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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 Affiliation des auteurs : non IGN 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]

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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 Affiliation des auteurs : non IGN 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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Land cover characterization for hydrological modelling using thermal infrared emissivities / A. French in International Journal of Remote Sensing IJRS, vol 31 n° 14 (July 2010)

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 31 n° 14 (July 2010) . - pp 3867 - 3883 Titre : Land cover characterization for hydrological modelling using thermal infrared emissivities Type de document : Article/Communication Auteurs : A. French, Auteur ; A. Inamdar, Auteur Année de publication : 2010 Article en page(s) : pp 3867 - 3883 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] caractérisation [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] emissivité [Termes descripteurs IGN] évapotranspiration [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] Kansas (Etats-Unis ; état) [Termes descripteurs IGN] modèle hydrographique [Termes descripteurs IGN] Oklahoma (Etats-Unis) Résumé : (Auteur) Remote sensing with multispectral thermal infrared can improve regional estimation of evapotranspiration (ET) by providing new constraints on land surface energy balance. Current models use visible and near infrared bands to obtain vegetative cover, and sometimes use thermal infrared data for land surface temperature. Together these can yield good ET estimates. However, it may be possible to do even better by using remotely sensed thermal infrared emissivity, a surface property related to fractional vegetative cover but independent of plant greenness. Emissivities derived from clear-sky Moderate Resolution Imaging Spectroradiometer (MODIS) observations obtained in 2007 over the Southern Great Plains (Oklahoma and Kansas), USA, were compared with changes in Normalized Difference Vegetation Index (NDVI) for winter wheat and grazing land. Emissivity changes were independent of NDVI and sensitive to standing canopies, regardless of growth stage or senescence. Thus emissivities were seasonally dynamic, able to detect wheat harvest timing, and helpful for modelling ET. Numéro de notice : A2010-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30565 [article]

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Assessing spatial uncertainty of LIDAR-derived building model: a case study in downtown Oklahoma city / M. Cheuk in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 3 (March 2009)

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 3 (March 2009) . - pp 257 - 269 Titre : Assessing spatial uncertainty of LIDAR-derived building model: a case study in downtown Oklahoma city Type de document : Article/Communication Auteurs : M. Cheuk, Auteur ; M. Yuan, Auteur Année de publication : 2009 Article en page(s) : pp 257 - 269 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] estimation de précision [Termes descripteurs IGN] incertitude géométrique [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] modèle numérique du bâti [Termes descripteurs IGN] Oklahoma (Etats-Unis) [Termes descripteurs IGN] reconstruction 3D du bâti Résumé : (Auteur) Light Detection and Ranging (lidar) technology enables cost-effective rapid production of digital models that capture topography and vertical structures of surface features at a fine spatial resolution. The capability has promoted lidar applications for mapping terrain, buildings, forest stands, and coastal features that cannot be adequately captured by other remote sensing means over a large area. However, in complex terrain, lidar data and lidar-derived products may contain significant uncertainty. This research provides a simple method to assess the spatial uncertainty of lidar-derived building model, using downtown Oklahoma City, Oklahoma as an example. Results indicate that significant uncertainty could be found in urban environment where: (a) building structures are complex, (b) buildings are constructed with reflective materials, and (c) vegetation grows near-by. In addition, cities under rapid development also challenge the accuracy assessment of 3D building models. To conclude, we suggest: (a) careful pre-flight planning before data collection, (b) improve the feature extraction algorithm if possible, (c) use of other remote sensing data, and (d) accuracy assessment on suggested urban environments to reduce the spatial uncertainty of lidar data and lidar-derived products. Copyright ASPRS Numéro de notice : A2009-036 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29666 [article]

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Using ENVISAT ASAR global mode data for surface soil moisture retrieval over Oklahoma, USA / C. Pathe in IEEE Transactions on geoscience and remote sensing, vol 47 n° 2 (February 2009)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 47 n° 2 (February 2009) . - pp 468 - 480 Titre : Using ENVISAT ASAR global mode data for surface soil moisture retrieval over Oklahoma, USA Type de document : Article/Communication Auteurs : C. Pathe, Auteur ; W. Wagner, Auteur ; D. Sabel, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 468 - 480 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] bande C [Termes descripteurs IGN] bruit (théorie du signal) [Termes descripteurs IGN] diffusométrie [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image Envisat-ASAR [Termes descripteurs IGN] image ERS-SAR [Termes descripteurs IGN] Oklahoma (Etats-Unis) Résumé : (Auteur) The advanced synthetic aperture radar (ASAR) onboard of the satellite ENVISAT can be operated in global monitoring (GM) mode. ASAR GM mode has delivered the first global multiyear C-band backscatter data set in HH polarization at a spatial resolution of 1 km. This paper investigates if ASAR GM can be used for retrieving soil moisture using a change detection approach over large regions. A method previously developed for the European Remote Sensing (ERS) scatterometer is adapted for use with ASAR GM and tested over Oklahoma, USA. The ASAR-GM-derived relative soil moisture index is compared to 50-km ERS soil moisture data and pointlike in situ measurements from the Oklahoma MESONET. Even though the scale gap from ASAR GM to the in situ measurements is less pronounced than in the case of the ERS scatterometer, the correlation for ASAR against the in situ measurements is, in general, somewhat weaker than for the ERS scatterometer. The analysis suggests that this is mainly due to the much higher noise level of ASAR GM compared to the ERS scatterometer. Therefore, some spatial averaging to 3-10 km is recommended to reduce the noise of the ASAR GM soil moisture images. Nevertheless, the study demonstrates that ASAR GM allows resolving spatial details in the soil moisture patterns not observable in the ERS scatterometer measurements while still retaining the basic capability of the ERS scatterometer to capture temporal trends over large areas. Copyright IEEE Numéro de notice : A2009-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29778 [article]

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Representing complex geographic phenomena in GIS / M. Yuan in Cartography and Geographic Information Science, vol 28 n° 2 (April 2001)

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Automated approaches for displaying spatially oriented time-series data through image processing techniques / I.E. Von Essen in Geocarto international, vol 4 n° 4 (December 1989 - February 1990)

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Estimating soil wetness using satellite data / B.J. Choudhury in International Journal of Remote Sensing IJRS, vol 9 n° 7 (July 1988)

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Recognition and assessment of error in geographic information systems / S.J. Walsh in Photogrammetric Engineering & Remote Sensing, PERS, vol 53 n° 10 Tome 1 (october 1987)

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Comparison of NOAA AVHRR data to meteorologic drought indices / S.J. Walsh in Photogrammetric Engineering & Remote Sensing, PERS, vol 53 n° 8 (august 1987)

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