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Monitoring and Analysis of Urban LandCover Changes over StockholmRegion between 1986 and 2004 usingRemote Sensing and Spatial Metrics
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Satellite Positioning.
2008 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Remote sensing and spatial metrics provide tools for qualitative landcover change analysis. The main objective for this thesis is to analyse theland cover changes that has occurred in Stockholm region between 1986and 2004 using remote sensing and spatial metrics. Two dates of SPOTHRV images were acquired on the June 13th 1986 and July 29th 2004over the greater Stockholm area.There are two approaches to perform change detection; one is tocompare remotely sensed images from different dates, i.e. image-to-imagechange detection, and the other is to compare classified maps, i.e. postclassificationchange detection. In this research the aim is to comparethese two techniques in their ability to detect new urban features in thelandscapes and to analyze the spatial patterns of the changes withspatial metrics.The first part of this thesis is to evaluate various image-to-imagechange detection methods. Three different image-to-image changedetection methods are tested; image differencing, multi-date compositeimage change detection and change vector analysis. Then the imagesfrom the two dates are classified and analysed with post-classificationcomparison method. A mask for the urban zones is used in combinationwith supervised and unsupervised classification methods. The finalclassification scheme consists of seven land cover classes:Transportation, High density built-up, Low density built-up, Open land,Coniferous forest, Deciduous and mixed forest and Water.The second part of the thesis is to investigate if any possiblefragmentation of the landscape has occurred due to the land coverchanges. Spatial metrics offers several measures that describe thedistribution and form of the land cover classes and these are applied todescribe the spatial attributes.It can be concluded from the resulting statistics that the image-toimagechange detection can be as accurate that the post-classificationcomparison, with overall accuracies up to 84.8% respectively 86.7%.Change vector analysis showed to be highly accurate in detecting newurban areas, when appropriate measures is chosen. The threedimensional change vector analysis using red band differencing, PCA andtriangular vegetation index showed to be the best combination.Post-classification comparison showed a growth of low-density builtupareas with 1.9% and the high-density built-up 0.4% over the wholestudy area. The forested area had grown with approximately 5.4%, whileopen land had decreased with 5.2%. The water area decreased slightlyfrom 1986 to 2004, due to the seasonal change that affects the waterlevel. PCC is dependent on the classification accuracy and theclassification errors cause false changes. The overall accuracy for thehybrid classification for the 1986 image was 92.3% with kappa statisticsof 0.909 and for the 2004 image 90.4% and 0.886 respectively. Accordingto the spatial metrics statistics the forested and urban area has changedin their spatial distribution. These classes have become more unite andcompact while open land is not affected.

Place, publisher, year, edition, pages
2008.
Series
TRITA-GIT EX ; 08-11
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-199697OAI: oai:DiVA.org:kth-199697DiVA, id: diva2:1065082
Supervisors
Available from: 2017-01-19 Created: 2017-01-13 Last updated: 2017-01-19Bibliographically approved

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