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Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification / Jose Aranha in Forests, vol 11 n° 5 (May 2020)
[article]
Titre : Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification Type de document : Article/Communication Auteurs : Jose Aranha, Auteur ; Teresa Enes, Auteur ; Ana Calvão, Auteur ; Hélder Viana, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbuste
[Termes IGN] biomasse
[Termes IGN] classification dirigée
[Termes IGN] gestion forestière
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] image Sentinel-MSI
[Termes IGN] incendie de forêt
[Termes IGN] modèle de croissance végétale
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Portugal
[Termes IGN] signature spectrale
[Termes IGN] sous-bois
[Termes IGN] système d'information géographique
[Termes IGN] zone sinistréeRésumé : (auteur) Shrubs growing in former burnt areas play two diametrically opposed roles. On the one hand, they protect the soil against erosion, promote rainwater infiltration, carbon sequestration and support animal life. On the other hand, after the shrubs’ density reaches a particular size for the canopy to touch and the shrubs’ biomass accumulates more than 10 Mg ha−1, they create the necessary conditions for severe wild fires to occur and spread. The creation of a methodology suitable to identify former burnt areas and to track shrubs’ regrowth within these areas in a regular and a multi temporal basis would be beneficial. The combined use of geographical information systems (GIS) and remote sensing (RS) supported by dedicated land survey and field work for data collection has been identified as a suitable method to manage these tasks. The free access to Sentinel images constitutes a valuable tool for updating the GIS project and for the monitoring of regular shrubs’ accumulated biomass. Sentinel 2 VIS-NIR images are suitable to classify rural areas (overall accuracy = 79.6% and Cohen’s K = 0.754) and to create normalized difference vegetation index (NDVI) images to be used in association to allometric equations for the shrubs’ biomass estimation (R2 = 0.8984, p-value Numéro de notice : A2020-654 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f11050555 Date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.3390/f11050555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96116
in Forests > vol 11 n° 5 (May 2020) . - 19 p.[article]Soil moisture estimation with SVR and data augmentation based on alpha approximation method / Wei Xu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
[article]
Titre : Soil moisture estimation with SVR and data augmentation based on alpha approximation method Type de document : Article/Communication Auteurs : Wei Xu, Auteur ; Zhaoxu Zhang, Auteur ; Qiming Qin, Auteur Année de publication : 2020 Article en page(s) : pp 3190 - 3201 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] approximation
[Termes IGN] erreur moyenne quadratique
[Termes IGN] humidité du sol
[Termes IGN] image ALOS
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] irrigation
[Termes IGN] modèle de régression
[Termes IGN] surveillance agricoleRésumé : (auteur) Soil moisture content is an important parameter in hydrological, meteorological, and agricultural applications. Balenzano et al. proposed the alpha approximation method in 2011 for solving some complex issues during the retrieval of soil moisture over agricultural crops with synthetic aperture radar data. However, determining the constraints and solving the underdetermined system of equations in this method add new challenges. Considering the questions of constraints and underdetermined system of equations, the alpha approximation method is used to augment the measured data, and can avoid solving the underdetermined system of equations with constraints directly. Then, these data are applied in a support vector regression machine for soil moisture estimation. It is found that when an optimal model is determined, the method proposed in this article is superior to the direct use of the alpha approximation method, and the root-mean-squared error (RMSE) decreased from 0.0775 to 0.0339 and R 2 increased from 0.0467 to 0.6491. In addition, the method obtained a good result from a data set collected that included a different growing period of crops by changing the standardized method from StandardScaler to Scale , where the RMSE is 0.0501 and R 2 is 0.3204. This indicates the good generalization capability of this method. In conclusion, the proposed method solves the two questions effectively and provides a potential way for long-time or large-scale soil moisture monitoring with much less in situ measurements. Numéro de notice : A2020-235 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2950321 Date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2950321 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94981
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3190 - 3201[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)
[article]
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 IGN] Bangladesh
[Termes IGN] climat tropical
[Termes IGN] eau de surface
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-8
[Termes IGN] image PlanetScope
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] plan d'eau
[Termes IGN] radar à antenne synthétique
[Termes IGN] reconnaissance de surface
[Termes IGN] surveillance hydrologique
[Termes IGN] télédétection spatiale
[Termes IGN] zone humideRésumé : (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 Affiliation des auteurs : non IGN 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
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2471 - 2480[article]Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series / Maylis Lopes in Methods in ecology and evolution, vol 11 n° 4 (April 2020)
[article]
Titre : Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series Type de document : Article/Communication Auteurs : Maylis Lopes, Auteur ; Pierre-Louis Frison , Auteur ; Merry Crowson, Auteur ; Eleanor Warren-Thomas, Auteur ; et al., Auteur Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : pp 532 - 541 Note générale : bibliography Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] classification
[Termes IGN] fusion d'images
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Indonésie
[Termes IGN] nébulosité
[Termes IGN] série temporelle
[Termes IGN] tourbière
[Termes IGN] zone intertropicaleRésumé : (auteur) The recent availability of high spatial and temporal resolution optical and radar satellite imagery has dramatically increased opportunities for mapping land cover at fine scales. Fusion of optical and radar images has been found useful in tropical areas affected by cloud cover because of their complementarity. However, the multitemporal dimension these data now offer is often neglected because these areas are primarily characterized by relatively low levels of seasonality and because the consideration of multitemporal data requires more processing time. Hence, land cover mapping in these regions is often based on imagery acquired for a single date or on an average of multiple dates. The aim of this work is to assess the added value brought by the temporal dimension of optical and radar time series when mapping land cover in tropical environments. Specifically, we compared the accuracies of classifications based on (a) optical time series, (b) their temporal average, (c) radar time series, (d) their temporal average, (e) a combination of optical and radar time series and (f) a combination of their temporal averages for mapping land cover in Jambi province, Indonesia, using Sentinel-1 and Sentinel-2 imagery. Using the full information contained in the time series resulted in significantly higher classification accuracies than using temporal averages (+14.7% for Sentinel-1, +2.5% for Sentinel-2 and +2% combining Sentinel-1 and Sentinel-2). Overall, combining Sentinel-2 and Sentinel-1 time series provided the highest accuracies (Kappa = 88.5%). Our study demonstrates that preserving the temporal information provided by satellite image time series can significantly improve land cover classifications in tropical biodiversity hotspots, improving our capacity to monitor ecosystems of high conservation relevance such as peatlands. The proposed method is reproducible, automated and based on open-source tools satellite imagery. Numéro de notice : A2020-875 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/2041-210X.13359 Date de publication en ligne : 27/01/2020 En ligne : https://doi.org/10.1111/2041-210X.13359 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99668
in Methods in ecology and evolution > vol 11 n° 4 (April 2020) . - pp 532 - 541[article]Monitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys / Ashutosh Tiwari in Geocarto international, vol 35 n° 5 ([01/04/2020])
[article]
Titre : Monitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys Type de document : Article/Communication Auteurs : Ashutosh Tiwari, Auteur ; Avadh Bihari Narayan, Auteur ; Ramji Dwivedi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 535 - 558 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arpentage
[Termes IGN] corrélation croisée maximale
[Termes IGN] covariance
[Termes IGN] données GNSS
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] effondrement de terrain
[Termes IGN] escarpement
[Termes IGN] image Sentinel-SAR
[Termes IGN] Inde
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] modèle numérique de surface
[Termes IGN] précipitation
[Termes IGN] surveillance géologique
[Termes IGN] tachéomètre électronique robotiséRésumé : (auteur) A robust geodetic framework comprising Terrestrial Laser Scanner (TLS), Global Navigation Satellite Systems (GNSS), Robotic Total Station (RTS) and Multi-temporal InSAR (MT-InSAR) was employed first in India to investigate a landslide-prone Sirobagarh region, Uttarakhand, at different spatial extents, and to evaluate the relationship amongst the displacement estimates obtained from the applied surveying techniques. TLS derived digital elevation models indicated displacements >5 m on the landslide upper scarp. GNSS- and RTS-based observations showed horizontal movements towards the Alaknanda river in the landslide slope direction (maximum values: 0.1305 and 0.045 m, respectively), and downward vertical motion (largest subsidence magnitude: −2.1315 and −0.030 m, respectively). MT-InSAR processing of Sentinel-1a images identified 21071 measurement pixels, highlighting subsidence around the landslide (mean velocity range: −0.110 to 0.008 m/year). Analysis of displacement vectors using vector equality, cross-covariance, cross-correlation and principal component analysis reveals that GNSS vertical displacement estimates were partially correlated with MT-InSAR measurements (correlated for epoch difference 2–3), whereas there was good cross-correlation between MT-InSAR and LiDAR observations throughout. The displacement estimates and their analyses evident unstable movement of the landslide scarp occurring due to debris flow and rainfall, and a relatively moderate subsidence activity in the surrounding areas lying in the landslide zone. Numéro de notice : A2020-144 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1524516 Date de publication en ligne : 23/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1524516 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94770
in Geocarto international > vol 35 n° 5 [01/04/2020] . - pp 535 - 558[article]Réservation
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