Using web-based remote sensing to measure the severity of forest fragmentation in Southeast Asian Sub Regions

Forest fragmentation is major threat to biodiversity, yet measuring it is still a challenge. Current techniques for measuring forest fragmentation is exclusively limited to experts of Geographic Information System (GIS) and remote sensing technology. The acquisition of satellite images as well as co...

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Main Author: Mohd Fauzi, Intan Nur Farisa
Format: Project Paper Report
Language:English
Published: 2018
Online Access:http://psasir.upm.edu.my/id/eprint/91328/1/FH%202018%2013%20IR.pdf
http://psasir.upm.edu.my/id/eprint/91328/
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spelling my.upm.eprints.913282021-11-15T00:46:15Z http://psasir.upm.edu.my/id/eprint/91328/ Using web-based remote sensing to measure the severity of forest fragmentation in Southeast Asian Sub Regions Mohd Fauzi, Intan Nur Farisa Forest fragmentation is major threat to biodiversity, yet measuring it is still a challenge. Current techniques for measuring forest fragmentation is exclusively limited to experts of Geographic Information System (GIS) and remote sensing technology. The acquisition of satellite images as well as commercial GIS and remote sensing software is extremely expensive to natural resource managers and scientists from developing countries. Hence, this study provides a simple but reliable new method to measure forest fragmentation using Google Earth Pro which relies on the area and perimeter of an existing forest patch that are benchmarked against those measured for an optimal (i.e. circular) shaped patch. A 120 random forest patches were selected from Southeast Asian sub regions namely, Borneo, Peninsular Malaysia, Sulawesi and Sumatra using Google Earth Pro. The spatial geometry of the forest patches (area and perimeter) of the existing patch and theoretical circular shape were measured, the forest fragmentation effect value was then derived from the data obtained: 1) Forest Fragmentation Effect Value based on Area [FEVba]. 2) Forest Fragmentation Effect Value based on Perimeter [FEVbp]. Based on [FEVba], Sulawesi has the highest mean (0.6313), followed by Borneo, Peninsular Malaysia and Sumatra. Based on [FEVbp], Sumatra has the highest mean (0.2633) followed by Sulawesi, Peninsular Malaysia and Borneo. The result obtained indicates that the method can be universally applied across region to guide conservation stakeholders and help scientists to study biodiversity in fragmented landscapes. 2018 Project Paper Report NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/91328/1/FH%202018%2013%20IR.pdf Mohd Fauzi, Intan Nur Farisa (2018) Using web-based remote sensing to measure the severity of forest fragmentation in Southeast Asian Sub Regions. [Project Paper Report]
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Forest fragmentation is major threat to biodiversity, yet measuring it is still a challenge. Current techniques for measuring forest fragmentation is exclusively limited to experts of Geographic Information System (GIS) and remote sensing technology. The acquisition of satellite images as well as commercial GIS and remote sensing software is extremely expensive to natural resource managers and scientists from developing countries. Hence, this study provides a simple but reliable new method to measure forest fragmentation using Google Earth Pro which relies on the area and perimeter of an existing forest patch that are benchmarked against those measured for an optimal (i.e. circular) shaped patch. A 120 random forest patches were selected from Southeast Asian sub regions namely, Borneo, Peninsular Malaysia, Sulawesi and Sumatra using Google Earth Pro. The spatial geometry of the forest patches (area and perimeter) of the existing patch and theoretical circular shape were measured, the forest fragmentation effect value was then derived from the data obtained: 1) Forest Fragmentation Effect Value based on Area [FEVba]. 2) Forest Fragmentation Effect Value based on Perimeter [FEVbp]. Based on [FEVba], Sulawesi has the highest mean (0.6313), followed by Borneo, Peninsular Malaysia and Sumatra. Based on [FEVbp], Sumatra has the highest mean (0.2633) followed by Sulawesi, Peninsular Malaysia and Borneo. The result obtained indicates that the method can be universally applied across region to guide conservation stakeholders and help scientists to study biodiversity in fragmented landscapes.
format Project Paper Report
author Mohd Fauzi, Intan Nur Farisa
spellingShingle Mohd Fauzi, Intan Nur Farisa
Using web-based remote sensing to measure the severity of forest fragmentation in Southeast Asian Sub Regions
author_facet Mohd Fauzi, Intan Nur Farisa
author_sort Mohd Fauzi, Intan Nur Farisa
title Using web-based remote sensing to measure the severity of forest fragmentation in Southeast Asian Sub Regions
title_short Using web-based remote sensing to measure the severity of forest fragmentation in Southeast Asian Sub Regions
title_full Using web-based remote sensing to measure the severity of forest fragmentation in Southeast Asian Sub Regions
title_fullStr Using web-based remote sensing to measure the severity of forest fragmentation in Southeast Asian Sub Regions
title_full_unstemmed Using web-based remote sensing to measure the severity of forest fragmentation in Southeast Asian Sub Regions
title_sort using web-based remote sensing to measure the severity of forest fragmentation in southeast asian sub regions
publishDate 2018
url http://psasir.upm.edu.my/id/eprint/91328/1/FH%202018%2013%20IR.pdf
http://psasir.upm.edu.my/id/eprint/91328/
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score 13.160551