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Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands / Katrin Krzepek in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol 90 n° 6 (December 2022)  

Public [article]  inPFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science > vol 90 n° 6 (December 2022) . - pp 561 - 575 Titre : Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands Type de document : Article/Communication Auteurs : Katrin Krzepek, Auteur ; Jacob Schmidt, Auteur ; Dorota Iwaszczuk, Auteur Année de publication : 2022 Article en page(s) : pp 561 - 575 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] apprentissage non-dirigé [Termes IGN] aquifère [Termes IGN] Bade-Wurtemberg (Allemagne) [Termes IGN] bande C [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] fusion d'images [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Normalized Difference Water Index [Termes IGN] puits de carbone [Termes IGN] seuillage d'image [Termes IGN] théorie de Dempster-Shafer [Termes IGN] tourbière Résumé : (auteur) Peatlands as natural carbon sinks have a major impact on the climate balance and should therefore be monitored and protected. The hydrology of the peatland serves as an indicator of the carbon storage capacity. Hence, we investigate the question how suitable different remote sensing data are for monitoring the size of open water surface and the water table depth (WTD) of a peatland ecosystem. Furthermore, we examine the potential of combining remote sensing data for this purpose. We use C-band synthetic aperture radar (SAR) data from Sentinel-1 and multi-spectral data from Sentinel-2. The radar backscatter σ0, the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) are calculated and used for consideration of the WTD and the lake size. For the measurement of the lake size, we implement and investigate the methods: random forest, adaptive thresholding and an analysis according to the Dempster–Shafer theory. Correlations between WTD and the remote sensing data σ0 as well as NDWI are investigated. When looking at the individual data sets the results of our case study show that the VH polarized σ0 data produces the clearest delineation of the peatland lake. However the adaptive thresholding of the weighted fusion image of σ0-VH, σ0-VV and MNDWI, and the random forest algorithm with all three data sets as input proves to be the most suitable for determining the lake area. The correlation coefficients between σ0/NDWI and WTD vary greatly and lie in ranges of low to moderate correlation. Numéro de notice : A2022-942 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s41064-022-00216-w Date de publication en ligne : 06/09/2022 En ligne : https://doi.org/10.1007/s41064-022-00216-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102876 [article]

Comparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping / Dang Hung Bui in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

Public [article]  inGeo-spatial Information Science > vol 25 n° 3 (October 2022) Titre : Comparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping Type de document : Article/Communication Auteurs : Dang Hung Bui, Auteur ; László Mucsi, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification pixellaire [Termes IGN] fusion d'images [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] théorie de Dempster-Shafer [Termes IGN] zone urbaine Résumé : (auteur) Data fusion has shown potential to improve the accuracy of land cover mapping, and selection of the optimal fusion technique remains a challenge. This study investigated the performance of fusing Sentinel-1 (S-1) and Sentinel-2 (S-2) data, using layer-stacking method at the pixel level and Dempster-Shafer (D-S) theory-based approach at the decision level, for mapping six land cover classes in Thu Dau Mot City, Vietnam. At the pixel level, S-1 and S-2 bands and their extracted textures and indices were stacked into the different single-sensor and multi-sensor datasets (i.e. fused datasets). The datasets were categorized into two groups. One group included the datasets containing only spectral and backscattering bands, and the other group included the datasets consisting of these bands and their extracted features. The random forest (RF) classifier was then applied to the datasets within each group. At the decision level, the RF classification outputs of the single-sensor datasets within each group were fused together based on D-S theory. Finally, the accuracy of the mapping results at both levels within each group was compared. The results showed that fusion at the decision level provided the best mapping accuracy compared to the results from other products within each group. The highest overall accuracy (OA) and Kappa coefficient of the map using D-S theory were 92.67% and 0.91, respectively. The decision-level fusion helped increase the OA of the map by 0.75% to 2.07% compared to that of corresponding S-2 products in the groups. Meanwhile, the data fusion at the pixel level delivered the mapping results, which yielded an OA of 4.88% to 6.58% lower than that of corresponding S-2 products in the groups. Numéro de notice : A2022-448 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10095020.2022.2035656 Date de publication en ligne : 03/03/2022 En ligne : https://doi.org/10.1080/10095020.2022.2035656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100398 [article]

Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery / Shengyuan Zou in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 113 (September 2022) . - n° 103018 Titre : Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery Type de document : Article/Communication Auteurs : Shengyuan Zou, Auteur ; Le Wang, Auteur Année de publication : 2022 Article en page(s) : n° 103018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection d'objet [Termes IGN] image à très haute résolution [Termes IGN] image Streetview [Termes IGN] logement [Termes IGN] New York (Etats-Unis ; état) [Termes IGN] théorie de Dempster-Shafer [Termes IGN] zone urbaine Résumé : (auteur) Abandoned houses (AH) present an utmost challenge confronting the urban environment in contemporary U.S. shrinking cities. Data accessibility is a major hurdle that prevents the acquisition of large-scale AH information at the individual property level. To this end, the latest revolution of open-access remote sensing platforms has witnessed a plethora of multi-source, multi-perspective fine-spatial-resolution data for urban environments, among which very-high-resolution (VHR) top-down view remote sensing images and horizontal-perspective Google Street View (GSV) images are prominent exemplifiers. In this study, we aim to map individual-level abandoned houses across cities by developing a method that can effectively leverage VHR remote sensing and GSV images. The proposed method is composed of four steps. First, we explored the feasibility of the three most relevant and complementary remote sensing data for individual-level AH detection, i.e., daytime VHR images, nighttime light VHR images, and GSV images. Second, we extracted discriminative features that are indicative of housing abandonment conditions from the three disparate data sources. Third, we applied decision-level fusion with Dempster-Shafer Theory (DST) to better leverage the prior knowledge about data effectiveness. In the last step, a geographical random forests (GRF) model was first implemented to improve the predictions of where houses were occluded on GSV images. We mapped individual AH in two typical U.S. shrinking cities, Buffalo, NY, and Cleveland, OH, which allowed us to further explore the individual-property-level spatial characteristics of AH. Results revealed that the proposed DST fusion and GRF prediction consistently achieved promising performance across the two cities. Given the merits of incorporating open-access and multi-perspective data, our proposed method has the potential to be generalized to understanding regional and national-scale urban environments tackling housing abandonment challenges. Numéro de notice : A2022-788 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103018 Date de publication en ligne : 18/09/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101894 [article]

Towards sustainable forestry: Using a spatial Bayesian belief network to quantify trade-offs among forest-related ecosystem services / Catherine Frizzle in Journal of Environmental Management, vol 301 ([01/01/2022])  

Public [article]  inJournal of Environmental Management > vol 301 [01/01/2022] . - n° 113817 Titre : Towards sustainable forestry: Using a spatial Bayesian belief network to quantify trade-offs among forest-related ecosystem services Type de document : Article/Communication Auteurs : Catherine Frizzle, Auteur ; Richard A. Fournier, Auteur ; Melanie Trudel, Auteur ; Joan E. Luther, Auteur Année de publication : 2022 Article en page(s) : n° 113817 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] gestion forestière durable [Termes IGN] réseau bayesien [Termes IGN] service écosystémique [Termes IGN] théorie de Dempster-Shafer [Vedettes matières IGN] Foresterie Résumé : (auteur) Assessing trade-offs among ecosystem services (ESs) that are provided by forests is necessary to support decision-making and to minimize negative effects of timber harvesting. In this study, we examined how spatial data, forest operational rules, ESs, and probabilistic statistics can be combined into a practical tool for trade-off analysis that could guide decision-making towards sustainable forestry. Our main goal was to analyze trade-offs among the wood provisioning ES and other forest ESs at the landscape level using a Bayesian belief network (BBN). We used LiDAR data to derive four ES layers as inputs to a spatial BBN: (i) wood provisioning; (ii) erosion regulating; (iii) climate regulating; and (iv) habitat supporting. We quantified operational constraints with four forest operational rules (FOR) that were defined in terms of: (i) potential harvest block size; (ii) distance between a small potential harvest block and a larger harvest block; (iii) gross merchantable volume (GMV); and (iv) distance to an existing resource road. Maps of the most probable trade-off classes between the wood provisioning ES and other ESs enabled us to identify areas where timber harvesting should be avoided or where timber harvesting should have a very low negative effect on other ESs. Even with our most restrictive management scenario, the total GMV that could be harvested met the annual allowable cut (AAC) volume required to meet sustainable forestry objectives. Through our study, we demonstrated that high-resolution spatial data could be used to quantify trade-offs among wood provisioning ES and other forest-related ESs and to simulate small changes in ES indicators within the BBN. We also demonstrated the potential to evaluate management scenarios to reduce trade-offs by considering FOR as inputs to the BBN. Maps of the most probable trade-off classes among two or three ESs under operational constraints provide key information to guide forest management decision-making towards sustainable forestry. Numéro de notice : A2022-338 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1016/j.jenvman.2021.113817 Date de publication en ligne : 01/10/2021 En ligne : https://doi.org/10.1016/j.jenvman.2021.113817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100709 [article]

A data fusion-based framework to integrate multi-source VGI in an authoritative land use database / Lanfa Liu in International Journal of Digital Earth, vol 14 n° 4 (April 2021)  

Public [article]  inInternational Journal of Digital Earth > vol 14 n° 4 (April 2021) . - pp 480 - 509 Titre : A data fusion-based framework to integrate multi-source VGI in an authoritative land use database Type de document : Article/Communication Auteurs : Lanfa Liu, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Laurence Jolivet , Auteur ; Arnaud Le Bris , Auteur ; Linda M. See, Auteur Année de publication : 2021 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : pp 480 - 509 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données d'occupation du sol [Termes IGN] base de données localisées de référence [Termes IGN] données hétérogènes [Termes IGN] données localisées des bénévoles [Termes IGN] fusion de données [Termes IGN] intégration de données [Termes IGN] mise à jour de base de données [Termes IGN] OCS GE [Termes IGN] théorie de Dempster-Shafer Résumé : (auteur) Updating an authoritative Land Use and Land Cover (LULC) database requires many resources. Volunteered geographic information (VGI) involves citizens in the collection of data about their spatial environment. There is a growing interest in using existing VGI to update authoritative databases. This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique, in order to update an authoritative land use database. Each VGI data source is considered to be an independent source of information, which is fused together using Dempster-Shafer Theory (DST). The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency. Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles. The data fusion approach achieved an overall accuracy of 85.6% for the 144 features having at least two contributions when the confidence threshold was set to 0.05. Despite the heterogeneity and limited amount of VGI used, the results are promising, with 99% of the LU polygons updated or enriched. These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally. Numéro de notice : A2021-069 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/17538947.2020.1842524 Date de publication en ligne : 05/11/2020 En ligne : https://hal.science/hal-03046640v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96522 [article]

Sensor tasking for search and catalog maintenance of geosynchronous space objects / Han Cai in Acta Astronautica, vol 175 (October 2020)  

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Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India / Subrata Mondal in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

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Application of machine learning techniques for evidential 3D perception, in the context of autonomous driving / Edouard Capellier (2020)  

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Multi-temporal image change mining based on evidential conflict reasoning / Fatma Haouas in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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Ensemble methods for pedestrian detection in dense crowds / Jennifer Vandoni (2019)  

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Salient object detection in complex scenes via D-S evidence theory based region classification / Chunlei Yang in The Visual Computer, vol 33 n° 11 (November 2017)  

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Occupancy modelling for moving object detection from Lidar point clouds: A comparative study / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W4 (September 2017)  

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Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models / Kai Cui in Geocarto international, vol 32 n° 9 (September 2017)  

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Learning to diversify deep belief networks for hyperspectral image classification / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)  

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Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models / Samira Ghorbani Nejad in Geocarto international, vol 32 n° 2 (February 2017)  

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