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Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging / Bo Li in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 161 -1 72 Titre : Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging Type de document : Article/Communication Auteurs : Bo Li, Auteur ; Xiangming Xu, Auteur ; Li Zhang, Auteur Année de publication : 2020 Article en page(s) : pp 161 -1 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] hauteur de la végétation [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] pomme de terre [Termes descripteurs IGN] régression des moindres carrés partiels [Termes descripteurs IGN] rendement agricole Résumé : (auteur) Rapid and accurate biomass and yield estimation facilitates efficient plant phenotyping and site-specific crop management. A low altitude unmanned aerial vehicle (UAV) was used to acquire RGB and hyperspectral imaging data for a potato crop canopy at two growth stages to estimate the above-ground biomass and predict crop yield. Field experiments included six cultivars and multiple treatments of nitrogen, potassium, and mixed compound fertilisers. Crop height was estimated using the difference between digital surface model and digital elevation models derived from RGB imagery. Combining with two narrow-band vegetation indices selected by the RReliefF feature selection algorithm. Random Forest regression models demonstrated high prediction accuracy for both fresh and dry above-ground biomass, with a coefficient of determination (r2) > 0.90. Crop yield was predicted using four narrow-band vegetation indices and crop height (r2 = 0.63) with imagery data obtained 90 days after planting. A Partial Least Squares regression model based on the full wavelength spectra demonstrated improved yield prediction (r2 = 0.81). This study demonstrated the merits of UAV-based RGB and hyperspectral imaging for estimating the above-ground biomass and yield of potato crops, which can be used to assist in site-specific crop management. Numéro de notice : A2020-125 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.013 date de publication en ligne : 28/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.013 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94750 [article]

Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 26 - 40 Titre : Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California Type de document : Article/Communication Auteurs : Matthew L. Clark, Auteur Année de publication : 2020 Article en page(s) : pp 26 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] carte forestière [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] image AVIRIS [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] Short Waves InfraRed Résumé : (Auteur) The current era of earth observation now provides constellations of open-access, multispectral satellite imagery with medium spatial resolution, greatly increasing the frequency of cloud-free data for analysis. The Landsat satellites have a long historical record, while the newer Sentinel-2 (S2) satellites offer higher temporal, spatial and spectral resolution. The goal of this study was to evaluate the relative benefits of single- and multi-seasonal multispectral satellite data for discriminating detailed forest alliances, as defined by the U.S. National Vegetation Classification system, in a Mediterranean-climate landscape (Sonoma County, California). Results were compared to a companion analysis of simulated hyperspectral satellite data (HyspIRI) for the same study site and reference data (Clark et al., 2018). Experiments used real and simulated S2 and Landsat 8 (L8) data. Simulated S2 and L8 were from HyspIRI images, thereby focusing results on differences in spectral resolution rather than other confounding factors. The Support Vector Machine (SVM) classifier was used in a hierarchical classification of land-cover (Level 1), followed by alliances (Level 2) in forest pixels, and included summer-only and multi-seasonal sets of predictor variables (bands, indices and bands plus indices). Both real and simulated multi-seasonal multispectral variables significantly improved overall accuracy (OA) by 0.2–1.6% for Level 1 tree/no tree classifications and 3.6–25.8% for Level 2 forest alliances. Classifiers with S2 variables tended to be more accurate than L8 variables, particularly for S2, which had 0.4–2.1% and 5.1–11.8% significantly higher OA than L8 for Level 1 tree/no tree and Level 2 forest alliances, respectively. Combining multispectral bands and indices or using just bands was generally more accurate than relying on just indices for classification. Simulated HyspIRI variables from past research had significantly greater accuracy than real L8 and S2 variables, with an average OA increase of 8.2–12.6%. A final alliance-level map used for a deeper analysis used simulated multi-seasonal S2 bands and indices, which had an overall accuracy of 74.3% (Kappa = 0.70). The accuracy of this classification was only 1.6% significantly lower than the best HyspIRI-based classification, which used multi-seasonal metrics (Clark et al., 2018), and there were alliances where the S2-based classifier was more accurate. Within the context of these analyses and study area, S2 spectral-temporal data demonstrated a strong capability for mapping global forest alliances, or similar detailed floristic associations, at medium spatial resolutions (10–30 m). Numéro de notice : A2020-011 Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.007 date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94399 [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 descripteurs IGN] bande L [Termes descripteurs IGN] bande P [Termes descripteurs IGN] canopée [Termes descripteurs IGN] constante diélectrique [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] micro-onde [Termes descripteurs IGN] rugosité du sol [Termes descripteurs 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 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]

Estimating pasture biomass and canopy height in brazilian savanna using UAV photogrammetry / Juliana Batistoti in Remote sensing, Vol 11 n° 20 (2 October 2019)  

Public [article]  inRemote sensing > Vol 11 n° 20 (2 October 2019) . - 12 p. Titre : Estimating pasture biomass and canopy height in brazilian savanna using UAV photogrammetry Type de document : Article/Communication Auteurs : Juliana Batistoti, Auteur ; José Marcato, Auteur ; Luis Itavo, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : 12 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] canopée [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] Poaceae [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] positionnement cinématique en temps réel Résumé : (auteur) The Brazilian territory contains approximately 160 million hectares of pastures, and it is necessary to develop techniques to automate their management and increase their production. This technical note has two objectives: First, to estimate the canopy height using unmanned aerial vehicle (UAV) photogrammetry; second, to propose an equation for the estimation of biomass of Brazilian savanna (Cerrado) pastures based on UAV canopy height. Four experimental units of Panicum maximum cv. BRS Tamani were evaluated. Herbage mass sampling, height measurements, and UAV image collection were simultaneously performed. The UAVs were flown at a height of 50 m, and images were generated with a mean ground sample distance (GSD) of approximately 1.55 cm. The forage canopy height estimated by UAVs was calculated as the difference between the digital surface model (DSM) and the digital terrain model (DTM). The R2 between ruler height and UAV height was 0.80; between biomass (kg ha−1 GB—green biomass) and ruler height, 0.81; and between biomass (kg ha−1 GB) and UAV height, 0.74. UAV photogrammetry proved to be a potential technique to estimate height and biomass in Brazilian Panicum maximum cv. BRS Tamani pastures located in the endangered Brazilian savanna (Cerrado) biome Numéro de notice : A2019-556 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11202447 date de publication en ligne : 22/10/2019 En ligne : https://doi.org/10.3390/rs11202447 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94212 [article]

Quantifying the impact of trees on land surface temperature: a downscaling algorithm at city-scale / Elena Barbierato in European journal of remote sensing, sans n° (2019)  

Public [article]  inEuropean journal of remote sensing > sans n° (2019) . - 11 p. Titre : Quantifying the impact of trees on land surface temperature: a downscaling algorithm at city-scale Type de document : Article/Communication Auteurs : Elena Barbierato, Auteur ; Iacopo Bernetti, Auteur ; Irene Capecchi, Auteur ; Claudio Saragosa, Auteur Année de publication : 2019 Article en page(s) : 11 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] climat urbain [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] flore urbaine [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] Toscane (Italie) Résumé : (auteur) The climate of a city influences the ways in which its outdoor spaces are used. Especially, public spaces intended for use by pedestrians and cyclists, such as parks, squares, residential and commercial streets, and foot and cycle paths will be used and enjoyed more frequently if they have a comfortable and healthy climate. Due to the predicted global temperature increase, urban climate is likely to become more uncomfortable, especially in summer when an increase in heat stress is expected. Urban forestry has been proposed as one approach for mitigating the human health consequences of increased temperature resulting from climate change. The aims of the current research were to (a) provide a transferable methodology useful for analyzing the effect of urban trees on surface temperature reduction, particularly in public spaces, and (b) provide high-resolution urban mapping for adaptation strategies to climate change based on green space projects. To achieve the established aims, we developed a methodology that uses multisource data: LiDAR data, high-resolution Landsat imagery, global climate model data from CMIP5 (IPPC Fifth Assessment), and data from meteorological stations. The proposed model can be a useful tool for validating the efficiency of design simulations of new green spaces for temperature mitigation. Numéro de notice : A2019-320 Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2019.1646104 date de publication en ligne : 29/07/2019 En ligne : https://doi.org/10.1080/22797254.2019.1646104 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93266 [article]

Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])  

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A new waveform decomposition method for multispectral LiDAR / Shalei Song in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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A new generation of the United States National Land Cover Database : Requirements, research priorities, design, and implementation strategies / Limin Yang in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

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Analyse du risque végétation dans les emprises ferroviaires à partir de données LiDAR acquises par drones / Luc Perrin in XYZ, n° 154 (mars - mai 2018)

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Mise en place d’un outil de classification et d’utilisation des données LiDAR pour l’étude du couvert arboré à Florence / Florian Thill (2018) 

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Restaurer les milieux et prévenir les inondations grâce au génie végétal / Freddy Rey (2018)    

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Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015) / Ronald C. Estoque in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

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An information fusion approach for PALSAR data to retrieve soil moisture / Ankita Jain in Geocarto international, vol 32 n° 9 (September 2017)  

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A time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter / Jeffrey D. Ouellette in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)  

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Total canopy transmittance estimated from small-footprint, full-waveform airborne LiDAR / Milutin Milenković in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)  

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