Centre de documentation
scientifique

Comparative use of PPK-integrated close-range terrestrial photogrammetry and a handheld mobile laser scanner in the measurement of forest road surface deformation / Remzi Eker in Measurement, vol 206 (January 2023)  

Public [article]  inMeasurement > vol 206 (January 2023) Titre : Comparative use of PPK-integrated close-range terrestrial photogrammetry and a handheld mobile laser scanner in the measurement of forest road surface deformation Type de document : Article/Communication Auteurs : Remzi Eker, Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre [Termes IGN] analyse comparative [Termes IGN] cartographie et localisation simultanées [Termes IGN] chemin forestier [Termes IGN] déformation de surface [Termes IGN] lidar mobile [Termes IGN] positionnement cinématique [Termes IGN] semis de points [Termes IGN] structure-from-motion [Termes IGN] télémétrie laser terrestre [Termes IGN] Turquie Résumé : (auteur) This study aimed to compare a handheld mobile laser scanning (HMLS), called TORCH that uses the SLAM algorithm, and a PPK-integrated close-range terrestrial photogrammetry (CRTP) to measure forest road surface deformation. The PPK-integrated CRTP includes a multiband GNSS-module and a camera mounted on a 5-m prism pole. 3D point-clouds were gathered/produced at three different dates with approximately 3-month intervals. And then road surface deformations were determined by applying the M3C2 algorithm. Each method was compared by considering some advantages and disadvantages. PPK-integrated CRTP, which could only be used in areas where the GPS signal is not blocked, provided highly denser 3D point clouds than HMLS. However, for the first period, the difference of mean deformation values between the two methods was not statistically significant, whereas it was statistically significant for the second period. Both methods can be suggested to use in forest road surface deformation yet considering their limitations. Numéro de notice : A2023-043 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.measurement.2022.112322 Date de publication en ligne : 14/12/2022 En ligne : https://doi.org/10.1016/j.measurement.2022.112322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102330 [article]

Forest road extraction from orthophoto images by convolutional neural networks / Erhan Çalişkan in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° inconnu [01/01/2023] . - pp Titre : Forest road extraction from orthophoto images by convolutional neural networks Type de document : Article/Communication Auteurs : Erhan Çalişkan, Auteur ; Yusuf Sevim, Auteur Année de publication : 2023 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] chemin forestier [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction automatique [Termes IGN] orthoimage [Termes IGN] segmentation sémantique Résumé : (auteur) Continuous monitoring of the forest road infrastructure and keeping track of the changes occurred are important for forestry practices, map updating, forest fire and forest transport decision support systems. In this context, the most of up to date data can be obtained by automatic forest road extraction from satellite images via machine learning (ML). Acquiring sufficient data is one of the most important factors which affect the success of ML and deep learning (DL). DL architectures yield more consistent results for complex data sets compared with ML algorithms. In the present study, three different deep learning (Resnet-18, MobileNet-V2 and Xception) architectures with semantic segmentation architecture were compared for extracting the forest road network from high-resolution orthophoto images and the results were analyzed. The architectures were evaluated through a multiclass statistical analysis based precision, recall, F1 score, intersection over union and overall accuracy (OA). The results present significant values obtained by the Resnet-18 architecture, with 99.72% of OA and 98.87% of precision and by the MobileNet-V2 architecture, with 97.76% of OA and 98.28% of precision. Also the results show that Resnet-18, MobileNet-V2 semantic segmentation architectures can be used efficiently for forest road extraction. Numéro de notice : A2022-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2060319 Date de publication en ligne : 06/04/2022 En ligne : https://doi.org/10.1080/10106049.2022.2060319 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100380 [article]

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]

An infrastructure perspective for enhancing multi-functionality of forests: A conceptual modeling approach / Mojtaba Houballah in Earth' future, vol 9 n° 1 (January 2021)  

Public [article]  inEarth' future > vol 9 n° 1 (January 2021) . - n° e2019EF001369 Titre : An infrastructure perspective for enhancing multi-functionality of forests: A conceptual modeling approach Type de document : Article/Communication Auteurs : Mojtaba Houballah, Auteur ; Jean-Denis Mathias, Auteur ; Thomas Cordonnier, Auteur Année de publication : 2021 Article en page(s) : n° e2019EF001369 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] chemin forestier [Termes IGN] conservation des ressources naturelles [Termes IGN] développement durable [Termes IGN] gestion forestière durable [Termes IGN] modèle conceptuel de données [Termes IGN] modèle mathématique [Termes IGN] production primaire brute [Termes IGN] service écosystémique [Termes IGN] tourisme [Vedettes matières IGN] Foresterie Résumé : (auteur) Many forest resource systems depend heavily on shared and coupled infrastructures in applying their management strategies. Addressing a question of sustainability for relevant contemporary social-ecological systems (SES) can be tackled by understanding how these shared infrastructures mediate the interaction between human and ecological environment. Shared infrastructures, which are mainly composed of roads (accessibility utilities), highlight the relation between the performance of ecosystem services and the multifunctional use of the forest. However, dilemmas associated with road provision pose some problems when applied in a forest multifunctional management context, because roads potentially diminish or enhance forest functions in a complex way. In this context, maintaining, fostering, and improving multifunctional management where the development of an ecosystem function can affect the performance of others is challenging. We propose to develop a mathematical model based on a recent study that links multifunctional forest management to the multifunctionality of forest roads by using the SES and robustness frameworks. With this model, we analyze the evolution of the forest system and three key forest functions (wood production, tourism, and nature conservation) when impacted by decisions of road provision. We then examine how governance provision strategies can affect the performance of functions and how these strategies can potentially foster forest multifunctionality. This approach allows us to derive conditions of sustainability in which decisions of shared infrastructure provisions can play an important role in the functionalities and performance of the forest. Numéro de notice : A2021-617 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1029/2019EF001369 Date de publication en ligne : 10/12/2020 En ligne : https://doi.org/10.1029/2019EF001369 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98581 [article]

Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology / Aura Salmivaara in Forestry, an international journal of forest research, vol 93 n° 5 (October 2020)  

Public [article]  inForestry, an international journal of forest research > vol 93 n° 5 (October 2020) . - pp 662 - 674 Titre : Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology Type de document : Article/Communication Auteurs : Aura Salmivaara, Auteur ; Samuli Launiainen, Auteur ; Jari Perttunen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 662 - 674 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Environnement [Termes IGN] apprentissage automatique [Termes IGN] chemin forestier [Termes IGN] classification barycentrique [Termes IGN] dégradation des sols [Termes IGN] dommage [Termes IGN] données localisées libres [Termes IGN] exploitation forestière [Termes IGN] Finlande [Termes IGN] humidité du sol [Termes IGN] modèle dynamique [Termes IGN] modèle hydrographique Résumé : (auteur) Forest harvesting operations with heavy machinery can lead to significant soil rutting. Risks of rutting depend on the soil bearing capacity which has considerable spatial and temporal variability. Trafficability prediction is required in the selection of suitable operation sites for a given time window and conditions, and for on-site route optimization during the operation. Integrative tools are necessary to plan and carry out forest operations with minimal negative ecological and economic impacts. This study demonstrates a trafficability prediction framework that utilizes a spatial hydrological model and a wide range of spatial data. Trafficability was approached by producing a rut depth prediction map at a 16 × 16 m grid resolution, based on the outputs of a general linear mixed model developed using field data from Southern Finland, modelled daily soil moisture, spatial forest inventory and topography data, along with field measured rolling resistance and information on the mass transported through the grid cells. Dynamic rut depth prediction maps were produced by accounting for changing weather conditions through hydrological modelling. We also demonstrated a generalization of the rolling resistance coefficient, measured with harvester CAN-bus channel data. Future steps towards a nationwide prediction framework based on continuous data flow, process-based modelling and machine learning are discussed. Numéro de notice : A2020-790 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1093/forestry/cpaa010 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1093/forestry/cpaa010 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96559 [article]

Improving drainage conditions of forest roads using the GIS and forest road simulator / Mehran Nasiri in Journal of forest science, vol 66 n° 9 (September 2020)  

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Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain / Convadonga Prendes in iForest, biogeosciences and forestry, vol 12 n° 4 (July 2019)  

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