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About tree height measurement: Theoretical and practical issues for uncertainty quantification and mapping / Samuele De petris in Forests, vol 13 n° 7 (July 2022)
Titre : About tree height measurement: Theoretical and practical issues for uncertainty quantification and mapping Type de document : Article/Communication Auteurs : Samuele De petris, Auteur ; Philippo Sarvia, Auteur ; Enrico Borgogno Mondino, Auteur Année de publication : 2022 Article en page(s) : n°969 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de sensibilité
[Termes IGN] biome
[Termes IGN] carte forestière
[Termes IGN] Google Earth Engine
[Termes IGN] hauteur des arbres
[Termes IGN] incertitude de mesurage
[Termes IGN] modèle de simulation
[Termes IGN] pente
[Termes IGN] statistiques
[Termes IGN] variance
[Vedettes matières IGN] ForesterieRésumé : (auteur) Forest height is a fundamental parameter in forestry. Tree height is widely used to assess a site’s productivity both in forest ecology research and forest management. Thus, a precise height measure represents a necessary step for the estimation of carbon storage at the local, national, and global scales. In this context, error in height measurement necessarily affects the accuracy of related estimates. Ordinarily, forest height is surveyed by ground sampling adopting hypsometers. The latter suffers from many errors mainly related to the correct tree apex identification (not always well visible in dense stands) and to the measurement process itself. In this work, a statistically based operative method for estimating height measurement uncertainty (σH) was proposed using the variance propagation law. Some simulations were performed involving several combinations of terrain slope, tree height, and survey distances by modelling the σH behaviour and its sensitivity to such parameters. Results proved that σH could vary between 0.5 m and up to 20 m (worst case). Sensitivity analysis shows that terrain slopes and distance poorly affect σH, while angles are the main drivers of height uncertainty. Finally, to give a practical example of such deductions, tree height uncertainty was mapped at the global scale using Google Earth Engine and summarized per forest biomes. Results proved that tropical biomes have higher uncertainty (from 1 m to 4 m) while shrublands and tundra have the lowest (under 1 m). Numéro de notice : A2022-546 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f13070969 Date de publication en ligne : 22/06/2022 En ligne : https://doi.org/10.3390/f13070969 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101131
in Forests > vol 13 n° 7 (July 2022) . - n°969[article]
Titre : Machine learning - advanced techniques and emerging applications Type de document : Monographie Auteurs : Hamed Farhadi, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2018 Importance : 230 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 9781789237528 9781789237535 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] données massives
[Termes IGN] informatique en nuage
[Termes IGN] processeur graphique
[Termes IGN] statistiquesRésumé : (éditeur) The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses. Note de contenu : 1- Hardware accelerator design for machine learning
2- Regression models to predict air pollution from affordable data collections
3- Multiple kernel-based multimedia fusion for automated event detection from tweets
4- Using sentiment analysis and machine learning algorithms to determine citizens’ perceptions
5- Overcoming challenges in predictive modeling of Laser-plasma interaction scenarios. The sinuous route from advanced machine learning to deep learning
6- Machine learning approaches for spectrum management in cognitive radio networks
7- Machine learning algorithm for wireless indoor localization
8- classification of malaria-infected cells using deep convolutional neuronal networks
9- Machine learning in educational technology
10- Sentiment-based semantic rule learning for improved product recommandations
11- A multilevel evolutionary algorithm applied to the maximum satisfiability problemsNuméro de notice : 25952 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.69783 En ligne : https://doi.org/10.5772/intechopen.69783 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96406
Titre : Applications of spatial statistics Type de document : Monographie Auteurs : Ming Hung, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2016 Importance : 154 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-953-51-2757-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] croissance urbaine
[Termes IGN] données localisées
[Termes IGN] géostatistique
[Termes IGN] interpolation spatiale
[Termes IGN] planification urbaine
[Termes IGN] qualité de vie
[Termes IGN] revenu
[Termes IGN] statistiques
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] transport
[Termes IGN] variogrammeRésumé : (éditeur) Spatial statistics has been widely used in many environmental studies. This book is a collection of recent studies on applying spatial statistics in subjects such as demography, transportation, precision agriculture and ecology. Different subjects require different aspects of spatial statistics. In addition to quantitative statements from statistics and tests, visualization in forms of maps, drawings, and images are provided to illustrate the relationship between data and locations. This book will be valuable to researchers who are interested in applying statistics to spatial data, as well as graduate students who know statistics and want to explore how it can be applied to spatial data. With the processing part being simplified to several mouse clicks by commercial software, one should pay more attention to justification of using spatial statistics, as well as interpretation and assessment of the results. GIScience proves to be a useful tool in visualization of spatial data, and such useful technology should be utilized, as part, for the interpretation and assessment of the results. Note de contenu : 1- Application of spatial statistics in transportation engineering
2- Comparison of spatial interpolation techniques using visualization and quantitative assessment
3- Wage concentration in Spain: A spatial analysis
4- Spatial optimization of urban cellular automata model
5- Structural diversity of plant populations: Insight from spatial analyses
6- Practical value of user‐centred spatial statistics for responsive urban planningNuméro de notice : 25935 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/61666 En ligne : https://doi.org/10.5772/61666 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96262
Titre : Data Analysis : Statistical and Computational Methods for Scientists and Engineers Type de document : Monographie Auteurs : Siegmund Brandt, Auteur Editeur : Springer International Publishing Année de publication : 2014 Importance : 532 p. ISBN/ISSN/EAN : 978-3-319-03762-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de variance
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] méthode des moindres carrés
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] probabilités
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] statistiques
[Termes IGN] variable aléatoireRésumé : (éditeur) The fourth edition of this successful textbook presents a comprehensive introduction to statistical and numerical methods for the evaluation of empirical and experimental data. Equal weight is given to statistical theory and practical problems. The concise mathematical treatment of the subject matter is illustrated by many examples, and for the present edition a library of Java programs has been developed. It comprises methods of numerical data analysis and graphical representation as well as many example programs and solutions to programming problems. The programs (source code, Java classes, and documentation) and extensive appendices to the main text are available for free download from the book’s page at www.springer.com.
Contents: Probabilities. Random variables. Random numbers and the Monte Carlo Method. Statistical distributions (binomial, Gauss, Poisson). Samples. Statistical tests. Maximum Likelihood. Least Squares. Regression. Minimization. Analysis of Variance. Time series analysis.
Audience: The book is conceived both as an introduction and as a work of reference. In particular it addresses itself to students, scientists and practitioners in science and engineering as a help in the analysis of their data in laboratory courses, working for bachelor or master degrees, in thesis work, and in research and professional work.Note de contenu : 1- Introduction
2- Probabilities
3- Random Variables: Distributions
4- Computer Generated Random Numbers: The Monte Carlo Method
5- Some Important Distributions and Theorems
6- Samples
7- The Method of Maximum Likelihood
8- Testing Statistical Hypotheses
9- The Method of Least Squares
10- Function Minimization
11- Analysis of Variance
12- Linear and Polynomial Regression
13- Time Series AnalysisNuméro de notice : 25778 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-03762-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94973 Les indicateurs de développement durable pour les territoires, édition 2014 / CGDD Commissariat Général au Développement Durable (2014)
Titre : Les indicateurs de développement durable pour les territoires, édition 2014 Type de document : Rapport Auteurs : CGDD Commissariat Général au Développement Durable, Auteur Editeur : La Défense : Commissariat Général au Développement Durable CGDD Année de publication : 2014 Collection : Repères Importance : 110 p. Format : 10 x 15 cm Langues : Français (fre) Descripteur : [Vedettes matières IGN] Environnement
[Termes IGN] changement climatique
[Termes IGN] consommation
[Termes IGN] eau
[Termes IGN] économie
[Termes IGN] France (géographie physique)
[Termes IGN] sciences humaines et sociales
[Termes IGN] statistiques
[Termes IGN] transportNote de contenu : Thème : Consommation et production durables
Thème : Société de la connaissance et développement économique et social
Thème : Bonne gouvernance
Thème : Changement climatique et maîtrise de l'énergie
Thème : Transport et mobilité durables
Thème : Conservation et gestion durable de la biodiversité et des ressources naturelles
Thème : Santé publique, prévention et gestion des risques
Thème : Cohésion sociale et territorialesNuméro de notice : 15793 Affiliation des auteurs : non IGN Nature : Rapport statistique En ligne : http://www.statistiques.developpement-durable.gouv.fr/publications/p/2113/1539/i [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=40758 Réservation
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