Calculation of Tree Height and Canopy Crown from Drone Images Using Segmentation

Seul, Ye; Lā,; Hien, Phu; Park, Hae‐Sim; Soo, Jong; Lee, Myeong Soo; Hee, M. C.; Pyeon,; Wook, Mu; Jee-In,

doi:10.7848/ksgpc.2015.33.6.605

Cited by 42 publications

(32 citation statements)

References 8 publications

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“…In particular, UAV equipped with consumer-grade onboard system camera and different sensors have been used to estimate tree counts, tree heights and crowns measurements due to its low cost and faster performance compared to traditional methods [22,23,[69][70][71][72][73][74]. In this paper, we present a simplified framework for automated ITD from UAV-SfM derived CHM based on algorithms designed for LiDAR data processing.…”

Section: Discussionmentioning

confidence: 99%

“…However, research in this area is still in its infancy, especially for ITD [70]. Lim et al [23] identified 11 of 13 trees, with one false positive, utilizing stacked RGB ortho-image and CHM segmentation. Trees not identified possessed small canopies which were not clearly defined from neighboring trees.…”

Section: Discussionmentioning

confidence: 99%

“…Advances in the fields of the Unmanned Aerial Vehicle (UAV) technology and data processing have broadened the horizons of remote sensing of forestry, and made the acquisition of high-resolution imagery and 3D data more easily available and affordable [22][23][24][25][26][27][28]. In fact, UAVs can be obtained at reasonable costs and can be perceived as a forester's eye in the sky, capable of performing forest inventory and analysis on a periodic basis [22,29].…”

mentioning

confidence: 99%

“…With the availability of a wide range of sensors, these UAVs allow the end users to define the spatial resolutions, thereby opening new opportunities to forest managers [30]. In the past decade, studies have focused on exploring the usage of UAV-derived Canopy Height Models (CHMs), suggesting its potential in detection of tree tops, delineation of tree crowns, and subsequently estimation of parameters of crown morphology such as height, diameter, and surface curvature [9,22,23,25,[30][31][32].…”

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confidence: 99%

“…Nevertheless, these methods were primarily designed for measuring large spaces or objects, and require expensive sensors, well-trained personnel, and precise computational technology to obtain accurate results. Therefore, how to collect high resolution data for individual tree attributes estimation in the case of smaller study areas over time, considering the cost associated with it, is a key challenge [22,23].…”

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confidence: 99%

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Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Mohan

Silva

Klauberg

et al. 2017

Forests

314

232

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Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m × 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Mohan

Silva

Klauberg

et al. 2017

Forests

314

232

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Drone‐based photogrammetry‐derived crown metrics for predicting tree and oil palm water use

et al. 2019

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Transpiration at the stand level is often estimated from water use measurements on a limited number of plants and then scaled up by predicting the remaining plants of a stand by plant size-related variables. Today, drone-based methods offer new opportunities for plant size assessments. We tested crown variables derived from drone-based photogrammetry for predicting and scaling plant water use. In an oil palm agroforest and an oil palm monoculture plantation in lowland Sumatra, Indonesia, tree and oil palm water use rates were measured by sap flux techniques.Simultaneously, aerial images were taken from an octocopter equipped with an Red Green Blue (RGB) camera. We used the structure from motion approach to compute several crown variables such as crown length, width, and volume. Crown volumes for both palms (69%) and trees (81%) explained much of the observed spatial variability in water use; however, the specific crown volume model differed between palms and trees and there was no single linear model fitting for both. Among the trees, crown volume explained more of the observed variability than stem diameter, and in consequence, uncertainties in stand level estimates resulting from scaling were largely reduced. For oil palms, an appropriate whole-plant size-related predictor variable was thus far not available. Stand level transpiration estimates in the studied oil palm agroforest were lower than those in the oil palm monoculture, which is probably due to the small-statured trees. In conclusion, we consider drone-derived crown metrics very useful for the scaling from single plant water use to stand-level transpiration.

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Supporting Oil Palm Replanting Programs Using UAV and GIS in Malaysia

Khiabani

Takeuchi

2019

Unmanned Aerial Vehicle: Applications in Agriculture and Environment

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Calculation of Tree Height and Canopy Crown from Drone Images Using Segmentation

Cited by 42 publications

References 8 publications

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Drone‐based photogrammetry‐derived crown metrics for predicting tree and oil palm water use

Supporting Oil Palm Replanting Programs Using UAV and GIS in Malaysia

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