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scientifique

Forest products in the global bioeconomy / P.J. Verkerk (2022)  

Public Titre : Forest products in the global bioeconomy : Enabling substitution by wood-based products and contributing to the sustainable development goals Type de document : Rapport Auteurs : P.J. Verkerk, Auteur ; M. Hassegawa, Auteur ; et al., Auteur Editeur : Rome [Italie] : Food and Agriculture Organization / Organisation des Nations Unies pour l'Alimentation et l'Agriculture FAO Année de publication : 2022 Importance : 168 p. ISBN/ISSN/EAN : 978-92-5-135151-2 Langues : Anglais (eng) Descripteur : [Termes IGN] développement durable [Termes IGN] économie forestière [Termes IGN] gaz à effet de serre [Termes IGN] gestion forestière durable [Termes IGN] produit forestier [Termes IGN] réduction [Vedettes matières IGN] Economie forestière Résumé : (auteur) This report addresses the role of forest products in replacing fossil-based and GHG-intensive products. The overarching objective is to provide recommendations to strengthen the contribution of substitution by forest products to sustainable development. To that end, this report firstly provides an overview of the understanding of the bioeconomy and the role of forest products across the world. Secondly, we present examples of conventional and innovative forest products and describe their role in the bioeconomy. Thirdly, we present a review of the quantitative and qualitative understanding of the environmental impacts and benefits of substituting fossil fuel-based or -intensive products with forest-based products, and of the contribution of substitution to SDGs. Fourthly, we outline the current understanding of the future global demand and supply dynamics of forest products and the potential impact that increased substitution may have on these dynamics. Fifthly, we identify gaps in the global forest product value chain. Finally, it provides recommendations and conclusions, respectively. Numéro de notice : 14516 Affiliation des auteurs : non IGN Thématique : FORET Nature : Rapport DOI : 10.4060/cb7274en En ligne : https://doi.org/10.4060/cb7274en Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101246

Hyperspectral band selection via optimal neighborhood reconstruction / Qi Wang in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8465 - 8476 Titre : Hyperspectral band selection via optimal neighborhood reconstruction Type de document : Article/Communication Auteurs : Qi Wang, Auteur ; Fahong Zhang, Auteur ; Xuelong Li, Auteur Année de publication : 2020 Article en page(s) : pp 8465 - 8476 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse combinatoire (maths) [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] optimisation (mathématiques) [Termes IGN] reconstruction d'image [Termes IGN] réduction Résumé : (auteur) Band selection is one of the most important technique in the reduction of hyperspectral image (HSI). Different from traditional feature selection problem, an important characteristic of it is that there is usually strong correlation between neighboring bands, that is, bands with close indexes. Aiming to fully exploit this prior information, a novel band selection method called optimal neighborhood reconstruction (ONR) is proposed. In ONR, band selection is considered as a combinatorial optimization problem. It evaluates a band combination by assessing its ability to reconstruct the original data, and applies a noise reducer to minimize the influence of noisy bands. Instead of using some approximate algorithms, ONR exploits a recurrence relation that underlies the optimization target to obtain the optimal solution in an efficient way. Besides, we develop a parameter selection approach to automatically determine the parameter of ONR, ensuring it is adaptable to different data sets. In experiments, ONR is compared with some state-of-the-art methods on six HSI data sets. The results demonstrate that ONR is more effective and robust than the others in most of the cases. Numéro de notice : A2020-742 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2987955 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2987955 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96372 [article]

A convolutional neural network with mapping layers for hyperspectral image classification / Rui Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3136 - 3147 Titre : A convolutional neural network with mapping layers for hyperspectral image classification Type de document : Article/Communication Auteurs : Rui Li, Auteur ; Zhibin Pan, Auteur ; Yang Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3136 - 3147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algèbre linéaire [Termes IGN] analyse discriminante [Termes IGN] analyse en composantes principales [Termes IGN] analyse multidimensionnelle [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couche thématique [Termes IGN] dispersion [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] réduction Résumé : (auteur) In this article, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification. The proposed mapping layers map the input patch into a low-dimensional subspace by multilinear algebra. We use our mapping layers to reduce the spectral and spatial redundancies and maintain most energy of the input. The feature extracted by our mapping layers can also reduce the number of following convolutional layers for feature extraction. Our MCNN architecture avoids the declining accuracy with increasing layers phenomenon of deep learning models for HSI classification and also saves the training time for its effective mapping layers. Furthermore, we impose the 3-D convolutional kernel on the convolutional layer to extract the spectral–spatial features for HSI. We tested our MCNN on three data sets of Indian Pines, University of Pavia, and Salinas, and we achieved the classification accuracy of 98.3%, 99.5%, and 99.3%, respectively. Experimental results demonstrate that the proposed MCNN can significantly improve classification accuracy and save much time consumption. Numéro de notice : A2020-234 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2948865 Date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2948865 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94980 [article]

Dimension reduction methods applied to coastline extraction on hyperspectral imagery / Ozan Arslan in Geocarto international, vol 35 n° 4 ([15/03/2020])  

Public [article]  inGeocarto international > vol 35 n° 4 [15/03/2020] . - pp 376 - 390 Titre : Dimension reduction methods applied to coastline extraction on hyperspectral imagery Type de document : Article/Communication Auteurs : Ozan Arslan, Auteur ; özer Akyürek, Auteur ; Sinasi Kaya, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 376 - 390 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] Bosphore, détroit du [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection de contours [Termes IGN] extraction [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] Istanbul (Turquie) [Termes IGN] littoral [Termes IGN] rapport signal sur bruit [Termes IGN] réduction [Termes IGN] télédétection [Termes IGN] trait de côte Résumé : (auteur) In this study, dimensionality reduction (DR) methods on a hyperspectral dataset to explore the influence on the process of extraction of coastlines were examined and performance of different DR algorithms on the detection of coastline in Bosphorus, Istanbul was investigated. Among these methods, principal component (PC) analysis, maximum noise fraction and independent component (IC) analysis were used in the experiments with the aim of comparing. The study was carried out using these well-known DR techniques on a real hyperspectral image, an Hyperion data set with 161 bands, in the course of the experiments. Three different classifiers (i.e. ML, SVM and neural network) were used for the classification of dimensionally reduced and original images to detect coastline in the region. The DR results were evaluated quantitatively and visually in order to determine the reduced dimensions of the image subsets. Findings show that there is no significant influence of using DR methods on the dataset on the detection of coastline. Numéro de notice : A2020-099 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1520920 Date de publication en ligne : 22/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1520920 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94690 [article]

Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis / Jiong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 76 - 89 Titre : Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis Type de document : Article/Communication Auteurs : Jiong Wang, Auteur ; Olivier Schmitz, Auteur ; Meng Lu, Auteur ; Derek Karssenberg, Auteur Année de publication : 2020 Article en page(s) : pp 76 - 89 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] données spatiotemporelles [Termes IGN] factorisation de matrice non-négative [Termes IGN] image Aqua-MODIS [Termes IGN] image Landsat [Termes IGN] image Terra-MODIS [Termes IGN] image thermique [Termes IGN] mise à l'échelle [Termes IGN] Pays-Bas [Termes IGN] radiance [Termes IGN] réduction [Termes IGN] température de surface [Termes IGN] variation diurne Résumé : (Auteur) Due to the limitation in the availability of airborne imagery data that are high in both spatial and temporal resolution, land surface temperature (LST) dense in both space and time can only be obtained through downscaling of frequently acquired LST with coarse resolution. Many conventional downscaling techniques are only feasible in an ideal situation, where land surface factors as LST predictors are continuously available for downscaling the LST. These techniques are also applied only at large scales ignoring sub-regional variations. Based upon unmixing based approaches, this study presents an LST downscaling workflow, where only the coarse resolution of 1 km LST image at the prediction time is required. The conceptual backbone of the study is assuming that the LST patterns are governed by thermal behaviors of a fixed set of temperature sensitive land surface components. In operation, the study focuses on central Netherlands covering an area of 90 × 90 km. The MODIS and Landsat imagery acquired simultaneously are used as a coarse-fine resolution pair to derive downscaling mechanism which is then applied to coarse imagery at a time with missing fine resolution imagery. First, an optimal number of thermal components are extracted at fine resolution through the application of the non-negative matrix factorization (NMF). These components are assumed to possess unique temperature change patterns caused by combined effects of land cover change, radiance change, or both. Given the LST change and thermal components at coarse resolution, the LST change load of each component can then be obtained at the coarse resolution by solving a system of linear equations encoding thermal component-LST relationship. Such LST change load of thermal components is further unmixed to fine resolution and linearly weighted by the component distribution at fine resolution to obtain the fine resolution LST change. During the process, the coarse LST data is used directly without any resampling practice as shown in previous studies. Thus the technique is less time consuming even with a large downscaling factor of 30. The downscaled fine resolution LST represents an R-squared of over 0.7 outperforming classic downscaling techniques. The downscaled LST differentiates temperature over major land types and captures both seasonal and diurnal LST dynamics. Numéro de notice : A2020-063 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.014 Date de publication en ligne : 16/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.014 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94580 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020031	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020033	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020032	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

PpC: a new method to reduce the density of lidar data. Does it affect the DEM accuracy? / Sandra Bujan in Photogrammetric record, vol 34 n° 167 (September 2019)  

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Reduction of measurement data before Digital Terrain Model generation vs. DTM generalisation / Wioleta Błaszczak-Bąk in Survey review, vol 51 n° 368 (September 2019)  

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Dimensionality reduction and classification of hyperspectral images using ensemble discriminative local metric learning / Yanni Dong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)  

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Empirical methods of reducing the observations in geodetic networks / Roman Kadaj in Geodesy and cartography, vol 65 n° 1 (June 2016)  

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A greedy-based multiquadric method for LiDAR-derived ground data reduction / Chuanfa Chen in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)  

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Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry : An interim report on a study sponsored by the National Science Foundation as a part of research grant GK-11655 / Y.I. Abdel-Aziz in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 2 (February 2015)  

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Spectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery / L. Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 9 (September 2014)  

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Dans un contexte de crise des finances publiques, faire plus avec moins, est-ce possible ? / Henri Pornon in Géomatique expert, n° 97 (01/03/2014)

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Efficient fusion of multidimensional descriptors for image retrieval / Neelanjan Bhowmik (2014)    

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Clutter reduction methods for point symbols in map mashups / Jari Korpi in Cartographic journal (the), vol 50 n° 3 (August 2013)  

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