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Soil erosion assessment using RUSLE model and its validation by FR probability model / Amiya Gayen in Geocarto international, vol 35 n° 15 ([01/11/2020])
[article]
Titre : Soil erosion assessment using RUSLE model and its validation by FR probability model Type de document : Article/Communication Auteurs : Amiya Gayen, Auteur ; Sunil Saha, Auteur ; Hamid Reza Pourghasemi, Auteur Année de publication : 2020 Article en page(s) : pp 1750 - 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de sensibilité
[Termes IGN] cartographie des risques
[Termes IGN] érosion
[Termes IGN] érosion hydrique
[Termes IGN] fréquence
[Termes IGN] Inde
[Termes IGN] modèle RUSLE
[Termes IGN] modèle stochastique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] occupation du sol
[Termes IGN] pente
[Termes IGN] surveillance géologique
[Termes IGN] utilisation du solRésumé : (auteur) The objective of the current study is to estimate the annual average soil loss through RUSLE model and furthermore assess the soil erosion risk and its distribution using frequency ratio (FR) probability algorithm. At first, soil erosion risk zones were identified using FR model by the consideration 14 soil erosion conditioning factors such as land use (LU/LC), slope, slope aspect, normalized difference vegetation index (NDVI), altitude, plan curvature, stream power index, distance from river, road, and lineament, soil types, rainfall erosivity, slope length and lineament density. Secondly, the spatial pattern of annual average soil loss rates was estimated using RUSLE model with consideration of five factors such as, rainfall erosivity (R), cover management (C), slope length (LS), soil erodability (K), and conservation practice factors (P). In order to map soil erosion susceptibility by the FR model, dataset divided randomly into parts 70/30 percent for training and validation purposes, respectively. Based on the FR value, the susceptibility map was reclassified into five different critical erosion probability zones. Among this, the severe and high erosion zones occupy 13.69% and 16.26%, respectively, of the total area, where as low and very low susceptibility zones together constitute 32.98% of the River Basin. The assessed high amount of average annual soil erosion (more than 100 t/ha/year) is occupied 9.55% of the total study area. It is conclude that high soil erosion susceptibility and yearly average soil loss were performed in this study area. Therefore, the produced soil erosion susceptibility maps and annual average soil erosion map can be very useful for primary land use planning and soil erosion hazard mitigation purpose for prioritizing areas. Numéro de notice : A2020-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581272 Date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96134
in Geocarto international > vol 35 n° 15 [01/11/2020] . - pp 1750 - 1768[article]De la bonne interprétation des documents / François Mazuyer in Géomètre, n° 2184 (octobre 2020)
[article]
Titre : De la bonne interprétation des documents Type de document : Article/Communication Auteurs : François Mazuyer, Auteur Année de publication : 2020 Article en page(s) : pp 26 - 27 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Topographie ancienne
[Termes IGN] bornage
[Termes IGN] géomètre-expert
[Termes IGN] instrument de mesure
[Termes IGN] précision des mesures
[Termes IGN] relevé
[Termes IGN] topographie ancienneRésumé : (auteur) Les anciens plans servent encore au géomètre-expert dans des opérations de délimitation de la propriété. Mais il faut les analyser avec prudence, car les outils et méthodes de l’époque n’offrent pas toujours la précision désormais requise. Numéro de notice : A2020-667 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96148
in Géomètre > n° 2184 (octobre 2020) . - pp 26 - 27[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2020091 RAB Revue Centre de documentation En réserve L003 Disponible Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones / Xun Liang in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)
[article]
Titre : Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones Type de document : Article/Communication Auteurs : Xun Liang, Auteur ; Xiaoping Liu, Auteur ; Guangliang Chen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1930 - 1952 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aide à la décision
[Termes IGN] analyse de groupement
[Termes IGN] automate cellulaire
[Termes IGN] Chine
[Termes IGN] classification floue
[Termes IGN] classification non dirigée
[Termes IGN] croissance urbaine
[Termes IGN] modèle de simulation
[Termes IGN] planification urbaine
[Termes IGN] zone d'activité économiqueRésumé : (auteur) Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (CA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified CA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, China from 2013 to 2019. The proposed model is applied to future simulation in 2019–2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs. Numéro de notice : A2020-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1741591 Date de publication en ligne : 23/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1741591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95668
in International journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 1930 - 1952[article]Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia / Jana Vojteková in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)
[article]
Titre : Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia Type de document : Article/Communication Auteurs : Jana Vojteková, Auteur ; Matej Vojtek, Auteur Année de publication : 2020 Article en page(s) : pp 131 - 148 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse multicritère
[Termes IGN] ArcGIS
[Termes IGN] cartographie des risques
[Termes IGN] effondrement de terrain
[Termes IGN] gestion des risques
[Termes IGN] modèle numérique de surface
[Termes IGN] pente
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] Slovaquie
[Termes IGN] utilisation du solRésumé : (auteur) Landslide susceptibility is an important topic mainly because its geo-spatial analysis provides a useful tool for planning, disaster management and hazard mitigation. In this study, the aim is to identify and analyze landslide susceptibility at a local spatial scale, which is represented by the town of Handlová, using the multi-criteria evaluation (i.e., the analytical hierarchy process technique – AHP) and geographic information systems (GIS). The following landslide conditioning factors were selected representing the local terrain predispositions: slope angle, geology, slope aspect, elevation, distance from rivers, distance from faults and land use. The raster-based analysis was performed using the spatial resolution of 10 × 10 m. The weights for each factor were determined by the AHP technique where slope angle had the highest relative importance. Based on the resulting susceptibility map, 51.98% out of the total study area is characterized by high and very high susceptibility class. The Atlas of Slope Stability of the Slovak Republic, which contains past landslides until 2006, was used for verification of the results. The verification confirmed a moderate accuracy between the landslide susceptibility map and landslide inventory from the atlas since 60.8% of all landslide areas from the atlas corresponded with high and very high susceptibility class. Numéro de notice : A2020-567 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475705.2020.1713233 Date de publication en ligne : 20/01/2020 En ligne : https://doi.org/10.1080/19475705.2020.1713233 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95893
in Geomatics, Natural Hazards and Risk > vol 11 n° 1 (2020) . - pp 131 - 148[article]Comparison of tree-based classification algorithms in mapping burned forest areas / Dilek Kucuk Matci in Geodetski vestnik, vol 64 n° 3 (September - November 2020)
[article]
Titre : Comparison of tree-based classification algorithms in mapping burned forest areas Type de document : Article/Communication Auteurs : Dilek Kucuk Matci, Auteur ; Resul Comert, Auteur ; Ugur Avdan, Auteur Année de publication : 2020 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image orientée objet
[Termes IGN] bassin méditerranéen
[Termes IGN] carte forestière
[Termes IGN] classification dirigée
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Landsat-8
[Termes IGN] incendie de forêt
[Termes IGN] matrice de confusion
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] Rotation Forest classification
[Termes IGN] Turquie
[Termes IGN] zone sinistréeRésumé : (auteur) In this study, we compared the performance of tree-based classification algorithms – Random Forest (RF), Rotation Forest (RotF), J48, The Alternating Decision Tree (ADTree), Forest by Penalising Attributes (Forest PA), Logical Analysis of Data Algorithm (LADTree) and Functional Trees (FT) – for mapping burned forest areas within the Mediterranean region in Turkey. Object-based image analysis (OBIA) was performed to pan-sharpened the Landsat 8 images. Four different burned areas, namely Kumluca, Adrasan, Anamur, and Alanya, were used as study areas. Kumluca, Anamur, and Alanya regions were used as training areas, and Adrasan region was used as the test area. Obtained results were evaluated with confusion matrix and statistically significant analysis. According to the results, FT and RotF produced more accurate results than other algorithms. Also, the results obtained with these algorithms are statistically significant. Numéro de notice : A2020-626 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2020.03.348-360 Date de publication en ligne : 23/08/2020 En ligne : https://doi.org/10.15292/geodetski-vestnik.2020.03.348-360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96087
in Geodetski vestnik > vol 64 n° 3 (September - November 2020) . - 13 p.[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2020031 RAB Revue Centre de documentation En réserve L003 Disponible Genèse d'une norme internationale géodésique : l'ITRS et ses réalisations / Thierry Gattacceca in XYZ, n° 164 (septembre 2020)PermalinkGeo-environment risk assessment in Zhengzhou City, China / Chuanming Ma in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)PermalinkMining regional patterns of land use with adaptive adjacent criteria / Xinmeng Tu in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)PermalinkLes pérégrinations d'un topographe en Chine / Bernard Flacelière in XYZ, n° 164 (septembre 2020)PermalinkRelevé 3D et classification de nuages de points de patrimoine bâti / Arnadi Murtiyoso in XYZ, n° 164 (septembre 2020)PermalinkAux sources institutionnelles de l’enregistrement et du cadastre fonciers au Québec / Francis Roy in XYZ, n° 164 (septembre 2020)PermalinkA spaceborne SAR-based procedure to support the detection of landslides / Giuseppe Esposito in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)PermalinkA spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery / Bo Yang in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)PermalinkUse of Bayesian modeling to determine the effects of meteorological conditions, prescribed burn season, and tree characteristics on litterfall of pinus nigra and pinus pinaster stands / Juncal Espinosa in Forests, vol 11 n° 9 (September 2020)PermalinkUsing OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)Permalink