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Spatio-temporal urban ecosystem service analysis with Sentinel-2A MSI data
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Continuous urbanization changes the surface of our globe raising questions of sustainability, ecological functionality and living quality in metropolitan regions. Remote sensing enables us to obtain timely and reliable information on the state of urban areas and their changing patterns. The objectives of this study are to evaluate the contribution of Sentinal-2A data for urban ecosystem service mapping and to evaluate spatio-temporal characteristics of ecosystem service provisional patches through landscape metrics as an extension of the ecosystem service concept. Changes in service patterns over a 10-year time frame are mapped in the megacity of Beijing, China using Landsat TM data from 2005 and Sentinel-2A data from 2015. Landscape metrics are generated based on the classification results to evaluate the changes of urban ecosystem service provision bundles. The images are segmented using KTH-SEG, an edge-aware region growing and merging algorithm. The segments are then classified using a SVM classifier according to a classification strategy that is designed to distinguish between four natural and managed urban classes based on underlying ecosystem function and three artificial urban structures, i.e. buildings and roads that negatively influence ecosystem service provision to varying degrees and in different ways. These negative impacts are quantified through seven spatial attributes of green and blue patches and their configuration, namely area (CA), connectivity (COHESION), core area (TCA), diversity (SHDI), edge effects (CWED), percentage of land cover (PLAND) and a proximity measure. The 2015 classification accuracy of 90.2% was higher than the 2005 classification accuracy with 84.7%. Beijing’s urban development is characterized by a decrease in agricultural areas in the urban fringe in favour of new high and low density built-up areas, urban green space and golf courses. In total, high density and low-density urban areas have increased ca. 21%. Furthermore, the deconstruction of former high density low-rise suburban agglomerations into urban green space can be observed. The planar increase in urban areas is partly counteracted by the creation of managed urban green spaces. Ecosystem service bundles based on underlying land cover classes and similar spatial factors that influence service quality were derived for 2005 and 2015. Changes in landscape composition and configuration resulted in decreases of more than 30% in the bundles that represent food supply, noise reduction, waste treatment, global climate regulation. Temperature regulation/moderation of climate extremes, recreation/place values and social cohesion, aesthetic benefits/cognitive development and least affected by the observed land cover changes. The extension of the ecosystem service concept through spatio-temporal characteristics of ecosystem service provisional patches by landscape metrics is believed to give a more realistic appraisal of ecosystem services in urban areas.

Keyword [en]
Ecosystem Services, KTH-SEG, Landscape Metrics, MSI, Sentinel-2, Urban land cover
National Category
Remote Sensing
Research subject
Geodesy and Geoinformatics
Identifiers
URN: urn:nbn:se:kth:diva-181866OAI: oai:DiVA.org:kth-181866DiVA: diva2:900894
Note

QS 2016

Available from: 2016-02-05 Created: 2016-02-05 Last updated: 2016-02-09Bibliographically approved
In thesis
1. Remote Sensing of Urbanization and Environmental Impacts
Open this publication in new window or tab >>Remote Sensing of Urbanization and Environmental Impacts
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis aims to establish analytical frameworks to map urban growth patterns with spaceborne remote sensing data and to evaluate environmental impacts through Landscape Metrics and Ecosystem Services. Urbanization patterns at regional scale were evaluated in China's largest urban agglomerations and at metropolitan scale in Shanghai, Stockholm and Beijing using medium resolution optical satellite data. High-resolution data was used to investigate changes in Shanghai’s urban core. The images were co-registered and mosaicked. Tasseled Cap transformations and texture features were used to increase class separabilities prior to pixel-based Random Forest and SVM classifications. Urban land cover in Shanghai and Beijing were derived through object-based SVM classification in KTH-SEG. After post-classification refinements, urbanization indices, Ecosystem Services and Landscape Metrics were used to quantify and characterize environmental impact. Urban growth was observed in all studies. China's urban agglomerations showed most prominent urbanization trends. Stockholm’s urban extent increased only little with minor environmental implications. On a regional/metropolitan scale, urban expansion progressed predominately at the expense of agriculture. Investigating urbanization patterns at higher detail revealed trends that counteracted negative urbanization effects in Shanghai's core and Beijing's urban-rural fringe. Beijing's growth resulted in Ecosystem Services losses through landscape structural changes, i.e. service area decreases, edge contamination or fragmentation. Methodological frameworks to characterize urbanization trends at different scales based on remotely sensed data were developed. For detailed urban analyses high-resolution data are recommended whereas medium-resolution data at metropolitan/regional scales is suggested. The Ecosystem Service concept was extended with Landscape Metrics to create a more differentiated picture of urbanization effects.​

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016
Series
TRITA-SOM, ISSN 1653-6126 ; 2016:01
Keyword
Remote Sensing, Urbanization, Land Use/Land Cover (LULC), Environmental Impact, Landscape Metrics, Ecosystem Services
National Category
Remote Sensing
Research subject
Geodesy and Geoinformatics
Identifiers
urn:nbn:se:kth:diva-181867 (URN)978-91-7595-852-1 (ISBN)
Public defence
2016-02-25, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20160205

Available from: 2016-02-05 Created: 2016-02-05 Last updated: 2016-02-09Bibliographically approved

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