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

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

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.

ALOS PALSAR, ALOS-2 PALSAR-2, Downscaling, Forest mapping, MODIS NDVI, Spatial-temporal, Super-resolution mapping
0034-4257
74-91
Zhang, Yihang
2d9dfa3a-92f3-46df-ab5c-b38c4ec6b9e4
Ling, Feng
49f95470-7461-424a-88e4-abbc7c6ca27b
Foody, Giles M.
62843823-1717-4a6e-9dd6-72539e7bf44e
Ge, Yong
f22fa40c-9a6a-456c-bdad-b322c3fd24ee
Boyd, Doreen S.
5283ac81-d41c-428e-9433-4b0c71dbc486
Li, Xiaodong
9a1d146e-1c0d-4b78-b84e-7706504a1cdc
Du, Yun
f6c7f992-f312-4c0c-ae40-f2c75a66326d
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Zhang, Yihang
2d9dfa3a-92f3-46df-ab5c-b38c4ec6b9e4
Ling, Feng
49f95470-7461-424a-88e4-abbc7c6ca27b
Foody, Giles M.
62843823-1717-4a6e-9dd6-72539e7bf44e
Ge, Yong
f22fa40c-9a6a-456c-bdad-b322c3fd24ee
Boyd, Doreen S.
5283ac81-d41c-428e-9433-4b0c71dbc486
Li, Xiaodong
9a1d146e-1c0d-4b78-b84e-7706504a1cdc
Du, Yun
f6c7f992-f312-4c0c-ae40-f2c75a66326d
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b

Zhang, Yihang, Ling, Feng, Foody, Giles M., Ge, Yong, Boyd, Doreen S., Li, Xiaodong, Du, Yun and Atkinson, Peter M. (2019) Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016. Remote Sensing of Environment, 224, 74-91. (doi:10.1016/j.rse.2019.01.038).

Record type: Article

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.

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More information

Accepted/In Press date: 30 January 2019
e-pub ahead of print date: 10 February 2019
Published date: 1 April 2019
Keywords: ALOS PALSAR, ALOS-2 PALSAR-2, Downscaling, Forest mapping, MODIS NDVI, Spatial-temporal, Super-resolution mapping

Identifiers

Local EPrints ID: 430786
URI: http://eprints.soton.ac.uk/id/eprint/430786
ISSN: 0034-4257
PURE UUID: a2db5a78-b51d-4b8c-a3c1-3d535844ac13
ORCID for Peter M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

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Date deposited: 10 May 2019 16:30
Last modified: 07 Oct 2020 01:37

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Contributors

Author: Yihang Zhang
Author: Feng Ling
Author: Giles M. Foody
Author: Yong Ge
Author: Doreen S. Boyd
Author: Xiaodong Li
Author: Yun Du
Author: Peter M. Atkinson ORCID iD

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