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

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Aimé Richard Hajalalaina, Manuel Grizonnet, Eric Delaitre, Solofo Rakotondraompiana, Dominique Hervé (2013); Discrimination des zones humides en foret Malgache, proposition d’une methodologie multiresolution et multisource utilisant orfeo toolbox;Revue de Photogrammetrie et de télédétection.
[2]	Avisoatolona Andrianarivo, Eric Delaitre, Anne Elisabeth Laques, Stéphanie. M. Carriere, Dominique Hervé (2015); Détection de changement de l’occupation du sol dans une commune à la peripherie de la forêt humide de Fianarantsoa;
[3]	Nadia OUARAB, Youcef SMARA, Jean-Paul RASSON (1999); Utilisation de methods de classification hierarchique pour une classification supervisée d’image; Dix-septième colloque GRETSI, Vannes;
[4]	Marine Campedel, Eric Moulines (2014); Méthodologie de sélection de caractéristiques pour la classification d’images satellitaires;
[5]	Salim CHITROUB (2004); Combinaison de classiffeurs:une approche pour l’amelioration de la classification d’images multisources/Multidates de teledetection; Télédétection,, vol. 4, n°3, p. 289-301;
[6]	Aimé Richard Hajalalaina, Dominique Hervé, J. P Razafimandimby, E. Delaitre, J. C. Desconnets, T. Libourel (2016); Formalisation des chaines de traitements de données spatiales satellitaires sur la forêt à Madagascar;
[7]	Rashmi S, Swapna Addamani, Venkatand Ravikiran S (2014); Spectral Angle Mapper Algorithm for Remote Sensing Image Classification; ISSN 2348 – 7968; Vol. 1 Issue 4;
[8]	Bertels Luc, Bart Deronde, Pieter Kempeneers, Walter Debruyn, Sam Provoost (2005); Optimized Spectral Angle Mapper classification of spatially heterogeneous dynamic dune vegetation, a case study along the Belgian coastiline;The 9^th international Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS). Beijing, October 17-19.
[9]	George P. Petropoulos, Krishna Prasad Vadrevu & Chariton Kalaitzidis (2012): Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region, Geocarto International, DOI:10.1080/10106049.2012.668950
[10]	Kruse, F. A., et al., 1993. The spectral image processing system (sips) – interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 44, 145–163.
[11]	Keerti Kulkarni, P. A. Vijaya (2019); Experiment of Multispectral Images usin Spectral Angle Mapper Algorithm for Land; International Journal of Innovative Technology and Exploring Engineering (IJITEE); ISSN: 2278-3075, Volume-8, Issue-6S4, April 2019.
[12]	Osmar Abílio de Carvalho Jr, Paulo Roberto Meneses; Spectral Correlation Mapper (SCM): An Improvement on the Spectral Angle Mapper (2000).
[13]	Natanael Antunus Abade, Osmar Abilio de Carvalho Junior, Renato Fontes Guimaraes, Sandro Nunes de Oliveira (2005); Comparative Analysis of Modis Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary; remote sensing ISSN 2072-4292;
[14]	Madhura M, Suganthi Venkatazchalam (2013); Comparison of Supervised Classification Methods on Remode Sensed Satellite Data:An Application In Chennai, South India; International Journal of Science and Research (IJSR); ISSN: 2319-7064;
[15]	F. S. Al-Ahmadi, A. S. Hames (2008); Comparaison of four classification Methods to Extract Land Use and Land Cover from Raw Satellite Images for Remote Arid Areas, Kingdom of Saudi Arabia; JKAU; Earth Sci., Vol. 20 No, 1, pp: 167-191 (2009 A.D./1430 A.H);
[16]	Mahendra H N, Mallikarjunaswamy S, Rekha V, Puspalatha V, Sharmila N(2019); Performance Analysis of Different Classifier for Remote Sensing Application; International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958, Volume-9 Issue-1;
[17]	Bahadir Durak (2011); A classification Algorithme using Mahalanobis Distance clustering of data with applications;
[18]	Umair Khan, Nasru Minallah, Ahmad Junaid, Kashaf Gul, Nasir Ahmad (2015); Parallelepiped and Mahalanobis Distance based Classification for forestry identification in Pakistan; IEEE 978-1-5090-0436-2
[19]	Ameur Zohra, Adane Abdelhamid, Ameur Soltane (2007); Etude comparative des méthodes de classification appliquée à la segmentation d’images texturées;
[20]	J. H. Lumme (2004), Classification of vegetation and soil using imaging spectrometer data; Geoscience and remote sensing symposium.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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