Efficacy of using heterogeneous lidar datasets in predicting canopy heights over a large region
Unbiased wall-to-wall maps of canopy heights for large regions are useful to forest scientists for several reasons,including the following: (1) estimating biomass for carbon accounting related to climate change; (2) assessing forest productivity and health,and (3) detecting and monitoring fuel load “hot spots where wildfire risk is high.Airborne lidar is the most promising technology for such efforts,with acquisition costs dropping and precision and quality increasing over time.However,mapping large areas necessitates using lidar data from different projects,involving various acquisition dates,sensors,pulse densities,flying heights,etc.In this work,we address the important question of whether one can predict and model canopy heights over large areas of the US Southeast with reasonable accuracy using such a heterogeneous lidar dataset (with more than 30 separate lidar projects).A unique aspect of this effort is the use of extensive field data (more than 1600 plots) from the Forest Inventory and Analysis (FIA) program of the US Forest Services.The use of a national forest inventory network with a uniform and robust sampling scheme is an added advantage to our work.
Ranjith Gopalakrishnan Valerie A.Thomas John Coulston Randolph H.Wynne
Dept. of Forest Resources and Environment Conservation, Virginia Tech, USA USDA Forest Service(Southern Research Station), USA
国际会议
北京
英文
145-153
2013-10-09（万方平台首次上网日期，不代表论文的发表时间）