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Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 10 (May-2 2022) . - n° 2446 Titre : Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning Type de document : Article/Communication Auteurs : Mingge Yu, Auteur ; Xiaoping Rui, Auteur ; Weiyi Xie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2446 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection automatique [Termes IGN] échantillonnage [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] image panchromatique [Termes IGN] image Worldview [Termes IGN] modèle de simulation [Termes IGN] surface cultivée [Termes IGN] terrasse Résumé : (auteur) Rapid, accurate extraction of terraces from high-resolution images is of great significance for promoting the application of remote-sensing information in soil and water conservation planning and monitoring. To solve the problem of how deep learning requires a large number of labeled samples to achieve good accuracy, this article proposes an automatic identification method for terraces that can obtain high precision through small sample datasets. Firstly, a terrace identification source model adapted to multiple data sources is trained based on the WorldView-1 dataset. The model can be migrated to other types of images for terracing extraction as a pre-trained model. Secondly, to solve the small sample problem, a deep transfer learning method for accurate pixel-level extraction of high-resolution remote-sensing image terraces is proposed. Finally, to solve the problem of insufficient boundary information and splicing traces during prediction, a strategy of ignoring edges is proposed, and a prediction model is constructed to further improve the accuracy of terrace identification. In this paper, three regions outside the sample area are randomly selected, and the OA, F1 score, and MIoU averages reach 93.12%, 91.40%, and 89.90%, respectively. The experimental results show that this method, based on deep transfer learning, can accurately extract terraced field surfaces and segment terraced field boundaries. Numéro de notice : A2022-402 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14102446 Date de publication en ligne : 19/05/2022 En ligne : https://doi.org/10.3390/rs14102446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100705 [article]

Effect of climate change on the growth of tree species: Dendroclimatological analysis / Archana Gauli in Forests, vol 13 n° 4 (April 2022)  

Public [article]  inForests > vol 13 n° 4 (April 2022) . - n° 496 Titre : Effect of climate change on the growth of tree species: Dendroclimatological analysis Type de document : Article/Communication Auteurs : Archana Gauli, Auteur ; Prem Raj Neupane, Auteur ; Philip Mundhenk, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 496 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne [Termes IGN] analyse diachronique [Termes IGN] croissance des arbres [Termes IGN] dendrochronologie [Termes IGN] dendrologie [Termes IGN] données météorologiques [Termes IGN] échantillonnage [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] prévision météorologique [Termes IGN] Pseudotsuga menziesii [Termes IGN] sécheresse [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Tree ring analyses can assist in revealing the effect of gradual change in climatic variables on tree growth. Dendroclimatic analyses are of particular importance in evaluating the climate variables that affect growth significantly and in determining the relative strength of different climatic factors. In this study, we investigated the growth performance of Pinus sylvestris, Picea abies, and Pseudotsuga menziesii in northern Germany using standard dendrochronological methods. The study further analyzed tree growth responses to different climatic variables over a period of a hundred years. Both response function analysis and moving correlation analysis confirmed that the climate and growth relationship is species-specific and variable and inconsistent over time. Scots pine and Douglas fir growth were stimulated mainly by the increase in winter temperatures, particularly the January, February, and March temperatures of the current year. In contrast, Norway spruce growth was stimulated mainly by the increase in precipitation in May, June, and July and the increase in temperature in March of the current year. Climate projections for central Europe foresee an increase in temperature and a decrease in the amount of summer precipitation. In a future, warmer climate with drier summers, the growth of Norway spruce might be negatively affected. Numéro de notice : A2022-259 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f13040496 Date de publication en ligne : 22/03/2022 En ligne : https://doi.org/10.3390/f13040496 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100237 [article]

Two-phase forest inventory using very-high-resolution laser scanning / Henrik J. Persson in Remote sensing of environment, vol 271 (March- 2 2022)  

Public [article]  inRemote sensing of environment > vol 271 (March- 2 2022) . - n° 112909 Titre : Two-phase forest inventory using very-high-resolution laser scanning Type de document : Article/Communication Auteurs : Henrik J. Persson, Auteur ; Kenneth Olofsson, Auteur ; Johan Holmgren, Auteur Année de publication : 2022 Article en page(s) : n° 112909 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] échantillonnage [Termes IGN] forêt boréale [Termes IGN] hauteur des arbres [Termes IGN] inférence statistique [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lasergrammétrie [Termes IGN] modélisation de la forêt [Termes IGN] peuplement forestier [Termes IGN] Suède [Termes IGN] télémétrie laser terrestre [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) In this study, we compared a two-phase laser-scanning-based forest inventory of stands versus a traditional field inventory using sample plots. The two approaches were used to estimate stem volume (VOL), Lorey's mean height (HL), Lorey's stem diameter (DL), and VOL per tree species in a study area in Sweden. The estimates were compared at the stand level with the harvested reference values obtained using a forest harvester. In the first phase, a helicopter acquired airborne laser scanning (ALS) data with >500 points/m2 along 50-m wide strips across the stands. These strips intersected systematic plots in phase two, where terrestrial laser scanning (TLS) was used to model DL for individual trees. In total, phase two included 99 plots across 10 boreal forest stands in Sweden (lat 62.9° N, long 16.9° E). The single trees were segmented in both the ALS and TLS data and linked to each other. The very-high-resolution ALS data enabled us to directly measure tree heights and also classify tree species using a convolutional neural network. Stem volume was predicted from the predicted DBH and the estimated height, using national models, and aggregated at the stand level. The study demonstrates a workflow to derive forest variables and stand-level statistics that has potential to replace many manual field inventories thanks to its time efficiency and improved accuracy. To evaluate the inventories, we estimated bias, RMSE, and precision, expressed as standard error. The laser-scanning-based inventory provided estimates with an accuracy considerably higher than the field inventory. The RMSE was 17 m3/ha (7.24%), 0.9 m (5.63%), and 16 mm (5.99%) for VOL, HL, and DL respectively. The tree species classification was generally successful and improved the three species-specific VOL estimates by 9% to 74%, compared to field estimates. In conclusion, the demonstrated laser-scanning-based inventory shows potential to replace some future forest inventories, thanks to the increased accuracy demonstrated empirically in the Swedish forest study area. Numéro de notice : A2022-249 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112909 Date de publication en ligne : 22/01/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100201 [article]

Survival time and mortality rate of regeneration in the deep shade of a primeval beech forest / R. Petrovska in European Journal of Forest Research, vol 141 n° 1 (February 2022)  

Public [article]  inEuropean Journal of Forest Research > vol 141 n° 1 (February 2022) . - pp 43 - 58 Titre : Survival time and mortality rate of regeneration in the deep shade of a primeval beech forest Type de document : Article/Communication Auteurs : R. Petrovska, Auteur ; Harald Bugmann, Auteur ; Martina Lena Hobi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 43 - 58 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer platanoïdes [Termes IGN] Acer pseudoplatanus [Termes IGN] analyse de données [Termes IGN] arbre mort [Termes IGN] biomasse aérienne [Termes IGN] croissance des arbres [Termes IGN] dendrochronologie [Termes IGN] échantillonnage [Termes IGN] Fagus sylvatica [Termes IGN] forêt primaire [Termes IGN] Leaf Mass per Area [Termes IGN] mortalité [Termes IGN] ombre [Termes IGN] régénération (sylviculture) [Termes IGN] Ukraine [Vedettes matières IGN] Sylviculture Résumé : (auteur) Low mortality rates and slow growth differentiate shade-tolerant from shade-intolerant species and define the survival strategy of juvenile trees growing in deep shade. While radial stem growth has been widely used to explain mortality in juvenile trees, the leaf area ratio (LAR), known to be a key component of shade tolerance, has been neglected so far. We assessed the effects of LAR, radial stem growth and tree height on survival time and the age-specific mortality rate of juvenile Fagus sylvatica L. (European beech), Acer pseudoplatanus L. (sycamore maple) and Acer platanoides L. (Norway maple) in a primeval beech forest (Ukraine). Aboveground and belowground biomass and radial stem growth were analysed for 289 living and 179 dead seedlings and saplings. Compared with the other species, F. sylvatica featured higher LAR, slower growth and a lower mortality rate. The average survival time of F. sylvatica juveniles (72 years) allows it to reach the canopy more often than its competitors in forests with low canopy turnover rate. In contrast, a combination of lower LAR, higher growth rate and higher age-specific mortality rate of the two Acer species resulted in their shorter survival times and thus render their presence in the canopy a rare event. Overall, this study suggests that shade tolerance, commonly defined as a relationship between sapling mortality and growth, can alternatively be formulated as a relationship between survival time and the interplay of growth and LAR. Numéro de notice : A2022-199 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-021-01427-3 Date de publication en ligne : 05/11/2021 En ligne : https://doi.org/10.1007/s10342-021-01427-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100000 [article]

Metalearning / Pavel Brazdil (2022)  

Public Titre : Metalearning : Applications to automated machine learning and data mining Type de document : Monographie Auteurs : Pavel Brazdil, Auteur ; Jan N. van Rijn, Auteur ; Carlos Soares, Auteur ; Joaquin Vanschoren, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2022 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-67024-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse de groupement [Termes IGN] apprentissage automatique [Termes IGN] chaîne de traitement [Termes IGN] échantillonnage [Termes IGN] modèle stochastique [Termes IGN] ontologie [Termes IGN] optimisation (mathématiques) [Termes IGN] régression [Termes IGN] science des données [Termes IGN] série temporelle Résumé : (éditeur) This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence. Note de contenu : 1- Basic concepts and architecture 2- Advanced techniques and methods 3- Organizing and Exploiting Metadata Numéro de notice : 28698 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-67024-5 En ligne : https://doi.org/10.1007/978-3-030-67024-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100469

Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models / Arne Nothdurft in Forest ecology and management, vol 502 (15 december 2021)  

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The efficiency of retention measures in continuous-cover forestry for conserving epiphytic cryptogams: A case study on Abies alba / Stefan Kaufmann in Forest ecology and management, vol 502 (15 december 2021)  

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Deep learning for toponym resolution: Geocoding based on pairs of toponyms / Jacques Fize in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)  

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Spatial variability of suspended sediments in San Francisco Bay, California / Niky C. Taylor in Remote sensing, vol 13 n° 22 (November-2 2021)  

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Uncertainties in measurements of leaf optical properties are small compared to the biological variation within and between individuals of European beech / Fanny Petibon in Remote sensing of environment, vol 264 (October 2021)  

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Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan / Dirk Tiede in Transactions in GIS, Vol 25 n° 3 (June 2021)  

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Mixture effect on radial stem and shoot growth differs and varies with temperature / Maude Toïgo in Forest ecology and management, vol 488 (15 May 2021)  

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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 ([01/05/2021])  

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Forest fragmentation assessment using field-based sampling data from forest inventories / Habib Ramezani in Scandinavian journal of forest research, vol 36 n° 4 ([01/05/2021])  

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Performance evaluation of artificial neural networks for natural terrain classification / Perpetual Hope Akwensi in Applied geomatics, vol 13 n° 1 (May 2021)  

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