Light Detection and Ranging (LiDAR) is an effective, rapid, remote-sensing technology for describing the structure of forest canopies, height of individual trees, and the size and location of forest disturbances. These data can also provide estimates of standing carbon stocks of entire forests at landscape scales. When used in conjunction with LiDAR, hyperspectral imagery, which measures the wavelength of light reflected from a canopy, can provide identification of individual forest species. At present, the cost of deploying LiDAR and hyperspectral imagery has limited its application mostly to temperate forests. A time-limited opportunity has arisen to acquire this imagery at a greatly reduced cost for Barro Colorado Nature Monument (BCNM), Panama, one of the best-studied tropical forests in the world.
LiDAR and hyperspectral imagery provide an exceptionally powerful tool for integrating data on the structure and biodiversity of tropical forests. These data will provide a valuable resource for diverse projects conducted at BCNM, and will stimulate new research aimed at translating existing ground-based research up to landscape scales relevant to forest management and conservation. The wealth of existing data at BCNM allow for a rigorous evaluation of LiDAR's potential to provide large-scale forest carbon estimates, which will play a key role in implementation of proposed carbon dioxide (CO2) emission control through Reduced Emissions from Deforestation and Degradation. Data will be maintained by Smithsonian's Environmental Science Program and made available online and as an Ecological Archives publication one year after acquisition. These data will contribute to the training and research of students at numerous collaborating institutions.
