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.
Highlights
Forest structure is a reflection of the principles of forest growth and disturbance, influenced by the spatial and temporal variability of resource availability, disturbance rates, and management [1,2,3,4]
The ability to quantify forest structure beyond standing biomass is critical to efforts such as Reducing Emissions from Deforestation and Forest Degradation (REDD+), which depends on characterization of forest structure to provide insight into the previous carbon dynamics and the potential future storage capabilities of a forest [7,8,9,10]
Our study examined the use of a terrestrial lidar scanner (TLS) to estimate forest structure using vegetation profiles derived from the three-dimensional point cloud
Summary
Forest structure is a reflection of the principles of forest growth and disturbance, influenced by the spatial and temporal variability of resource availability, disturbance rates, and management [1,2,3,4]. Tropical forests have additional complexity in regard to species diversity and are thought to be among the most structurally complex of all forested ecosystems [6]. Tropical forest structure characterization is important in understanding ecological and earth system processes, knowledge of which proves vital in efforts to mitigate climate change through the reduction of greenhouse gases emissions. Ground-based measurements allow for accurate mapping of vegetation structure, but only on very limited spatial and temporal scales, and with high costs and unknown biases [11]
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