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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]

Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes / Meng Zhang in Geocarto international, vol 34 n° 10 (October 2019)  

Public [article]  inGeocarto international > vol 34 n° 10 (October 2019) . - pp 1144 - 1161 Titre : Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes Type de document : Article/Communication Auteurs : Meng Zhang, Auteur ; Yongnian Zeng, Auteur ; Wei Huang, Auteur ; Songnian Li, Auteur Année de publication : 2019 Article en page(s) : pp 1144 - 1161 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] Chine [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] hétérogénéité [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] paysage urbain [Termes descripteurs IGN] zone humide Résumé : (auteur) Remote sensing has been proven promising in wetland mapping. However, conventional methods in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM + images, which have great uncertainty due to the spectral similarity of different land covers, and pixel-based classifications may not meet the accuracy requirement. This paper proposes an approach that combines spatiotemporal fusion and object-based image analysis, using the spatial and temporal adaptive reflectance fusion model to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice, and the time series of MODIS NDVI to calculate phenological parameters for identifying wetlands with an object-based method. The results of a case study indicate that different types of wetlands can be successfully identified, with 92.38%. The overall accuracy and 0.85 Kappa coefficient, and 85% and 90% for the user’s accuracies of sedge swamp and paddy respectively. Numéro de notice : A2019-302 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1474275 date de publication en ligne : 17/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1474275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93222 [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]

Multi-sensor prediction of Eucalyptus stand volume: A support vector approach / Guilherme Silverio Aquino de Souza 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 135 - 146 Titre : Multi-sensor prediction of Eucalyptus stand volume: A support vector approach Type de document : Article/Communication Auteurs : Guilherme Silverio Aquino de Souza, Auteur ; Vicente Paulo Soares, Auteur ; Helio Garcia Leite, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 135 - 146 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bande L [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] Eucalyptus (genre) [Termes descripteurs IGN] image ALOS-AVNIR2 [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] radar à antenne synthétique [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] taux d'échantillonnage Résumé : (Auteur) Stem volume is a key attribute of Eucalyptus forest plantations upon which decision-making is based at diverse levels of planning. Quantifying volume through remote sensing can support a proper management of forests. Because of limitations on spaceborne optical and synthetic aperture radar sensors, this study integrated both types of datasets assembled using support vector regression (SVR) to retrieve the stand volume of Eucalyptus plantations. We assessed different combinations of sensors and a minimum number of plots to develop an SVR model. Finally, the best SVR performance was compared with other analytical methods already tested and in the literature: multilinear regression, artificial neural networks (ANN), and random forest (RF). Here, we introduce a test for comparative analysis of the performance of different methods. We found that SVR accurately predicted stem volume of Brazilian fast-growing Eucalyptus forest plantations. Gaussian radial basis was the most suitable kernel function. Integrating the optical and L-band backscatter data increased the predictive accuracy compared to a single sensor model. Combining NIR-band data from ALOS AVNIR-2 and backscatter of L-band horizontal emitted and vertical received (HV) electric fields from ALOS PALSAR produced the most accurate SVR model (with an R2 of 0.926 and root mean square error of 11.007 m3/ha). The number of field plots sufficient for model development with non-redundant explanatory variables was 77. Under this condition, SVR performed similarly to ANN and outperformed the multiple linear regression and random forest methods. Numéro de notice : A2019-319 Thématique : FORET/IMAGERIE Nature : Article DOI : doi.org/10.1016/j.isprsjprs.2019.08.002 date de publication en ligne : 20/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93357 [article]

Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

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Quantifying the impact of trees on land surface temperature: a downscaling algorithm at city-scale / Elena Barbierato in European journal of remote sensing, sans n° (2019)  

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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)  

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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)  

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Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators / Dinesh Babu Irulappa Pillai Vijayakumar in Remote sensing, vol 11 n° 8 (August 2019)  

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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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High-resolution large-area digital orthophoto map generation using LROC NAC images / Kaichang Di in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)  

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Improved algorithms for the measurement of total precipitable water and cloud liquid water from SARAL microwave radiometer observations / Rajput Neha Mangalsinh in Marine geodesy, vol 42 n° 4 (July 2019)  

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A novel method for separating woody and herbaceous time series / Qiang Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)  

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Sea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach / Jian Zhao in Marine geodesy, vol 42 n° 4 (July 2019)  

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