Centre dedocumentation
scientifique

Spatio-temporal evolution, future trend and phenology regularity of net primary productivity of forests in Northeast China / Chunli Wang in Remote sensing, vol 12 n° 21 (November - 1 2020)  

Public [article]  inRemote sensing > vol 12 n° 21 (November - 1 2020) . - n° 3670 Titre : Spatio-temporal evolution, future trend and phenology regularity of net primary productivity of forests in Northeast China Type de document : Article/Communication Auteurs : Chunli Wang, Auteur ; Qun’Ou Jiang, Auteur ; Xiangzheng Deng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 3670 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] développement durable [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] production primaire brute [Termes descripteurs IGN] variation saisonnière [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Net Primary Productivity (NPP) is one of the significant indicators to measure environmental changes; thus, the relevant study of NPP in Northeast China, Asia, is essential to climate changes and ecological sustainable development. Based on the Global Production Efficiency (GLO-PEM) model, this study firstly estimated the NPP in Northeast China, from 2001 to 2019, and then analyzed its spatio-temporal evolution, future changing trend and phenology regularity. Over the years, the NPP of different forests type in Northeast China showed a gradual increasing trend. Compared with other different time stages, the high-value NPP (700–1300 gC·m−2·a−1) in Changbai Mountain, from 2017 to 2019, is more widely distributed. For instance, the NPP has an increasing rate of 6.92% compared to the stage of 2011–2015. Additionally, there was a significant advance at the start of the vegetation growth season (SOS), and a lag at the end of the vegetation growth season (EOS), from 2001 to 2019. Thus, the whole growth period of forests in Northeast China became prolonged with the change of phenology. Moreover, analysis on the sustainability of NPP in the future indicates that the reverse direction feature of NPP change will be slightly stronger than the co-directional feature, meaning that about 30.68% of the study area will switch from improvement to degradation. To conclude, these above studies could provide an important reference for the sustainable development of forests in Northeast China. Numéro de notice : A2020-719 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12213670 date de publication en ligne : 09/11/2020 En ligne : https://doi.org/10.3390/rs12213670 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96308 [article]

Challenges in flood modeling over data-scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco / El Mahdi El Khalk in Natural Hazards and Earth System Sciences, vol 20 n° 10 (October 2020)  

Public [article]  inNatural Hazards and Earth System Sciences > vol 20 n° 10 (October 2020) . - pp 2591 - 2607 Titre : Challenges in flood modeling over data-scarce regions: how to exploit globally available soil moisture products to estimate antecedent soil wetness conditions in Morocco Type de document : Article/Communication Auteurs : El Mahdi El Khalk, Auteur ; Yves Tramblay, Auteur ; Christian Massari, Auteur Année de publication : 2020 Article en page(s) : pp 2591 - 2607 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Advanced scatterometer [Termes descripteurs IGN] Atlas marocain [Termes descripteurs IGN] bassin hydrographique [Termes descripteurs IGN] crue [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image SMOS [Termes descripteurs IGN] inondation [Termes descripteurs IGN] Maroc [Termes descripteurs IGN] modèle hydrographique [Termes descripteurs IGN] modélisation [Termes descripteurs IGN] variation saisonnière [Termes descripteurs IGN] zone semi-aride Résumé : (auteur) The Mediterranean region is characterized by intense rainfall events giving rise to devastating floods. In Maghreb countries such as Morocco, there is a strong need for forecasting systems to reduce the impacts of floods. The development of such a system in the case of ungauged catchments is complicated, but remote-sensing products could overcome the lack of in situ measurements. The soil moisture content can strongly modulate the magnitude of flood events and consequently is a crucial parameter to take into account for flood modeling. In this study, different soil moisture products (European Space Agency Climate Change Initiative, ESA-CCI; Soil Moisture and Ocean Salinity, SMOS; Soil Moisture and Ocean Salinity by the Institut National de la Recherche Agronomique and Centre d'Etudes Spatiales de la Biosphère, SMOS-IC; Advanced Scatterometer, ASCAT; and ERA5 reanalysis) are compared to in situ measurements and one continuous soil-moisture-accounting (SMA) model for basins located in the High Atlas Mountains, upstream of the city of Marrakech. The results show that the SMOS-IC satellite product and the ERA5 reanalysis are best correlated with observed soil moisture and with the SMA model outputs. The different soil moisture datasets were also compared to estimate the initial soil moisture condition for an event-based hydrological model based on the Soil Conservation Service curve number (SCS-CN). The ASCAT, SMOS-IC, and ERA5 products performed equally well in validation to simulate floods, outperforming daily in situ soil moisture measurements that may not be representative of the whole catchment soil moisture conditions. The results also indicated that the daily time step may not fully represent the saturation state before a flood event due to the rapid decay of soil moisture after rainfall in these semiarid environments. Indeed, at the hourly time step, ERA5 and in situ measurements were found to better represent the initial soil moisture conditions of the SCS-CN model by comparison with the daily time step. The results of this work could be used to implement efficient flood modeling and forecasting systems in semiarid regions where soil moisture measurements are lacking. Numéro de notice : A2020-610 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/nhess-20-2591-2020 date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.5194/nhess-20-2591-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95974 [article]

See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning / Zhouxin Xi in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 168 (October 2020) . - pp 1 - 16 Titre : See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning Type de document : Article/Communication Auteurs : Zhouxin Xi, Auteur ; Christopher Hopkinson, Auteur ; Stewart B. Rood, Auteur ; Derek R. Peddle, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] gestion forestière [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] variation saisonnière Résumé : (auteur) Determining tree species composition in natural forests is essential for effective forest management. Species classification at the individual tree level requires fine-scale traits which can be derived through terrestrial laser scanning (TLS) point clouds. A generalizable species classification framework also needs to decouple seasonal foliage variation from deciduous species, for which wood filtering is applicable. Different machine learning and deep learning models are feasible for wood filtering and species classification. We investigated 13 machine learning and deep learning classifiers for 9 species, and 15 classifiers for filtering wood points from TLS plot scans. Each classifier was evaluated using the criteria of mean Intersection over Union accuracy (mIoU), training stability and time cost. On average, deep learning classifiers outperformed machine learning classifiers by 10% and 5% in terms of wood and species classification mIoU, respectively. PointNet++ provided the best species classifier, with the highest mIoU (0.906), stability, and moderate time cost. Among wood classifiers, UNet achieved the top mIoU (0.839) while ResNet-50 was recommended for rapid trial and error testing. Across the classifications, the factors of input resolution, attributes and features were also analyzed. Hot zones of species classification with PointNet++ were visualized to indicate how AI interpret species traits. Numéro de notice : A2020-533 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.08.001 date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.08.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95718 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020101	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2020103	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2020102	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications / T. Chakraborty in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 168 (October 2020) . - pp 74 - 88 Titre : A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications Type de document : Article/Communication Auteurs : T. Chakraborty, Auteur ; A. Hsu, Auteur ; D. Manya, Auteur ; G. Sheriff, Auteur Année de publication : 2020 Article en page(s) : pp 74 - 88 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse socio-économique [Termes descripteurs IGN] base de données localisées [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] variation saisonnière Résumé : (auteur) The urban heat island (UHI) effect is strongly modulated by urban-scale changes to the aerodynamic, thermal, and radiative properties of the Earth’s land surfaces. Interest in this phenomenon, both from the climatological and public health perspectives, has led to hundreds of UHI studies, mostly conducted on a city-by-city basis. These studies, however, do not provide a complete picture of the UHI for administrative units using a consistent methodology. To address this gap, we characterize clear-sky surface UHI (SUHI) intensities for all urbanized areas in the United States using a modified Simplified Urban-Extent (SUE) approach by combining a fusion of remotely-sensed data products with multiple US census-defined administrative urban delineations. We find the highest daytime SUHI intensities during summer (1.91 ± 0.97 °C) for 418 of the 497 urbanized areas, while the winter daytime SUHI intensity (0.87 ± 0.45 °C) is the lowest in 439 cases. Since urban vegetation has been frequently cited as an effective way to mitigate UHI, we use NDVI, a satellite-derived proxy for live green vegetation, and US census tract delineations to characterize how vegetation density modulates inter-urban, intra-urban, and inter-seasonal variability in SUHI intensity. In addition, we also explore how elevation and distance from the coast confound SUHI estimates. To further quantify the uncertainties in our estimates, we analyze and discuss some limitations of these satellite-derived products across climate zones, particularly issues with using remotely sensed radiometric temperature and vegetation indices as proxies for urban heat and vegetation cover. We demonstrate an application of this spatially explicit dataset, showing that for the majority of the urbanized areas, SUHI intensity is lower in census tracts with higher median income and higher proportion of white people. Our analysis also suggests that poor and non-white urban residents may suffer the possible adverse effects of summer SUHI without reaping the potential benefits (e.g., warmer temperatures) during winter, though establishing this result requires future research using more comprehensive heat stress metrics. This study develops new methodological advancements to characterize SUHI and its intra-urban variability at levels of aggregation consistent with sources of other socioeconomic information, which can be relevant in future inter-disciplinary research and as a possible screening tool for policy-making. The dataset developed in this study is visualized at: https://datadrivenlab.users.earthengine.app/view/usuhiapp. Numéro de notice : A2020-635 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.021 date de publication en ligne : 13/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96058 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020101	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2020103	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2020102	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Spatio-temporal relationship between land cover and land surface temperature in urban areas: A case study in Geneva and Paris / Xu Ge in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 10 (October 2020) . - 24 p. Titre : Spatio-temporal relationship between land cover and land surface temperature in urban areas: A case study in Geneva and Paris Type de document : Article/Communication Auteurs : Xu Ge, Auteur ; Dasaraden Mauree, Auteur ; Roberto Castello, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] espace vert [Termes descripteurs IGN] Genève [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] Normalized Difference Built-up Index [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] Paris (75) [Termes descripteurs IGN] surface imperméable [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] variation saisonnière [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Currently, more than half of the world’s population lives in cities, which leads to major changes in land use and land surface temperature (LST). The associated urban heat island (UHI) effects have multiple impacts on energy consumption and human health. A better understanding of how different land covers affect LST is necessary for mitigating adverse impacts, and supporting urban planning and public health management. This study explores a distance-based, a grid-based and a point-based analysis to investigate the influence of impervious surfaces, green area and waterbodies on LST, from large (distance and grid based analysis with 400 m grids) to smaller (point based analysis with 30 m grids) scale in the two mid-latitude cities of Paris and Geneva. The results at large scale confirm that the highest LST was observed in the city centers. A significantly positive correlation was observed between LST and impervious surface density. An anticorrelation between LST and green area density was observed in Paris. The spatial lag model was used to explore the spatial correlation among LST, NDBI, NDVI and MNDWI on a smaller scale. Inverse correlations between LST and NDVI and MNDWI, respectively, were observed. We conclude that waterbodies display the greatest mitigation on LST and UHI effects both on the large and smaller scale. Green areas play an important role in cooling effects on the smaller scale. An increase of evenly distributed green area and waterbodies in urban areas is suggested to lower LST and mitigate UHI effects. Numéro de notice : A2020-666 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9100593 date de publication en ligne : 10/10/2020 En ligne : https://doi.org/10.3390/ijgi9100593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96143 [article]

Study on the inter-annual hydrology-induced deformations in Europe using GRACE and hydrological models / Artur Lenczuk in Journal of applied geodesy, vol 14 n° 4 (October 2020)  

Permalink

Automated estimation and tools to extract positions, velocities, breaks, and seasonal terms from daily GNSS measurements: illuminating nonlinear Salton Trough deformation / Michael B. Heflin in Earth and space science, vol 7 n° 7 (July 2020)  

Permalink

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam / Khanh Giang Le in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

Permalink

Improved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation / Eslam Ali in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Permalink

Year-to-year crown condition poorly contributes to ring width variations of beech trees in French ICP level I network / Clara Tallieu in Forest ecology and management, Vol 465 (1st June 2020)  

Permalink

Visualizing when, where, and how fires happen in U.S. parks and protected areas / Nicole C. Inglis in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)  

Permalink

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (2020)  

Permalink

Recent sea level change in the black sea from satellite altimetry and tide gauge observations / Nevin Betül Avsar in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)  

Permalink

Xylem anatomy of Robinia pseudoacacia L. and Quercus robur L. is differently affected by climate in a temperate alluvial forest / Paola Nola in Annals of Forest Science [en ligne], Vol 77 n° 1 (March 2020)  

Permalink

Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Permalink