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You searched for +publisher:"Delft University of Technology" +contributor:("Vargas-Luna, A."). One record found.

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Delft University of Technology

1. Alphenaar, K.J. An intensified vegetation classification of the Dutch river forelands: Designing a new, automatic classification and change detection method :.

Degree: 2016, Delft University of Technology

The current vegetation monitoring method of Rijkswaterstaat is a manual, time consuming and costly method and therefore only is performed once in six years. The vegetatielegger is a map, which shows the standard situation of the vegetation types that are allowed at any location in the river foreland area of the Netherlands. No rougher vegetation is allowed than displayed in the vegetatielegger. However, vegetation grows and new, rougher vegetation forms within those six years between monitoring cycles. So, to ensure the conservation of the situation as given in the vegetatielegger, a new, faster method had to be designed. This research designed and automatic post classification change detection and tested it on a representative test site. The object based classification used is a kNN-classifier and a region growing segmentation. The object-based approach ensures a classification that is well comparable to the polygons of the vegetatielegger. The classification is done with summer aerial photographs as well as with winter satellite images. The aerial photographs have a higher resolution than the satellite images, but in the winter satellite some vegetation classes are better distinguishable. The six classes that are classified are: water, built-up, forest, grass, tall herb vegetation and trees. Aerial photographs give a high overall accuracy for the classification; satellite images score better on tall herb classification accuracy. All generated results have a higher accuracy than the current classification method. The change detection is generated taking into account the Rijkswaterstaat rules for change in the forelands: only changes to a rougher vegetation type of more than 500m2 are important. The satellite image classification generates more accurate change detections than the aerial photographs. Advisors/Committee Members: Gorte, B.G.H., Menenti, M., Vargas-Luna, A..

Subjects/Keywords: classification; vegetation monitoring; river forelands; change detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Alphenaar, K. J. (2016). An intensified vegetation classification of the Dutch river forelands: Designing a new, automatic classification and change detection method :. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:20867a0e-e012-44bd-a210-c9c4a0901f06

Chicago Manual of Style (16th Edition):

Alphenaar, K J. “An intensified vegetation classification of the Dutch river forelands: Designing a new, automatic classification and change detection method :.” 2016. Masters Thesis, Delft University of Technology. Accessed October 16, 2019. http://resolver.tudelft.nl/uuid:20867a0e-e012-44bd-a210-c9c4a0901f06.

MLA Handbook (7th Edition):

Alphenaar, K J. “An intensified vegetation classification of the Dutch river forelands: Designing a new, automatic classification and change detection method :.” 2016. Web. 16 Oct 2019.

Vancouver:

Alphenaar KJ. An intensified vegetation classification of the Dutch river forelands: Designing a new, automatic classification and change detection method :. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2019 Oct 16]. Available from: http://resolver.tudelft.nl/uuid:20867a0e-e012-44bd-a210-c9c4a0901f06.

Council of Science Editors:

Alphenaar KJ. An intensified vegetation classification of the Dutch river forelands: Designing a new, automatic classification and change detection method :. [Masters Thesis]. Delft University of Technology; 2016. Available from: http://resolver.tudelft.nl/uuid:20867a0e-e012-44bd-a210-c9c4a0901f06

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