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Identifying surface urban heat island drivers and their spatial heterogeneity in China’s 281 cities: An empirical study based on multiscale geographically weighted regression / Lu Niu in Remote sensing, vol 13 n° 21 (November-1 2021)
[article]
Titre : Identifying surface urban heat island drivers and their spatial heterogeneity in China’s 281 cities: An empirical study based on multiscale geographically weighted regression Type de document : Article/Communication Auteurs : Lu Niu, Auteur ; Zhengfeng Zhang, Auteur ; Peng Zhong, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4428 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse géovisuelle
[Termes IGN] analyse multiéchelle
[Termes IGN] analyse spatio-temporelle
[Termes IGN] Chine
[Termes IGN] distribution spatiale
[Termes IGN] échelle géographique
[Termes IGN] hétérogénéité spatiale
[Termes IGN] ilot thermique urbain
[Termes IGN] image Terra-MODIS
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] nuit
[Termes IGN] régression géographiquement pondérée
[Termes IGN] variation diurne
[Termes IGN] variation saisonnière
[Termes IGN] zone urbaineRésumé : (auteur) The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers. Numéro de notice : A2021-821 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13214428 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.3390/rs13214428 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98931
in Remote sensing > vol 13 n° 21 (November-1 2021) . - n° 4428[article]Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency / Jiaqi Tian in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)
[article]
Titre : Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency Type de document : Article/Communication Auteurs : Jiaqi Tian, Auteur ; Xiaolin Zhu, Auteur ; Jin Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 29 - 44 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Enhanced vegetation index
[Termes IGN] filtrage du bruit
[Termes IGN] image Terra-MODIS
[Termes IGN] indice de végétation
[Termes IGN] lissage de données
[Termes IGN] nébulosité
[Termes IGN] phénologie
[Termes IGN] série temporelleRésumé : (auteur) Vegetation phenology can be extracted from vegetation index (VI) time series of satellite data. The maximum value composite (MVC) procedure and smoothing filters have been conventionally used as standard methods to exclude noises in the VI time series before extracting the vegetation phenology [e.g., National Aeronautics and Space Administration (NASA) VNP22Q2 and United States Geological Survey (USGS) MCD12Q2 phenology products]. However, it is unclear how to optimize the MVC and smoothing filters to produce the most accurate phenology metrics given that cloud frequency varies spatially. This study designed two simulation experiments, namely (1) using only the MVC and (2) using the MVC and smoothing filters together to smooth the enhanced vegetation index (EVI) time series for detecting spring phenology, i.e., start of season (SOS), over the northern hemisphere (north of 30°N) on a 5° × 5° grid cell basis by the inflection point and relative threshold algorithms. The results revealed that (1) the inappropriate selection of MVC periods (e.g., too short or too long) affected the accuracy of the SOS extracted by both phenology detection algorithms; (2) a filtering process with optimal parameters can reduce the effects of the MVC period on SOS extraction to a considerable extent, i.e., 65% and 61% for iterative Savitzky–Golay (SG) and penalized cubic splines (SP) filters, respectively; (3) optimal parameters for both the MVC and smoothing filters showed significant spatial heterogeneity; and (4) validation with ground PhenoCam data indicated that optimal parameters of the MVC and smoothing filters can produce more accurate results than official vegetation phenology products that use uniform parameters. Specifically, the R2 values of the NASA product and the USGS product were 0.58 and 0.67, which were increased to 0.70 and 0.81, respectively, by the optimal smoothing process. Optimal parameters of the MVC and smoothing filters provided by this study in each 5° × 5° sub-region may help future studies to improve the accuracy of phenology detection from satellite VI time series. Numéro de notice : A2021-653 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.08.003 Date de publication en ligne : 14/08/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.08.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98383
in ISPRS Journal of photogrammetry and remote sensing > vol 180 (October 2021) . - pp 29 - 44[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021101 SL Revue Centre de documentation Revues en salle Disponible 081-2021103 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021102 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Estimating regional soil moisture with synergistic use of AMSR2 and MODIS images / Majid Rahimzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)
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Titre : Estimating regional soil moisture with synergistic use of AMSR2 and MODIS images Type de document : Article/Communication Auteurs : Majid Rahimzadegan, Auteur ; Arash Davari, Auteur ; Ali Sayadi, Auteur Année de publication : 2021 Article en page(s) : pp 649-660 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Advanced Microwave Scanning Radiometer
[Termes IGN] coefficient de corrélation
[Termes IGN] humidité du sol
[Termes IGN] image Aqua-AMSR
[Termes IGN] image Terra-MODIS
[Termes IGN] indice d'humidité
[Termes IGN] Iran
[Termes IGN] polarisation
[Termes IGN] réflectance du solRésumé : (Auteur) Soil moisture content (SMC), product of Advanced Microwave Scanning Radiometer 2 (AMSR2), is not at an adequate level of accuracy on a regional scale. The aim of this study is to introduce a simple method to estimate SMC while synergistically using AMSR2 and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements with a higher accuracy on a regional scale. Two MODIS products, including daily reflectance (MYD021) and nighttime land surface temperature (LST) products were used. In 2015, 1442 in situ SMC measurements from six stations in Iran were used as ground-truth data. Twenty models were evaluated using combinations of polarization index (PI), index of soil wetness (ISW), normalized difference vegetation index (NDVI), and LST. The model revealed the best results using a quadratic combination of PI and ISW, a linear form of LST, and a constant value. The overall correlation coefficient, root-mean-square error, and mean absolute error were 0.59, 4.62%, and 3.01%, respectively. Numéro de notice : A2021-673 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00085 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.14358/PERS.20-00085 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98835
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 9 (September 2021) . - pp 649-660[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021091 SL Revue Centre de documentation Revues en salle Disponible Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico / Yao Gao in Geocarto international, vol 36 n° 15 ([15/08/2021])
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Titre : Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico Type de document : Article/Communication Auteurs : Yao Gao, Auteur ; Alexander Quevedo, Auteur ; Zoltan Szantoi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1768 - 1784 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] détection de changement
[Termes IGN] fonction harmonique
[Termes IGN] image Terra-MODIS
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Mexique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] variation saisonnièreRésumé : (auteur) MODIS-based NDVI time series (2000–2016) was applied to monitor sub-annual forest disturbance in the Mexican state of Michoacán, with an algorithm that decomposes the time-series data into a harmonic function and a trend. To detect change, a moving sum of residuals between the observed and predicted NDVI values was compared with that from the reference period. Magnitude of change was computed by subtracting the predicted NDVI from the observed one. By comparing the detected changes with reference data through visual interpretation, a threshold of |0.05| was established as the magnitude of change for forest disturbance detection. The method detected more forest gain than loss for 2013–2016, a result which is supported by recent findings from the national forest inventory. Forest loss decreases yearly for 2013–2016, and forest gain peaks at 2014 and 2015. We verified the findings with data from the global forest cover change project. Numéro de notice : A2021-580 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1661032 Date de publication en ligne : 09/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1661032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98185
in Geocarto international > vol 36 n° 15 [15/08/2021] . - pp 1768 - 1784[article]Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data / Xiaofang Sun in Geocarto international, vol 36 n° 14 ([01/08/2021])
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Titre : Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data Type de document : Article/Communication Auteurs : Xiaofang Sun, Auteur ; Bai Li, Auteur ; Zhengping Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1549 - 1564 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] carbone
[Termes IGN] carte de la végétation
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données ICEsat
[Termes IGN] données lidar
[Termes IGN] données multisources
[Termes IGN] Geoscience Laser Altimeter System
[Termes IGN] image Terra-MODIS
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Kiangsi (Chine)
[Termes IGN] krigeage
[Termes IGN] modèle numérique de surface
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression des moindres carrés partielsRésumé : (auteur) An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R2). A forest AGB map of the study area was generated using the optimal model. Numéro de notice : A2021-555 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1655799 Date de publication en ligne : 28/08/2019 En ligne : https://doi.org/10.1080/10106049.2019.1655799 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98108
in Geocarto international > vol 36 n° 14 [01/08/2021] . - pp 1549 - 1564[article]Atmospheric correction to passive microwave brightness temperature in snow cover mapping over china / Yubao Qiu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkAn integrated methodology for surface soil moisture estimating using remote sensing data approach / Rida Khellouk in Geocarto international, vol 36 n° 13 ([15/07/2021])PermalinkEvaluation of sum-NDVI values to estimate wheat grain yields using multi-temporal Landsat OLI data / Asadollah Mirasi in Geocarto international, vol 36 n° 12 ([01/07/2021])PermalinkFeux de forêts et technologies spatiales / Laurent Polidori in Géomètre, n° 2193 (juillet-août 2021)PermalinkA combined drought monitoring index based on multi-sensor remote sensing data and machine learning / Hongzhu Han in Geocarto international, vol 36 n° 10 ([01/06/2021])PermalinkOn the relationship between normalized difference vegetation index and land surface temperature: MODIS-based analysis in a semi-arid to arid environment / Salahuddin M. Jaber in Geocarto international, vol 36 n° 10 ([01/06/2021])PermalinkRapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkReference evapotranspiration (ETo) methods implemented as ArcMap models with remote-sensed and ground-based inputs, examined along with MODIS ET, for Peloponnese, Greece / Stavroula Dimitriadou in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)PermalinkResolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkA 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)Permalink