Estimating forest resources using airbone LiDAR-Estimating stand parameters of Sugi (Cryptomeria japonica D. Don) and Hinoki (Chamaecyparis obtusa Endl.) stands with differing densities.

Matsue, Keigo; Itoh, Takuya; Naito, Kenji

doi:10.4287/jsprs.45.4

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…In our previous study (Takahashi et al 2006), the laser penetration rate in the hinoki cypress stand (1.1%) was found to be lower than that in the sugi stand (8.1%), and it was often much lower than the rates reported in the previous studies (e.g., Naesset 2002;Naesset and Økland 2002;Popescu et al 2002;Hollaus et al 2006). Matsue et al (2006) also reported a lower laser penetration rate for the last-pulses in hinoki cypress stands (2% on average) than that in sugi stands (24% on average). Naesset (2002) reported that the mean laser penetration rates varied between 17 and 38% in areas covered by Norway spruce and Scots pine, and Hollaus et al (2006) also reported the laser penetration rate in areas mainly covered by a dense coniferous forest as 17% for winter first pulse data and 13% for summer first pulse data.…”

Section: Introductionmentioning

confidence: 64%

Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model

Yamamoto

Takahashi

Miyachi

et al. 2011

Journal of Forest Research

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In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the ''top surface model'' or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTH E ) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTH E and the observed mean tree height (MTH O ) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy.

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Section: Introductionmentioning

confidence: 64%

Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model

Yamamoto

Takahashi

Miyachi

et al. 2011

Journal of Forest Research

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“…Oono et al (2008) studied that with an increase in tree density, the detection accuracy decreases by 10 to 20%, especially within a dense forest stand. Matsue et al (2006) emphasized that stand density and crown shape highly influence tree detection rates, where overlapping crowns cannot be completely differentiated by the filtering process. In most studies, the detected crowns were observed to be the dominant layer, where the understory layer remains unseen.…”

Section: Height Estimation and Tree Detection Based On Plot Level Assmentioning

confidence: 99%

Accuracy of LiDAR-based tree height estimation and crown recognition in a subtropical evergreen broad-leaved forest in Okinawa, Japan

Zawawi¹,

Shiba

Jemali

2015

For. syst.

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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.

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“…Airbone LiDAR systems emit laser pulses from the air to the ground to measure distance to ground objects, making the measurement of 3D structures possible [43]. Various forest-related applications have been executed over large areas, including estimation of tree height and stem density [44]; analysis of the relationship between tree height and topography [45]; evaluation of forest degradation [20]; estimation of biomass [32,[46][47][48][49][50][51]; classification of species using vertical and horizontal texture, degree of leaf clumping, gap distribution, and canopy shape [29,[52][53][54][55]; gap mapping [56]; and gap monitoring [57][58][59]. LiDAR data have also been used to estimate PAI and vertical plant area density (PAD) distribution [60][61][62][63].…”

Section: Introductionmentioning

confidence: 99%

Estimating Three-Dimensional Distribution of Leaf Area Using Airborne LiDAR in Deciduous Broad-Leaved Forest

Awaya

Araki

2023

Remote Sensing

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We examined the performance of airborne light detection and ranging (LiDAR) data obtained in 2011 for leaf area estimation in deciduous broad-leaved forest using the Beer–Lambert law in Takayama, Gifu, Japan. We estimated leaf area index (LAI, allometry-LAI) and vertical leaf area density (LAD) using field survey data by applying allometric equations to estimate leaf-area of trees and a Weibull distribution equation to estimate vertical leaf distribution. We then estimated extinction coefficients (Ke) of LiDAR data for three height layers from the ground to the canopy top using the vertical LAD and vertical laser pulse distribution. The estimated PAI (LiDAR-PAI) using the Beer–Lambert law and Ke, when treating the canopies as three height layers, showed a significant linear relationship with allometry-LAI (p < 0.001). However, LiDAR-PAI when treating the canopies as single layer saturated at a PAI of six. It was similar to the lesser PAI estimation by hemispherical photography or relative photosynthetic photon flux density which treated the canopy as a single layer, compared to LAI measurements by litter traps. It is therefore important to allocate distinct Ke values to each of the multiple height layers for an accurate estimation of PAI and vertical PAD when applying the Beer–Lambert law to airborne LiDAR data.

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Estimating forest resources using airbone LiDAR-Estimating stand parameters of Sugi (Cryptomeria japonica D. Don) and Hinoki (Chamaecyparis obtusa Endl.) stands with differing densities.

Cited by 11 publications

References 5 publications

Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model

Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model

Accuracy of LiDAR-based tree height estimation and crown recognition in a subtropical evergreen broad-leaved forest in Okinawa, Japan

Estimating Three-Dimensional Distribution of Leaf Area Using Airborne LiDAR in Deciduous Broad-Leaved Forest

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