Producing a Canopy Height Map Over a Large Region Using Heterogeneous LIDAR Datasets

Abstract

Accurate and unbiased wall-to-wall canopy height maps for large regions are useful to forest scientists and managers for several reasons such as carbon accounting and wildfire fuel-load monitoring. Airborne lidar is establishing itself as the most promising technology for this. However, mapping large areas often involves using lidar data from different projects executed by different agencies, involving varying acquisition dates, sensors, pulse densities, etc. In this work, we address the important question of how accurately one can predict and model canopy heights over large areas of the Southeastern US using a heterogeneous lidar datasets (with more than 90 separate lidar projects). A unique aspect of this effort is the use of extensive and robust field data from the Forest Inventory and Analysis (FIA) program of the US Forest Service. We construct a simple linear model to predict canopy height at plots from distributional lidar metrics. Preliminary results are quite promising: over all lidar projects, we observe a correlation of 81.8% between the 95th percentile of lidar heights and field-measured height, with an RMSE of 3.66 meters (n=3078). We further estimated that ~1.21 m (33%) of this RMSE could be attributed to co-registration inaccuracies. The RMSE of 3.66 m compares quite well to previous efforts that used spaceborne lidar sensors to estimate canopy heights over large regions. We also identify and quantify the importance of several factors (like point density, the predominance of hardwoods or softwood) that also influence the efficacy of our prediction model.