Individual Tree-Crown Delineation and Treetop Detection in High-Spatial-Resolution Aerial Imagery

2004 ◽  
Vol 70 (3) ◽  
pp. 351-357 ◽  
Author(s):  
Le Wang ◽  
Peng Gong ◽  
Gregory S. Biging
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Aerial Imagery ◽  
Tree Crown ◽  
Individual Tree ◽  
Crown Delineation ◽  
Tree Crown Delineation

scholarly journals Automated Individual Tree Detection and Crown Delineation Using High Spatial Resolution RGB Aerial Imagery

2011 ◽  
Vol 27 (6) ◽  
pp. 703-715 ◽  
Author(s):  
Tae-Jin Park ◽  
Jong-Yeol Lee ◽  
Woo-Kyun Lee ◽  
Doo-Ahn Kwak ◽  
Han-Bin Kwak ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Aerial Imagery ◽  
Individual Tree ◽  
Tree Detection ◽  
Crown Delineation

scholarly journals A New Individual Tree Species Recognition Method Based on a Convolutional Neural Network and High-Spatial Resolution Remote Sensing Imagery

2021 ◽  
Vol 13 (3) ◽  
pp. 479
Author(s):  
Shijie Yan ◽  
Linhai Jing ◽  
Huan Wang
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Tree Crown ◽  
Recognition Method ◽  
Individual Tree ◽  
Tree Crowns ◽  
Multi Scale ◽  
Crown Delineation ◽  
Tree Crown Delineation

Tree species surveys are crucial to forest resource management and can provide references for forest protection policy making. The traditional tree species survey in the field is labor-intensive and time-consuming, supporting the practical significance of remote sensing. The availability of high-resolution satellite remote sensing data enable individual tree species (ITS) recognition at low cost. In this study, the potential of the combination of such images and a convolutional neural network (CNN) to recognize ITS was explored. Firstly, individual tree crowns were delineated from a high-spatial resolution WorldView-3 (WV3) image and manually labeled as different tree species. Next, a dataset of the image subsets of the labeled individual tree crowns was built, and several CNN models were trained based on the dataset for ITS recognition. The models were then applied to the WV3 image. The results show that the distribution maps of six ITS offered an overall accuracy of 82.7% and a kappa coefficient of 0.79 based on the modified GoogLeNet, which used the multi-scale convolution kernel to extract features of the tree crown samples and was modified for small-scale samples. The ITS recognition method proposed in this study, with multi-scale individual tree crown delineation, avoids artificial tree crown delineation. Compared with the random forest (RF) and support vector machine (SVM) approaches, this method can automatically extract features and outperform RF and SVM in the classification of six tree species.

scholarly journals A Region-Based Hierarchical Cross-Section Analysis for Individual Tree Crown Delineation Using ALS Data

2017 ◽  
Vol 9 (10) ◽  
pp. 1084 ◽  
Author(s):  
Yinghui Zhao ◽  
Yuanshuo Hao ◽  
Zhen Zhen ◽  
Ying Quan
Keyword(s):  
Cross Section ◽  
Tree Crown ◽  
Individual Tree ◽  
Crown Delineation ◽  
Tree Crown Delineation ◽  
Section Analysis

scholarly journals ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data

2019 ◽  
Vol 231 ◽  
pp. 111256
Author(s):  
Jon Murray ◽  
David Gullick ◽  
George Alan Blackburn ◽  
James Duncan Whyatt ◽  
Christopher Edwards
Keyword(s):  
Remotely Sensed ◽  
Tree Crown ◽  
Remotely Sensed Data ◽  
Individual Tree ◽  
Crown Delineation ◽  
Tree Crown Delineation ◽  
New Framework

scholarly journals A Comparison of Forest Tree Crown Delineation from Unmanned Aerial Imagery Using Canopy Height Models vs. Spectral Lightness

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 605 ◽  
Author(s):  
Jianyu Gu ◽  
Heather Grybas ◽  
Russell G. Congalton
Keyword(s):  
Spectral Data ◽  
Spatial Resolution ◽  
Forest Type ◽  
Canopy Height ◽  
Aerial Imagery ◽  
Tree Level ◽  
Tree Crown ◽  
Remotely Sensed Data ◽  
Individual Tree ◽  
Tree Crowns

Improvements in computer vision combined with current structure-from-motion photogrammetric methods (SfM) have provided users with the ability to generate very high resolution structural (3D) and spectral data of the forest from imagery collected by unmanned aerial systems (UAS). The products derived by this process are capable of assessing and measuring forest structure at the individual tree level for a significantly lower cost compared to traditional sources such as LiDAR, satellite, or aerial imagery. Locating and delineating individual tree crowns is a common use of remotely sensed data and can be accomplished using either UAS-based structural or spectral data. However, no study has extensively compared these products for this purpose, nor have they been compared under varying spatial resolution, tree crown sizes, or general forest stand type. This research compared the accuracy of individual tree crown segmentation using two UAS-based products, canopy height models (CHM) and spectral lightness information obtained from natural color orthomosaics, using maker-controlled watershed segmentation. The results show that single tree crowns segmented using the spectral lightness were more accurate compared to a CHM approach. The optimal spatial resolution for using lightness information and CHM were found to be 30 and 75 cm, respectively. In addition, the size of tree crowns being segmented also had an impact on the optimal resolution. The density of the forest type, whether predominately deciduous or coniferous, was not found to have an impact on the accuracy of the segmentation.

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