Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 57-69 |
| Journal | International Journal of Applied Earth Observation and Geoinformation (JAG) |
| Volume | 15 |
| DOIs | |
| Publication status | Published - 2012 |
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Keywords
- Context classification
- GEOBIA
- Tree crown identification
- IR-83742
- Object based image analysis
- Object classification
- METIS-293869
- Tree mapping
- ITC-ISI-JOURNAL-ARTICLE
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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 journal › Article › Academic › peer-review
TY - JOUR
T1 - Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images
AU - Ardila, Juan Pablo
AU - Bijker, W.
AU - Tolpekin, V.A.
AU - Stein, A.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Context classification
KW - GEOBIA
KW - Tree crown identification
KW - IR-83742
KW - Object based image analysis
KW - Object classification
KW - METIS-293869
KW - Tree mapping
KW - ITC-ISI-JOURNAL-ARTICLE
UR - https://ezproxy2.utwente.nl/login?url=http://dx.doi.org/10.1016/j.jag.2011.06.005
UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2012/isi/ardilalopez_con.pdf
U2 - 10.1016/j.jag.2011.06.005
DO - 10.1016/j.jag.2011.06.005
M3 - Article
VL - 15
SP - 57
EP - 69
JO - International Journal of Applied Earth Observation and Geoinformation (JAG)
JF - International Journal of Applied Earth Observation and Geoinformation (JAG)
SN - 1569-8432
ER -