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The impacts of spatial baseline on forest canopy height model and digital terrain model retrieval using P-band PolInSAR data

Liao, Zhanmang; He, Binbin; Van Dijk, Albert; Bai, Xiaojing; Quan, Xingwen

Description

Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) has shown potential for the retrieval of a forest canopy height model (CHM) and the underlying solid earth digital terrain model (DTM). However, because of non-volume decorrelation and other unavoidable errors, the robustness of retrieval heights is sensitive to the spatial baseline of the selected InSAR pairs, which relates forest parameters to measured coherence. Within the context of the random volume over ground (RVoG) model...[Show more]

dc.contributor.authorLiao, Zhanmang
dc.contributor.authorHe, Binbin
dc.contributor.authorVan Dijk, Albert
dc.contributor.authorBai, Xiaojing
dc.contributor.authorQuan, Xingwen
dc.date.accessioned2019-04-20T08:00:42Z
dc.identifier.issn0034-4257
dc.identifier.urihttp://hdl.handle.net/1885/160506
dc.description.abstractPolarimetric Synthetic Aperture Radar Interferometry (PolInSAR) has shown potential for the retrieval of a forest canopy height model (CHM) and the underlying solid earth digital terrain model (DTM). However, because of non-volume decorrelation and other unavoidable errors, the robustness of retrieval heights is sensitive to the spatial baseline of the selected InSAR pairs, which relates forest parameters to measured coherence. Within the context of the random volume over ground (RVoG) model and the three-stage inversion method, we aimed to quantify the influence of spatial baseline on the inversions at P-band, which are distinct from the inversions at higher frequency due to the non-negligible ground contributions. This information assists in optimal baseline selection and the development of robust inversion schemes. Assumptions about the extinction coefficient and additional DTM or DEM were used to reduce the influence of ground contribution on CHM and DTM inversion, respectively. Inversions from published airborne repeat-pass P-band PolInSAR data with four different spatial baselines were validated against LiDAR-derived DTM and CHM data. The results show that a longer spatial baseline performed better in DTM inversion. The longest baseline produced the best R2 of 0.995 and RMSE of 0.555 m, much better than the smallest baseline with an R2 of 0.794 and RMSE of 3.74 m. A threshold height could be identified that determines the overestimation and underestimation of CHM inversion due to the non-volume decorrelation. Different baselines produced different threshold heights, making CHM inversion only accurate for a limited range of forest height around the threshold. The optimal baseline produced a CHM with R2 of 0.605 and RMSE of 2.67 m. Additionally, we found that using multiple baselines has the potential to improve CHM inversion, improving the R2 to 0.827 and RMSE to 0.876 m in our study.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherElsevier
dc.sourceRemote Sensing of Environment
dc.titleThe impacts of spatial baseline on forest canopy height model and digital terrain model retrieval using P-band PolInSAR data
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume210
dc.date.issued2018
local.identifier.absfor060102 - Bioinformatics
local.identifier.ariespublicationu4485658xPUB1988
local.type.statusPublished Version
local.contributor.affiliationLiao, Zhanmang, University of Electronic Science and Technology of China
local.contributor.affiliationHe, Binbin, University of Electronic Science and Technology of China
local.contributor.affiliationVan Dijk, Albert, College of Science, ANU
local.contributor.affiliationBai, Xiaojing, Nanjing University of Information Science & Technology
local.contributor.affiliationQuan, Xingwen, University of Electronic Science and Technology of China
local.description.embargo2040-01-01
local.bibliographicCitation.startpage403
local.bibliographicCitation.lastpage421
local.identifier.doi10.1016/j.rse.2018.03.033
local.identifier.absseo820104 - Native Forests
dc.date.updated2019-03-12T07:32:03Z
local.identifier.scopusID2-s2.0-85044634454
local.identifier.thomsonID000431164300029
dc.provenanceJournal: Remote Sensing of Environment (ISSN: 0034-4257, ESSN: 1879-0704) RoMEO: This is a RoMEO green journal Paid OA: A paid open access option is available for this journal. Author's Pre-print: green tick author can archive pre-print (ie pre-refereeing) Author's Post-print: green tick author can archive post-print (ie final draft post-refereeing) Publisher's Version/PDF: cross author cannot archive publisher's version/PDF
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