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Automatic mapping of standing dead trees after an insect outbreak using the Window Independent Context Segmentation method
Stockholms universitet, Samhällsvetenskapliga fakulteten, Kulturgeografiska institutionen.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Kulturgeografiska institutionen.
2014 (engelsk)Inngår i: Journal of forestry, ISSN 0022-1201, E-ISSN 1938-3746, Vol. 112, nr 6, s. 564-571Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Since the 1980s, there has been an increase in the spruce bark beetle population in the Bavarian Forest National Park in southeastern Germany. There is a need for accurate and time-effective methods for monitoring the outbreak, because manual interpretation of image data is time-consuming and expensive. In this article, the window independent context segmentation method is used to map deadwood areas. The aim is to evaluate the method’s ability to monitor deadwood areas on a yearly basis. Two-color infrared scenes with a spatial resolution of 40 × 40 cm from 2001 and 2008 were used for the study. The method was found to be effective with an overall accuracy of 88% for the 2001 scene and 90% for the 2008 scene.

sted, utgiver, år, opplag, sider
2014. Vol. 112, nr 6, s. 564-571
HSV kategori
Forskningsprogram
kulturgeografi
Identifikatorer
URN: urn:nbn:se:su:diva-103087DOI: 10.5849/jof.13-050ISI: 000344981800003OAI: oai:DiVA.org:su-103087DiVA, id: diva2:715289
Tilgjengelig fra: 2014-05-02 Laget: 2014-05-02 Sist oppdatert: 2017-12-05bibliografisk kontrollert
Inngår i avhandling
1. Inferring Land Use from Remote Sensing Imagery: A context-based approach
Åpne denne publikasjonen i ny fane eller vindu >>Inferring Land Use from Remote Sensing Imagery: A context-based approach
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

This doctoral thesis investigates the potential of classification methods based on spatial context to infer specific forms of land use from remote sensing data. The problem is that some types of land use are characterized by a complex configuration of land covers that traditional per-pixel based methods have problems classifying due to spectral heterogeneity. The problem of spectral heterogeneity is also present in classification of high resolution imagery. Two novel methods based on contextual information are evaluated, Spatial Relational Post-Classification (SRPC) and Window Independent Context Segmentation (WICS). The thesis includes six case studies in rural and urban areas focusing on the classification of: agricultural systems, urban characteristics, and dead wood areas. In the rural case studies specific types of agricultural systems associated with different household strategies are mapped by inferring the physical expression of land use using the SRPC method. The urban remote sensing studies demonstrate how the WICS method is able to extract information corresponding to different phases of development. Additionally, different urban classes are shown to correspond to different socioeconomic profiles, demonstrating how urban remote sensing can be used to make a connection between the physical environment and the social lives of residents. Finally, in one study the WICS method is used to successfully classify dead trees from high resolution imagery. Taken together these studies demonstrate how approaches based on spatial context can be used to extract information on land use in rural and urban environments where land use manifests itself in the form of complex spectral class and land cover patterns. The thesis, thus, contributes to the research field by showing that contextual methods can capture multifaceted patterns that can be linked to land use. This, in turn, enables an increased use of remote sensing data, particularly in the social sciences.

sted, utgiver, år, opplag, sider
Stockholm: Department of Human Geography, Stockholm University, 2014. s. 174
Serie
Meddelanden från Kulturgeografiska institutionen vid Stockholms universitet, ISSN 0585-3508 ; 147
Emneord
land use, remote sensing, urban remote sensing, image analysis, segmentation, spatial context, land cover, land cover configuration, farming types, bark beetle, dead trees, forest inventory
HSV kategori
Forskningsprogram
kulturgeografi
Identifikatorer
urn:nbn:se:su:diva-103082 (URN)978-91-7447-887-7 (ISBN)
Disputas
2014-06-11, De Geersalen, Geovetenskapens Hus, Svante Arrhenius väg 14, Stockholm, 10:00 (engelsk)
Opponent
Veileder
Merknad

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript. Paper 5: Manuscript. Paper 6: Manuscript.

Tilgjengelig fra: 2014-05-20 Laget: 2014-05-02 Sist oppdatert: 2014-09-30bibliografisk kontrollert

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