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Mapping urban growth of the capital city of Honduras from Landsat data using the impervious surface fraction algorithm / Nguyen-Thanh Son in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)
[article]
Titre : Mapping urban growth of the capital city of Honduras from Landsat data using the impervious surface fraction algorithm Type de document : Article/Communication Auteurs : Nguyen-Thanh Son, Auteur ; Chi-Farn Chen, Auteur ; Cheng-Ru Chen, Auteur ; Shou-Hao Chiang, Auteur Année de publication : 2016 Article en page(s) : pp 328 - 341 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] croissance urbaine
[Termes IGN] Honduras
[Termes IGN] image Landsat
[Termes IGN] surface imperméable
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] urbanisationRésumé : (Auteur) This study developed an impervious surface fraction algorithm (ISFA) for automatic mapping of urban areas from Landsat data. We processed the data for 2001 and 2014 to trace the urbanization of Tegucigalpa, the capital city of Honduras, using a four-step procedure: (1) data pre-processing to perform image reflectance normalization, (2) quantification of impervious surface area (ISA) using ISFA, (3) accuracy assessment of mapping results and (4) change analysis of urban growth. The mapping results compared with the ground reference data confirmed the validity of ISFA for automatic delineation of ISA in the study region. The overall accuracy and Kappa coefficient achieved for 2001 were 92.8% and 0.86, while the values for 2014 were 91.8% and 0.84, respectively. The results of change detection between the classification maps indicated that ISA increased approximately 1956.7 ha from 2001 to 2014, mainly attributing to the increase of the city’s population. Numéro de notice : A2016-153 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047469 Date de publication en ligne : 22/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047469 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80390
in Geocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 328 - 341[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016021 RAB Revue Centre de documentation En réserve L003 Disponible Pan-tropical hinterland forests: mapping minimally disturbed forests / Alexandra Tyukavina in Global ecology and biogeography, vol 25 n° 2 (February 2016)
[article]
Titre : Pan-tropical hinterland forests: mapping minimally disturbed forests Type de document : Article/Communication Auteurs : Alexandra Tyukavina, Auteur ; Matthew C. Hansen, Auteur ; P.V. Potapov, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 151 - 163 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte forestière
[Termes IGN] dégradation de la flore
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tropicale
[Termes IGN] image LandsatRésumé : (auteur) Aim : Tropical forest degradation is a significant source of carbon emissions due to selective logging, fragmentation and other disturbance factors. However, methods for mapping and monitoring pan-tropical forest degradation are still in their infancy. Here we present a new and automated approach to differentiate forests likely to be affected by degradation dynamics from more structurally intact forests, referred to as hinterland forests.
Location : Pan-tropical.
Methods : Inputs required for hinterland forest mapping include the extent of the initial forest cover and subsequent forest cover loss data, in this case global-scale Landsat-derived tree cover and stand-replacement disturbance maps. User-defined parameters employed to generate the extent and change of hinterland forest include: (1) minimum size of hinterland forest patch, (2) minimum corridor width, (3) distance from disturbance, and (4) extant history.
Results : Hinterland forest extent was mapped using forest cover loss data from 2000 to 2012 and hinterland forest loss was quantified from 2007 to 2013. Lidar-modelled forest height data were shown to be different within and outside hinterland forests, demonstrating the biophysical basis of the hinterland concept in discriminating likely degradation. Overall, hinterland forests experienced an 18% decline from 2007 to 2013. Regional variation in hinterland forest extent and loss was high. Data on 2013 pan-tropical hinterland forest extent can be downloaded from http://glad.geog.umd.edu/hinterland/index.html and viewed online at http://earthenginepartners.appspot.com/science-2013-global-forest.
Main conclusions : The largest extent of hinterland forests and of hinterland forest loss was found in Latin America, followed by Africa and Southeast Asia, respectively. The highest proportional loss of hinterland forest occurred in Southeast Asia, followed by Africa and Latin America, respectively. Nearly 95% of all 2013 hinterland forests were found in 17 of the 69 tropical forest countries studied. The extent and loss of hinterland forest can be an input to national monitoring and management programmes focused on forest carbon stocks, biodiversity conservation and other ecosystem services.Numéro de notice : A2016--199 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1111/geb.12394 En ligne : http://dx.doi.org/10.1111/geb.12394 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80340
in Global ecology and biogeography > vol 25 n° 2 (February 2016) . - pp 151 - 163[article]An assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series / Charlotte Pelletier (2016)
Titre : An assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Gérard Dedieu, Auteur ; Nicolas Champion , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2016 Conférence : IGARSS 2016, International Geoscience And Remote Sensing Symposium 10/07/2016 15/07/2016 Pékin Chine Proceedings IEEE Importance : pp 3338 - 3341 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image à haute résolution
[Termes IGN] image Landsat-8
[Termes IGN] image SPOT 4
[Termes IGN] série temporelleRésumé : (auteur) New high resolution Satellite Image Time Series (SITS) are becoming crucial to land cover mapping over large areas. Their high temporal resolution will allow to better depict scene dynamics. However, it will also increase the amount of data to process. The classification of these data involves therefore new challenges such as: (1) selecting the best feature set to use as input data, (2) dealing with data variability coming from landscape diversity, and (3) establishing the robustness of existing classifiers over large areas. This work aims at addressing these questions through three different studies. Experimental results are obtained by using SPOT-4 and Landsat-8 SITS. Numéro de notice : C2016-034 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2016.7729863 Date de publication en ligne : 03/11/2016 En ligne : https://doi.org/10.1109/IGARSS.2016.7729863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91791 Automatic detection of clouds and shadows using high resolution satellite image time series / Nicolas Champion (2016)
Titre : Automatic detection of clouds and shadows using high resolution satellite image time series Type de document : Article/Communication Auteurs : Nicolas Champion , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2016 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3 Projets : 1-Pas de projet / Conférence : ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3 Importance : pp 475 - 479 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'ombre
[Termes IGN] détection des nuages
[Termes IGN] image Landsat-8
[Termes IGN] image Pléiades-HR
[Termes IGN] orthoimage
[Termes IGN] réflectance de surface
[Termes IGN] séquence d'images
[Termes IGN] série temporelleRésumé : (auteur) Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences. Numéro de notice : C2016-038 Affiliation des auteurs : IGN (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLI-B3-475-2016 Date de publication en ligne : 09/06/2016 En ligne : http://dx.doi.org/10.5194/isprs-archives-XLI-B3-475-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91849 A Bayesian network-based method to alleviate the ill-posed inverse problem: A case study on leaf area index and canopy water content retrieval / Xingwen Quan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)
[article]
Titre : A Bayesian network-based method to alleviate the ill-posed inverse problem: A case study on leaf area index and canopy water content retrieval Type de document : Article/Communication Auteurs : Xingwen Quan, Auteur ; Binbin He, Auteur ; Xing Li, Auteur Année de publication : 2015 Article en page(s) : pp 6507 - 6517 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] appariement d'images
[Termes IGN] image Landsat-8
[Termes IGN] Leaf Area Index
[Termes IGN] probabilité
[Termes IGN] problème inverse
[Termes IGN] réseau bayesien
[Termes IGN] teneur en eau de la végétationRésumé : (auteur) Retrieval of vegetation parameters from remotely sensed data using a radiative transfer model is generally hampered by the ill-posed inverse problem, which dramatically decreases the precision level of retrieved parameters. The purpose of this study was to use a Bayesian network-based method to allow the alleviation of the ill-posed inverse problem. This was achieved by introducing the correlations between the model free parameters into their prior joint probability distribution (PJPD), allowing the reduction of the probabilities of unrealistic combinations. Three sampling strategies intended to design three types of PJPDs that considered different correlations (represented by a correlation matrix) were presented. They were multivariate uniform distribution composed by independent free parameters, multivariate uniform distribution based on a simple correlation matrix, and multivariate Gaussian distribution based on a complicated correlation matrix, respectively. A case study of the presented method to retrieve leaf area index (LAI) and canopy water content (CWC) using the PROSAIL_5B (PROSPECT-5 + 4SAIL) model from Landsat 8 products was implemented. Results indicate that the presented method greatly improves the precision level of target parameters, with the coefficient of determination R2 of 0.69, 0.77, and 0.82 and root-mean-square error (RMSE) of 0.55, 0.51, and 0.44 m2 · m-2 for LAI and R2 = 0.68, 0.78, and 0.84 and RMSE = 230, 198, and 166 g · m-2 for CWC, respectively. Hence, the ill-posed inverse problem can be alleviated by the presented method, which can be widely applied for vegetation parameters retrieval. Numéro de notice : A2015-838 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2442999 Date de publication en ligne : 30/06/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2442999 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79172
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 12 (December 2015) . - pp 6507 - 6517[article]Réservation
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