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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)
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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 IGN] bande infrarouge
[Termes IGN] carte forestière
[Termes IGN] changement climatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Google Earth Engine
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image Landsat
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] image satellite
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] plaine
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] UkraineRésumé : (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 Affiliation des auteurs : non IGN Thématique : FORET/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
in Remote sensing > vol 12 n° 1 (January 2020) . - 24 p.[article]Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands / Mohammad El Hajj in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)
[article]
Titre : Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands Type de document : Article/Communication Auteurs : Mohammad El Hajj, Auteur ; Nicolas Baghdadi, Auteur ; Mehrez Zribi, Auteur Année de publication : 2019 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] humidité du sol
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Water Index
[Termes IGN] réseau neuronal artificiel
[Termes IGN] surface cultivéeRésumé : (auteur) Surface soil moisture (SSM) estimation is of great importance in several areas, such as hydrology, agriculture and risk assessment. C-band SAR (synthetic aperture radar) data have been widely used to estimate SSM, whereas few studies have been performed using L-band SAR due to the low availability of L-band SAR data. In this context, the objective of the present paper is to compare the SSM estimation potentials of the C- (Sentinel-1) and L-bands (PALSAR) for wheat and grassland plots. The inversion approach developed in this study uses neural networks to invert the SAR signal and estimate the SSM. For each radar frequency, the developed neural networks were trained using the following as an input vector: SAR incidence angle, SAR polarization (VV for the C-band and HH for the L-band), and NDVI from optical images. Artificial Neural networks (ANNs) were developed and validated using synthetic and real databases. The results showed that the L-band provided slightly less accurate SSM estimates than the C-band. Moreover, the results showed that the accuracies of the SSM estimates for both frequencies strongly depended on the soil roughness (Hrms) and SSM values. From the synthetic database at SSM values less than 25 vol.%, the ANNs underestimated the SSM for Hrms values less than 1.5 cm and overestimated the SSM for Hrms values greater than 1.5 cm. In addition, the ANNs underestimated the SSM value regardless of the Hrms value when the SSM value was greater than 25 vol.%. An RMSE analysis of the SSM estimates showed that the highest RMSE values were observed for the L-band regardless of the SSM value, and high RMSE values were observed for the C-band only in very wet soil conditions (SSM>25 vol.%). From the real database at NDVI values less than 0.7, the RMSE (root mean square error) of the SSM estimates was 4.6 vol.% for the C-band and 5.3 vol.% for the L-band. Most importantly, the L-band enabled the estimation of the SSM under a well-developed vegetation cover (NDVI > 0.7) with an RMSE of 6.7 vol.%, whereas the C-band SAR signal became completely attenuated for some crops when the NDVI value was greater than 0.7, and thus the estimation of SSM was impossible using the C-band. Numéro de notice : A2019-473 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.05.021 Date de publication en ligne : 29/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.05.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93634
in International journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 13 p.[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)
[article]
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] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] bande L
[Termes IGN] Brésil
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] Eucalyptus (genre)
[Termes IGN] image ALOS-AVNIR2
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] régression multiple
[Termes IGN] taux d'échantillonnage
[Termes IGN] volume en boisRésumé : (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 Affiliation des auteurs : non IGN 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
in ISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 135 - 146[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019103 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019102 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Geometric accuracy improvement of WorldView‐2 imagery using freely available DEM data / Mateo Gašparović in Photogrammetric record, vol 34 n° 167 (September 2019)
[article]
Titre : Geometric accuracy improvement of WorldView‐2 imagery using freely available DEM data Type de document : Article/Communication Auteurs : Mateo Gašparović, Auteur ; Dino Dobrinić, Auteur ; Damir Medak, Auteur Année de publication : 2019 Article en page(s) : pp 266 - 281 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] image à très haute résolution
[Termes IGN] image ALOS
[Termes IGN] image panchromatique
[Termes IGN] image Worldview
[Termes IGN] modèle numérique de surface
[Termes IGN] orientation du capteur
[Termes IGN] orthorectification
[Termes IGN] point d'appui
[Termes IGN] précision géométrique (imagerie)Résumé : (auteur) This paper provides geometric accuracy improvements for very highresolution WorldView-2 (WV2) Ortho Ready Standard Level-2A (ORS2A) satelliteimages in the panchromatic (PAN) band. The entire process can be divided intotwo steps: (1) sensor orientation, which was conducted on 40 ground controlpoints (GCPs) to determine which sensor model was best depending on variousnumbers of GCPs; and (2) orthorectification, whose main objective was tocompare the accuracy of the process when using different freely available globaldigital elevation models (DEMs). The research showed that a sensor model basedon rational polynomial coefficients (RPCs) with a shift or zero-order biascorrection attained the best geopositioning accuracy during the orientation stepwith the use of seven GCPs. The highest accuracy was achieved for imagesorthorectified with the ALOS World 3D-30 m DEM (AW3D30). Numéro de notice : A2019-432 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12292 Date de publication en ligne : 10/10/2019 En ligne : https://doi.org/10.1111/phor.12292 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93759
in Photogrammetric record > vol 34 n° 167 (September 2019) . - pp 266 - 281[article]Polarimétrie radar complète et partielle pour le suivi des surfaces terrestres / Pierre-Louis Frison in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)
[article]
Titre : Polarimétrie radar complète et partielle pour le suivi des surfaces terrestres Type de document : Article/Communication Auteurs : Pierre-Louis Frison , Auteur ; Cédric Lardeux, Auteur ; Bénédicte Fruneau , Auteur ; Jean-Paul Rudant , Auteur Année de publication : 2019 Article en page(s) : pp 33 - 39 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] carte de la végétation
[Termes IGN] classification
[Termes IGN] extraction de la végétation
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] polarimétrie radar
[Termes IGN] sédimentation
[Termes IGN] TunisieRésumé : (auteur) This article presents some illustrations of (fully or partial) polarimetric radar data applications for the monitoring of terrestrial surfaces. The first part is dedicated to fully polarimetric radar data. Firstly, a theoretical reminder presents the specificity of fully polarimetric data. Then illustrations are given for vegetation types cartography as well as spatio-temporal processes of sedimentation in a semi-arid area in Tunisia. The second part focuses on partially polarimetric data, of the type acquired by the Sentinel-1A/1B satellite SAR sensors, which will be widely used in future years due to their significant contribution to land surface observations studies for environmental sciences. Numéro de notice : A2019-346 Affiliation des auteurs : UPEM-LASTIG+Ext (2016-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.52638/rfpt.2019.464 En ligne : https://doi.org/10.52638/rfpt.2019.464 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93383
in Revue Française de Photogrammétrie et de Télédétection > n° 219-220 (juin - octobre 2019) . - pp 33 - 39[article]Generation of large-scale moderate-resolution forest height mosaic with spaceborne repeat-pass SAR interferometry and lidar / Yang Lei in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)PermalinkPermalinkPolarimetric radar vegetation index for biomass estimation in desert fringe ecosystems / Jisung Geba Chang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkAccuracy assessment of different digital surface models / Ugur Alganci in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)PermalinkActive tectonics of the onshore Hengchun Fault using UAS DSM combined with ALOS PS-InSAR time series (Southern Taiwan) / Benoit Deffontaines in Natural Hazards and Earth System Sciences, vol 18 n° 3 ([01/03/2018])PermalinkExploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area / Saygin Abdikan in Geocarto international, vol 33 n° 1 (January 2018)PermalinkBayesian data combination for the estimation of ionospheric effects in SAR interferograms / Giorgio Gomba in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkThe potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas / Emanuele Santi in Remote sensing of environment, vol 200 (October 2017)PermalinkAn information fusion approach for PALSAR data to retrieve soil moisture / Ankita Jain in Geocarto international, vol 32 n° 9 (September 2017)PermalinkCritical analysis of model-based incoherent polarimetric decomposition methods and investigation of deorientation effect / Pooja Mishra in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkMise en place d'une méthode semi-automatique de cartographie de l'occupation des sols à partir d'images SAR polarimétriques / Monique Moine in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)PermalinkActive interseismic shallow deformation of the Pingting terraces (Longitudinal Valley – Eastern Taiwan) from UAV high-resolution topographic data combined with InSAR time series / Benoit Deffontaines in Geomatics, Natural Hazards and Risk, vol 8 (2017)PermalinkDetection of ground surface deformation caused by the 2016 Kumamoto earthquake by InSAR using ALOS-2 data / Basara Miyahara in Bulletin of the GeoSpatial Information authority of Japan, vol 64 (December 2016)PermalinkRelative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation / Alyssa Endres in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkInvestigation of ionospheric effects on SAR Interferometry (InSAR): A case study of Hong Kong / Wu Zhu in Advances in space research, vol 58 n° 4 (August 2016)PermalinkSource model from ALOS-2 ScanSAR of the 2015 Nepal earthquakes / Youtian Liu in Journal of applied geodesy, vol 10 n° 2 (June 2016)PermalinkToward operational compensation of ionospheric effects in SAR interferograms: the split-spectrum method / Giorgio Gomba in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkApport de la télédétection radar satellitaire pour la cartographie de la forêt des Landes / Yousra Hamrouni (2016)PermalinkEstimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)PermalinkRadar based classification prior to biomass retrieval from P-Band SAR data / Pierre-Louis Frison (2016)Permalink