Biomass estimation of alpine grasslands under different grazing intensities using spectral vegetation indices

We measured and analyzed the spectral reflectance characteristics and biophysical properties such as biomass and vegetation cover of purple feather-grass (Stipa purpurea) alpine steppe grassland under different grazing intensities (no grazing, light, moderate, and heavy grazing) during the growing season. We investigated the effects of grazing intensities on the spectral reflectance and red edge parameters of S. purpurea alpine steppe grassland and established potential models of biomass estimation based on the relationship of spectral vegetation indices and measured alpine grassland biomass in Northern Tibet, China. The results indicated that the spectral reflectance of alpine grassland is similar to the spectral characteristics of typical green vegetation. Reflectance in the near-infrared region of the no grazing treatment was higher than the grazing treatments, while the reflectance of the no grazing treatment in the visible light region was weaker than the grazing treatments. With enhanced grazing intensity, the spectral reflectance of grass canopy followed an increasing trend; the red edge position moved to longer wavelengths, and the amplitude of the red edge decreased in alpine grassland. A significant relationship of measured aboveground biomass and vegetation indices was caculated based on spectral reflectance. Hence, the optimized soil adjusted vegetation index is prefered for the estimation of aboveground biomass of S. purpurea alpine steppe in Northern Tibet, China.

[1]  B. Wylie,et al.  Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands a case study , 2002 .

[2]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[3]  R. Harris.  Rangeland degradation on the Qinghai-Tibetan plateau: A review of the evidence of its magnitude and causes , 2010 .

[4]  Andrew K. Skidmore,et al.  Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Josep Peñuelas,et al.  Evaluating Wheat Nitrogen Status with Canopy Reflectance Indices and Discriminant Analysis , 1995 .

[6]  R. Person.  Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie, Pawnee National Grasslands, Colorado , 1972 .

[7]  J. Paruelo,et al.  ANPP ESTIMATES FROM NDVI FOR THE CENTRAL GRASSLAND REGION OF THE UNITED STATES , 1997 .

[8]  L. D. Miller,et al.  Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado , 1972 .

[9]  Qingzhu Gao,et al.  Dynamics of alpine grassland NPP and its response to climate change in Northern Tibet , 2009 .

[10]  L. Su.  VEGETATION INDEXES-BIOMASS MODELS FOR TYPICAL SEMI-ARID STEPPE—A CASE STUDY FOR XILINHOT IN NORTHERN CHINA , 2007 .

[11]  J. Peñuelas,et al.  The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. , 1994 .

[12]  Fuan Tsai,et al.  Derivative analysis of hyperspectral data , 1996, Remote Sensing.

[13]  W. P. Ball,et al.  Comparison of quantification methods to measure fire‐derived (black/elemental) carbon in soils and sediments using reference materials from soil, water, sediment and the atmosphere , 2007 .

[14]  O. Sala,et al.  Vegetation structure constrains primary production response to water availability in the Patagonian steppe. , 2006, Ecology.

[15]  N. K. Patel,et al.  Study of fractional vegetation cover using high spectral resolution data , 2007 .

[16]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[17]  Chen Xi,et al.  Study on the biomass change derived from the hyperspectral data of cotton leaves in canopy under moisture stress , 2006 .

[18]  Yuhong He,et al.  Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices , 2006 .

[19]  J. Dungan,et al.  The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration , 1991 .

[20]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[21]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[22]  S. Piao,et al.  Changes in biomass carbon stocks in China's grasslands between 1982 and 1999 , 2007 .

[23]  P. Curran,et al.  A new technique for interpolating the reflectance red edge position , 1998 .

[24]  Paul T. Tueller,et al.  Remote sensing technology for rangeland management applications. , 1989 .

[25]  Yuhong He,et al.  Reflectance measures of grassland biophysical structure , 2009 .

[26]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[27]  D. Sims,et al.  Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland , 2003 .

[28]  Masayuki Tamura,et al.  Hyperspectral identification of grassland vegetation in Xilinhot, Inner Mongolia, China , 2003 .

[29]  Jingyun Fang,et al.  Aboveground biomass in Tibetan grasslands , 2009 .

[30]  Zheng Du,et al.  The system of physico-geographical regions of the Qinghai-Xizang (Tibet) Plateau , 1996 .

[31]  S. Dobrowski,et al.  Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects , 2003 .

[32]  A. Skidmore,et al.  Red edge shift and biochemical content in grass canopies , 2007 .

[33]  M. Fladeland,et al.  Remote sensing for biodiversity science and conservation , 2003 .