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Titre : Remote Sensing Applications for Agriculture and Crop Modelling Type de document : Monographie Auteurs : Piero Toscano, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 310 p. ISBN/ISSN/EAN : ISBN 978-3-03928-227-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] agriculture
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte d'utilisation du sol
[Termes IGN] changement climatique
[Termes IGN] changement d'occupation du sol
[Termes IGN] engrais chimique
[Termes IGN] image infrarouge
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] image satellite
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] surface cultivéeRésumé : (éditeur) Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. This book sets the context for remote sensing and modelling for agricultural systems as a mean to minimize the environmental impact, while increasing production and productivity. The eighteen papers published in this Special Issue, although not representative of all the work carried out in the field of Remote Sensing for agriculture and crop modeling, provide insight into the diversity and the complexity of developments of RS applications in agriculture. Five thematic focuses have emerged from the published papers: yield estimation, land cover mapping, soil nutrient balance, time-specific management zone delineation and the use of UAV as agricultural aerial sprayers. All contributions exploited the use of remote sensing data from different platforms (UAV, Sentinel, Landsat, QuickBird, CBERS, MODIS, WorldView), their assimilation into crop models (DSSAT, AQUACROP, EPIC, DELPHI) or on the synergy of Remote Sensing and modeling, applied to cardamom, wheat, tomato, sorghum, rice, sugarcane and olive. The intended audience is researchers and postgraduate students, as well as those outside academia in policy and practice. Numéro de notice : 25747 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif En ligne : https://www.mdpi.com/books/pdfview/book/2023 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94932
Titre : Remote sensing technology applications in forestry and REDD+ Type de document : Monographie Auteurs : Kim Calders, Éditeur scientifique ; Inge Jonckheere, Éditeur scientifique ; Mikko Vastaranta, Éditeur scientifique ; Joanne Nightingale, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 244 p. ISBN/ISSN/EAN : 978-3-03928-471-9 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] canopée
[Termes IGN] cartographie des risques
[Termes IGN] déboisement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image Landsat
[Termes IGN] image multibande
[Termes IGN] image Sentinel
[Termes IGN] Pinus massoniana
[Termes IGN] polarimétrie radar
[Termes IGN] Réduction des émissions dues à la déforestation et la dégradation des forêts, REDD
[Termes IGN] télémétrie laser aéroporté
[Termes IGN] télémétrie laser terrestreRésumé : (Editeur) Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion. Numéro de notice : 26296 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03928-471-9 Date de publication en ligne : 07/04/2020 En ligne : https://doi.org/10.3390/books978-3-03928-471-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95009 A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems / Dong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
[article]
Titre : A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems Type de document : Article/Communication Auteurs : Dong Chen, Auteur ; Tatiana V. Loboda, Auteur ; Joanne V. Hall, Auteur Année de publication : 2020 Article en page(s) : pp 63 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Alaska (Etats-Unis)
[Termes IGN] Canada
[Termes IGN] changement climatique
[Termes IGN] écosystème forestier
[Termes IGN] forêt boréale
[Termes IGN] image Landsat
[Termes IGN] incendie de forêt
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] perturbation écologique
[Termes IGN] Short Waves InfraRed
[Termes IGN] toundraRésumé : (Auteur) Satellite imagery has been widely used for the assessment of wildfire burn severity within the scientific community and fire management agencies. Multiple indices have been proposed to assess burn severity, among which the differenced Normalized Burn Ratio (dNBR) is arguably the most commonly used index that is expected to provide an objective and consistent assessment. However, although evidence of variability in the dNBR-based assessment of burn severity driven by image pair selection has been shown in many studies, the comprehensive examination of the extent of the bias resulting from the image selection has been lacking. In this study, we focus on three factors of the image selection process which are encountered by most Landsat-derived dNBR applications, including the sensor combination and the difference in timing of image acquisition (for both the year and seasonality) of pre- and post-fire image pairs. Through separate analyses, each targeting a single factor, we show that Landsat sensor combination between the pre- and post-fire images has a limited impact on the dNBR values. The difference in the year of acquisition between the images in the image pairs is shown to influence dNBR assessment with a noticeable increase in mean dNBR (>0.1) with only a single year difference between images compared to multi-year differences. However, differences in the image acquisition seasons and the resulting phenological differences is shown to impact dNBR values most considerably. Based on our results, we warn against the calculation of dNBR when the images are acquired in different seasons. We believe that despite the existence of multiple derivatives of dNBR, there remains a need for an improved version; one that is less susceptible to the phenological impacts introduced by the selected images. Numéro de notice : A2020-012 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.011 Date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94400
in ISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 63 - 77[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020011 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020013 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Using remote sensing to assess the effect of time of day on the spatial and temporal variation of LST in urban areas / Akram Abdulla (2020)
Titre : Using remote sensing to assess the effect of time of day on the spatial and temporal variation of LST in urban areas Type de document : Thèse/HDR Auteurs : Akram Abdulla, Auteur ; Kevin Tansey, Directeur de thèse ; Kristen Barrett, Directeur de thèse Editeur : Leicester [Royaume-Uni] : University of Leicester Année de publication : 2020 Importance : 128 p. Format : 21 x 30 cm Note générale : bibliographie
Thesis submitted for the degree of Doctor of Philosophy at The University of Leicester, School of Geography, Geology and EnvironmentLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] données spatiotemporelles
[Termes IGN] ilot thermique urbain
[Termes IGN] image infrarouge
[Termes IGN] image Landsat
[Termes IGN] image Terra-MODIS
[Termes IGN] image thermique
[Termes IGN] occupation du sol
[Termes IGN] phénomène climatique extrême
[Termes IGN] température au sol
[Termes IGN] variation diurne
[Termes IGN] variation saisonnière
[Termes IGN] variation temporelle
[Termes IGN] zone urbaineIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis seeks to add to the study of the relationship between land surface temperature (LST) and urban land cover by presenting a method to project Landsat LST data from the satellite overpass time (9:40 am) to a local peak of temperature (estimated to be around 1:15 pm locally), to investigate the impact of the time of image acquisition on modelling the spatial and temporal variations of LST. Additionally, it would also verify the effects of extreme temperature to reach more representative seasonal images.The study uses remote sensing data extracted from Landsat 5 and 8 (30 m resolution) and the Spinning Enhanced Visible and Infrared Imager LST products (SEVIRI 3 km resolution), in addition to LST-based measurements collected from the ground. The study presented a method to convert Landsat images to be estimated during local peaks in LST with an accuracy of: standard error of 1.7°C and an R of 0.82 in comparison with actual ground-based measurements. This allowed an investigation of the effects of time of day on the spatial and temporal variation of LST, where it was found that this factor has clearly affected the relationship between LST and urban land cover. Similarly, the time of day has caused differences in estimating LST change over several years. It is also found that the extreme values of temperature can affect the trend of LST temporal variation, and which can be minimized by using the images in the form of the average of seasonal images for each year rather than images being used in a standalone manner. This study contributes to the improved study of LST by minimizing the uncertainty that can occur because of the angle of the sun and associated factors such as shadows, which has long been a controversial issue among researches due to the lack of appropriate satellite data. Note de contenu : 1- Introduction
2- Literature review
3- Study area
4- Converting Landsat LST data from morning to peak temperatures(9:40 am to 1:15 pm)
5- Assessing the effect of the time of day on the spatial variation of LST
6- Assessment and enhancement of the temporal variation of LST over a time series
7- General Discussion and ConclusionsNuméro de notice : 28304 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Geography, Geology and Environment : University of Leicester : 2020 DOI : sans En ligne : https://doi.org/10.25392/leicester.data.14518848.v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98068 A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal / Upama A. Koju in Journal of Forestry Research, vol 30 n° 6 (December 2019)
[article]
Titre : A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal Type de document : Article/Communication Auteurs : Upama A. Koju, Auteur ; Jiahua Zhang, Auteur ; Shashish Maharjan, Auteur ; Sha Zhang, Auteur ; Yun Bai, Auteur ; Dinesh Babu Irulappa-Pillai-Vijayakumar , Auteur ; Fengmei Yao, Auteur Année de publication : 2019 Projets : 3-projet - voir note / Article en page(s) : pp 2119 - 2136 Note générale : bibliographie
The work was supported by the CAS Strategic Priority Research Program (No. XDA19030402), the National Key Research and Development Program of China (No. 2016YFD0300101), the Natural Science Foundation of China (Nos. 31571565, 31671585), the Key Basic Research Project of the Shandong Natural Science Foundation of China (No. ZR2017ZB0422), and Research Funding of Qingdao University (No. 41117010153).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse multiéchelle
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] Google Earth
[Termes IGN] image Geoeye
[Termes IGN] image Landsat
[Termes IGN] image optique
[Termes IGN] image Quickbird
[Termes IGN] NépalRésumé : (auteur) Forests account for 80% of the total carbon exchange between the atmosphere and terrestrial ecosystems. Thus, to better manage our responses to global warming, it is important to monitor and assess forest aboveground carbon and forest aboveground biomass (FAGB). Different levels of detail are needed to estimate FAGB at local, regional and national scales. Multi-scale remote sensing analysis from high, medium and coarse spatial resolution data, along with field sampling, is one approach often used. However, the methods developed are still time consuming, expensive, and inconvenient for systematic monitoring, especially for developing countries, as they require vast numbers of field samples for upscaling. Here, we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites. The study was conducted in the Chitwan district of Nepal using GeoEye-1 (0.5 m), Landsat (30 m) and Google Earth very high resolution (GEVHR) Quickbird (0.65 m) images. For the local scale (Kayerkhola watershed), tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images. An overall accuracy of 83% was obtained in the delineation of tree canopy cover (TCC) per plot. A TCC vs. FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots. A coefficient of determination (R2) of 0.76 was obtained in the modelling, and a value of 0.83 was obtained in the validation of the model. To upscale FAGB to the entire district, open source GEVHR images were used as virtual field plots. We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model. Using the multivariate adaptive regression splines machine learning algorithm, we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices. The model was then used to extrapolate FAGB to the entire district. This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution (30 m) and accuracy (R2 = 0.76 and 0.7) with minimal error (RMSE = 64 and 38 tons ha−1) at local and regional scales. This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time. The method is especially applicable for developing countries that have low budgets for carbon estimations, and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation (REDD +) monitoring reporting and verification processes. Numéro de notice : A2019-664 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11676-018-0743-1 Date de publication en ligne : 09/07/2018 En ligne : https://doi.org/10.1007/s11676-018-0743-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99699
in Journal of Forestry Research > vol 30 n° 6 (December 2019) . - pp 2119 - 2136[article]Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images / Cheolhee Yoo in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)PermalinkUtilisation des SIG et de la télédétection pour la cartographie de la susceptibilité aux mouvements d'instabilité de versant dans l'Ouest montagneux de la Côte d'Ivoire / Boyossoro Hélène Kouadio in Revue Française de Photogrammétrie et de Télédétection, n° 221 (novembre 2019)PermalinkPotential of Landsat-8 and Sentinel-2A composite for land use land cover analysis / Divyesh Varade in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkResidences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkEvolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco / Ali Aydda in Geocarto international, vol 34 n° 13 ([15/10/2019])PermalinkLandsats 1–5 multispectral scanner system sensors radiometric calibration update / Cibele Teixeira-Pinto in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)PermalinkMultitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador / Nguyen-Thanh Son in Geocarto international, vol 34 n° 12 ([15/09/2019])PermalinkChange detection work-flow for mapping changes from arable lands to permanent grasslands with advanced boosting methods / Jiří Šandera in Geodetski vestnik, vol 63 n° 3 (September - November 2019)PermalinkExploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data / Alexander Cass in Applied geomatics, vol 11 n° 3 (September 2019)PermalinkImplementing Moran eigenvector spatial filtering for massively large georeferenced datasets / Daniel A. Griffith in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)Permalink