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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])  

Public [article]  inGeocarto international > vol 37 n° 8 [01/05/2022] . - pp 2175 - 2197 Titre : Production of optimum forest roads and comparison of these routes with current forest roads: a case study in Maçka, Turkey Type de document : Article/Communication Auteurs : Faruk Yildirim, Auteur ; Fatih Kadi, Auteur Année de publication : 2022 Article en page(s) : pp 2175 - 2197 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse comparative [Termes IGN] carte forestière [Termes IGN] chemin forestier [Termes IGN] interface graphique [Termes IGN] Matlab [Termes IGN] processus de hiérarchisation analytique [Termes IGN] recherche du chemin optimal, algorithme de [Termes IGN] Turquie Résumé : (auteur) Forest roads are a basic necessity in forestry policies and should be planned by considering many factors. This study aims to generate optimum forest road routes and to compare them with current forest roads. First, FRNSM has been produced according to AHP, using nine factors for the study area. Then, risk statuses of the current forest roads are examined. According to results, 35% of the total forest road has high risk. A MATLAB-GUI based an application using optimal path algorithm developed for the second stage of the study has been produced. Using this application, optimum forest road routes have been produced for 11 pilot areas selected from the region. Generated routes have been compared with current forest roads in the region. It has been observed that generated routes in all areas are more suitable than current forest roads in terms of total length and average risk of suitability. Numéro de notice : A2022-504 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1818852 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1080/10106049.2020.1818852 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101025 [article]

Mapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data / Santanu Malik in Geocarto international, vol 37 n° 8 ([01/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 8 [01/05/2022] . - pp 2198 - 2214 Titre : Mapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data Type de document : Article/Communication Auteurs : Santanu Malik, Auteur ; Tridip Bhowmik, Auteur ; Umesh Mishra, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2198 - 2214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte d'occupation du sol [Termes IGN] estimation bayesienne [Termes IGN] géostatistique [Termes IGN] gestion durable [Termes IGN] Inde [Termes IGN] krigeage [Termes IGN] modèle de simulation [Termes IGN] puits de carbone [Termes IGN] régression [Termes IGN] réseau neuronal artificiel [Termes IGN] sol arable Résumé : (auteur) Prediction and accurate digital soil mapping (DSM) of soil organic carbon (SOC) at a local scale is a key factor for any agro-ecological modelling. This study aims to use remote sensing and terrain derivatives to provide a reliable method for SOC prediction. An advanced geostatistical-based empirical Bayesian Kriging regression (EBKR) method was used and performance was compared with the artificial neural network (ANN) and hybrid ANN, i.e. ANN-OK (ordinary kriging) and ANN-CK (cokriging). The result showed that the hybrid ANN model performs better than ANN, whereas the EBKR method outperforms all other methods with the highest R2 of 0.936. The DSM map shows that the highest SOC concentration was found in easternmost part of the study area with grass and agricultural land. This work shows the robustness of the EBKR prediction method over other techniques. The study will also aid the policymakers in adopting sustainable land use management. Numéro de notice : A2022-505 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1815864 Date de publication en ligne : 24/09/2020 En ligne : https://doi.org/10.1080/10106049.2020.1815864 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101026 [article]

Human cognition based framework for detecting roads from remote sensing images / Naveen Chandra in Geocarto international, vol 37 n° 8 ([01/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 8 [01/05/2022] . - pp 2365 - 2384 Titre : Human cognition based framework for detecting roads from remote sensing images Type de document : Article/Communication Auteurs : Naveen Chandra, Auteur ; Himadri Vaidya, Auteur ; Jayanta Kumar Ghosh, Auteur Année de publication : 2022 Article en page(s) : pp 2365 - 2384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse d'image numérique [Termes IGN] classification [Termes IGN] cognition [Termes IGN] extraction du réseau routier [Termes IGN] image à haute résolution [Termes IGN] interprétation (psychologie) [Termes IGN] représentation cognitive [Termes IGN] segmentation d'image Résumé : (auteur) The complete extraction of roads from remote sensing images (RSIs) is an emergent area of research. It is an interesting topic as it involves diverse procedures for detecting roads. The detection of roads using high-resolution-satellite-images (HRSi) is challenging because of the occurrence of several types of noise such as bridges, vehicles, and crossing lines, etc. The extraction of the correct road network is crucial due to its broad range of applications such as transportation, map updating, navigation, and generating maps. Therefore our paper concentrates on understanding the cognitive processes, reasoning, and knowledge used by the analyst through visual cognition while performing the task of road detection from HRSi. The novel process is performed emulating human cognition within cognitive task analysis which is carried out in five different stages. The suggested cognitive procedure for road extraction is validated with the fifteen HRSi of four different land cover patterns specifically developed-sub-urban (DSUr), developed-urban (DUr), emerging-sub-urban (ESUr), and emerging-urban (EUr). The experimental results and the comparative assessment prove the impact of the presented cognitive method. Numéro de notice : A2022-506 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1810330 Date de publication en ligne : 14/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1810330 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101027 [article]