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WREP : A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops / Dong Li in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)
[article]
Titre : WREP : A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops Type de document : Article/Communication Auteurs : Dong Li, Auteur ; Tao Cheng, Auteur ; Kai Zhou, Auteur Année de publication : 2017 Article en page(s) : pp 103 - 117 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande rouge
[Termes IGN] canopée
[Termes IGN] céréales
[Termes IGN] cultures
[Termes IGN] ondelette
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] réflectance spectrale
[Termes IGN] teneur en chlorophylle des feuilles
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Red edge position (REP), defined as the wavelength of the inflexion point in the red edge region (680–760 nm) of the reflectance spectrum, has been widely used to estimate foliar chlorophyll content from reflectance spectra. A number of techniques have been developed for REP extraction in the past three decades, but most of them require data-specific parameterization and the consistence of their performance from leaf to canopy levels remains poorly understood. In this study, we propose a new technique (WREP) to extract REPs based on the application of continuous wavelet transform to reflectance spectra. The REP is determined by the zero-crossing wavelength in the red edge region of a wavelet transformed spectrum for a number of scales of wavelet decomposition. The new technique is simple to implement and requires no parameterization from the user as long as continuous wavelet transforms are applied to reflectance spectra. Its performance was evaluated for estimating leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) of cereal crops (i.e. rice and wheat) and compared with traditional techniques including linear interpolation, linear extrapolation, polynomial fitting and inverted Gaussian.
Our results demonstrated that WREP obtained the best estimation accuracy for both LCC and CCC as compared to traditional techniques. High scales of wavelet decomposition were favorable for the estimation of CCC and low scales for the estimation of LCC. The difference in optimal scale reveals the underlying mechanism of signature transfer from leaf to canopy levels. In addition, crop-specific models were required for the estimation of CCC over the full range. However, a common model could be built with the REPs extracted with Scale 5 of the WREP technique for wheat and rice crops when CCC was less than 2 g/m2 (R2 = 0.73, RMSE = 0.26 g/m2). This insensitivity of WREP to crop type indicates the potential for aerial mapping of chlorophyll content between growth seasons of cereal crops. The new REP extraction technique provides us a new insight for understanding the spectral changes in the red edge region in response to chlorophyll variation from leaf to canopy levels.Numéro de notice : A2017-348 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.04.024 En ligne : https://dx.doi.org/10.1016/j.isprsjprs.2017.04.024 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85616
in ISPRS Journal of photogrammetry and remote sensing > vol 129 (July 2017) . - pp 103 - 117[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017073 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017072 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis / Arati Paul in Geocarto international, vol 32 n° 6 (June 2017)
[article]
Titre : Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis Type de document : Article/Communication Auteurs : Arati Paul, Auteur ; V.M. Chowdary, Auteur ; Y.K. Srivastava, Auteur ; Debsunder Dutta, Auteur ; J.R. Sharma, Auteur Année de publication : 2017 Article en page(s) : pp 640 - 654 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Calcutta
[Termes IGN] densité spectrale de puissance
[Termes IGN] détection de changement
[Termes IGN] détection de contours
[Termes IGN] Google Earth
[Termes IGN] image Cartosat-1
[Termes IGN] image IRS-LISS
[Termes IGN] occupation du sol
[Termes IGN] seuillage d'imageRésumé : (Auteur) Automatic change detection of land cover features using high-resolution satellite images, is a challenging problem in the field of intelligent remote sensing data interpretation, and is becoming more and more effective for its applications viz. urban planning and monitoring, disaster assessment etc. In the present study, a change in detection approach based on the image morphology that analyses change in the local image grids is proposed. In this approach, edges from both the images are extracted and grid wise comparison is made by probabilistic thresholding and power spectral density analysis for identifying change area. One of the advantages of the proposed methodology is that the temporal images used in the change analysis need not be radiometrically corrected as analysis is based on edge extractions. The grid-based analysis further reduces the error, which might have been introduced by image mis-registration. The proposed methodology is validated by finding the temporal changes in the linear land cover features in parts of Kolkata city, India using three different image data-sets from LISS IV, Cartosat-1 and Google earth having varied spatial resolutions of 5.8 m, 2.5 m and about 1 m, respectively. The overall accuracy in identifying changes is found to be 64.82, 73.86 and 80.93% for LISS IV, Cartosat-1 and Google earth data-set, respectively. Numéro de notice : A2017-275 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1167966 Date de publication en ligne : 01/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1167966 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85304
in Geocarto international > vol 32 n° 6 (June 2017) . - pp 640 - 654[article]Effects of urban tree canopy loss on land surface temperature magnitude and timing / Arthur Elmes in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
[article]
Titre : Effects of urban tree canopy loss on land surface temperature magnitude and timing Type de document : Article/Communication Auteurs : Arthur Elmes, Auteur ; John Rogan, Auteur ; Christopher Williams, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 338 - 353 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] arbre urbain
[Termes IGN] densité de la végétation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étude d'impact
[Termes IGN] ilot thermique urbain
[Termes IGN] image Worldview
[Termes IGN] Massachusetts (Etats-Unis)
[Termes IGN] température de surface
[Termes IGN] variation saisonnière
[Termes IGN] zone urbaineRésumé : (Auteur) Urban Tree Canopy (UTC) plays an important role in moderating the Surface Urban Heat Island (SUHI) effect, which poses threats to human health due to substantially increased temperatures relative to rural areas. UTC coverage is associated with reduced urban temperatures, and therefore benefits both human health and reducing energy use in cities. Measurement of this relationship relies on accurate, fine spatial resolution UTC mapping, and on time series analysis of Land Surface Temperatures (LST). The City of Worcester, Massachusetts underwent extensive UTC loss and gain during the relatively brief period from 2008 to 2015, providing a natural experiment to measure the UTC/LST relationship. This paper consists of two elements to this end. First, it presents methods to map UTC in urban and suburban locations at fine spatial resolution (∼0.5 m) using image segmentation of a fused Lidar/WorldView-2 dataset, in order to show UTC change over time. Second, the areas of UTC change are used to explore changes in LST magnitude and seasonal variability using a time series of all available Landsat data for the study area over the eight-year period from 2007 to 2015. Fractional UTC change per unit area was determined using fine resolution UTC maps for 2008, 2010, and 2015, covering the period of large-scale tree loss and subsequent planting. LST changes were measured across a series of net UTC change bins, providing a relationship between UTC net change and LST trend. LST was analyzed for both monotonic trends over time and changes to seasonal magnitude and timing, using Theil-Sen slopes and Seasonal Trend Analysis (STA), respectively. The largest magnitudes of UTC loss occurred in residential neighborhoods, causing increased exposure of impervious (road) and pervious (grass) surfaces. Net UTC loss showed higher monotonic increases in LST than persistence and gain areas. STA indicated that net UTC loss was associated greater difference between 2008 and 2015 seasonal temperature curves than persistence areas, and also larger peak LST values, with peak increases ranging from 1 to 6 °C. Timing of summer warm period was extended in UTC loss areas by up to 15 days. UTC gain provided moderate LST mitigation, with lower monotonic trends, lower peak temperatures, and smaller seasonal curve changes than both persistence and loss locations. This study shows that urban trees mitigate the magnitude and timing of the surface urban heat island effect, even in suburban areas with less proportional impervious coverage than the dense urban areas traditionally associated with SUHI. Trees can therefore be seen as an effective means of offsetting the energy-intensive urban heat island effect. Numéro de notice : A2017-338 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.04.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.04.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85506
in ISPRS Journal of photogrammetry and remote sensing > vol 128 (June 2017) . - pp 338 - 353[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017063 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017062 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery / Gabriel Navarro in International journal of applied Earth observation and geoinformation, vol 58 (June 2017)
[article]
Titre : Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery Type de document : Article/Communication Auteurs : Gabriel Navarro, Auteur ; Isabel Caballero, Auteur ; Gustavo Silva, Auteur ; Pedro-Cecilio Parra, Auteur ; Águeda Vázquez, Auteur ; Rui Caldeira, Auteur Année de publication : 2017 Article en page(s) : pp 97 - 106 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] correction atmosphérique
[Termes IGN] image Sentinel-MSI
[Termes IGN] incendie de forêt
[Termes IGN] indice de végétation
[Termes IGN] Madère, île de
[Termes IGN] signature spectrale
[Termes IGN] surveillance de la végétationRésumé : (auteur) A forest fire started on August 8th, 2016 in several places on Madeira Island causing damage and casualties. As of August 10th the local media had reported the death of three people, over 200 people injured, over 950 habitants evacuated, and 50 houses damaged. This study presents the preliminary results of the assessment of several spectral indices to evaluate the burn severity of Madeira fires during August 2016. These spectral indices were calculated using the new European satellite Sentinel-2A launched in June 2015. The study confirmed the advantages of several spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVIreXn) using red-edge spectral bands to assess the post-fire conditions. Results showed high correlation between NDVI, GNDVI, NBR and NDVIre1n spectral indices and the analysis performed by Copernicus Emergency Management Service (EMSR175), considered as the reference truth. Regarding the red-edge spectral indices, the NDVIre1n (using band B5, 705 nm) presented better results compared with B6 (740 nm) and B7 (783 nm) bands. These preliminary results allow us to assume that Sentinel-2 will be a valuable tool for post-fire monitoring. In the future, the two twin Sentinel-2 satellites will offer global coverage of the Madeira Archipelago every five days, therefore allowing the simultaneous study of the evolution of the burnt area and reforestation information with high spatial (up to 10 m) and temporal resolution (5 days). Numéro de notice : A2017-366 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2017.02.003 En ligne : https://doi.org/10.1016/j.jag.2017.02.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85794
in International journal of applied Earth observation and geoinformation > vol 58 (June 2017) . - pp 97 - 106[article]Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
[article]
Titre : Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms Type de document : Article/Communication Auteurs : Lien T.H. Pham, Auteur Année de publication : 2017 Article en page(s) : pp 86 - 97 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] analyse spectrale
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse forestière
[Termes IGN] carte thématique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de changement
[Termes IGN] image SPOT 4
[Termes IGN] image SPOT 5
[Termes IGN] mangrove
[Termes IGN] surveillance de la végétation
[Termes IGN] teneur en carbone
[Termes IGN] texture d'image
[Termes IGN] Viet NamRésumé : (Auteur) Mangrove forests are well-known for their provision of ecosystem services and capacity to reduce carbon dioxide concentrations in the atmosphere. Mapping and quantifying mangrove biomass is useful for the effective management of these forests and maximizing their ecosystem service performance. The objectives of this research were to model, map, and analyse the biomass change between 2000 and 2011 of mangrove forests in the Cangio region in Vietnam. SPOT 4 and 5 images were used in conjunction with object-based image analysis and machine learning algorithms. The study area included natural and planted mangroves of diverse species. After image preparation, three different mangrove associations were identified using two levels of image segmentation followed by a Support Vector Machine classifier and a range of spectral, texture and GIS information for classification. The overall classification accuracy for the 2000 and 2011 images were 77.1% and 82.9%, respectively. Random Forest regression algorithms were then used for modelling and mapping biomass. The model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy (R2adj = 0.73). Among the different variables, vegetation association type was the most important variable identified by the Random Forest model. Based on the biomass maps generated from the Random Forest, total biomass in the Cangio mangrove forest increased by 820,136 tons over this period, although this change varied between the three different mangrove associations. Numéro de notice : A2017-332 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.03.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.03.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85485
in ISPRS Journal of photogrammetry and remote sensing > vol 128 (June 2017) . - pp 86 - 97[article]Réservation
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