The Experts below are selected from a list of 26115 Experts worldwide ranked by ideXlab platform
Thomas L. Noland - One of the best experts on this subject based on the ideXlab platform.
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Automated Tree Crown delineation from imagery based on morphological techniques
IOP Conference Series: Earth and Environmental Science, 2014Co-Authors: Linhai Jing, Thomas L. Noland, H GuoAbstract:In current Tree Crown delineation from imagery, Treetops and three dimensional (3D) radiometric shapes of Tree Crowns are frequently extracted from a spectral band or a brightness component of the image and taken as references to localize and delineate Tree Crowns. However, color components of the image are rarely used together with the brightness component of the image to facilitate localizing and delineating Crowns. The 3D radiometric shape of a Crown can be derived from a brightness or color component and may be taken as a half-ellipsoid. From top to bottom of such a half-ellipsoid, multiple horizontal slices can be drawn, contain the Treetop, and indicate both the location and the horizontal extent of the Crown. Based on such a concept of horizontal slices of Crowns, a novel multi-scale method for individual Tree Crown delineation from imagery was proposed in this study. In this method, the brightness and color components of the image are morphologically opened within the scale range of target Crowns, horizontal slices of target Crowns are extracted from the resulting opened images and integrated together to localize Crowns, and one component is segmented using the watershed approach with reference to the integrated slices. In an experiment on high spatial resolution aerial imagery over natural closed canopy forests, the proposed method correctly delineated approximately 74% of mixedwood Tree Crowns and 59% of deciduous Crowns in the natural forests.
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Automated individual Tree Crown delineation from LIDAR data using morphological techniques
IOP Conference Series: Earth and Environmental Science, 2014Co-Authors: L Jing, Thomas L. NolandAbstract:In current Tree Crown delineation from LiDAR data, Treetops and 3D geometric shapes of Tree Crowns are frequently extracted from LiDAR-derived Crown Height Model (CHM) and used as references to localize and delineate Crowns. However, it is difficult to detect deciduous Treetops and delineate deciduous Tree Crowns. The 3D shape of a Crown, which can be derived from CHM, may be taken as a half ellipsoid, and any horizontal slice of the ellipsoid contains the Treetop and indicates not only the location but also the spatial extent of the Crown. Based on such slices, a novel multi-scale method for individual Tree Crown delineation from CHM was proposed in this study. This method consists mainly of two steps: (1) morphologically open the CHM over the scale range of target Tree Crowns; and (2) take local maxima within each resulting opened CHM as the horizontal slices of target Crowns at the corresponding scale level and integrate all the slices within the scale range together to represent the spatial distribution of target Crowns. In an experiment on CHMs over two natural closed canopy forests in Ontario, Canada, the proposed method accurately delineated the majority of the Tree Crowns from closed canopy forests.
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An individual Tree Crown delineation method based on multi-scale segmentation of imagery
ISPRS Journal of Photogrammetry and Remote Sensing, 2012Co-Authors: Linhai Jing, Thomas L. NolandAbstract:Abstract A forest consists of multi-scale branches, Tree Crowns, and Tree clusters. Similar to small Tree Crowns in shape and scale, branches normally cause over-segmentation of imagery when a watershed segmentation approach is used to segment imagery for Tree Crown delineation. In order to eliminate such over-segmentation, a new method for individual Tree Crown delineation from optical imagery was proposed based on multi-scale filtering and segmentation in this study. In this method, the dominant sizes of Tree Crowns are first determined; Gaussian filters are designed to fit the three-dimensional radiometric shapes of multi-scale Tree Crowns; the grayscale image is smoothed using the Gaussian filters and segmented using the watershed segmentation approach; and finally, the resulting multiple segmentation maps are integrated together to generate a Tree Crown map. In an experiment on aerial imagery of forests consisting of multi-scale Tree Crowns, the proposed method yielded high-quality Tree Crown maps.
Lindi J. Quackenbush - One of the best experts on this subject based on the ideXlab platform.
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a novel transferable individual Tree Crown delineation model based on fishing net dragging and boundary classification
Isprs Journal of Photogrammetry and Remote Sensing, 2015Co-Authors: Tao Liu, Lindi J. QuackenbushAbstract:Abstract This study provides a novel approach to individual Tree Crown delineation (ITCD) using airborne Light Detection and Ranging (LiDAR) data in dense natural forests using two main steps: Crown boundary refinement based on a proposed Fishing Net Dragging (FiND) method, and segment merging based on boundary classification. FiND starts with approximate Tree Crown boundaries derived using a traditional watershed method with Gaussian filtering and refines these boundaries using an algorithm that mimics how a fisherman drags a fishing net. Random forest machine learning is then used to classify boundary segments into two classes: boundaries between Trees and boundaries between branches that belong to a single Tree. Three groups of LiDAR-derived features—two from the pseudo waveform generated along with Crown boundaries and one from a canopy height model (CHM)—were used in the classification. The proposed ITCD approach was tested using LiDAR data collected over a mountainous region in the Adirondack Park, NY, USA. Overall accuracy of boundary classification was 82.4%. Features derived from the CHM were generally more important in the classification than the features extracted from the pseudo waveform. A comprehensive accuracy assessment scheme for ITCD was also introduced by considering both area of Crown overlap and Crown centroids. Accuracy assessment using this new scheme shows the proposed ITCD achieved 74% and 78% as overall accuracy, respectively, for deciduous and mixed forest.
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agent based region growing for individual Tree Crown delineation from airborne laser scanning als data
Journal of remote sensing, 2015Co-Authors: Zhen Zhen, Lindi J. Quackenbush, Stephen V Stehman, Lianjun ZhangAbstract:Individual Tree Crown delineation ITCD is a fundamental and vital component of individual Tree-based forest inventory. Region-growing algorithms have been widely developed and applied in ITCD studies. Although individual Treetops are typically designated as initial seeds, most region-growing algorithms do not consider Tree competition when Tree Crowns touch each other and have some overlapping area. An agent-based region-growing ABRG algorithm was constructed for ITCD using airborne laser scanning ALS data considering both growth and competition mechanisms. Three region-growing algorithms were compared for both coniferous and deciduous Trees. The algorithms were: 1 marker-controlled region growing with simultaneous growth MCRG; 2 agent-based region growing with one-way competition ABRG1W in which taller Trees limit the growth of shorter Trees, but shorter Trees have no impact on taller Trees; and 3 agent-based region growing with two-way competition ABRG2W in which taller and shorter Trees influence each other. Incorporating competition into the ABRG algorithm resulted in a statistically significant improvement of the accuracy of individual Tree delineations for conifers and a statistically significant improvement in the accuracy of plot-level Crown area for deciduous Trees. For conifers, two-way competition provided more accurate results because of increased competition in coniferous plots. For deciduous plots, the accuracy of one-way competition had a strong correlation 0.92 with relative spacing an index of competition level. This result is consistent with the one-way competition mechanism because the taller larger deciduous Trees would limit the growth of small Trees as the competition level declines. The degree of improvement in Tree Crown delineation accuracy by ABRG was related to the characteristics of Tree height and density i.e. distance between Trees in the plots.
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a comparison of three methods for automatic Tree Crown detection and delineation from high spatial resolution imagery
Journal of remote sensing, 2011Co-Authors: Lindi J. QuackenbushAbstract:This article compares the performance of three algorithms representative of published methods for Tree Crown detection and delineation from high spatial resolution imagery, and demonstrates a standardized accuracy assessment framework. The algorithms-watershed segmentation, region growing and valley-following-were tested on softwood and hardwood sites using Emerge natural colour vertical aerial imagery with 60 cm ground sampled distance and QuickBird panchromatic imagery with an 11˚ look angle. The evaluation considered both plot-level and individual Tree Crown detection and delineation results. The study shows that while all three methods reasonably delineate Crowns in the softwood stand on the Emerge image, region growing provided the highest accuracies, with producer's and user's accuracy for Tree detection reaching 70% and root mean square error for Crown diameter estimation of 15%. Crown detection accuracies were lower on the QuickBird image. No algorithm proved accurate for the hardwood stand on either image set (both producer's and user's accuracies < 30%).
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Active contour and hill climbing for Tree Crown detection and delineation.
Photogrammetric Engineering & Remote Sensing, 2010Co-Authors: Wenhua Zhang, Lindi J. QuackenbushAbstract:This paper presents a new approach for individual Tree Crown detection and delineation that is applicable under various imaging conditions. The approach extracts Crown area using a region-based active contour model and then detects Tree tops within the Crown area by considering both spectral and shape characteristics of the Crown. The detected Tree tops allow subsequent clustering of Crown pixels using a new hill-climbing algorithm. We tested the approach on a Norway spruce stand using three types of high spatial resolution imagery: an Emerge natural color vertical aerial image, an off nadir QuickBird panchromatic image, and a natural color digital orthoimage. In comparison to the published region growing algorithm, our approach improved Tree Crown detection by over 10 percent for all three types of imagery, and provided accurate Tree Crown diameter estimation, which has utility in Tree volume estimation, species composition, and forest health analysis.
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COMPARISON OF INDIVIDUAL Tree Crown DETECTION AND DELINEATION METHODS
2008Co-Authors: Lindi J. QuackenbushAbstract:Efficient forest management increases the demand for detailed, timely information. High spatial resolution remotely sensed imagery provides viable sources and opportunities for automated forest interpretation at an individual Tree level. Recent research, which aims at providing Tree-based forest inventory measurements, has considered automatic individual Tree Crown detection and delineation. A range of algorithms have been developed for different types of images, tested on different forest areas and evaluated using different methods of accuracy assessment. However, no research exists that compares the performance of these methods using a common dataset and the same evaluation approach. In this paper, we compared the performances of three algorithms representative of current published methods for Tree Crown detection and delineation. The three algorithms—marker-controlled watershed segmentation, region growing and valley-following—were tested on Emerge natural color vertical aerial image with 60 cm ground sampled distance (GSD) of a softwood study site and a hardwood study site. Overall, producer’s and user’s accuracy were applied in segmentation evaluation. While forest stand density and variation in Tree Crown size influenced performance, the results demonstrated that all three algorithms effectively delineate the Norway spruce Tree Crowns in the softwood stand, with the region growing method obtaining the best overall accuracy. However, no algorithm proved accurate for the hardwood stand. This analysis suggested that each algorithm has advantages and limitations based on stand characteristics. Future research is needed to explore adaptive algorithms that are capable of accurately delineating Crowns in stands where Trees vary in size and density.
Roeland Samson - One of the best experts on this subject based on the ideXlab platform.
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Influence of Tree Crown characteristics on the local PM10 distribution inside an urban sTreet canyon in Antwerp (Belgium) : a model and experimental approach
Urban Forestry & Urban Greening, 2016Co-Authors: Jelle Hofman, Harm Bartholomeus, Stijn Janssen, Kim Calders, Karen Wuyts, Shari Van Wittenberghe, Roeland SamsonAbstract:Abstract Apart from influencing the amount of leaf-deposited particles, Tree Crown morphology will influence the local distribution of atmospheric particles. Nevertheless, Tree Crowns are often represented very rudimentary in three-dimensional air quality models. Therefore, the influence of Tree Crown representation on the local ambient PM 10 concentration and resulting leaf-deposited PM 10 mass was evaluated, using the three-dimensional computational fluid dynamics (CFD) model ENVI-met ® and ground-based LiDAR imaging. The modelled leaf-deposited PM 10 mass was compared to gravimetric results within three different particle size fractions (0.2–3, 3–10 and >10 μm), obtained at 20 locations within the Tree Crown. Modelling of the LiDAR-derived Tree Crown resulted in altered atmospheric PM 10 concentrations in the vicinity of the Tree Crown. Although this model study was limited to a single Tree and model configuration, our results demonstrate that improving Tree Crown characteristics (shape, dimensions and LAD) affects the resulting local PM 10 distribution in ENVI-met. An accurate Tree Crown representation seems, therefore, of great importance when aiming at modelling the local PM distribution.
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on the relation between Tree Crown morphology and particulate matter deposition on urban Tree leaves a ground based lidar approach
Atmospheric Environment, 2014Co-Authors: Jelle Hofman, Harm Bartholomeus, Kim Calders, Karen Wuyts, Shari Van Wittenberghe, Roeland SamsonAbstract:Abstract Urban dwellers often breathe air that does not meet the European and WHO standards. Next to legislative initiatives to lower atmospheric pollutants, much research has been conducted on the potential of urban Trees as mitigation tool for atmospheric particles. While leaf-deposited dust has shown to vary significantly throughout single Tree Crowns, this study evaluated the influence of micro-scale Tree Crown morphology (leaf density) on the amount of leaf-deposited dust. Using a ground-based LiDAR approach, the three-dimensional Tree Crown morphology was obtained and compared to gravimetric measurements of leaf-deposited dust within three different size fractions (>10, 3–10 and 0.2–3 μm). To our knowledge, this is the first application of ground-based LiDAR for comparison with gravimetric results of leaf-deposited particulate matter. Overall, an increasing leaf density appears to reduce leaf-deposition of atmospheric particles. This might be explained by a reduced wind velocity, suppressing turbulent deposition of atmospheric particles through impaction. Nevertheless, the effect of Tree Crown morphology on particulate deposition appears almost negligible (7% AIC decrease) compared to the influence of physical factors like height, azimuth and Tree position.
Donald G. Leckie - One of the best experts on this subject based on the ideXlab platform.
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combined high density lidar and multispectral imagery for individual Tree Crown analysis
Canadian Journal of Remote Sensing, 2003Co-Authors: Donald G. Leckie, François A. Gougeon, David A Hill, Rick Quinn, Lynne Armstrong, Roger ShreenanAbstract:Lidar technology has reached a point where ground and forest canopy elevation models can be produced at high spatial resolution. Individual Tree Crown isolation and classification methods are developing rapidly for multispectral imagery. Analysis of multispectral imagery, however, does not readily provide Tree height information and lidar data alone cannot provide species and health attributes. The combination of lidar and multispectral data at the individual Tree level may provide a very useful forest inventory tool. A valley following approach to individual Tree isolation was applied to both high resolution digital frame camera imagery and a canopy height model (CHM) created from high-density lidar data over a test site of even aged (55 years old) Douglas-fir plots of varying densities (300, 500, and 725 stems/ha) on the west coast of Canada. Tree height was determined from the laser data within the automated Crown delineations. Automated Tree isolations of the multispectral imagery achieved 80%‐90% goo...
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Forest information extraction from high spatial resolution images using an individual Tree Crown approach
2003Co-Authors: François A. Gougeon, Donald G. LeckieAbstract:RESUME iv INTRODUCTION 1 TECHNIQUES AND METHODS 2 IMAGE PREPROCESSING AND MASKS GENERATION 4 INDIVIDUAL Tree Crown DELINEATION 6 INDIVIDUAL Tree Crown CLASSIFICATION 10 ACCURACY ASSESSMENTS 11 INDIVIDUAL Tree Crown REGROUPING AND STAND GENERATION 12 THE Tree-TOP APPROACH 14 FOREST INVENTORY APPLICATIONS 16 CONCLUSION 22 REFERENCES 23 Cover Image – Results of species classification and regrouping of individual Tree Crowns and Tree clusters over the original panchromatic IKONOS image (1 m/pixel) for part of a 10 000 ha area (11.7 x 8.6 km2) in the Lac a lʼOurs region of Quebec that was analyzed with the individual Tree Crown approach. This work was done in collaboration with CLC-Camint (Gatineau) and Industries Davidson Inc. and was funded in part by the “Programme de mise en valeur des ressources du milieu forestier Volet 1” of the Quebec Department of Natural Resources. The Tree species in the forested areas are indicated by the following colours:
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Individual Tree Crown Image Analysis - A Step Towards Precision Forestry ∗
2001Co-Authors: François A. Gougeon, Donald G. LeckieAbstract:Worldwide economy, environmental concerns, and stricter legislation governing forestry practices have put increased demands on forest managers. Riparian zone delineation, helicopter logging, plantation monitoring, selective cuts, just in time delivery, biodiversity and wildlife management are all various aspects of the same coin. The information requirements brought on by these activities is staggering. Existing information tools are inadequate and hamper the progress of forest management activities such as precision forestry. The use of high spatial resolution (10-100cm/pixel) remotely sensed images (aerial or satellite) or scanned aerial photographs, presents possibilities to analyze forested areas on an individual Tree Crown (ITC) basis. The Canadian Forest Service is at the forefront of research on individual Tree Crown based image analysis. We have developed techniques, methods and processes to separate forested from non-forested areas, delineate individual Tree Crowns, identify their species, and if needed, regroup them into forest stands or environmental strata. Eventually, forest managers will forgo static regroupings in favor of keeping all of the information about the individual Tree Crowns themselves (e.g., position, Crown area, height, species, and dominance). Regrouping would be done on demand, for each specific application, if done at all. In addition, the unprecedented level of details afforded by ITC techniques should allow us to extract a variety of additional forest management information such as: snag locations, forest gap sizes and distribution, highlyvalued Tree locations, detailed damage and regeneration assessments. This may also lead to more precise volume and biomass estimates and foster the use of individual Tree growth models. This paper first presents some of the image analysis concepts, methods and tools behind producing ITC-based forest inventories and then, reports on some successful applications, limitations, and ongoing research.
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individual Tree Crown image analysis a step towards precision forestry
2001Co-Authors: François A. Gougeon, Donald G. LeckieAbstract:Worldwide economy, environmental concerns, and stricter legislation governing forestry practices have put increased demands on forest managers. Riparian zone delineation, helicopter logging, plantation monitoring, selective cuts, just in time delivery, biodiversity and wildlife management are all various aspects of the same coin. The information requirements brought on by these activities is staggering. Existing information tools are inadequate and hamper the progress of forest management activities such as precision forestry. The use of high spatial resolution (10-100cm/pixel) remotely sensed images (aerial or satellite) or scanned aerial photographs, presents possibilities to analyze forested areas on an individual Tree Crown (ITC) basis. The Canadian Forest Service is at the forefront of research on individual Tree Crown based image analysis. We have developed techniques, methods and processes to separate forested from non-forested areas, delineate individual Tree Crowns, identify their species, and if needed, regroup them into forest stands or environmental strata. Eventually, forest managers will forgo static regroupings in favor of keeping all of the information about the individual Tree Crowns themselves (e.g., position, Crown area, height, species, and dominance). Regrouping would be done on demand, for each specific application, if done at all. In addition, the unprecedented level of details afforded by ITC techniques should allow us to extract a variety of additional forest management information such as: snag locations, forest gap sizes and distribution, highlyvalued Tree locations, detailed damage and regeneration assessments. This may also lead to more precise volume and biomass estimates and foster the use of individual Tree growth models. This paper first presents some of the image analysis concepts, methods and tools behind producing ITC-based forest inventories and then, reports on some successful applications, limitations, and ongoing research.
François A. Gougeon - One of the best experts on this subject based on the ideXlab platform.
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comparison of six individual Tree Crown detection algorithms evaluated under varying forest conditions
Journal of remote sensing, 2011Co-Authors: Morten Andreas Dahl Larsen, Xavier Descombes, Mats Eriksson, Guillaume Perrin, Tomas Brandtberg, François A. GougeonAbstract:In this article, six individual Tree Crown ITC detection/delineation algorithms are evaluated, using an image data set containing six diverse forest types at different geographical locations in three European countries. The algorithms use fundamentally different techniques, including local maxima detection, valley following VF, region-growing RG, template matching TM, scale-space SS theory and techniques based on stochastic frameworks. The structurally complexity of the forests in the aerial images used ranges from a homogeneous plantation and an area with isolated Tree Crowns to an extremely dense deciduous forest type. None of the algorithms alone could successfully analyse all different cases. The study shows that it is important to partition the imagery into homogeneous forest stands prior to the application of individual Tree detection algorithms. It furthermore suggests a need for a common, publicly available suite of test images and common test procedures for evaluation of individual Tree detection/delineation algorithms. Finally, it highlights that, for complex forest types, monoscopic images are insufficient for consistent Tree Crown detection, even by human interpreters.
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combined high density lidar and multispectral imagery for individual Tree Crown analysis
Canadian Journal of Remote Sensing, 2003Co-Authors: Donald G. Leckie, François A. Gougeon, David A Hill, Rick Quinn, Lynne Armstrong, Roger ShreenanAbstract:Lidar technology has reached a point where ground and forest canopy elevation models can be produced at high spatial resolution. Individual Tree Crown isolation and classification methods are developing rapidly for multispectral imagery. Analysis of multispectral imagery, however, does not readily provide Tree height information and lidar data alone cannot provide species and health attributes. The combination of lidar and multispectral data at the individual Tree level may provide a very useful forest inventory tool. A valley following approach to individual Tree isolation was applied to both high resolution digital frame camera imagery and a canopy height model (CHM) created from high-density lidar data over a test site of even aged (55 years old) Douglas-fir plots of varying densities (300, 500, and 725 stems/ha) on the west coast of Canada. Tree height was determined from the laser data within the automated Crown delineations. Automated Tree isolations of the multispectral imagery achieved 80%‐90% goo...
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Forest information extraction from high spatial resolution images using an individual Tree Crown approach
2003Co-Authors: François A. Gougeon, Donald G. LeckieAbstract:RESUME iv INTRODUCTION 1 TECHNIQUES AND METHODS 2 IMAGE PREPROCESSING AND MASKS GENERATION 4 INDIVIDUAL Tree Crown DELINEATION 6 INDIVIDUAL Tree Crown CLASSIFICATION 10 ACCURACY ASSESSMENTS 11 INDIVIDUAL Tree Crown REGROUPING AND STAND GENERATION 12 THE Tree-TOP APPROACH 14 FOREST INVENTORY APPLICATIONS 16 CONCLUSION 22 REFERENCES 23 Cover Image – Results of species classification and regrouping of individual Tree Crowns and Tree clusters over the original panchromatic IKONOS image (1 m/pixel) for part of a 10 000 ha area (11.7 x 8.6 km2) in the Lac a lʼOurs region of Quebec that was analyzed with the individual Tree Crown approach. This work was done in collaboration with CLC-Camint (Gatineau) and Industries Davidson Inc. and was funded in part by the “Programme de mise en valeur des ressources du milieu forestier Volet 1” of the Quebec Department of Natural Resources. The Tree species in the forested areas are indicated by the following colours:
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Individual Tree Crown Image Analysis - A Step Towards Precision Forestry ∗
2001Co-Authors: François A. Gougeon, Donald G. LeckieAbstract:Worldwide economy, environmental concerns, and stricter legislation governing forestry practices have put increased demands on forest managers. Riparian zone delineation, helicopter logging, plantation monitoring, selective cuts, just in time delivery, biodiversity and wildlife management are all various aspects of the same coin. The information requirements brought on by these activities is staggering. Existing information tools are inadequate and hamper the progress of forest management activities such as precision forestry. The use of high spatial resolution (10-100cm/pixel) remotely sensed images (aerial or satellite) or scanned aerial photographs, presents possibilities to analyze forested areas on an individual Tree Crown (ITC) basis. The Canadian Forest Service is at the forefront of research on individual Tree Crown based image analysis. We have developed techniques, methods and processes to separate forested from non-forested areas, delineate individual Tree Crowns, identify their species, and if needed, regroup them into forest stands or environmental strata. Eventually, forest managers will forgo static regroupings in favor of keeping all of the information about the individual Tree Crowns themselves (e.g., position, Crown area, height, species, and dominance). Regrouping would be done on demand, for each specific application, if done at all. In addition, the unprecedented level of details afforded by ITC techniques should allow us to extract a variety of additional forest management information such as: snag locations, forest gap sizes and distribution, highlyvalued Tree locations, detailed damage and regeneration assessments. This may also lead to more precise volume and biomass estimates and foster the use of individual Tree growth models. This paper first presents some of the image analysis concepts, methods and tools behind producing ITC-based forest inventories and then, reports on some successful applications, limitations, and ongoing research.
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individual Tree Crown image analysis a step towards precision forestry
2001Co-Authors: François A. Gougeon, Donald G. LeckieAbstract:Worldwide economy, environmental concerns, and stricter legislation governing forestry practices have put increased demands on forest managers. Riparian zone delineation, helicopter logging, plantation monitoring, selective cuts, just in time delivery, biodiversity and wildlife management are all various aspects of the same coin. The information requirements brought on by these activities is staggering. Existing information tools are inadequate and hamper the progress of forest management activities such as precision forestry. The use of high spatial resolution (10-100cm/pixel) remotely sensed images (aerial or satellite) or scanned aerial photographs, presents possibilities to analyze forested areas on an individual Tree Crown (ITC) basis. The Canadian Forest Service is at the forefront of research on individual Tree Crown based image analysis. We have developed techniques, methods and processes to separate forested from non-forested areas, delineate individual Tree Crowns, identify their species, and if needed, regroup them into forest stands or environmental strata. Eventually, forest managers will forgo static regroupings in favor of keeping all of the information about the individual Tree Crowns themselves (e.g., position, Crown area, height, species, and dominance). Regrouping would be done on demand, for each specific application, if done at all. In addition, the unprecedented level of details afforded by ITC techniques should allow us to extract a variety of additional forest management information such as: snag locations, forest gap sizes and distribution, highlyvalued Tree locations, detailed damage and regeneration assessments. This may also lead to more precise volume and biomass estimates and foster the use of individual Tree growth models. This paper first presents some of the image analysis concepts, methods and tools behind producing ITC-based forest inventories and then, reports on some successful applications, limitations, and ongoing research.
