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A compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study / Hannah Vickers in Remote sensing, vol 13 n°10 (May-2 2021)
[article]
Titre : A compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study Type de document : Article/Communication Auteurs : Hannah Vickers, Auteur ; Eirik Malnes, Auteur ; Ward van Pelt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2002 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] données multicapteurs
[Termes IGN] image à haute résolution
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] manteau neigeux
[Termes IGN] modélisation
[Termes IGN] Normalized Difference Snow Index
[Termes IGN] série temporelle
[Termes IGN] surveillance hydrologique
[Termes IGN] SvalbardRésumé : (auteur) Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets. Numéro de notice : A2021-438 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102002 Date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97822
in Remote sensing > vol 13 n°10 (May-2 2021) . - n° 2002[article]A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration / Daeyong Jin in Remote sensing, vol 13 n°10 (May-2 2021)
[article]
Titre : A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration Type de document : Article/Communication Auteurs : Daeyong Jin, Auteur ; Eojin Lee, Auteur ; Kyonghwan Kwon, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2003 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] chlorophylle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Corée du sud
[Termes IGN] distribution spatiale
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] hydrodynamique
[Termes IGN] image COMS-GOCIRésumé : (auteur) In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a. Numéro de notice : A2021-417 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102003 Date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97759
in Remote sensing > vol 13 n°10 (May-2 2021) . - n° 2003[article]Detection of rainstorm pattern in arid regions using MODIS NDVI time series analysis / Mohamed E. Hereher in Geocarto international, vol 36 n° 8 ([01/05/2021])
[article]
Titre : Detection of rainstorm pattern in arid regions using MODIS NDVI time series analysis Type de document : Article/Communication Auteurs : Mohamed E. Hereher, Auteur Année de publication : 2021 Article en page(s) : pp 861 - 873 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Arabie
[Termes IGN] bassin hydrographique
[Termes IGN] gestion de l'eau
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] orage
[Termes IGN] pluie
[Termes IGN] précipitation
[Termes IGN] ressources en eau
[Termes IGN] série temporelle
[Termes IGN] zone arideRésumé : (auteur) The normalized difference vegetation index (NDVI) was used to delineate potential water suppliers west of the Arabian Peninsula. Time series NDVI data extracted from the moderate resolution imaging spectroradiometer NDVI product were used to develop a robust estimate of rainstorm frequency and intensity. A total of 216 NDVI images were acquired between February 2000 and January 2018 to carry out this investigation. As NDVI values of negative records correspond to water, it was possible to address and delineate the occurrence and duration of temporal ponded water. Results showed that at least 7 locations are potential to harvest water from flashfloods. Some locations witnessed 10, 11 and 13 rainstorms and ponding of water ranged from 1 to 20 months. These locations, if properly managed, could sustain a fresh water resource for local uses. The study demonstrates that NDVI time series curves could help identify the time/duration of previous rainstorms. Numéro de notice : A2021-482 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1629643 Date de publication en ligne : 19/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1629643 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97433
in Geocarto international > vol 36 n° 8 [01/05/2021] . - pp 861 - 873[article]Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])
[article]
Titre : Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; İlker Ercanlı, Auteur ; Muammer Şenyurt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 918 - 935 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] échantillonnage
[Termes IGN] fonction de base radiale
[Termes IGN] gestion forestière
[Termes IGN] image proche infrarouge
[Termes IGN] image Worldview
[Termes IGN] matrice de co-occurrence
[Termes IGN] peuplement forestier
[Termes IGN] Pinus nigra
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificiel
[Termes IGN] texture d'image
[Termes IGN] TurquieRésumé : (auteur) The aim of this research is to assess some stand parameters such as stand volume (SV), basal area (BA), number of trees (NT) and aboveground biomass (AGB) of pure Crimean pine forest stands in Turkey by using ground measurements and remote sensing techniques. For this purpose, 86 sample plots were collected from pure Crimean pine stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. The stand parameters of each sample area were estimated using the data obtained from the sample plots. Subsequently, we calculated the values of contrast (CON), correlation (COR), dissimilarity (DIS), entropy (ENT), homogeneity (HOM), mean (M), second moment (SM) and variance (VAR) from WorldView-2 imagery using a grey-level co-occurrence matrix method. Eight textural features and twelve different window sizes ranging from 3 × 3 to 25 × 25 were generated from blue, green, red and near-infrared bands of the WorldView-2 satellite image. For predicting the relationships between WorldView-2 textural features and stand parameters of each sample plot, regression models were developed by using multiple linear regression (MLR) analysis. Additionally, artificial neural networks (ANNs) based on the multilayer perceptron (MLP) and the radial basis function (RBF) architectures were trained by comparing various numbers of neurons and activation functions in their network types. The results showed that the MLR models had low the coefficient of determination (R2) values (0.32 for SV, 0.35 for BA, 0.33 for NT and 0.34 for AGB), and the most of the ANNs models (MLP and RBF) were better than the regression models for estimating stand parameters. The ANNs model containing MLP and RBF for SV (R2 = 0.40; R2 = 0.56), for BA (R2 = 0.34; R2 = 0.51), for NT (R2 = 0.34; R2 = 0.37) and for AGB (R2 = 0.34, R2 = 0.57) were found the best results, respectively. Our results revealed that the ANNs models developed with WorldView-2 satellite image were beneficial to estimate stand parameters better than the MLR model in pure Crimean pine stands. Numéro de notice : A2021-484 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1629644 Date de publication en ligne : 25/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1629644 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97443
in Geocarto international > vol 36 n° 8 [01/05/2021] . - pp 918 - 935[article]Inversion of solar-induced chlorophyll fluorescence using polarization measurements of vegetation / Haiyan Yao in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)
[article]
Titre : Inversion of solar-induced chlorophyll fluorescence using polarization measurements of vegetation Type de document : Article/Communication Auteurs : Haiyan Yao, Auteur ; Ziying Li, Auteur ; Yang Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 331-338 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] chlorophylle
[Termes IGN] couvert végétal
[Termes IGN] données polarimétriques
[Termes IGN] fluorescence
[Termes IGN] polarisationRésumé : (Auteur) In vegetation remote sensing, the apparent radiation of the vegetation canopy is often combined with three components derived from different parts of vegetation that have different production mechanisms and optical properties: volume scattering Lvol, polarized light Lpol, and chlorophyll fluorescence ChlF. The chlorophyll fluorescence plays a very important role in vegetation remote sensing, and the polarization information in vegetation remote sensing has become an effective way to characterize the physical characteristics of vegetation. This study analyzes the difference between these three types of radiation flux and utilizes polarization radiation to separate them from the apparent radiation of the vegetation canopy. Specifically, solar-induced chlorophyll fluorescence is extracted from vegetation canopy radiation data using standard Fraunhofer-line discrimination. The results show that polarization measurements can quantitatively separate Lvol, Lpol, and ChlF and extract the solar-induced chlorophyll fluorescence. This study improves our understanding of the light-scattering properties of vegetation canopies and provides insights for developing building models and research algorithms. Numéro de notice : A2021-365 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.5.331 Date de publication en ligne : 01/05/2021 En ligne : https://doi.org/10.14358/PERS.87.5.331 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97694
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 5 (May 2021) . - pp 331-338[article]Réservation
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