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Land cover harmonization using Latent Dirichlet Allocation / Zhan Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
[article]
Titre : Land cover harmonization using Latent Dirichlet Allocation Type de document : Article/Communication Auteurs : Zhan Li, Auteur ; Joanne C. White, Auteur ; Michael A. Wulder, Auteur Année de publication : 2021 Article en page(s) : pp 348 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] allocation de Dirichlet latente
[Termes IGN] Canada
[Termes IGN] carte d'occupation du sol
[Termes IGN] chevauchement
[Termes IGN] erreur de classification
[Termes IGN] harmonisation des données
[Termes IGN] matrice d'erreur
[Termes IGN] matrice de co-occurrence
[Termes IGN] segmentation sémantique
[Termes IGN] utilisation du solRésumé : (auteur) Large-area land cover maps are produced to satisfy different information needs. Land cover maps having partial or complete spatial and/or temporal overlap, different legends, and varying accuracies for similar classes, are increasingly common. To address these concerns and combine two 30-m resolution land cover products, we implemented a harmonization procedure using a Latent Dirichlet Allocation (LDA) model. The LDA model used regionalized class co-occurrences from multiple maps to generate a harmonized class label for each pixel by statistically characterizing land attributes from the class co-occurrences. We evaluated multiple harmonization approaches: using the LDA model alone and in combination with more commonly used information sources for harmonization (i.e. error matrices and semantic affinity scores). The results were compared with the benchmark maps generated using simple legend crosswalks and showed that using LDA outputs with error matrices performed better and increased harmonized map overall accuracy by 6–19% for areas of disagreement between the source maps. Our results revealed the importance of error matrices to harmonization, since excluding error matrices reduced overall accuracy by 4–20%. The LDA-based harmonization approach demonstrated in this paper is quantitative, transparent, portable, and efficient at leveraging the strengths of multiple land cover maps over large areas. Numéro de notice : A2021-027 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1796131 Date de publication en ligne : 27/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1796131 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96701
in International journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 348 - 374[article]Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery / Rajeev Bhattarai in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
[article]
Titre : Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery Type de document : Article/Communication Auteurs : Rajeev Bhattarai, Auteur ; Parinaz Rahimzadeh-Bajgiran, Auteur ; Aaron R. Weiskittel, Auteur Année de publication : 2021 Article en page(s) : pp 28 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Abies balsamea
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] défoliation
[Termes IGN] dégradation de la flore
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] insecte phyllophage
[Termes IGN] Nouveau-Brunswick (Canada)
[Termes IGN] Picea abiesRésumé : (auteur) Spruce budworm (Choristoneura fumiferana; SBW) is the most destructive forest pest of northeastern Canada and United States. SBW occurrence as well as the extent and severity of its damage are highly dependent on the characteristics of the forests and the availability of host species namely, spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Remote sensing satellite imagery represents a valuable data source for seamless regional-scale mapping of forest composition. This study developed and evaluated new models to map the distribution and abundance of SBW host species at 20 m spatial resolution using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery in combination with several site variables for a total of 191 variables in northern New Brunswick, Canada using the Random Forest (RF) algorithm. We found Sentinel-2 multi-temporal single spectral bands and numerous spectral vegetation indices (SVIs) yielded the classification of SBW host species with an overall accuracy (OA) of 72.6% and kappa coefficient (K) of 0.65. Incorporating Sentinel-1 SAR data with Sentinel-2 variables coupled with elevation, only marginally improved the performance of the model (OA: 73.0% and K: 0.66). The use of Sentinel-1 SAR data with elevation resulted in a reasonable OA of 57.5% and K of 0.47. These spatially explicit up-to-date SBW host species maps are essential for identifying susceptible forests, monitoring SBW defoliation, and minimizing forest losses from insect impacts at landscape scale in the current SBW outbreak in the region. Numéro de notice : A2021-085 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.023 Date de publication en ligne : 15/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.023 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96845
in ISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 28 - 40[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021021 SL Revue Centre de documentation Revues en salle Disponible 081-2021022 DEP-RECF Revue Nancy Bibliothèque Nancy IFN Exclu du prêt Topoclimatic zoning of continental Chile / Donna Cortez in Journal of maps, vol 17 n° 2 (February 2021)
[article]
Titre : Topoclimatic zoning of continental Chile Type de document : Article/Communication Auteurs : Donna Cortez, Auteur ; Sebastián Herrera, Auteur ; Daniela Araya-Osses, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : *pp 114 - 124 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse en composantes principales
[Termes IGN] carte climatique
[Termes IGN] Chili
[Termes IGN] climat continental
[Termes IGN] climat de montagne
[Termes IGN] climatologie
[Termes IGN] littoral
[Termes IGN] partition d'image
[Termes IGN] topographie localeRésumé : (article) In this study, the topoclimates of continental Chile are mapped. The mapping involves the identification of homogeneous zones based on the relationships between the climatic variables that characterize a location and the topography that influences the spatial behavior of these variables. The climatic and topographical zoning of the study area is conducted using a statistical methodology based on a combination of principal component analysis and cluster analysis. The climate, topography, and topoclimatic zoning yield 20, 8, and 96 clusters, respectively. Maximum topoclimatic variability is identified in sectors with mountain ranges and intermediate depression (especially in valley areas), and minimum variability is detected in the coastal sector. Furthermore, only one of the topoclimatic units has an area larger than 50,000 km2, whereas 46.8% of the units have surface areas below 2,000 km2. Numéro de notice : A2021-410 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17445647.2021.1886188 Date de publication en ligne : 10/03/2021 En ligne : https://doi.org/10.1080/17445647.2021.1886188 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97732
in Journal of maps > vol 17 n° 2 (February 2021) . - *pp 114 - 124[article]Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain / R. Niederheiser in GIScience and remote sensing, vol 58 n° 1 (February 2021)
[article]
Titre : Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain Type de document : Article/Communication Auteurs : R. Niederheiser, Auteur ; M. Winkler, Auteur ; V. Di Cecco, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 120 - 137 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre
[Termes IGN] Alpes
[Termes IGN] caméra numérique
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification semi-dirigée
[Termes IGN] couvert végétal
[Termes IGN] distribution de Poisson
[Termes IGN] données topographiques
[Termes IGN] indice de végétation
[Termes IGN] module linéaire
[Termes IGN] montagne
[Termes IGN] occupation du sol
[Termes IGN] photogrammétrie métrologique
[Termes IGN] semis de pointsRésumé : (auteur) In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance. Numéro de notice : A2021-258 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2020.1859264 Date de publication en ligne : 13/01/2021 En ligne : https://doi.org/10.1080/15481603.2020.1859264 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97295
in GIScience and remote sensing > vol 58 n° 1 (February 2021) . - pp 120 - 137[article]Mapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series / Misganu Debella-Gilo in Remote sensing, Vol 13 n° 2 (January-2 2021)
[article]
Titre : Mapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series Type de document : Article/Communication Auteurs : Misganu Debella-Gilo, Auteur ; Arnt Kristian Gjertsen, Auteur Année de publication : 2021 Article en page(s) : n° 289 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] carte agricole
[Termes IGN] carte d'utilisation du sol
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image Sentinel-MSI
[Termes IGN] Norvège
[Termes IGN] série temporelle
[Termes IGN] Soil Adjusted Vegetation Index
[Termes IGN] surface cultivée
[Termes IGN] utilisation du sol
[Termes IGN] variation saisonnièreRésumé : (auteur) The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas. Numéro de notice : A2021-198 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13020289 Date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.3390/rs13020289 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97149
in Remote sensing > Vol 13 n° 2 (January-2 2021) . - n° 289[article]Amélioration des systèmes de suivi des cultures à l’aide de la télédétection multi-source et des techniques d’apprentissage profond / Yawogan Gbodjo (2021)PermalinkAnalyse de la dynamique d’embroussaillement des pelouses calcaires par traitement d’images / Théo Mesure (2021)PermalinkAnalyse spatio-temporaire des dégradations et évolution des forêts par télédétection : cas du Parc National de Theniet El Had (Algérie) / Faouzi Berrichi in Bulletin des sciences géographiques, n° 32 (2019 - 2021)PermalinkAnalysing 18th century hydrographic data: a campaign in the Bay of Biscay, 1750-1751 / Helen Mair Rawsthorne (2021)PermalinkApplications of remote sensing data in mapping of forest growing stock and biomass / Jose Aranha (2021)PermalinkApport des données satellitaires Sentinel-1 et Sentinel-2 pour la détection des surfaces irriguées et l'estimation des besoins et des consommations en eau des cultures d'été dans les zones tempérées / Yann Pageot (2021)PermalinkApport de la modélisation physique pour la cartographie de la biodiversité végétale en forêts tropicales par télédétection optique / Dav Ebengo Mwampongo (2021)PermalinkApport de la photogrammétrie satellite pour la modélisation du manteau neigeux / César Deschamps-Berger (2021)PermalinkApport de la télédétection pour la simulation spatialisée des composantes du bilan carbone des cultures et des effets d'atténuation biogéochimiques et biogéophysiques des cultures intermédiaires / Gaétan Pique (2021)PermalinkApports des méthodes d'apprentissage profond pour la reconnaissance automatique des modes d'occupation des sols et d'objets par télédétection en milieu tropical / Guillaume Rousset (2021)PermalinkPermalinkPermalinkAssessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)PermalinkAssessment of combining convolutional neural networks and object based image analysis to land cover classification using Sentinel 2 satellite imagery (Tenes region, Algeria) / N. Zaabar (2021)PermalinkAutomated detection of lineaments express geological linear features of a tropical region using topographic fabric grain algorithm and the SRTM DEM / Samy Ismail Elmahdy in Geocarto international, vol 36 n° 1 ([01/01/2021])PermalinkBenchmarking of convolutional neural network approaches for vegetation land cover mapping / Benjamin Carpentier (2021)PermalinkPermalinkDevelopment and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors / Marta Sapena Moll (2021)PermalinkDiurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations / Adnane Chakir (2021)PermalinkDiurnal cycles of C-band temporal coherence and backscattering coefficient over a wheat field in a semi-arid area / Nadia Ouaadi (2021)Permalink