Título
Improving species diversity and biomass estimates of tropical dry forests using airborne LiDAR
Autor
JOSE LUIS HERNANDEZ STEFANONI
JUAN MANUEL DUPUY RADA
Richard Birdsey
FERNANDO JESUS TUN DZUL
Alicia Peduzzi
JUAN PABLO CAAMAL SOSA
Nivel de Acceso
Acceso Abierto
Referencia de datos
doi: DOI: 10.3390/rs6064741
Materias
Resumen o descripción
The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure.
Fecha de publicación
2014
Tipo de publicación
Artículo
Versión de la publicación
Versión publicada
Recurso de información
Formato
application/pdf
Fuente
Remote Sensing, 6(6), 4741-4763, 2014
Idioma
Inglés
Relación
&
López-Merlín, D. (2014). Improving species diversity and biomass estimates of tropical dry forests using airborne LiDAR. Remote Sensing, 6(6), 4741-4763.
Sugerencia de citación
Hernández-Stefanoni, J. L., Dupuy, J. M., Johnson, K. D., Birdsey, R., Tun-Dzul, F., Peduzzi, A., ...
Repositorio Orígen
Repositorio Institucional CICY
Descargas
663