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Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 95 - 117 Titre : Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping Type de document : Article/Communication Auteurs : Alvin B. Baloloy, Auteur ; Ariel C. Blanco, Auteur ; Raymund Rhommel StaAna, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 95 - 117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] Asie du sud-est [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] espèce exotique envahissante [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] orthophotographie [Termes descripteurs IGN] Philippines [Termes descripteurs IGN] Short Waves InfraRed [Termes descripteurs IGN] surveillance du littoral Résumé : (auteur) Numéro de notice : A2020-354 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.001 date de publication en ligne : 11/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95240 [article]

A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions / Shahryar K. Ahmad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2471 - 2480 Titre : A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions Type de document : Article/Communication Auteurs : Shahryar K. Ahmad, Auteur ; Faisal Hossain, Auteur ; Hisham Eldardiry, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2471 - 2480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Bangladesh [Termes descripteurs IGN] climat tropical [Termes descripteurs IGN] eau de surface [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image PlanetScope [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] plan d'eau [Termes descripteurs IGN] radar à antenne synthétique [Termes descripteurs IGN] reconnaissance de surface [Termes descripteurs IGN] surveillance hydrologique [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] zone humide Résumé : (auteur) Consistent estimation of water surface area from remote sensing remains challenging in regions such as South Asia with vegetation, mountainous topography, and persistent monsoonal cloud cover. High-resolution optical imagery, which is often used for global inundation mapping, is highly impacted by clouds, while synthetic aperture radar (SAR) imagery is not impacted by clouds and is affected by both topographic layover and vegetation. Here, we compare and contrast inundation extent measurements from visible (Landsat-8 and Sentinel-2) and SAR (Sentinel-1) imagery. Each data type (wavelength) has complementary strengths and weaknesses which were gauged separately over selected water bodies in Bangladesh. High-resolution cloud-free PlanetScope imagery at 3-m resolution was used as a reference to check the accuracy of each technique and data type. Next, the optical and radar images were fused for a rule-based water area classification algorithm to derive the optimal decision for the water mask. Results indicate that the fusion approach can improve the overall accuracy by up to 3.8%, 18.2%, and 8.3% during the wet season over using the individual products of Landsat8, Sentinel-1, and Sentinel-2, respectively, at three sites, while providing increased observational frequency. The fusion-derived products resulted in overall accuracy ranging from 85.8% to 98.7% and Kappa coefficient varying from 0.61 to 0.83. The proposed SAR-visible fusion technique has potential for improving satellite-based surface water monitoring and storage changes, especially for smaller water bodies in humid tropical climate of South Asia. Numéro de notice : A2020-198 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2950705 date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2950705 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94868 [article]

Plant survival monitoring with UAVs and multispectral data in difficult access afforested areas / Maria Luz Gil-Docampo in Geocarto international, vol 35 n° 2 ([01/02/2020])  

Public [article]  inGeocarto international > vol 35 n° 2 [01/02/2020] . - pp 128 - 140 Titre : Plant survival monitoring with UAVs and multispectral data in difficult access afforested areas Type de document : Article/Communication Auteurs : Maria Luz Gil-Docampo, Auteur ; Juan Ortiz-Sanz, Auteur ; S. Martínez-Rodríguez, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 128 - 140 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] aire protégée [Termes descripteurs IGN] analyse de survie [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] climat aride [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] mortalité [Termes descripteurs IGN] reboisement [Termes descripteurs IGN] ressources en eau [Termes descripteurs IGN] surveillance de la végétation [Termes descripteurs IGN] télédétection aérienne Résumé : (Auteur) Water supply devices enable afforestation in dry climates and on poor lands with generally high success rates. Previous survival analyses have been based on the direct observation of each individual plant in the field, which entails considerable effort and costs. This study provides a low-cost method to discriminate between live and dead plants in afforestation that can efficiently replace traditional field inspections through the use of unmanned aerial vehicles (UAVs) equipped with RGB and NIR sensors. The method combines the use of a conventional camera with an identical camera modified to record the NIR channel. Survival analysis was performed with digital image processing techniques based on calculated indices associated with plant vigour and PCA-based decorrelation. The method yielded results with high global accuracy rates (∼96.2%) with a minimum percentage of doubtful plants, even in young plantations (seedlings Numéro de notice : A2020-035 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1508312 date de publication en ligne : 02/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1508312 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94517 [article]

Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery / Yuanheng Sun in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 826 - 840 Titre : Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery Type de document : Article/Communication Auteurs : Yuanheng Sun, Auteur ; Qiming Qin, Auteur ; Huazhong Ren, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 826 - 840 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] bande rouge [Termes descripteurs IGN] canopée [Termes descripteurs IGN] Chine [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] teneur en chlorophylle des feuilles Résumé : (auteur) The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5–7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images. Numéro de notice : A2020-069 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2940826 date de publication en ligne : 27/09/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2940826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94615 [article]

Regional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification / Viktor Myroniuk in Remote sensing, vol 12 n°1 (January 2020)  

Public [article]  inRemote sensing > vol 12 n°1 (January 2020) . - 24 p. Titre : Regional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification Type de document : Article/Communication Auteurs : Viktor Myroniuk, Auteur ; Mykola Kutia, Auteur ; Arbi J. Sarkissian, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bande infrarouge [Termes descripteurs IGN] carte forestière [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Global Forest Change map [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] plaine [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance forestière [Termes descripteurs IGN] Ukraine Résumé : (auteur) Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15% Numéro de notice : A2020-225 Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12010187 date de publication en ligne : 05/01/2020 En ligne : https://doi.org/10.3390/rs12010187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94940 [article]

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

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Un été brûlant sous l’oeil des satellites / Laurent Polidori in Géomètre, n° 2173 (octobre 2019)

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A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

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Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])  

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Satellite information classification and interpretation / Rustam B. Rustamov (2019)  

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Télédétection multispectrale et hyperspectrale des eaux littorales turbides / Morgane Larnicol (2018)  

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Semiautomatic detection and classification of materials in historic buildings with low-cost photogrammetric equipment / Javier Sanchez in Journal of Cultural Heritage, vol 25 (May - June 2017)  

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Unsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

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Thermal infrared reveals vegetation stress / Thomas A. Groen in GIM international [en ligne], vol 30 n° 6 (June 2016)

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Real-time atmospheric correction of AVIRIS-NG imagery / Brian D. Bue in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)  

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