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Evaluating narrative in geoportals for territorial public policies / Luis Manuel Batista in Cartographica, vol 56 n° 4 (Winter 2021)
[article]
Titre : Evaluating narrative in geoportals for territorial public policies Type de document : Article/Communication Auteurs : Luis Manuel Batista, Auteur ; Ana Figueiras, Auteur Année de publication : 2021 Article en page(s) : pp 303 - 319 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse comparative
[Termes IGN] carte thématique
[Termes IGN] collectivité territoriale
[Termes IGN] communication cartographique
[Termes IGN] compréhension de l'image
[Termes IGN] conception cartographique
[Termes IGN] géoportail
[Termes IGN] plan local d'urbanisme
[Termes IGN] politique territoriale
[Termes IGN] Portugal
[Termes IGN] web mappingRésumé : (auteur) To do territorial planning, we need several maps referring to different layers of information necessary to represent the territory according to a vast set of variables. At the Portuguese municipal level, the municipal master plan (PDM – plano diretor municipal) is the territorial management tool responsible for long-term territorial planning and ordering. Since the PDM will constrain the citizens’ lives, it should be of easy access and interpretation. However, due to its large amount of information, it is often hard for them to understand what is presented using only static cartographic elements. Comparing 60 Web sites that present geographical information for a wider public, we found that narrative, visual, and interactive elements and data presentation in flexible portions with the necessary information for partitioning, browsing, or querying made the data more engaging. This flexibility is particularly important with large data sets, where decreasing the level of complexity is vital for proper understanding and analysis. We found that geoportals dedicated to territorial public policies lack narrative elements that would improve comprehension. Since it is difficult for the general public to understand the strategy the municipality wants to implement, the desired public participation, although possible, will return poor results. Numéro de notice : A2021-887 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2021-0023 Date de publication en ligne : 02/12/2021 En ligne : https://doi.org/10.3138/cart-2021-0023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99227
in Cartographica > vol 56 n° 4 (Winter 2021) . - pp 303 - 319[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2021041 SL Revue Centre de documentation Revues en salle Disponible Extensification and afforestation of cultivated mineral soil for climate change mitigation in Finland / Boris Tupek in Forest ecology and management, vol 501 (December-1 2021)
[article]
Titre : Extensification and afforestation of cultivated mineral soil for climate change mitigation in Finland Type de document : Article/Communication Auteurs : Boris Tupek, Auteur ; Aleski Lehtonen, Auteur ; Raisa Mäkipää, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119672 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] afforestation
[Termes IGN] Betula (genre)
[Termes IGN] boisement artificiel
[Termes IGN] changement d'occupation du sol
[Termes IGN] dioxyde de carbone
[Termes IGN] écologie forestière
[Termes IGN] Finlande
[Termes IGN] modèle de croissance végétale
[Termes IGN] Picea abies
[Termes IGN] puits de carbone
[Termes IGN] reboisement
[Termes IGN] surface cultivée
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Offsetting nation-wide CO2 emissions by carbon sinks from land use change (LUC), e.g. agricultural fields extensification and afforestation, is considered as a major climate change mitigation option. We evaluated the LUC potential for reducing emissions and creating annual soil and ecosystem carbon sinks in Finland. We used agricultural statistics, the forest growth model MOTTI, the soil carbon model Yasso07, and the RCP8.5 climate scenario. The soil carbon stock (SOC) of extensified grasslands showed on average less carbon loss than cropland, thus reducing future carbon emissions by LUC between 0.17 Mg ha−1 y-1, initially, and 0.08 Mg ha−1 y-1 after 50 years. The annual rate of such carbon gain was in comparison to SOC between 1.4‰ and 0.7‰ which is lower than proposed by the Paris 4‰ initiative for offsetting global anthropogenic CO2 emissions. Furthermore, after afforestation, estimated SOC is expected to increase above pre-LUC levels with 30 years lag. Estimated SOC sink from afforestation when compared to continuous cultivation varied depending on dominant tree species and soil fertility from between 0.19 Mg ha−1 y-1 (1.7‰ for spruce in medium fertile soil) to 0.46 Mg ha−1 y-1 (3.7‰ for silver birch in highly fertile soil). Future total soil and biomass carbon sink attributed to afforestation ranged between 1.65 and 2.44 Mg ha−1 y-1. Combined carbon sinks created by the present LUC could with 30 years lag offset annually between 0.01 and 4% of the present national net CO2 emissions in Finland. The long delay and a small scale of potential future carbon emission reduction by the LUC highlighted the importance of employing additional tools to reach the national neutrality targets due in next 15 or 30 years. Numéro de notice : A2021-744 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119672 Date de publication en ligne : 22/09/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98685
in Forest ecology and management > vol 501 (December-1 2021) . - n° 119672[article]Lithological mapping based on fully convolutional network and multi-source geological data / Ziye Wang in Remote sensing, vol 13 n° 23 (December-1 2021)
[article]
Titre : Lithological mapping based on fully convolutional network and multi-source geological data Type de document : Article/Communication Auteurs : Ziye Wang, Auteur ; Renguang Zuo, Auteur ; Hao Liu, Auteur Année de publication : 2021 Article en page(s) : n° 4860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] carte géologique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données géologiques
[Termes IGN] fusion de données multisource
[Termes IGN] Himalaya
[Termes IGN] lithologie
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping. Numéro de notice : A2021-878 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13234860 Date de publication en ligne : 30/11/2021 En ligne : https://doi.org/10.3390/rs13234860 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99146
in Remote sensing > vol 13 n° 23 (December-1 2021) . - n° 4860[article]National scale mapping of larch plantations for Wales using the Sentinel-2 data archive / Suvarna M. Punalekar in Forest ecology and management, vol 501 (December-1 2021)
[article]
Titre : National scale mapping of larch plantations for Wales using the Sentinel-2 data archive Type de document : Article/Communication Auteurs : Suvarna M. Punalekar, Auteur ; Carole Planque, Auteur ; Richard M. Lucas, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119679 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] carte forestière
[Termes IGN] coupe rase (sylviculture)
[Termes IGN] gestion forestière
[Termes IGN] image infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Larix decidua
[Termes IGN] maladie phytosanitaire
[Termes IGN] modélisation de la forêt
[Termes IGN] Pays de Galles
[Termes IGN] surveillance forestièreRésumé : (auteur) Accurate spatial information regarding forest types and tree species is immensely important for efficient forest management strategies. In the UK and particularly in Wales, creating a spatial inventory of larch (Larix sps.) plantations that encompasses both the public and private forests has become one of the highest priorities of woodland management policies, particularly given the need to respond to the rapid spread of Phytophthora ramorum fungal disease. For directing disease control measures, national scale, regularly updated mapping of larch distributions is essential. In this study, we applied a ExtraTree classifier machine learning algorithm to multi-year (June 2015 and December 2019) multi-path composites of vegetation indices derived from 10 m Sentinel-2 satellite data (spectral range used in this study: 490–2190 nm) to map the extent of larch plantations across Wales. For areas identified as woody vegetation, areas under larch plantations were associated with a needle-leaved leaf type and deciduous phenology, allowing differentiation from broad-leaved deciduous and needle-leaved evergreen types. The model accuracies for validation, which included overall accuracy, producer’s and user’s accuracies, exceeded 95% and the F1-score was greater than 0.97 for all forest types. Comparison against an independent reference dataset indicated all map accuracies above 90% (F1-score higher than 0.92) with the lowest value being 90.3% for the producer’s accuracy for larch. Short wave infrared and red-edge based indices were particularly useful for discriminating larch from other forest types. Capacity for updating information on clear-felling of larch stands through annual updates of a woody mask was also introduced. The resulting maps of larch plantations for Wales are the most current for Wales covering public as well as private woodlands and can be routinely updated. The classification approach has potential to be transferred to a wider geographical area given the availability of open-source multi-year Sentienl-2 datasets and robust calibration datasets. Numéro de notice : A2021-741 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119679 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98657
in Forest ecology and management > vol 501 (December-1 2021) . - n° 119679[article]OBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry / Xuan Fang in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)
[article]
Titre : OBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry Type de document : Article/Communication Auteurs : Xuan Fang, Auteur ; Jincheng Li, Auteur ; Ying Zhu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 805 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse d'image orientée objet
[Termes IGN] Chine
[Termes IGN] classification barycentrique
[Termes IGN] dommage matériel
[Termes IGN] données de terrain
[Termes IGN] érosion
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] pente
[Termes IGN] photogrammétrie aérienne
[Termes IGN] segmentation d'image
[Termes IGN] surface cultivée
[Termes IGN] terrasseRésumé : (auteur) Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. Automatic high-accuracy mapping of terrace damages is the basis of monitoring and related studies. Researchers have achieved artificial terrace damage mapping mainly via manual field investigation, but an automatic method is still lacking. In this study, given the success of high-resolution unmanned aerial vehicle (UAV) photogrammetry and object-based image analysis (OBIA) for image processing tasks, an integrated framework based on OBIA and UAV photogrammetry is proposed for terrace damage mapping. The Pujiawa terrace in the Loess Plateau of China was selected as the study area. Firstly, the segmentation process was optimised by considering the spectral features and the terrains and corresponding textures obtained from high-resolution images and digital surface models. The feature selection was implemented via correlation analysis, and the optimised segmentation parameter was achieved using the estimation of scale parameter algorithm. Then, a supervised k-nearest neighbourhood classifier was used to identify the terrace damages in the segmented objects, and additional geometric features at the object level were considered for classification. The comparison with the ground truth, as delineated by the image and field survey, showed that proposed classification can be adequately performed. The F-measures of extraction on three terrace damages were 92.07% (terrace sinkhole), 81.95% (ridge sinkhole), and 85.17% (collapse), and the Kappa coefficient was 85.34%. Finally, the potential application and spatial distribution of the terrace damages in this study were determined. We believe that this work can provide a credible framework for mapping terrace damages in the Loess Plateau of China. Numéro de notice : A2021-882 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10120805 Date de publication en ligne : 27/11/2021 En ligne : https://doi.org/10.3390/ijgi10120805 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99178
in ISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 805[article]Crop rotation modeling for deep learning-based parcel classification from satellite time series / Félix Quinton in Remote sensing, vol 13 n° 22 (November-2 2021)PermalinkEfficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine / Yongjing Mao in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)PermalinkEvaluation of watershed soil erosion hazard using combination weight and GIS: a case study from eroded soil in Southern China / Shifa Chen in Natural Hazards, vol 109 n° 2 (November 2021)PermalinkPotential flood hazard zone mapping based on geomorphologic considerations and fuzzy analytical hierarchy model in a data scarce West African basin / Olabanji Aladejana in Geocarto international, vol 36 n° 19 ([01/11/2021])PermalinkTidal flood area mapping in the face of climate change scenarios: case study in a tropical estuary in the Brazilian semi-arid region / Paulo Victor N. Araújo in Natural Hazards and Earth System Sciences, vol 21 n° 11 (November 2021)PermalinkAnalyzing routes in Ottoman Greater Syria using historical GIS: The 1849 Saida map / Motti Zohar in Transactions in GIS, vol 25 n° 5 (October 2021)PermalinkAnthropogenic degradation of dunes within a city: a disappearing feature of the cultural landscape of Toruń (Poland) / Pawel Molewski in Journal of maps, vol 17 n° 4 (October 2021)PermalinkComparison of digital elevation models through the analysis of geomorphic surface remnants in the Desatoya Mountains, Nevada / Bernadett Dobre in Transactions in GIS, vol 25 n° 5 (October 2021)PermalinkComplexity-based matching between image resolution and map scale for multiscale image-map generation / Qian Peng in International journal of geographical information science IJGIS, vol 35 n° 10 (October 2021)PermalinkEpistemological thoughts on the success of maps and the role of cartography / David Fairbairn in International journal of cartography, vol 7 n° 3 (October 2021)Permalink