Using LiDAR and normalized difference vegetation index to remotely determine LAI and percent canopy cover at varying scales

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Title: Using LiDAR and normalized difference vegetation index to remotely determine LAI and percent canopy cover at varying scales
Author: Griffin, Alicia Marie Rutledge
Abstract: The use of airborne LiDAR (Light Detection and Ranging ) as a direct method to evaluate forest canopy parameters is vital in addressing both forest management and ecological concerns . The overall goal of this study was to develop the use of airborne LiDAR in evaluating canopy parameters such as percent canopy cover (PCC ) and leaf area index (LAI ) for mixed pine and hardwood forests (primarily loblolly pine , Pinus taeda , forests ) of the southeastern United States . More specific objectives were to : (1 ) Develop scanning LiDAR and multispectral imagery methods to estimate PCC and LAI over both hardwood and coniferous forests ; (2 ) investigate whether a LiDAR and normalized difference vegetation index (NDVI ) data fusion through linear regression improve estimates of these forest canopy characteristics ; (3 ) generate maps of PCC and LAI for the study region , and (4 ) compare local scale LiDAR -derived PCC and regional scale MODIS -based PCC and investigate the relationship . Scanning LiDAR data was used to derive local scale PCC estimates , and TreeVaW , a LiDAR software application , was used to locate individual trees to derive an estimate of plot -level PCC . A canopy height model (CHM ) was created from the LiDAR dataset and used to determine tree heights per plot . QuickBird multispectral imagery was used to calculate the NDVI for the study area . LiDAR - and NDVI -derived estimates of plot -level PCC and LAI were compared to field observations for 53 plots over 47 square kilometers . Linear regression analysis resulted in models explaining 84 % and 78 % of the variability associated with PCC and LAI , respectively . For these models to be of use in future studies , LiDAR point density must be 2 .5 m . The relationship between regional scale PCC and local scale PCC was investigated by resizing the local scale LiDAR -derived PCC map to lower resolution levels , then determining a regression model relating MODIS data to the local values of PCC . The results from this comparison showed that MODIS PCC data is not very accurate at local scales . The methods discussed in this paper show great potential for improving the speed and accuracy of ecological studies and forest management .
URI: http : / /hdl .handle .net /1969 .1 /ETD -TAMU -1117
Date: 2009-05-15

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Using LiDAR and normalized difference vegetation index to remotely determine LAI and percent canopy cover at varying scales. Available electronically from http : / /hdl .handle .net /1969 .1 /ETD -TAMU -1117 .

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