Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data

NWIPB OpenIR

	Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data
	Chen, Jin 1,2; Gu, Song 3; Shen, Miaogen 2; Tang, Yanhong 1; Matsushita, Bunkei 4
	2009
发表期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
卷号	30 期号:24 页码:6497-6517
文章类型	Article
摘要	To improve the estimation of aboveground biomass of grassland having a high canopy cover based on remotely sensed data, we measured in situ hyperspectral reflectance and the aboveground green biomass of 42 quadrats in an alpine meadow ecosystem on the Qinghai-Tibetan Plateau. We examined the relationship between aboveground green biomass and the spectral features of original reflectance, first-order derivative reflectance (FDR), and band-depth indices by partial least squares (PLS) regression, as well as the relationship between the aboveground biomass and narrow-band vegetation indices by linear and nonlinear regression analyses. The major findings are as follows. (1) The effective portions of spectra for estimating aboveground biomass of a high-cover meadow were within the red-edge and near infrared (NIR) regions. (2) The band-depth ratio (BDR) feature, using NIR region bands (760-950 nm) in combination with the red-edge bands, yields the best predictive accuracy (RMSE = 40.0 gm(-2)) for estimating biomass among all the spectral features used as independent variables in the partial least squares regression method. (3) The ratio vegetation index (RVI2) and the normalized difference vegetation index (NDVI2) proposed by Mutanga and Skidmore (Mutanga, O. and Skidmore, A. K., 2004a, Narrow band vegetation indices solve the saturation problem in biomass estimation. International Journal of Remote Sensing, 25, pp. 1-6) are better correlated to the aboveground biomass than other VIs (R(2) = 0.27 for NDVI2 and 0.26 for RVI2), while RDVI, TVI and MTV1 predicted biomass with higher accuracy (RMSE - 37.2 g m(-2), 39.9 gm(-2) and 39.8 g m(-2), respectively). Although all of the models developed in this study are probably acceptable, the models developed in this study still have low accuracy, indicating the urgent need for further efforts.; To improve the estimation of aboveground biomass of grassland having a high canopy cover based on remotely sensed data, we measured in situ hyperspectral reflectance and the aboveground green biomass of 42 quadrats in an alpine meadow ecosystem on the Qinghai-Tibetan Plateau. We examined the relationship between aboveground green biomass and the spectral features of original reflectance, first-order derivative reflectance (FDR), and band-depth indices by partial least squares (PLS) regression, as well as the relationship between the aboveground biomass and narrow-band vegetation indices by linear and nonlinear regression analyses. The major findings are as follows. (1) The effective portions of spectra for estimating aboveground biomass of a high-cover meadow were within the red-edge and near infrared (NIR) regions. (2) The band-depth ratio (BDR) feature, using NIR region bands (760-950 nm) in combination with the red-edge bands, yields the best predictive accuracy (RMSE = 40.0 gm(-2)) for estimating biomass among all the spectral features used as independent variables in the partial least squares regression method. (3) The ratio vegetation index (RVI2) and the normalized difference vegetation index (NDVI2) proposed by Mutanga and Skidmore (Mutanga, O. and Skidmore, A. K., 2004a, Narrow band vegetation indices solve the saturation problem in biomass estimation. International Journal of Remote Sensing, 25, pp. 1-6) are better correlated to the aboveground biomass than other VIs (R(2) = 0.27 for NDVI2 and 0.26 for RVI2), while RDVI, TVI and MTV1 predicted biomass with higher accuracy (RMSE - 37.2 g m(-2), 39.9 gm(-2) and 39.8 g m(-2), respectively). Although all of the models developed in this study are probably acceptable, the models developed in this study still have low accuracy, indicating the urgent need for further efforts.
WOS标题词	Science & Technology ; Technology
关键词[WOS]	LEAST-SQUARES REGRESSION ; ADJUSTED VEGETATION INDEX ; YELLOWSTONE-NATIONAL-PARK ; RED EDGE POSITION ; SPECTRAL INDEXES ; AREA INDEX ; BROAD-BAND ; REFLECTANCE ; PHYTOMASS ; MODIS
收录类别	SCI
语种	英语
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000273641500008
引用统计	被引频次：101[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://210.75.249.4/handle/363003/1920
专题	中国科学院西北高原生物研究所
作者单位	1.Natl Inst Environm Studies, Tsukuba, Ibaraki 3058569, Japan 2.Beijing Normal Univ, Minist Educ China, Key Lab Environm Change & Nat Disaster, Beijing 100875, Peoples R China 3.Chinese Acad Sci, NW Plateau Inst Biol, Xining 810001, Peoples R China 4.Univ Tsukuba, Grad Sch Life & Environm Sci, Tsukuba, Ibaraki 3058572, Japan
推荐引用方式 GB/T 7714	Chen, Jin,Gu, Song,Shen, Miaogen,et al. Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2009,30(24):6497-6517.
APA	Chen, Jin,Gu, Song,Shen, Miaogen,Tang, Yanhong,&Matsushita, Bunkei.(2009).Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data.INTERNATIONAL JOURNAL OF REMOTE SENSING,30(24),6497-6517.
MLA	Chen, Jin,et al."Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data".INTERNATIONAL JOURNAL OF REMOTE SENSING 30.24(2009):6497-6517.