INDIVIDUAL TREE CROWN DELINEATION FROM HIGH SPATIAL RESOLUTION IMAGERY USING U-NET

Abstract. The objective of this study was to explore the utilization of deep learning networks in individual tree crown (ITC) delineation, a very important step in individual tree analysis. Even though many traditional machine learning methods have been developed for ITC delineation, the accuracy remains low, especially for dense forests where branches, crowns, and clusters of trees usually have similar characteristics and boundaries of tree crowns are not distinct. Advance in deep learning provides a good opportunity to improve ITC delineation. In this study, U-net, Residual U-net, and attention U-net were implemented for the first time in ITC delineation. In order to ensure that the boundaries of tree crowns were classified correctly, a weight map was generated to give more weights to boundary pixels between two close crowns in the loss function. These three networks were trained and tested using optical imagery obtained over a study site within the Great Lakes-St. Lawrence forest region, Ontario Canada. Based on two test sites dominated by open mixed forest and closed deciduous forests, respectively, the overall accuracies were 0.94 and 0.90, respectively for U-net, 0.89 and 0.62 for Residual U-net, and 0.96 and 0.83 for attention U-net.

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Analysis of multitemporal aerial images for fenyőfő Forest change detection

Landscape & Environment ◽

10.21120/le/10/2/4 ◽

2016 ◽

Vol 10 (2) ◽

pp. 89-100

Author(s):

Shukhrat Shokirov ◽

Géza Király

Keyword(s):

Forest Type ◽

Measurement Data ◽

Aerial Images ◽

Tree Crown ◽

Individual Tree ◽

Tree Crowns ◽

Clear Cut ◽

Crown Delineation ◽

Tree Crown Delineation ◽

Type Classification

This study evaluated the use of 40 cm spatial resolution aerial images for individual tree crown delineation, forest type classification, health estimation and clear-cut area detection in Fenyőfő forest reserves in 2012 and 2015 years. Region growing algorithm was used for segmentation of individual tree crowns. Forest type (coniferous/deciduous trees) were distinguished based on the orthomosaic images and segments. Research also investigated the height of individual trees, clear-cut areas and cut crowns between 2012 and 2015 years using Canopy Height Models. Results of the research were examined based on the field measurement data. According to our results, we achieved 75.2% accuracy in individual tree crown delineation. Heights of tree crowns have been calculated with 88.5% accuracy. This study had promising result in clear cut area and individual cut crown detection. Overall accuracy of classification was 77.2%, analysis showed that coniferous tree type classification was very accurate, but deciduous tree classification had a lot of omission errors. Based on the results and analysis, general information about forest health conditions has been presented. Finally, strengths and limitations of the research were discussed and recommendations were given for further research.

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

Remote Sensing ◽

10.3390/rs13030479 ◽

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.

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Individual Tree Crown Delineation Using Multispectral LiDAR Data

Sensors ◽

10.3390/s19245421 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5421 ◽

Cited By ~ 1

Author(s):

Faizaan Naveed ◽

Baoxin Hu ◽

Jianguo Wang ◽

G. Brent Hall

Keyword(s):

Lidar Data ◽

Segmentation Method ◽

Tree Crown ◽

Individual Tree ◽

Tree Crowns ◽

Multi Scale ◽

Crown Delineation ◽

Tree Crown Delineation ◽

The City ◽

532 Nm

In this study, multispectral Light Detection and Ranging (LiDAR) data were utilized to improve delineation of individual tree crowns (ITC) as an important step in individual tree analysis. A framework to integrate spectral and height information for ITC delineation was proposed, and the multi-scale algorithm for treetop detection developed in one of our previous studies was improved. In addition, an advanced region-based segmentation method that used detected treetops as seeds was proposed for segmentation of individual crowns based on their spectral, contextual, and height information. The proposed methods were validated with data acquired using Teledyne Optech’s Titan LiDAR sensor. The sensor was operated at three wavelengths (1550 nm, 1064 nm, and 532 nm) within a study area located in the city of Toronto, ON, Canada. The proposed method achieved 80% accuracy, compared with manual delineation of crowns, considering both matched and partially matched crowns, which was 12% higher than that obtained by the earlier marker-controlled watershed (MCW) segmentation technique. Furthermore, the results showed that the integration of spectral and height information improved ITC delineation using either the proposed framework or MCW segmentation, compared with using either spectral or height information individually.

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Individual Tree Crown Detection Using UAV Orthomosaic

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.4093 ◽

2021 ◽

Vol 11 (2) ◽

pp. 7047-7053

Author(s):

K. N. Tahar ◽

M. A. Asmadin ◽

S. A. H. Sulaiman ◽

N. Khalid ◽

A. N. Idris ◽

...

Keyword(s):

Unmanned Aerial Vehicles ◽

Aerial Images ◽

Tree Crown ◽

Individual Tree ◽

Tree Crowns ◽

Crown Diameter ◽

Economic Information ◽

Algorithm Operation ◽

Crown Delineation ◽

Tree Crown Delineation

Unmanned Aerial Vehicles (UAVs) are increasingly used in forestry as they are economical and flexible. This study aims to present the advantages of the drone photogrammetry method in collecting individual tree crowns, as individual tree crown detection could deliver essential ecological and economic information. The referred accuracy for individual tree crown extraction is 79.2%. Only crowns that were clearly visible were selected and manually delineated on the image because the distribution of the true crown size is significantly different from the segmented crowns. The aim of this study is to investigate UAVs orthomosaics in individual tree crown detection. The objectives of this study are to produce the orthomosaic of tree crown extraction mapping using the Pix4D software and analyze the tree crowns using tree crown delineation and the OBIA algorithm. Data processing involves the processing of aerial images using Pix4Dmapper. Automatic tree crown detection involves a tree crown delineation algorithm and OBIA operations to process the tree crown extraction. The crown delineation algorithm and OBIA algorithm operation will be compared to the actual tree crown measurement in terms of diameter and area. The tree crown delineation method obtained a 0.347m mean diameter difference from the actual tree crown diameter, while the OBIA approach obtained 4.98m. The tree crown delineation method obtained 97.26% of the actual tree crown area, while OBIA obtained 91.74%.

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Automatic Tree Crown Feature Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in an Urban Forest

10.5194/egusphere-egu21-8297 ◽

2021 ◽

Author(s):

Robert Minařík ◽

Jakub Langhammer ◽

Theodora Lendzioch

Keyword(s):

Feature Extraction ◽

Bark Beetle ◽

Urban Forest ◽

Tree Crown ◽

Individual Tree ◽

Tree Crowns ◽

Reference Tree ◽

Crown Delineation ◽

Tree Crown Delineation ◽

Conifer Trees

Multispectral imaging using unmanned aerial systems (UAS) enables rapid and accurate detection of pest insect infestations, which are an increasing threat to midlatitude natural forests. Pest detection at the level of an individual tree is of particular importance in mixed forests, where it enables a sensible forest management approach. Moreover, urban forests may be affected more seriously because an urban environment produces additional stressors. The stressors include changes in forest soil properties, tree species diversity, higher temperatures, and carbon dioxide content. The stressed trees are then optimal material for a bark beetle feeding. Therefore, it is necessary to use an appropriate method for the detection of individual infested trees.In this contribution, we present a novel method for individual tree crown delineation (ITCD) followed by feature extraction to detect a bark beetle disturbance in a mixed urban forest using a photogrammetric point cloud (PPC) and a multispectral orthomosaic. An excess green index (ExG) threshold mask was applied before the ITCD to separate targeted coniferous trees from deciduous trees and backgrounds. The individual crowns of conifer trees were automatically delineated as (i) a full tree crown using marker-controlled watershed segmentation (MCWS), Dalponte2016, and Li 2012 region growing algorithms or (ii) a buffer around a treetop from the masked PPC.We statistically compared selected spectral and elevation features extracted from automatically delineated crowns of each method to reference tree crowns to distinguish between the forest disturbance classes and two tree species. Moreover, the effect of PPC density on the ITCD accuracy and feature extraction was investigated. The ExG threshold mask application resulted in the excellent separability of targeted conifer trees and the increasing shape similarity of automatically delineated crowns compared to reference tree crowns. The results revealed a strong effect of PPC density on treetop detection and ITCD. If the PPC density is sufficient (> 10 points/m2), the automatically delineated crowns produced by Dalponte2016, MCWS, and Li 2012 methods are comparable, and the extracted feature statistics insignificantly differ from reference tree crowns. The buffer method is less suitable for detecting a bark beetle disturbance in the mixed forest because of the simplicity of crown delineation. It caused significant differences in extracted feature statistics compared to reference tree crowns. Therefore, the point density was found to be more significant than the algorithm used.We conclude that the automatic methods may constitute a reliable substitute for the time-consuming manual tree crown delineation in tree-based bark beetle disturbance detection and sanitation of individual infested trees using the suggested methodology and high-density (>20 points/m2, 10 points/m2 minimum) PPC.

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