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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)  

Public [article]  inGeocarto international > vol 32 n° 9 (September 2017) . - pp 935 - 955 Titre : Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models Type de document : Article/Communication Auteurs : Kai Cui, Auteur ; Dong Lu, Auteur ; Wei Li, Auteur Année de publication : 2017 Article en page(s) : pp 935 - 955 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aléa [Termes IGN] analyse comparative [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] effondrement de terrain [Termes IGN] inventaire [Termes IGN] pondération [Termes IGN] risque naturel [Termes IGN] système d'information géographique [Termes IGN] théorie de Dempster-Shafer [Termes IGN] vulnérabilité Résumé : (Auteur) The main aim of this study was to produce landslide susceptibility maps using statistical index (SI), certainty factors (CF), weights of evidence (WoE) and evidential belief function (EBF) models for the Long County, China. Firstly, a landslide inventory map, including a total of 171 landslides, was compiled on the basis of earlier reports, interpretation of aerial photographs and supported by extensive field surveys. Thereafter, all landslides were randomly separated into two data sets: 70% landslides (120 points) were selected for establishing the model and the remaining landslides (51 points) were used for validation purposes. Eleven landslide conditioning factors, such as slope aspect, slope angle, plan curvature, profile curvature, altitude, distance to faults, distance to roads, distance to rivers, lithology, NDVI and land use, were considered for landslide susceptibility mapping in this study. Then, the SI, CF, WoE and EBF models were used to produce the landslide susceptibility maps for the study area. Finally, the four models were validated using area under the curve (AUC) method. According to the validation results, the EBF model (AUC = 78.93%) has a higher prediction accuracy than the SI model (AUC = 77.72%), the WoE model (AUC = 77.62%) and the CF model (AUC = 77.72%). Similarly, the validation results also indicate that the EBF model has the highest training accuracy of 80.25%, followed by SI (79.80%), WoE (79.71%) and CF (79.67%) models. Numéro de notice : A2017-457 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.0/10106049.2016.1195886 Date de publication en ligne : 16/06/2016 En ligne : http://dx.doi.org108/10.0/10106049.2016.1195886 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86382 [article]

Investigation of automatic feature weighting methods (Fisher, Chi-square and Relief-F) for landslide susceptibility mapping / Emrehan Kutlug Sahin in Geocarto international, vol 32 n° 9 (September 2017)  

Public [article]  inGeocarto international > vol 32 n° 9 (September 2017) . - pp 956 - 977 Titre : Investigation of automatic feature weighting methods (Fisher, Chi-square and Relief-F) for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Emrehan Kutlug Sahin, Auteur ; Cengizhan Ipbuker, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2017 Article en page(s) : pp 956 - 977 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse comparative [Termes IGN] cartographie des risques [Termes IGN] distribution de Fisher [Termes IGN] effondrement de terrain [Termes IGN] khi carré [Termes IGN] pondération [Termes IGN] processus de hiérarchisation analytique [Termes IGN] risque naturel [Termes IGN] surveillance géologique [Termes IGN] test de performance [Termes IGN] vulnérabilité Résumé : (Auteur) In landslide susceptibility mapping, factor weights have been usually determined by expert judgements. A novel methodology for weighting landslide causative factors by integrating statistical feature weighting algorithms was proposed. The primary focus of this study is to investigate the effectiveness of automatic feature weighting algorithms, namely Fisher, Chi-square and Relief-F algorithms. Analytic hierarchy process (AHP) method was used as a benchmark method to compare the performances of the weighting algorithms. All weighted factors were tested using factor-weighted overlay method, and quality of these maps was assessed using overall accuracy, area under the ROC curve (AUC) and success rate curve. In addition, Wilcoxon’s signed-rank test was applied to evaluate statistical differences between both estimated overall accuracies and AUCs, respectively. Results showed that the weights determined by feature weighting methods outperformed the conventional AHP method by about 6% and this level of differences was found to be statistically significant. Numéro de notice : A2017-458 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1170892 Date de publication en ligne : 11/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1170892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86383 [article]

An information fusion approach for PALSAR data to retrieve soil moisture / Ankita Jain in Geocarto international, vol 32 n° 9 (September 2017)  

Public [article]  inGeocarto international > vol 32 n° 9 (September 2017) . - pp 1017 - 1033 Titre : An information fusion approach for PALSAR data to retrieve soil moisture Type de document : Article/Communication Auteurs : Ankita Jain, Auteur ; Dharmendra Singh, Auteur Année de publication : 2017 Article en page(s) : pp 1017 - 1033 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande spectrale [Termes IGN] couvert végétal [Termes IGN] données polarimétriques [Termes IGN] fusion de données [Termes IGN] humidité du sol [Termes IGN] image ALOS-PALSAR [Termes IGN] polarimétrie radar Résumé : (Auteur) Estimation of vegetation covered soil moisture with satellite images is still a challenging task. Several models are available for soil moisture retrieval in which water cloud model (WCM) is most common. But, it requires an estimation of accurate vegetation parameterization. Thus, there is a need to develop such an approach for soil moisture retrieval which minimize these limitations. Therefore, this paper deals with the soil moisture retrieval using fully polarimetric SAR data by fusing the information from different bands. Various polarimetric indices and observables were critically analysed, and found that the index; SPAN (total scattered power) gives better information of vegetation cover as compared to other indices/observables. Based on this, WCM model has been modified using SPAN as parameter and soil moisture content were retrieved. Numéro de notice : A2017-459 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1188163 Date de publication en ligne : 10/06/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1188163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86384 [article]