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Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions

Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterat...

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Autores principales: Razali, Sheriza Mohd, Marin, Arnaldo, Nuruddin, Ahmad Ainuddin, Shafri, Helmi Zulhaidi Mohd, Hamid, Hazandy Abdul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4063057/
https://www.ncbi.nlm.nih.gov/pubmed/24811079
http://dx.doi.org/10.3390/s140508259
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author Razali, Sheriza Mohd
Marin, Arnaldo
Nuruddin, Ahmad Ainuddin
Shafri, Helmi Zulhaidi Mohd
Hamid, Hazandy Abdul
author_facet Razali, Sheriza Mohd
Marin, Arnaldo
Nuruddin, Ahmad Ainuddin
Shafri, Helmi Zulhaidi Mohd
Hamid, Hazandy Abdul
author_sort Razali, Sheriza Mohd
collection PubMed
description Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.
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spelling pubmed-40630572014-06-19 Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions Razali, Sheriza Mohd Marin, Arnaldo Nuruddin, Ahmad Ainuddin Shafri, Helmi Zulhaidi Mohd Hamid, Hazandy Abdul Sensors (Basel) Article Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions. Molecular Diversity Preservation International (MDPI) 2014-05-07 /pmc/articles/PMC4063057/ /pubmed/24811079 http://dx.doi.org/10.3390/s140508259 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Razali, Sheriza Mohd
Marin, Arnaldo
Nuruddin, Ahmad Ainuddin
Shafri, Helmi Zulhaidi Mohd
Hamid, Hazandy Abdul
Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions
title Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions
title_full Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions
title_fullStr Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions
title_full_unstemmed Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions
title_short Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions
title_sort capability of integrated modis imagery and alos for oil palm, rubber and forest areas mapping in tropical forest regions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4063057/
https://www.ncbi.nlm.nih.gov/pubmed/24811079
http://dx.doi.org/10.3390/s140508259
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