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An ecological approach to climate change-informed tree species selection for reforestation / William H. MacKenzie in Forest ecology and management, vol 481 (February 2021)
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Titre : An ecological approach to climate change-informed tree species selection for reforestation Type de document : Article/Communication Auteurs : William H. MacKenzie, Auteur ; Colin R. Mahony, Auteur Année de publication : 2021 Article en page(s) : n° 118705 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] altitude
[Termes IGN] bioclimatologie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Colombie-Britannique (Canada)
[Termes IGN] écosystème forestier
[Termes IGN] facteur édaphique
[Termes IGN] reboisement
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Accounting for climate change in reforestation practices has the potential to be one of the most efficacious adaptation strategies for maintaining future forest ecosystem services. There is a rich literature projecting spatial shifts in climatic suitability for tree species and strong scientific evidence for the necessity of assisted migration. However, there has been limited translation of this research into operational reforestation, due in part to mismatches to the information needs of practitioners. Here, we describe a practitioner-focused climate change informed tree species selection (CCISS) model to support reforestation decisions in British Columbia (BC). CCISS projects the climate change redistribution of bioclimate units from the multi-scaled Biogeoclimatic Ecosystem Classification (BEC) system with machine-learning for 90 modelled futures. It leverages the reforestation knowledge from BEC to make site-specific species projections of reforestation feasibility with climate change uncertainty metrics. We present 21st-century feasibility projections for a comprehensive set of tree species native to western North America. Some general trends are evident: augmentation of the number of feasible species in sub-boreal regions due to the rapid expansion of feasibility for temperate species; attrition at low elevations in southern BC due to declines in the feasibility of native species with little compensation by non-native species; and turnover at mid-elevations as declining feasibility for subalpine species is compensated by uphill expansion of climatic feasibility for submontane species. Edaphic (soil) factors are important; feasibility declines are higher on relatively dry sites than on wetter sites for most species. Our analysis emphasizes that changes in feasibility are species-specific, spatially variable, and influenced by edaphic site factors. By employing the multi-scaled BEC system that currently informs operational reforestation, CCISS facilitates translation of research into actionable guidance for practitioners. Numéro de notice : A2021-226 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2020.118705 Date de publication en ligne : 01/11/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118705 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97203
in Forest ecology and management > vol 481 (February 2021) . - n° 118705[article]An improved rainfall-threshold approach for robust prediction and warning of flood and flash flood hazards / Geraldo Moura Ramos Filho in Natural Hazards, Vol 105 n° 3 (February 2021)
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Titre : An improved rainfall-threshold approach for robust prediction and warning of flood and flash flood hazards Type de document : Article/Communication Auteurs : Geraldo Moura Ramos Filho, Auteur ; Victor Hugo Rabelo Coelho, Auteur ; Emerson da Silva Freitas, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2409 - 2429 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] crue
[Termes IGN] Indice de précipitations antérieures
[Termes IGN] indice de risque
[Termes IGN] inondation
[Termes IGN] méthode robuste
[Termes IGN] prévention des risques
[Termes IGN] risque naturel
[Termes IGN] Sao Paulo
[Termes IGN] seuillage
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) This paper presents an improved method of using threshold of peak rainfall intensity for robust flood/flash flood evaluation and warnings in the state of São Paulo, Brazil. The improvements involve the use of two tolerance levels and the delineating of an intermediate threshold by incorporating an exponential curve that relates rainfall intensity and Antecedent Precipitation Index (API). The application of the tolerance levels presents an average increase of 14% in the Probability of Detection (POD) of flood and flash flood occurrences above the upper threshold. Moreover, a considerable exclusion (63%) of non-occurrences of floods and flash floods in between the two thresholds significantly reduce the number of false alarms. The intermediate threshold using the exponential curves also exhibits improvements for almost all time steps of both hydrological hazards, with the best results found for floods correlating 8-h peak intensity and 8 days API, with POD and Positive Predictive Value (PPV) values equal to 81% and 82%, respectively. This study provides strong indications that the new proposed rainfall threshold-based approach can help reduce the uncertainties in predicting the occurrences of floods and flash floods. Numéro de notice : A2020-204 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04405-x Date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.1007/s11069-020-04405-x Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97167
in Natural Hazards > Vol 105 n° 3 (February 2021) . - pp 2409 - 2429[article]A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping / Zhice Fang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
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Titre : A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Zhice Fang, Auteur ; Yi Wang, Auteur ; Ling Peng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 321 - 347 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] effondrement de terrain
[Termes IGN] géomorphologie locale
[Termes IGN] pondération
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal récurrent
[Termes IGN] risque naturelRésumé : (auteur) This study introduces four heterogeneous ensemble-learning techniques, that is, stacking, blending, simple averaging, and weighted averaging, to predict landslide susceptibility in Yanshan County, China. These techniques combine several state-of-the-art classifiers of convolutional neural network, recurrent neural network, support vector machine, and logistic regression in specific ways to produce reliable results and avoid problems with the model selection. The study consists of three main steps. The first step establishes a spatial database consisting of 16 landslide conditioning factors and 380 historical landslide locations. The second step randomly selects training (70% of the total) and test (30%) datasets out of grid cells corresponding to landslide and non-slide locations in the study area. The final step constructs the proposed heterogeneous ensemble-learning methods for landslide susceptibility mapping. The proposed ensemble-learning methods show higher prediction accuracy than the individual classifiers mentioned above based on statistical measures. The blending ensemble-learning method achieves the highest overall accuracy of 80.70% compared to the other ensemble-learning methods. Numéro de notice : A2021-028 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808897 Date de publication en ligne : 15/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808897 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96704
in International journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 321 - 347[article]Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control / Adolfo Lozano-Tello in European journal of remote sensing, vol 54 n° 1 (2021)
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Titre : Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control Type de document : Article/Communication Auteurs : Adolfo Lozano-Tello, Auteur ; Marcos Fernández-Sellers, Auteur ; Elia Quirós, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1 - 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification pixellaire
[Termes IGN] Estrémadure (Espagne)
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] politique agricole commune
[Termes IGN] réseau neuronal artificiel
[Termes IGN] surface cultivée
[Termes IGN] surveillance agricoleRésumé : (auteur) The early and automatic identification of crops declared by farmers is essential for streamlining European Union Common Agricultural Policy (CAP) payment processes. Currently, field inspections are partial, expensive and entail a considerable delay in the process. Chronological satellite images of cultivated plots can be used so that neural networks can form the model of the declared crop. Once the patterns of a crop are obtained, the correspondence of the declaration with the model of the neural network can be systematically predicted, and can be used for monitoring the CAP. In this article, we propose a learning model with neural networks, using as examples of training the pixels of the cultivated plots from the satellite images over a period of time. We also propose using several years in the training model to generalise the patterns without linking them to the climatic characteristics of a specific year. The article also describes the use of the model in learning the multi-year pattern of tobacco cultivation with very good results. Numéro de notice : A2021-138 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1858723 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/22797254.2020.1858723 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97012
in European journal of remote sensing > vol 54 n° 1 (2021) . - pp 1 - 12[article]Detection of pictorial map objects with convolutional neural networks / Raimund Schnürer in Cartographic journal (the), vol 58 n° 1 (February 2021)
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Titre : Detection of pictorial map objects with convolutional neural networks Type de document : Article/Communication Auteurs : Raimund Schnürer, Auteur ; René Sieber, Auteur ; Jost Schmid-Lanter, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 50 - 68 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] base de données d'images
[Termes IGN] bibliothèque numérique
[Termes IGN] carte ancienne
[Termes IGN] carte numérique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] objet cartographique
[Termes IGN] pictogrammeRésumé : (auteur) In this work, realistically drawn objects are identified on digital maps by convolutional neural networks. For the first two experiments, 6200 images were retrieved from Pinterest. While alternating image input options, two binary classifiers based on Xception and InceptionResNetV2 were trained to separate maps and pictorial maps. Results showed that the accuracy is 95–97% to distinguish maps from other images, whereas maps with pictorial objects are correctly classified at rates of 87–92%. For a third experiment, bounding boxes of 3200 sailing ships were annotated in historic maps from different digital libraries. Faster R-CNN and RetinaNet were compared to determine the box coordinates, while adjusting anchor scales and examining configurations for small objects. A resulting average precision of 32% was obtained for Faster R-CNN and of 36% for RetinaNet. Research outcomes are relevant for trawling map images on the Internet and for enhancing the advanced search of digital map catalogues. Numéro de notice : A2021-651 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2020.1738112 Date de publication en ligne : 11/09/2020 En ligne : https://doi.org/10.1080/00087041.2020.1738112 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98381
in Cartographic journal (the) > vol 58 n° 1 (February 2021) . - pp 50 - 68[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 030-2021011 RAB Revue Centre de documentation En réserve L003 Disponible Fully convolutional neural network for impervious surface segmentation in mixed urban environment / Joseph McGlinchy in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)
PermalinkGeographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling / Stefanos Georganos in Geocarto international, vol 36 n° 2 ([01/02/2021])
PermalinkGTP-PNet: A residual learning network based on gradient transformation prior for pansharpening / Hao Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
PermalinkA heuristic approach to the generalization of complex building groups in urban villages / Wenhao Yu in Geocarto international, vol 36 n° 2 ([01/02/2021])
PermalinkIdentifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena Moll in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
PermalinkImproving trajectory estimation using 3D city models and kinematic point clouds / Lucas Lucks in Transactions in GIS, Vol 25 n° 1 (February 2021)
PermalinkLand cover harmonization using Latent Dirichlet Allocation / Zhan Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
PermalinkMultiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification / Zitong Wu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
PermalinkRoom semantics inference using random forest and relational graph convolutional networks: A case study of research building / Xuke Hu in Transactions in GIS, Vol 25 n° 1 (February 2021)
PermalinkA simplified ICA-based local similarity stereo matching / Suting Chen in The Visual Computer, vol 37 n° 2 (February 2021)
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