Next Article in Journal
Next Article in Special Issue
Previous Article in Journal
Previous Article in Special Issue
Forests 2013, 4(4), 984-1002; doi:10.3390/f4040984
Article

Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest

1,* , 2
, 1
 and 1
Received: 8 August 2013; in revised form: 28 October 2013 / Accepted: 15 November 2013 / Published: 20 November 2013
View Full-Text   |   Download PDF [2163 KB, updated 21 November 2013; original version uploaded 20 November 2013]   |   Browse Figures
Abstract: This study aims to estimate forest above-ground biomass and biomass components in a stand of Picea crassifolia (a coniferous tree) located on Qilian Mountain, western China via low density small-footprint airborne LiDAR data. LiDAR points were first classified into ground points and vegetation points. After, vegetation statistics, including height quantiles, mean height, and fractional cover were calculated. Stepwise multiple regression models were used to develop equations that relate the vegetation statistics from field inventory data with field-based estimates of biomass for each sample plot. The results showed that stem, branch, and above-ground biomass may be estimated with relatively higher accuracies; estimates have adjusted R2 values of 0.748, 0.749, and 0.727, respectively, root mean squared error (RMSE) values of 9.876, 1.520, and 15.237 Mg·ha−1, respectively, and relative RMSE values of 12.783%, 12.423%, and 14.163%, respectively. Moreover, fruit and crown biomass may be estimated with relatively high accuracies; estimates have adjusted R2 values of 0.578 and 0.648, respectively, RMSE values of 1.022 and 5.963 Mg·ha−1, respectively, and relative RMSE values of 23.273% and 19.665%, respectively. In contrast, foliage biomass estimates have relatively low accuracies; they had an adjusted R2 value of 0.356, an RMSE of 3.691 Mg·ha−1, and a relative RMSE of 26.953%. Finally, above-ground biomass and biomass component spatial maps were established using stepwise multiple regression equations. These maps are very useful for updating and modifying forest base maps and registries.
Keywords: above-ground biomass; biomass components; LiDAR; coniferous forest; Qilian Mountain above-ground biomass; biomass components; LiDAR; coniferous forest; Qilian Mountain
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

He, Q.; Chen, E.; An, R.; Li, Y. Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest. Forests 2013, 4, 984-1002.

AMA Style

He Q, Chen E, An R, Li Y. Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest. Forests. 2013; 4(4):984-1002.

Chicago/Turabian Style

He, Qisheng; Chen, Erxue; An, Ru; Li, Yong. 2013. "Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest." Forests 4, no. 4: 984-1002.


Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert