IGSNRR OpenIR
Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007-2016
Zhang, Yihang1; Ling, Feng1; Foody, Giles M.2; Ge, Yong3; Boyd, Doreen S.2; Li, Xiaodong1,2; Du, Yun1; Atkinson, Peter M.3,4,5,6
2019-04-01
Source PublicationREMOTE SENSING OF ENVIRONMENT
ISSN0034-4257
Volume224Pages:74-91
Corresponding AuthorLing, Feng(lingf@whigg.ac.cn)
AbstractAdvanced 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.
KeywordALOS PALSAR ALOS-2 PALSAR-2 Forest mapping MODIS NDVI Spatial-temporal Downscaling Super-resolution mapping
DOI10.1016/j.rse.2019.01.038
WOS KeywordSOUTHEAST-ASIA ; LANDSAT DATA ; ALOS PALSAR ; RAIN-FOREST ; SERIES ; ALGORITHMS ; MAPS ; CLASSIFICATION ; BIOMASS ; DYNAMICS
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of Chinese Academy of Sciences[XDA2003030201] ; National Natural Science Foundation of China[41801292] ; Natural Science Foundation of Hubei Province of China[ZRMS2018001622] ; Youth Innovation Promotion Association CAS[2017384] ; Natural Science Foundation of China[61671425]
Funding OrganizationStrategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; Natural Science Foundation of Hubei Province of China ; Youth Innovation Promotion Association CAS ; Natural Science Foundation of China
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000462421200006
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/48658
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLing, Feng
Affiliation1.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
Recommended Citation
GB/T 7714
Zhang, Yihang,Ling, Feng,Foody, Giles M.,et al. Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007-2016[J]. REMOTE SENSING OF ENVIRONMENT,2019,224:74-91.
APA Zhang, Yihang.,Ling, Feng.,Foody, Giles M..,Ge, Yong.,Boyd, Doreen S..,...&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.
MLA Zhang, Yihang,et al."Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007-2016".REMOTE SENSING OF ENVIRONMENT 224(2019):74-91.
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