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The role of blue green infrastructure in the urban thermal environment across seasons and local climate zones in East Africa / Xueqin Li in Sustainable Cities and Society, vol 80 (May 2022)
[article]
Titre : The role of blue green infrastructure in the urban thermal environment across seasons and local climate zones in East Africa Type de document : Article/Communication Auteurs : Xueqin Li, Auteur ; Lindsay C. Stringer, Auteur ; Martin Dallimer, Auteur Année de publication : 2022 Article en page(s) : n° 103798 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement climatique
[Termes IGN] climat local
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] croissance urbaine
[Termes IGN] espace vert
[Termes IGN] Ethiopie
[Termes IGN] Google Earth Engine
[Termes IGN] ilot thermique urbain
[Termes IGN] indice de végétation
[Termes IGN] Ouganda
[Termes IGN] saison
[Termes IGN] série temporelle
[Termes IGN] Soudan
[Termes IGN] Tanzanie
[Termes IGN] température au sol
[Termes IGN] zone urbaine denseRésumé : (auteur) Rapid urbanisation and climate change are two major trends in Africa in need of further investigation. In this paper, the urban thermal environment and vegetation abundance in four East African cities (Khartoum, Addis Ababa, Kampala and Dar es Salaam) were characterised, providing new insights into the role and potentials of blue green infrastructure in differing climate regions. The Local Climate Zone (LCZ) framework was employed to detect the seasonal Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) derived from Landsat-8 data. Significant LST differences between LCZs in dry and rainy seasons were confirmed using a Welch's T test. The LCZs were found to offer potentially new approaches to investigating issues pertaining to urban heating in data-scarce regions. Greater surface urban heat island (SUHI) intensity during the rainy season was apparent in Khartoum and Addis Ababa, emphasising the importance of seasonality in urban thermal studies. Spatial correlations between EVI and LST within each LCZ were analysed through Moran's I and further illustrated the complex relationships of vegetation and thermal behaviour in urban areas. Despite these complexities, urban blue green infrastructure was found to moderate the SUHI, with different types of intervention required across different LCZs. Numéro de notice : A2022-269 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.103798 Date de publication en ligne : 23/02/2022 En ligne : https://doi.org/10.1016/j.scs.2022.103798 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100280
in Sustainable Cities and Society > vol 80 (May 2022) . - n° 103798[article]Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information / Murali Krishna Gumma in Geocarto international, vol 37 n° 7 ([15/04/2022])
[article]
Titre : Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information Type de document : Article/Communication Auteurs : Murali Krishna Gumma, Auteur ; Kimeera Tummala, Auteur ; Sreenath Dixit, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1833 - 1849 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] appariement spectral
[Termes IGN] blé (céréale)
[Termes IGN] carte de la végétation
[Termes IGN] distribution spatiale
[Termes IGN] image Sentinel-MSI
[Termes IGN] Inde
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelle
[Termes IGN] surface cultivée
[Termes IGN] variation saisonnièreRésumé : (auteur) Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in drylands where variability in cropping is very high owing to erratic weather conditions. Dryland cereals and grain legumes are key to ensuring the food and nutritional security of a large number of vulnerable populations living in the drylands. Reliable information on area cultivated to such crops forms part of the national accounting of food production and supply in many Asian countries, many of which are employing remote sensing tools to improve the accuracy of assessments of cultivated areas. This paper assesses the capabilities and limitations of mapping cultivated areas in the Rabi (winter) season and corresponding cropping patterns in three districts characterized by small-plot agriculture. The study used Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series at 10 m resolution by employing a Spectral Matching Technique (SMT) approach. The use of SMT is based on the well-studied relationship between temporal NDVI signatures and crop phenology. The rabi season in India, dominated by non-rainy days, is best suited for the application of this method, as persistent cloud cover will hamper the availability of images necessary to generate clearly differentiating temporal signatures. Our study showed that the temporal signatures of wheat, chickpea and mustard are easily distinguishable, enabling an overall accuracy of 84%, with wheat and mustard achieving 86% and 94% accuracies, respectively. The most significant misclassifications were in irrigated areas for mustard and wheat, in small-plot mustard fields covered by trees and in fragmented chickpea areas. A comparison of district-wise national crop statistics and those obtained from this study revealed a correlation of 96%. Numéro de notice : A2022-497 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1805029 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1080/10106049.2020.1805029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100989
in Geocarto international > vol 37 n° 7 [15/04/2022] . - pp 1833 - 1849[article]Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data / Yanyan Wang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)
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Titre : Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data Type de document : Article/Communication Auteurs : Yanyan Wang, Auteur ; Shenghui Fang, Auteur ; Lingli Zhao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102720 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] carte de la végétation
[Termes IGN] Chine
[Termes IGN] croissance végétale
[Termes IGN] données spatiotemporelles
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] maïs (céréale)
[Termes IGN] mesure de similitude
[Termes IGN] phénologie
[Termes IGN] saison
[Termes IGN] segmentation d'image
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) This study aims to map the planting area of summer maize and estimate the spatiotemporal phenology information with parcel-based classification method through integration of Sentinel-1/2 data in Jiaozuo located in North China Plain. For the maize mapping, the combination of Sentinel-1/2 data with the parcel-based method has the highest classification accuracy, suggesting that the integration of Sentinel-1/2 data with parcel-based method has great potential for regional maize mapping. For the estimation of maize phenology, the dynamic threshold method is used to extract the tasseling and milk ripening date through the time series σ0VH. In order to reduce the influence of precipitation or irrigation on SAR data, a Local Minimum Value Composite (LMVC) method is proposed to filter the original time series SAR data. The systematic phenology estimation method mainly includes LMVC, S-G filtering, Fourier curve fitting and dynamic threshold points extracting. Compared with the actual phenology date by field investigation, the errors of estimated tasseling and milk ripening date are 4.3 days and 5.5 days respectively, indicating that the time series σ0VH derived from the SAR data has great potential in spatiotemporal phenology estimation of field maize. Finally, the scattering mechanism of the maize field to C-band microwave in different growth periods was analyzed. It was also found that the phenology of maize was delayed in the coal mining subsidence areas and the areas with insufficient field management. Numéro de notice : A2022-232 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102720 Date de publication en ligne : 24/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102720 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100121
in International journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102720[article]Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space / Cheikh Mohamedou in Canadian Journal of Forest Research, Vol 52 n° 4 (April 2022)
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Titre : Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space Type de document : Article/Communication Auteurs : Cheikh Mohamedou, Auteur ; Annika S. Kangas, Auteur ; Alireza Hamedianfar, Auteur ; Jari Vauhkonen, Auteur Année de publication : 2022 Article en page(s) : pp 439 - 449 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données spatiotemporelles
[Termes IGN] dynamique de la végétation
[Termes IGN] estimation bayesienne
[Termes IGN] fusion de données
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] série temporelle
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Forest resource assessments based on multi-source and multi-temporal data have become more common. Therefore, enhancing the prediction capabilities of forestry dynamics by efficiently pooling and analyzing time-series and spatial sequential data is now more pivotal. Bayesian filtering and smoothing provide a well-defined formalism for the fusion or assimilation of various data. We ascertained how often the generic, standardized Bayesian framework is used in the scientific literature and whether such an approach is beneficial for forestry applications. A review of the literature showed that the use of Bayesian methods appears to be less common in forestry than in other disciplines, particularly remote sensing. Specifically, time-series analyses were found to favor ad hoc methods. Our review did not reveal strong numeric evidence for better performance by the various Bayesian approaches, but this result may be partly due to the challenge in comparing a variety of methods for different prediction tasks. We identified methodological challenges related to assimilating predictions of forest development; in particular, combining modelled growth with disturbances due to both forest operations and natural phenomena. Nevertheless, the Bayesian frameworks provide possibilities to efficiently combine and update prior and posterior predictive distributions and derive related uncertainty measures that appear under-utilized in forestry. Numéro de notice : A2022-315 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1139/cjfr-2021-0145 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.1139/cjfr-2021-0145 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100415
in Canadian Journal of Forest Research > Vol 52 n° 4 (April 2022) . - pp 439 - 449[article]Land surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data / Jun Lu in Remote sensing, vol 14 n° 5 (March-1 2022)
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Titre : Land surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data Type de document : Article/Communication Auteurs : Jun Lu, Auteur ; Tao He, Auteur ; Dan-Xia Song, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1296 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] fusion de données multisource
[Termes IGN] harmonisation des données
[Termes IGN] image Gaofen
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] indice de végétation
[Termes IGN] phénologie
[Termes IGN] réflectance spectrale
[Termes IGN] série temporelleRésumé : (auteur) Land Surface Phenology is an important characteristic of vegetation, which can be informative of its response to climate change. However, satellite-based identification of vegetation transition dates is hindered by inconsistencies in different observation platforms, including band settings, viewing angles, and scale effects. Therefore, time-series data with high consistency are necessary for monitoring vegetation phenology. This study proposes a data harmonization approach that involves band conversion and bidirectional reflectance distribution function (BRDF) correction to create normalized reflectance from Landsat-8, Sentinel-2A, and Gaofen-1 (GF-1) satellite data, characterized by the same spectral and illumination-viewing angles as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Nadir BRDF Adjusted Reflectance (NBAR). The harmonized data are then subjected to the spatial and temporal adaptive reflectance fusion model (STARFM) to produce time-series data with high spatio–temporal resolution. Finally, the transition date of typical vegetation was estimated using regular 30 m spatial resolution data. The results show that the data harmonization method proposed in this study assists in improving the consistency of different observations under different viewing angles. The fusion result of STARFM was improved after eliminating differences in the input data, and the accuracy of the remote-sensing-based vegetation transition date was improved by the fused time-series curve with the input of harmonized data. The root mean square error (RMSE) estimation of the vegetation transition date decreased by 9.58 days. We concluded that data harmonization eliminates the viewing-angle effect and is essential for time-series vegetation monitoring through improved data fusion. Numéro de notice : A2022-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14051296 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.3390/rs14051296 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100027
in Remote sensing > vol 14 n° 5 (March-1 2022) . - n° 1296[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)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)PermalinkMonthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning / Feng Zhao in Remote sensing of environment, vol 269 (February 2022)PermalinkRecurrent origin–destination network for exploration of human periodic collective dynamics / Xiaojian Chen in Transactions in GIS, vol 26 n° 1 (February 2022)PermalinkSpatiotemporal temperature fusion based on a deep convolutional network / Xuehan Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)PermalinkApport des nouveaux systèmes GNSS de cartographie du niveau marin à l’exploitation des données altimétriques en zone côtière / Clémence Chupin (2022)PermalinkApprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques / Jean-Yves Franceschi (2022)PermalinkContraintes observationnelles historiques sur la sensibilité climatique : implications pour les projections de la hausse du niveau de la mer / Jonathan Chenal (2022)PermalinkEstimation of Lesser Antilles vertical velocity fields using a GNSS-PPP software comparison / Pierre Sakic-Kieffer (2022)PermalinkÉvolution rétrospective et prospective d’un massif dunaire par imagerie multispectrale et LiDAR / Iris Jeuffrard (2022)Permalink