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Automatic information extraction and prediction of karst rocky desertification in Puding using remote sensing data
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Industrial Development, IT and Land Management, Land management, GIS.
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Sustainable development
The essay/thesis is partially on sustainable development according to the University's criteria
Abstract [en]

Karst rocky desertification (KRD) is one kind of severe environmental problem existing in southwest of China. Reveal KRD condition is vital to solve the problem. A way to address the problem is by identifying KRD areas, so that policy-makers and researchers may get a better view of the issue and know where the areas affected by the problem are located. The study area is called Puding which is a county located in the central part of Guizhou province. Based on Landsat data, by using GIS and RS techniques, KRD information of Puding was extracted. Furthermore, the study monitored decades of change of the environmental problem in Puding and predicted possible condition in the future. Other researchers and decision makers may get a better view of the issue from the study results. In addition to Landsat data, other used data includes: ASTER Global digital elevation model data, Modis data, Google Earth data and other thematic maps. In the study, expert classification system and spectral features based model two methods were applied to extract KRD information and compare with each other. Their classified rules were taken from previous studies separately. Necessary preprocessing procedures such as atmospheric correction and geometrical correction were performed before extraction. After extraction relevant results were evaluated and analyzed. Predictions were made by cellular automata Markov module. Based on extracted KRD results, the distribution, percentage, change, and prediction of KRD conditions in Puding were presented. The results of the accuracy evaluation showed that the spectral features based model had acceptable performance. However, the KRD results extracted by expert classification system method were poor. The extracted KRD results, including KRD maps and the prediction map, both indicated that KRD areas in Puding were decreased from 1993 (spring) to 2016 (spring) and suggested to pay more attention to KRD areas changes with the seasons

Place, publisher, year, edition, pages
2016. , 46 p.
Keyword [en]
GIS, remote sensing, KRD, change detection, CA Markov prediction, Puding, China
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:hig:diva-23988OAI: oai:DiVA.org:hig-23988DiVA: diva2:1093467
Subject / course
Geomatics
Educational program
Geomatics – bachelor’s programme (swe or eng)
Presentation
2016-12-09, 13:15 (English)
Supervisors
Examiners
Available from: 2017-05-18 Created: 2017-05-07 Last updated: 2017-08-21Bibliographically approved

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fulltext(2761 kB)25 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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