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Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([15/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 8 [15/04/2021] . - pp 918 - 935 Titre : Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; İlker Ercanlı, Auteur ; Muammer Şenyurt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 918 - 935 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] classification par Perceptron multicouche [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] fonction de base radiale [Termes descripteurs IGN] gestion forestière [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] matrice de co-occurrence [Termes descripteurs IGN] peuplement forestier [Termes descripteurs IGN] Pinus nigra [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] texture d'image [Termes descripteurs IGN] Turquie Résumé : (auteur) The aim of this research is to assess some stand parameters such as stand volume (SV), basal area (BA), number of trees (NT) and aboveground biomass (AGB) of pure Crimean pine forest stands in Turkey by using ground measurements and remote sensing techniques. For this purpose, 86 sample plots were collected from pure Crimean pine stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. The stand parameters of each sample area were estimated using the data obtained from the sample plots. Subsequently, we calculated the values of contrast (CON), correlation (COR), dissimilarity (DIS), entropy (ENT), homogeneity (HOM), mean (M), second moment (SM) and variance (VAR) from WorldView-2 imagery using a grey-level co-occurrence matrix method. Eight textural features and twelve different window sizes ranging from 3 × 3 to 25 × 25 were generated from blue, green, red and near-infrared bands of the WorldView-2 satellite image. For predicting the relationships between WorldView-2 textural features and stand parameters of each sample plot, regression models were developed by using multiple linear regression (MLR) analysis. Additionally, artificial neural networks (ANNs) based on the multilayer perceptron (MLP) and the radial basis function (RBF) architectures were trained by comparing various numbers of neurons and activation functions in their network types. The results showed that the MLR models had low the coefficient of determination (R2) values (0.32 for SV, 0.35 for BA, 0.33 for NT and 0.34 for AGB), and the most of the ANNs models (MLP and RBF) were better than the regression models for estimating stand parameters. The ANNs model containing MLP and RBF for SV (R2 = 0.40; R2 = 0.56), for BA (R2 = 0.34; R2 = 0.51), for NT (R2 = 0.34; R2 = 0.37) and for AGB (R2 = 0.34, R2 = 0.57) were found the best results, respectively. Our results revealed that the ANNs models developed with WorldView-2 satellite image were beneficial to estimate stand parameters better than the MLR model in pure Crimean pine stands. Numéro de notice : A2021-308 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1629644 date de publication en ligne : 25/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1629644 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97443 [article]

Cloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques / Miao Tian in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 2781 - 2793 Titre : Cloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques Type de document : Article/Communication Auteurs : Miao Tian, Auteur ; Hao Chen, Auteur ; Guanghui Liu, Auteur Année de publication : 2021 Article en page(s) : pp 2781 - 2793 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par Perceptron multicouche [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] détection des nuages [Termes descripteurs IGN] dioxyde de carbone [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] modèle atmosphérique [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] vapeur d'eau Résumé : (auteur) Accurate cloud detection using infrared (IR) data is very challenging due to the limitations and uncertainties from many aspects in the satellite IR remote sensing. This article proposes an end-to-end cloud detection method for the Cross-track IR Sounder (CrIS) using machine learning (ML) techniques. The brightness temperatures from paired CrIS channels in the longwave and midwave water vapor bands and the longwave and shortwave CO 2 bands are used. After obtaining the linear regression coefficients for each of the selected channel pairs, a complete set of CrIS full spectral resolution (FSR) cloud detection index (FCDI) is derived from the temperature difference between the regression and observation for each channel pair. It is shown that FCDI captures cloud location and structure well by comparing with the cloud products (CPs) from the Visible IR Imaging Radiometer Suite (VIIRS). After collocating FCDI with VIIRS CP, ML techniques such as the extreme learning machine, support vector machine, and multilayer perceptron are used to train the collocated FCDIs for cloud detection. Simulation results show that the accuracy of FCDI cloud detection is slightly above 80%. Moreover, the results encourage the use of water vapor bands in FCDI, in addition to CO 2 bands. Numéro de notice : A2021-281 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3020120 date de publication en ligne : 18/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3020120 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97387 [article]

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 [en ligne], vol 78 n° 1 (March 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 1 (March 2021) . - n° 4 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 descripteurs IGN] Acer velutinum [Termes descripteurs IGN] alnus cordata [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] carpinus betulus [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] dynamique de la végétation [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] Fagus orientalis [Termes descripteurs IGN] forêt inéquienne [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] Iran [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] volume en bois [Vedettes matières IGN] Sylviculture Résumé : (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 [article]

Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships / Sensen Wu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 582 - 608 Titre : Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships Type de document : Article/Communication Auteurs : Sensen Wu, Auteur ; Zhongyi Wang, Auteur ; Zhenhong Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 582 - 608 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] espace-temps [Termes descripteurs IGN] estimation par noyau [Termes descripteurs IGN] littoral [Termes descripteurs IGN] modélisation environnementale [Termes descripteurs IGN] raisonnement spatiotemporel [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] régression linéaire Résumé : (auteur) Geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) are classic methods for estimating non-stationary relationships. Although these methods have been widely used in geographical modeling and spatiotemporal analysis, they face challenges in adequately expressing space-time proximity and constructing a kernel with optimal weights. This probably results in an insufficient estimation of spatiotemporal non-stationarity. To address complex non-linear interactions between time and space, a spatiotemporal proximity neural network (STPNN) is proposed in this paper to accurately generate space-time distance. A geographically and temporally neural network weighted regression (GTNNWR) model that extends geographically neural network weighted regression (GNNWR) with the proposed STPNN is then developed to effectively model spatiotemporal non-stationary relationships. To examine its performance, we conducted two case studies of simulated datasets and environmental modeling in coastal areas of Zhejiang, China. The GTNNWR model was fully evaluated by comparing with ordinary linear regression (OLR), GWR, GNNWR, and GTWR models. The results demonstrated that GTNNWR not only achieved the best fitting and prediction performance but also exactly quantified spatiotemporal non-stationary relationships. Further, GTNNWR has the potential to handle complex spatiotemporal non-stationarity in various geographical processes and environmental phenomena. Numéro de notice : A2021-167 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1775836 date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1080/13658816.2020.1775836 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97102 [article]

Machine learning in ground motion prediction / Farid Khosravikia in Computers & geosciences, vol 148 (March 2021)  

Public [article]  inComputers & geosciences > vol 148 (March 2021) . - n° 104700 Titre : Machine learning in ground motion prediction Type de document : Article/Communication Auteurs : Farid Khosravikia, Auteur ; Patricia Clayton, Auteur Année de publication : 2021 Article en page(s) : n° 104700 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] mouvement de terrain [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] sismicité Résumé : (auteur) This paper studies the advantages and disadvantages of different machine learning techniques in predicting ground-motion intensity measures given source characteristics, source-to-site distance, and local site conditions. Typically, linear regression-based models with predefined equations and coefficients are used in ground motion prediction. However, restrictions of the linear regression models may limit their capabilities in extracting complex nonlinear behaviors in the data. Therefore, the present paper comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. This study quantifies event-to-event and site-to-site variability of the ground motions by implementing them as random effect terms to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4–500 km in Oklahoma, Kansas, and Texas since 2005. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring predefined equations or coefficients. Moreover, it is found that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Numéro de notice : A2021-230 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1016/j.cageo.2021.104700 date de publication en ligne : 21/01/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104700 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97220 [article]

Developing a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data / Juan Guerra-Hernández in Forest ecology and management, vol 481 (February 2021)  

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Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs / Yang Bai in Computers & geosciences, vol 146 (January 2021)  

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Retrieving surface soil water content using a soil texture adjusted vegetation index and unmanned aerial system images / Haibin Gu in Remote sensing, vol 13 n° 1 (January 2021)  

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Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) / Mirza Razi Imam Baig in Annals of GIS, vol 26 n° 4 (December 2020)  

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Optimizing local geoid undulation model using GPS/levelling measurements and heuristic regression approaches / Mosbeh R. Kaloop in Survey review, vol 52 n° 375 (November 2020)  

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Semi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)  

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Predicting biomass dynamics at the national extent from digital aerial photogrammetry / Bronwyn Price in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)  

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Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects / Jike Chen in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)  

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Coastline change modelling induced by climate change using geospatial techniques in Togo (West Africa) / Yawo Konko in Advances in Remote Sensing, vol 9 n° 2 (June 2020)  

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Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data / Rochelle Schneider dos Santos in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

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