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Improving Species Diversity and Biomass Estimates of Tropical Dry Forests Using Airborne LiDAR

J. Hernández‐Stefanoni,J. Dupuy,Kristofer D. Johnson,R. Birdsey,Fernando Tun-Dzul,A. Peduzzi,Juan Pablo Caamal-Sosa,Gonzalo Sánchez-Santos,D. López-Merlín

Published 2014 in Remote Sensing

ABSTRACT

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.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    Remote Sensing

  • Publication date

    2014-05-26

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs6064741
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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