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imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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Monitoring coastal vulnerability by using DEMs based on UAV spatial data / Antonio Minervino Amodio in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)
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Titre : Monitoring coastal vulnerability by using DEMs based on UAV spatial data Type de document : Article/Communication Auteurs : Antonio Minervino Amodio, Auteur ; Gianluigi Di Paola, Auteur ; Carmen Maria Rosskopf, Auteur Année de publication : 2022 Article en page(s) : n° 155 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Adriatique, mer
[Termes IGN] détection de changement
[Termes IGN] érosion côtière
[Termes IGN] géoréférencement
[Termes IGN] image captée par drone
[Termes IGN] Italie
[Termes IGN] littoral méditerranéen
[Termes IGN] modèle numérique de surface
[Termes IGN] orthophotographie
[Termes IGN] point d'appui
[Termes IGN] structure-from-motion
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côte
[Termes IGN] vulnérabilitéRésumé : (auteur) The use of Unmanned Aerial Vehicles (UAVs) represents a rather innovative, quick, and low-cost methodological approach offering applications in several fields of investigation. The present study illustrates the developed method using Digital Elevation Models (DEMs) based on UAV-derived data for evaluating short-term morphological-topographic changes of the beach system and related implications for coastal vulnerability assessment. UAV surveys were performed during the summers of 2019 and 2020 along a beach stretch affected by erosion, located along the central Adriatic coast. Acquired high-resolution aerial photos were used to generate large-scale DEMs as well as orthophotos of the beach using the Structure from Motion (SfM) image processing tool. Comparison of the generated 2019 and 2020 DEMs highlighted significant morphological changes and a sediment volume loss of about 780 m3 within a surface area of about 4400 m2. Based on 20 m spaced beach profiles derived from the DEMs, a coastal vulnerability assessment was performed using the CVA approach that highlighted some significant variations in the CVA index between 2019 and 2020. Results evidence that UAV surveys provide high-resolution topographic data, suitable for specific beach monitoring activities and the updating of some parameters that enter in the CVA model contributing to its correct application. Numéro de notice : A2022-185 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11030155 Date de publication en ligne : 22/02/2022 En ligne : https://doi.org/10.3390/ijgi11030155 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99895
in ISPRS International journal of geo-information > vol 11 n° 3 (March 2022) . - n° 155[article]Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images / Omer Gokberk Narin in Geocarto international, vol 37 n° 5 ([01/03/2022])
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Titre : Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images Type de document : Article/Communication Auteurs : Omer Gokberk Narin, Auteur ; Saygin Abdikan, Auteur Année de publication : 2022 Article en page(s) : pp 1378 - 1392 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image multitemporelle
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] phénologie
[Termes IGN] rendement agricole
[Termes IGN] tournesol
[Termes IGN] TurquieRésumé : (Auteur) With the increase of the world’s population, while urbanization is increasing, agricultural lands are decreasing. Therefore, monitoring of up-to-date agricultural lands is important for agricultural product estimation. The study investigates suitability of Sentinel-2 data for the phenological stage analysis and yield estimation of sunflower plant. To this aim, fieldworks was conducted and sunflower parcels were identified in Zile district of Tokat province, Turkey which has dense sunflower production. In this study, ten Vegetation Indices (VIs) were performed by using multi-temporal Sentinel-2 data obtained during the growth stages of sunflower plant and yield estimation was obtained. As a result, the indices obtained on 30 June, at the stage of inflorescence emergence, provided coefficient of determination (R2) higher than 0.67 and The Root Mean Square Error (RMSE) lower than 13 kg/da. Among the VIs, the best forecast obtained by NDVI (R2 = 0.74 and RMSE = 10.80 kg/da) approximately three months before the harvest of sunflower. Numéro de notice : A2022-276 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1765886 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.1080/10106049.2020.1765886 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100784
in Geocarto international > vol 37 n° 5 [01/03/2022] . - pp 1378 - 1392[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2022051 RAB Revue Centre de documentation En réserve L003 Disponible A novel regression method for harmonic analysis of time series / Qiang Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)
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Titre : A novel regression method for harmonic analysis of time series Type de document : Article/Communication Auteurs : Qiang Zhou, Auteur ; Zhe Zhu, Auteur ; George Xian, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 48 - 61 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse harmonique
[Termes IGN] détection de changement
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-SWIR
[Termes IGN] modèle de régression
[Termes IGN] réflectance
[Termes IGN] régression harmonique
[Termes IGN] série temporelle
[Termes IGN] variation saisonnièreRésumé : (auteur) Harmonic analysis of time series is an important technique to reveal seasonal land surface dynamics using remote sensing information. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other hand, it is challenging to obtain temporally continuous satellite data with high quality because of atmospheric contamination. We developed a novel regression method named Harmonic Adaptive Penalty Operator (HAPO) for harmonic analysis of unevenly distributed time series. We introduced a new penalty function to minimize unexpected fluctuations in the model, which can substantially reduce the overfitting issue of regression in time series with temporal gaps. Specifically, the new penalty function minimizes the length of the model curve and the value range difference between the model and time series observations. We compared HAPO with three widely used regression methods (OLS: Ordinary Least Squares; LASSO: Least Absolute Shrinkage and Selection Operator; and Ridge) with different scenarios using Landsat time series data across the United States. First, we evaluated methods using Landsat surface reflectance time series within a single year. HAPO showed small and consistent monthly Root Mean Square Deviation (RMSD) values, in which most of the time RMSD values of predicted reflectance were less than 0.04. More importantly, HAPO showed consistent and less bias given varying density and irregularity of time series. Second, we evaluated methods using multi-year time series and the result suggested that HAPO was a better predictor of relatively short time series (less than4 years) with steady small RMSD values. When a longer time series (≥4 years) was used, all four methods disclosed similar RMSD values, but HAPO outperformed other three methods when there were temporal gaps. Last, we preliminarily tested how regression methods affected change detection and classification accuracy. HAPO showed the highest change detection accuracy of all tests in terms of F1 score when using the change threshold of 0.9999. In classification, HAPO produced the highest accuracy for short time series segments (one- or two-year time series). In contrast, all methods reached similar accuracy for 5-year time series. These results suggest that for areas that have large seasonal observation gaps or for time series that have less than 4 years records, HAPO can provide more consistent and accurate analytical results than other regression methods for harmonic analysis of time series. Numéro de notice : A2022-133 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.01.006 Date de publication en ligne : 21/01/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.01.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99729
in ISPRS Journal of photogrammetry and remote sensing > vol 185 (March 2022) . - pp 48 - 61[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2022031 SL Revue Centre de documentation Revues en salle Disponible Probabilistic unsupervised classification for large-scale analysis of spectral imaging data / Emmanuel Paradis in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)
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Titre : Probabilistic unsupervised classification for large-scale analysis of spectral imaging data Type de document : Article/Communication Auteurs : Emmanuel Paradis, Auteur Année de publication : 2022 Article en page(s) : n° 102675 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] analyse spectrale
[Termes IGN] classification barycentrique
[Termes IGN] classification ISODATA
[Termes IGN] classification non dirigée
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de changement
[Termes IGN] entropie
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] Matlab
[Termes IGN] occupation du solRésumé : (auteur) Land cover classification of remote sensing data is a fundamental tool to study changes in the environment such as deforestation or wildfires. A current challenge is to quantify land cover changes with real-time, large-scale data from modern hyper- or multispectral sensors. A range of methods are available for this task, several of them being based on the k-means classification method which is efficient when classes of land cover are well separated. Here a new algorithm, called probabilistic k-means, is presented to solve some of the limitations of the standard k-means. It is shown that the new algorithm performs better than the standard k-means when the data are noisy. If the number of land cover classes is unknown, an entropy-based criterion can be used to select the best number of classes. The proposed new algorithm is implemented in a combination of R and C computer codes which is particularly efficient with large data sets: a whole image with more than 3 million pixels and covering more than 10,000 km2 can be analysed in a few minutes. Four applications with hyperspectral and multispectral data are presented. For the data sets with ground truth data, the overall accuracy of the probabilistic k-means was substantially improved compared to the standard k-means. One of these data sets includes more than 120 million pixels, demonstrating the scalability of the proposed approach. These developments open new perspectives for the large scale analysis of remote sensing data. All computer code are available in an open-source package called sentinel. Numéro de notice : A2022-193 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102675 Date de publication en ligne : 06/01/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102675 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99954
in International journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102675[article]Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)
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Titre : Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 Type de document : Article/Communication Auteurs : Nima Pahlevan, Auteur ; Brandon Smith, Auteur ; Krista Alikas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] appariement d'images
[Termes IGN] apprentissage automatique
[Termes IGN] chlorophylle
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] correction atmosphérique
[Termes IGN] données multisources
[Termes IGN] eaux côtières
[Termes IGN] image Landsat-OLI
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-OLCI
[Termes IGN] matière organique
[Termes IGN] Oregon (Etats-Unis)
[Termes IGN] qualité des eauxRésumé : (auteur) Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (acdom(440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and acdom(440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and acdom(440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and acdom(440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model. Numéro de notice : A2022-126 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112860 Date de publication en ligne : 04/01/2022 En ligne : https://doi.org/10.1016/j.rse.2021.112860 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99705
in Remote sensing of environment > vol 270 (March 2022) . - n° 112860[article]Traffic sign three-dimensional reconstruction based on point clouds and panoramic images / Minye Wang in Photogrammetric record, vol 37 n° 177 (March 2022)
PermalinkUltrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)
PermalinkUsing street view images to identify road noise barriers with ensemble classification model and geospatial analysis / Kai Zhang in Sustainable Cities and Society, vol 78 (March 2022)
PermalinkAboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models / Dibyendu Deb in Geocarto international, vol 37 n° 4 ([15/02/2022])
PermalinkComparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion / Nitzan Malachy in Remote sensing, vol 14 n° 4 (February-2 2022)
PermalinkMulti-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests / Chong Zhang in Remote sensing, vol 14 n° 4 (February-2 2022)
PermalinkA national fuel type mapping method improvement using sentinel-2 satellite data / Alexandra Stefanidou in Geocarto international, vol 37 n° 4 ([15/02/2022])
PermalinkSimulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model / Hasan Aksoy in Geocarto international, vol 37 n° 4 ([15/02/2022])
PermalinkAn open science and open data approach for the statistically robust estimation of forest disturbance areas / Saverio Francini in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)
PermalinkBuilding footprint extraction in Yangon city from monocular optical satellite image using deep learning / Hein Thura Aung in Geocarto international, vol 37 n° 3 ([01/02/2022])
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