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Beyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? / Arthur Sanguet in Global ecology and conservation, vol 39 (November 2022)
[article]
Titre : Beyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? Type de document : Article/Communication Auteurs : Arthur Sanguet, Auteur ; Nicolas Wyler, Auteur ; Blaise Petitpierre, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° e02286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte d'occupation du sol
[Termes IGN] changement climatique
[Termes IGN] distribution spatiale
[Termes IGN] échantillonnage de données
[Termes IGN] habitat (nature)
[Termes IGN] modèle de simulation
[Termes IGN] montagne
[Termes IGN] pédologie locale
[Termes IGN] Suisse
[Termes IGN] télédétection
[Termes IGN] topographie locale
[Termes IGN] zone humide
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) Species Distribution Models (SDM) represent a powerful tool to predict species’ habitat suitability on a landscape and fill the gap between truncated observation data and all possible locations. SDMs have been widely used in theoretical studies of species niches as well as in conservation applications. Here, we evaluated the impacts of predictors’ type on models’ performances and spatial predictions using 72 plant species belonging to six ecological groups at a regional scale in the area of Geneva (Switzerland). Twelve models were created using various combinations of high-resolution (25 m) explanatory variables including topography, pedology, climate, habitats and remote sensing data. Models integrating a combination of habitats and topopedo-climatic predictors had significantly higher performances, while remote sensing predictors showed low performances. Our results suggest that the number and the level of details of habitat predictors (broad or very precise) do not fundamentally affect prediction maps. However, selecting too few, overly simplified or exceedingly complex habitat predictors tend to lower models’ performances. The use of eight habitat categories complemented with eight topopedo-climatic predictors produced models with the highest performances. Ecological groups of species responded differently to models and while alpine and ruderal species have greater average performances due to a high affinity with topopedo-climatic predictors, wetlands’ species were less performant on average. These results underline the necessity of developing or having access to habitats distribution data especially in a conservation context. Numéro de notice : A2022-815 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1016/j.gecco.2022.e02286 Date de publication en ligne : 13/09/2022 En ligne : https://doi.org/10.1016/j.gecco.2022.e02286 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101977
in Global ecology and conservation > vol 39 (November 2022) . - n° e02286[article]Integrating Bayesian networks to forecast sea-level rise impacts on barrier island characteristics and habitat availability / Benjamin T. Gutierrez in Earth and space science, vol 9 n° 11 (November 2022)
[article]
Titre : Integrating Bayesian networks to forecast sea-level rise impacts on barrier island characteristics and habitat availability Type de document : Article/Communication Auteurs : Benjamin T. Gutierrez, Auteur ; Sarah Zeigler, Auteur ; Erika Lentz, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de sensibilité
[Termes IGN] changement climatique
[Termes IGN] géomorphologie
[Termes IGN] habitat animal
[Termes IGN] île
[Termes IGN] modèle de simulation
[Termes IGN] montée du niveau de la mer
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] planification côtière
[Termes IGN] réseau bayesien
[Termes IGN] submersion marine
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côteRésumé : (auteur) Evaluation of sea-level rise (SLR) impacts on coastal landforms and habitats is a persistent need for informing coastal planning and management, including policy decisions, particularly those that balance human interests and habitat protection throughout the coastal zone. Bayesian networks (BNs) are used to model barrier island change under different SLR scenarios that are relevant to management and policy decisions. BNs utilized here include a shoreline change model and two models of barrier island biogeomorphological evolution at different scales (50 and 5 m). These BNs were then linked to another BN to predict habitat availability for piping plovers (Charadrius melodus), a threatened shorebird reliant on beach habitats. We evaluated the performance of the two linked geomorphology BNs and further examined error rates by generating hindcasts of barrier island geomorphology and habitat availability for 2014 conditions. Geomorphology hindcasts revealed that model error declined with a greater number of known inputs, with error rates reaching 55% when multiple outputs were hindcast simultaneously. We also found that, although error in predictions of piping plover nest presence/absence increased when outputs from the geomorphology BNs were used as inputs in the piping plover habitat BN, the maximum error rate for piping plover habitat suitability in the fully-linked BNs was only 30%. Our findings suggest this approach may be useful for guiding scenario-based evaluations where known inputs can be used to constrain variables that produce higher uncertainty for morphological predictions. Overall, the approach demonstrates a way to assimilate data and model structures with uncertainty to produce forecasts to inform coastal planning and management. Numéro de notice : A2022-883 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1029/2022EA002286 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1029/2022EA002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102024
in Earth and space science > vol 9 n° 11 (November 2022) . - 24 p.[article]Tidal level prediction using combined methods of harmonic analysis and deep neural networks in Southern coastline of Iran / Kourosh Shahryari Nia in Marine geodesy, vol 45 n° 6 (November 2022)
[article]
Titre : Tidal level prediction using combined methods of harmonic analysis and deep neural networks in Southern coastline of Iran Type de document : Article/Communication Auteurs : Kourosh Shahryari Nia, Auteur ; Mohammad Ali Sharifi, Auteur ; Saeed Farzaneh, Auteur Année de publication : 2022 Article en page(s) : pp 645 - 669 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse harmonique
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] données marégraphiques
[Termes IGN] Iran
[Termes IGN] marée océanique
[Termes IGN] modèle de simulation
[Termes IGN] niveau de la mer
[Vedettes matières IGN] AltimétrieRésumé : (auteur) Predicting tides and water levels had always been such an important topic for researchers and professionals since the study of tidal level has pivotal role in supporting marine economy, port construction projects and maritime transportation. Tidal water levels are a combination of astronomical (deterministic part) and non-astronomical (stochastic part) water levels. In this study, we combined Harmonic Analysis (HA) with three Deep Neural Networks (DNNs), namely the Long-Short Term Memory (LSTM), Convolution Neural Network (CNN), and Multilayer Perceptron (MLP). The HA method is used for predicting the astronomical components, while DNNs are used to predict the non-astronomical water level. We have used tide gauge data from three stations along the southern coastline of Iran to demonstrate the effectiveness and accuracy of our model. We utilized RMSE, MAE, R2 (r-squared), and MAPE to evaluate the performance of the model. Finally, The LSTM network shown superior performance in most of the cases, although other networks also show good results. All three DNNs have R2 of 0.99, and the RMSE, MAE, and MAPE indicate that errors are low. Numéro de notice : A2022-783 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2022.2116615 Date de publication en ligne : 28/08/2022 En ligne : https://doi.org/10.1080/01490419.2022.2116615 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101880
in Marine geodesy > vol 45 n° 6 (November 2022) . - pp 645 - 669[article]A model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe / Bingbin Wen in Forest ecology and management, vol 522 (October-15 2022)
[article]
Titre : A model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe Type de document : Article/Communication Auteurs : Bingbin Wen, Auteur ; Haben Blondeel, Auteur ; Dries Landuyt, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120465 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de sensibilité
[Termes IGN] azote
[Termes IGN] biodiversité
[Termes IGN] changement climatique
[Termes IGN] dynamique de la végétation
[Termes IGN] Europe centrale
[Termes IGN] forêt tempérée
[Termes IGN] gestion forestière durable
[Termes IGN] impact sur l'environnement
[Termes IGN] modèle de simulation
[Termes IGN] sous-étage
[Termes IGN] système d'aide à la décision
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) The temperate forest understorey is rich in terms of vascular plant diversity and plays a vital functional role. Given the sensitivity of this forest layer to forest management and global environmental change and the limited knowledge on its long-term dynamics, there is a need for decision support systems that can guide temperate forest managers to optimize their management in terms of understorey outcomes. In this study, using understorey resurvey data collected from across temperate Europe, we developed Generalized Additive Models (GAM) to predict four understorey properties based on forest management and environmental change data, and implemented this model in a web-based tool as a prototype understorey Decision Support System (DSS). Using seventy-two combined climate change, nitrogen(N) deposition and forest management scenarios, applied to two case study regions in Europe, we predicted temperate forest understorey biodiversity dynamics between 2020 and 2050. A sensitivity analysis subsequently allowed to quantify the relative importance of canopy opening, N deposition and climate change on understorey dynamics. Our study showed that, regardless of regions, understorey richness and the proportion of forest specialists generally decreased among most scenarios, but the proportion of woody species and the understorey vegetation total cover increased. Climate warming, N deposition, and increases in canopy openness all influenced understorey dynamics. Climate warming will shift composition towards a selection of forest generalists and woody species, but a less open canopy could mitigate this shift by increasing the proportion of forest specialists. The case studies also showed that these responses can be context-dependent, especially in terms of responses to N deposition. Numéro de notice : A2022-710 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120465 Date de publication en ligne : 19/08/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101587
in Forest ecology and management > vol 522 (October-15 2022) . - n° 120465[article]Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model / Santanu Dinda in Geocarto international, vol 37 n° 22 ([10/10/2022])
[article]
Titre : Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model Type de document : Article/Communication Auteurs : Santanu Dinda, Auteur ; Nilanjana Das Chatterjee, Auteur ; Subrata Ghosh, Auteur Année de publication : 2022 Article en page(s) : pp 6551 - 6578 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse diachronique
[Termes IGN] chaîne de Markov
[Termes IGN] changement d'occupation du sol
[Termes IGN] croissance urbaine
[Termes IGN] densité du bâti
[Termes IGN] espace vert
[Termes IGN] Inde
[Termes IGN] logique floue
[Termes IGN] modèle de simulation
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] vulnérabilité
[Termes IGN] zone urbaine denseRésumé : (auteur) Changes in land-use and land-cover (LULC) in urban areas affect the natural environment, especially urban green spaces (UGS). The present study examines the loss of UGS due to LULC transformation at different periods to predict the future vulnerable zone of UGS, based on the 'Pressure-State-Response’ framework. To calculate the weight of each factor, a combined Analytical Hierarchical Process and Fuzzy Comprehensive Evaluation method have been used. An integrated multilayer perceptron based artificial neural network and Markov chain (MLP-ANN-MC) model has been employed to predict the UGS vulnerable area in Kolkata. Results indicated that growth rates of built-up area, land-use dynamic degree, change intensity index, and proximity factors are the major responsible for UGS vulnerability. Applying the MLP-ANN-MC model, future vulnerable zones were identified for management and conservation of UGS. The methodology developed and demonstrated in this study expands LULC change analysis and provide a new dimension for UGS vulnerability assessment. Numéro de notice : A2022-726 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1952315 Date de publication en ligne : 16/07/2021 En ligne : https://doi.org/10.1080/10106049.2021.1952315 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101672
in Geocarto international > vol 37 n° 22 [10/10/2022] . - pp 6551 - 6578[article]An estimation method to reduce complete and partial nonresponse bias in forest inventory / James A. Westfall in European Journal of Forest Research, vol 141 n° 5 (October 2022)PermalinkEvaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks / Abdelkrim Bouasria in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkModelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches / Wenzong Gao in Journal of geodesy, vol 96 n° 10 (October 2022)PermalinkSimulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata: a case study on city of Toronto / Xiaocong Xu in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkSpatio-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)PermalinkPrediction of suspended sediment concentration using hybrid SVM-WOA approaches / Sandeep Samantaray in Geocarto international, vol 37 n° 19 ([15/09/2022])PermalinkExploring 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)PermalinkFlood 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)PermalinkSimulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices / Najmeh Mozaffaree Pour in Environmental Monitoring and Assessment, vol 194 n° 9 (September 2022)PermalinkClimatic sensitivities derived from tree rings improve predictions of the forest vegetation simulator growth and yield model / Courtney L. Giebink in Forest ecology and management, vol 517 (August-1 2022)Permalink