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.

参考文献 34

[1]	Adams JB, Sabol DE, Kapos V, Almeida Filho RA, Roberts DA, Smith MO, Gillespie AR (1995). Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon.Remote Sensing of Environment, 52, 137-154.
[2]	Archer S, Scifres C, Bassham CR, Maggio R (1988). Autogenic succession in a subtropical savanna: Conversion of grassland to thorn woodland.Ecological Monographs, 58, 111-127.
[3]	Asner GP (1998). Biophysical and biochemical sources of variability in canopy reflectance.Remote Sensing of Environment, 64, 234-253.
[4]	Cao C, Chen W, Li G, Jia H, Ji W, Xu M, Gao M, Ni X, Zhao J, Zheng S (2011). The retrieval of shrub fractional cover based on a geometric-optical model in combination with linear spectral mixture analysis.Canadian Journal of Remote Sensing, 37, 348-358.
[5]	Castro H, Freitas H (2009). Above-ground biomass and productivity in the Montado: From herbaceous to shrub dominated communities.Journal of Arid Environments, 73, 506-511.
[6]	Chen LY, Shen HH, Fang JY (2014). Shrub-encroached grassland: A new vegetation type.Chinese Journal of Nature, 6, 391-396.(in Chinese with English abstract) [陈蕾伊, 沈海花, 方精云 (2014). 灌丛化草原: 一种新的植被景观. 自然杂志, 6, 391-396.]
[7]	Chen XQ, Li J (2009). Relationships between Leymus chinensis phenology and meteorological factors in Inner Mongolia grasslands.Acta Ecologica Sinica, 10, 5280-5290.(in Chinese with English abstract)[陈效逑, 李倞 (2009). 内蒙古草原羊草物候与气象因子的关系. 生态学报,10, 5280-5290.]
[8]	Cho MA, Skidmore AK (2006). A new technique for extracting the red edge position from hyper-spectral data: The linear extrapolation method.Remote Sensing of Environment, 101, 181-193.
[9]	Deng SB, Chen QJ (2010). Spectral Characteristics of Vegeta- tion and Vegetation Index Summary. The Application of Remote Sensing China Association 2010 Conference and Regional Remote Sensing and Industrial Development Forum. China Association of Remote Sensing Application, Beijing. 9.(in Chinese)[邓书斌, 陈秋锦 (2010).植被波谱特征与植被指数综述. 中国遥感应用协会2010年会暨区域遥感发展与产业高层论坛论文集. 中国遥感应用协会, 北京. 9.]
[10]	Dong Z, Zhao X, Liang D, Huang WJ, Peng DL, Huang LS (2014). Remote sensing identification of shrub encroach- ment in Xianghuangqi, Nei Mongol.Transactions of the Chinese Society of Agricultural Engineering, 11, 152-158.(in Chinese with English abstract)[董洲, 赵霞, 梁栋, 黄文江, 彭代亮, 黄林生 (2014). 内蒙古灌丛化草原分布特征的遥感辨识. 农业工程学报,11, 152-158.]
[11]	Eldridge DJ, Bowker MA, Maestre FT, Roger E, Reynolds JF, Whitford WG (2011). Impacts of shrub encroachment on ecosystem structure and functioning: Towards a global synthesis.Ecology Letters, 14, 709-722.
[12]	Elmore AJ, Mustard JF, Manning SJ, Lobell DB (2000). Quantifying vegetation change in semiarid environments.Remote Sensing of Environment, 73, 87-102.
[13]	Fan Y, Li XY, Li GY (2014). Variation characteristics of shrub phenology in grassland of Inner Mongolia based on MODIS.Journal of Arid Meteorology, 6, 902-908.(in Chinese with English abstract) [范瑛, 李小雁, 李广泳 (2014).基于遥感数据的内蒙古草原灌丛物候变化研究. 干旱气象,6, 902-908.]
[14]	Goslee SC, Havstad KM, Peters DC, Rango A, Schlesinger WH (2003). High-resolution images reveal rate and pattern of shrub encroachment over six decades in New Mexico, USA.Journal of Arid Environments, 54, 755-767.
[15]	Hamada Y, Stow DA, Roberts DA, Franklin J, Kyriakidis PC (2013). Assessing and monitoring semi-arid shrublands using object-based image analysis and multiple endmember spectral mixture analysis.Environmental Monitoring and Assessment, 185, 3173-3190.
[16]	Hostert P, Roder A, Hill J (2003). Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands.Remote Sensing of Environment, 87, 183-197.
[17]	Jurena PN, Archer S (2003). Woody plant establishment and spatial heterogeneity in grasslands.Ecology, 84, 907-919.
[18]	Kuemmerle T, Roder A, Hill J (2006). Separating grassland and shrub vegetation by multidate pixel-adaptive spectral mixture analysis.International Journal of Remote Sensing, 27, 3251-3271.
[19]	Laliberte AS, Rango A, Havstad KM, Paris JF, Beck RF, Mcneely R, Gonzalez AL (2004). Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico.Remote Sensing of Environment, 93, 198-210.
[20]	Mcpherson GR, Wright HA, Wester DB (1988). Patterns of shrub invasion in semiarid Texas grasslands.The American Midland Naturalist, 120, 391-397.
[21]	Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution im- agery.Remote Sensing of Environment, 115, 1145-1161.
[22]	Niu XW (1998). Biological characteristics of Caragana microphylla.Acta Agriculturae Boreali-Sinica, 4, 123-130.(in Chinese with English abstract)[牛西午 (1998). 柠条生物学特性研究. 华北农学报,4, 123-130.]
[23]	Peng HY, Li XY, Tong SY (2014). Advance in shrub en- croachment in arid and semiarid region. Acta Prataculturae Sinica, 23, 313-322.(in Chinese with English abstract)[彭海英, 李小雁, 童绍玉 (2014). 干旱半干旱区草原灌丛化研究进展. 草业学报,23, 313-322.]
[24]	Roberts DA, Gardner M, Church R, Ustin S, Scheer G, Green RO (1998). Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models.Remote Sensing of Environment, 65, 267-279.
[25]	Shan LY (2004). Spectrum Analysis and Information Obtaining on TM Image of Xianghuang Banner Grassland in Inner Mongolia. Master degree dissertation, Gansu Agricultural University, Lanzhou. 4-9.(in Chinese with English abstract)[单丽燕 (2004). 内蒙古镶黄旗草原TM影像的波谱分析及信息提取. 硕士学位论文, 甘肃农业大学, 兰州. 4-9.]
[26]	Shi GH (2014). Effects of climate change on phenophase of forage grass in Xilinguole typical grassland.Chinese Agricultural Science Bulletin, 29, 197-204.(in Chinese with English abstract)[师桂花 (2014). 气候变化对锡林郭勒盟典型草原天然牧草物候期的影响. 中国农学通报,29, 197-204.]
[27]	Smith MO, Ustin SL, Adams JB, Gillespie AR (1990). Vegeta- tion in deserts: I. A regional measure of abundance from multispectral images.Remote Sensing of Environment, 31, 1-26.
[28]	Sohn Y, Mccoy RM (1997). Mapping desert shrub rangeland using spectral unmixing and modeling spectral mixtures with TM data.Photogrammetric Engineering and Remote Sensing, 63, 707-716.
[29]	Wang H, Li XB, Long HL, Xu X, Zhang C (2008). Simulating vegetation fractional coverage for temperate grassland in Northern China combining NDVI with precipitation time series from 1982 to 1999.Journal of Basic Science and Engineering, 4, 525-536.(in Chinese with English abstract)[王宏, 李晓兵, 龙慧灵, 许旭, 张程 (2008). 整合1982-1999年NDVI与降雨量时间序列模拟中国北方温带草原植被盖度. 应用基础与工程科学学报,4, 525-536.]
[30]	Woebbecke DM, Meyer GE, von Bargen K, Mortensen DA (1992). Plant species identification, size, and enumeration using machine vision techniques on near-binary images.SPIE Optics in Agriculture and Forestry, 1836, 208-212.
[31]	Xiao J, Moody A (2005). A comparison of methods for estima- ting fractional green vegetation cover within a desert-to- upland transition zone in central New Mexico, USA.Remote Sensing of Environment, 98, 237-250.
[32]	Xu N, Ding JL, Liu HX (2012). Extraction of vegetation information in arid area based on NDVI and LSMM: A case study of Turpan.Geomatics & Spatial Information Technology, 7, 52-57.(in Chinese with English abstract)[徐娜, 丁建丽, 刘海霞 (2012). 基于NDVI和LSMM的干旱区植被信息提取研究——以新疆吐鲁番市为例. 测绘与空间地理信息,7, 52-57.]
[33]	Zhang FH, Huang MX, Zhang J, Bao G, Bao YH (2014). Identification of grass species based on hyperspectrum—A case study of Xilin Gol Grassland. Bulletin of Surveying and Mapping, (7), 66-69.(in Chinese with English abstract)[张富华, 黄明祥, 张晶, 包钢, 包玉海 (2014). 利用高光谱识别草地种类的研究——以锡林郭勒草原为例. 测绘通报, (7), 66-69.]
[34]	Zhou Y, Chen J, Chen XH, Cao X, Zhu XL (2013). Two important indicators with potential to identify Caragana microphylla in Xilin Gol grassland from temporal MODIS data.Ecological Indicators, 34, 520-527.