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Forest fire susceptibility assessment using Google Earth engine in Gangwon-do, Republic of Korea / Yong Piao in Geomatics, Natural Hazards and Risk, vol 13 (2022)
[article]
Titre : Forest fire susceptibility assessment using Google Earth engine in Gangwon-do, Republic of Korea Type de document : Article/Communication Auteurs : Yong Piao, Auteur ; Dongkun Lee, Auteur ; Sangjin Park, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 432 - 450 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aléa
[Termes IGN] cartographie des risques
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Corée du sud
[Termes IGN] Google Earth Engine
[Termes IGN] incendie de forêt
[Termes IGN] pente
[Termes IGN] risque naturel
[Termes IGN] vulnérabilitéRésumé : (auteur) Forest fires are one of the most frequently occurring natural hazards, causing substantial economic loss and destruction of forest cover. As the Gangwon-do region in Korea has abundant forest resources and ecological diversity as Korea's largest forest area, spatial data on forest fire susceptibility of the region are urgently required. In this study, a forest fire susceptibility map (FFSM) of Gangwon-do was constructed using Google Earth Engine (GEE) and three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Trees (BRT). The factors related to climate, topography, hydrology, and human activity were constructed. To verify the accuracy, the area under the receiver operating characteristic curve (AUC) was used. The AUC values were 0.846 (BRT), 0.835 (RF), 0.751 (CART). Factor importance analysis was performed to identify the important factors of the occurrence of forest fires in Gangwon-do. The results show that the most important factor in the Gangwon-do region is slope. A slope of approximately 17° (moderately steep) has a considerable impact on the occurrence of forest fires. Human activity and interference are the other important factors that affect forest fires. The established FFSM can support future efforts on forest resource protection and environmental management planning in Gangwon-do. Numéro de notice : A2022-445 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2022.2030808 Date de publication en ligne : 02/02/2022 En ligne : https://doi.org/10.1080/19475705.2022.2030808 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99942
in Geomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 432 - 450[article]
Titre : Forêts et changement climatique : comprendre et modéliser le fonctionnement hydrique des arbres Type de document : Monographie Auteurs : François Courbet, Éditeur scientifique ; Claude Doussan, Éditeur scientifique ; Jean-Marc Limousin, Éditeur scientifique ; et al., Auteur Editeur : Versailles : Quae Année de publication : 2022 Collection : Synthèses, ISSN 1777-4624 Importance : 144 p. ISBN/ISSN/EAN : 978-2-7592-3457-8 Note générale : Glossaire et bibliographie Langues : Français (fre) Descripteur : [Termes IGN] arbre (flore)
[Termes IGN] bilan hydrique
[Termes IGN] changement climatique
[Termes IGN] écophysiologie
[Termes IGN] foresterie
[Termes IGN] humidité du sol
[Termes IGN] indicateur hydrographique
[Termes IGN] risque naturel
[Termes IGN] sécheresse
[Termes IGN] stress hydrique
[Termes IGN] vulnérabilité
[Vedettes matières IGN] Végétation et changement climatiqueIndex. décimale : 48.30 Végétation et changement climatique Résumé : (Editeur) Face au changement climatique, le risque de sécheresse est un risque majeur auquel les forêts sont confrontées. L'ouvrage présente les connaissances de bases du fonctionnement hydrique des arbres, les indicateurs observables des effets de la sécheresse et les modèles capables de simuler le fonctionnement des forêts en fonction du climat et de son évolution. Note de contenu :
Introduction
Chapitre 1. Fonctionnement hydrique des arbres forestiers
- Les voies de transfert de l’eau dans le système sol-arbre-atmosphère
- L’eau dans le sol
- L’eau dans l’arbre
- Transfert de l’eau : concepts et formalisation
- Le fonctionnement d’un arbre en cas de sécheresse
- Les paramètres de la vulnérabilité à la sécheresse
- Les indicateurs des effets de la sécheresse
- Récapitulatif
Chapitre 2. Bilan hydrique et modèles : des outils au service des chercheurs et des praticiens
- Caractériser le niveau de sécheresse subie par les arbres : le bilan hydrique
- Des modèles intégrateurs des connaissances sur le fonctionnement des arbres
- Conclusion
Chapitre 3. Fiches variables écophysiologiques
- Le potentiel hydrique
- Flux hydrique
- Conductance et conductivité
- Efficience d’utilisation de l’eau (WUE pour Water Use Efficiency)
- Rapports isotopiques
- Cavitation
Chapitre 4. Processus et paramètres impliqués dans les modèles de fonctionnement de la végétation
- Principaux processus impliqués dans les modèles de fonctionnement
- Principaux paramètres et variables impliqués dans les modèles de fonctionnement
Chapitre 5. Fiches modèles fonctionnels
- Biljou
- CASTANEA
- CONTINUUM
- GO+
- ISBA
- MAIDEN
- MuSICA
- NOTG
- ORCHIDÉE
- PHENOFIT4
- RReShar
- SAMSARA2
- SIERRA
- SurEauNuméro de notice : 26950 Affiliation des auteurs : non IGN Thématique : FORET Nature : Monographie nature-HAL : OuvrScient DOI : 10.17528/cifor/008565 En ligne : https://dx.doi.org/10.17528/cifor/008565 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102377 A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)
[article]
Titre : A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods Type de document : Article/Communication Auteurs : Pengxiang Zhao, Auteur ; Zohreh Masoumi, Auteur ; Maryam Kalantari, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] cartographie des risques
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] effondrement de terrain
[Termes IGN] Iran
[Termes IGN] modèle numérique de surface
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] risque naturel
[Termes IGN] système d'information géographiqueRésumé : (auteur) Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management. Numéro de notice : A2022-056 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/rs14010211 Date de publication en ligne : 04/01/2022 En ligne : https://doi.org/10.3390/rs14010211 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99459
in Remote sensing > vol 14 n° 1 (January-1 2022) . - n° 211[article]Investigating the role of wind disturbance in tropical forests through a forest dynamics model and satellite observations / E-Ping Rau (2022)
Titre : Investigating the role of wind disturbance in tropical forests through a forest dynamics model and satellite observations Type de document : Thèse/HDR Auteurs : E-Ping Rau, Auteur ; Jérôme Chave, Directeur de thèse Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2022 Importance : 184 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse 3 Paul SabatierLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse forestière
[Termes IGN] canopée
[Termes IGN] chablis (sylviculture)
[Termes IGN] cyclone
[Termes IGN] forêt tropicale
[Termes IGN] Guyane française
[Termes IGN] image Sentinel-SAR
[Termes IGN] modèle dynamique
[Termes IGN] perturbation écologique
[Termes IGN] précipitation
[Termes IGN] risque naturel
[Termes IGN] sécheresse
[Termes IGN] traitement d'image radar
[Termes IGN] ventIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Natural disturbances have an important influence on the structure, composition and functioning of tropical forests and a role in the regulation of biogeochemical cycles. The frequency and intensity of natural disturbances are modified by climate change: a better knowledge of their mechanism of action is necessary to predict the consequences of this modification. Modeling allows us to evaluate the role of each of the ecological processes and their link with environmental factors. Remote sensing tools inform us about the structure and functioning of forests at large scales, and can be useful for the calibration and validation of vegetation models. In this thesis, I employed both approaches to examine how tropical forests are shaped by natural disturbances, particularly wind, which is a major disturbance factor in many tropical regions. First, I evaluated the transferability of a spatially explicit, individual-based model via sensitivity testing and calibration of global parameters. The model correctly predicts forest structure at two contrasting sites, and its response is consistent with variations in climate forcing. Calibration of a small number of key parameters was required, including the parameter controlling mortality and crown allometry. To investigate the sensitivity of the model to mortality, I implemented a wind damage module based on biophysical principles and coupled with wind speed to model forest responses to extreme wind events. With increasing disturbance level, canopy height decreased steadily but biomass showed a non-linear response. Wind intensity had a strong impact on canopy height and biomass, but not the frequency of extreme wind events. Finally, I tested whether radar data from Sentinel-1 satellites could be used to detect gaps due to natural disturbances in French Guiana. The Sentinel-1 data detected more natural gaps above 0.2 ha than the optical satellite data, and they showed a spatial pattern consistent with the optical images. The level of disturbance did not vary with altitude. We found more disturbance during dry seasons, which could be due to the delayed response of precipitation rather than the direct response of drought. In conclusion, this thesis demonstrates that the integration between modeling and remote sensing sheds light on the effects of natural disturbances on tropical forests. The resulting results can be used to study other types of disturbances and their interactions on a large scale. Note de contenu : General introduction
General methods
1: Transferability of an individual- and trait-based forest dynamics model: a test case across the tropics
1.1 Abstract
1.2 Introduction
1.3 Materials and methods
1.4 Results
1.5 Discussion
1.6 Acknowledgements and author contributions
1.7 Supplementary data
2: Wind speed controls forest structure in subtropical forests exposed to cyclones: a case study using an individual-based model
2.1 Abstract
2.2 Introduction
2.3 Material and methods
2.4 Results
2.5 Discussion
2.6 Acknowledgments and author contributions
2.7 Supplementary data
3: Detecting Natural Disturbances in Tropical Forests Using Sentinel-1 SAR Data: a Test in French Guiana
3.1 Abstract
3.2 Introduction
3.3 Methods
3.4 Results
3.5 Discussions
3.6 Acknowledgments and author contributions
3.7 Supplementary data
General discussion and conclusionsNuméro de notice : 26836 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Ecologie, biodiversité et évolution : Toulouse 3 : 2022 nature-HAL : Thèse DOI : sans Date de publication en ligne : 20/06/2022 En ligne : https://tel.hal.science/tel-03699667 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101075 Mapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 Imagery from Google Earth Engine / Jiyu Liu in Geomatics, Natural Hazards and Risk, vol 13 (2022)
[article]
Titre : Mapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 Imagery from Google Earth Engine Type de document : Article/Communication Auteurs : Jiyu Liu, Auteur ; David Freudenberger, Auteur ; Lim Samsung, Auteur Année de publication : 2022 Article en page(s) : pp 1867 - 1897 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse spectrale
[Termes IGN] approche hiérarchique
[Termes IGN] Australie
[Termes IGN] carte thématique
[Termes IGN] écosystème forestier
[Termes IGN] Google Earth Engine
[Termes IGN] image infrarouge
[Termes IGN] image Landsat-8
[Termes IGN] incendie
[Termes IGN] Indien (océan)
[Termes IGN] segmentation d'image
[Termes IGN] utilisation du sol
[Termes IGN] zone sinistréeRésumé : (auteur) In Australia, fire has become part of the natural ecosystem. Severe fires have devastated Australia's unique forest ecosystems due to the global climate change. In this study, we integrated a multi-resolution segmentation method and a hierarchical classification framework based on expert-based knowledge to classify the burned areas and land-uses in Kangaroo Island, South Australia. Using an object-based image classification framework that combines colour and shape features from input layers, we demonstrated that the objects segmented from the multi-source data lead to a higher accuracy in classification with an overall accuracy of 90.2% and a kappa coefficient of 85.2%. On the other hand, the single source data from post-fire Landsat-8 imagery showed an overall accuracy of 87.4% which is also statistically acceptable. According to our experiment results, more than 30.44% of the study area was burned during the 2019–2020 ‘Black-Summer’ fire season in Australia. Among the burned areas, high severity accounted for 12.14%, moderate severity for 11.48%, while low severity was 6.82%. For unburned areas, farmland accounted for 45.52% of the study area, of which about one-third was affected by the disturbances other than fire. The remaining area consists of 19.42% unaffected forest, 3.48% building and bare land, and 1.14% water. The comparison analysis shows that our object-based image classification framework takes full advantage of the multi-source data and generates the edges of burned areas more clearly, which contributes to the improved fire management and control. Numéro de notice : A2022-873 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/19475705.2022.2098066 Date de publication en ligne : 02/08/2022 En ligne : https://doi.org/10.1080/19475705.2022.2098066 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102171
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