Light detection and ranging and hyperspectral data for estimation of forest biomass: a review

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 ver- tical 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 chal- lenges are also discussed. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.8.081598)