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Journal of Applied Remote Sensing • Open Access

Light detection and ranging and hyperspectral data for estimation of forest biomass: a review
Author(s): Qixia Man; Pinliang Dong; Huadong Guo; Guang Liu; Runhe Shi

Paper Abstract

Forests are one of the most important sinks for carbon. Estimating the amount of carbon stored in forests is a major task for understanding the global carbon cycle. From local to global scales, remote sensing has been extensively used for forest biomass estimation. With the availability of multisensor image data, fusion has become a valuable method in remote sensing applications. Light detection and ranging (LiDAR) can provide information on the vertical structure of forests, whereas hyperspectral images can provide detailed spectral information of forests. Effective fusion of LiDAR and hyperspectral data is expected to help extract important biophysical parameters of forests. However, it is still unclear as to how forest biophysical and biochemical attributes derived from hyperspectral data relate to structural attributes derived from LiDAR data. A summary of previous research on LiDAR-hyperspectral fusion for forest biomass estimation is valuable for further improvement of biomass estimation methods. A review on the status of hyperspectral data, LiDAR data, and the fusion of these two data sources for forest biomass estimation in the last decade is provided. Some future research topics and major challenges are also discussed.

Paper Details

Date Published: 18 December 2014
PDF: 21 pages
J. Appl. Rem. Sens. 8(1) 081598 doi: 10.1117/1.JRS.8.081598
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Qixia Man, East China Normal Univ. (China)
Chinese Academy of Sciences (China)
Institute of Remote Sensing and Digital Earth (China)
Pinliang Dong, Univ. of North Texas (United States)
Huadong Guo, East China Normal Univ. (China)
Chinese Academy of Sciences (China)
Guang Liu, Institute of Remote Sensing and Digital Earth (China)
Runhe Shi, East China Normal Univ. (China)
Institute of Remote Sensing and Digital Earth (China)

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