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Developing reliably distinguishable color schemes for legends of natural resource taxonomy-based maps / Virgil Vlad in Cartography and Geographic Information Science, Vol 48 n° 5 (September 2021)  

Public [article]  inCartography and Geographic Information Science > Vol 48 n° 5 (September 2021) . - pp 393 - 416 Titre : Developing reliably distinguishable color schemes for legends of natural resource taxonomy-based maps Type de document : Article/Communication Auteurs : Virgil Vlad, Auteur ; Mihai Toti, Auteur ; Sorina Dumitru, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 393 - 416 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique [Termes IGN] carte d'occupation du sol [Termes IGN] colorimétrie [Termes IGN] couleur (rédaction cartographique) [Termes IGN] couleur primaire [Termes IGN] lecture de carte [Termes IGN] légende cartographique [Termes IGN] palette de couleurs [Termes IGN] ressources naturelles [Termes IGN] Roumanie [Termes IGN] taxinomie [Termes IGN] visualisation cartographique Résumé : (auteur) The legends of natural resource taxonomy-based maps (e.g. soil, geological, geomorphological, vegetation, and land cover/land use) need many different distinguishable colors. The existing methods of color selection for map legends are based on the designer subjectivity, ensuring schemes having few colors. An analysis of the modeling and management of colors in digital applications has led to define an algorithm to calculate an objective colorimetric measure of color difference – “DE*ab” – based on the perceptually uniform color model CIELAB. The proposed method consists of a set of specific rules for developing hierarchically structured color schemes and a specific procedure for ensuring selection of a large number of reliably distinguishable colors, based on a color difference threshold. The accuracy of color reproduction in printing processes is also taken into account. The method has been applied to develop a standard of colors for soil maps. It contains 63 colors and has been used for developing a soil map having 41 standard colors. A user test of the method results proved that thresholds of 10 DE*ab units and 15 DE*ab units ensure obtaining acceptably distinguishable colors for displaying/printing maps by using high-quality, respectively, current devices. Three datasets that support the research are given. Numéro de notice : A2021-643 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1942218 date de publication en ligne : 23/08/2021 En ligne : https://doi.org/10.1080/15230406.2021.1942218 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98340 [article]

Geoglam, l'agriculture par satellite / Laurent Polidori in Géomètre, n° 2194 (septembre 2021)

Public [article]  inGéomètre > n° 2194 (septembre 2021) . - pp 17 - 17 Titre : Geoglam, l'agriculture par satellite Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2021 Article en page(s) : pp 17 - 17 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] blé (céréale) [Termes IGN] carte d'occupation du sol [Termes IGN] Glycine max [Termes IGN] image à haute résolution [Termes IGN] Leaf Area Index [Termes IGN] maïs (céréale) [Termes IGN] modèle dynamique [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] observation de la Terre [Termes IGN] Oryza (genre) [Termes IGN] série temporelle [Termes IGN] surface cultivée Résumé : (Auteur) Des satellites pour la sécurité alimentaire et la transparence du marché agricole Numéro de notice : A2021-579 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans date de publication en ligne : 13/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98400 [article]

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Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine / Tongxi Hu in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 176 (June 2021) . - pp 250 - 261 Titre : Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine Type de document : Article/Communication Auteurs : Tongxi Hu, Auteur ; Elizabeth Myers Toman, Auteur ; Gang Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 250 - 261 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bassin hydrographique [Termes IGN] carte d'occupation du sol [Termes IGN] classification bayesienne [Termes IGN] détection de changement [Termes IGN] estimation bayesienne [Termes IGN] Google Earth Engine [Termes IGN] image Landsat [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Ohio (Etats-Unis) [Termes IGN] précision infrapixellaire [Termes IGN] série temporelle Résumé : (auteur) Large fractions of human-altered lands are working landscapes where people and nature interact to balance social, economic, and ecological needs. Achieving these sustainability goals requires tracking human footprints and landscape disturbance at fine scales over time—an effort facilitated by remote sensing but still under development. Here, we report a satellite time-series analysis approach to detecting fine-scale human disturbances in an Ohio watershed dominated by forests and pastures but with diverse small-scale industrial activities such as hydraulic fracturing (HF) and surface mining. We leveraged Google Earth Engine to stack decades of Landsat images and explored the effectiveness of a fuzzy change detection algorithm called the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) to capture fine-scale disturbances. BEAST is an ensemble method, capable of estimating changepoints probabilistically and identifying sub-pixel disturbances. We found the algorithm can successfully capture the patterns and timings of small-scale disturbances, such as grazing, agriculture management, coal mining, HF, and right-of-ways for gas and power lines, many of which were not captured in the annual land cover maps from Cropland Data Layers—one of the most widely used classification-based land dynamics products in the US. For example, BEAST could detect the initial HF wellpad construction within 60 days of the registered drilling dates on 88.2% of the sites. The wellpad footprints were small, disturbing only 0.24% of the watershed in area, which was dwarfed by other activities (e.g., right-of-ways of utility transmission lines). Together, these known activities have disturbed 9.7% of the watershed from the year 2000 to 2017 with evergeen forests being the most affected land cover. This study provides empirical evidence on the effectiveness and reliability of BEAST for changepoint detection as well as its capability to detect disturbances from satellite images at sub-pixel levels and also documents the value of Google Earth Engine and satellite time-series imaging for monitoring human activities in complex working landscapes. Numéro de notice : A2021-415 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.008 date de publication en ligne : 17/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97746 [article]

Resolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 6 (June 2021) . - pp 405 - 412 Titre : Resolution enhancement for large-scale land cover mapping via weakly supervised deep learning Type de document : Article/Communication Auteurs : Qiutong Yu, Auteur ; Wei Liu, Auteur ; Wesley Nunes Gonçalves, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 405 - 412 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] apprentissage profond [Termes IGN] apprentissage semi-dirigé [Termes IGN] carte d'occupation du sol [Termes IGN] changement d'occupation du sol [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'apprentissage [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] image Terra-MODIS [Termes IGN] série temporelle Résumé : (Auteur) Multispectral satellite imagery is the primary data source for monitoring land cover change and characterizing land cover globally. However, the consistency of land cover monitoring is limited by the spatial and temporal resolutions of the acquired satellite images. The public availability of daily high-resolution images is still scarce. This paper aims to fill this gap by proposing a novel spatiotemporal fusion method to enhance daily low spatial resolution land cover mapping using a weakly supervised deep convolutional neural network. We merge Sentinel images and moderate resolution imaging spectroradiometer (MODIS )-derived thematic land cover maps under the application background of massive remote sensing data and the large spatial resolution gaps between MODIS data and Sentinel images. The neural network training was conducted on the public data set SEN12MS, while the validation and testing used ground truth data from the 2020 IEEE Geoscience and Remote Sensing Society data fusion contest. The proposed data fusion method shows that the synthesized land cover map has significantly higher spatial resolution than the corresponding MODIS-derived land cover map. The ensemble approach can be implemented for generating high-resolution time series of satellite images by fusing fine images from Sentinel-1 and -2 and daily coarse images from MODIS. Numéro de notice : A2021-373 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.6.405 date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.14358/PERS.87.6.405 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97825 [article]

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A user-driven process for INSPIRE-compliant land use database: example from Wallonia, Belgium / Benjamin Beaumont in Annals of GIS, vol 27 n° 2 (April 2021)  

Public [article]  inAnnals of GIS > vol 27 n° 2 (April 2021) . - pp 211 - 224 Titre : A user-driven process for INSPIRE-compliant land use database: example from Wallonia, Belgium Type de document : Article/Communication Auteurs : Benjamin Beaumont, Auteur ; Tais Grippa, Auteur ; Moritz Lennert, Auteur Année de publication : 2021 Article en page(s) : pp 211 - 224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] aménagement du territoire [Termes IGN] base de données foncières [Termes IGN] carte d'occupation du sol [Termes IGN] chaîne de traitement [Termes IGN] données massives [Termes IGN] édition en libre accès [Termes IGN] infrastructure européenne de données localisées [Termes IGN] INSPIRE [Termes IGN] utilisateur [Termes IGN] utilisation du sol [Termes IGN] Wallonie (Belgique) Résumé : (auteur) Regional land use monitoring at high spatial, temporal, and thematic resolution is an important expectation of Walloon stakeholders. Over the last decade, increased data-processing capacities and the annual acquisition of remotely sensed data have resulted in the production of a large amount of relevant geodata. The INSPIRE directive and its obligations for 2020 serve as a path for the development of a new user-driven and open-source hierarchical land use classification system mapping scheme, as presented in this paper. The process includes intensive user consultation, the development of an entire automatic processing chain, and efforts to address challenges such as big data handling, the variability of input data properties, and reproducibility. The thematically detailed land use map, with its 69 classes, is already widely used by Walloon stakeholders, and new demands for updating have already emerged. Based on a European classification system that is compulsory for all member states, INSPIRE-compliant land use maps will make it possible to carry out cross-border studies and compare spatial planning strategies between states. Numéro de notice : A2021-626 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2021.1875047 date de publication en ligne : 17/01/2021 En ligne : https://doi.org/10.1080/19475683.2021.1875047 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98256 [article]

Toward a yearly country-scale CORINE land-cover map without using images: A map translation approach / Luc Baudoux in Remote sensing, Vol 13 n° 6 (March 2021)  

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Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena Moll in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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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)  

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Apport de la photogrammétrie satellite pour la modélisation du manteau neigeux / César Deschamps-Berger (2021)  

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Assessment 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)  

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Benchmarking of convolutional neural network approaches for vegetation land cover mapping / Benjamin Carpentier (2021)  

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Development 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)  

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Télédétection et intégration de connaissances via la modélisation spatiale pour une cartographie plus cohérente des systèmes agricoles complexes / Arthur Crespin-Boucaud (2021)  

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Analyse de la déforestation dans la périphérie ouest de la réserve de biosphère du Dja au Cameroun, à partir d'une série multi-annuelle d'images Landsat / Eric Wilson Tegno Nguekam in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)  

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Semi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)  

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