Asmamaw Gebrehiwot North Carolina A&T State University
Leila Hashemi-Beni North Carolina A&T State University
Lyubov A Kurkalova North Carolina A&T State University
Chinazor S. Azubike North Carolina A&T State University
Timothy J. Mulrooney North Carolina Central University

24P

Protecting and monitoring forests throughout the world is an essential part of the international effort to reduce greenhouse gas emissions in the fight against global warming. Quantifying above ground biomass in forest ecosystems is critical for terrestrial carbon cycle and further develops a capacity for monitoring carbon stocks over time. Airborne laser scanning (LIDAR), a high-resolution active remote sensing  technology that uses light in the form of a pulsed laser to densely  sample the earth’s surface, provides an accurate and efficient  measurement of three-dimensional forest structures over an extensive area. This study catalogs geospatial data to calculate forest acreage and to estimate biomass in North Carolina with an end-goal of performing economic analysis of NC woodlands. The data include LiDAR datasets acquired March through April 2005 (FIRS) and QL2 LiDAR with resolution of 2 points per meter collected from 2014 to present covering all counties in North Carolina; National Land Cover Database (NLCD), which is updated once every five years; and the US Department of Agriculture (USDA) National Agricultural Statistics Service Cropland Data Layer (CDL), which is collected yearly.

The analysis proceeds in four major steps. First, we compare study area forest land location and acreage estimates derived from NLCD and CDL. Then, LiDAR data is used to validate and resolve any inconsistencies in forest land estimation from a combination of NCLD and CDL data. Third, we develop and apply a framework to measure the vertical structures of the canopy in Duplin County by creating a DEM and calculating canopy density and height, and biomass estimation. The canopy height is determined by subtracting the bare earth surface and digital surface model created using bare earth and vegetation LIDAR points. The density of the canopy is estimated by comparing the number of vegetation points to the total number of points. Finally, we combine a) forest land area estimates and b) canopy height and density estimates with c) known distributions of the diameter of trees in the area encompassing the study area, to estimate forest biomass available in the study area.

Our estimates supplement and complement those relying solely on the survey-based, USDA Southern Research Station’s Forest Inventory and Analysis data, collected yearly on one fifth of the state’s forest area.  The developed methodology and forest biomass estimation underscore the importance of comparing, reconciling, and combining of alternative data sources for forest biomass estimation.

13:45 Biomass Estimation Using Lidar Data in North Carolina, Asmamaw Gebrehiwot