Individual urban trees detection based on point clouds derived from UAV-RGB imagery and local maxima algorithm, a case study of Fateh Garden, Iran

Azizi, Zahra; Miraki, Mojdeh

doi:10.1007/s10668-022-02820-7

Cited by 6 publications

(1 citation statement)

References 36 publications

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“…First, the top of the crowns of pine trees in the study area was determined by using a local maximum filter (Local maximum: LM). Since the control points were the crown tops of pine trees, the area marked around each point is a decreasing gradient and represents the area of the tree crown (Azizi & Miraki 2022). The crown tops of the pine trees detected with the LM filter on the CHM were used to determine the height of each tree.…”

Section: Tree Height Estimationmentioning

confidence: 99%

Height estimation of pine (Pinus eldarica) single trees using slope corrected shadow length on unmanned aerial vehicle (UAV) imagery in a plantation forest

Hosingholizade,

Erfanifard,

Alavipanah

et al. 2023

AFR

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Tree height is one of the key parameters in forest plantations that plays a crucial role in estimation of above-ground biomass (AGB) of trees and stands. The parameter may be obtained by different methods from airborne remotely sensed datasets such shadow length of each tree individual or crown height models (CHMs). However, tree height estimation based on shadow length might be biased considering diverse topography of forest sites. Therefore, this study aims to develop a reliable method to estimate tree height in a plantation forest using shadow length on UAV imagery. First, heights of 151 pine (Pinus eldarica) trees were precisely measured in Pardisan Park, North Khorasan province, Iran. Additionally, a collection of images was captured by a Phantom 4Pro UAV in order to illustrate the study area. Then, two different approaches were considered to estimate the height of trees. In the first approach, tree heights were estimated based on shadow length on the UAV orthomosaic and correcting the effect of slope. The second approach considered the UAV-based CHM and height estimation using CHM segmentation and local maximum filtering. The results showed that tree heights estimated by the first approach were not significantly different from the insitu data (p=0.298). Furthermore, the heights estimated by the slope corrected shadow length showed higher precision compared to the heights estimated by the shadow length without slope correction (Relative root mean squared error, RRMSE 5.6% and 8.2% respectively). However, the heights obtained from the first approach were less precise than the second approach (RRMSE 5.6% and 4.2% respectively). In general, it was concluded that height estimation of pine trees based on shadow length after correction of slope effects can be considered as a reliable approach, although CHM is more efficient in estimating the tree heights. The findings of this study are applicable for height estimation of pine trees within plantation forests on UAV imagery.

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Section: Tree Height Estimationmentioning

confidence: 99%