Next Article in Journal
Atmospheric Trace Gas (NO2 and O3) Variability in South Korean Coastal Waters, and Implications for Remote Sensing of Coastal Ocean Color Dynamics
Previous Article in Journal
Status of Aquarius and Salinity Continuity
Open AccessArticle

Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests

1
Centro de Investigación Científica de Yucatán A.C., Unidad de Recursos Naturales, Calle 43 # 130 , x 32 y 34 Colonia Chuburná de Hidalgo, Mérida CP 97205, Yucatán, Mexico
2
Laboratorio de Análisis de Información Geográfica y Estadística, El Colegio de la Frontera Sur, Carretera Panamericana y Periférico Sur s/n, San Cristóbal de las Casas CP 29290, Chiapas, Mexico
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1586; https://doi.org/10.3390/rs10101586
Received: 28 August 2018 / Revised: 18 September 2018 / Accepted: 29 September 2018 / Published: 2 October 2018
(This article belongs to the Section Forest Remote Sensing)
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 Yucatán, México. We sampled woody plants of three size classes in 29 nested plots (80 m2, 400 m2 and 1000 m2) 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 m2 to 1000 m2 (R2 = 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. View Full-Text
Keywords: airborne laser scanner; forest biomass; plot size; co-registration error; Monte Carlo simulation airborne laser scanner; forest biomass; plot size; co-registration error; Monte Carlo simulation
Show Figures

Figure 1

MDPI and ACS Style

Hernández-Stefanoni, J.L.; Reyes-Palomeque, G.; Castillo-Santiago, M.Á.; George-Chacón, S.P.; Huechacona-Ruiz, A.H.; Tun-Dzul, F.; Rondon-Rivera, D.; Dupuy, J.M. Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests. Remote Sens. 2018, 10, 1586.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop