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.

scholarly journals Analysis of multitemporal aerial images for fenyőfő Forest change detection

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.

scholarly journals Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data

Forests ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 759 ◽  
Author(s):  
Wan Wan Mohd Jaafar ◽  
Iain Woodhouse ◽  
Carlos Silva ◽  
Hamdan Omar ◽  
Khairul Abdul Maulud ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Aboveground Biomass ◽  
Airborne Lidar ◽  
Tree Level ◽  
Lidar Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Tree Crowns ◽  
Crown Width ◽  
3D Lidar

Individual tree crown (ITC) segmentation is an approach to isolate individual tree from the background vegetation and delineate precisely the crown boundaries for forest management and inventory purposes. ITC detection and delineation have been commonly generated from canopy height model (CHM) derived from light detection and ranging (LiDAR) data. Existing ITC segmentation methods, however, are limited in their efficiency for characterizing closed canopies, especially in tropical forests, due to the overlapping structure and irregular shape of tree crowns. Furthermore, the potential of 3-dimensional (3D) LiDAR data is not fully realized by existing CHM-based methods. Thus, the aim of this study was to develop an efficient framework for ITC segmentation in tropical forests using LiDAR-derived CHM and 3D point cloud data in order to accurately estimate tree attributes such as the tree height, mean crown width and aboveground biomass (AGB). The proposed framework entails five major steps: (1) automatically identifying dominant tree crowns by implementing semi-variogram statistics and morphological analysis; (2) generating initial tree segments using a watershed algorithm based on mathematical morphology; (3) identifying “problematic” segments based on predetermined set of rules; (4) tuning the problematic segments using a modified distance-based algorithm (DBA); and (5) segmenting and counting the number of individual trees based on the 3D LiDAR point clouds within each of the identified segment. This approach was developed in a way such that the 3D LiDAR points were only examined on problematic segments identified for further evaluations. 209 reference trees with diameter at breast height (DBH) ≥ 10 cm were selected in the field in two study areas in order to validate ITC detection and delineation results of the proposed framework. We computed tree crown metrics (e.g., maximum crown height and mean crown width) to estimate aboveground biomass (AGB) at tree level using previously published allometric equations. Accuracy assessment was performed to calculate percentage of correctly detected trees, omission and commission errors. Our method correctly identified individual tree crowns with detection accuracy exceeding 80 percent at both forest sites. Also, our results showed high agreement (R2 > 0.64) in terms of AGB estimates using 3D LiDAR metrics and variables measured in the field, for both sites. The findings from our study demonstrate the efficacy of the proposed framework in delineating tree crowns, even in high canopy density areas such as tropical rainforests, where, usually the traditional algorithms are limited in their performances. Moreover, the high tree delineation accuracy in the two study areas emphasizes the potential robustness and transferability of our approach to other densely forested areas across the globe.

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 Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1218 ◽  
Author(s):  
Julia Maschler ◽  
Clement Atzberger ◽  
Markus Immitzer
Keyword(s):  
Random Forest ◽  
Scots Pine ◽  
Tree Species ◽  
Near Infrared ◽  
Mean Shift ◽  
Hyperspectral Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Tree Crowns

Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve ‘Wienerwald’, a temperate Austrian forest. The study also assesses the automation potential for the delineation of tree crowns using a mean shift segmentation algorithm in order to permit model application over large areas. Object-based Random Forest classification was carried out on variables that were derived from 699 manually delineated as well as automatically segmented reference trees. The models were trained separately for two strata: small and/or conifer stands and high broadleaf forests. The two strata were delineated beforehand using CHM-based tree height and NDVI. The predictor variables encompassed spectral reflectance, vegetation indices, textural metrics and principal components. After feature selection, the overall classification accuracy (OA) of the classification based on manual delineations of the 13 tree species was 91.7% (Cohen’s kappa (κ) = 0.909). The highest user’s and producer’s accuracies were most frequently obtained for Weymouth pine and Scots Pine, while European ash was most often associated with the lowest accuracies. The classification that was based on mean shift segmentation yielded similarly good results (OA = 89.4% κ = 0.883). Based on the automatically segmented trees, the Random Forest models were also applied to the whole study site (1050 ha). The resulting tree map of the study area confirmed a high abundance of European beech (58%) with smaller amounts of oak (6%) and Scots pine (5%). We conclude that highly accurate tree species classifications can be obtained from hyperspectral data covering the visible and near-infrared parts of the electromagnetic spectrum. Our results also indicate a high automation potential of the method, as the results from the automatically segmented tree crowns were similar to those that were obtained for the manually delineated tree crowns.

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 Tree Crown Delineation Algorithm Based on a Convolutional Neural Network

2020 ◽  
Vol 12 (8) ◽  
pp. 1288 ◽  
Author(s):  
José R. G. Braga ◽  
Vinícius Peripato ◽  
Ricardo Dalagnol ◽  
Matheus P. Ferreira ◽  
Yuliya Tarabalka ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Tropical Forests ◽  
Large Scale ◽  
Satellite Images ◽  
Tree Level ◽  
Tree Crown ◽  
Individual Tree ◽  
High Resolution Satellite Images

Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network—Mask R-CNN algorithm—to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising—the R e c a l l , P r e c i s i o n , and F 1 score values obtained were were 0.81 , 0.91 , and 0.86 , respectively. In the study site, the total of tree crowns delineated was 59,062 . These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions.

scholarly journals Comparative performances of new and existing indices of crown asymmetry: an evaluation using tall trees of Eucalyptus pilularis (Smith)

2020 ◽  
Vol 32 (1) ◽  
pp. 43-65
Author(s):  
Fanlin Kong ◽  
Huiquan Bi ◽  
Michael McLean ◽  
Fengri Li
Keyword(s):  
Rank Order ◽  
Bilateral Symmetry ◽  
Tree Crown ◽  
Remotely Sensed Data ◽  
Individual Tree ◽  
True Value ◽  
Crown Shape ◽  
Crown Morphology ◽  
Eucalyptus Pilularis ◽  
Relative Scale

AbstractOver the past 50 years, crown asymmetry of forest trees has been evaluated through several indices constructed from the perspective of projected crown shape or displacement but often on an ad hoc basis to address specific objectives related to tree growth and competition, stand dynamics, stem form, crown structure and treefall risks. Although sharing some similarities, these indices are largely incoherent and non-comparable as they differ not only in the scale but also in the direction of their values in indicating the degree of crown asymmetry. As the first attempt at devising normative measures of crown asymmetry, we adopted a relative scale between 0 for perfect symmetry and 1 for extreme asymmetry. Five existing crown asymmetry indices (CAIs) were brought onto this relative scale after necessary modifications. Eight new CAIs were adapted from measures of circularity for digital images in computer graphics, indices of income inequality in economics, and a bilateral symmetry indicator in plant leaf morphology. The performances of the 13 CAIs were compared over different numbers of measured crown radii for 30 projected crowns of mature Eucalyptus pilularis trees through benchmarking statistics and rank order correlation analysis. For each CAI, the index value based on the full measurement of 36 evenly spaced radii of a projected crown was taken as the true value in the benchmarking process. The index (CAI13) adapted from the simple bilateral symmetry measure proved to be the least biased and most precise. Its performance was closely followed by that of three other CAIs. The minimum number of crown radii that is needed to provide at least an indicative measure of crown asymmetry is four. For more accurate and consistent measures, at least 6 or 8 crown radii are needed. The range of variability in crown morphology of the trees under investigation also needs to be taken into consideration. Although the CAIs are from projected crown radii, they can be readily extended to individual tree crown metrics that are now commonly extracted from LiDAR and other remotely sensed data. Adding a normative measure of crown asymmetry to individual tree crown metrics will facilitate the process of big data analytics and artificial intelligence in forestry wherever crown morphology is among the factors to be considered for decision making in forest management.

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