The use of time series analysis in land USE/COVER change

Fulya Basak Sariyilmaz, Nebiye Musaoǧlu

Research output: Contribution to conferencePaperpeer-review

Abstract

This study aims to determine Land Use/Cover (LULC) classes by using satellite images, and to make a statistical analysis of any changes through time series analysis. Sariyer, one of Istanbul's major districts, was selected as the study area. To obtain LULC change classes, Landsat TM and + ETM satellite images from different dates were classified. Four classes based on level one of the CORINE database were determined as a result of the classification process. These are artificial surfaces, forested and semi-natural areas, agricultural areas and water bodies. After the accuracy assessment process, the classification results were analyzed by time series. The results of the classification and linear trend analysis method were interpreted, and significant results were obtained for the years 2012, 2015, 2017, 2021, 2025, 2027, and 2030. The standard deviations and confidence intervals were calculated for each year. The estimations that resulted from the time series analysis for 2012 and 2015 were compared with the satellite imagery classification results from the same dates. According the time series results for the study area, it is possible to predict that while the value of agricultural areas, forested and semi-natural areas are decreasing, the value of artificial surfaces is increasing. In addition, it can be claimed that time series analysis provides useful results with regard to the analysis of land use/cover change.

Original languageEnglish
Publication statusPublished - 2015
Event36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015 - Quezon City, Metro Manila, Philippines
Duration: 24 Oct 201528 Oct 2015

Conference

Conference36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015
Country/TerritoryPhilippines
CityQuezon City, Metro Manila
Period24/10/1528/10/15

Keywords

  • Change detection
  • Linear trend analysis
  • Remote sensing
  • Sariyer

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