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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)  

Public [article]  inRemote sensing of environment > vol 273 (May 2022) . - n° 112985 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 végétation Résumé : (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 [article]

Simultaneous estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from multiple-satellite data / Han Ma in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 43334 - 4354 Titre : Simultaneous estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from multiple-satellite data Type de document : Article/Communication Auteurs : Han Ma, Auteur ; Giang Liu, Auteur ; Shunlin Liang, Auteur ; Zhiqiang Xiao, Auteur Année de publication : 2017 Article en page(s) : pp 43334 - 4354 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] albedo [Termes IGN] image SPOT-Végétation [Termes IGN] image Terra-MISR [Termes IGN] image Terra-MODIS [Termes IGN] indice foliaire [Termes IGN] Leaf Area Index [Termes IGN] modèle de transfert radiatif [Termes IGN] modèle numérique de surface [Termes IGN] Normalized Difference Snow Index [Termes IGN] photosynthèse [Termes IGN] surveillance écologique Résumé : (Auteur) Leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface broadband albedo are three routinely generated land-surface parameters from satellite observations, which have been widely used in land-surface modeling and environmental monitoring. Currently, most global land products are retrieved separately from individual satellite data. Many issues, such as data gaps, spatial and temporal inconsistencies, and insufficient accuracy under certain conditions resulting from the inadequacies of single-sensor observations, have made the incorporation of multiple sensors a reasonable solution. In this paper, an approach to simultaneous estimation of LAI, broadband albedo, and FAPAR from multiple-satellite sensors is further refined. The method, improved from that proposed in an earlier study using Moderate Resolution Imaging Spectroradiometer (MODIS) data, consists of several steps. First, a coupled dynamic and radiative-transfer model based on MODIS, SPOT/VEGETATION, and Multiangle Imaging SpectroRadiometer data was developed to retrieve LAI values and use them to construct a time-evolving dynamic model. Second, an iteration process with predefined exit criteria was developed to obtain consistent gap-filled LAI estimates. Third, a spectral albedo based on the retrieved LAI values was simulated using a radiative-transfer model and then converted to a broadband albedo using empirical methods. Snow-covered pixels identified by normalized difference snow index thresholds were adjusted to the weighted average of the underlying albedo and the maximum snow albedo. Finally, the FAPAR of green vegetation was calculated as a combination of the albedo at the top of the canopy, the soil albedo, and the transmittance of the PAR down to the background. Validation of retrieved LAI, albedo, and FAPAR values obtained from multiple-satellite data over ten study sites has demonstrated that the proposed method can produce more accurate products than presently distributed global products. Numéro de notice : A2017-495 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2691542 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2691542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86435 [article]

Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data / Kun Jia in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 93 (July 2014) . - pp. 49 - 55 Titre : Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data Type de document : Article/Communication Auteurs : Kun Jia, Auteur ; Shunlin Liang, Auteur ; Ning Zhang, Auteur ; Xiangqin Wei, Auteur ; Xingfa Gu, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp. 49 - 55 Langues : Anglais (eng) Descripteur : [Termes IGN] changement d'occupation du sol [Termes IGN] classification dirigée [Termes IGN] fusion de données [Termes IGN] image à basse résolution [Termes IGN] occupation du sol [Termes IGN] précision des données [Termes IGN] représentation du temps [Termes IGN] série temporelle Résumé : (Auteur) Land cover classification of finer resolution remote sensing data is always difficult to acquire high-frequency time series data which contains temporal features for improving classification accuracy. This paper proposed a method of land cover classification with finer resolution remote sensing data integrating temporal features extracted from time series coarser resolution data. The coarser resolution vegetation index data is first fused with finer resolution data to obtain time series finer resolution data. Temporal features are extracted from the fused data and added to improve classification accuracy. The result indicates that temporal features extracted from coarser resolution data have significant effect on improving classification accuracy of finer resolution data, especially for vegetation types. The overall classification accuracy is significantly improved approximately 4% from 90.4% to 94.6% and 89.0% to 93.7% for using Landsat 8 and Landsat 5 data, respectively. The user and producer accuracies for all land cover types have been improved. Numéro de notice : A2014-328 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.04.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.04.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73677 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014071	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Research on urban influence domains in China / Shunlin Liang in International journal of geographical information science IJGIS, vol 23 n°11-12 (november 2009)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 23 n°11-12 (november 2009) . - pp 1527 - 1539 Titre : Research on urban influence domains in China Type de document : Article/Communication Auteurs : Shunlin Liang, Auteur Année de publication : 2009 Article en page(s) : pp 1527 - 1539 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] ArcGIS [Termes IGN] Chine [Termes IGN] mégalopole [Termes IGN] milieu urbain Résumé : (Auteur) Through research on the gravity model, the paper studies the geometric characteristics of urban influence domain and the principles for the change of urban influence domain along with the evolution of distance decay index, calculates the distribution of the gravitational field by using ARCGIS, establishes a spatial cluster system for the megalopolis in china, delineates urban influence domains by dissolving spatial features, and compartmentalizes China into 13 economic regions based on the megalopolis clusters and urban influence domains combining with the physical and economic locations. Major conclusions are: the distribution of urban gravitational field is the manifestation of regional unbalanced development; the spatial structure and characteristic of urban system can be studied through the distribution situation of urban gravitational field; the urban influence domains in China have not formed the mosaic structure of a standard regular hexagon; the economic region with weak urban gravitational field may be compressed by the region with stronger urban gravitational field. Copyright Taylor & Francis Numéro de notice : A2009-572 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810802363614 En ligne : https://doi.org/10.1080/13658810802363614 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30148 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-09071	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-09072	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spatially and temporally continuous LAI data sets based on an integrated filtering method: examples from North America / H. Fang in Remote sensing of environment, vol 112 n° 1 (15/01/2008)  

Public [article]  inRemote sensing of environment > vol 112 n° 1 (15/01/2008) . - pp 75 - 93 Titre : Spatially and temporally continuous LAI data sets based on an integrated filtering method: examples from North America Type de document : Article/Communication Auteurs : H. Fang, Auteur ; Shunlin Liang, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 75 - 93 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de filtrage [Termes IGN] Amérique du nord [Termes IGN] analyse comparative [Termes IGN] analyse diachronique [Termes IGN] données spatiotemporelles [Termes IGN] image Landsat-ETM+ [Termes IGN] image Terra-MODIS [Termes IGN] Leaf Area Index [Termes IGN] qualité des données Résumé : (Auteur) Leaf Area Index (LAI) is an important biophysical variable for characterizing the land surface vegetation. Global LAI product has been routinely produced from the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellite platforms. However, the MODIS standard LAI product is not continuous both spatially and temporally. To fill the gaps and improve the quality, we have developed a data filtering algorithm. This filter, called the temporal spatial filter (TSF), integrates both spatial and temporal characteristics for different plant functional types. The spatial gaps are first filled with the multi-year averages of the same day. If the values are missing over all years, the pixel is filled with a new estimate using the vegetation continuous field–ecosystem curve fitting method. The TSF integrates both the multi-seasonal average trend (background) and the seasonal observation. We implement this algorithm using the MODIS Collection 4 LAI product over North America. Comparison of the TSF results with the Savitzky–Golay filter indicates that the TSF performs much better in restoring the spatial and temporal distribution of seasonal LAI trends. The new LAI product has been validated by comparing with field measurements and the derived LAI maps from ETM+ data at a broadleaf forest site and an agricultural site. The validation results indicate that the new LAI product agrees better with both the field measurements and LAI values obtained from the ETM+ than does the MODIS LAI standard product, which usually shows higher LAI values. Copyright Elsevier Numéro de notice : A2008-026 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.07.026 En ligne : https://doi.org/10.1016/j.rse.2006.07.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29021 [article]

Improved estimation of aerosol optical depth from MODIS imagery over land surfaces / B. Zhong in Remote sensing of environment, vol 104 n° 4 (30/10/2006)  

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Quantitative remote sensing of land surfaces / Shunlin Liang (2004)

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vol 18 n°2-4 - June 2000 - Land surface bidirectional reflectance distribution function (BDRF): Recent advances and future prospects (Bulletin de Remote sensing reviews) / Shunlin Liang

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