Literature DB >> 29862420

Object-based land-use/land-cover change detection using Landsat imagery: a case study of Ardabil, Namin, and Nir counties in northwest Iran.

Farnoosh Aslami1, Ardavan Ghorbani2.   

Abstract

In this study, land-use/land-cover (LULC) change in the Ardabil, Namin, and Nir counties, in the Ardabil province in the northwest of Iran, was detected using an object-based method. Landsat images including Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) were used. Preprocessing methods, including geometric and radiometric correction, and topographic normalization were performed. Image processing was conducted according to object-based image analysis using the nearest neighbor algorithm. An accuracy assessment was conducted using overall accuracy and Kappa statistics. Results show that maps obtained from images for 1987, 2002, and 2013 had an overall accuracy of 91.76, 91.06, and 93.00%, and a Kappa coefficient of 0.90, 0.83, and 0.91, respectively. Change detection between 1987 and 2013 shows that most of the rangelands (97,156.6 ha) have been converted to dry farming; moreover, residential and other urban land uses have also increased. The largest change in land use has occurred for irrigated farming, rangelands, and dry farming, of which approximately 3539.8, 3086.9, and 2271.9 ha, respectively, have given way to urban land use for each of the studied years.

Keywords:  Ardabil province; Change detection; Land use/land cover; Landsat; Object-based image analysis; Remote sensing

Mesh:

Year:  2018        PMID: 29862420     DOI: 10.1007/s10661-018-6751-y

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  1 in total

1.  Mapping LULC types in the Cerrado-Atlantic Forest ecotone region using a Landsat time series and object-based image approach: A case study of the Prata River Basin, Mato Grosso do Sul, Brazil.

Authors:  Elias Rodrigues da Cunha; Celso Augusto Guimarães Santos; Richarde Marques da Silva; Vitor Matheus Bacani; Paulo Eduardo Teodoro; Elói Panachuki; Naelmo de Souza Oliveira
Journal:  Environ Monit Assess       Date:  2020-01-24       Impact factor: 2.513

  1 in total

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