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dc.contributor.authorShi, Jianminen_UK
dc.contributor.authorZhang, Wangfeien_UK
dc.contributor.authorMarino, Armandoen_UK
dc.contributor.authorZeng, Pengen_UK
dc.contributor.authorJi, Yongjieen_UK
dc.contributor.authorZhao, Hanen_UK
dc.contributor.authorHuang, Guoranen_UK
dc.contributor.authorWang, Mengjinen_UK
dc.description.abstractA reliable evaluation of biomass is a vital prerequisite for realizing the international goal of “emission peak and carbon neutrality”. It is critical to estimate the components of forest biomass, for ecosystem management. Additionally, working on components we may solve the saturation problems in AGB estimation using remote sensing features. In our previous works we proposed GA-SVR (Genetic algorithms and support vector regression) algorithm with polarimetric SAR (Synthetic Aperture Rader) to retrieve total forest Above Ground Biomass (AGB) estimation in our previous works, however, the potential of GA-SVR algorithm applied in component AGB estimation especially using combination of multi-frequency polarimetric SAR features deserves further exploration. In this study, we use quad-polarimetric SAR data at C- and L- bands, extracting the backscatter coefficients and polarimetric features derived from four polarization decomposition methods (Yamaguchi 3-component decomposition, Freeman 2-component decomposition, H/A/alpha decomposition, and TSVM decomposition) as the input to the GA-SVR for forest component AGB estimation. The effectiveness of 66 polarimetric features derived from C-, L-band at each test site was evaluated for forest component AGB prediction at two test sites. The outcomes demonstrated that the GA-SVR attained high estimation accuracy according to the values of coefficient of determination R2, root mean square error, relative root mean square error, mean deviation, mean absolute deviation, mean percentage error, and mean absolute percentage error. The highest attained values of them were 0.77, 1.01 Mg/ha, 23.02%, −0.07 Mg/ha, 0.71 Mg/ha, 0.15%, and 18.42%, respectively. The study reconfirmed the robustness of GA-SVR algorithm and effectiveness of polarimetric SAR features extracted from four decomposition methods for forest total and AGB estimation. It also revealed that the capability of combining C- band L-band SAR polarimetric features for improving forest total and component AGB relies on the difference of forest structures.en_UK
dc.publisherElsevier BVen_UK
dc.relationShi J, Zhang W, Marino A, Zeng P, Ji Y, Zhao H, Huang G & Wang M (2023) Forest total and component biomass retrieval via GA-SVR algorithm and quad-polarimetric SAR data. <i>International Journal of Applied Earth Observation and Geoinformation</i>, 118, Art. No.: 103275.
dc.rightsPublished by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( nc-nd/4.0/).en_UK
dc.subjectForest total and component AGBen_UK
dc.subjectPolarimetric SARen_UK
dc.titleForest total and component biomass retrieval via GA-SVR algorithm and quad-polarimetric SAR dataen_UK
dc.typeJournal Articleen_UK
dc.citation.jtitleInternational Journal of Applied Earth Observation and Geoinformationen_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
rioxxterms.typeJournal Article/Reviewen_UK
local.rioxx.authorShi, Jianmin|en_UK
local.rioxx.authorZhang, Wangfei|en_UK
local.rioxx.authorMarino, Armando|0000-0002-4531-3102en_UK
local.rioxx.authorZeng, Peng|0000-0002-5678-5906en_UK
local.rioxx.authorJi, Yongjie|en_UK
local.rioxx.authorZhao, Han|en_UK
local.rioxx.authorHuang, Guoran|en_UK
local.rioxx.authorWang, Mengjin|en_UK
local.rioxx.projectInternal Project|University of Stirling|
Appears in Collections:Biological and Environmental Sciences Journal Articles

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