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A national fuel type mapping method improvement using sentinel-2 satellite data / Alexandra Stefanidou in Geocarto international, vol 37 n° 4 ([15/02/2022])
[article]
Titre : A national fuel type mapping method improvement using sentinel-2 satellite data Type de document : Article/Communication Auteurs : Alexandra Stefanidou, Auteur ; Ioannis Z. Gitas, Auteur ; Thomas Katagis, Auteur Année de publication : 2022 Article en page(s) : pp 1022 - 1042 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image orientée objet
[Termes IGN] carte de la végétation
[Termes IGN] carte thématique
[Termes IGN] combustible
[Termes IGN] distribution spatiale
[Termes IGN] Grèce
[Termes IGN] image Sentinel-MSI
[Termes IGN] incendie de forêt
[Termes IGN] prévention des risquesRésumé : (auteur) Despite the fact that wildland fires have always been an integral part of many ecosystems, their increased frequency and intensity have reinforced the need of fire managers for updated and highly accurate information associated with the spatial distribution of forest fuels. In 2015, a fuel type mapping method was developed in the framework of the “National Observatory of Forest Fires (NOFFi)” project resulting in the generation of a national fuel type map. In this study, we aimed at examining the potential of the newly available Sentinel-2 satellite images for the improvement of the NOFFi’s mapping method in terms of accuracy and update effectiveness of the national fuel type map. Results demonstrate Sentinel-2 data will likely improve the resolution and reliability of national fuel type maps, increasing mapping efficiency for operational purposes. Numéro de notice : A2022-393 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2020.1756460 Date de publication en ligne : 28/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1756460 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100687
in Geocarto international > vol 37 n° 4 [15/02/2022] . - pp 1022 - 1042[article]Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model / Hasan Aksoy in Geocarto international, vol 37 n° 4 ([15/02/2022])
[article]
Titre : Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model Type de document : Article/Communication Auteurs : Hasan Aksoy, Auteur ; Sinan Kaptan, Auteur Année de publication : 2022 Article en page(s) : pp 1183 - 1202 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] automate cellulaire
[Termes IGN] classification dirigée
[Termes IGN] détection de changement
[Termes IGN] gestion forestière
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-TM
[Termes IGN] modèle de Markov
[Termes IGN] occupation du sol
[Termes IGN] surface cultivée
[Termes IGN] surface forestière
[Termes IGN] Turquie
[Termes IGN] utilisation du solRésumé : (auteur) This study aimed to simulate and assess forest cover and land use/land cover (LULC) changes between 2019 and 2039 using the cellular automata-Markov model. The performance of the model was evaluated by comparing the 2019 simulation map with the 2019 supervised classified map, and it was found to be reliable, with a similarity rate of 85.43%. The LULC analysis and estimates were carried out for a total of six classes: coniferous, broad-leaf, mixed forest, settlement, water and agriculture. Between 1999 and 2019, the areas of total forest increased by 17.4%, settlement by 84.6% and water by 20.1%, whereas the agriculture area decreased by 33.2%. According to 2019‒2039 land use/cover simulation results, there were decreases of 2.4% in total forest area and 3.7% in residential and water surface areas, but a 6.9% decrease in the agriculture class. Tracking these changes will contribute to decision making and strategy development efforts of forest planners and managers. Numéro de notice : A2022-397 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1778102 Date de publication en ligne : 22/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1778102 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100691
in Geocarto international > vol 37 n° 4 [15/02/2022] . - pp 1183 - 1202[article]Assessment and mapping soil water erosion using RUSLE approach and GIS tools: Case of Oued el-Hai watershed, Aurès West, Northeastern of Algeria / Aida Bensekhria in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)
[article]
Titre : Assessment and mapping soil water erosion using RUSLE approach and GIS tools: Case of Oued el-Hai watershed, Aurès West, Northeastern of Algeria Type de document : Article/Communication Auteurs : Aida Bensekhria, Auteur ; Rabah Bouhata, Auteur Année de publication : 2022 Article en page(s) : n° 84 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Algérie
[Termes IGN] Aurès, massif des
[Termes IGN] bassin hydrographique
[Termes IGN] carte thématique
[Termes IGN] conservation des ressources naturelles
[Termes IGN] érosion hydrique
[Termes IGN] modèle RUSLE
[Termes IGN] outil d'aide à la décision
[Termes IGN] système d'information géographiqueRésumé : (auteur) The problem of soil water erosion is one of the primary causes of agro-pedological heritage degradation. The combined effect of natural factors and inappropriate human actions has weakened the soil, which seriously threatens the region’s fertile lands and soils, which can ultimately lead to an irreversible situation of desertification. This study focuses on analysis and mapping of the vulnerability to erosion in Oued el-Hai watershed, Algeria, based on a technical methodology that combines the universal soil loss equation (USLE) with the geographic information system (GIS) tools. The results are organized into three main classes of different rate values, from one area to another, depending on the influence of different factors that control the erosion process. The highest loss rate value is greater than 30 t·ha−1·yr−1 and covers 23.2% of the total area, mainly located in the mountainous areas with steep slopes. However, the minimum potential erosion rate value is mainly located on the plain, with an average of 10 t·ha−1·yr−1 covering 45.2% of the total area of the watershed. The estimate of potential water erosion has given alarming results. The total area of the watershed could lose a rate of 16.69 t·ha−1·yr−1 (on average) each year. The method and results described in this article are valuable for understanding the soil erosion risk and are useful for managing and planning land use that will avoid land degradation. Hence, the results of this study are considered an important document which constitutes a decision support tool in terms of the management and protection of natural resources. Numéro de notice : A2022-119 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11020084 Date de publication en ligne : 24/01/2022 En ligne : https://doi.org/10.3390/ijgi11020084 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99650
in ISPRS International journal of geo-information > vol 11 n° 2 (February 2022) . - n° 84[article]Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 3 ([01/02/2022])
[article]
Titre : Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Adam Sheka Kanu, Auteur ; Lingbo Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 828 - 840 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Extreme Gradient Machine
[Termes IGN] Green Leaf Area Index
[Termes IGN] image Gaofen
[Termes IGN] image HJ-1A
[Termes IGN] image HJ-1B
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice foliaire
[Termes IGN] modèle de régression
[Termes IGN] rizièreRésumé : (auteur) Optical satellite imagery has been widely used to monitor leaf area index (LAI). However, most studies have focussed on single- or dual-source data, thus making little use of a growing repository of freely available optical imagery. Hence this study has evaluated the feasibility of quad-source optical satellite imagery involving Landsat-8, Sentinel-2A, China’s environment satellite constellation (HJ-1 A and B) and Gaofen-1 (GF-1) in modelling rice green LAI over a test site located in southeast China at two growing seasons. With the application of machine learning regression models including Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbour (k-NN) and Gradient Boosting Decision Tree (GBDT), results indicated that regression models based on an ensemble of decision trees (RF and GBDT) were more suitable for modelling rice green LAI. The current study has demonstrated the feasibility of quad-source optical imagery in modelling rice green LAI and this is relevant for cloudy areas. Numéro de notice : A2022-346 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1745299 Date de publication en ligne : 03/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1745299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100530
in Geocarto international > vol 37 n° 3 [01/02/2022] . - pp 828 - 840[article]Mapping abundance distributions of allergenic tree species in urbanized landscapes: A nation-wide study for Belgium using forest inventory and citizen science data / Sébastien Dujardin in Landscape and Urban Planning, vol 218 (February 2022)
[article]
Titre : Mapping abundance distributions of allergenic tree species in urbanized landscapes: A nation-wide study for Belgium using forest inventory and citizen science data Type de document : Article/Communication Auteurs : Sébastien Dujardin, Auteur ; Michiel Stas, Auteur ; Camille Van Eupen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Alnus (genre)
[Termes IGN] Belgique
[Termes IGN] Betula (genre)
[Termes IGN] carte de la végétation
[Termes IGN] carte forestière
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Corylus (genre)
[Termes IGN] distribution spatiale
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] milieu urbain
[Termes IGN] modèle mathématique
[Termes IGN] régression
[Termes IGN] santé
[Termes IGN] science citoyenne
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Mapping the distribution of allergenic plants in urbanized landscapes is of high importance to evaluate its impact on human health. However, data is not always available for the allergy-relevant species such as alder, birch, hazel, especially within cities where systematic inventories are often missing or not readily available. This research presents an approach to produce high-resolution abundance maps of allergenic tree species using existing forest inventories and opportunistic open-access citizen science data. Following a two-step approach, we first built species distribution models (SDMs) to predict species habitat suitability, using environmental characteristics as predictors. Second, we used statistical regressions to model the relationships between abundance, the habitat suitability predicted by the SDMs, and additional vegetation cover covariates. The combination of forest inventory data with citizen science data improves the accuracy of abundance distribution models of allergenic tree species. This produces a continuous, 1-hectare resolution map of alder, birch, and hazel showing spatial variations of abundance distributions both within the urban fabric and along the urban–rural gradient. Species abundance modelling can offer a better understanding of the existing and potential future allergy risk posed by green spaces and pave the way for a wide variety of applications at fine-scale, which is indispensable for evidence-based urban green space policy and planning in support of public health. Numéro de notice : A2022-248 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1016/j.landurbplan.2021.104286 Date de publication en ligne : 31/10/2021 En ligne : https://doi.org/10.1016/j.landurbplan.2021.104286 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100196
in Landscape and Urban Planning > vol 218 (February 2022) . - n° 104286[article]Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery / Donato Morresi in Remote sensing of environment, vol 269 (February 2022)PermalinkMapping global flying aircraft activities using Landsat 8 and cloud computing / Fen Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)PermalinkMonthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning / Feng Zhao in Remote sensing of environment, vol 269 (February 2022)PermalinkMulti-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers / Jacques Mourey in Natural Hazards and Earth System Sciences, vol 22 n° 2 (February 2022)PermalinkSpatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria / Maninder Singh Dhillon in Remote sensing, vol 14 n° 3 (February-1 2022)PermalinkSynergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images / Alireza Hamedianfar in Geocarto international, vol 37 n° 3 ([01/02/2022])PermalinkIncreasing territorial planning activities through viewshed analysis / Gheorghe-Gavrilă Hognogi in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkSoil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS / Sumedh R. Kashiwar in Natural Hazards, vol 110 n° 2 (January 2022)PermalinkAirborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape / Niva Kiran Verma in Geocarto international, vol 37 n° 1 ([01/01/2022])PermalinkAn assessment of forest loss and its drivers in protected areas on the Copperbelt province of Zambia: 1972–2016 / Darius Phiri in Geomatics, Natural Hazards and Risk, vol 13 (2022)Permalink