In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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VALIDATION OF A SEMI-AUTOMATED CLASSIFICATION APPROACH FOR URBAN 
GREEN STRUCTURE 
0ivind Due Trier 3 ’ * and Einar Lieng b 
3 Norwegian Computing Center, Gaustadalleen 23, P.O. Box 114 Blindem, N0-0314 Oslo, Norway - trier@nr.no 
b Asplan Viak AS, P.O. Box 701, N0-4808 Arendal, Norway - einar.lieng@asplanviak.no 
KEY WORDS: Segmentation, classification, multispectral Quickbird imagery, urban vegetation 
ABSTRACT: 
Municipalities in Norway need to develop an urban green structure plan. Traditional mapping has its limitation, since the land use is 
in focus and not the actual land cover. This study evaluated the appropriateness of using multispectral Quickbird images for the 
semi-automated mapping of green structures in urban and suburban areas. A Quickbird image of Oslo from 2 June 2008 was used. A 
classification algorithm was implemented in Definiens Developer. The algorithm was applied to the whole image, and tested on six 
randomly selected subsets. The validation was performed by manual editing of the classification result. The main focus of the 
editing process was to detect misclassifications between grey areas (such as roads and buildings) and green areas (trees, grass, and 
sparse vegetation). The most striking problem with the automated method was that the object borders were very rugged. However, 
these segmentation problems were to some extent ignored in the evaluation process, concentrating on correcting major parts of 
objects being misclassified rather than correcting all minor segmentation inaccuracies. The classification step had approximately 9% 
misclassification rate in the two-class problem grey area versus green area. This is a very good basis for further improvement. The 
obvious segmentation problems are clearly the first things to address when further improving the method. Another problem is to 
what extent the automated method can be used on other images with different light conditions, e.g., with the presence of clouds or 
light haze and another solar elevation. Will a simple retraining of the classification rules be sufficient, or will the rules have to be 
redesigned? It could even happen that redesigning the rules is not sufficient, so that other methods have to be developed. 
1. INTRODUCTION 
This work was initiated to meet the need of municipalities in 
Norway to develop a green structure plan. Traditional mapping 
has its limitation, since the land use is in focus and not the 
actual land cover. Therefore, other sources of information about 
urban and suburban green structure are being sought. A 
municipality is interested in a green structure plan for several 
reasons: 
1. To map current status of green areas and their 
changes over time. For example, what happens with 
the vegetation in public parks over time, even if the 
mapped land use does not change? 
2. To maintain biological diversity. Different species or 
groups of species use different varieties of green 
structure as corridors. For example, small birds would 
avoid open areas, and need a corridor of trees to move 
safely. In open areas, they would expose themselves 
to predators. 
3. Green structures are being used for recreation. 
4. Vegetation converts carbon dioxide to oxygen, 
reduces noise, and has aesthetical value. Vegetation 
also binds water, reducing the prospect of floods after 
heavy rainfall. 
5. If accurate, the green structure map can be used in 
overlays 
The green structure includes private gardens. Although not 
accessible to the public, private gardens containing trees 
contributes to items 2 and 4 above. 
* Corresponding author. 
Page 1 of 6 
Forest and farmland are not in the focus of this study, since they 
are well mapped, and the land cover aligns well with the land 
use classification of traditional mapping. 
The purpose of this study was to evaluate the appropriateness of 
using Quickbird 0.6 m - 2.4 m resolution satellite images for 
the automatic mapping of green structures in urban and 
suburban areas. The rest of the report is organized as follows: 
Section 2 presents the available Quickbird image data, followed 
by a description of the segmentation, training, classification and 
postprocessing steps of the automatic algorithm in Section 3. In 
section 4, the validation methodology is described. The 
validation results are presented in Section 5 and discussed in 
Section 6. This paper is a condensed version of a project report 
(Trier, 2009), available at http://publ.nr.no. 
2. DATA 
The project has acquired parts of a cloud-free Quickbird scene 
of parts of Oslo and surrounding area, acquired on 2 June 2008. 
The image has a 0.6 m ground resolution panchromatic band, 
and four 2.4 m resolution multispectral bands (blue, green, red 
and near infrared). 
3. CLASSIFICATION PROCEDURE 
Definiens Developer (Definiens, 2007) was used to segment the 
image, based on pixel colors and parameters describing the 
segment shapes. Then the user defined a set of rules to classify 
the segments based on texture, neighborhood, color and other 
attributes. The final classification result consists of five classes: 
(1) grey areas, (2) grass, (3) trees, (4) little vegetation, and (5) 
water and missing data.