Environmental Applications of Remote Sensing 2016
DOI: 10.5772/62122
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Detection of Tree Crowns in Very High Spatial Resolution Images

Abstract: The requirements for advanced knowledge on forest resources have led researchers to develop efficient methods to provide detailed information about trees. Since 1999, orbital remote sensing has been providing very high resolution (VHR) image data. The new generation of satellite allows individual tree crowns to be visually identifiable. The increase in spatial resolution has also had a profound effect in image processing techniques and has motivated the development of new object-based procedures to extract inf… Show more

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Cited by 18 publications
(27 citation statements)
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“…However, the sample size was limited and the number and complexity of tree crowns analyzed should be increased to provide greater confidence in this type of UAV-mapping application. Additional research is warranted to better understand and fully capture species-specific tree crown geometry, structure, and texture for use in automated tree crown delineation (e.g., Gomes and Maillard 2016) and accurate species classifications (e.g., St-Onge et al 2015).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…However, the sample size was limited and the number and complexity of tree crowns analyzed should be increased to provide greater confidence in this type of UAV-mapping application. Additional research is warranted to better understand and fully capture species-specific tree crown geometry, structure, and texture for use in automated tree crown delineation (e.g., Gomes and Maillard 2016) and accurate species classifications (e.g., St-Onge et al 2015).…”
Section: Resultsmentioning
confidence: 99%
“…Clusters and training areas may consist of individual distinct tree crowns or selected portions of several crowns; therefore, the image analyst must consider crown structure, illumination differences, partial occlusions, understory species, and other factors (such as crown transparency). Objectbased methods attempt to simplify the image prior to classification using coherent segments or objects that correspond to individual species' crowns (Gougeon 1995;Gomes and Maillard 2016). The object-recognition and classification success of traditional pattern recognition machine learning algorithms, which rely on manually crafted training data, has led to the development of complex and powerful neural networks or deep learning systems (LeCun et al 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Advances in airborne and spaceborne optical remote sensing technologies and the increasing availability of VHR optical imagery (one meter or submeter in spatial resolution) [55] in the past few decades have enabled researchers and experts to conduct detailed tree inventory and analysis projects at the single-tree level. Among the numerous existing studies that examine the VHR products of airborne and spaceborne optical technical remote sensors for ITC detection/delineation [32,51,[56][57][58][59][60][61], there is a considerable body of literature on using of VHR optical imagery for mapping and inventory of orchard trees [62][63][64][65].…”
Section: Aerial and Satellite Remote Sensingmentioning
confidence: 99%
“…Individual tree extraction using spectral and textural information derived from the optical data has been studied extensively over the past two decades [32,51,57,60]. However, despite the impressive developments in the algorithms for processing optical imagery as well as in the methods for incorporating spectral and contextual information for ITC detection/delineation, several factors still negatively affect tree detection performance, particularly in the urban context, including the complexity and heterogeneity of urban environments, the low spectral separability between tree crowns, and other types of understory vegetation (e.g., shrubs, grass), the large within-crown spectral variance in VHR imagery, the limited spatial resolution of satellite imageries with regard to the size of tree crowns, and the limitations for conducting fieldwork and providing ground reference datasets for the supervised ITC detection/delineation algorithms (particularly on private properties) [32,53,56,66,67].…”
Section: Aerial and Satellite Remote Sensingmentioning
confidence: 99%
“…Local maxima methods depend on locating the pixel of highest brightness inside a certain exploration window to identify the trees in the image [11,28,35]. In the scouting window, the pixel with the highest reflectance compared to the other pixels in the same window is determined and recognized as a potential tree location.…”
Section: Image Data and Processingmentioning
confidence: 99%