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scientifique

Spatial assessment of ecosystem services provisioning changes in a forest-dominated protected area in NE Turkey / Can Vatandaslar in Environmental Monitoring and Assessment, vol 194 n° 8 (August 2022)  

Public [article]  inEnvironmental Monitoring and Assessment > vol 194 n° 8 (August 2022) . - n° 539 Titre : Spatial assessment of ecosystem services provisioning changes in a forest-dominated protected area in NE Turkey Type de document : Article/Communication Auteurs : Can Vatandaslar, Auteur Année de publication : 2022 Article en page(s) : n° 539 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] parc naturel national [Termes IGN] protection de l'environnement [Termes IGN] service écosystémique [Termes IGN] Turquie Résumé : (auteur) Forested landscapes offer high provisioning capacities for many ecosystem services (ES), yet their capabilities may change in time due to multifaceted ES drivers. Therefore, assessing the changes in individual ES is critical for ecosystem-based management. This study analyzes the spatio-temporal changes in ES provided by a forest-dominated protected area in NE, Turkey. To this end, 18 ES were quantified and mapped using the ES matrix approach for 1985 and 2021. Then, the status of the ES and potential drivers of landscape changes were revealed through the assessment of demographic and management structure changes. The results showed that the multiple ES provisioning capacity of the landscape increased by 7% over 35 years. The capacities for “crops” and “livestock” ES decreased for the same period. The most prominent ES were “wild foods,” “erosion regulation,” and “knowledge systems.” Spatially, ES hotspots accumulated in the northern parts and the core zone of the protected area. The most significant changes occurred in the lowlands, mostly composed of degraded forests and coppices as of 1985 after their transformation into productive forests. The spatio-temporal changes in many ES can be attributed to the declaration of the landscape as a protected area in 1994. The removal of anthropogenic pressure and the impact of conservation management can be evaluated as the main drivers for the positive changes in the total ES capacity. Thus, sound policy structures and effective conservation strategies should be further encouraged for increasing protected areas’ capacities to provide the large array of ES. Numéro de notice : A2022-459 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1007/s10661-022-10212-7 Date de publication en ligne : 29/06/2022 En ligne : http://dx.doi.org/10.1007/s10661-022-10212-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101260 [article]

Effects of offsets and outliers on the sea level trend at Antalya 2 tide gauge within the Eastern Mediterranean Sea / Mehmet Emin Ayhan in Marine geodesy, vol 45 n° 4 (July 2022)  

Public [article]  inMarine geodesy > vol 45 n° 4 (July 2022) . - pp 329 - 359 Titre : Effects of offsets and outliers on the sea level trend at Antalya 2 tide gauge within the Eastern Mediterranean Sea Type de document : Article/Communication Auteurs : Mehmet Emin Ayhan, Auteur Année de publication : 2022 Article en page(s) : pp 329 - 359 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] autocorrélation [Termes IGN] compensation [Termes IGN] données marégraphiques [Termes IGN] Méditerranée, mer [Termes IGN] modèle statistique [Termes IGN] niveau de la mer [Termes IGN] niveau moyen des mers [Termes IGN] Turquie [Termes IGN] valeur aberrante [Termes IGN] variation saisonnière [Vedettes matières IGN] Altimétrie Résumé : (auteur) Antalya 2 tide gauge (TG) station is located on the coast of Turkey within the Eastern Mediterranean Sea. Relative sea level trends 6.0 ± 1.5 and 6.44 ± 0.45 mm/year over 1985–2009 at Antalya 2 TG are different from the trend (1.6 ± 1.5 mm/year over 1935–1977) at Antalya TG within 10 km. In order to investigate this trend discrepancy, the monthly mean series at Antalya 2 TG is re-analyzed for offsets, outliers and trend estimation. The Zivot-Andrews method and the Qp outlier test result in one offset at 1994.0417 year with magnitude of 71.24 ± 13.48 mm and nine outliers. The series, corrected for the offset and outliers, de-seasonalized and filled for missed points, is identified as trend-stationary process and analyzed for trend estimation by various models. The optimal model providing the lowest Akaike Information Criteria is polynomial linear trend with multiplicative seasonal Autoregressive Moving Average (ARMA(2,0)x(1,0)12). The estimated relative sea level trend by the optimal model is 1.77 ± 0.65 mm/year. The large trend discrepancy at Antalya 2 TG is accounted for by one offset primarily (∼71%) and nine outliers (∼3%). Numéro de notice : A2022-516 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2022.2047843 Date de publication en ligne : 11/03/2022 En ligne : https://doi.org/10.1080/01490419.2022.2047843 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101064 [article]

Assessing and mapping landslide susceptibility using different machine learning methods / Osman Orhan in Geocarto international, vol 37 n° 10 ([01/06/2022])  

Public [article]  inGeocarto international > vol 37 n° 10 [01/06/2022] . - pp 2795 - 2820 Titre : Assessing and mapping landslide susceptibility using different machine learning methods Type de document : Article/Communication Auteurs : Osman Orhan, Auteur ; Suleyman Sefa Bilgilioglu, Auteur ; Zehra Kaya, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2795 - 2820 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] carte thématique [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] effondrement de terrain [Termes IGN] lithologie [Termes IGN] pente [Termes IGN] régression logistique [Termes IGN] réseau neuronal artificiel [Termes IGN] séparateur à vaste marge [Termes IGN] Turquie Résumé : (auteur) The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide incidents occur in Turkey. Firstly, a landslide inventory was produced by identifying a total of 252 landslides. Secondly, a total of 11 landslide conditioning factors were considered for the landslide susceptibility mapping. Subsequently, the five machine learning techniques were constructed with the help of the training dataset for the landslide susceptibility maps. Finally, the receiver operating characteristic (ROC), sensitivity, specificity, F-measure, accuracy and kappa index were applied to compare and validate the performance of the five machine learning techniques. Numéro de notice : A2022-594 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1837258 Date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1837258 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101298 [article]

GIS-based assessment of long-term traffic accidents using spatiotemporal and empirical Bayes analysis in Turkey / Saffet Erdoğan in Applied geomatics, vol 14 n° 2 (June 2022)  

Public [article]  inApplied geomatics > vol 14 n° 2 (June 2022) . - pp 147 - 162 Titre : GIS-based assessment of long-term traffic accidents using spatiotemporal and empirical Bayes analysis in Turkey Type de document : Article/Communication Auteurs : Saffet Erdoğan, Auteur ; Mehmet Ali Dereli, Auteur ; Halil İbrahim Şenol, Auteur Année de publication : 2022 Article en page(s) : pp 147 - 162 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accident de la route [Termes IGN] analyse de groupement [Termes IGN] distribution spatiale [Termes IGN] données spatiotemporelles [Termes IGN] données statistiques [Termes IGN] sécurité routière [Termes IGN] système d'information géographique [Termes IGN] théorème de Bayes [Termes IGN] trafic routier [Termes IGN] Turquie Résumé : (auteur) The number of traffic fatalities continues to rise steadily throughout the world. In 2016, it reached 1.35 million. The spatiotemporal analysis makes a big contribution when used with spatial and statistical analysis together in terms of the understanding of the change. This study focuses on spatiotemporal fluctuations in traffic accident hotspots to gain useful insights into traffic safety in Turkey in 2004–2017 period. For this purpose, 372,800 accident records are arranged on a GIS platform. The areas that lack traffic safety and require more attention were determined using spatial, temporal, and empirical Bayesian analysis. Although similar results were detected with spatiotemporal and empiric Bayes analysis, spatiotemporal analysis was used to understand where traffic accidents clustering, and how the trends of traffic accidents change whether are increasing or decreasing. As a result of the analysis, an increasing trend has been found in many locations in Turkey from 2004 to 2017. Numéro de notice : A2022-461 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-022-00419-1 Date de publication en ligne : 02/02/2022 En ligne : https://doi.org/10.1007/s12518-022-00419-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100788 [article]

Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping / Emrehan Kutlug Sahin in Geocarto international, vol 37 n° 9 ([15/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 9 [15/05/2022] . - pp 2441 - 2465 Titre : Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Emrehan Kutlug Sahin, Auteur Année de publication : 2022 Article en page(s) : pp 2441 - 2465 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse comparative [Termes IGN] cartographie thématique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] effondrement de terrain [Termes IGN] Extreme Gradient Machine [Termes IGN] khi carré [Termes IGN] Turquie Résumé : (auteur) The aim of the study is to compare four recent gradient boosting algorithms named as Gradient Boosting Machine (GBM), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) for modelling landslide susceptibility (LS). In the first step of the study, the geodatabase including landslide inventory map and landslide conditioning factors was constructed. In the second step, chi-square (CHI) statistic-based feature selection (FS) technique was utilized to compute the importance of the landslide causative factors. In the third step, tree-based ensemble learning algorithms were applied to predict the potential distribution of landslide susceptibility. Also, the prediction performance of ensemble methods was compared to that of Random Forest (RF) ensemble method. Finally, the prediction capabilities of the methods were assessed using overall accuracy (Acc), area under the receiver operating characteristic curve (AUC), kappa index, root mean square error (RMSE), and F score measures. In order to further evaluation, the McNemar's test was utilized to assess statistical significance in the differences between the four gradient boosting models. The accuracy results indicated that the CatBoost model had the highest prediction capability (Acc= 0.8503 and AUC= 0.8975), followed by the XGBoost (Acc= 0.8336 and AUC= 0.8860), the LightGBM (Acc= 0.8244 and AUC= 0.8796) and the GBM (Acc= 0.8080 and AUC= 0.8685). On the other hand, the estimated accuracy measures considered in this study showed that the RF method had the lowest prediction capability of compared the others. Although the individual performances of the methods were found to be acceptable level, the CatBoost method showed the superior performance compared to others with respect to the AUC and Acc values estimated in this study. The results of the study confirmed that the relatively new ensemble learning techniques were efficient and robust for producing LS maps and furthermore, it is probably that these algorithms will be preferred more often in the future studies due to their robustness. Numéro de notice : A2022-564 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1831623 Date de publication en ligne : 16/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1831623 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101244 [article]

Building Information Modelling (BIM) for property valuation: A new approach for Turkish Condominium Ownership / Nida Celik Simsek in Survey review, vol 54 n° 384 (May 2022)  

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Production of optimum forest roads and comparison of these routes with current forest roads: a case study in Maçka, Turkey / Faruk Yildirim in Geocarto international, vol 37 n° 8 ([01/05/2022])  

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Species level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery / Semiha Demirbaş Çağlayana in Geocarto international, vol 37 n° 6 ([01/04/2022])  

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Estimation of uneven-aged forest stand parameters, crown closure and land use/cover using the Landsat 8 OLI satellite image / Sinan Kaptan in Geocarto international, vol 37 n° 5 ([01/03/2022])  

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Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images / Omer Gokberk Narin in Geocarto international, vol 37 n° 5 ([01/03/2022])  

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Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model / Hasan Aksoy in Geocarto international, vol 37 n° 4 ([15/02/2022])  

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Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network / Ekrem Saralioglu in Geocarto international, vol 37 n° 2 ([15/01/2022])  

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An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web / Abdulkadir Memduhoglu in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)  

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Investigation of the landslides in Beylikdüzü-Esenyurt districts of Istanbul from InSAR and GNSS observations / Caglar Bayik in Natural Hazards, vol 109 n° 1 (October 2021)  

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Quantifying coherence between TDM90, SRTM90 and ASTER90 / Umut Gunes Sefercik in Geocarto international, vol 36 n° 15 ([15/08/2021])  

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