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Prescribed fire after thinning increased resistance of sub-Mediterranean pine forests to drought events and wildfires / Lena Vilà-Vilardell in Forest ecology and management, vol 527 (January-1 2023)  

Public [article]  inForest ecology and management > vol 527 (January-1 2023) . - n° 120602 Titre : Prescribed fire after thinning increased resistance of sub-Mediterranean pine forests to drought events and wildfires Type de document : Article/Communication Auteurs : Lena Vilà-Vilardell, Auteur ; Miquel De Cáceres, Auteur ; Míriam Piqué, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 120602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] brûlis [Termes IGN] éclaircie (sylviculture) [Termes IGN] incendie de forêt [Termes IGN] Pinus nigra [Termes IGN] sécheresse [Termes IGN] sous-étage [Termes IGN] stress hydrique [Termes IGN] structure d'un peuplement forestier [Termes IGN] vulnérabilité [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Vegetation structure affects the vulnerability of a forest to drought events and wildfires. Management decisions, such as thinning intensity and type of understory treatment, influence competition for water resources and amount of fuel available. While heavy thinning effectively reduces tree water stress and intensity of a crown fire, the duration of these benefits may be limited by a fast growth response of the understory. Our aim was to study the effect of forest structure on pine forests vulnerability to extreme drought events and on the potential wildfire behaviour after management, with a special focus on the role of the understory. In three sub-Mediterranean sites of NE Spain dominated by Pinus nigra, two intensities of thinning (light: aiming at 70–75% canopy cover; and heavy: aiming at 50–60% canopy cover) followed by two understory treatments (mechanical only and mechanical plus prescribed burning) were applied, resulting in four differently managed stands plus an untreated control per site. Four to five years after management, we measured forest structure (overstory in one 314 m2 circular plot and understory in 20 quadrats of 1 m2 per treatment unit) and fuel load (in two 10 m transects per treatment unit) and simulated water balance and fire behaviour under extreme weather conditions. Understory contribution was assessed comparing the real structure with a virtual forest stand where understory vegetation equalled the one of the untreated control. Our results suggest that the resulting mid-term structure following treatments effectively reduced water stress and fire behaviour compared with untreated control, and that the most effective treatments were the ones where prescribed burning was applied after light or heavy thinning. While understory clearing contributes to increase the resistance to both disturbances, an additive effect of burning the debris reduced the vulnerability to drought and wildfires after treatments. Our study highlights the importance of managing the understory to further increase forest resistance to both disturbances. Numéro de notice : A2022-858 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120602 Date de publication en ligne : 08/11/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102109 [article]

Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale / Shengwu Qin in Natural Hazards, vol 114 n° 3 (December 2022)  

Public [article]  inNatural Hazards > vol 114 n° 3 (December 2022) . - pp 2709 - 2738 Titre : Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale Type de document : Article/Communication Auteurs : Shengwu Qin, Auteur ; Shuangshuang Qiao, Auteur ; Jingyu Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2709 - 2738 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] éboulement [Termes IGN] hétérogénéité spatiale [Termes IGN] prévention des risques [Termes IGN] séparateur à vaste marge [Termes IGN] système d'information géographique Résumé : (auteur) Susceptibility mapping is an effective means of preventing debris flow disasters. However, previous studies have failed to solve spatial heterogeneity well, especially at the regional scale. The main objective of this study is to solve the spatial heterogeneity of regional-scale debris flow susceptibility (DFS) mapping by establishing a geographic information system (GIS)-based processing framework. The framework was realized by integrating the determination factor (DFactor) model with machine learning models. The DFactor model established different combinations of evaluation factors in each local region and clarified the differing contributions of influencing factors to DFS. To test the feasibility of the framework, the support vector machine (SVM) and two-dimensional convolutional neural network (CNN) were integrated with the DFactor model (DFactor-SVM and DFactor-CNN) to evaluate DFS in Jilin Province, China. The individual models (SVM and CNN) were also used to map the DFS for comparison with the integrated models. For debris flow modeling, 868 debris flow samples were collected and randomly divided into two datasets: 70% of the samples were used for training and the result was used for verification. The results of the receiver operating characteristic curve showed that the integrated models performed better. The DFactor-CNN model had the highest predictive accuracy, followed by the DFactor-SVM, CNN and SVM models. In general, the GIS-based processing framework maximizes the contribution of the influencing factors to debris flows and enhances the prediction ability of models. Furthermore, it provides a reliable means to predict debris flows at the regional scale. Numéro de notice : A2022-854 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-022-05487-5 Date de publication en ligne : 06/08/2022 En ligne : https://doi.org/10.1007/s11069-022-05487-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102101 [article]

Modelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)  

Public [article]  inSustainable Cities and Society > vol 87 (December 2022) . - n° 104257 Titre : Modelling evacuation preparation time prior to floods: A machine learning approach Type de document : Article/Communication Auteurs : R. Sreejith, Auteur ; K.R. Sinimole, Auteur Année de publication : 2022 Article en page(s) : n° 104257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] apprentissage automatique [Termes IGN] chronométrie [Termes IGN] données spatiotemporelles [Termes IGN] gestion de crise [Termes IGN] inondation [Termes IGN] Kerala (Inde ; état) [Termes IGN] modèle de simulation [Termes IGN] plan de prévention des risques [Termes IGN] questionnaire [Termes IGN] risque naturel [Termes IGN] secours d'urgence Résumé : (auteur) Flooding is a significant hazard responsible for substantial damage and risks to human life worldwide. Effective emergency evacuation to a safer location remains a concern even though the crisis can be predicted and warnings were given. During a calamity, most residents cannot quickly and securely flee. As it is crucial to start evacuation at the right time to have a safe evacuation, this study focuses on a machine learning-based model for predicting a household's evacuation preparation time in the incident of a flood. The study is based on the data collected from flood-affected people from Kerala, India, through a questionnaire. The study indicates that people's demographic, geographical and behavioural aspects, awareness of natural hazards and management are the critical components for improved emergency actions. Further, the article also analysed the characteristics of the respondents and successfully created clusters in which the respondents broadly belong, which will help the rescue team operationalize the evacuation process. Numéro de notice : A2022-819 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104257 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104257 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101986 [article]

«Accompagner le rétablissement spontané de la forêt après un incendie» / Jacques Hazera in Géomètre, n° 2207 (novembre 2022)

Public [article]  inGéomètre > n° 2207 (novembre 2022) . - pp 40 - 42 Titre : «Accompagner le rétablissement spontané de la forêt après un incendie» Type de document : Article/Communication Auteurs : Jacques Hazera, Auteur Année de publication : 2022 Article en page(s) : pp 40 - 42 Langues : Français (fre) Descripteur : [Termes IGN] dégradation de l'environnement [Termes IGN] écosystème forestier [Termes IGN] incendie de forêt [Termes IGN] reboisement [Termes IGN] régénération (sylviculture) [Vedettes matières IGN] Sylviculture Résumé : (Auteur) En complément de la tribune de Jacques Hazera (lire page 36), la rédaction de Géomètre a souhaité interroger l’expert fores?tier sur d’autres problématiques. En premier lieu, les désastres causés par les terribles incendies de cet été, dont la propagation rapide a été favorisée par des conditions exceptionnelles. (PROPOS RECUEILLIS PAR MICHEL EPINAT Géomètre-expert honoraire et président du Cnarège) Numéro de notice : A2022-803 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102127 [article]

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Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope / V.S. Martins in Remote sensing of environment, vol 280 (October 2022)  

Public [article]  inRemote sensing of environment > vol 280 (October 2022) . - n° 113203 Titre : Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope Type de document : Article/Communication Auteurs : V.S. Martins, Auteur ; D.P. Roy, Auteur ; H. Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113203 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Afrique (géographie politique) [Termes IGN] apprentissage profond [Termes IGN] carte thématique [Termes IGN] cartographie automatique [Termes IGN] correction radiométrique [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] forêt tropicale [Termes IGN] image Landsat-OLI [Termes IGN] image PlanetScope [Termes IGN] incendie [Termes IGN] précision de la classification [Termes IGN] régression [Termes IGN] savane Résumé : (auteur) High spatial resolution commercial satellite data provide new opportunities for terrestrial monitoring. The recent availability of near-daily 3 m observations provided by the PlanetScope constellation enables mapping of small and spatially fragmented burns that are not detected at coarser spatial resolution. This study demonstrates, for the first time, the potential for automated PlanetScope 3 m burned area mapping. The PlanetScope sensors have no onboard calibration or short-wave infrared bands, and have variable overpass times, making them challenging to use for large area, automated, burned area mapping. To help overcome these issues, a U-Net deep learning algorithm was developed to classify burned areas from two-date Planetscope 3 m image pairs acquired at the same location. The deep learning approach, unlike conventional burned area mapping algorithms, is applied to image spatial subsets and not to single pixels and so incorporates spatial as well as spectral information. Deep learning requires large amounts of training data. Consequently, transfer learning was undertaken using pre-existing Landsat-8 derived burned area reference data to train the U-Net that was then refined with a smaller set of PlanetScope training data. Results across Africa considering 659 PlanetScope radiometrically normalized image pairs sensed one day apart in 2019 are presented. The U-Net was first trained with different numbers of randomly selected 256 × 256 30 m pixel patches extracted from 92 pre-existing Landsat-8 burned area reference data sets defined for 2014 and 2015. The U-Net trained with 300,000 Landsat patches provided about 13% 30 m burn omission and commission errors with respect to 65,000 independent 30 m evaluation patches. The U-Net was then refined by training on 5,000 256 × 256 3 m patches extracted from independently interpreted PlanetScope burned area reference data. Qualitatively, the refined U-Net was able to more precisely delineate 3 m burn boundaries, including the interiors of unburned areas, and better classify “faint” burned areas indicative of low combustion completeness and/or sparse burns. The refined U-Net 3 m classification accuracy was assessed with respect to 20 independently interpreted PlanetScope burned area reference data sets, composed of 339.4 million 3 m pixels, with low 12.29% commission and 12.09% omission errors. The dependency of the U-Net classification accuracy on the burned area proportion within 3 m pixel 256 × 256 patches was also examined, and patches Numéro de notice : A2022-774 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113203 Date de publication en ligne : 08/08/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101802 [article]

Investigation of recognition and classification of forest fires based on fusion color and textural features of images / Cong Li in Forests, vol 13 n° 10 (October 2022)  

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Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)  

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Exploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 36 n° 9 (September 2022)  

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Feux de forêt : un drone traque les risques de reprise / Nathalie Da Cruz in Géomètre, n° 2205 (septembre 2022)

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Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach / Quoc Bao Pham in Natural Hazards, vol 113 n° 2 (September 2022)  

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Large-area high spatial resolution albedo retrievals from remote sensing for use in assessing the impact of wildfire soot deposition on high mountain snow and ice melt / André Bertoncini in Remote sensing of environment, vol 278 (September 2022)  

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Rapid source models of the 2021 Mw 7.4 Maduo, China, earthquake inferred from high-rate BDS3/2, GPS, Galileo and GLONASS observations / Jianfei Zang in Journal of geodesy, vol 96 n° 9 (September 2022)  

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Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping / Sandro Martinis in Remote sensing of environment, vol 278 (September 2022)  

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Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM / Jiehua Cai in Engineering Geology, vol 305 (August 2022)  

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Influence of the declaration of protected natural areas on the evolution of forest fires in collective lands in Galicia (Spain) / Gervasio Lopez Rodriguez in Forests, Vol 13 n° 8 (August 2022)  

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