Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques



Mapping forest biomass is fundamental for estimating CO2 emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m × 31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10–40, 40–70 and >70 % of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m3 ha−1. The total growing stock of the forest was found to be 2,024,652.88 m3. The AGWB ranged from 143 to 421 Mgha−1. Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE) = 42.25 Mgha−1) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha−1 respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha−1. The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping.


Biomass mapping Forest inventory Remote sensing Direct radiometric relationships k-nearest neighbours Cokriging 



The authors sincerely thank the Director, Indian Institute of Remote Sensing, Dehradun and the Director, Centre for Space Science and Technology Education in Asia and the Pacific (CSSTEAP), Dehradun, for the encouragement and support for this study. The first author wish to acknowledge Ministry of Forests and Soil Conservation, Nepal for nomination and CSSTEAP, Dehradun, India, to carry out this study as a partial fulfilment of Post Graduate course. Grateful thanks are due to the forest officers and staff of Timli Forest Range, Kalsi Soil and Water Conservation Division, Uttarakhand, India, for field support.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of ForestsKathmanduNepal
  2. 2.Forestry and Ecology DepartmentIndian Institute of Remote Sensing, ISRODehradunIndia

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