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Regional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification / Viktor Myroniuk in Remote sensing, vol 12 n°1 (January 2020)
Titre : Regional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification Type de document : Article/Communication Auteurs : Viktor Myroniuk, Auteur ; Mykola Kutia, Auteur ; Arbi J. Sarkissian, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] bande infrarouge
[Termes descripteurs IGN] carte forestière
[Termes descripteurs IGN] changement climatique
[Termes descripteurs IGN] classification par forêts aléatoires
[Termes descripteurs IGN] Global Forest Change map
[Termes descripteurs IGN] Google Earth Engine
[Termes descripteurs IGN] image ALOS-PALSAR
[Termes descripteurs IGN] image Landsat
[Termes descripteurs IGN] image proche infrarouge
[Termes descripteurs IGN] image RVB
[Termes descripteurs IGN] image satellite
[Termes descripteurs IGN] Normalized Difference Vegetation Index
[Termes descripteurs IGN] plaine
[Termes descripteurs IGN] série temporelle
[Termes descripteurs IGN] surveillance forestière
[Termes descripteurs IGN] UkraineRésumé : (auteur) Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15% Numéro de notice : A2020-225 Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12010187 date de publication en ligne : 05/01/2020 En ligne : https://doi.org/10.3390/rs12010187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94940
in Remote sensing > vol 12 n°1 (January 2020) . - 24 p.[article]
Titre : Estimation of metabolic flows of urban environment based on fuzzy expert knowledge Type de document : Article/Communication Auteurs : Igor Patrakeyev, Auteur ; Victor Ziborov, Auteur ; Oleksii Mikhno, Auteur Année de publication : 2020 Article en page(s) : pp 8 – 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information
[Termes descripteurs IGN] aménagement durable
[Termes descripteurs IGN] croissance urbaine
[Termes descripteurs IGN] environnement
[Termes descripteurs IGN] logique floue
[Termes descripteurs IGN] système d'information urbain
[Termes descripteurs IGN] système expert flou
[Termes descripteurs IGN] Ukraine
[Termes descripteurs IGN] ville
[Termes descripteurs IGN] zone urbaineRésumé : (auteur) The quality and comfort of the urban environment serve as one of the most important factors for ensuring the competitiveness of municipalities, regions and countries. The quality of the urban environment is determined by the qual-ity of its three components: anthropogenic, natural and social environment. The main problem of assessing the state of the urban environment is the fragmentation of methodological approaches and adequate tools for assessing the qualitative state of the urban environment. This objectively makes it difficult for municipal authorities to use this assessment as an ele-ment in the system of urban planning decision making. We have developed an intelligent information system to provide an assessment of potential, real and lost opportunities of the urban environment using fuzzy expert knowledge. This system operates in the conditions of using non-numeric, inaccurate and incomplete information to ensure the management of sustainable city development. The system for assessing the potential, real and lost opportunities of the urban environment is based on the use of fuzzy logic equations. It allows to evaluate the effectiveness of metabolic transformations of each subsystem of the urban environment. Numéro de notice : A2020-221 Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.3846/gac.2020.8560 date de publication en ligne : 27/02/2020 En ligne : https://doi.org/10.3846/gac.2020.8560 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94926
in Geodesy and cartography > vol 46 n° 1 (January 2020) . - pp 8 – 16[article]
Titre : Spatially constrained regionalization with multilayer perceptron Type de document : Article/Communication Auteurs : Michael Govorov, Auteur ; Giedre Beconyte, Auteur ; Gennady Gienko, Auteur ; Victor Putrenko, Auteur Année de publication : 2019 Article en page(s) : pp 1048 - 1077 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] analyse de groupement
[Termes descripteurs IGN] apprentissage automatique
[Termes descripteurs IGN] classification dirigée
[Termes descripteurs IGN] données géologiques
[Termes descripteurs IGN] Perceptron multicouche
[Termes descripteurs IGN] programmation par contraintes
[Termes descripteurs IGN] régionalisation
[Termes descripteurs IGN] réseau neuronal artificiel
[Termes descripteurs IGN] segmentation par graphes d'adjacence de régions
[Termes descripteurs IGN] Ukraine
[Termes descripteurs IGN] uraniumRésumé : (auteur) In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on multivariate homogeneity measures. The study focuses on non stationarity and autocorrelation in spatial data. Supervised MLP machine learning algorithms with spatial constraints have been implemented and tested on a point dataset. MLP spatially weighted classification models and an MLP contiguity constrained classification model are developed to conduct spatially constrained regionalization. The proposed methods have been tested with an attribute‐rich point dataset of geological surveys in Ukraine. The experiments show that consideration of the spatial effects, such as the use of spatial attributes and their respective whitening, improve the output of regionalization. It is also shown that spatial sorting used to preserve spatial contiguity leads to improved regionalization performance. Numéro de notice : A2019-552 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12557 date de publication en ligne : 09/07/2019 En ligne : https://doi.org/10.1111/tgis.12557 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94202
in Transactions in GIS > Vol 23 n° 5 (October 2019) . - pp 1048 - 1077[article]
Titre : Automated annual cropland mapping using knowledge-based temporal features Type de document : Article/Communication Auteurs : François Waldner, Auteur ; Guadalupe Sepulcre Canto, Auteur ; Pierre Defourny, Auteur Année de publication : 2015 Article en page(s) : pp 1 – 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] Argentine
[Termes descripteurs IGN] Belgique
[Termes descripteurs IGN] carte agricole
[Termes descripteurs IGN] carte d'occupation du sol
[Termes descripteurs IGN] Chine
[Termes descripteurs IGN] classification dirigée
[Termes descripteurs IGN] cultures
[Termes descripteurs IGN] série temporelle
[Termes descripteurs IGN] surveillance agricole
[Termes descripteurs IGN] UkraineRésumé : (auteur) Global, timely, accurate and cost-effective cropland mapping is a prerequisite for reliable crop condition monitoring. This article presented a simple and comprehensive methodology capable to meet the requirements of operational cropland mapping by proposing (1) five knowledge-based temporal features that remain stable over time, (2) a cleaning method that discards misleading pixels from a baseline land cover map and (3) a classifier that delivers high accuracy cropland maps (>>80%). This was demonstrated over four contrasted agrosystems in Argentina, Belgium, China and Ukraine. It was found that the quality and accuracy of the baseline impact more the certainty of the classification rather than the classification output itself. In addition, it was shown that interpolation of the knowledge-based features increases the stability of the classifier allowing for its re-use from year to year without recalibration. Hence, the method shows potential for application at larger scale as well as for delivering cropland map in near real time. Numéro de notice : A2015-889 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.09.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79438
in ISPRS Journal of photogrammetry and remote sensing > vol 110 (December 2015) . - pp 1 – 13[article]
Titre : Regional dynamics of terrestrial vegetation productivity and climate feedbacks for territory of Ukraine Type de document : Article/Communication Auteurs : Dmytro Movchan, Auteur ; Yuriy V. Kostyuchenko, Auteur Année de publication : 2015 Article en page(s) : pp 1490 - 1505 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse diachronique
[Termes descripteurs IGN] analyse en composantes principales
[Termes descripteurs IGN] biome
[Termes descripteurs IGN] carbone
[Termes descripteurs IGN] changement climatique
[Termes descripteurs IGN] données météorologiques
[Termes descripteurs IGN] forêt
[Termes descripteurs IGN] production végétale
[Termes descripteurs IGN] régression multiple
[Termes descripteurs IGN] steppe
[Termes descripteurs IGN] Ukraine
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (Auteur) Climate change has the potential to affect terrestrial ecosystems and, thereby, the carbon cycle. Various vegetation biomes are likely to respond differently to changes in climatic factors. The purpose of this study was to analyse the trends of the terrestrial vegetation productivity and climate drivers on regional levels and relations between ones. The gross primary productivity from the global satellite-based terrestrial production efficiency model (PEM) MOD17 as the vegetation productivity indicator and meteorological data from the weather station network as climatic indicators were used. The analysis covered a period from 2000 to 2012. Correlation analysis was used to quantify the association between the vegetation productivity and climatic indicators for different growing seasons and landscape-climatic zones of Ukraine. The Mann–Kendall trend test was applied to take into account seasonal features. Multiple linear regression models for corresponding seasons and zones have been simulated using the principal component analysis. The results showed no detectable limiting effect of the climatic drivers on plant productivity for forest areas. The limiting effect of the temperature increasing and precipitation amount decreasing for the steppe zone and eastern forest-steppe subzone of Ukraine for summer was observed. Numéro de notice : A2015-606 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1051985 En ligne : https://doi.org/10.1080/13658816.2015.1051985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78035
in International journal of geographical information science IJGIS > vol 29 n° 8 (August 2015) . - pp 1490 - 1505[article]PermalinkPermalinkPermalinkPermalinkNimekirjutusraamat [Livre sur l'écriture des noms géographiques en Russie et républiques de l'ex-URSS avec tableaux de translittération et liste de toponymes] / T. Erelt (1993)
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