The Semi-Individual Tree Crown Approach

An international data science challenge, called National Ecological Observatory Network—National Institute of Standards and Technology data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: (1) individual tree crown (ITC) delineation, for identifying the location and size of individual trees; (2) alignment between field surveyed trees and ITCs delineated on remote sensing data; and (3) tree species classification. In this paper, the methods and results of team Fondazione Edmund Mach (FEM) are presented. The ITC delineation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the delineation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the delineated ITCs and the field surveyed trees was done using the Euclidean distance among the position, the height, and the crown radius of the ITCs and the field surveyed trees. The classification (Task 3) was performed using a support vector machine classifier applied to a selection of the hyperspectral bands and the canopy height model. The selection of the bands was done using the sequential forward floating selection method and the Jeffries Matusita distance. The results of the three tasks were very promising: team FEM ranked first in the data science competition in Task 1 and 2, and second in Task 3. The Jaccard score of the delineated crowns was 0.3402, and the results showed that the proposed approach delineated both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good (overall accuracy of 88.1%, kappa accuracy of 75.7%, and mean class accuracy of 61.5%), although the accuracy was biased toward the most represented species.

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

Remote Sensing ◽

10.3390/rs9101084 ◽

2017 ◽

Vol 9 (10) ◽

pp. 1084 ◽

Cited By ~ 9

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

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

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111256 ◽

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

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Individual Tree Measurement in Tropical Environment using Terrestrial Laser Scanning

Jurnal Teknologi ◽

10.11113/jt.v73.4329 ◽

2015 ◽

Vol 73 (5) ◽

Cited By ~ 1

Author(s):

Muhammad Zulkarnain Abdul Rahman ◽

Zulkepli Majid ◽

Md Afif Abu Bakar ◽

Abd Wahid Rasib ◽

Wan Hazli Wan Kadir

Keyword(s):

Laser Scanning ◽

Mean Squared Error ◽

Tree Height ◽

Point Clouds ◽

Tree Crown ◽

Individual Tree ◽

Branch Diameter ◽

Reference Tree ◽

Crown Volume ◽

Tree Branch

Detailed forest inventory and mensuration of individual trees have drawn attention of research society mainly to support sustainable forest management. This study aims at estimating individual tree attributes from high density point cloud obtained by terrestrial laser scanner (TLS). The point clouds were obtained over single reference tree and group of trees in forest area. The reference tree is treated as benchmark since detailed measurements of branch diameter were made on selected branches with different sizes and locations. Diameter at breast height (DBH) was measured for trees in forest. Furthermore tree height, height to crown base, crown volume and tree branch volume were also estimated for each tree. Branch diameter is estimated directly from the point clouds based on semi-automatic approach of model fitting i.e. sphere, ellipse and cylinder. Tree branch volume is estimated based on the volume of the fitted models. Tree height and height to crown base are computed using histogram analysis of the point clouds elevation. Tree crown volume is estimated by fitting a convex-hull on the tree crown. The results show that the Root Mean Squared Error (RMSE) of the estimated tree branch diameter does not have a specific trend with branch sizes and number of points used for fitting process. This explains complicated distribution of point clouds over the branches. Overall cylinder model produces good results with most branch sizes and number of point clouds for fitting. The cylinder fitting approach shows significantly better estimation results compared to sphere and ellipse fitting models.

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Individual tree crown segmentation based on aerial image using superpixel and topological features

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.14.022210 ◽

2020 ◽

Vol 14 (02) ◽

pp. 1

Author(s):

Yuhan Zhou ◽

Liwen Wang ◽

Kang Jiang ◽

Lianfeng Xue ◽

Feng An ◽

...

Keyword(s):

Aerial Image ◽

Tree Crown ◽

Individual Tree ◽

Topological Features

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

Forests ◽

10.3390/f11060605 ◽

2020 ◽

Vol 11 (6) ◽

pp. 605 ◽

Cited By ~ 1

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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Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

Remote Sensing ◽

10.3390/rs12040651 ◽

2020 ◽

Vol 12 (4) ◽

pp. 651

Author(s):

Joyson Ahongshangbam ◽

Alexander Röll ◽

Florian Ellsäßer ◽

Hendrayanto ◽

Dirk Hölscher

Keyword(s):

Spatial Heterogeneity ◽

Water Use ◽

Tropical Rainforests ◽

Sap Flux ◽

Tree Crown ◽

Individual Tree ◽

Canopy Transpiration ◽

Tree Water Use ◽

Climate Regulation ◽

The Relationship

Tropical rainforests comprise complex 3D structures and encompass heterogeneous site conditions; their transpiration contributes to climate regulation. The objectives of our study were to test the relationship between tree water use and crown metrics and to predict spatial variability of canopy transpiration across sites. In a lowland rainforest of Sumatra, we measured tree water use with sap flux techniques and simultaneously assessed crown metrics with drone-based photogrammetry. We observed a close linear relationship between individual tree water use and crown surface area (R2 = 0.76, n = 42 trees). Uncertainties in predicting stand-level canopy transpiration were much lower using tree crown metrics than the more conventionally used stem diameter. 3D canopy segmentation analyses in combination with the tree crown–water use relationship predict substantial spatial heterogeneity in canopy transpiration. Among our eight study plots, there was a more than two-fold difference, with lower transpiration at riparian than at upland sites. In conclusion, we regard drone-based canopy segmentation and crown metrics to be very useful tools for the scaling of transpiration from tree- to stand-level. Our results indicate substantial spatial variation in crown packing and thus canopy transpiration of tropical rainforests.

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