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Potential of texture from SAR tomographic images for forest aboveground biomass estimation / Zhanmang Liao in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 88 (June 2020) . - 15 p. Titre : Potential of texture from SAR tomographic images for forest aboveground biomass estimation Type de document : Article/Communication Auteurs : Zhanmang Liao, Auteur ; Binbin He, Auteur ; Xingwen Quan, Auteur Année de publication : 2020 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse texturale [Termes IGN] bande P [Termes IGN] biomasse aérienne [Termes IGN] canopée [Termes IGN] image radar moirée [Termes IGN] rétrodiffusion [Termes IGN] tomographie radar Résumé : (auteur) Synthetic Aperture Radar (SAR) texture has been demonstrated to have the potential to improve forest biomass estimation using backscatter. However, forests are 3D objects with a vertical structure. The strong penetration of SAR signals means that each pixel contains the contributions of all the scatterers inside the forest canopy, especially for the P-band. Consequently, the traditional texture derived from SAR images is affected by forest vertical heterogeneity, although the influence on texture-based biomass estimation has not yet been explicitly explored. To separate and explore the influence of forest vertical heterogeneity, we introduced the SAR tomography technique into the traditional texture analysis, aiming to explore whether TomoSAR could improve the performance of texture-based aboveground biomass (AGB) estimation and whether texture plus tomographic backscatter could further improve the TomoSAR-based AGB estimation. Based on the P-band TomoSAR dataset from TropiSAR 2009 at two different sites, the results show that ground backscatter variance dominated the texture features of the original SAR image and reduced the biomass estimation accuracy. The texture from upper vegetation layers presented a stronger correlation with forest biomass. Texture successfully improved tomographic backscatter-based biomass estimation, and the texture from upper vegetation layers made AGB models much more transferable between different sites. In addition, the correlation between texture indices varied greatly among different tomographic heights. The texture from the 10 to 30 m layers was able to provide more independent information than the other layers and the original images, which helped to improve the backscatter-based AGB estimation. Numéro de notice : A2020-447 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2020.102049 Date de publication en ligne : 12/02/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102049 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95523 [article]

Retrieving grassland canopy water content by considering the information from neighboring pixels / Binbin He in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 8 (August 2017) . - pp 553 - 565 Titre : Retrieving grassland canopy water content by considering the information from neighboring pixels Type de document : Article/Communication Auteurs : Binbin He, Auteur ; Xingwen Quan, Auteur ; Dasong Xu, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 553 - 565 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Chine [Termes IGN] classification barycentrique [Termes IGN] classification pixellaire [Termes IGN] modèle de transfert radiatif [Termes IGN] prairie [Termes IGN] réponse spectrale [Termes IGN] teneur en eau liquide Résumé : (auteur) Accurate and robust retrieval of grassland canopy water content (CWC) using a radiative transfer model (RTM) is generally affected by the ill-posed inversion problem due to the lack of enough available a priori information. To alleviate this problem when inversing the RTM, a two-step inversion method was proposed. The key point of this method was to simultaneously consider the spectral information from neighboring pixels and the spatial dependency among these pixels, with the purpose to win more information from these neighboring pixels. The proposed methodology was then applied to retrieve CWC using the PROSAIL RTM from Landsat-8 OLI data for a plateau grassland in China. The results showed that the estimated CWC using the proposed method (RMSE = 67.31 g m-2 and R2 = 0.81) was better than that from the traditional method (RMSE = 80.11 g m-2 and R2 = 0.78) which only considered the information of single pixel. Numéro de notice : A2017-436 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.8.553 En ligne : https://doi.org/10.14358/PERS.83.8.553 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86340 [article]

A Bayesian network-based method to alleviate the ill-posed inverse problem: A case study on leaf area index and canopy water content retrieval / Xingwen Quan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 12 (December 2015) . - pp 6507 - 6517 Titre : A Bayesian network-based method to alleviate the ill-posed inverse problem: A case study on leaf area index and canopy water content retrieval Type de document : Article/Communication Auteurs : Xingwen Quan, Auteur ; Binbin He, Auteur ; Xing Li, Auteur Année de publication : 2015 Article en page(s) : pp 6507 - 6517 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] appariement d'images [Termes IGN] image Landsat-8 [Termes IGN] Leaf Area Index [Termes IGN] probabilité [Termes IGN] problème inverse [Termes IGN] réseau bayesien [Termes IGN] teneur en eau de la végétation Résumé : (auteur) Retrieval of vegetation parameters from remotely sensed data using a radiative transfer model is generally hampered by the ill-posed inverse problem, which dramatically decreases the precision level of retrieved parameters. The purpose of this study was to use a Bayesian network-based method to allow the alleviation of the ill-posed inverse problem. This was achieved by introducing the correlations between the model free parameters into their prior joint probability distribution (PJPD), allowing the reduction of the probabilities of unrealistic combinations. Three sampling strategies intended to design three types of PJPDs that considered different correlations (represented by a correlation matrix) were presented. They were multivariate uniform distribution composed by independent free parameters, multivariate uniform distribution based on a simple correlation matrix, and multivariate Gaussian distribution based on a complicated correlation matrix, respectively. A case study of the presented method to retrieve leaf area index (LAI) and canopy water content (CWC) using the PROSAIL_5B (PROSPECT-5 + 4SAIL) model from Landsat 8 products was implemented. Results indicate that the presented method greatly improves the precision level of target parameters, with the coefficient of determination R2 of 0.69, 0.77, and 0.82 and root-mean-square error (RMSE) of 0.55, 0.51, and 0.44 m2 · m-2 for LAI and R2 = 0.68, 0.78, and 0.84 and RMSE = 230, 198, and 166 g · m-2 for CWC, respectively. Hence, the ill-posed inverse problem can be alleviated by the presented method, which can be widely applied for vegetation parameters retrieval. Numéro de notice : A2015-838 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2442999 Date de publication en ligne : 30/06/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2442999 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79172 [article]

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An extended approach for biomass estimation in a mixed vegetation area using ASAR and TM data / Minfeng Xing in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 5 (May 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 5 (May 2014) . - pp 429 - 438 Titre : An extended approach for biomass estimation in a mixed vegetation area using ASAR and TM data Type de document : Article/Communication Auteurs : Minfeng Xing, Auteur ; Binbin He, Auteur ; Xingwen Quan, Auteur ; Xiaowen Li, Auteur Année de publication : 2014 Article en page(s) : pp 429 - 438 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse (combustible) [Termes IGN] image Envisat-ASAR [Termes IGN] image Landsat-TM [Termes IGN] télédétection en hyperfréquence [Termes IGN] végétation Résumé : (Auteur) The use of microwave remote sensing for estimating vegeta-tion biomass is limited in arid regions because of the het-erogeneous distribution of vegetation, variable scattering mechanisms from different vegetation components, and the strong influence from underlying ground surface. In order to minimize this problem, a synergistic method of optical and microwave remote sensing data for the retrieval of aboveg-round biomass (agb) based on the modified water cloud model (WCM) was developed in this paper. Vegetation cover-age which can be easily estimated from optical data as ad-ditional information was combined in this method. Dimidiate pixel model (dpm) and phenological subtraction methodology (psm) were used to estimate vegetation coverage and differen--tiate vegetation types in the sub-pixel domain, respectively. The percentage cover of unmixed vegetation was incorporated to minimize problems associated with heterogeneous vegeta-tion and sparse vegetation cover. Finally, the accuracy and sources of error in this novel AGB retrieval method were evalu-ated. The results showed that the predicted AGB correlated with the measured AGB (R2 = 0.8007; RMSE = 0.2808 kg/m2). Numéro de notice : A2014-241 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.80.5.429 En ligne : https://doi.org/10.14358/PERS.80.5.429 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33144 [article]