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A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration / Daeyong Jin in Remote sensing, vol 13 n°10 (May 2021)  

Public [article]  inRemote sensing > vol 13 n°10 (May 2021) . - n° 2003 Titre : A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration Type de document : Article/Communication Auteurs : Daeyong Jin, Auteur ; Eojin Lee, Auteur ; Kyonghwan Kwon, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2003 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] chlorophylle [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] Corée du sud [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] hydrodynamique [Termes descripteurs IGN] image COMS-GOCI Résumé : (auteur) In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a. Numéro de notice : A2021-417 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102003 date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97759 [article]

Evaluation of light pollution in global protected areas from 1992 to 2018 / Haowei Mu in Remote sensing, vol 13 n° 9 (May 2021)  

Public [article]  inRemote sensing > vol 13 n° 9 (May 2021) . - n° 1849 Titre : Evaluation of light pollution in global protected areas from 1992 to 2018 Type de document : Article/Communication Auteurs : Haowei Mu, Auteur ; Xuecao Li, Auteur ; Xiaoping Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 1849 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] aire protégée [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] carte thématique [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] image DMSP-OLS [Termes descripteurs IGN] image VIIRS [Termes descripteurs IGN] nuit [Termes descripteurs IGN] politique de conservation (biodiversité) [Termes descripteurs IGN] pollution lumineuse [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] urbanisation Résumé : (auteur) Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate change, the light pollution status in global PAs deserves attention for mitigation and adaptation. In this study, we developed a framework to evaluate the light pollution status in global PAs, using the global NTL time series data. First, we classified global PAs (30,624) into three pollution categories: non-polluted (5974), continuously polluted (8141), and discontinuously polluted (16,509), according to the time of occurrence of lit pixels in/around PAs from 1992 to 2018. Then, we explored the NTL intensity (e.g., digital numbers) and its trend in those polluted PAs and identified those hotspots of PAs at the global scale with consideration of global urbanization. Our study shows that global light pollution is mainly distributed within the range of 30°N and 60°N, including Europe, north America, and East Asia. Although the temporal trend of NTL intensity in global PAs is increasing, Japan and the United States of America (USA) have opposite trends due to the implementation of well-planned ecological conservation policies and declining population growth. For most polluted PAs, the lit pixels are close to their boundaries (i.e., less than 10 km), and the NTL in/around these lit areas has become stronger over the past decades. The identified hotspots of PAs (e.g., Europe, the USA, and East Asia) help support decisions on global biodiversity conservation, particularly with global urbanization and climate change. Numéro de notice : A2021-407 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/rs13091849 date de publication en ligne : 09/05/2021 En ligne : https://doi.org/10.3390/rs13091849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97723 [article]

Identifying urban neighborhoods with higher potential for social investment using GIS-FIS approach / Hossein Aghajani in Applied geomatics, vol 13 n° 1 (May 2021)  

Public [article]  inApplied geomatics > vol 13 n° 1 (May 2021) . - pp 1 - 13 Titre : Identifying urban neighborhoods with higher potential for social investment using GIS-FIS approach Type de document : Article/Communication Auteurs : Hossein Aghajani, Auteur ; Ali Alizadeh-Zoeram, Auteur Année de publication : 2021 Article en page(s) : pp 1 - 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] ArcGIS [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] Inférence floue [Termes descripteurs IGN] Iran [Termes descripteurs IGN] Matlab [Termes descripteurs IGN] politique sociale [Termes descripteurs IGN] processus d'analyse hiérarchique [Termes descripteurs IGN] quartier [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) Today, participation in social investment is a critical concern of urban management in metropolitan areas. To be effective in both public and private sectors, such participation requires an accurate knowledge of geographical distribution of resources and social needs in different parts of the city in order to better manage these investments. To this end, the present research aimed at providing an objective and tangible integrated approach to identify urban neighborhoods with high potential for social investment. The research area was neighborhoods located in Mashhad, Iran. Thirty-one criteria in three dimensions of empowerment, education, and culture were identified. Having determined, normalized, and weighted the values of each criteria in different neighborhoods using the analytic hierarchy process (AHP) method, the weight scores of each neighborhood in each dimension were added up by GIS approach and Arc GIS 10.1 software. Then, in order to conclude the overall status of social investment in different neighborhoods, a fuzzy inference system (FIS) was designed in the MATLAB 2014a environment according to experts’ opinions. Finally, based on the output of the designed system, the overall status of each neighborhood studied was determined in terms of social investment and neighborhoods with high potential for social investment were identified. The approach presented provides an insight for urban mangers, companies, and philanthropic investors to invest more effectively given the appropriate social investment opportunities. Numéro de notice : A2021-236 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-020-00317-4 date de publication en ligne : 27/05/2020 En ligne : https://doi.org/10.1007/s12518-020-00317-4 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97243 [article]

An analysis of the spatial and temporal distribution of large‐scale data production events in OpenStreetMap / A. Yair Grinberger in Transactions in GIS, Vol 25 n° 2 (April 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 2 (April 2021) . - pp 622 - 641 Titre : An analysis of the spatial and temporal distribution of large‐scale data production events in OpenStreetMap Type de document : Article/Communication Auteurs : A. Yair Grinberger, Auteur ; Moritz Schott, Auteur ; Martin Raifer, Auteur ; Alexander Zipf, Auteur Année de publication : 2021 Article en page(s) : pp 622 - 641 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données localisées libres [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] extraction de données [Termes descripteurs IGN] grande échelle [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] qualité des données Résumé : (Auteur) Organized mapping activities within OpenStreetMap frequently lead to the production of massive amounts of data over a short period. In this article we utilize a novel procedure to identify such large‐scale data production events in the history of OpenStreetMap and analyze their patterns. We find that events account for a significant share of OpenStreetMap data and that organizational practices have shifted over time towards local knowledge‐based events and well‐organized data imports. However, regions in the “Global South” remain dependent on remote mapping events, pointing to uneven geographies of representation. We also find that events are frequently followed by periods of increased activity, with the exact nature of effects depending on contextual elements such as previous events. These findings portray organized activities as a significant and unique component which requires consideration when using OpenStreetMap data and analyzing their quality. Numéro de notice : A2021-360 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12746 date de publication en ligne : 19/03/2021 En ligne : https://doi.org/10.1111/tgis.12746 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97624 [article]

Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine / Dong Liang in Remote sensing of environment, Vol 256 (April 2020)  

Public [article]  inRemote sensing of environment > Vol 256 (April 2020) . - n° 112318 Titre : Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine Type de document : Article/Communication Auteurs : Dong Liang, Auteur ; Huadong Guo, Auteur ; Lu Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112318 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] albedo [Termes descripteurs IGN] Antarctique [Termes descripteurs IGN] calotte glaciaire [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] coefficient de rétrodiffusion [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] fonte des glaces [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] montée du niveau de la mer [Termes descripteurs IGN] série temporelle Résumé : (auteur) The Antarctic ice sheet is an important mass of glacier ice. It is particularly sensitive to climate change, and the flow of Antarctica's inland glaciers into the sea, accelerated by collapsing ice shelves, threatens global sea level rise. The amount of snowmelt on the surface of the ice sheet is an important metric for accurately assessing surface material loss and albedo change, which affect the stability of the ice sheet. This study proposes a framework for quickly extracting time-series freeze-thaw information at the continental scale and 40 m resolution by taking advantage of the huge amount of synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites over the Antarctic, available for rapid processing on Google Earth Engine. Co-orbit normalization is used in the proposed framework to establish a unified standard of judgement by reducing the variations in the backscattering coefficient introduced by observation geometry, terrain fluctuations, and melt conditions between images acquired at different times. We implemented the framework to produce a massive dataset of both monthly freeze-thaw information over the Antarctic and higher temporal resolution freeze-thaw information for the Larsen C ice shelf from 2015 to 2019, with overall accuracies of 93% verified by a manual visual interpretation method and 84% evaluated from automatic weather station temperatures. Due to its effectiveness and robustness, the framework can be used to analyse the spatiotemporal distribution of snowmelt, the change in melt area, and anomalous melt events in Antarctica, especially those in Larsen C caused by foehn wind. Numéro de notice : A2021-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112318 date de publication en ligne : 10/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112318 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97117 [article]

Comparison of two parameter recovery methods for the transformation of Pinus sylvestris yield tables into a diameter distribution model / Francisco Mauro in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)  

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GIS-based spatial landslide distribution analysis of district Neelum, AJ&K, Pakistan / Shah Naseer in Natural Hazards, vol 106 n° 1 (March 2021)  

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Suitability assessment of urban land use in Dalian, China using PNN and GIS / Ziqian Kang in Natural Hazards, vol 106 n° 1 (March 2021)  

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What factors shape spatial distribution of biomass in riparian forests? Insights from a LiDAR survey over a large area / Leo Huylenbroeck in Forests, vol 12 n° 3 (March 2021)  

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Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model / Yizhuo Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling / Stefanos Georganos in Geocarto international, vol 36 n° 2 ([01/02/2021])  

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A density-based algorithm for the detection of individual trees from LiDAR data / Melissa Latella in Remote sensing, Vol 13 n° 2 (January-2 2021)  

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Chinese tourists in Nordic countries: An analysis of spatio-temporal behavior using geo-located travel blog data / Yunhao Zheng in Computers, Environment and Urban Systems, vol 85 (January 2021)  

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LANet: Local attention embedding to improve the semantic segmentation of remote sensing images / Lei Ding in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

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Spatial characterization and distribution modelling of Ensete ventricosum (wild and cultivated) in Ethiopia / Meron Awoke Eshetae in Geocarto international, vol 36 n° 1 ([01/01/2021])  

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