Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images

Juan Pablo Ardila, W. Bijker, V.A. Tolpekin, A. Stein

Research output: Contribution to journalArticleAcademicpeer-review

47 Citations (Scopus)

Abstract

Municipalities need accurate and updated inventories of urban vegetation in order to manage green resources and estimate their return on investment in urban forestry activities. Earlier studies have shown that semi-automatic tree detection using remote sensing is a challenging task. This study aims to develop a reproducible geographic object-based image analysis (GEOBIA) methodology to locate and delineate tree crowns in urban areas using high resolution imagery. We propose a GEOBIA approach that considers the spectral, spatial and contextual characteristics of tree objects in the urban space. The study presents classification rules that exploit object features at multiple segmentation scales modifying the labeling and shape of image-objects. The GEOBIA methodology was implemented on QuickBird images acquired over the cities of Enschede and Delft (The Netherlands), resulting in an identification rate of 70% and 82% respectively. False negative errors concentrated on small trees and false positive errors in private gardens. The quality of crown boundaries was acceptable, with an overall delineation error <0.24 outside of gardens and backyards.
Original languageEnglish
Pages (from-to)57-69
JournalInternational Journal of Applied Earth Observation and Geoinformation (JAG)
Volume15
DOIs
Publication statusPublished - 2012

Fingerprint

Image analysis
urban area
Satellites
image analysis
garden
Forestry
urban forestry
Labeling
QuickBird
methodology
Remote sensing
segmentation
imagery
remote sensing
satellite image
vegetation
resource

Keywords

  • Context classification
  • GEOBIA
  • Tree crown identification
  • IR-83742
  • Object based image analysis
  • Object classification
  • METIS-293869
  • Tree mapping
  • ITC-ISI-JOURNAL-ARTICLE

Cite this

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title = "Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images",
abstract = "Municipalities need accurate and updated inventories of urban vegetation in order to manage green resources and estimate their return on investment in urban forestry activities. Earlier studies have shown that semi-automatic tree detection using remote sensing is a challenging task. This study aims to develop a reproducible geographic object-based image analysis (GEOBIA) methodology to locate and delineate tree crowns in urban areas using high resolution imagery. We propose a GEOBIA approach that considers the spectral, spatial and contextual characteristics of tree objects in the urban space. The study presents classification rules that exploit object features at multiple segmentation scales modifying the labeling and shape of image-objects. The GEOBIA methodology was implemented on QuickBird images acquired over the cities of Enschede and Delft (The Netherlands), resulting in an identification rate of 70{\%} and 82{\%} respectively. False negative errors concentrated on small trees and false positive errors in private gardens. The quality of crown boundaries was acceptable, with an overall delineation error <0.24 outside of gardens and backyards.",
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Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images. / Ardila, Juan Pablo; Bijker, W.; Tolpekin, V.A.; Stein, A.

In: International Journal of Applied Earth Observation and Geoinformation (JAG), Vol. 15, 2012, p. 57-69.

Research output: Contribution to journalArticleAcademicpeer-review

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