Combining remote sensing imagery and forest age inventory for biomass mapping.

Aboveground biomass (AGB) of forests is an important component of the global carbon cycle. In this study, Landsat ETM(+) images and field forest inventory data were used to estimate AGB of forests in Liping County, Guizhou Province, China. Three different vegetation indices, including simple ratio (SR), reduced simple ratio (RSR), and normalized difference vegetation index (NDVI), were calculated from atmospherically corrected ETM(+) reflectance images. A leaf area index (LAI) map was produced from the RSR map using a regression model based on measured LAI and RSR. The LAI map was then used to develop an initial AGB map, from which forest stand age was deduced. Vegetation indices, LAI, and forest stand age were together used to develop AGB estimation models for different forest types through a stepwise regression analysis. Significant predictors of AGB changed with forest types. LAI and NDVI were significant predictors of AGB for Chinese fir (R(2)=0.93). The model using LAI and stand age as predictors explained 94% of the AGB variance for coniferous forests. Stand age captured 79% of the AGB variance for broadleaved forests (R(2)=0.792). AGB of mixed forests was predicted well by LAI and SR (R(2)=0.931). Without differentiating among forest types, the model with SR and LAI as predictors was able to explain 90% of AGB variances of all forests. In Liping County, AGB shows a strong gradient that increases from northeast to southwest. About 64% of the forests have AGB in the range from 90 to 180 t ha(-1).

[1]  Yrjö Rauste,et al.  Multi-temporal JERS SAR data in boreal forest biomass mapping , 2005 .

[2]  J. Cihlar,et al.  Plant canopy gap-size analysis theory for improving optical measurements of leaf-area index. , 1995, Applied optics.

[3]  Jingyun Fang,et al.  FOREST BIOMASS OF CHINA: AN ESTIMATE BASED ON THE BIOMASS–VOLUME RELATIONSHIP , 1998 .

[4]  S. Running,et al.  Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System , 2000 .

[5]  G. S. Haripriya,et al.  Estimates of biomass in Indian forests , 2000 .

[6]  R. B. Jackson,et al.  Methods in Ecosystem Science , 2000, Springer New York.

[7]  Scott J. Goetz,et al.  Interannual variability of global terrestrial primary production: Results of a model driven with satellite observations , 2000 .

[8]  A. C. Seijmonsbergen,et al.  Improved landsat-based forest mapping in steep mountainous terrain , 2003 .

[9]  A. Gitelson.  Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation. , 2004, Journal of plant physiology.

[10]  M. A. Green,et al.  Integrating Eucalypt Forest Inventory and GIs in Western Australia Practical Paper , 1997 .

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

[12]  Thuy Le Toan,et al.  Biomass quantification of Andean wetland forages using ERS satellite SAR data for optimizing livestock management , 2003 .

[13]  E. Vermote,et al.  Second Simulation Of The Satellite Signal In The Solar Spectrum - 6s Code , 1990, 10th Annual International Symposium on Geoscience and Remote Sensing.

[14]  S. Leblanc,et al.  A Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests: An Image and Model Analysis , 2000 .

[15]  A. Gillespie,et al.  Topographic Normalization of Landsat TM Images of Forest Based on Subpixel Sun–Canopy–Sensor Geometry , 1998 .

[16]  R. Lacaze,et al.  Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption , 2003 .

[17]  S. Goward,et al.  Global Primary Production: A Remote Sensing Approach , 1995 .

[18]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..