灌丛化草原灌木和草本植物光谱特征差异及灌木盖度反演——以内蒙古镶黄旗为例

doi:10.17521/cjpe.2016.0101

摘要/Abstract

摘要：

灌丛化草原已成为我国干旱、半干旱地区一种重要的植被类型, 但目前有关灌丛化草原灌木和草本植物光谱特征以及灌木盖度的遥感反演研究鲜有报道。相比传统的野外调查方法, 基于遥感影像的灌木盖度反演为实现长时间、大范围灌丛化草原灌木盖度监测提供了可能。该研究综合利用灌木和草本植物光谱特征差异以及季相差异, 以内蒙古镶黄旗灌丛化草原区为例, 通过线性模型和多端元混合光谱分解模型, 实现了利用中分辨率Landsat卫星影像的灌木盖度反演。对镶黄旗优势灌木和草本植物群落的光谱特征分析表明, 小叶锦鸡儿(Caragana microphylla)灌木群落的红边斜率、归一化植被指数和改进红边归一化植被指数值均高于以羊草(Leymus chinensis)、克氏针茅(Stipa krylovii)为优势种的草本植物群落, 并且其红边位置有“红移”趋势。两种模型反演所得镶黄旗灌丛化草原区灌木盖度平均值均为13%, 绝大多数区域灌木盖度低于25%。相比基于盛夏时节影像的多端元混合光谱分解模型, 利用灌木和草本植物季相特征差异建立的基于初秋时节影像的线性模型更适合灌丛化草原灌木盖度的遥感反演。

关键词: 灌丛化草原, 光谱特征, 灌木盖度, 遥感反演, 线性模型, 多端元混合光谱模型

Abstract:

Aims Shrub-encroached grassland has become an important vegetation type in China’s arid and semi-arid region. Our study objective is to explore the spectral features of shrub and grass communities, as well as their empirical relationships with shrub coverage. The quantitative estimation of shrub cover based on medium-resolution Landsat satellite imagery provides the practical basis for long term retrieval of large areas of shrub expansion in the grassland region. Methods Linear models and Multiple Endmember Spectral Analysis Model (MESMA) based on medium resolution Landsat satellite imagery were developed to quantify the shrub coverage in a shrub-encroached grassland region in Xianghuang Banner, Nei Mongol using the spectral features and their seasonal differences between the shrub and grass communities. Important findings Compared to Leymus chinensis and Stipa krylovii dominated grass communities, Caragana microphylla community had a higher normalized difference vegetation index (NDVI), modified red edge normalized difference vegetation index (mNDVI₇₀₅), and red edge slope. The red edge position of C. microphylla community shifted to longer wavelengths. The average and the maximum shrub coverage was 13% and 25%, respectively, in the shrub-encroached grassland based on both models. The correlation coefficient of determination (R²) and root mean square error (RMSE) of the linear model was 0.31 and 0.05, respectively. We found that the linear model based on seasonal differences of shrub and grass community was more suitable for retrieving shrub coverage in the study area from medium resolution imagery than the MESMA model that is based on mid-summer images.

Key words: shrub encroachment, spectral features, shrub coverage, remote sensing, linear model, multiple endmember spectral mixture analysis model

刘涛宇, 赵霞, 沈海花, 胡会峰, 黄文江, 方精云. 灌丛化草原灌木和草本植物光谱特征差异及灌木盖度反演——以内蒙古镶黄旗为例. 植物生态学报, 2016, 40(10): 969-979. DOI: 10.17521/cjpe.2016.0101

Tao-Yu LIU, Xia ZHAO, Hai-Hua SHEN, Hui-Feng HU, Wen-Jiang HUANG, Jing-Yun FANG. Spectral feature differences between shrub and grass communities and shrub coverage retri- eval in shrub-encroached grassland in Xianghuang Banner, Nei Mongol, China. Chinese Journal of Plant Ecology, 2016, 40(10): 969-979. DOI: 10.17521/cjpe.2016.0101

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2016.0101

https://www.plant-ecology.com/CN/Y2016/V40/I10/969

图/表 8

图1 研究区域位置及野外调查点分布图。

Fig. 1 Geographic location of study area and the distributions of field sampling.

图2 1 m × 1 m小样方照片及植被覆盖自动化提取效果图。A, 植被覆盖度为34%的草本植物小样方。B, 植被覆盖度为4%的草本植物小样方。C, 植被覆盖度为91%的灌木小样方。D, 植被覆盖度为32%的灌木小样方。E, F, G, H分别为软件提取出的A, B, C, D的植被覆盖(绿色部分表示植被覆盖)。

Fig. 2 Photos of 1 m × 1 m sampling plots and the auto-extracted pictures of vegetation coverage. A, Grass sample plot with a 34% vegetation coverage. B, Grass sample plot with a 34% vegetation coverage. C, Shrub sample plot with a 91% vegetation coverage. D, Shrub sample plot with a 32% vegetation coverage. E, F, G, H the vegetation coverage of A, B, C, D retrieved by the software, respectively (Vegetation coverage was colored green).

图3 不同小样方植被覆盖度下优势灌木、草本植物群落光谱曲线及灌草光谱比较。A, 不同植被覆盖度下羊草群落光谱曲线。B, 不同植被覆盖度下克氏针茅群落光谱曲线。C, 不同植被覆盖度下小叶锦鸡儿群落光谱曲线。D, 优势灌木、草本植物群落光谱曲线比较。

Fig. 3 Spectral features of different vegetation coverage between the shrub and grass communities. A, Spectra of Leymus chinensis with different vegetation coverage. B, Spectra of Stipa krylovii with different vegetation coverage. C, Spectra of Caragana microphylla with different vegetation coverage. D, Comparison of dominant shrub and grass communities’ spectra.

表1 不同植被覆盖度盖度优势草本植物群落光谱特征(平均值±标准偏差)

Table 1 Changes in spectral features with shrub and grass dominancy and vegetation coverage (mean ± SD)

1 m × 1 m小样方植被覆盖度 Vegetation cover within 1 m × 1 m plot	羊草 Leymus chinensis			克氏针茅 Stipa krylovii
1 m × 1 m小样方植被覆盖度 Vegetation cover within 1 m × 1 m plot	mNDVI₇₀₅	REP	NDVI	mNDVI₇₀₅	REP	NDVI
<10%	0.17 ± 0.01	705.22 ± 1.46	0.24 ± 0.02	0.19 ± 0.04	704.65 ± 4.20	0.26 ± 0.05
10%-20%	-	-	-	0.23 ± 0.03	702.81 ± 2.52	0.33 ± 0.06
20%-35%	0.35 ± 0.02	708.45 ± 2.32	0.49 ± 0.05	0.27 ± 0.04	702.62 ± 1.29	0.40 ± 0.06

图4 基于不同时相影像的线性模型及不同季节灌丛化草原景观。A, 基于盛夏季影像的线性模型。B, 盛夏季灌丛化草原景观。C, 基于初秋季影像的线性模型。D, 初秋季灌丛化草原景观。

Fig. 4 Linear models based on multi-temporal images and landscape of shrub-encroached grassland in different seasons. A, Linear model in the mid-summer. B, Landscape of shrub-encroached grassland in mid-summer. C, Linear model in early autumn. D, Landscape of shrub-encroached grassland in early autumn. NDVI, normalized difference vegetation index.

表2 不同植被覆盖度小叶锦鸡儿灌木群落光谱特征(平均值±标准偏差)

Table 2 Spectral features of Caragana microphylla by vegetation coverage (mean ± SD)

1 m × 1 m小样方植被覆盖度 Vegetation coverage within 1 m × 1 m plot	小叶锦鸡儿 Caragana microphylla
1 m × 1 m小样方植被覆盖度 Vegetation coverage within 1 m × 1 m plot	mNDVI₇₀₅	REP	NDVI
35%-50%	0.41 ± 0.06	716.15 ± 2.69	0.49 ± 0.07
50%-70%	0.51 ± 0.04	718.78 ± 2.63	0.63 ± 0.08
70%-90%	0.62 ± 0.09	722.94 ± 5.06	0.77 ± 0.11

图5 线性模型反演所得镶黄旗灌丛化草原灌木盖度分布图。

Fig. 5 Predicted shrub coverage based on the linear model in shrub encroached grassland area in Xianghuang Banner.

图6 多端元混合光谱分解模型端元光谱及反演所得镶黄旗灌丛化草原灌木盖度分布图。A, MESMA模型端元光谱。B, 镶黄旗灌丛化草原灌木盖度分布图。

Fig. 6 Endmember spectra of MESMA model and predicted shrub coverage in shrub-encroached grassland area in Xianghuang Banner. A, Endmember spectra in MESMA model. B, Shrub coverage distribution in shrub-encroached grassland in Xianghuang Banner.

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