Abstract
Tree spatial distribution patterns such as random, regular, and clustered play a crucial role in numerical simulations of carbon and water cycles and energy exchanges between forest ecosystems and the atmosphere. An efficient approach is needed to characterize tree spatial distribution patterns quantitatively. This study aims to employ increasingly available aerial laser scanning (ALS) data to capture individual tree locations and further characterize their spatial distribution patterns at the landscape or regional levels. First, we use the pair correlation function to identify the categories (i.e., random, regular, and clustered) of tree spatial distribution patterns, and then determine the unknown parameters of statistical models used for approximating each tree spatial distribution pattern using ALS-based metrics. After applying the proposed method in both natural and urban forest sites, our results show that ALS-based tree crown radii can capture 58%–77% (p < 0.001) variations of visual-based measurements depending on forest types and densities. The root mean squared errors (RMSEs) of ALS-based tree locations increase from 1.46 m to 2.51 m as the forest densities increasing. The Poisson, soft-core, and hybrid-Gibbs point processes are determined as the optimal models to approximate random, regular, and clustered tree spatial distribution patterns, respectively. This work provides a solid foundation for improving the simulation accuracy of forest canopy bidirectional reflectance distribution function (BRDF) and further obtain a better understanding of the processes of carbon and water cycles of forest ecosystems.
Highlights
Tree spatial distribution patterns, as an essential characteristic of forest structure, play a vital role in forest succession, regeneration, growth, and understory development [1,2,3,4,5,6,7]
Better representations of tree spatial distribution patterns will improve the accuracy of estimating the proportions of visible forest overstory and ground components in a forest stand, which further simulate the forest canopy bidirectional reflectance distribution function (BRDF) and retrieve forest structural parameters such as leaf area index (LAI)
By comparing the visual- and Aerial laser scanning data (ALS)-based tree crown radii (Figure 6), we found that the values of the correlation coefficient increased from 0.61 to 0.77 as forest densities decreased in urban forest plots WPA-Hard-core distance (HC), WPA-MC, and WPA-LC (Figure 6a–c)
Summary
As an essential characteristic of forest structure, play a vital role in forest succession, regeneration, growth, and understory development [1,2,3,4,5,6,7]. It thoroughly explains the coexistence relationship and community structure of plant species based on past spatial distribution and ecosystem processes [8,9]. The same spatial coverage of a forest stand might exhibit different tree spatial distribution patterns under various sizes of observation windows [17]
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