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Feasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques / J.O. Ondieki in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)
[article]
Titre : Feasibility of mapping radioactive minerals in high background radiation areas using remote sensing techniques Type de document : Article/Communication Auteurs : J.O. Ondieki, Auteur ; C.O. Mito, Auteur ; M.I. Kaniu, Auteur Année de publication : 2022 Article en page(s) : n° 102700 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de groupement
[Termes IGN] carte thématique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données géologiques
[Termes IGN] image Landsat-OLI
[Termes IGN] Kappa de Cohen
[Termes IGN] Kenya
[Termes IGN] minerai
[Termes IGN] pollution radioactive
[Termes IGN] précision de la classification
[Termes IGN] radioactivité
[Termes IGN] signature spectraleRésumé : (auteur) This study investigates the utility of using remote sensing and geographic information system techniques to accurately infer the presence of radioactive minerals in a typical high background radiation area (HBRA) by analyzing spectral signatures of associated soil, rocks and vegetation. To accomplish this, both unsupervised (K-Means Clustering) and supervised classification techniques based on a maximum likelihood classifier (MLC) were applied to Landsat-8 Imager data from Mrima Hill on Kenya's south coast. The hill is surrounded by dense tropical forest and deeply weathered soils which are rich in Nb, Th, and rare earth elements. Due to high activity concentrations of 232Th (>8 times higher than the world average value for soil), the hill has been designated as a geogenic HBRA. Based on the underlying geological formations, four classifications of vegetation and two classifications of soil/rocks were established and used to indicate the presence of radioactive minerals in the area. Measurements of air-absorbed gamma dose-rates in the area were successfully used to validate these findings. The application of the MLC method on Landsat satellite data shows that this method can be used as a powerful tool to explore and improve radioactive minerals mapping in HBRAs, the overall classification accuracy of Landsat8 OLI data using botanical technique is 80% and the Kappa Coefficient is 0.6. The overall classification accuracy using soil/rocks spectral signatures is 91% and the Kappa Coefficient is 0.7. Finally, the study demonstrated the general utility of remote sensing techniques in radioactive mineral surveys as well as environmental radiological assessments, particularly in resource-constrained settings. Numéro de notice : A2022-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102700 Date de publication en ligne : 02/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102700 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99956
in International journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102700[article]A novel regression method for harmonic analysis of time series / Qiang Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)
[article]
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]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2022031 SL Revue Centre de documentation Revues en salle Disponible 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)
[article]
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]Aboveground 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])
[article]
Titre : Aboveground 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 Type de document : Article/Communication Auteurs : Dibyendu Deb, Auteur ; Shovik Deb, Auteur ; Debasis Chakraborty, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1043 - 1058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] distribution spatiale
[Termes IGN] image Landsat-8
[Termes IGN] Inde
[Termes IGN] indice de végétation
[Termes IGN] modèle de régression
[Termes IGN] point d'appui
[Termes IGN] régression linéaire
[Termes IGN] régression multiple
[Termes IGN] séparateur à vaste marge
[Termes IGN] zone semi-arideRésumé : (auteur) This study compared the traditional regression models and support vector machine (SVM) for estimation of aboveground biomass (ABG) of an agro-pastoral ecology using vegetation indices derived from Landsat 8 satellite data as explanatory variables . The area falls in the Shivpuri Tehsil of Madhya Pradesh, India, which is predominantly a semi-arid tract of the Bundelkhand region. The Enhanced Vegetation Index-1 (EVI-1) was identified as the most suitable input variable for the regression models, although the collective effect of a number of the vegetation indices was evident. The EVI-1 was also the most suitable input variable to SVM, due to its capacity to distinctly differentiate diverse vegetation classes. The performance of SVM was better over regression models for estimation of the AGB. Based on the SVM-derived and the ground observations, the AGB of the area was precisely mapped for croplands, grassland and rangelands over the entire region. Numéro de notice : A2022-394 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1756461 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1756461 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100688
in Geocarto international > vol 37 n° 4 [15/02/2022] . - pp 1043 - 1058[article]Simulation 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])
[article]
Titre : Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model Type de document : Article/Communication Auteurs : Hasan Aksoy, Auteur ; Sinan Kaptan, Auteur Année de publication : 2022 Article en page(s) : pp 1183 - 1202 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] automate cellulaire
[Termes IGN] classification dirigée
[Termes IGN] détection de changement
[Termes IGN] gestion forestière
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-TM
[Termes IGN] modèle de Markov
[Termes IGN] occupation du sol
[Termes IGN] surface cultivée
[Termes IGN] surface forestière
[Termes IGN] Turquie
[Termes IGN] utilisation du solRésumé : (auteur) This study aimed to simulate and assess forest cover and land use/land cover (LULC) changes between 2019 and 2039 using the cellular automata-Markov model. The performance of the model was evaluated by comparing the 2019 simulation map with the 2019 supervised classified map, and it was found to be reliable, with a similarity rate of 85.43%. The LULC analysis and estimates were carried out for a total of six classes: coniferous, broad-leaf, mixed forest, settlement, water and agriculture. Between 1999 and 2019, the areas of total forest increased by 17.4%, settlement by 84.6% and water by 20.1%, whereas the agriculture area decreased by 33.2%. According to 2019‒2039 land use/cover simulation results, there were decreases of 2.4% in total forest area and 3.7% in residential and water surface areas, but a 6.9% decrease in the agriculture class. Tracking these changes will contribute to decision making and strategy development efforts of forest planners and managers. Numéro de notice : A2022-397 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1778102 Date de publication en ligne : 22/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1778102 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100691
in Geocarto international > vol 37 n° 4 [15/02/2022] . - pp 1183 - 1202[article]Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 3 ([01/02/2022])PermalinkLandsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest / Ran Meng in Remote sensing of environment, vol 269 (February 2022)PermalinkMapping global flying aircraft activities using Landsat 8 and cloud computing / Fen Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)PermalinkSpatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria / Maninder Singh Dhillon in Remote sensing, vol 14 n° 3 (February-1 2022)PermalinkSpatiotemporal temperature fusion based on a deep convolutional network / Xuehan Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)PermalinkMulti-temporal remote sensing data to monitor terrestrial ecosystem responses to climate variations in Ghana / Ram Avtar in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkSoil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS / Sumedh R. Kashiwar in Natural Hazards, vol 110 n° 2 (January 2022)PermalinkUse of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa / Mangana Rampheri in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkApport de la télédétection et des variables auxiliaires dans l'étude de l'évolution des périodes de sécheresse / Nesrine Farhani (2022)PermalinkExamining the integration of Landsat operational land imager with Sentinel-1 and vegetation indices in mapping southern yellow pines (Loblolly, Shortleaf, and Virginia pines) / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)PermalinkHistorical shoreline analysis and field monitoring at Ennore coastal stretch along the Southeast coast of India / M. Dhananjayan in Marine geodesy, vol 45 n° 1 (January 2022)PermalinkImproving LSMA for impervious surface estimation in an urban area / Jin Wang in European journal of remote sensing, vol 55 n° 1 (2022)PermalinkMapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 Imagery from Google Earth Engine / Jiyu Liu in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkMonitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)PermalinkPermalinkPermalinkPermalinkAbove-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat / Stefano Puliti in Remote sensing of environment, vol 265 (November 2021)PermalinkEfficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine / Yongjing Mao in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)PermalinkLandsat, un demi-siècle de tradition / Laurent Polidori in Géomètre, n° 2196 (novembre 2021)Permalink