Title
Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests.
Abstract
Accurate estimates of above ground biomass (AGB) are needed for monitoring carbon in tropical forests. LiDAR data can provide precise AGB estimations because it can capture the horizontal and vertical structure of vegetation. However, the accuracy of AGB estimations from LiDAR is affected by a co-registration error between LiDAR data and field plots resulting in spatial discrepancies between LiDAR and field plot data. Here, we evaluated the impacts of plot location error and plot size on the accuracy of AGB estimations predicted from LiDAR data in two types of tropical dry forests in Yucatan, Mexico. We sampled woody plants of three size classes in 29 nested plots (80 m(2), 400 m(2) and 1000 m(2)) in a semi-deciduous forest (Kiuic) and 28 plots in a semi-evergreen forest (FCP) and estimated AGB using local allometric equations. We calculated several LiDAR metrics from airborne data and used a Monte Carlo simulation approach to assess the influence of plot location errors (2 to 10 m) and plot size on ABG estimations from LiDAR using regression analysis. Our results showed that the precision of AGB estimations improved as plot size increased from 80 m(2) to 1000 m(2) (R-2 = 0.33 to 0.75 and 0.23 to 0.67 for Kiuic and FCP respectively). We also found that increasing GPS location errors resulted in higher AGB estimation errors, especially in the smallest sample plots. In contrast, the largest plots showed consistently lower estimation errors that varied little with plot location error. We conclude that larger plots are less affected by co-registration error and vegetation conditions, highlighting the importance of selecting an appropriate plot size for field forest inventories used for estimating biomass.
Year
DOI
Venue
2018
10.3390/rs10101586
REMOTE SENSING
Keywords
Field
DocType
airborne laser scanner,forest biomass,plot size,co-registration error,Monte Carlo simulation
Biomass,Remote sensing,Lidar,Global Positioning System,Tropical and subtropical dry broadleaf forests,Geology
Journal
Volume
Issue
ISSN
10
10
2072-4292
Citations 
PageRank 
References 
0
0.34
3
Authors
8