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UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1) / Martin Štroner in European journal of remote sensing, vol 56 n° 1 (2023)  

Public [article]  inEuropean journal of remote sensing > vol 56 n° 1 (2023) . - n° 2179942 Titre : UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1) Type de document : Article/Communication Auteurs : Martin Štroner, Auteur ; Rudolf Urban, Auteur ; Thomas Křemen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2179942 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] densité de la végétation [Termes IGN] données lidar [Termes IGN] image captée par drone [Termes IGN] modèle numérique de terrain [Termes IGN] rugosité du sol [Termes IGN] semis de points [Termes IGN] structure-from-motion Résumé : (auteur) In this paper, we evaluated the results in terms of accuracy and coverage of the LiDAR-UAV system DJI Zenmuse L1 and Digital Aerial Photogrammetric system (DAP – UAV) DJI Zenmuse P1 in a forested area under leaf-off conditions on three sites with varying terrain ruggedness/tree type combinations. Detailed reference clouds were obtained using terrestrial scanning by Leica P40. Our results show that branches pose no problem to the accuracy of LiDAR-UAV and DAP-UAV derived terrain clouds. Elevation accuracies for photogrammetric data were even better than for LiDAR data – as low as 0.015 m on all sites. However, the LiDAR system provided better coverage, with almost full coverage at all sites, while the DAP-UAV coverage declined with the increasing density of branches (being worst in the young forest). In the very dense young forest (Site 1), the coverage by photogrammetrically extracted terrain cloud using high calculation quality and no filtering achieved 80.7% coverage, while LiDAR-UAV reached almost 100% coverage. The importance of the use of multiple (or last) returns when using LiDAR-UAV systems was demonstrated by the fact that on the site with the densest vegetation, only 11% of the ground points were represented by first returns. Numéro de notice : A2023-219 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/22797254.2023.2179942 Date de publication en ligne : 01/03/2023 En ligne : https://doi.org/10.1080/22797254.2023.2179942 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103161 [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])  

Public [article]  inGeocarto international > vol 36 n° 5 [15/03/2021] . - pp 581 - 598 Titre : A soil texture categorization mapping from empirical and semi-empirical modelling of target parameters of synthetic aperture radar Type de document : Article/Communication Auteurs : Shoba Periasamy, Auteur ; Divya Senthil, Auteur ; Ramakrishnan S Shanmugam, Auteur Année de publication : 2021 Article en page(s) : pp 581 - 598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Argile [Termes IGN] bande C [Termes IGN] coefficient de rétrodiffusion [Termes IGN] constante diélectrique [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] indice de végétation [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] limon [Termes IGN] polarisation croisée [Termes IGN] rugosité du sol [Termes IGN] sable [Termes IGN] texture du sol Résumé : (auteur) The present study investigates the potential of synthetic aperture radar in demonstrating the relative percentage of sand, silt and clay content in the soil. The contribution of vegetation and topography in the backscattering coefficient has been significantly reduced by employing the terrain correction model, dual polarized SAR vegetation index and water cloud model. The target parameters namely ‘Soil Roughness (hrms-soil)’ and ‘Dielectric Constant’ (ε′vv−soil ) has arrived from cross-polarization ratio and modified Dubois model. The extracted target parameters are sufficiently correlated with in situ sand (R2 = 0.81) and clay measurements (R2 = 0.78). The relative percentage of silt was mapped by the novel idea of performing the correlation analysis between hrms-soil and ε′vv−soil and thus represented the percentage of silt with reasonable accuracy (R2 = 0.77). From the soil triangle formed with three estimated target parameters, we found that the clay category has shared around 35% of the total area followed by sandy loam (23%). Numéro de notice : A2021-253 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1618924 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1618924 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97276 [article]

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Soil and vegetation scattering contributions in L-Band and P-Band polarimetric SAR observations / S. Hamed Alemohammad in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8417 - 8429 Titre : Soil and vegetation scattering contributions in L-Band and P-Band polarimetric SAR observations Type de document : Article/Communication Auteurs : S. Hamed Alemohammad, Auteur ; Thomas Jagdhuber, Auteur ; Mahta Moghaddam, Auteur ; Dara Entekhabi, Auteur Année de publication : 2019 Article en page(s) : pp 8417 - 8429 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande L [Termes IGN] bande P [Termes IGN] canopée [Termes IGN] constante diélectrique [Termes IGN] couvert végétal [Termes IGN] données polarimétriques [Termes IGN] humidité du sol [Termes IGN] image captée par drone [Termes IGN] image radar moirée [Termes IGN] micro-onde [Termes IGN] rugosité du sol [Termes IGN] teneur en eau de la végétation Résumé : (auteur) Active microwave-based retrieval of soil moisture in vegetated areas has uncertainties due to the sensitivity of the signal to both soil (dielectric constant and roughness) and vegetation (dielectric constant and structure) properties. A multi-frequency acquisition system would increase the number of observations that may constrain soil and/or vegetation parameter retrievals. In order to realize this constraint, an understanding of microwaves interaction with the surface and vegetation across frequencies is necessary. Different microwave frequencies have varied interactions with the soil-vegetation medium and increasing penetration into the soil and canopy with the decreasing frequency. In this study, we examine the contributions of different scattering mechanisms to coincident observations from two microwave frequencies (L and P) of airborne synthetic aperture radar instruments. We quantify contributions of surface, vegetation volume, and double-bounce scattering components. Results are analyzed and discussed to guide future multi-frequency retrieval algorithm designs. Numéro de notice : A2019-594 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2920995 Date de publication en ligne : 27/06/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2920995 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94586 [article]

Soil roughness retrieval from TerraSar-X data using neural network and fractal method / Mohammad Maleki in Advances in space research, vol 64 n°5 (1 September 2019)  

Public [article]  inAdvances in space research > vol 64 n°5 (1 September 2019) . - pp 1117-1129 Titre : Soil roughness retrieval from TerraSar-X data using neural network and fractal method Type de document : Article/Communication Auteurs : Mohammad Maleki, Auteur ; Jalal Amini, Auteur ; Claudia Notarnicola, Auteur Année de publication : 2019 Article en page(s) : pp 1117-1129 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse fractale [Termes IGN] bande X [Termes IGN] équation intégrale [Termes IGN] image TerraSAR-X [Termes IGN] modèle d'inversion [Termes IGN] modèle numérique de terrain [Termes IGN] Perceptron multicouche [Termes IGN] polarimétrie radar [Termes IGN] rugosité du sol Résumé : (auteur) The purpose of this study is to estimate the surface roughness (rms) using TerraSar-X data in HH polarization. Simulation of data is carried out at a wide range of moisture and roughness using the Integral Equation Model (IEM). The inversion method is based on Multi-Layer Perceptron neural network. Inversion technique is performed in two steps. In the first step, the neural network is trained using synthetic data. The inputs of the first neural network are the backscattering coefficient and incidence angle, and the moisture is the output. In the next step, three neural networks are built based on a prior and without prior information on roughness. The inputs of three neural network are backscattering coefficient, estimated moisture in the first step and incidence angle and the roughness is output. The validation of the proposed methods is carried out based on synthetic and real data. Ground roughness measurements are extracted from Digital Terrain Model (DTM) using the fractal method. The accuracy of moisture from synthetic data is 6.1 vol% without prior information on moisture and roughness. The roughness (rms) accuracy of synthetic datasets is 0. 61 cm without prior information and is 0.31 cm and 0.38 cm for rms lower than 2 cm and rms between 2 and 4 cm, with prior information on roughness. The result's analysis of the simulated data showed that the prior information on roughness strongly improves the accuracy of roughness and moisture estimates. The accuracy of rms estimates for the TerraSar-X image in the HH polarization is about 0.9 cm in the case of no prior information on roughness. The accuracy improves to 0.57 cm for rms lower than 2 cm and 0.54 cm for rms between 2 and 4 cm with prior information on roughness. An overestimation of rms for rms lower than 2 cm and an underestimation of rms for rms higher than 2 cm are observed. The results of the accuracy of the synthetic and real data showed that the X band in HH polarization has a very good potential to estimate the soil roughness. Numéro de notice : A2019-411 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2019.04.019 Date de publication en ligne : 24/04/2019 En ligne : https://doi.org/10.1016/j.asr.2019.04.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93527 [article]

Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth / Sébastien Labarre in Remote sensing of environment, vol 225 (May 2019)  

Public [article]  inRemote sensing of environment > vol 225 (May 2019) . - pp 1 - 15 Titre : Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth Type de document : Article/Communication Auteurs : Sébastien Labarre, Auteur ; Stéphane Jacquemoud, Auteur ; Cécile Ferrari, Auteur ; Arthur Delorme, Auteur ; Allan Derrien, Auteur ; Raphaël Grandin, Auteur ; Mohamed Jalludin, Auteur ; F. LemaÎtre, Auteur ; Marianne Metois, Auteur ; Marc Pierrot-Deseilligny , Auteur ; Ewelina Rupnik , Auteur ; Bernard Tanguy, Auteur Année de publication : 2019 Projets : CAROLInA / Jacquemoud, Stéphane Article en page(s) : pp 1 - 15 Note générale : Bibliographie The PhD thesis of Sébastien Labarre was funded by the Direction générale de l'armement (DGA) and by the Commissariat à l'énergie atomique et aux énergies alternatives (CEA). Field data were acquired in the frame of the CAROLInA (Characterization of Multi-Scale Roughness using OpticaL ImAgery) project funded by CNES. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Djibouti [Termes IGN] goniomètre [Termes IGN] image optique [Termes IGN] image Pléiades-HR [Termes IGN] modèle numérique de surface [Termes IGN] réflectance du sol [Termes IGN] rugosité du sol [Termes IGN] sol nu Résumé : (Auteur) Surface roughness can be defined as the mean slope angle integrated over all scales from the grain size to the local topography. It controls the energy balance of bare soils, in particular the angular distribution of scattered and emitted radiation. This provides clues to understand the intimate structure and evolution of planetary surfaces over ages. In this article we investigate the capacity of the Hapke photometric model, the most widely used in planetary science, to retrieve surface roughness from multiangular reflectance data. Its performance is still a question at issue and we lack validation experiments comparing model retrievals with ground measurements. To address this issue and to show the potentials and limits of the Hapke model, we compare the mean slope angle determined from very high resolution digital elevation models of volcanic and sedimentary terrains sampled in the Asal-Ghoubbet rift (Republic of Djibouti), to the photometric roughness estimated by model inversion on multiangular reflectance data measured on the ground (Chamelon field goniometer) and from space (Pleiades images). The agreement is good on moderately rough surfaces, in the domain of validity of the Hapke model, and poor on others. Numéro de notice : A2019-154 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.02.014 Date de publication en ligne : 02/03/2019 En ligne : https://doi.org/10.1016/j.rse.2019.02.014 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92492 [article]

Estimation of surface roughness over bare agricultural soil from Sentinel-1 data / Mohammad Choker (2018)  

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Fusing microwave and optical satellite observations to simultaneously retrieve surface soil moisture, vegetation water content, and surface soil roughness / Yohei Sawada in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

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Global sensitivity analysis of the L-MEB model for retrieving soil moisture / Zengyan Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

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LiDAR-derived surface roughness texture mapping: Application to mount St. Helens Pumice Plain deposit analysis / Patrick L. Whelley in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 2 (January 2014)  

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Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z) / Dimitri Lague in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)  

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Evaluating an improved parameterization of the soil emission in L-MEB / Jean-Pierre Wigneron in IEEE Transactions on geoscience and remote sensing, vol 49 n° 4 (April 2011)  

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Physical limitations on detecting tunnels using underground-focusing spotlight synthetic aperture radar / J. Martinez-Lorenzo in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 1 (January 2011)  

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Assessment of erosion, deposition and rill development on irregular soil surfaces using close range digital photogrammetry / G. Gessesse in Photogrammetric record, vol 25 n° 131 (September - November 2010)  

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Influence of macroscale and microscale surface roughness on multi-beam RADARSAT-1 data: implications for geological mapping in the Curaçá Valley, Brazil / W.R. Paradella in Photo interprétation, European journal of applied remote sensing, vol 45 n° 2 (juin 2009)

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A method for soil moisture estimation in Western Africa based on the ERS scatterometer / Mehrez Zribi in IEEE Transactions on geoscience and remote sensing, vol 46 n° 2 (February 2008)  

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