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Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science, vol 78 n° 1 (March 2021)
[article]
Titre : Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest Type de document : Article/Communication Auteurs : Seyedeh Kosar Hamidi, Auteur ; Eric K. Zenner, Auteur ; Mahmoud Bayat, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer velutinum
[Termes IGN] Alnus cordata
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] Carpinus betulus
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] dynamique de la végétation
[Termes IGN] écosystème forestier
[Termes IGN] Fagus orientalis
[Termes IGN] forêt inéquienne
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Iran
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle de simulation
[Termes IGN] peuplement mélangé
[Termes IGN] régression linéaire
[Termes IGN] volume en bois
[Vedettes matières IGN] SylvicultureRésumé : (auteur) Key message: We modeled 10-year net stand volume growth with four machine learning (ML) methods, i.e., artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and nearest neighbor analysis (NN), and with linear regression analysis. Incorporating interactions of multiple variables, the ML methods ANN and SVM predicted nonlinear system behavior and unraveled complex relations with greater accuracy than regression analysis.
Context: Investigating the quantitative and qualitative characteristics of short-term forest dynamics is essential for testing whether the desired goals in forest-ecosystem conservation and restoration are achieved. Inventory data from the Jojadeh section of the Farim Forest located in the uneven-aged, mixed Hyrcanian Forest were used to model and predict 10-year net annual stand volume increment with new machine learning technologies.
Aims: The main objective of this study was to predict net annual stand volume increment as the preeminent factor of forest growth and yield models.
Methods: In the current study, volume increment was modeled from two consecutive inventories in 2003 and 2013 using four machine learning techniques that used physiographic data of the forest as input for model development: (i) artificial neural networks (ANN), (ii) support vector machines (SVM), (iii) random forests (RF), and (iv) nearest neighbor analysis (NN). Results from the various machine learning technologies were compared against results produced with regression analysis.
Results: ANNs and SVMs with a linear kernel function that incorporated field-measurements of terrain slope and aspect as input variables were able to predict plot-level volume increment with a greater accuracy (94%) than regression analysis (87%).
Conclusion: These results provide compelling evidence for the added utility of machine learning technologies for modeling plot-level volume increment in the context of forest dynamics and management.Numéro de notice : A2021-071 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01011-6 Date de publication en ligne : 12/01/2021 En ligne : https://doi.org/10.1007/s13595-020-01011-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96794
in Annals of Forest Science > vol 78 n° 1 (March 2021) . - n° 4[article]Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)
[article]
Titre : Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB Type de document : Article/Communication Auteurs : Mahya Norallahi, Auteur ; Hesam Seyed Kaboli, Auteur Année de publication : 2021 Article en page(s) : pp119 - 137 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage automatique
[Termes IGN] cartographie des risques
[Termes IGN] classification bayesienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] entropie maximale
[Termes IGN] inondation
[Termes IGN] Iran
[Termes IGN] zone urbaineRésumé : (auteur) Rapid urban development, increasing impermeable surfaces, poor drainage system and changes in extreme precipitations are the most important factors that nowadays lead to increased urban flooding and it has become an urban problem. Urban flood mapping and its use in making an urban development plan can reduce flood damages and losses. Constantly producing urban flood hazard maps using models that rely on the availability of detailed hydraulic-hydrological data is a major challenge especially in developing countries. In this study, urban flood hazard map was produced with limited data using three machine learning models: Genetic Algorithm Rule-Set Production, Maximum Entropy (MaxEnt), Random Forest (RF) and Naïve Bayes for Kermanshah city, Iran. The flood hazard predicting factors used in modeling were: slope, land use, precipitation, distance to river, distance to channel, curve number (CN) and elevation. Flood inventory map was produced based on available reports and field surveys, that 117 flooded points and 163 non-flooded points were identified. Models performance was evaluated based on area under the receiver-operator characteristic curve (AUC-ROC), Kappa statistic and hits and miss analysis. The results show that RF model (AUC-ROC = 99.5%, Kappa = 98%, Accuracy = 90%, Success ratio = 99%, Threat score = 90% and Heidke skill score = 98%) performed better than other models. The results also showed that distance to canal, land use and CN have shown more contribution among others for modeling the flood and precipitation had the least effect among other factors. The findings show that machine learning methods can be a good alternative to distributed models to predict urban flood-prone areas where there are lack of detailed hydraulic and hydrological data. Numéro de notice : A2021-418 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04453-3 Date de publication en ligne : 04/01/2021 En ligne : https://doi.org/10.1007/s11069-020-04453-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97768
in Natural Hazards > vol 106 n° 1 (March 2021) . - pp119 - 137[article]Integrating runoff map of a spatially distributed model and thematic layers for identifying potential rainwater harvesting suitability sites using GIS techniques / Hamid Karimi in Geocarto international, vol 36 n° 3 ([15/02/2021])
[article]
Titre : Integrating runoff map of a spatially distributed model and thematic layers for identifying potential rainwater harvesting suitability sites using GIS techniques Type de document : Article/Communication Auteurs : Hamid Karimi, Auteur ; Hossein Zeinivand, Auteur Année de publication : 2021 Article en page(s) : pp 320 - 339 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] carte hydrographique
[Termes IGN] combinaison linéaire ponderée
[Termes IGN] couche thématique
[Termes IGN] eau pluviale
[Termes IGN] écoulement des eaux
[Termes IGN] étang
[Termes IGN] Iran
[Termes IGN] modèle hydrographique
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] ruissellementRésumé : (auteur) Rainwater harvesting (RWH) is one of the major techniques that is investigated in the present study using Analytic Hierarchy Process (AHP) and Weighted Linear Combination (WLC) methods as two tools for decision-making, weighting and combining different thematic layers include land use, slope, drainage density and hydrological soil groups (HSG). The runoff map obtained by the distributed spatial-physical WetSpa model is considered as a useful layer that is integrated with other thematic layers in the geographic information system (GIS) environment for identifying RWH sites. Kakareza watershed (1132 km2) in Iran was selected as a study area to carry out the foregoing approach. The results showed that 256 km2 of the study area is good for RWH, 360 km2 is moderate and 516 km2 is poor. Thus, about 22.61% (256 km2) of Kakareza watershed is highly suitable for farm ponds. This article recommends the RWH suitable sites to a judicious decision for better water management in the area. Numéro de notice : A2021-141 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1608590 Date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/10106049.2019.1608590 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97037
in Geocarto international > vol 36 n° 3 [15/02/2021] . - pp 320 - 339[article]An improved approach based on terrain-dependent mathematical models for georeferencing pushbroom satellite images / Behrooz Moradi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)
[article]
Titre : An improved approach based on terrain-dependent mathematical models for georeferencing pushbroom satellite images Type de document : Article/Communication Auteurs : Behrooz Moradi, Auteur ; Mohammad Javad Valadan Zoej, Auteur ; Sayad Yaghoobi, Auteur ; Somayeh Yavari, Auteur Année de publication : 2021 Article en page(s) : pp 53 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] géoréférencement
[Termes IGN] image à haute résolution
[Termes IGN] image Geoeye
[Termes IGN] image Ikonos
[Termes IGN] Iran
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] modélisation 3DRésumé : (Auteur) Recently, linear features in remotely sensed imagery have gained much attention because of their unique characteristics compared to other control features. For georeferencing high-resolution satellite images, the observations in the mathematical equations (slope and y-intercept) of the corresponding control lines in the two spaces are considered the same based on recent studies. However, the use of such assumptions causes error and reduces the accuracy of registration. The aim of this article is to present a methodology based on a quasi-observation assumption in the mathematical equations in the process of georeferencing. Experimental results for IKONOS and GeoEye images over two different cities of Iran indicate that the quasi-observation assumption can increase the average registration accuracy up to 48.96% and 24.77% using 3D-affine and rational function models, respectively. This improvement in accuracy increases the processing time by 31.48% over traditional approaches; however, the proposed methodology can be regarded as an efficient solution. Numéro de notice : A2021-057 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.1.53 Date de publication en ligne : 01/01/2021 En ligne : https://doi.org/10.14358/PERS.87.1.53 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96768
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 1 (January 2021) . - pp 53 - 69[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021011 SL Revue Centre de documentation Revues en salle Disponible Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / Roholah Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
[article]
Titre : Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation Type de document : Article/Communication Auteurs : Roholah Yazdan, Auteur ; Masood Varshosaz, Auteur Année de publication : 2021 Article en page(s) : pp 18 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] base de données d'images
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] corrélation à l'aide de traits caractéristiques
[Termes IGN] corrélation croisée normalisée
[Termes IGN] couple stéréoscopique
[Termes IGN] détection automatique
[Termes IGN] modèle stéréoscopique
[Termes IGN] reconnaissance d'objets
[Termes IGN] segmentation d'image
[Termes IGN] SIFT (algorithme)
[Termes IGN] signalisation routière
[Termes IGN] SURF (algorithme)
[Termes IGN] Téhéran
[Termes IGN] transformation de Hough
[Termes IGN] zone urbaineRésumé : (auteur) Automatic detection and recognition of traffic signs have many applications. However, some problems can affect the accuracy of the existing algorithms, such as changes in environmental light conditions, shadows, the presence of objects of the same colour, significant changes in scale and rotation, as well as obstacles in front of the traffic signs. To overcome these difficulties, a reference image database is usually used that includes different modes of appearing the traffic signs in the images. In order to overcome the effects of scale and rotation, in this paper a new method is presented in which only one reference image is needed for each sign to recognise the traffic sign in an image. In the proposed method, imaging is done in stereo. Using the captured image pair, a virtual image is generated which is then used to recognise the sign. As a result, the recognition is carried out with a minimum number of reference images. Experiments show that the proposed algorithm significantly improves recognition results. The traffic signs are recognised with 93.1% accuracy that enjoys a 4.9% improvement over traditional methods. Numéro de notice : A2021-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.003 Date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96304
in ISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 18 - 35[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021011 SL Revue Centre de documentation Revues en salle Disponible 081-2021013 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt The effect of different sampling schemes on estimation precision of snow water equivalent (SWE) using geostatistics techniques in a semi-arid region of Iran / Hojatolah Ganjkhanlo in Geocarto international, vol 35 n° 16 ([01/12/2020])PermalinkImproving 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)PermalinkEstimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study / Mir Reza Ghaffari Razin in GPS solutions, Vol 24 n° 3 (July 2020)PermalinkCrowdsource mapping of target buildings in hazard: the utilization of smartphone technologies and geographic services / Mohammad H. Vahidnia in Applied geomatics, vol 12 n° 1 (April 2020)PermalinkGeneration of digital terrain model for forest areas using a new particle swarm optimization on LiDAR data / Behnaz Bigdeli in Survey review, vol 52 n° 371 (March 2020)PermalinkAutomatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkCalculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2 / Ali Mokhtari in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkThe Iranian height datum offset from the GBVP solution and spirit-leveling/gravimetry data / Amir Ebadi in Journal of geodesy, vol 93 n° 8 (August 2019)PermalinkDesigning an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes / Sepideh Saeidi in Geocarto international, vol 33 n° 12 (December 2018)PermalinkApplication of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak, Kerman, south of Iran / Morteza Safari in Geocarto international, vol 33 n° 11 (November 2018)Permalink