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Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model / Han Ma in Remote sensing of environment, vol 273 (May 2022)
[article]
Titre : Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model Type de document : Article/Communication Auteurs : Han Ma, Auteur ; Shunlin Liang, Auteur Année de publication : 2022 Article en page(s) : n° 112985 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] cohérence temporelle
[Termes IGN] image Terra-MODIS
[Termes IGN] Leaf Area Index
[Termes IGN] réflectance de surface
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (auteur) Leaf area index (LAI) is a terrestrial essential climate variable that is required in a variety of ecosystem and climate models. The Global LAnd Surface Satellite (GLASS) LAI product has been widely used, but its current version (V5) from Moderate Resolution Imaging Spectroradiometer (MODIS) data has several limitations, such as frequent temporal fluctuation, large data gaps, high dependence on the quality of surface reflectance, and low computational efficiency. To address these issues, this paper presents a deep learning model to generate a new version of the LAI product (V6) at 250-m resolution from MODIS data from 2000 onward. Unlike most existing algorithms that estimate one LAI value at one time for each pixel, this model estimates LAI for 2 years simultaneously. Three widely used LAI products (MODIS C6, GLASS V5, and PROBA-V V1) are used to generate global representative time-series LAI training samples using K-means clustering analysis and least difference criteria. We explore four machine learning models, the general regression neural network (GRNN), long short-term memory (LSTM), gated recurrent unit (GRU), and Bidirectional LSTM (Bi-LSTM), and identify Bi-LSTM as the best model for product generation. This new product is directly validated using 79 high-resolution LAI reference maps from three in situ observation networks. The results show that GLASS V6 LAI achieves higher accuracy, with a root mean square (RMSE) of 0.92 at 250 m and 0.86 at 500 m, while the RMSE is 0.98 for PROBA-V at 300 m, 1.08 for GLASS V5, and 0.95 for MODIS C6 both at 500 m. Spatial and temporal consistency analyses also demonstrate that the GLASS V6 LAI product is more spatiotemporally continuous and has higher quality in terms of presenting more realistic temporal LAI dynamics when the surface reflectance is absent for a long period owing to persistent cloud/aerosol contaminations. The results indicate that the new Bi-LSTM deep learning model runs significantly faster than the GLASS V5 algorithm, avoids the reconstruction of surface reflectance data, and is resistant to the noises (cloud and snow contamination) or missing values contained in surface reflectance than other methods, as the Bi-LSTM can effectively extract information across the entire time series of surface reflectance rather than a single time point. To our knowledge, this is the first global time-series LAI product at the 250-m spatial resolution that is freely available to the public (www.geodata.cn and www.glass.umd.edu). Numéro de notice : A2022-284 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112985 Date de publication en ligne : 10/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100303
in Remote sensing of environment > vol 273 (May 2022) . - n° 112985[article]Significant loss of ecosystem services by environmental changes in the Mediterranean coastal area / Adriano Conte in Forests, vol 13 n° 5 (May 2022)
[article]
Titre : Significant loss of ecosystem services by environmental changes in the Mediterranean coastal area Type de document : Article/Communication Auteurs : Adriano Conte, Auteur ; Ilaria Zappitelli, Auteur ; Lina Fusaro, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 689 Note générale : bilbliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Ecologie
[Termes IGN] biodiversité
[Termes IGN] écosystème
[Termes IGN] forêt méditerranéenne
[Termes IGN] Leaf Area Index
[Termes IGN] littoral méditerranéen
[Termes IGN] Pinus (genre)
[Termes IGN] pollution atmosphérique
[Termes IGN] puits de carbone
[Termes IGN] Quercus suber
[Termes IGN] Rome
[Termes IGN] service écosystémiqueRésumé : (auteur) Mediterranean coastal areas are among the most threated forest ecosystems in the northern hemisphere due to concurrent biotic and abiotic stresses. These may affect plants functionality and, consequently, their capacity to provide ecosystem services. In this study, we integrated ground-level and satellite-level measurements to estimate the capacity of a 46.3 km2 Estate to sequestrate air pollutants from the atmosphere, transported to the study site from the city of Rome. By means of a multi-layer canopy model, we also evaluated forest capacity to provide regulatory ecosystem services. Due to a significant loss in forest cover, estimated by satellite data as −6.8% between 2014 and 2020, we found that the carbon sink capacity decreased by 34% during the considered period. Furthermore, pollutant deposition on tree crowns has reduced by 39%, 46% and 35% for PM, NO2 and O3, respectively. Our results highlight the importance of developing an integrated approach combining ground measurements, modelling and satellite data to link air quality and plant functionality as key elements to improve the effectiveness of estimate of ecosystem services. Numéro de notice : A2022-350 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.3390/f13050689 Date de publication en ligne : 28/04/2022 En ligne : https://doi.org/10.3390/f13050689 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100537
in Forests > vol 13 n° 5 (May 2022) . - n° 689[article]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]Detecting and mapping drought severity using multi-temporal Landsat data in the uMsinga region of KwaZulu-Natal, South Africa / Shenelle Lottering in Geocarto international, vol 37 n° 6 ([01/04/2022])
[article]
Titre : Detecting and mapping drought severity using multi-temporal Landsat data in the uMsinga region of KwaZulu-Natal, South Africa Type de document : Article/Communication Auteurs : Shenelle Lottering, Auteur ; Paramu Mafongoyab, Auteur ; Romano Lottering, Auteur Année de publication : 2022 Article en page(s) : pp 1574 - 1586 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique du sud (état)
[Termes IGN] cartographie thématique
[Termes IGN] données météorologiques
[Termes IGN] données multitemporelles
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-TM
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] sécheresse
[Termes IGN] stress hydrique
[Termes IGN] température au solRésumé : (auteur) Drought has become a more frequent phenomenon under changing climatic conditions, particularly in Sub Saharan Africa. This study tested the utility of a newly proposed Temperature-Vegetation Water Stress Index (T-VWSI) in detecting drought severity using Landsat data for the years 2008, 2012, 2016 and 2018. This index was created using both NDVI and LST to detect drought severity within the region. The results show that the year 2016 experienced the most severe levels of drought, with the northern areas of the uMsinga region being most severely affected. SPI was used to corroborate the findings of the T-VWSI index and also established that the year 2016 was the year of severe drought in uMsinga. The results of this study have illustrated the potential of the T-VWSI index in effectively mapping and detecting drought over large spatial areas. Numéro de notice : A2022-473 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1783580 Date de publication en ligne : 08/07/2020 En ligne : https://doi.org/10.1080/10106049.2020.1783580 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100820
in Geocarto international > vol 37 n° 6 [01/04/2022] . - pp 1574 - 1586[article]Comparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)PermalinkEvaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 5 ([01/03/2022])PermalinkLand 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)PermalinkMonitoring 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])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])PermalinkDevelopment of earth observational diagnostic drought prediction model for regional error calibration: A case study on agricultural drought in Kyrgyzstan / Eunbeen Park in GIScience and remote sensing, vol 59 n° 1 (2022)PermalinkDynamic 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])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)PermalinkSymbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography / Somnath Paramanik in Applied Geography, vol 139 (February 2022)Permalink3D modeling of urban area based on oblique UAS images - An end-to-end pipeline / Valeria-Ersilia Oniga in Remote sensing, vol 14 n° 2 (January-2 2022)Permalink