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Topographic Correction of LAPAN-A3/LAPAN-IPB Multispectral Image: A Comparison of Five Different Algorithms

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  • Zylshal Zylshal

    (Remote Sensing Technology and Data Center, Indonesian National Institute of Aeronautics and Space (LAPAN), Jakarta Timur, Indonesia)

Abstract

Reflectance variability in mountainous regions caused by steep slopes can decrease the accuracy of landcover mapping. Topographic correction aims to reduce this effect, and various techniques have been proposed to conduct such correction on satellite imagery. This paper presents the initial results of five different topographic correction techniques applied to LAPAN-A3 multispectral images, namely cosine correction, improved cosine correction, Minnaert correction, modified Minnaert correction and two-stage normalization. The widely-available ALOS World 3D 30 meter DEM was employed, with the evaluation made in a mountainous area in South Sulawesi, Indonesia, located in an ancient volcanic region, with slopes ranging from 0 to 60 degrees. The slope aspect was almost equally distributed in all directions. Visual and statistical analysis was conducted before and after the topographic correction to evaluate the results. Standard deviation (SD) and the coefficient variation (CV) were calculated; the results show that the topographic corrections were able to reduce the effect of shadows and relief. Minnaert correction proved to be the best method in terms of visual appearance and spectral variability reduction.

Suggested Citation

  • Zylshal Zylshal, 2020. "Topographic Correction of LAPAN-A3/LAPAN-IPB Multispectral Image: A Comparison of Five Different Algorithms," Quaestiones Geographicae, Sciendo, vol. 39(3), pages 33-45, September.
    • Handle: RePEc:vrs:quageo:v:39:y:2020:i:3:p:33-45:n:2
    DOI: 10.2478/quageo-2020-0021
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    References listed on IDEAS

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    1. Uday Pimple & Asamaporn Sitthi & Dario Simonetti & Sukan Pungkul & Kumron Leadprathom & Amnat Chidthaisong, 2017. "Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand," Sustainability, MDPI, vol. 9(2), pages 1-26, February.
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    Cited by:

    1. Sheoran Savita Kumari & Parmar Vinti, 2022. "GeoWebCln: An Intensive Cleaning Architecture for Geospatial Metadata," Quaestiones Geographicae, Sciendo, vol. 41(1), pages 51-62, March.

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