmites australis meadow | 23.1% | 57.7% | 19.2% | |
翅碱蓬群落 Suaeda heteroptera community | 23.1% | 22.8% | 54.1% | |
环境变量+光谱变量 Environmental variable + spectral variable | 柽柳灌丛 Tamarix chinensis shrub | 52.1% | 30.6% | 17.3% |
芦苇草甸 Phragmites australis meadow | 10.5% | 82.7% | 6.8% | |
翅碱蓬群落 Suaeda heteroptera community | 16.1% | 16.6% | 67.3% |
表4 广义加法模型验证结果
Table 4 Validation results of generalized additive model
预测变量 Predictive variables | 验证样方的植被类型 Vegetation type of the quadrat for validation | 广义加法模型预测的植被类型 Predicted vegetation type by generalized additive model | ||
---|---|---|---|---|
柽柳灌丛 Tamarix chinensis shrub | 芦苇草甸 Phragmites australis meadow | 翅碱蓬群落 Suaeda heteroptera community | ||
光谱变量 Spectral variable | 柽柳灌丛 Tamarix chinensis shrub | 36.7% | 43.4% | 19.9% |
芦苇草甸 Phragmites australis meadow | 14.7% | 58.9% | 26.4% | |
翅碱蓬群落 Suaeda heteroptera community | 18.4% | 30.4% | 51.2% | |
环境变量 Environmental variable | 柽柳灌丛 Tamarix chinensis shrub | 34.1% | 37.2% | 28.7% |
芦苇草甸 Phragmites australis meadow | 23.1% | 57.7% | 19.2% | |
翅碱蓬群落 Suaeda heteroptera community | 23.1% | 22.8% | 54.1% | |
环境变量+光谱变量 Environmental variable + spectral variable | 柽柳灌丛 Tamarix chinensis shrub | 52.1% | 30.6% | 17.3% |
芦苇草甸 Phragmites australis meadow | 10.5% | 82.7% | 6.8% | |
翅碱蓬群落 Suaeda heteroptera community | 16.1% | 16.6% | 67.3% |
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