Centre dedocumentation
scientifique

The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland / Ranjith Gopalakrishnan in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 77 n° 4 (December 2020) . - 18 p. Titre : The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland Type de document : Article/Communication Auteurs : Ranjith Gopalakrishnan, Auteur ; Petteri Packalen, Auteur ; Veli-Pekka Ikonen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] paysage forestier [Termes descripteurs IGN] télémétrie laser aéroporté [Termes descripteurs IGN] tempête [Termes descripteurs IGN] vent Résumé : (auteur) Key message: The potential of airborne laser scanning (ALS) and multispectral remote sensing data to aid in generating improved wind damage risk maps over large forested areas is demonstrated. This article outlines a framework to generate such maps, primarily utilizing the horizontal structural information contained in the ALS data. Validation was done over an area in Eastern Finland that had experienced sporadic wind damage. Context: Wind is the most prominent disturbance element for Finnish forests. Hence, tools are needed to generate wind damage risk maps for large forested areas, and their possible changes under planned silvicultural operations. Aims: (1) How effective are ALS-based forest variables (e.g. distance to upwind forest stand edge, gap size) for identifying high wind damage risk areas? (2) Can robust estimates of predicted critical wind speeds for uprooting of trees be derived from these variables? (3) Can these critical wind speed estimates be improved using wind multipliers, which factor in topography and terrain roughness effects? Methods: We first outline a framework to generate several wind damage risk–related parameters from remote sensing data (ALS + multispectral). Then, we assess if such parameters have predictive power. That is, whether they help differentiate between damaged and background points. This verification exercise used 42 wind damaged points spread over a large area. Results: Parameters derived from remote sensing data are shown to have predictive power. Risk models based on critical wind speeds are not that robust, but show potential for improvement. Conclusion: Overall, this work described a framework to get several wind risk–related parameters from remote sensing data. These parameters are shown to have potential in generating wind damage risk maps over large forested areas. Numéro de notice : A2020-629 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00992-8 date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1007/s13595-020-00992-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96045 [article]

Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea / Sunmin Lee in Geocarto international, vol 35 n° 15 ([01/11/2020])  

Public [article]  inGeocarto international > vol 35 n° 15 [01/11/2020] . - pp 1665 - 1679 Titre : Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea Type de document : Article/Communication Auteurs : Sunmin Lee, Auteur ; Moung-Jin Lee, Auteur ; Hyung-Sup Jung, Auteur ; Saro Lee, Auteur Année de publication : 2020 Article en page(s) : pp 1665 - 1679 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] carte forestière [Termes descripteurs IGN] carte topographique [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] catastrophe naturelle [Termes descripteurs IGN] Corée du sud [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] réseau bayesien [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] zone urbaine Résumé : (auteur) In recent years, machine learning techniques have been increasingly applied to the assessment of various natural disasters, including landslides and floods. Machine learning techniques can be used to make predictions based on the relationships among events and their influencing factors. In this study, a machine learning approaches were applied based on landslide location data in a geographic information system environment. Topographic maps were used to determine the topographical factors. Additional soil and forest parameters were examined using information obtained from soil and forest maps. A total of 17 factors affecting landslide occurrence were selected and a spatial database was constructed. Naïve Bayes and Bayesian network models were applied to predict landslides based on selected risk factors. The two models showed accuracies of 78.3 and 79.8%, respectively. The results of this study provide a useful foundation for effective strategies to prevent and manage landslides in urban areas. Numéro de notice : A2020-658 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1585482 date de publication en ligne : 16/04/2019 En ligne : https://doi.org/10.1080/10106049.2019.1585482 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96130 [article]

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])  

Public [article]  inGeocarto international > vol 35 n° 15 [01/11/2020] . - pp 1750 - 1768 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 descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] érosion [Termes descripteurs IGN] érosion hydrique [Termes descripteurs IGN] fréquence [Termes descripteurs IGN] Inde [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] pente [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] utilisation du sol Résumé : (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 [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)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 11 n° 1 (2020) . - pp 131 - 148 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 descripteurs IGN] analyse multicritère [Termes descripteurs IGN] ArcGIS [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] gestion des risques [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] pente [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] Slovaquie [Termes descripteurs IGN] utilisation du sol Résumé : (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 [article]

Geo-environment risk assessment in Zhengzhou City, China / Chuanming Ma in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 11 n° 1 (2020) . - pp 40 - 70 Titre : Geo-environment risk assessment in Zhengzhou City, China Type de document : Article/Communication Auteurs : Chuanming Ma, Auteur ; Wu Yan, Auteur ; Xinjie Hu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 40 - 70 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] Chine [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] évaluation des données [Termes descripteurs IGN] gestion des risques [Termes descripteurs IGN] pollution des eaux [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] risque environnemental [Termes descripteurs IGN] séisme [Termes descripteurs IGN] structure hiérarchique de données [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] urbanisme [Termes descripteurs IGN] zone urbaine dense Résumé : (auteur) The urban geological environment risk assessment is based on the research and analysis of the main geological environmental problems of the city, comprehensively assessing the risk of urban geological environment problems and the possible losses, and studying the degree of matching between the natural and social attributes of the geological environment. According to the urban planning of Zhengzhou City, the different types of functional areas of the city were used as evaluation objects, and the analytic hierarchy-composite index model was used to evaluate the geological environment risk and social economic vulnerability. The risk assessment model was used to evaluate the geological environment risk of Zhengzhou City. The evaluation results show that the area of high-risk area in Zhengzhou accounts for 4.05%; the area of medium-high risk area accounts for 12.89%; the area of medium-low and low-risk area accounts for 83.06%. According to the assessment results, suggestions are put forward to provide service for the urban planning, development and risk management. Highlights: * An urban geo-environment risk assessment technique system combining with the AHP - composite index assessment model is proposed. * Different types of functional zones in Zhengzhou City are taken as assessment units. * Geo-environment risk in Zhengzhou City is qualitatively and quantitatively evaluated. Numéro de notice : A2020-565 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475705.2019.1701571 date de publication en ligne : 27/12/2019 En ligne : https://doi.org/10.1080/19475705.2019.1701571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95890 [article]

A 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)  

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GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia / Dhekra Souissi in Geocarto international, vol 35 n° 9 ([01/07/2020])  

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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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Evaluating the impact of visualization of risk upon emergency route-planning / Lisa Cheong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

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Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping / Kristofer Lasko in Geocarto international, vol 35 n° 6 ([01/05/2020])  

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Assessment of malaria hazard, vulnerability, and risks in Dire Dawa City Administration of eastern Ethiopia using GIS and remote sensing / Abdinasir Moha in Applied geomatics, vol 12 n° 1 (April 2020)  

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A novel method of spatiotemporal dynamic geo-visualization of criminal data, applied to command and control centers for public safety / Mayra Salcedo-Gonzalez in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)  

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Optimizing arbovirus surveillance using risk mapping and coverage modelling / Joni A. Downs in Annals of GIS, Vol 26 n° 1 (January 2020)  

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Remote Sensing Technology Applications in Forestry and REDD+ / Kim Calders (2020)  

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A thematic mapping method to assess and analyze potential urban hazards and risks caused by flooding / Mohammad Khalid Hossain in Computers, Environment and Urban Systems, vol 79 (January 2020)  

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