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Coniferous and broad-leaved forest distinguishing using L-band polarimetric SAR data / Fang Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7487 - 7499 Titre : Coniferous and broad-leaved forest distinguishing using L-band polarimetric SAR data Type de document : Article/Communication Auteurs : Fang Shang, Auteur ; Taiga Saito, Auteur ; Saya Ohi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7487 - 7499 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande L [Termes IGN] détection de changement [Termes IGN] détection de cible [Termes IGN] distribution spatiale [Termes IGN] forêt de feuillus [Termes IGN] image ALOS-PALSAR [Termes IGN] image radar moirée [Termes IGN] Japon [Termes IGN] Pinophyta [Termes IGN] polarimétrie radar Résumé : (auteur) This article proposes a coniferous and broad-leaved forest distinguishing method using L-band polarimetric SAR data based on the structure-orientation parameter. The structure-orientation parameter is one of the averaged Stokes vector-based discriminators which is sensitive to the composition of equivalent horizontal and vertical structures. In the proposed method, the structure-orientation parameters is compensated by employing the scattered power information to remove the influence of the topography. The final distinguishing result is generated based on the statistical feature of the compensated parameters. The experiments using several sets of ALOS2-PALSAR2 level 1.1 data prove that the proposed method has high performance for forest-type distinguishing. Numéro de notice : A2021-648 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3032468 date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3032468 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98355 [article]

Remote sensing method for extracting topographic information on tidal flats using spatial distribution features / Yang Lijun in Marine geodesy, vol 44 n° 5 (September 2021)  

Public [article]  inMarine geodesy > vol 44 n° 5 (September 2021) . - pp 408 - 431 Titre : Remote sensing method for extracting topographic information on tidal flats using spatial distribution features Type de document : Article/Communication Auteurs : Yang Lijun, Auteur ; Xiao Yao, Auteur ; Jie Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 408 - 431 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] alluvion [Termes IGN] arpentage [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] données topographiques [Termes IGN] extraction de données [Termes IGN] Fleuve bleu (Chine) [Termes IGN] géomorphologie locale [Termes IGN] image Landsat [Termes IGN] marée océanique [Termes IGN] modèle numérique de surface [Termes IGN] positionnement cinématique en temps réel [Termes IGN] Shanghai (Chine) [Termes IGN] vase Résumé : (Auteur) A remote sensing method combining remote sensing and ground surveying is proposed to extract tidal flat topographic information via the spatial distribution characteristics of tidal flat surface features. Based on the eastern Chongming beach of the Yangtze Estuary and Landsat-5 satellite images, this study identifies the spatial distribution characteristics of tidal flat features using field-based RTK data and spectral data. The remote sensing method for extracting the geometric and physical characteristics of linear and surface geographical elements on tidal flats and the elevation assignment method are discussed. The effectiveness of this method is verified by the quality of the resultant tidal flat DEM. The results show that the use of spatial distribution features in remote sensing images can provide rich topographic information. The DEM results have an accuracy of 0.16 m, are in line with the basic topographic patterns of tidal flats, and can describe local microscale geomorphic features. This technique solves the problem of a single topographic information source in current remote sensing measurement methods and provides technical support for detecting dynamic changes in coastal zones by using remote sensing technology. Numéro de notice : A2021-577 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2021.1925791 date de publication en ligne : 04/06/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925791 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98230 [article]

Unsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, Vol 36 n° 15 ([10/08/2021])  

Public [article]  inGeocarto international > Vol 36 n° 15 [10/08/2021] . - pp 1709 - 1731 Titre : Unsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification Type de document : Article/Communication Auteurs : Divyesh Varade, Auteur ; Ajay K. Maurya, Auteur ; Onkar Dikshit, Auteur Année de publication : 2021 Article en page(s) : pp 1709 - 1731 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] bande spectrale [Termes IGN] classification floue [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par nuées dynamiques [Termes IGN] distribution spatiale [Termes IGN] entropie [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] Inde [Termes IGN] manteau neigeux [Termes IGN] neige [Termes IGN] réflectance spectrale Résumé : (auteur) Information on the spatial and temporal extent of snow cover distribution is a significant input in hydrological processes and climate models. Although hyperspectral remote sensing provides significant opportunities in the assessment of land cover, the applications of such data are limited in the snow-covered alpine regions. A major issue with hyperspectral data is the larger dimensionality. Feature selection methods are often used to derive the most informative subset of bands from the hyperspectral data. In this study, a band selection technique is proposed which utilizes the mutual information (MI) between hyperspectral bands and a reference band. The first principal component of the hyperspectral data is selected as the reference band. Two variants of this approach are proposed involving preclustering of bands using: (1) the k-means and (2) the fuzzy k-means algorithms. The MI is derived from weighted entropy of the hyperspectral band and the reference band. The weights are computed from the cluster distance ratio and the cluster membership function for the k-means and fuzzy k-means algorithm, respectively. The selected bands were classified using random forest classifier. The proposed methods are evaluated with four datasets, two Hyperion datasets corresponding to the geographical locations of Dhundi and Solang in India, corresponding to snow covered terrain and two benchmark AVIRIS datasets of Indian Pines and Salinas. The average classification accuracy (0.995 and 0.721 for Dhundi and Solang datasets, respectively) for the proposed approach were observed to be better as compared with those from other state of the art techniques. Numéro de notice : A2021-568 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1665717 date de publication en ligne : 18/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1665717 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98183 [article]

Mapping sandy land using the new sand differential emissivity index from thermal infrared emissivity data / Shanshan Chen in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5464 - 5478 Titre : Mapping sandy land using the new sand differential emissivity index from thermal infrared emissivity data Type de document : Article/Communication Auteurs : Shanshan Chen, Auteur ; Huazhong Ren, Auteur ; Rongyuan Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 5464 - 5478 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] désertification [Termes IGN] détection de changement [Termes IGN] distribution spatiale [Termes IGN] ensablement [Termes IGN] image TASI [Termes IGN] image Terra-ASTER [Termes IGN] image thermique [Termes IGN] sable [Termes IGN] Sinkiang (Chine) Résumé : (auteur) On the basis of the spectral shape of thermal infrared (TIR) emissivity for sandy land, a remote sensing sand index called the sand differential emissivity index (SDEI) is proposed in this article to simply and conveniently detect sandy land over large areas. The SDEI is evaluated on ground, airborne, and spaceborne thermal emissivity data, and it shows good characterization of sandy land and performs better in sandy land identification than two previous indices. The SDEI was also evaluated in the transition zones of China’s four mega-sandy lands and was applied to long-term land surface emissivity to obtain the spatial distribution and variation in China’s sandy land from 2000 to 2016. The findings showed that a mean accuracy of 96% and a mean kappa coefficient of 0.83 were obtained in the transition zones, and the sandy land in the transition zone exhibited a decreasing trend over the past 17 years and a significant decline in the Mu Us sandy land. Meanwhile, the sandy land area in China decreased by 3.6×104 km 2 (1.53%) by the end of 2016 compared with that in early 2000. Numéro de notice : A2021-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3022772 date de publication en ligne : 25/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3022772 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97977 [article]

Role of maximum entropy and citizen science to study habitat suitability of jacobin cuckoo in different climate change scenarios / Priyinka Singh in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 7 (July 2021) . - n° 463 Titre : Role of maximum entropy and citizen science to study habitat suitability of jacobin cuckoo in different climate change scenarios Type de document : Article/Communication Auteurs : Priyinka Singh, Auteur ; Sameer Saran, Auteur ; Sultan Kocaman, Auteur Année de publication : 2021 Article en page(s) : n° 463 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] Aves [Termes IGN] changement climatique [Termes IGN] distribution spatiale [Termes IGN] données spatiotemporelles [Termes IGN] entropie maximale [Termes IGN] habitat animal [Termes IGN] migration animale [Termes IGN] mousson [Termes IGN] science citoyenne Résumé : (auteur) Recent advancements in spatial modelling and mapping methods have opened up new horizons for monitoring the migration of bird species, which have been altered due to the climate change. The rise of citizen science has also aided the spatiotemporal data collection with associated attributes. The biodiversity data from citizen observatories can be employed in machine learning algorithms for predicting suitable environmental conditions for species’ survival and their future migration behaviours. In this study, different environmental variables effective in birds’ migrations were analysed, and their habitat suitability was assessed for future understanding of their responses in different climate change scenarios. The Jacobin cuckoo (Clamator jacobinus) was selected as the subject species, since their arrival to India has been traditionally considered as a sign for the start of the Indian monsoon season. For suitability predictions in current and future scenarios, maximum entropy (Maxent) modelling was carried out with environmental variables and species occurrences observed in India and Africa. For modelling, the correlation test was performed on the environmental variables (bioclimatic, precipitation, minimum temperature, maximum temperature, precipitation, wind and elevation). The results showed that precipitation-related variables played a significant role in suitability, and through reclassified habitat suitability maps, it was observed that the suitable areas of India and Africa might decrease in future climatic scenarios (SSPs 2.6, 4.5, 7.0 and 8.5) of 2030 and 2050. In addition, the suitability and unsuitability areas were calculated (in km2) to observe the subtle changes in the ecosystem. Such climate change studies can support biodiversity research and improve the agricultural economy. Numéro de notice : A2021-545 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10070463 date de publication en ligne : 06/07/2021 En ligne : https://doi.org/10.3390/ijgi10070463 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98046 [article]

Simulating multi-exit evacuation using deep reinforcement learning / Dong Xu in Transactions in GIS, Vol 25 n° 3 (June 2021)  

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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)  

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

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

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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)  

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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)  

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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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