An individual tree crown delineation method based on multi-scale segmentation of imagery

Jing, Linhai; Hu, Baoxin; Noland, Thomas L.; Li, Jili

doi:10.1016/j.isprsjprs.2012.04.003

Cited by 144 publications

(113 citation statements)

References 21 publications

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“…The popular methods are, but not limited to, the template matching approaches [20][21][22] , the valley following approach [23,24], the local-maxima [25], texture grouping [26], the use of morphological operators [27], and joining of convex edges [28]. Since the segmentation is not the focus of this study, an automatic processing was tested using the watershed approach proposed by [29] for a potential of large size image processing. Even the overall segmentation accuracy was over 80%, the main errors occurred when a few trees group together.…”

Section: Introductionmentioning

confidence: 99%

Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles

Zhang

2012

Remote Sensing

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Individual tree species identification is important for urban forest inventory and ecology management. Recent advances in remote sensing technologies facilitate more detailed estimation of individual urban tree characteristics. This study presents an approach to improve the classification of individual tree species via longitudinal profiles from very high spatial resolution airborne imagery. The longitudinal profiles represent the side view tree shape, which play a very important role in individual tree species on-site identification. Decision tree classification was employed to conduct the final classification result. Using this profile approach, six major species (Maple, Ash, Birch, Oak, Spruce, Pine) of trees on the York University (Ontario, Canada) campus were successfully identified. Two decision trees were constructed, one knowledge-based and one derived from gain ratio criteria. The classification accuracy achieved were 84% and 86%, respectively.

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Section: Introductionmentioning

confidence: 99%

Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles

Zhang

2012

Remote Sensing

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“…Besides active data, imagebased approaches still form another important research area of tree detection studies (e.g. Jing et al, 2012;Hung, et al, 2012;Malek et al, 2014;Ozdarici-Ok, 2015;Leckie, et al, 2016). According to the literature published up to now, most of the tree detection studies are related with the detection of areal and/or individual mixed and coniferous forests (e.g.…”

Section: Previous Studiesmentioning

confidence: 99%

Detection of Citrus Trees From Uav DSMS

Ozdarici-Ok

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents an automated approach to detect citrus trees from digitals surface models (DSMs) as a single source. The DSMs in this study are generated from Unmanned Aerial Vehicles (UAVs), and the proposed approach first considers the symmetric nature of the citrus trees, and it computes the orientation-based radial symmetry in an efficient way. The approach also takes into account the local maxima (LM) information to verify the output of the radial symmetry. Our contributions in this study are twofold: (i) Such an integrated approach (symmetry + LM) has not been tested to detect (citrus) trees (in orchards), and (ii) the validity of such an integrated approach has not been experienced for an input, e.g. a single DSM. Experiments are performed on five test patches. The results reveal that our approach is capable of counting most of the citrus trees without manual intervention. Comparison to the stateof-the-art reveals that the proposed approach provides notable detection performance by providing the best balance between precision and recall measures.

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“…Table 8 summarizes the accuracy of LiDAR-derived mangrove biophysical parameters using the VWF and IWS methods compared with previous studies in different forest types. The accuracy of the height estimation using LiDAR data for mangrove forests is lower than the accuracy for coniferous [31,43] spruce, pine [41,42,44], and boreal [54] forests. However, the results of this study cannot be compared directly with previous studies because of the differences in the LiDAR point cloud density, tree species, forest structure, and crown density.…”

Section: Measurement Of Crown Diametermentioning

confidence: 99%

“…VWF is an adaptive local maximum filtering approach with a circular window of variable window size, which is useful for individual tree crown delineation. IWS initially aims to segment the tree crown over the high-spatial-resolution image [44] and has been recently applied to the LiDAR-derived CHM [43,45]. IWS starts with the inversion of the raster canopy surface into the equivalent of individual hydrologic drainage basins [29,45].…”

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Extraction of Mangrove Biophysical Parameters Using Airborne LiDAR

et al. 2013

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Tree parameter determinations using airborne Light Detection and Ranging (LiDAR) have been conducted in many forest types, including coniferous, boreal, and deciduous. However, there are only a few scientific articles discussing the application of LiDAR to mangrove biophysical parameter extraction at an individual tree level. The main objective of this study was to investigate the potential of using LiDAR data to estimate the biophysical parameters of mangrove trees at an individual tree scale. The Variable Window Filtering (VWF) and Inverse Watershed Segmentation (IWS) methods were investigated by comparing their performance in individual tree detection and in deriving tree position, crown diameter, and tree height using the LiDAR-derived Canopy Height Model (CHM). The results demonstrated that each method performed well in mangrove forests with a low percentage of crown overlap conditions. The VWF method yielded a slightly higher accuracy for mangrove parameter extractions from LiDAR data compared with the IWS method. This is because the VWF method uses an adaptive circular filtering window size based on an allometric relationship. As a result of the VWF method, the position measurements of individual tree indicated a mean distance error value of 1.10 m. The In this study, the accuracy of LiDAR-derived biophysical parameters in mangrove forests using the VWF and IWS methods is lower than in coniferous, boreal, pine, and deciduous forests. An adaptive allometric equation that is specific for the level of tree density and percentage of crown overlap is a solution for improving the predictive accuracy of the VWF method.

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An individual tree crown delineation method based on multi-scale segmentation of imagery

Cited by 144 publications

References 21 publications

Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles

Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles

Detection of Citrus Trees From Uav DSMS

Extraction of Mangrove Biophysical Parameters Using Airborne LiDAR

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