Deforestation and carbon loss estimation at tropical forest using multispectral remote sensing: Case study of Besul Tambahan Permanent Forest Reserve
This study presents the utilization of remote sensing technology in detecting the logging operation at tropical forest. The technology of remote sensing is used to extract the the logging activity without going to the site. In order to detect the logging operations, SPOT-5 satellite imagery was used...
Published in: | International Conference on Space Science and Communication, IconSpace |
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IEEE Computer Society
2015
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2-s2.0-84962533849 Latif Z.A.; Zaqwan H.M.; Saufi M.; Adnan N.A.; Omar H. Deforestation and carbon loss estimation at tropical forest using multispectral remote sensing: Case study of Besul Tambahan Permanent Forest Reserve 2015 International Conference on Space Science and Communication, IconSpace 2015-September 10.1109/IconSpace.2015.7283797 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962533849&doi=10.1109%2fIconSpace.2015.7283797&partnerID=40&md5=e51b631be4fb0e71c1987fd155e21bc3 This study presents the utilization of remote sensing technology in detecting the logging operation at tropical forest. The technology of remote sensing is used to extract the the logging activity without going to the site. In order to detect the logging operations, SPOT-5 satellite imagery was used to detect the deforested area and to extract parameters to calculate volume of biomass and amount of carbon stock loss. The image classification technique and on-screen digitizing was conducted to delineate the logging areas. There are four (4) maps produced from the analysis, which are the classified maps of the Besul Tambahan Permanent Forest Reserve before and after the logging operations, and digitized maps of the Besul Tambahan Permanent Reserve Forest (PRF) before and after logging operation. The results show the biomass volume and carbon stock losses of the study area due to the logging operations. From the classified images of SPOT-5 2012 and 2014, the estimated area due the logging operations is 113.49 ha. As for the biomass and carbon stock losses, 28.95% and 27.91% before the forest was logged respectively. Based on the digitized map, the logging operations have extracted 56.26 ha of the forest area before logged. Meanwhile, for the biomass and carbon stock loss, the logging operations took out about 14.09% and 14.01% of the forest volume before logged respectively. The study is hope to assist the forest managers to identify the effect of logging activities towards biomass and carbon loss in tropical forest. © 2015 IEEE. IEEE Computer Society 21654301 English Conference paper |
author |
Latif Z.A.; Zaqwan H.M.; Saufi M.; Adnan N.A.; Omar H. |
spellingShingle |
Latif Z.A.; Zaqwan H.M.; Saufi M.; Adnan N.A.; Omar H. Deforestation and carbon loss estimation at tropical forest using multispectral remote sensing: Case study of Besul Tambahan Permanent Forest Reserve |
author_facet |
Latif Z.A.; Zaqwan H.M.; Saufi M.; Adnan N.A.; Omar H. |
author_sort |
Latif Z.A.; Zaqwan H.M.; Saufi M.; Adnan N.A.; Omar H. |
title |
Deforestation and carbon loss estimation at tropical forest using multispectral remote sensing: Case study of Besul Tambahan Permanent Forest Reserve |
title_short |
Deforestation and carbon loss estimation at tropical forest using multispectral remote sensing: Case study of Besul Tambahan Permanent Forest Reserve |
title_full |
Deforestation and carbon loss estimation at tropical forest using multispectral remote sensing: Case study of Besul Tambahan Permanent Forest Reserve |
title_fullStr |
Deforestation and carbon loss estimation at tropical forest using multispectral remote sensing: Case study of Besul Tambahan Permanent Forest Reserve |
title_full_unstemmed |
Deforestation and carbon loss estimation at tropical forest using multispectral remote sensing: Case study of Besul Tambahan Permanent Forest Reserve |
title_sort |
Deforestation and carbon loss estimation at tropical forest using multispectral remote sensing: Case study of Besul Tambahan Permanent Forest Reserve |
publishDate |
2015 |
container_title |
International Conference on Space Science and Communication, IconSpace |
container_volume |
2015-September |
container_issue |
|
doi_str_mv |
10.1109/IconSpace.2015.7283797 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962533849&doi=10.1109%2fIconSpace.2015.7283797&partnerID=40&md5=e51b631be4fb0e71c1987fd155e21bc3 |
description |
This study presents the utilization of remote sensing technology in detecting the logging operation at tropical forest. The technology of remote sensing is used to extract the the logging activity without going to the site. In order to detect the logging operations, SPOT-5 satellite imagery was used to detect the deforested area and to extract parameters to calculate volume of biomass and amount of carbon stock loss. The image classification technique and on-screen digitizing was conducted to delineate the logging areas. There are four (4) maps produced from the analysis, which are the classified maps of the Besul Tambahan Permanent Forest Reserve before and after the logging operations, and digitized maps of the Besul Tambahan Permanent Reserve Forest (PRF) before and after logging operation. The results show the biomass volume and carbon stock losses of the study area due to the logging operations. From the classified images of SPOT-5 2012 and 2014, the estimated area due the logging operations is 113.49 ha. As for the biomass and carbon stock losses, 28.95% and 27.91% before the forest was logged respectively. Based on the digitized map, the logging operations have extracted 56.26 ha of the forest area before logged. Meanwhile, for the biomass and carbon stock loss, the logging operations took out about 14.09% and 14.01% of the forest volume before logged respectively. The study is hope to assist the forest managers to identify the effect of logging activities towards biomass and carbon loss in tropical forest. © 2015 IEEE. |
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IEEE Computer Society |
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21654301 |
language |
English |
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Conference paper |
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scopus |
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Scopus |
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1809677786242088960 |