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  • Mapping Annual Forest Cover by Fusing PALSARPALSAR-2 and MODIS NDVI During 2007-2016

    Rights statement: This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 224, 2019 DOI: 10.1016/j.rse.2019.01.038

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Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

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Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016. / Zhang, Yihang; Ling, Feng; Foody, Giles M.; Ge, Yong; Boyd, Doreen S.; Li, Xiaodong; Du, Yun; Atkinson, P.M.

In: Remote Sensing of Environment, Vol. 224, 01.04.2019, p. 74-91.

Research output: Contribution to journalJournal article

Harvard

Zhang, Y, Ling, F, Foody, GM, Ge, Y, Boyd, DS, Li, X, Du, Y & Atkinson, PM 2019, 'Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016', Remote Sensing of Environment, vol. 224, pp. 74-91. https://doi.org/10.1016/j.rse.2019.01.038

APA

Zhang, Y., Ling, F., Foody, G. M., Ge, Y., Boyd, D. S., Li, X., Du, Y., & Atkinson, P. M. (2019). Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016. Remote Sensing of Environment, 224, 74-91. https://doi.org/10.1016/j.rse.2019.01.038

Vancouver

Zhang Y, Ling F, Foody GM, Ge Y, Boyd DS, Li X et al. Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016. Remote Sensing of Environment. 2019 Apr 1;224:74-91. https://doi.org/10.1016/j.rse.2019.01.038

Author

Zhang, Yihang ; Ling, Feng ; Foody, Giles M. ; Ge, Yong ; Boyd, Doreen S. ; Li, Xiaodong ; Du, Yun ; Atkinson, P.M. / Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016. In: Remote Sensing of Environment. 2019 ; Vol. 224. pp. 74-91.

Bibtex

@article{fc69ad29030d42a0b24f889d23fb973a,
title = "Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016",
abstract = "Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce annual, global 25 m forest maps between 2007 and 2010, and the latest global forest maps of 2015 and 2016 were produced by using the ALOS-2 PALSAR-2 data. However, annual 25 m spatial resolution forest maps during 2011–2014 are missing because of the gap in operation between ALOS and ALOS-2, preventing the construction of a continuous, fine resolution time-series dataset on the world's forests. In contrast, the MODerate Resolution Imaging Spectroradiometer (MODIS) NDVI images were available globally since 2000. This research developed a novel method to produce annual 25 m forest maps during 2007–2016 by fusing the fine spatial resolution, but asynchronous PALSAR/PALSAR-2 with coarse spatial resolution, but synchronous MODIS NDVI data, thus, filling the four-year gap in the ALOS and ALOS-2 time-series, as well as enhancing the existing mapping activity. The method was developed concentrating on two key objectives: 1) producing more accurate 25 m forest maps by integrating PALSAR/PALSAR-2 and MODIS NDVI data during 2007–2010 and 2015–2016; 2) reconstructing annual 25 m forest maps from time-series MODIS NDVI images during 2011–2014. Specifically, a decision tree classification was developed for forest mapping based on both the PALSAR/PALSAR-2 and MODIS NDVI data, and a new spatial-temporal super-resolution mapping was proposed to reconstruct the 25 m forest maps from time-series MODIS NDVI images. Three study sites including Paraguay, the USA and Russia were chosen, as they represent the world's three main forest types: tropical forest, temperate broadleaf and mixed forest, and boreal conifer forest, respectively. Compared with traditional methods, the proposed approach produced the most accurate continuous time-series of fine spatial resolution forest maps both visually and quantitatively. For the forest maps during 2007–2010 and 2015–2016, the results had greater overall accuracy values (>98%) than those of the original JAXA forest product. For the reconstructed 25 m forest maps during 2011–2014, the increases in classifications accuracy relative to three benchmark methods were statistically significant, and the overall accuracy values of the three study sites were almost universally >92%. The proposed approach, therefore, has great potential to support the production of annual 25 m forest maps by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016.",
keywords = "ALOS PALSAR, ALOS-2 PALSAR-2, Downscaling, Forest mapping, MODIS NDVI, Spatial-temporal, Super-resolution mapping, Continuous time systems, Decision trees, Forestry, Image reconstruction, Image resolution, Optical resolving power, Radiometers, Synthetic aperture radar, Time series, Down-scaling, Modis ndvi, Spatial temporals, Super-resolution mappings, Mapping, Coniferophyta",
author = "Yihang Zhang and Feng Ling and Foody, {Giles M.} and Yong Ge and Boyd, {Doreen S.} and Xiaodong Li and Yun Du and P.M. Atkinson",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 224, 2019 DOI: 10.1016/j.rse.2019.01.038 ",
year = "2019",
month = apr
day = "1",
doi = "10.1016/j.rse.2019.01.038",
language = "English",
volume = "224",
pages = "74--91",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

AU - Zhang, Yihang

AU - Ling, Feng

AU - Foody, Giles M.

AU - Ge, Yong

AU - Boyd, Doreen S.

AU - Li, Xiaodong

AU - Du, Yun

AU - Atkinson, P.M.

N1 - This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 224, 2019 DOI: 10.1016/j.rse.2019.01.038

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce annual, global 25 m forest maps between 2007 and 2010, and the latest global forest maps of 2015 and 2016 were produced by using the ALOS-2 PALSAR-2 data. However, annual 25 m spatial resolution forest maps during 2011–2014 are missing because of the gap in operation between ALOS and ALOS-2, preventing the construction of a continuous, fine resolution time-series dataset on the world's forests. In contrast, the MODerate Resolution Imaging Spectroradiometer (MODIS) NDVI images were available globally since 2000. This research developed a novel method to produce annual 25 m forest maps during 2007–2016 by fusing the fine spatial resolution, but asynchronous PALSAR/PALSAR-2 with coarse spatial resolution, but synchronous MODIS NDVI data, thus, filling the four-year gap in the ALOS and ALOS-2 time-series, as well as enhancing the existing mapping activity. The method was developed concentrating on two key objectives: 1) producing more accurate 25 m forest maps by integrating PALSAR/PALSAR-2 and MODIS NDVI data during 2007–2010 and 2015–2016; 2) reconstructing annual 25 m forest maps from time-series MODIS NDVI images during 2011–2014. Specifically, a decision tree classification was developed for forest mapping based on both the PALSAR/PALSAR-2 and MODIS NDVI data, and a new spatial-temporal super-resolution mapping was proposed to reconstruct the 25 m forest maps from time-series MODIS NDVI images. Three study sites including Paraguay, the USA and Russia were chosen, as they represent the world's three main forest types: tropical forest, temperate broadleaf and mixed forest, and boreal conifer forest, respectively. Compared with traditional methods, the proposed approach produced the most accurate continuous time-series of fine spatial resolution forest maps both visually and quantitatively. For the forest maps during 2007–2010 and 2015–2016, the results had greater overall accuracy values (>98%) than those of the original JAXA forest product. For the reconstructed 25 m forest maps during 2011–2014, the increases in classifications accuracy relative to three benchmark methods were statistically significant, and the overall accuracy values of the three study sites were almost universally >92%. The proposed approach, therefore, has great potential to support the production of annual 25 m forest maps by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016.

AB - Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce annual, global 25 m forest maps between 2007 and 2010, and the latest global forest maps of 2015 and 2016 were produced by using the ALOS-2 PALSAR-2 data. However, annual 25 m spatial resolution forest maps during 2011–2014 are missing because of the gap in operation between ALOS and ALOS-2, preventing the construction of a continuous, fine resolution time-series dataset on the world's forests. In contrast, the MODerate Resolution Imaging Spectroradiometer (MODIS) NDVI images were available globally since 2000. This research developed a novel method to produce annual 25 m forest maps during 2007–2016 by fusing the fine spatial resolution, but asynchronous PALSAR/PALSAR-2 with coarse spatial resolution, but synchronous MODIS NDVI data, thus, filling the four-year gap in the ALOS and ALOS-2 time-series, as well as enhancing the existing mapping activity. The method was developed concentrating on two key objectives: 1) producing more accurate 25 m forest maps by integrating PALSAR/PALSAR-2 and MODIS NDVI data during 2007–2010 and 2015–2016; 2) reconstructing annual 25 m forest maps from time-series MODIS NDVI images during 2011–2014. Specifically, a decision tree classification was developed for forest mapping based on both the PALSAR/PALSAR-2 and MODIS NDVI data, and a new spatial-temporal super-resolution mapping was proposed to reconstruct the 25 m forest maps from time-series MODIS NDVI images. Three study sites including Paraguay, the USA and Russia were chosen, as they represent the world's three main forest types: tropical forest, temperate broadleaf and mixed forest, and boreal conifer forest, respectively. Compared with traditional methods, the proposed approach produced the most accurate continuous time-series of fine spatial resolution forest maps both visually and quantitatively. For the forest maps during 2007–2010 and 2015–2016, the results had greater overall accuracy values (>98%) than those of the original JAXA forest product. For the reconstructed 25 m forest maps during 2011–2014, the increases in classifications accuracy relative to three benchmark methods were statistically significant, and the overall accuracy values of the three study sites were almost universally >92%. The proposed approach, therefore, has great potential to support the production of annual 25 m forest maps by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016.

KW - ALOS PALSAR

KW - ALOS-2 PALSAR-2

KW - Downscaling

KW - Forest mapping

KW - MODIS NDVI

KW - Spatial-temporal

KW - Super-resolution mapping

KW - Continuous time systems

KW - Decision trees

KW - Forestry

KW - Image reconstruction

KW - Image resolution

KW - Optical resolving power

KW - Radiometers

KW - Synthetic aperture radar

KW - Time series

KW - Down-scaling

KW - Modis ndvi

KW - Spatial temporals

KW - Super-resolution mappings

KW - Mapping

KW - Coniferophyta

U2 - 10.1016/j.rse.2019.01.038

DO - 10.1016/j.rse.2019.01.038

M3 - Journal article

VL - 224

SP - 74

EP - 91

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -