Abstract
Aim of study: To present an approach for estimating tree heights, stand density and crown patches using LiDAR data in a subtropical broad-leaved forest.Area of study: The study was conducted within the Yambaru subtropical evergreen broad-leaved forest, Okinawa main island, Japan.Materials and methods: A digital canopy height model (CHM) was extracted from the LiDAR data for tree height estimation and a watershed segmentation method was applied for the individual crown delineation. Dominant tree canopy layers were estimated using multi-scale filtering and local maxima detection. The LiDAR estimation results were then compared to the ground inventory data and a high resolution orthophoto image for accuracy assessment. Main results: A Wilcoxon matched pair test suggests that LiDAR data is highly capable of estimating tree height in a subtropical forest (z = 4.0, p = 0.345), but has limitation to detect small understory trees and a single tree delineation. The results show that there is a statistically significant different type of crown detection from LiDAR data over forest inventory (z = 0, p = 0.043). We also found that LiDAR computation results underestimated the stand density and overestimated the crown size.Research highlights: Most studies involving crown detection and tree height estimation have focused on the analysis of plantations, boreal forests and temperate forests, and less was conducted on tropical and/or subtropical forests. Our study tested the capability of LiDAR as an effective application for analyzing a highly dense forest.Key words: Broad-leaved; inventory; LiDAR; subtropical; tree height.Abbreviations: DBH: Diameter at Breast Height, CHM: Canopy Height Model, DEM: Digital Elevation Model, DSM: Digital Surface Model, LiDAR: Light Detection and Ranging, YFA: Yambaru Forest Area.
Highlights
Quantitative forest information, such as tree counts, diameter at breast height (DBH), tree heights, crown diameter and biomass are critical for effective forest management
The main objective of this study is to present a potential approach for estimating tree heights, stand density and crown patch characteristics using LiDAR data in a subtropical broad-leaved forest
Plot 3, which is characterized by a higher slope value recorded the highest mean value of tree height in both field inventory and LiDAR estimations
Summary
Quantitative forest information, such as tree counts, diameter at breast height (DBH), tree heights, crown diameter and biomass are critical for effective forest management. Field measurements and remote sensing represent two primary ways to assess forest inventories. The traditional method of field measurements involved a labor-intensive forest inventory, required supplementary work, and was more time-consuming and more applicable to a small area (Avery & Bukhart, 1994; Shivers & Borders, 1996). New technologies such as remote sensing and satellite imagery have been introduced and provide an observation for large areas through the application of different types of high resolution sensors, including the most recently used LiDAR (Light Detection and Ranging).
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