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Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring / Gopal Krishna in Geocarto international, vol 36 n° 5 ([15/03/2021])
[article]
Titre : Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring Type de document : Article/Communication Auteurs : Gopal Krishna, Auteur ; Rabi N. Sahoo, Auteur ; Prafull Singh, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 481 - 498 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] image hyperspectrale
[Termes IGN] image thermique
[Termes IGN] indice de stress
[Termes IGN] Oryza (genre)
[Termes IGN] réflectance spectrale
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] rizière
[Termes IGN] sécheresse
[Termes IGN] stress hydrique
[Termes IGN] teneur en eau de la végétationRésumé : (auteur) Water deficit in crops induces a stress that may ultimately result in low production. Identification of response of genotypes towards water deficit stress is very crucial for plant phenotyping. The study was carried out with the objective to identify the response of different rice genotypes to water deficit stress. Ten rice genotypes were grown each under water deficit stress and well watered or nonstress conditions. Thermal images coupled with visible images were recorded to quantify the stress and response of genotypes towards stress, and relative water content (RWC) synchronized with image acquisition was also measured in the lab for rice leaves. Synced with thermal imaging, Canopy reflectance spectra from same genotype fields were also recorded. For quantification of water deficit stress, Crop Water Stress Index (CWSI) was computed and its mode values were extracted from processed thermal imageries. It was ascertained from observations that APO and Pusa Sugandha-5 genotypes exhibited the highest resistance to the water deficit stress or drought whereas CR-143, MTU-1010, and Pusa Basmati-1 genotypes ascertained the highest sensitiveness to the drought. The study reveals that there is an effectual relationship (R2 = 0.63) between RWC and CWSI. The relationship between canopy reflectance spectra and CWSI was also established through partial least square regression technique. A very efficient relationship (calibration R2 = 0.94 and cross-validation R2 = 0.71) was ascertained and 10 most optimal wavebands related to water deficit stress were evoked from hyperspectral data resampled at 5 nm wavelength gap. The identified ten most optimum wavebands can contribute in the quick detection of water deficit stress in crops. This study positively contributes towards the identification of drought tolerant and drought resistant genotypes of rice and may provide valuable input for the development of drought-tolerant rice genotypes in future. Numéro de notice : A2021-250 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1618922 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1618922 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97272
in Geocarto international > vol 36 n° 5 [15/03/2021] . - pp 481 - 498[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2021051 RAB Revue Centre de documentation En réserve L003 Disponible 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]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)
[article]
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 IGN] Chine
[Termes IGN] classification par réseau neuronal
[Termes IGN] espace-temps
[Termes IGN] estimation par noyau
[Termes IGN] littoral
[Termes IGN] modélisation environnementale
[Termes IGN] raisonnement spatiotemporel
[Termes IGN] régression géographiquement pondérée
[Termes IGN] régression linéaireRésumé : (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
in International journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 582 - 608[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021031 SL Revue Centre de documentation Revues en salle Disponible Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions / Xin-Ming Zhu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
[article]
Titre : Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions Type de document : Article/Communication Auteurs : Xin-Ming Zhu, Auteur ; Xiao-Ning Song, Auteur ; Pei Leng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2680 - 2697 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Chine
[Termes IGN] correction atmosphérique
[Termes IGN] hétérogénéité spatiale
[Termes IGN] image Landsat-8
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression
[Termes IGN] température au sol
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi’an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases. Numéro de notice : A2021-219 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3002821 Date de publication en ligne : 26/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3002821 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97211
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2680 - 2697[article]Machine learning in ground motion prediction / Farid Khosravikia in Computers & geosciences, vol 148 (March 2021)
[article]
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 IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Etats-Unis
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] mouvement de terrain
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] sismicitéRésumé : (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
in Computers & geosciences > vol 148 (March 2021) . - n° 104700[article]Le nivellement par GNSS chez SNCF Réseau / Antoine Beuvain Pacheco in XYZ, n° 166 (mars 2021)PermalinkSpace-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach / Bisong Hu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkModelling potential density of natural regeneration of European oak species (Quercus robur L., Quercus petraea (Matt.) Liebl.) depending on the distance to the potential seed source: Methodological approach for modelling dispersal from inventory data at forest enterprise level / Maximilian Axer in Forest ecology and management, vol 482 ([15/02/2021])PermalinkA comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping / Zhice Fang in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)PermalinkDeveloping 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)PermalinkEstimating the impacts of proximity to public transportation on residential property values: An empirical analysis for Hartford and Stamford areas, Connecticut / Bo Zhang in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)PermalinkGeo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan / Muhammad Imran in Geocarto international, vol 36 n° 2 ([01/02/2021])PermalinkGTP-PNet: A residual learning network based on gradient transformation prior for pansharpening / Hao Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkUsing Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas / José P. Granadeiro in Remote sensing, Vol 13 n° 2 (January-2 2021)PermalinkAssessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)Permalink