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Effects of sample plot size and GPS location errors on aboveground biomass estimates from LiDAR in tropical dry forests

Hernández Stefanoni, José Luis | Reyes Palomeque, Gabriela [autor/a] | Castillo Santiago, Miguel Ángel [autor/a] | George Chacón, Stephanie P [autor/a] | Huechacona Ruiz, Astrid Helena [autor/a] | Tun Dzul, Fernando Jesús [autor/a] | Rondon Rivera, Dinosca [autor/a] | Dupuy Rada, Juan Manuel [autor/a].
Tipo de material: Artículo en línea Artículo en línea Tema(s): Biomasa forestal | Monitoreo forestal | Bosques tropicales secos | Inventarios forestalesTema(s) en inglés: Forest biomass | Forest monitoring | Tropical dry forest | Forest inventoriesDescriptor(es) geográficos: Reserva Biocultural Kaxil Kiuic, Oxkutzcab (Yucatán, México) | Felipe Carrillo Puerto (Quintana Roo, México) Nota de acceso: Acceso en línea sin restricciones En: Remote Sensing. volumen 10, número 10, 1586 (October 2018), páginas 1-15. --ISSN: 2072-4292Número de sistema: 59088Resumen:
Inglés

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.

Recurso en línea: https://www.mdpi.com/2072-4292/10/10/1586
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Acceso en línea sin restricciones

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. eng

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