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Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)
[article]
Titre : Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach Type de document : Article/Communication Auteurs : Hakan Oktay Aydınlı, Auteur ; Ali Ekincek, Auteur ; Mervegül Aykanat-Atay, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 669 - 678 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 récurrent
[Termes IGN] détection de changement
[Termes IGN] données Copernicus
[Termes IGN] image Aqua-MODIS
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle de simulation
[Termes IGN] Noire, mer
[Termes IGN] optimisation (mathématiques)
[Termes IGN] série temporelle
[Termes IGN] température de surface de la merRésumé : (auteur) High temporal resolution remote sensing images provide continuous data about the marine environment, which is critical for gaining extensive knowledge about the aquatic environment and marine species. Sea surface temperature (SST) is one of the basic parameters that can be obtained with the help of remote sensing. Long-term alterations in the SST can affect the aquatic environment and marine species, such as the life expectancy of anchovies in the Black Sea. Forecasting the dynamics of SSTs is crucial for detecting and eliminating the SST-oriented impacts. The goal of the current study is to construct a predictive model to estimate the daily SST value for the mid-Black Sea using a machine learning approach by employing time-series satellite data from 2008 to 2021. Turkey’s mid-Black Sea coastal line, comprising Ordu, Samsun, and Sinop stations, was chosen as the study area. The SST predictive model was represented by applying the recurrent neural network (RNN) long- and short-term memory (LSTM). Adam stochastic optimization was used for validation, and the mean square error (MSE) for each location was found to be 0.914, 0.815, and 0.802, respectively. The findings indicate that our model is significantly promising for accurate and effective short- and midterm daily SST prediction. Numéro de notice : A2022-894 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-022-00462-y Date de publication en ligne : 23/08/2022 En ligne : https://doi.org/10.1007/s12518-022-00462-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102242
in Applied geomatics > vol 14 n° 4 (December 2022) . - pp 669 - 678[article]The simulation and prediction of land surface temperature based on SCP and CA-ANN models using remote sensing data: A case study of Lahore / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)
[article]
Titre : The simulation and prediction of land surface temperature based on SCP and CA-ANN models using remote sensing data: A case study of Lahore Type de document : Article/Communication Auteurs : Muhammad Nasar Ahmad, Auteur ; Shao Zhengfeng, Auteur ; Andaleeb Yaseen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 783 - 790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] changement climatique
[Termes IGN] changement d'utilisation du sol
[Termes IGN] classification par réseau neuronal
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] MNS SRTM
[Termes IGN] modèle de simulation
[Termes IGN] Pakistan
[Termes IGN] planification urbaine
[Termes IGN] température au solRésumé : (auteur) Over the last two decades, urban growth has become a major issue in Lahore, accelerating land surface temperature (LST) rise. The present study focused on estimating the current situation and simulating the future LST patterns in Lahore using remote sensing data and machine learning models. The semi-automated classification model was applied for the estimation of LST from 2000 to 2020. Then, the cellular automata-artificial neural networks (CA-ANN) module was implemented to predict future LST patterns for 2030 and 2040, respectively. Our research findings revealed that an average of 2.8 °C of land surface temperature has increased, with a mean LST value from 37.25 °C to 40.10 °C in Lahore during the last two decades from 2000 to 2020. Moreover, keeping CA-ANN simulations for land surface temperature, an increase of 2.2 °C is projected through 2040, and mean LST values will be increased from 40.1 °C to 42.31 °C by 2040. The CA-ANN model was validated for future LST simulation with an overall Kappa value of 0.82 and 86.2% of correctness for the years 2030 and 2040 using modules for land-use change evaluation. The study also indicates that land surface temperature is an important factor in environmental changes. Therefore, it is suggested that future urban planning should focus on urban rooftop plantations and vegetation conservation to minimize land surface temperature increases in Lahore. Numéro de notice : A2022-886 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00071R2 Date de publication en ligne : 01/12/2022 En ligne : https://doi.org/10.14358/PERS.22-00071R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102208
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 12 (December 2022) . - pp 783 - 790[article]A whale optimization algorithm–based cellular automata model for urban expansion simulation / Yuan Ding in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)
[article]
Titre : A whale optimization algorithm–based cellular automata model for urban expansion simulation Type de document : Article/Communication Auteurs : Yuan Ding, Auteur ; Kai Cao, Auteur ; Weifeng Qiao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103093 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] Canton (Kouangtoung)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] coefficient de Gini
[Termes IGN] croissance urbaine
[Termes IGN] étalement urbain
[Termes IGN] itération
[Termes IGN] modèle de simulation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau neuronal artificiel
[Termes IGN] utilisation du solRésumé : (auteur) Numéro de notice : A2022-826 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.jag.2022.103093 Date de publication en ligne : 07/11/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103093 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102010
in International journal of applied Earth observation and geoinformation > vol 115 (December 2022) . - n° 103093[article]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)PermalinkA 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)PermalinkModelling 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])PermalinkAn 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)Permalink