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Automatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 63 - 76 Titre : Automatic canola mapping using time series of Sentinel 2 images Type de document : Article/Communication Auteurs : Davoud Ashourloo, Auteur ; Hamid Salehi Shahrabi, Auteur ; Mohsen Azadbakht, Auteur ; Hossein Aghighi, Auteur ; Hamed Nematollahi, Auteur ; Abbas Alimohammadi, Auteur ; Ali Akbar Matkan, Auteur Année de publication : 2019 Article en page(s) : pp 63 - 76 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] agriculture de précision [Termes descripteurs IGN] Brassica napus subsp. napus [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Iran [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Oklahoma (Etats-Unis) [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] série temporelle Résumé : (Auteur) Different techniques utilized for mapping various crops are mainly based on using training dataset. But, due to difficulties of access to a well-represented training data, development of automatic methods for detection of crops is an important need which has not been considered as it deserves. Therefore, main objective of present study was to propose a new automatic method for canola (Brassica napus L.) mapping based on Sentinel 2 satellite time series data. Time series data of three study sites in Iran (Moghan, Gorgan, Qazvin) and one site in USA: (Oklahoma), were used. Then, spectral reflectance values of canola in various spectral bands were compared with those of the other crops during the growing season. NDVI, Red and Green spectral bands were successfully applied for automatic identification of canola flowering date using the threshold values. Examination of the fisher function indicated that multiplication of the near-infrared (NIR) band by the sum of red and green bands during the flowering date is an efficient index to differentiate canola from the other crops. The Kappa and overall accuracy (OA) for the four study sites were more than 0.75 and 88%, respectively. Results of this research demonstrated the potential of the proposed approach for canola mapping using time series of remotely sensed data. Numéro de notice : A2019-317 Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.08.007 date de publication en ligne : 09/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93355 [article]

Classification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data / Prateek Verma in Geocarto international, vol 34 n° 10 (October 2019)  

Public [article]  inGeocarto international > vol 34 n° 10 (October 2019) . - pp 1075 - 1088 Titre : Classification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data Type de document : Article/Communication Auteurs : Prateek Verma, Auteur ; Sanjay Kumar Ghosh, Auteur Année de publication : 2019 Article en page(s) : pp 1075 - 1088 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] glacier [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] lac glaciaire [Termes descripteurs IGN] Normalized Difference Water Index [Termes descripteurs IGN] seuillage d'image [Termes descripteurs IGN] Uttarakhand (Inde ; état) Résumé : (auteur) It is important to identify and locate glacial lakes for assessing any potential hazard. This study presents a combination of semi-automatic method Double-Window Flexible Pace Search (DFPS) and edge detection technique to identify glacial lakes using Sentinel 2A satellite data. Initially, Normalized Difference Water Index (NDWI) has been used to identify water and non-water areas, while DFPS and Edge detection technique has been used to identify an optimum threshold value to distinguish between water and shadow areas. The optimal threshold from DFPS process is 0.21, while threshold value of gradient magnitude using edge detection process is 0.318. The number of glacial lakes identified using the above algorithm is in close agreement with previously published results on glacial lakes in Gangotri glacier using different techniques. Thus, a combination of DFPS and edge detection process has successfully segregated glacial lakes from other features present in Gangotri glacier. Numéro de notice : A2019-300 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1469677 date de publication en ligne : 15/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1469677 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93220 [article]

Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data / Charles Otunga in Geocarto international, vol 34 n° 10 (October 2019)  

Public [article]  inGeocarto international > vol 34 n° 10 (October 2019) . - pp 1123 - 1143 Titre : Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data Type de document : Article/Communication Auteurs : Charles Otunga, Auteur ; John Odindi, Auteur ; Onisimo Mutanga, Auteur ; Clément Adjorlolo, Auteur Année de publication : 2019 Article en page(s) : pp 1123 - 1143 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Afrique du sud (état) [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] bande rouge [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] Festuca (genre) [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] paturage [Termes descripteurs IGN] prairie [Termes descripteurs IGN] répartition géographique Résumé : (auteur) Integrating the Red Edge channel in satellite sensors is valuable for plant species discrimination. Sentinel-2 MSI and Rapid Eye are some of the new generation satellite sensors that are characterized by finer spatial and spectral resolution, including the red edge band. The aim of this study was to evaluate the potential of the red edge band of Sentinel-2 and Rapid Eye, for mapping festuca C3 grass using discriminant analysis and maximum likelihood classification algorithms. Spectral bands, vegetation indices and spectral bands plus vegetation indices were analysed. Results show that the integration of the red edge band improved the festuca C3 grass mapping accuracy by 5.95 and 4.76% for Sentinel-2 and Rapid Eye when the red edge bands were included and excluded in the analysis, respectively. The results demonstrate that the use of sensors with strategically positioned red edge bands, could offer information that is critical for the sustainable rangeland management. Numéro de notice : A2019-301 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1474274 date de publication en ligne : 24/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1474274 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93221 [article]

Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2 / Ali Mokhtari in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 231 - 245 Titre : Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2 Type de document : Article/Communication Auteurs : Ali Mokhtari, Auteur ; Hamideh Noory, Auteur ; Farrokh Pourshakouri, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 231 - 245 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bilan énergétique [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] cultures [Termes descripteurs IGN] évapotranspiration [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] orge (céréale) [Termes descripteurs IGN] Téhéran Résumé : (Auteur) Evapotranspiration is considered to be an important component of allocating water to agricultural sector; therefore, the more accurate this parameter is, the more optimized the water use can be. This study was conducted in order to evaluate the Landsat 8 and Sentinel-2 data (A and B), both separately and combined, in potential evapotranspiration (ETp) and single crop coefficient (Kc) estimations. Field measurements such as crop height, leaf area index (LAI), land surface temperature (LST), air temperature above canopy (AT), and spectral data were exploited in the evaluating process throughout the entirety of 2017–18 growing season under winter wheat and barley cultivations in the Agricultural Research Farms of the University of Tehran. The novel method of Multi-Sensor Data Fusion using the Priestly-Taylor equation was taken into practice for satellite-based ETp (MSDF-ET) calculation from the combination of MODIS thermal and Landsat 8 and Sentinel-2 multispectral data. Thermal images were downscaled by the means of the TsHARP algorithm. Thus, prior to ETp calculation, the thermal sharpening algorithm calculated using different spectral indices (SI) was assessed. The SI included NDVI, SAVI, SR, NDWI, NDWIg, and LSWI. The subsequent results were representative of the LSWI qualification under both Landsat 8 and Sentinel-2 conditions against thermal and spectral measurements. Also the satellite-based ETp strongly correlated with the ETp derived from the field data illuminating the promising accuracy of the MSDF-ET method in both Landsat 8 and Sentinel-2 data. In the end, the time series of Kc obtained from the combination of satellites were fairly indicative of the real-world variations under different vegetation cover and crop growth stages. Overall, using Landsat 8 and Sentinel-2 products in integration with each other could significantly result in more reliable decisions in agricultural water resources management. Numéro de notice : A2019-270 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.011 date de publication en ligne : 24/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93088 [article]

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Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images / Jie Wang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 189 - 201 Titre : Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images Type de document : Article/Communication Auteurs : Jie Wang, Auteur ; Xiangming Xiao, Auteur ; Rajen Bajgain, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 189 - 201 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] Oklahoma (Etats-Unis) [Termes descripteurs IGN] paturage [Termes descripteurs IGN] phénologie [Termes descripteurs IGN] régression multiple Résumé : (Auteur) Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10–30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI  2 m2/m2, AGB > 500 g/m2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management. Numéro de notice : A2019-269 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.007 date de publication en ligne : 21/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93086 [article]

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Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery / Yanan Liu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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Cartographie des défoliations du massif forestier du Pays des étangs en Lorraine : Apports potentiels de la télédétection / Thierry Bélouard in Revue forestière française [en ligne], vol 70 n° 5 (2018)  

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DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn / Roberto Interdonato in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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A novel sharpening approach for superresolving multiresolution optical images / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)  

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Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors / Pauline Perbet in International Journal of Remote Sensing IJRS, vol 40 n°19 (February 2019)  

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Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 (2019)  

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Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)  

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Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands / Fabio Castaldi in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)  

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Géomatique webmapping en open source / David Collado (2019)

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