A 50-m Forest Cover Map in Southeast Asia from ALOS/PALSAR and Its Application on Forest Fragmentation Assessment
Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial di...
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| Published in | PloS one Vol. 9; no. 1; p. e85801 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
22.01.2014
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0085801 |
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| Abstract | Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity. |
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| AbstractList | Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73x10.sup.6 km.sup.2 (GlobCover) to 2.69x10.sup.6 km.sup.2 (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity. Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity. Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity.Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity. Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×106 km2 (GlobCover) to 2.69×106 km2 (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity. |
| Audience | Academic |
| Author | Sheldon, Sage Xiao, Xiangming Biradar, Chandrashekhar Dong, Jinwei Takeuhci, Wataru Zhang, Geli Dinh Duong, Nguyen Hazarika, Manzul Wikantika, Ketut Moore, Berrien |
| AuthorAffiliation | 2 International Center for Agricultural Research in Dry Areas, Amman, Jordan 6 Center for Remote Sensing, Institute of Technology Bandung, Bandung, Indonesia DOE Pacific Northwest National Laboratory, United States of America 1 Department of Microbiology and Plant Biology, and Center for Spatial Analysis, University of Oklahoma, Norman, Oklahoma, United States of America 5 Geoinformatics Center, Asian Institute of Technology, Pathumthani, Thailand 4 Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam 3 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China 7 Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, Japan 8 College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, Oklahoma, United States of America |
| AuthorAffiliation_xml | – name: 4 Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam – name: 2 International Center for Agricultural Research in Dry Areas, Amman, Jordan – name: 3 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China – name: 1 Department of Microbiology and Plant Biology, and Center for Spatial Analysis, University of Oklahoma, Norman, Oklahoma, United States of America – name: 5 Geoinformatics Center, Asian Institute of Technology, Pathumthani, Thailand – name: 7 Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, Japan – name: DOE Pacific Northwest National Laboratory, United States of America – name: 6 Center for Remote Sensing, Institute of Technology Bandung, Bandung, Indonesia – name: 8 College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, Oklahoma, United States of America |
| Author_xml | – sequence: 1 givenname: Jinwei surname: Dong fullname: Dong, Jinwei – sequence: 2 givenname: Xiangming surname: Xiao fullname: Xiao, Xiangming – sequence: 3 givenname: Sage surname: Sheldon fullname: Sheldon, Sage – sequence: 4 givenname: Chandrashekhar surname: Biradar fullname: Biradar, Chandrashekhar – sequence: 5 givenname: Geli surname: Zhang fullname: Zhang, Geli – sequence: 6 givenname: Nguyen surname: Dinh Duong fullname: Dinh Duong, Nguyen – sequence: 7 givenname: Manzul surname: Hazarika fullname: Hazarika, Manzul – sequence: 8 givenname: Ketut surname: Wikantika fullname: Wikantika, Ketut – sequence: 9 givenname: Wataru surname: Takeuhci fullname: Takeuhci, Wataru – sequence: 10 givenname: Berrien surname: Moore fullname: Moore, Berrien |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24465714$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2014 Public Library of Science 2014 Dong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2014 Dong et al 2014 Dong et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: JD XX. Performed the experiments: JD XX SS CB. Analyzed the data: JD XX SS CB GZ ND MH KW WT BM. Contributed reagents/materials/analysis tools: JD XX SS CB WT. Wrote the paper: JD XX SS CB GZ ND MH KW WT BM. Competing Interests: The authors have declared that no competing interests exist. |
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| Snippet | Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity... |
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| SubjectTerms | Accuracy Agriculture Algorithms Artificial satellites in remote sensing Asia, Southeastern Biodiversity Biological diversity Biological diversity conservation Biology Biomass Carbon Cloud cover Computer Science Conservation Conservation of Natural Resources - methods Conservation of Natural Resources - statistics & numerical data Continents Crops, Agricultural - growth & development Data processing Deforestation Earth Sciences Emission inventories Emissions control Engineering Fluxes Forest degradation Forest management Forest resources Forests Fragmentation Geographic Information Systems - statistics & numerical data Geography Habitat destruction Image resolution Landscape ecology Mapping Measuring instruments Models, Theoretical Neural networks Plant biology Plantations Pollution monitoring Protection and preservation Radar Radar imaging Remote sensing Remote Sensing Technology - methods Remote Sensing Technology - statistics & numerical data Reproducibility of Results Rubber Satellite observation Satellites Science Spatial analysis Spatial distribution Spatial resolution Synthetic aperture radar Tropical Climate Tropical forests Wildlife conservation |
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| Title | A 50-m Forest Cover Map in Southeast Asia from ALOS/PALSAR and Its Application on Forest Fragmentation Assessment |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/24465714 https://www.proquest.com/docview/1491119116 https://www.proquest.com/docview/1492707595 https://pubmed.ncbi.nlm.nih.gov/PMC3899076 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0085801&type=printable https://doaj.org/article/26b8da89dcde4885bdd46cc8add3bd58 http://dx.doi.org/10.1371/journal.pone.0085801 |
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