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Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China / Kezhen Yao in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 5 (May 2022) . - n° 269 Titre : Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China Type de document : Article/Communication Auteurs : Kezhen Yao, Auteur ; Saini Yang, Auteur ; Shengnan Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] dispersion [Termes IGN] effondrement de terrain [Termes IGN] Extreme Gradient Machine [Termes IGN] modèle de simulation [Termes IGN] régression linéaire [Termes IGN] risque naturel [Termes IGN] vulnérabilité Résumé : (auteur) Landslide susceptibility assessment serves as a critical scientific reference for geohazard control, land use, and sustainable development planning. The existing research has not fully considered the potential impact of the spatial agglomeration and dispersion of landslides on assessments. This issue may cause a systematic evaluation bias when the field investigation data are insufficient, which is common due to limited human resources. Accordingly, this paper proposes two novel strategies, including a clustering algorithm and a preprocessing method, for these two ignored features to strengthen assessments, especially in high-susceptibility regions. Multiple machine learning models are compared in a case study of the city of Bijie (Guizhou Province, China). Then we generate the optimal susceptibility map and conduct two experiments to test the validity of the proposed methods. The primary conclusions of this study are as follows: (1) random forest (RF) was superior to other algorithms in the recognition of high-susceptibility areas and the portrayal of local spatial features; (2) the susceptibility map incorporating spatial feature messages showed a noticeable improvement over the spatial distribution and gradual change of susceptibility, as well as the accurate delineation of critical hazardous areas and the interpretation of historical hazards; and (3) the spatial distribution feature had a significant positive effect on modeling, as the accuracy increased by 5% and 10% after including the spatial agglomeration and dispersion consideration in the RF model, respectively. The benefit of the agglomeration is concentrated in high-susceptibility areas, and our work provides insight to improve the assessment accuracy in these areas, which is critical to risk assessment and prevention activities. Numéro de notice : A2022-371 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11050269 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.3390/ijgi11050269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100613 [article]

Modelling potential density of natural regeneration of European oak species (Quercus robur L., Quercus petraea (Matt.) Liebl.) depending on the distance to the potential seed source: Methodological approach for modelling dispersal from inventory data at forest enterprise level / Maximilian Axer in Forest ecology and management, vol 482 ([15/02/2021])  

Public [article]  inForest ecology and management > vol 482 [15/02/2021] . - n° 118802 Titre : Modelling potential density of natural regeneration of European oak species (Quercus robur L., Quercus petraea (Matt.) Liebl.) depending on the distance to the potential seed source: Methodological approach for modelling dispersal from inventory data at forest enterprise level Type de document : Article/Communication Auteurs : Maximilian Axer, Auteur ; Robert Schlicht, Auteur ; Sven Wagner, Auteur Année de publication : 2021 Article en page(s) : n° 118802 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] conversion forestière [Termes IGN] dispersion [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Pinophyta [Termes IGN] planification [Termes IGN] Quercus pedunculata [Termes IGN] Quercus sessiliflora [Termes IGN] régénération (sylviculture) [Termes IGN] régression par quantile [Termes IGN] Saxe (Allemagne) [Termes IGN] semis (sylviculture) [Vedettes matières IGN] Foresterie Résumé : (auteur) The use of natural oak regeneration from admixed oaks and neighbouring oak stands provides an interesting alternative to cost-intensive artificial oak regeneration when aiming for forest conversion of pure coniferous stands. In this study analysis of forest inventory data is done on how far and in what density natural regeneration of both Pedunculate and Sessile oak occurs in coniferous stands. In order to investigate as exclusively as possible the effect of distance to the seed source on the regeneration density of both oaks, the regeneration potential was determined by using quantile regression. By applying a .995th quantile, reducing factors on seedling density, e.g. desiccation, browsing, pathogens or limited resource availability, were excluded as much as possible. Thus, the effect of zoochorus vectors on effective dispersal could be quantified. The regeneration potential was determined based on data from the forest inventory of the Saxony state forest enterprise, Germany, including 2357 sample plots. Remote sensing data, including the location of oaks in the overstorey, were used to determine the distance to the nearest potential seed source. The results of the present study demonstrate that the highest regeneration densities are not found in the immediate vicinity of the nearest seed source, but at distances between 60 and 140 m to it,i.e. the maximum of seedling density per area unit is in some distance to the trees trunk. In the present study, dispersal distances of established regeneration up to 1565 m were detected. From a distance of 1570–2300 m on, there was no oak regeneration. The results prove that acorns are taken from the seed source and that, in addition to barochorus dispersal, the zoochorus dispersal is of great importance for the succession of coniferous stands. The position of potential seed sources is therefore an important information for silvicultural planning, in order to estimate potential oak regeneration densities. Numéro de notice : A2021-228 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2020.118802 Date de publication en ligne : 13/12/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118802 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97208 [article]

A convolutional neural network with mapping layers for hyperspectral image classification / Rui Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3136 - 3147 Titre : A convolutional neural network with mapping layers for hyperspectral image classification Type de document : Article/Communication Auteurs : Rui Li, Auteur ; Zhibin Pan, Auteur ; Yang Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3136 - 3147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algèbre linéaire [Termes IGN] analyse discriminante [Termes IGN] analyse en composantes principales [Termes IGN] analyse multidimensionnelle [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couche thématique [Termes IGN] dispersion [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] réduction Résumé : (auteur) In this article, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification. The proposed mapping layers map the input patch into a low-dimensional subspace by multilinear algebra. We use our mapping layers to reduce the spectral and spatial redundancies and maintain most energy of the input. The feature extracted by our mapping layers can also reduce the number of following convolutional layers for feature extraction. Our MCNN architecture avoids the declining accuracy with increasing layers phenomenon of deep learning models for HSI classification and also saves the training time for its effective mapping layers. Furthermore, we impose the 3-D convolutional kernel on the convolutional layer to extract the spectral–spatial features for HSI. We tested our MCNN on three data sets of Indian Pines, University of Pavia, and Salinas, and we achieved the classification accuracy of 98.3%, 99.5%, and 99.3%, respectively. Experimental results demonstrate that the proposed MCNN can significantly improve classification accuracy and save much time consumption. Numéro de notice : A2020-234 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2948865 Date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2948865 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94980 [article]

The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study / Yanan Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 161 - 167 Titre : The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study Type de document : Article/Communication Auteurs : Yanan Yan, Auteur ; Lei Deng, Auteur ; L. Xian-Lin, Auteur Année de publication : 2020 Article en page(s) : pp 161 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] agrégation spatiale [Termes IGN] anisotropie [Termes IGN] bande spectrale [Termes IGN] classification pixellaire [Termes IGN] détection d'objet [Termes IGN] dispersion [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] distribution spatiale [Termes IGN] extraction de la végétation [Termes IGN] hétérogénéité spatiale [Termes IGN] modèle de simulation [Termes IGN] modèle de transfert radiatif [Termes IGN] réflectance [Termes IGN] régression linéaire [Termes IGN] télédétection Résumé : (auteur) Spectral decomposition of mixed pixels can provide information about the abundance of end members but fails to indicate the spatial distribution of end members in vegetation remote sensing. This work is a significant attempt to use the bidirectional reflectance distribution function (BRDF) characteristics of mixed pixels in the prediction of spatial-heterogeneity metrics. Data sets from this function with different spatial distributions were constructed by the discrete anisotropic radiative transfer model, and three spatial aggregation and dispersion metrics were calculated: percentage of like adjacencies, spatial division index, and aggregation index. A simple linear regression method was used to construct the prediction model of spatial aggregation and dispersion metrics. The potential of multiangle remote sensing model for identifying spatial patterns well was demonstrated, and its importance was found to differ for different spatial aggregation and dispersion metrics. Specifically, the precision of the model based on multiangle reflectance used for predicting the spatial division index could meet a minimum root mean square of 5.95%. The reflectance features from backward observation on the principal plane play the leading role in recognizing the spatial heterogeneity of mixed pixels. The prediction model is sufficiently robust to distinguish the same vegetation with different growth trends, but also performs well when the ground objects have a smaller reflectance difference in the mixed pixels in a certain band. This study is expected to offer a new thought for spatial-heterogeneity identification of ground objects and thus promote the development of remote sensing technology in assessing spatial distribution. Numéro de notice : A2020-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.161 Date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.161 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94775 [article]

Classification d’aires de dispersion à l’aide d’un facteur géographique - Application à la dialectologie / Clément Chagnaud in Revue internationale de géomatique, vol 30 n° 1-2 (janvier - juin 2020)  

Public [article]  inRevue internationale de géomatique > vol 30 n° 1-2 (janvier - juin 2020) . - pp 67 - 83 Titre : Classification d’aires de dispersion à l’aide d’un facteur géographique - Application à la dialectologie Type de document : Article/Communication Auteurs : Clément Chagnaud, Auteur ; Philippe Garat, Auteur ; Paule-Annick Davoine, Auteur Année de publication : 2020 Article en page(s) : pp 67 - 83 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aire de répartition [Termes IGN] analyse de données [Termes IGN] dialecte [Termes IGN] dispersion [Termes IGN] linguistique [Termes IGN] objet géographique zonal Résumé : (auteur) Nous proposons une procédure d’analyse statistique multidimensionnelle couplant des méthodes de projection et de classification pour identifier des ensembles cohérents au sein d’un corpus d’entités géographiques surfaciques que l’on appelle aires de dispersion. La méthodologie intègre un facteur géographique dans la construction de l’espace de représentation pour la projection des données. En appliquant ces méthodes sur des données géolinguistiques, nous pouvons identifier et expliquer de nouvelles structures spatiales au sein d’un corpus d’aires de dispersion de traits linguistiques. Numéro de notice : A2021-344 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3166/rig.2020.00107 Date de publication en ligne : 16/04/2021 En ligne : https://doi.org/10.3166/rig.2020.00107 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97588 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
047-2020011	SL	Revue	Centre de documentation	Revues en salle	Disponible

Phase quality optimization in polarimetric differential SAR interferometry / Rubén Iglesias in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)  

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Efficiently acquiring reflectance fields using patterned illumination / Gaurav Garg (2006)  

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La thermographie infrarouge / G. Gaussorgues (1999)

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La thermographie infrarouge / G. Gaussorgues (1984)

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The theory of dispersion applied to electro-optical distance measurement and angle measurement / J.C. de Munck (1970)  

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Cours d'astronomie à l'usage des étudiants des facultés des sciences, 1. Première partie Quelques théories applicables à l'étude des sciences expérimentales / B. Baillaud (1893)

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