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How do species and data characteristics affect species distribution models and when to use environmental filtering? / Lukáš Gábor in International journal of geographical information science IJGIS, vol 34 n° 8 (August 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 8 (August 2020) . - pp 1567 - 1584 Titre : How do species and data characteristics affect species distribution models and when to use environmental filtering? Type de document : Article/Communication Auteurs : Lukáš Gábor, Auteur ; Vítězslav Moudrý, Auteur ; Vojtěch Barták, Auteur ; Vincent Lecours, Auteur Année de publication : 2020 Article en page(s) : pp 1567 - 1584 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données environnementales [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] échantillonnage (statistique) [Termes descripteurs IGN] entropie maximale [Termes descripteurs IGN] erreur d'échantillon [Termes descripteurs IGN] filtrage d'information [Termes descripteurs IGN] interaction spatiale Résumé : (auteur) Species distribution models (SDMs) are widely used in ecology and conservation. However, their performance is known to be affected by a variety of factors related to species occurrence characteristics. In this study, we used a virtual species approach to overcome the difficulties associated with testing of combined effects of those factors on performance of presence-only SDMs when using real data. We focused on the individual and combined roles of factors related to response variable (i.e. sample size, sampling bias, environmental filtering, species prevalence, and species response to environmental gradients). Results suggest that environmental filtering is not necessarily helpful and should not be performed blindly, without evidence of bias in species occurrences. The more gradual the species response to environmental gradients is, the greater is the model sensitivity to an inappropriate use of environmental filtering, although this sensitivity decreases with higher species prevalence. Results show that SDMs are affected to the greatest degree by the species response to environmental gradients, species prevalence, and sample size. Models’ accuracy decreased with sample size below 300 presences. Furthermore, a high level of interactions among individual factors was observed. Ignoring the combined effects of factors may lead to misleading outcomes and conclusions. Numéro de notice : A2020-408 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615070 date de publication en ligne : 14/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615070 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95465 [article]

Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya / Vijendra Kumar Pandey in Geocarto international, vol 35 n° 2 ([01/02/2020])  

Public [article]  inGeocarto international > vol 35 n° 2 [01/02/2020] . - pp 168 - 187 Titre : Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya Type de document : Article/Communication Auteurs : Vijendra Kumar Pandey, Auteur ; Hamid Reza Pourghasemi, Auteur ; Milap Chand Sharma, Auteur Année de publication : 2020 Article en page(s) : pp 168 - 187 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] autoroute [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] entropie maximale [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] image IRS-LISS [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] Linear Imaging Self-Scanning System [Termes descripteurs IGN] modèle statistique [Termes descripteurs IGN] mousson [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] test statistique Résumé : (Auteur) The main objective of this study to produce landslide susceptibility zones using maximum entropy (MaxEnt) and support vector machine (SVM) data-driven models along the Tipari to Ghuttu highway corridors in the Garhwal Himalaya. A landslide inventory has been prepared through field surveys and LISS-IV and Landsat 8 satellite images. The datasets of 85 landslides were categorised into training and test sets. In this study 11 landslide conditioning variables were used that are; altitude, slope angle, aspect, plan curvature, topographic wetness index, normalised difference vegetation index (NDVI), land use, soil texture, distance to rivers, distance to faults, and distance to the road. The result produced using MaxEnt and SVM model were subsequently validated using receiver operating characteristics curve (ROC) with test sets of landslide dataset. Both the models have good prediction capabilities. MaxEnt has ROC value of 0.78 while SVM has the highest prediction rate of 0.85. Numéro de notice : A2020-036 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1510038 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1510038 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94519 [article]

Combining land cover products using a minimum divergence and a Bayesian data fusion approach / Sarah Gengler in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 3-4 (March - April 2018) . - pp 806 - 826 Titre : Combining land cover products using a minimum divergence and a Bayesian data fusion approach Type de document : Article/Communication Auteurs : Sarah Gengler, Auteur ; Patrick Bogaert, Auteur Année de publication : 2018 Article en page(s) : pp 806 - 826 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] Belgique [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] distance de Kullback-Leibler [Termes descripteurs IGN] entropie maximale [Termes descripteurs IGN] entropie relative [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] source de données Résumé : (Auteur) Land cover mapping plays an important role for a wide spectrum of applications that are ranging from climate modeling to food security. However, it is a common case that several and partially conflicting land cover products are available at the same time over a same area, where each product suffers from specific limitations and lack of accuracy. In order to take advantage of the best features of each product while at the same time attenuating their respective weaknesses, this paper is proposing a methodology that allows the user to combine these products together based on a general framework involving maximum entropy/minimum divergence principles, Bayesian data fusion and Bayesian updating. First, information brought by each land cover product is coded in terms of inequality constraints so that a first estimation of their quality can be computed based on a maximum entropy/minimum divergence principle. Information from these various land cover products can then be fused afterwards in a Bayesian framework, leading to a single map with an associated measure of uncertainty. Finally, it is shown how the additional information brought by control data can help improving this fused map through a Bayesian updating procedure. The first part of the paper is briefly presenting the most important theoretical results, while the second part is illustrating the use of this suggested approach for a specific area in Belgium, where five different land cover products are at hand. The benefits and limitations of this approach are finally discussed by the light of the results for this case study. Numéro de notice : A2018-045 Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1413577 En ligne : https://doi.org/10.1080/13658816.2017.1413577 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89267 [article]

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Development and Comparison of Species Distribution Models for Forest Inventories / Óscar Rodríguez de Rivera in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)  

Public [article]  inISPRS International journal of geo-information > vol 6 n° 6 (June 2017) Titre : Development and Comparison of Species Distribution Models for Forest Inventories Type de document : Article/Communication Auteurs : Óscar Rodríguez de Rivera, Auteur ; Antonio López-Quílez, Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] arbre (flore) [Termes descripteurs IGN] classification et arbre de régression [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] entropie maximale [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] modèle mathématique [Termes descripteurs IGN] régression multivariée par spline adaptative [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) A comparison of several statistical techniques common in species distribution modeling was developed during this study to evaluate and obtain the statistical model most accurate to predict the distribution of different forest tree species (in our case presence/absence data) according environmental variables. During the process we have developed maximum entropy (MaxEnt), classification and regression trees (CART), multivariate adaptive regression splines (MARS), showing the statistical basis of each model and, at the same time, we have developed a specific additive model to compare and validate their capability. To compare different results, the area under the receiver operating characteristic (ROC) function (AUC) was used. Every AUC value obtained with those models is significant and all of the models could be useful to represent the distribution of each species. Moreover, the additive model with thin plate splines gave the best results. The worst capability was obtained with MARS. This model’s performance was below average for several species. The additive model developed obtained better results because it allowed for changes and calibrations. In this case we were aware of all of the processes that occurred during the modeling. By contrast, models obtained using specific software, in general, perform like “hermetic machines”, because it could sometimes be impossible to understand the stages that led to the final results. Numéro de notice : A2017-810 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi6060176 En ligne : https://doi.org/10.3390/ijgi6060176 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89250 [article]

Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants / Fangyuan Yu in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 1-2 (January - February 2017) . - pp 190 - 212 Titre : Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants Type de document : Article/Communication Auteurs : Fangyuan Yu, Auteur ; Thomas A. Groen, Auteur ; Tiejun Wang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 190 - 212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] aire de répartition [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] Chine [Termes descripteurs IGN] climat de montagne [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] entropie maximale [Termes descripteurs IGN] région [Termes descripteurs IGN] rhododendron (genre) [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Understanding the environmental factors determining the distribution of species with different range sizes can provide valuable insights for evolutionary ecology and conservation biology in the face of expected climate change. However, little is known about what determines the variation in geographical and elevational ranges of alpine and subalpine plant species. Here, we examined the relationship between geographical and elevational range sizes for 80 endemic rhododendron species in China using Spearman’s rank-order correlation. We ran the species distribution model – maximum entropy modelling (MaxEnt) – with 27 environmental variables. The importance of each variable to the model prediction was compared for species groups with different geographical and elevational range sizes. Our results showed that the correlation between geographical and elevational range sizes of rhododendron species was not significant. Climate-related variables were found to be the most important factors in shaping the distributional ranges of alpine and subalpine plant species across China. Species with geographically and elevationally narrow ranges had distinct niche requirements. For geographical ranges, the narrow-ranged species showed less tolerance to niche conditions than the wide-ranged species. For elevational ranges, compared with the wide-ranged species, the narrow-ranged species showed an equivalent niche breadth, but occurred at different niche position along the environmental gradient. Our findings suggest that over large spatial extents the elevational range size can be a complementary trait of alpine and subalpine plant species to geographical range size. Climatic niche breadth, especially the range of seasonal variability, can explain species’ geographical range sizes. Changes in climate may influence the distribution of rhododendrons, with the effects likely being felt most by species with either a narrow geographical or narrow elevational range. Numéro de notice : A2017-031 Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1195502 En ligne : http://dx.doi.org/10.1080/13658816.2016.1195502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84024 [article]

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Introduction to Artificial Intelligence / Wolfgang Ertel (2017)  

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An entropy-based multispectral image classification algorithm / Di Long in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)  

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