The use of broadband vegetation indices in cultivated land detection with Landsat 8 OLI multi-temporal images

Ugur Alganci*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Determination of cotton and maize cultivated areas with multi-temporal satellite images using vegetation indices is the mam objective of this research. Study area was located on Sanlmrfa province, Turkey/which hosts huge amount of agricultural production of cotton and maize in spring-summer season with its suitable and effective irrigation system. The Landsat 8 OLI multi-temporal images acquired with 16-day interval were used to identify cultivated areas in the study area. Image acquisition dates from 30 April 2015 to 21 September 2015 completely covers the phenological development period of cultivated crop types. Terrain corrected images were radiometncally calibrated to produce Top of Atmosphere (ToA) reflectance images, in order to reduce the atmospheric and illumination effects, thus providing efficient multi-temporal analysis. ToA reflectance images were then used in vegetation index (VI) production. Normalized Difference Vegetation Index (NDVI), Transformed Difference Vegetation Index (TDVI), Enhanced Vegetation Index (EVI) and Green Normalized Difference Vegetation Index (GNDVI) were used in this research as vegetation suppression and data dimension reduction methods. Then VI image stacks were classified with pixel based Support Vector Machine (SVM) algorithm and results were compared with statistical production database to evaluate the effectiveness of broadband Vis in cultivated area and crop pattern detection. Results of the analysis provided that, GNDVI based dataset provided highest accuracies according to areal comparison and point based accuracy assessment. NDVI and TDVI based datasets were ranked as the second with similar accuracy results, while EVI based dataset was in the last place when compared to remaining VI datasets. Additionally, area determination efficiency and classification accuracy for the cotton was higher than the maize nearly in all regions.

Original languageEnglish
Pages (from-to)739-744
Number of pages6
JournalFresenius Environmental Bulletin
Volume28
Issue number2
Publication statusPublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 Parlar Scientific Publications. All rights reserved.

Funding

Author acknowledge the support of United Sates Geological Survey (USGS) by providing the free of charge Landsat 8 OLI image data and Turkish Statistical Institute (TUIK) for providing farmer registration system statistics. Author decleras no conflict of interest.

FundersFunder number
U.S. Geological Survey

    Keywords

    • Broadband vegetation indices
    • Crop type identification
    • Cultivated area mapping
    • Multi-temporal satellite image
    • Support vector machine classification

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