Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index

Forum Geografi

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Title Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index
Creator Hidayati, Iswari Nur
Suharyadi, R
Danoedoro, Projo
Subject geography; urban remote sensing
built-up area extraction; remote sensing; index transformation; Landsat 8 OLI
landsat imagery; built up area
Description Studying urban areas using remote sensing imagery has become a challenge, both visually and digitally. Supervised classification, one of the digital classification approaches to differentiate between built-up and non-built-up area, used to be leading in digital studies of urban area. Then the next generation uses index transformation for automatic urban data extraction. The extraction of urban built-up land can be automatically done with NDBI although it has one limitation on separating built-up land and bare land. The previous studies provide opportunities for further research to increase the accuracy of the extraction, particularly using index transformation. This study aims to obtain the maximum accuracy of the extraction by merging several indices including NDBI, NDVI, MNDWI, NDWI, and SAVI. The merging of the indices is using four stages: merging of two indices, three indices, four indexes and five indices. Several operations were experimented to merge the indices, either by addition, subtraction, or multiplication. The results show that merging NDBI and MNDWI produce the highest accuracy of 90.30% either by multiplication (overlay) or reduction. Application of SAVI, NDBI, and NDWI also gives a good effect for extracting urban built-up areas and has 85.72% mapping accuracy.
Publisher Universitas Muhammadiyah Surakarta
Contributor Faculty of Geography, Gadjah Mada University
Date 2018-04-23
Type info:eu-repo/semantics/article

Quantitative approach, principle component analysis, index combination approach
Format application/pdf
Source Forum Geografi; Vol 32, No 1 (2018): July 2018; 96-108
Language eng
Coverage Yogyakarta
Landsat imagery; radiometric correction; NDVI, NDBI, SAVI, MNDWI, NDWI; built up extraction data using several mathematics algorithm; Principle component analysis, overall accuracy
Stratified random sampling; 240 samples; pixel area; grid analysis; index combination
Rights Copyright (c) 2018 Iswari Nur Hidayati, R Suharyadi, Projo Danoedoro

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