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

REMOTE SENSING OF ENVIRONMENT

卷:224

Article

Zhang, Yihang¹  Ling, Feng¹  Foody, Giles M.²  Ge, Yong³  Boyd, Doreen S.²  Li, Xiaodong^1,2  Du, Yun¹  Atkinson, Peter M.^3,4,5,6 

[1] Chinese Acad Sci, Inst Geodesy & Geophys, Key Lab Monitoring & Estimate Environm & Disaster, Wuhan 430077, Hubei, Peoples R China

[2] Univ Nottingham, Sch Geog, Nottingham NG7 2RD, England

[3] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China

[4] Univ Lancaster, Fac Sci & Technol, Lancaster Environm Ctr, Lancaster LA1 4YQ, England

[5] Univ Southampton, Sch Geog & Environm Sci, Southampton SO17 1BJ, Hants, England

[6] Queens Univ, Sch Nat & Built Environm, Belfast BT7 1NN, Antrim, North Ireland

关键词: ALOS PALSAR;    ALOS-2 PALSAR-2;    Forest mapping;    MODIS NDVI;    Spatial-temporal;    Downscaling;    Super-resolution mapping;   

DOI  :  10.1016/j.rse.2019.01.038

来源: Elsevier

【 摘 要 】

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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