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Geo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan / Muhammad Imran in Geocarto international, vol 36 n° 2 ([01/02/2021])
[article]
Titre : Geo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan Type de document : Article/Communication Auteurs : Muhammad Imran, Auteur ; Yasra Hamid, Auteur ; Abeer Mazher, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 197 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] cartographie des risques
[Termes IGN] diptère
[Termes IGN] image Landsat
[Termes IGN] maladie tropicale
[Termes IGN] modélisation spatiale
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Pakistan
[Termes IGN] régression géographiquement pondérée
[Termes IGN] régression logistique
[Termes IGN] risque sanitaire
[Termes IGN] série temporelle
[Termes IGN] zone intertropicale
[Termes IGN] zone urbaineRésumé : (auteur) The study objective is to predict the epidemiological impact of dengue fever arbovirosis in urban tropical areas of Pakistan. To do so, we used the GPS-based data of the Aedes larvae collected during 2014–2015 in Lahore. We developed a Geographically Weighted Logistic Regression (GWLR) model for Geospatially predicting larvae presence or absence in Lahore. Data on rainfall, temperature are included along with time series of the normalized difference vegetation index (NDVI) derived from Landsat imagery. We observed a high spatial variability of the GWLR parameter estimates of these variables in the study area. The GWLR model significantly (R2a = 0.78) explained the presence or absence of Aedes larvae with temperature, rainfall and NDVI variables in South and Southeast of the study area. In the North and North-West, however, GWLR relationships were observed weak in highly populated areas. Interpolating GWLR coefficients generate more accurate maps of Aedes larvae presence or absence. Numéro de notice : A2021-474 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1614100 Date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1080/10106049.2019.1614100 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96932
in Geocarto international > vol 36 n° 2 [01/02/2021] . - pp 197 - 211[article]Using Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas / José P. Granadeiro in Remote sensing, Vol 13 n° 2 (January-2 2021)
[article]
Titre : Using Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas Type de document : Article/Communication Auteurs : José P. Granadeiro, Auteur ; João Belo, Auteur ; Mohamed Henriques, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 320 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bathymétrie
[Termes IGN] carte bathymétrique
[Termes IGN] écosystème
[Termes IGN] estran
[Termes IGN] Guinée-Bissao
[Termes IGN] habitat (nature)
[Termes IGN] image Sentinel-MSI
[Termes IGN] modèle numérique de surface
[Termes IGN] régression logistique
[Termes IGN] topographie localeRésumé : (auteur) Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation. Numéro de notice : A2021-197 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article DOI : 10.3390/rs13020320 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/rs13020320 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97148
in Remote sensing > Vol 13 n° 2 (January-2 2021) . - n° 320[article]Evaluation of a neural network with uncertainty for detection of ice and water in SAR imagery / Nazanin Asadi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
[article]
Titre : Evaluation of a neural network with uncertainty for detection of ice and water in SAR imagery Type de document : Article/Communication Auteurs : Nazanin Asadi, Auteur ; K. Andrea Scott, Auteur ; Alexander S. Komarov, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 247 - 259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] assimilation des données
[Termes IGN] classification pixellaire
[Termes IGN] glace de mer
[Termes IGN] image radar moirée
[Termes IGN] incertitude des données
[Termes IGN] modèle d'incertitude
[Termes IGN] Perceptron multicouche
[Termes IGN] pondération
[Termes IGN] précision de la classification
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Synthetic aperture radar (SAR) sea ice imagery is a promising source of data for sea ice data assimilation. Classification of SAR sea ice imagery into ice and water is of particular relevance due to its relationship with ice concentration, a key variable in sea ice data assimilation systems. With increasing volumes of SAR data, automated methods to carry out these classifications are of particular importance. Although several automated approaches have been proposed, none look at the impact of including an estimate of uncertainty of the model parameters and input features on the classification output. This article uses an established database of SAR image features to train a multilayer perceptron (MLP) neural network to classify pixel locations as either ice, water, or unknown. The classification accuracies are benchmarked using a recently developed logistic regression approach for the same database. The two methods are found to be comparable. The MLP approach is then enhanced to allow uncertainty to be estimated at each pixel location. Following methods proposed in the deep learning community, two kinds of uncertainty are considered. The first, epistemic uncertainty, is that due to uncertainty in the MLP weights. The second kind of uncertainty, aleatoric uncertainty, is that which cannot be explained by the model, and is therefore associated with the input data. It is found that including these uncertainties in the MLP models reduces their accuracies slightly, but also reduces misclassification rates. This is of particular importance for data assimilation applications, where misclassifications could severely degrade the analysis. Numéro de notice : A2021-033 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992454 Date de publication en ligne : 09/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992454 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96735
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 247 - 259[article]Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)
[article]
Titre : Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy Type de document : Article/Communication Auteurs : Vasil Yordanov, Auteur ; Maria Antonia Brovelli, Auteur Année de publication : 2020 Article en page(s) : 23 p. 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] cartographie géomorphologique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] effondrement de terrain
[Termes IGN] figuré linéaire
[Termes IGN] indice de risque
[Termes IGN] inventaire
[Termes IGN] Lombardie
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] modèle statistique
[Termes IGN] régression logistiqueRésumé : (auteur) Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations. Numéro de notice : A2020-695 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00344-1 Date de publication en ligne : 09/11/2020 En ligne : https://doi.org/10.1007/s12518-020-00344-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96244
in Applied geomatics > vol 12 n° 4 (December 2020) . - 23 p.[article]Stand-level mortality models for Nordic boreal forests / Jouni Siipilehto in Silva fennica, vol 54 n° 5 (December 2020)
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Titre : Stand-level mortality models for Nordic boreal forests Type de document : Article/Communication Auteurs : Jouni Siipilehto, Auteur ; Micky Allen, Auteur ; Urban Nilsson, Auteur ; Andreas Brunner, Auteur ; et al., Auteur ; Urban Nilsson Année de publication : 2020 Article en page(s) : n° 10414 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] âge du peuplement forestier
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] modélisation de la forêt
[Termes IGN] mortalité
[Termes IGN] Norvège
[Termes IGN] régression logistique
[Termes IGN] Suède
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) New mortality models were developed for the purpose of improving long-term growth and yield simulations in Finland, Norway, and Sweden and were based on permanent national forest inventory plots from Sweden and Norway. Mortality was modelled in two steps. The first model predicts the probability of survival, while the second model predicts the proportion of basal area in surviving trees for plots where mortality has occurred. In both models, the logistic function was used. The models incorporate the variation in prediction period length and in plot size. Validation of both models indicated unbiased mortality rates with respect to various stand characteristics such as stand density, average tree diameter, stand age, and the proportion of different tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), and broadleaves. When testing against an independent dataset of unmanaged spruce-dominated stands in Finland, the models provided unbiased prediction with respect to stand age. Numéro de notice : A2020-854 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.14214/sf.10414 Date de publication en ligne : 01/12/2020 En ligne : https://doi.org/10.14214/sf.10414 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98710
in Silva fennica > vol 54 n° 5 (December 2020) . - n° 10414[article]Tree species classification using structural features derived from terrestrial laser scanning / Louise Terryn in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkIndividual tree detection and classification for mapping pine wilt disease using multispectral and visible color imagery acquired from unmanned aerial vehicle / Takeshi Hoshikawa in Journal of The Remote Sensing Society of Japan, vol 40 n° 1 (2020)PermalinkPermalinkA representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena / Guiming Zhang in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)PermalinkAnalyzing the recent dynamics of wildland fires in Quercus suber L. woodlands in Sardinia (Italy), Corsica (France) and Catalonia (Spain) / Michele Salis in European Journal of Forest Research, vol 138 n° 3 (June 2019)PermalinkImproving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco / Hassane Rahali in Geocarto international, vol 34 n° 1 ([01/01/2019])PermalinkCAVIAR: an R package for checking, displaying and processing wood-formation-monitoring data / Cyrille B.K. Rathgeber in Tree Physiology, vol 38 n° 8 (August 2018)PermalinkPermalinkModeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules / Yongjiu Feng in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkA GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping / Wei Chen in Geocarto international, vol 32 n° 4 (April 2017)Permalink