title: Estimating Tropical Forest Structure Using a Terrestrial Lidar.
abstract: Forest structure comprises numerous quantifiable biometric components and characteristics, which include tree geometry and stand architecture. These structural components are important in the understanding of the past and future trajectories of these biomes. Tropical forests are often considered the most structurally complex and yet least understood of forested ecosystems. New technologies have provided novel avenues for quantifying biometric properties of forested ecosystems, one of which is LIght Detection And Ranging (lidar). This sensor can be deployed on satellite, aircraft, unmanned aerial vehicles, and terrestrial platforms. In this study we examined the efficacy of a terrestrial lidar scanner (TLS) system in a tropical forest to estimate forest structure. Our study was conducted in January 2012 at La Selva, Costa Rica at twenty locations in a predominantly undisturbed forest. At these locations we collected field measured biometric attributes using a variable plot design. We also collected TLS data from the center of each plot. Using this data we developed relative vegetation profiles (RVPs) and calculated a series of parameters including entropy, Fast Fourier Transform (FFT), number of layers and plant area index to develop statistical relationships with field data. We developed statistical models using a series of multiple linear regressions, all of which converged on significant relationships with the strongest relationship being for mean crown depth (r2 = 0.88, p < 0.001, RMSE = 1.04 m). Tree density was found to have the poorest significant relationship (r2 = 0.50, p < 0.01, RMSE = 153.28 n ha-1). We found a significant relationship between basal area and lidar metrics (r2 = 0.75, p < 0.001, RMSE = 3.76 number ha-1). Parameters selected in our models varied, thus indicating the potential relevance of multiple features in canopy profiles and geometry that are related to field-measured structure. Models for biomass estimation included structural canopy variables in addition to height metrics. Our work indicates that vegetation profiles from TLS data can provide useful information on forest structure.
authors.affiliation: Earth System Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, New Hampshire, United States of America.|Department of Earth Sciences, University of New Hampshire, Durham, New Hampshire, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Palace
authors.affiliation: Earth System Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, New Hampshire, United States of America.
authors.forename: Franklin B
authors.initials: FB
authors.lastname: Sullivan
authors.affiliation: Department of Natural Resources, University of New Hampshire, Durham, New Hampshire, United States of America.
authors.forename: Mark
authors.initials: M
authors.lastname: Ducey
authors.affiliation: Earth System Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, New Hampshire, United States of America.
authors.forename: Christina
authors.initials: C
authors.lastname: Herrick
mesh_terms: D018533:Biomass; D003364:Costa Rica; D017753:Ecosystem; D004784:Environmental Monitoring; D065928:Forests; D008027:Light; D015233:Models, Statistical; D063148:Plant Dispersal / Q000502:physiology*; D063809:Satellite Imagery / Q000379:methods*; D013816:Thermodynamics; D014197:Trees / Q000502:physiology*; D014329:Tropical Climate