Forest aboveground biomass estimation using SPOT-5 texture indices and spectral derivatives

In this paper, spectral derivatives and textural indices derived from a SPOT-5 image have been investigated for the potential of estimating aboveground biomass (AGB) in the region of Liangshui (47 10'N, 128 53'E), Heilongjiang Provence, China. The results indicated that textural indices were more accurate than spectral derivatives and the best model could be obtained by combining textural indices and spectral derivatives (R2=0.77 and RMSE=55.96 t/ha).

[1]  P. S. Roy,et al.  Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest , 1996, Journal of Biosciences.

[2]  Xavier Pons,et al.  On the reliability of Landsat TM for estimating forest variables by regression techniques: a methodological analysis , 1998, IEEE Trans. Geosci. Remote. Sens..

[3]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[4]  S. Vieira,et al.  Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil) , 2010 .

[5]  W. Cohen,et al.  Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery , 1992 .

[6]  Michael A. Wulder,et al.  Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters , 1998 .

[7]  D. Lu.  Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon , 2005 .

[8]  Craig A. Coburn,et al.  A multiscale texture analysis procedure for improved forest stand classification , 2004 .

[9]  W. Walker,et al.  Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy , 2006 .

[10]  C. Kleinn,et al.  Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory , 2009 .

[11]  Thomas R. Crow,et al.  Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA , 2004 .

[12]  R. Hall,et al.  Incorporating texture into classification of forest species composition from airborne multispectral images , 2000 .

[13]  Pang Yong,et al.  Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China , 2012 .

[14]  Jorge M. Palmeirim,et al.  Mapping Mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour , 2004 .

[15]  Yong Pang,et al.  Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China: Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China , 2013 .