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Examining the integration of Landsat operational land imager with Sentinel-1 and vegetation indices in mapping southern yellow pines (Loblolly, Shortleaf, and Virginia pines) / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 1 (January 2022) . - pp 29 - 38 Titre : Examining the integration of Landsat operational land imager with Sentinel-1 and vegetation indices in mapping southern yellow pines (Loblolly, Shortleaf, and Virginia pines) Type de document : Article/Communication Auteurs : Clement E. Akumu, Auteur ; Eze O. Amadi, Auteur Année de publication : 2022 Article en page(s) : pp 29 - 38 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] bande C [Termes IGN] canopée [Termes IGN] carte de la végétation [Termes IGN] coefficient de rétrodiffusion [Termes IGN] image Landsat-OLI [Termes IGN] image Sentinel-SAR [Termes IGN] indice de végétation [Termes IGN] intégration de données [Termes IGN] inventaire forestier local [Termes IGN] Pinus (genre) [Termes IGN] Pinus ponderosa [Termes IGN] précision de la classification [Termes IGN] Soil Adjusted Vegetation Index Résumé : (Auteur) The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines. Numéro de notice : A2022-062 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00024R2 Date de publication en ligne : 01/01/2022 En ligne : https://doi.org/10.14358/PERS.21-00024R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99706 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2022011	SL	Revue	Centre de documentation	Revues en salle	Disponible

In situ C-band data for wheat physiological functioning monitoring in the South Mediterranean region / Nadia Ouaadi (2022)  

Public Titre : In situ C-band data for wheat physiological functioning monitoring in the South Mediterranean region Type de document : Article/Communication Auteurs : Nadia Ouaadi, Auteur ; Ludovic Villard, Auteur ; Saïd Khabba, Auteur ; Pierre-Louis Frison , Auteur ; Jamal Ezzahar, Auteur ; Mohamed Kasbani, Auteur ; Adnane Chakir , Auteur ; Pascal Fanise, Auteur ; Valérie Le Dantec, Auteur ; Mehrez Zribi, Auteur ; Salah Er-Raki, Auteur ; Lionel Jarlan, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : IGARSS 2022, IEEE International Geoscience And Remote Sensing Symposium 17/07/2022 22/07/2022 Kuala Lumpur Malaysie Proceedings IEEE Importance : pp 4951 - 4954 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande C [Termes IGN] blé (céréale) [Termes IGN] coefficient de rétrodiffusion [Termes IGN] cohérence photométrique [Termes IGN] variation diurne Résumé : (auteur) Irrigated agriculture is the largest consumer of freshwater in the world, particularly in the South Mediterranean region that is already suffering from water shortages. Monitoring the water stress status of plants can contribute to an optimal use of irrigation. C-band radar data have shown great potential for monitoring soil and vegetation hydric conditions. While a diurnal cycle up to 1 dB has been observed over tropical forests, the behavior of annual crops is yet to be investigated. In this context, an experiment composed of a radar setup with 6 C-band antennas was installed in Morocco over a wheat field. 15 minutes full polarization acquisitions of the backscattering coefficient and the interferometric coherence are analyzed in relation with the physiological functioning of wheat. In this paper, the first results from the analysis of data collected during the 2020 growing season are presented. The results reveal the existence of a diurnal cycle of the interferometric coherence and the backscattering coefficient (up to 0.45 and 1.5 dB, respectively) with amplitudes increase in relation with vegetation development. Numéro de notice : C2022-041 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS46834.2022.9884289 Date de publication en ligne : 28/09/2022 En ligne : https://doi.org/10.1109/IGARSS46834.2022.9884289 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101769

A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lan Xun in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 148 - 166 Titre : A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery Type de document : Article/Communication Auteurs : Lan Xun, Auteur ; Jiahua Zhang, Auteur ; Dan Cao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 148 - 166 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] cartographie automatique [Termes IGN] Chine [Termes IGN] coefficient de rétrodiffusion [Termes IGN] distribution spatiale [Termes IGN] Etats-Unis [Termes IGN] Gossypium (genre) [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] indice de végétation [Termes IGN] polarisation [Termes IGN] réflectance spectrale [Termes IGN] série temporelle Résumé : (auteur) Cotton is an important cash crop in the world, as the main source of natural and renewable fiber for textiles. Accurate and timely monitoring of the cotton distribution is crucial for cotton cultivation management and international trade. However, most of the previous researches on cotton identification using remotely sensed images are highly dependent on training samples, and the collection of samples is time-consuming and expensive. To overcome this limitation, a new index, termed as Cotton Mapping Index (CMI), was developed in this study for automatic cotton mapping using time series of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) satellite data. Four sites in the United States (U.S.) and four sites in China were selected to develop and assess the performance of the CMI. The spectral characteristics derived from Sentinel-2 and backscattering coefficients derived from Sentinel-1 for cotton and non-cotton crops during the cotton growth period were analyzed. Considering the phenology differences of crops in different regions, the features at an adaptive window were adopted to construct the CMI. The results showed that at the peak greenness period, the multiplication of red-edge 1 and red-edge 2 band for cotton samples were much larger than those for non-cotton samples, whereas the spectral angle at the red band as well as the absolute values of backscattering coefficients in vertical transmit and vertical receive (VV) polarization for cotton samples were much smaller than those for non-cotton samples. Based on these findings, the CMI was developed to identify cotton cultivated area within the cropland area. The overall accuracy of classification results for the sites in the U.S. was higher than 81.20%, and the mean relative error for the sites in Xinjiang of China was 26.69%. The CMI, which incorporated optical and radar features, had a better performance than the indices using optical features solely. The advantage of the CMI over supervised classifiers (i.e., k-nearest neighbors, support vector machine and random forest) is that no training samples are required. Moreover, the cotton distribution map can be obtained before the harvest using the CMI. These results indicated the potential of the CMI for cotton mapping. The applicability of CMI in other regions with different cropping systems and crop types needs to be further assessed in the future study. Numéro de notice : A2021-775 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.08.021 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.08.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98836 [article]

Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)  

Public [article]  inRemote sensing of environment > vol 264 (October 2021) . - n° 112575 Titre : Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data Type de document : Article/Communication Auteurs : Qi Zhang, Auteur ; Linlin Ge, Auteur ; Ruiheng Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112575 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Californie (Etats-Unis) [Termes IGN] cartographie thématique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par Perceptron multicouche [Termes IGN] coefficient de rétrodiffusion [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] incendie [Termes IGN] réflectance du sol [Termes IGN] réseau neuronal siamois Résumé : (auteur) Around 350 million hectares of land are affected by wildfires every year influencing the health of ecosystems and leaving a trail of destruction. Accurate information over burned areas (BA) is essential for governments and communities to prioritize recovery actions. Prior research over the past decades has established the potentials and limitations of space-borne earth observation for mapping BA over large geographic areas at various scales. The operational deployment of Sentinel-1 and Sentinel-2 constellations significantly improved the quality and quantity of the imagery from the microwave (C-band) and optical regions on the spectrum. Based on that, this study set to investigate whether the existing coarse BA products can be further improved by the synergy of optical surface reflectance (SR), radar backscatter coefficient (BS), and/or radar interferometric coherence (COR) data with higher spatial resolutions. A Siamese Self-Attention (SSA) classification strategy is proposed for the multi-sensor BA mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by test sites, feature sources, and classification strategies to appraise the improvements achieved by the proposed method. Numéro de notice : A2021-807 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112575 Date de publication en ligne : 06/07/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98866 [article]

Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine / Dong Liang in Remote sensing of environment, Vol 256 (April 2020)  

Public [article]  inRemote sensing of environment > Vol 256 (April 2020) . - n° 112318 Titre : Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine Type de document : Article/Communication Auteurs : Dong Liang, Auteur ; Huadong Guo, Auteur ; Lu Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112318 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] albedo [Termes IGN] Antarctique [Termes IGN] calotte glaciaire [Termes IGN] changement climatique [Termes IGN] coefficient de rétrodiffusion [Termes IGN] distribution spatiale [Termes IGN] fonte des glaces [Termes IGN] Google Earth Engine [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] montée du niveau de la mer [Termes IGN] série temporelle Résumé : (auteur) The Antarctic ice sheet is an important mass of glacier ice. It is particularly sensitive to climate change, and the flow of Antarctica's inland glaciers into the sea, accelerated by collapsing ice shelves, threatens global sea level rise. The amount of snowmelt on the surface of the ice sheet is an important metric for accurately assessing surface material loss and albedo change, which affect the stability of the ice sheet. This study proposes a framework for quickly extracting time-series freeze-thaw information at the continental scale and 40 m resolution by taking advantage of the huge amount of synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites over the Antarctic, available for rapid processing on Google Earth Engine. Co-orbit normalization is used in the proposed framework to establish a unified standard of judgement by reducing the variations in the backscattering coefficient introduced by observation geometry, terrain fluctuations, and melt conditions between images acquired at different times. We implemented the framework to produce a massive dataset of both monthly freeze-thaw information over the Antarctic and higher temporal resolution freeze-thaw information for the Larsen C ice shelf from 2015 to 2019, with overall accuracies of 93% verified by a manual visual interpretation method and 84% evaluated from automatic weather station temperatures. Due to its effectiveness and robustness, the framework can be used to analyse the spatiotemporal distribution of snowmelt, the change in melt area, and anomalous melt events in Antarctica, especially those in Larsen C caused by foehn wind. Numéro de notice : A2021-477 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112318 Date de publication en ligne : 10/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112318 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97117 [article]

A soil texture categorization mapping from empirical and semi-empirical modelling of target parameters of synthetic aperture radar / Shoba Periasamy in Geocarto international, vol 36 n° 5 ([15/03/2021])  

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Diurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations / Adnane Chakir (2021)  

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Diurnal cycles of C-band temporal coherence and backscattering coefficient over a wheat field in a semi-arid area / Nadia Ouaadi (2021)  

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Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas / Nadia Ouaadi in Remote sensing of environment, Vol 251 (15 December 2020)  

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Time series potential assessment for biophysical characterization of orchards and crops in a mixed scenario with Sentinel-1A SAR data / Hemant Sahu in Geocarto international, vol 35 n° 14 ([15/10/2020])  

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C band radar crops monitoring at high temporal frequency: first results of the MOCTAR campaign / Pierre-Louis Frison (2020)  

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Combination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan / Emal Wali in Remote sensing, vol 12 n° 1 (January 2020)  

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Surface soil moiture retrieval over irrigated wheat crops in semi-arid areas using Sentinel-1 data / Nadia Ouaadi (2020)  

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Water stress detection over irrigated wheat crops in semi-arid areas using the diurnal differences of Sentinel-1 backscatter / Nadia Ouaadi (2020)  

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Lettre : Existe-t-il des relations formelles entre coefficients de diffusion radar et facteurs de réflectance en optique ? / Jean-Paul Rudant in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)  

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