Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve

Arisetra Razafinimaro; Aimé Richard Hajalalaina; ZoJaona Tantely Reziky; Eric Delaitre; Avisoa Andrianarivo

doi:doi:10.11648/j.ajrs.20210901.12

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Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve

Arisetra Razafinimaro, Aimé Richard Hajalalaina, ZoJaona Tantely Reziky, Eric Delaitre, Avisoa Andrianarivo

Published in American Journal of Remote Sensing (Volume 9, Issue 1)

Received: 17 December 2020 Accepted: 24 December 2020 Published: 23 February 2021

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Abstract

This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018. For this, we adopted the methodology of satellite image processing based on supervised classification algorithms. The processing was moved to spectral preparation and improvement of spatial resolution using the blue, green, red, near infrared and panchromatic channels. Then, a comparison study of the supervised classification algorithms was done to obtain a more accurate result. The validation of the classification results was performed using several reference points, a previous national processing result already validated in the field and the Google earth image of the same year. After repeating the classification several times, we obtained accuracies of 77%, 75%, 88%, 84% and 90% with Kappa indices of 0.64, 0.61, 0.80, 0.76 and 0.84 for the Spectral Angle Mapper, Spectral Correlation Mapper, Maximum Likelihood, Mahalanobis Distance and Minimum Distance. Based on these results, the minimum distance showed a higher accuracy and gave us 13462.1842 ha of forest area, 16798.8006 ha of prairie for the year 2018.

Published in	American Journal of Remote Sensing (Volume 9, Issue 1)
DOI	10.11648/j.ajrs.20210901.12
Page(s)	16-22
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Image Processing, Landsat8, Forest Area Kalambatritra, Erdas Imagine, Land Use, Classification

References

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APA Style

Arisetra Razafinimaro, Aimé Richard Hajalalaina, ZoJaona Tantely Reziky, Eric Delaitre, Avisoa Andrianarivo. (2021). Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve. American Journal of Remote Sensing, 9(1), 16-22. https://doi.org/10.11648/j.ajrs.20210901.12

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ACS Style

Arisetra Razafinimaro; Aimé Richard Hajalalaina; ZoJaona Tantely Reziky; Eric Delaitre; Avisoa Andrianarivo. Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve. Am. J. Remote Sens. 2021, 9(1), 16-22. doi: 10.11648/j.ajrs.20210901.12

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AMA Style

Arisetra Razafinimaro, Aimé Richard Hajalalaina, ZoJaona Tantely Reziky, Eric Delaitre, Avisoa Andrianarivo. Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve. Am J Remote Sens. 2021;9(1):16-22. doi: 10.11648/j.ajrs.20210901.12

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@article{10.11648/j.ajrs.20210901.12,
  author = {Arisetra Razafinimaro and Aimé Richard Hajalalaina and ZoJaona Tantely Reziky and Eric Delaitre and Avisoa Andrianarivo},
  title = {Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve},
  journal = {American Journal of Remote Sensing},
  volume = {9},
  number = {1},
  pages = {16-22},
  doi = {10.11648/j.ajrs.20210901.12},
  url = {https://doi.org/10.11648/j.ajrs.20210901.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20210901.12},
  abstract = {This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018. For this, we adopted the methodology of satellite image processing based on supervised classification algorithms. The processing was moved to spectral preparation and improvement of spatial resolution using the blue, green, red, near infrared and panchromatic channels. Then, a comparison study of the supervised classification algorithms was done to obtain a more accurate result. The validation of the classification results was performed using several reference points, a previous national processing result already validated in the field and the Google earth image of the same year. After repeating the classification several times, we obtained accuracies of 77%, 75%, 88%, 84% and 90% with Kappa indices of 0.64, 0.61, 0.80, 0.76 and 0.84 for the Spectral Angle Mapper, Spectral Correlation Mapper, Maximum Likelihood, Mahalanobis Distance and Minimum Distance. Based on these results, the minimum distance showed a higher accuracy and gave us 13462.1842 ha of forest area, 16798.8006 ha of prairie for the year 2018.},
 year = {2021}
}

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TY  - JOUR
T1  - Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve
AU  - Arisetra Razafinimaro
AU  - Aimé Richard Hajalalaina
AU  - ZoJaona Tantely Reziky
AU  - Eric Delaitre
AU  - Avisoa Andrianarivo
Y1  - 2021/02/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ajrs.20210901.12
DO  - 10.11648/j.ajrs.20210901.12
T2  - American Journal of Remote Sensing
JF  - American Journal of Remote Sensing
JO  - American Journal of Remote Sensing
SP  - 16
EP  - 22
PB  - Science Publishing Group
SN  - 2328-580X
UR  - https://doi.org/10.11648/j.ajrs.20210901.12
AB  - This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018. For this, we adopted the methodology of satellite image processing based on supervised classification algorithms. The processing was moved to spectral preparation and improvement of spatial resolution using the blue, green, red, near infrared and panchromatic channels. Then, a comparison study of the supervised classification algorithms was done to obtain a more accurate result. The validation of the classification results was performed using several reference points, a previous national processing result already validated in the field and the Google earth image of the same year. After repeating the classification several times, we obtained accuracies of 77%, 75%, 88%, 84% and 90% with Kappa indices of 0.64, 0.61, 0.80, 0.76 and 0.84 for the Spectral Angle Mapper, Spectral Correlation Mapper, Maximum Likelihood, Mahalanobis Distance and Minimum Distance. Based on these results, the minimum distance showed a higher accuracy and gave us 13462.1842 ha of forest area, 16798.8006 ha of prairie for the year 2018.
VL  - 9
IS  - 1
ER  -

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Author Information

Arisetra Razafinimaro

Department of Informatics, School of Management and Technological Innovation, Fianarantsoa, Madagascar
Aimé Richard Hajalalaina

Department of Informatics, School of Management and Technological Innovation, Fianarantsoa, Madagascar
ZoJaona Tantely Reziky

Higher Polytechnic School of Antananarivo, Antananarivo University, Antananarivo, Madagascar
Eric Delaitre

Research Institute for Development, Montpellier, France
Avisoa Andrianarivo

Department of Geography, Antananarivo University, Antananarivo, Madagascar

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APA Style

Arisetra Razafinimaro, Aimé Richard Hajalalaina, ZoJaona Tantely Reziky, Eric Delaitre, Avisoa Andrianarivo. (2021). Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve. American Journal of Remote Sensing, 9(1), 16-22. https://doi.org/10.11648/j.ajrs.20210901.12

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ACS Style

Arisetra Razafinimaro; Aimé Richard Hajalalaina; ZoJaona Tantely Reziky; Eric Delaitre; Avisoa Andrianarivo. Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve. Am. J. Remote Sens. 2021, 9(1), 16-22. doi: 10.11648/j.ajrs.20210901.12

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AMA Style

Arisetra Razafinimaro, Aimé Richard Hajalalaina, ZoJaona Tantely Reziky, Eric Delaitre, Avisoa Andrianarivo. Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve. Am J Remote Sens. 2021;9(1):16-22. doi: 10.11648/j.ajrs.20210901.12

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@article{10.11648/j.ajrs.20210901.12,
  author = {Arisetra Razafinimaro and Aimé Richard Hajalalaina and ZoJaona Tantely Reziky and Eric Delaitre and Avisoa Andrianarivo},
  title = {Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve},
  journal = {American Journal of Remote Sensing},
  volume = {9},
  number = {1},
  pages = {16-22},
  doi = {10.11648/j.ajrs.20210901.12},
  url = {https://doi.org/10.11648/j.ajrs.20210901.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20210901.12},
  abstract = {This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018. For this, we adopted the methodology of satellite image processing based on supervised classification algorithms. The processing was moved to spectral preparation and improvement of spatial resolution using the blue, green, red, near infrared and panchromatic channels. Then, a comparison study of the supervised classification algorithms was done to obtain a more accurate result. The validation of the classification results was performed using several reference points, a previous national processing result already validated in the field and the Google earth image of the same year. After repeating the classification several times, we obtained accuracies of 77%, 75%, 88%, 84% and 90% with Kappa indices of 0.64, 0.61, 0.80, 0.76 and 0.84 for the Spectral Angle Mapper, Spectral Correlation Mapper, Maximum Likelihood, Mahalanobis Distance and Minimum Distance. Based on these results, the minimum distance showed a higher accuracy and gave us 13462.1842 ha of forest area, 16798.8006 ha of prairie for the year 2018.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve
AU  - Arisetra Razafinimaro
AU  - Aimé Richard Hajalalaina
AU  - ZoJaona Tantely Reziky
AU  - Eric Delaitre
AU  - Avisoa Andrianarivo
Y1  - 2021/02/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ajrs.20210901.12
DO  - 10.11648/j.ajrs.20210901.12
T2  - American Journal of Remote Sensing
JF  - American Journal of Remote Sensing
JO  - American Journal of Remote Sensing
SP  - 16
EP  - 22
PB  - Science Publishing Group
SN  - 2328-580X
UR  - https://doi.org/10.11648/j.ajrs.20210901.12
AB  - This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018. For this, we adopted the methodology of satellite image processing based on supervised classification algorithms. The processing was moved to spectral preparation and improvement of spatial resolution using the blue, green, red, near infrared and panchromatic channels. Then, a comparison study of the supervised classification algorithms was done to obtain a more accurate result. The validation of the classification results was performed using several reference points, a previous national processing result already validated in the field and the Google earth image of the same year. After repeating the classification several times, we obtained accuracies of 77%, 75%, 88%, 84% and 90% with Kappa indices of 0.64, 0.61, 0.80, 0.76 and 0.84 for the Spectral Angle Mapper, Spectral Correlation Mapper, Maximum Likelihood, Mahalanobis Distance and Minimum Distance. Based on these results, the minimum distance showed a higher accuracy and gave us 13462.1842 ha of forest area, 16798.8006 ha of prairie for the year 2018.
VL  - 9
IS  - 1
ER  -

Copy | Download