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Assessing environmental impacts of urban growth using remote sensing / John C. Trinder in Geo-spatial Information Science, vol 23 n° 1 (March 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 1 (March 2020) . - pp 20 - 39 Titre : Assessing environmental impacts of urban growth using remote sensing Type de document : Article/Communication Auteurs : John C. Trinder, Auteur ; Qingxiang Liu, Auteur Année de publication : 2020 Article en page(s) : pp 20 - 39 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] développement durable [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] impact sur l'environnement [Termes descripteurs IGN] multiple endmember spectral mixture analysis [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] Sydney (Nouvelle-Galles du Sud) [Termes descripteurs IGN] Wuhan (Chine) Résumé : (auteur) This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities. Numéro de notice : A2020-162 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2019.1710438 date de publication en ligne : 21/01/2020 En ligne : https://doi.org/10.1080/10095020.2019.1710438 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94822 [article]

Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 102 - 118 Titre : Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data Type de document : Article/Communication Auteurs : Alfonso Fernández-Manso, Auteur ; Carmen Quintano, Auteur ; Dar A. Roberts, Auteur Année de publication : 2019 Article en page(s) : pp 102 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] entropie [Termes descripteurs IGN] forêt méditerranéenne [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] incendie de forêt [Termes descripteurs IGN] multiple endmember spectral mixture analysis Résumé : (Auteur) All ecosystems and in particular ecosystems in Mediterranean climates are affected by fires. Knowledge of the drivers that most influence burn severity patterns as well an accurate map of post-fire effects are key tools for forest managers in order to plan an adequate post-fire response. Remote sensing data are becoming an indispensable instrument to reach both objectives. This work explores the relative influence of pre-fire vegetation structure and topography on burn severity compared to the impact of post-fire damage level, and evaluates the utility of the Maximum Entropy (MaxEnt) classifier trained with post-fire EO-1 Hyperion data and pre-fire LiDAR to model three levels of burn severity at high accuracy. We analyzed a large fire in central-eastern Spain, which occurred on 16–19 June 2016 in a maquis shrubland and Pinus halepensis forested area. Post-fire hyperspectral Hyperion data were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA) and five fraction images were generated: char, green vegetation (GV), non-photosynthetic vegetation, soil (NPVS) and shade. Metrics associated with vegetation structure were calculated from pre-fire LiDAR. Post-fire MESMA char fraction image, pre-fire structural metrics and topographic variables acted as inputs to MaxEnt, which built a model and generated as output a suitability surface for each burn severity level. The percentage of contribution of the different biophysical variables to the MaxEnt model depended on the burn severity level (LiDAR-derived metrics had a greater contribution at the low burn severity level), but MaxEnt identified the char fraction image as the highest contributor to the model for all three burn severity levels. The present study demonstrates the validity of MaxEnt as one-class classifier to model burn severity accurately in Mediterranean countries, when trained with post-fire hyperspectral Hyperion data and pre-fire LiDAR. Numéro de notice : A2019-313 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.003 date de publication en ligne : 14/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93339 [article]

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Hyperspectral analysis of soil polluted with four types of hydrocarbons / Laura A. Reséndez-Hernández in Geocarto international, vol 34 n° 9 ([15/06/2019])  

Public [article]  inGeocarto international > vol 34 n° 9 [15/06/2019] . - pp 925 - 942 Titre : Hyperspectral analysis of soil polluted with four types of hydrocarbons Type de document : Article/Communication Auteurs : Laura A. Reséndez-Hernández, Auteur ; Daniel Prudencio-Csapek, Auteur ; Diego Fabian Lozano Garcia, Auteur Année de publication : 2019 Article en page(s) : pp 925 - 942 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] classification Spectral angle mapper [Termes descripteurs IGN] hydrocarbure [Termes descripteurs IGN] multiple endmember spectral mixture analysis [Termes descripteurs IGN] pétrole [Termes descripteurs IGN] pollution des sols [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] spectroradiomètre Résumé : (auteur) In this study, a high spectral resolution GER-2600 spectroradiometer was used to obtain the spectral data of soil samples that were polluted with four different types of petroleum–hydrocarbons products: Diesel, Gasoline, Crude Oil and Fuel Oil. The polluted soil samples were prepared in the laboratory at five concentrations levels: unpolluted soil, 2500, 100,000, 250,000 ppm and pure pollutant. Spectral data were pre-processed and then analysed with various approaches: Principal Components Transformation and ANOVA, Spectral Angle Mapper (SAM), Hydrocarbon Index (HI) and Spectral Mixture Analysis (SMA). The results showed that it was possible to determine the different spectral response between clean soil and some of the polluted soils: crude oil at concentrations higher than 100,000 ppm were the easiest to recognize; while samples polluted with gasoline at concentrations below 250,000 ppm were the most difficult to distinguish from non-polluted samples. Numéro de notice : A2019-516 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1451921 date de publication en ligne : 28/03/2019 En ligne : https://doi.org/10.1080/10106049.2018.1451921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93870 [article]

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Influence of tree species complexity on discrimination performance of vegetation indices / Azadeh Ghiyamat in European journal of remote sensing, vol 49 (2016)  

Public [article]  inEuropean journal of remote sensing > vol 49 (2016) . - pp 15 - 37 Titre : Influence of tree species complexity on discrimination performance of vegetation indices Type de document : Article/Communication Auteurs : Azadeh Ghiyamat, Auteur ; Helmi Zulhaidi Mohd Shafri, Auteur ; Abdul Rashid Mohamed Shariff, Auteur Année de publication : 2016 Article en page(s) : pp 15 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] information complexe [Termes descripteurs IGN] multiple endmember spectral mixture analysis [Termes descripteurs IGN] Pinus nigra corsicana [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] test de performance Résumé : (auteur) Performance of different vegetation indices (VIs) in combination with single- and multipleendmember (SEM and MEM) for discriminating Corsican and Scots pines with different ages and Broadleaves tree species is demonstrated by using an airborne hyperspectral data. The analysis is performed in three different complexity levels. The results show by increasing tree species complexity, overall accuracy significantly reduced. An overall accuracy up to 90% is obtained from the first category with the least complexity; however, it is reduced to 55% in the third category with the highest complexity. By employing MEM, performance of normalized difference vegetation index (NDVI) is increased by 10%. Numéro de notice : A2016-834 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article En ligne : http://dx.doi.org/10.5721/EuJRS20164902 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82723 [article]

Leveraging in-scene spectra for vegetation species discrimination with MESMA-MDA / Brian D. Bue in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 108 (October 2015) . - pp 33 - 48 Titre : Leveraging in-scene spectra for vegetation species discrimination with MESMA-MDA Type de document : Article/Communication Auteurs : Brian D. Bue, Auteur ; David R. Thompson, Auteur ; R. Glenn Sellar, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 33 - 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] multiple endmember spectral mixture analysis [Termes descripteurs IGN] réflectance végétale [Termes descripteurs IGN] signature spectrale [Termes descripteurs IGN] spectromètre imageur Résumé : (auteur) We describe an approach to improve Multiple Endmember Spectral Mixture Analysis (MESMA) results for applications involving discrimination among spectrally-similar species, and commonly occur in multispectral and hyperspectral vegetation remote sensing studies. Such applications are inherently difficult, due to the high degree of similarity between distinct species, coupled with potentially high intra-species variability caused by factors such as growing conditions, canopy structure, ambient illumination, or substrate characteristics. We describe a method to map spectra to a feature space where distinctions between plant species are emphasized using a transformation based on Multiclass Discriminant Analysis. We compute this transformation using groups of pixels that represent individual plant canopies similar to the endmembers in MESMA’s spectral library, and describe a technique to automatically select such spectra from a given image. Compared to conventional MESMA, and also to several alternative MESMA formulations, we observe up to twofold increases in accuracy, along with a factor of ten reduction in computation time using our MESMA approach in several species discrimination applications. We demonstrate the effectiveness of our approach for agricultural species discrimination applications using spectra captured by two different imaging spectrometers. Numéro de notice : A2015-850 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79220 [article]

A novel negative abundance‐oriented hyperspectral unmixing algorithm / Rubén Marrero in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)

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Estimating fractional land cover in semi-arid central Kalahari: the impact of mapping method (spectral unmixing vs. object-based image analysis) and vegetation morphology / Niti B. Mishra in Geocarto international, vol 29 n° 7 - 8 (November - December 2014)  

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The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques / Chengbin Deng in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)

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Mapping and assessing of urban impervious areas using multiple endmember spectral mixture analysis: a case study in the city of Tampa, Florida / Fenqing Weng in Geocarto international, vol 28 n° 7-8 (November - December 2013)  

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Piecewise convex multiple-model endmember detection and spectral unmixing / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)  

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Multiple endmember unmixing of CHRIS/Proba imagery for mapping impervious surfaces in urban and suburban environments / Luca Demarchi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)  

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Mapping impervious surfaces from superresolution enhanced CHRIS/Proba imagery using multiple endmember unmixing / Luca Demarchi in ISPRS Journal of photogrammetry and remote sensing, vol 72 (August 2012)

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A pre-screened and normalized multiple endmember spectral mixture analysis [MESMA] for mapping impervious surface area in Lake Kasumigaura Basin, Japan / F. Yang in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 5 (September - October 2010)

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Application of multiple endmember spectral mixture analysis (MESMA) to AVIRIS imagery for coastal salt marsh mapping: a case study in China Camp, CA, USA / L. Li in International Journal of Remote Sensing IJRS, vol 26 n° 23 (December 2005)

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A comparison of error metrics and constraints for multiple endmember spectral analysis and spectral angle mapper / P.E. Dennison in Remote sensing of environment, vol 93 n° 3 (15/11/2004)

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Measuring the physical composition of urban morphology using multiple endmember spectral mixture models / T. Rashed in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)

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