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Spatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China / Mingyue Wang in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 4 (April 2020) . - 17 p. Titre : Spatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China Type de document : Article/Communication Auteurs : Mingyue Wang, Auteur ; Jun’e Fu, Auteur ; Zhitao Wu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 17 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] Fleuve jaune (Chine) [Termes descripteurs IGN] image Aqua-MODIS [Termes descripteurs IGN] image SPOT [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] précipitation [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] température [Termes descripteurs IGN] variation saisonnière Résumé : (auteur) Research on vegetation variation is an important aspect of global warming studies. The quantification of the relationship between vegetation change and climate change has become a central topic and challenge in current global change studies. The source region of the Yellow River (SRYR) is an appropriate area to study global change because of its unique natural conditions and vulnerable terrestrial ecosystem. Therefore, we chose the SRYR for a case study to determine the driving forces behind vegetation variation under global warming. Using the Normalized Difference Vegetation Index (NDVI) and climate data, we investigated the NDVI variation in the growing season in the region from 1998 to 2016 and its response to climate change based on trend analysis, the Mann–Kendall trend test and partial correlation analysis. Finally, an NDVI–climate mathematical model was built to predict the NDVI trends from 2020 to 2038. The results indicated the following: (1) over the past 19 years, the NDVI showed an increasing trend, with a growth rate of 0.00204/a. There was an upward trend in NDVI over 71.40% of the region. (2) Both the precipitation and temperature in the growing season showed upward trends over the last 19 years. NDVI was positively correlated with precipitation and temperature. The areas with significant relationships with precipitation covered 31.01% of the region, while those with significant relationships with temperature covered 56.40%. The sensitivity of the NDVI to temperature was higher than that to precipitation. Over half (56.58%) of the areas were found to exhibit negative impacts of human activities on the NDVI. (3) According to the simulation, the NDVI will increase slightly over the next 19 years, with a linear tendency of 0.00096/a. From the perspective of spatiotemporal changes, we combined the past and future variations in vegetation, which could adequately reflect the long-term vegetation trends. The results provide a theoretical basis and reference for the sustainable development of the natural environment and a response to vegetation change under the background of climate change in the study area. Numéro de notice : A2020-262 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9040282 date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.3390/ijgi9040282 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95022 [article]

Change-detection map learning using matching pursuit / Y. Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 8 (August 2015) . - pp 4712 - 4723 Titre : Change-detection map learning using matching pursuit Type de document : Article/Communication Auteurs : Y. Li, Auteur ; Maoguo Gong, Auteur ; Licheng Jiao, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 4712 - 4723 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] couple stéréoscopique [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] Fleuve jaune (Chine) [Termes descripteurs IGN] image ERS-SAR Résumé : (Auteur) Learning can be of great use when dealing with problems in various fields. Inspired by locally linear embedding from manifold, we propose a novel automatic change-detection method through an offline learning approach. The proposed method comprises three steps. First, two coupled dictionaries of the difference image (DI) patches and change-detection map patches are generated from known image pairs. Second, we approximately represent each patch of the input DI with respect to the DI dictionary by using the matching the pursuit algorithm. Third, the coefficients of this representation are applied with the change-detection map dictionary to generate the output change-detection map. This way, we exploit the relationship between the DI patches and the corresponding change-detection map patches based on two coupled dictionaries. In addition, the relationship guides us to construct the change-detection map for any given input DI. Experimental results on real synthetic aperture radar databases show that the proposed method is superior to its counterparts. Our method can obtain promising results, even though the dictionaries are prepared by simple random sampling from fixed training images. Numéro de notice : A2015-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76867 [article]

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Combination of overlap-driven adjustment and Phong model for LiDAR intensity correction / Q. Ding in ISPRS Journal of photogrammetry and remote sensing, vol 75 (January 2013)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 75 (January 2013) . - pp 40 - 47 Titre : Combination of overlap-driven adjustment and Phong model for LiDAR intensity correction Type de document : Article/Communication Auteurs : Q. Ding, Auteur ; W. Chen, Auteur ; B. King, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 40 - 47 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] capteur multibande [Termes descripteurs IGN] Chine [Termes descripteurs IGN] correction du signal [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] Fleuve jaune (Chine) [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] réflexion spéculaire [Termes descripteurs IGN] télémétrie laser aéroporté [Termes descripteurs IGN] zone humide Résumé : (Auteur) Airborne laser scanning LiDAR systems deliver not only geometric (X, Y, Z) information of the scanned surfaces but also the returned intensity of the laser pulse. Recent studies have shown the potential of using intensity data for many applications. However, there are limitations in using the raw intensity data because of radiometric system bias, reflectance noise and variations between adjacent strips. To overcome these limitations, a three-step LiDAR intensity correction algorithm is proposed. Following corrections for environmental and surface effects, an overlap-driven least-squares adjustment model that does not rely on the selection of homologous points minimizes intensity differences in the overlap area of strips. Finally, the Phong reflection model, which describes both diffuse and specular reflectance, is used to attenuate the effects of strong reflections that typically occur over wet or water dominated areas. The algorithm was applied to a multi-strip LiDAR dataset that covers wetlands in the estuary of the Yellow River, People’s Republic of China. Results demonstrated a significant reduction in radiometric differences in the overlap areas, and strong specular reflections in the nadir regions were reduced. Objects which were obscured by the specular reflection in the original intensity data were clearly identifiable after the adjustment. Numéro de notice : A2013-031 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32169 [article]

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Temporal change in the landscape erosion pattern in the Yellow River basin / W. Siyuan in International journal of geographical information science IJGIS, vol 21 n° 9-10 (october 2007)

Public [article]  inInternational journal of geographical information science IJGIS > vol 21 n° 9-10 (october 2007) . - pp 1077 - 1092 Titre : Temporal change in the landscape erosion pattern in the Yellow River basin Type de document : Article/Communication Auteurs : W. Siyuan, Auteur ; L. Jingshi, Auteur ; Y. Cunjian, Auteur Année de publication : 2007 Article en page(s) : pp 1077 - 1092 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] agriculture [Termes descripteurs IGN] bassin hydrographique [Termes descripteurs IGN] Chine [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] érosion hydrique [Termes descripteurs IGN] Fleuve jaune (Chine) [Termes descripteurs IGN] gestion durable [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] impact sur l'environnement [Termes descripteurs IGN] urbanisation [Termes descripteurs IGN] utilisation du sol Résumé : (Auteur) Using Landsat TM data from 1995 and 2000, changes in the landscape erosion pattern of the Yellow River Basin, China were analysed. The aim was to improve our understanding of soil-erosion change so that sustainable land use could be established. First, a soil-erosion intensity index model was developed to study soil-erosion intensity change in the study area. Over the 5 years, the areas of weak erosion, moderate erosion, severe erosion, and very severe erosion all increased. The area of weak erosion increased dramatically by 7.94*105 ha, and areas of slight erosion and acute erosion decreased by 1.93*106 ha and 4.50*104 ha, respectively. The results show that while the intensity of soil erosion has gradually been decreasing as a whole, in some regions the soil erosion is becoming more severe. Based on landscape indices, the pattern of changes in soil erosion over the past 5 years was analysed. The changes in landscape pattern of soil erosion resulted from human activities. Analysis showed that human impact increases fragmentation, having three major effects on landscape pattern, reduction in patch area, variations in patch shape, and changes in spatial pattern. In the study area, population growth, farming, governmental policy and forest degradation are the major factors causing soil erosion change over a 5-year period. Copyright Taylor & Francis Numéro de notice : A2007-557 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28920 [article]

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Evaporing estimation in the Yellow River basin, China using integrated NDVI data / R. Sun in International Journal of Remote Sensing IJRS, vol 25 n° 13 (July 2004)

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 13 (July 2004) . - pp 2523 - 2534 Titre : Evaporing estimation in the Yellow River basin, China using integrated NDVI data Type de document : Article/Communication Auteurs : R. Sun, Auteur ; X. Gao, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 2523 - 2534 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] bassin hydrographique [Termes descripteurs IGN] Chine [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] évapotranspiration [Termes descripteurs IGN] Fleuve jaune (Chine) [Termes descripteurs IGN] humidité de l'air [Termes descripteurs IGN] indice d'humidité [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] production agricole [Termes descripteurs IGN] ressources en eau [Termes descripteurs IGN] sécheresse Résumé : (Auteur) It is important to estimate land surface evapotranspiration (ET) for water resources evaluation, drought monitoring and crop production simulation. In this paper, a relationship between annual ET, integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) and Relative Moisture Index (RMI) was established. Based on this relationship, the spatial distribution and dynamic change of annual ET were estimated for the Yellow River Basin, China from 1982 to 2000. Our analyses involved the use of integrated NDVI data, monthly mean air temperature, and precipitation. Our results showed that the integrated AVHRR NDVI can be used to effectively estimate annual ET in the Yellow River Basin, with an accuracy over 90% for the whole basin. Numéro de notice : A2004-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26786 [article]

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Using Thematic Mapper data for change detection and sustainable use of cultivated land: a case study in the Yellow River delta, China / G.X. Zhao in International Journal of Remote Sensing IJRS, vol 25 n° 13 (July 2004)

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Landsat-MSS radiance as a measure of suspended sediment in the lower yellow river (Hwang-Ho) / S. Aranuvachapun in Remote sensing of environment, vol 25 n° 2 (01/07/1988)

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