Characterizing Tropical Forest Structure using Field-based Measurements and a Terrestrial Lidar

Abstract:

Forest structure comprises numerous quantifiable components of forest biometric characteristics, one of which is tree architecture. This structural component is 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 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 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, FFT, number of layers and plant area index to develop statistical relationships with field data. We developed statistical models using multiple linear regressions, all of which converged on statistically significant relationships with the strongest relationship being for mean crown depth (r2 = 0.87, p < 0.01, RMSE = 1.1 m). Tree density was found to have the least strong statistical relationship (r² = 0.45, p < 0.01, RMSE = 160.7 n ha^-1). We found significant relationship between basal area and lidar metrics (r² = 0.76, p < 0.001, RMSE = 3.68 number ha^-1). Models developed for biomass 1 had a higher r-squared value and lower RMSE than that of biomass2 (biomass1: r² = 0.7, p < 0.01, RMSE = 28.94 Mg ha^-1; biomass2: r² = 0.67, p < 0.01, RMSE = 40.62 Mg 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. Our work indicates that TLS data can provide useful information on tropical forest structure.