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A Landsat data tiling and compositing approach optimized for change detection in the conterminous United States / Kurtis J. Nelson in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 7 (July 2015)
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Titre : A Landsat data tiling and compositing approach optimized for change detection in the conterminous United States Type de document : Article/Communication Auteurs : Kurtis J. Nelson, Auteur ; Daniel Steinwand, Auteur Année de publication : 2015 Article en page(s) : pp 573 - 586 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] dalle
[Termes IGN] détection de changement
[Termes IGN] Etats-Unis
[Termes IGN] image Landsat
[Termes IGN] incendie de forêt
[Termes IGN] optimisation spatiale
[Termes IGN] traitement d'image
[Termes IGN] végétationRésumé : (auteur) Annual disturbance maps are produced by the LANDFIRE program across the conterminous United States (CONUS). Existing LANDFIRE disturbance data from 1999 to 2010 are available and current efforts will produce disturbance data through 2012. A tiling and compositing approach was developed to produce bi-annual images optimized for change detection. A tiled grid of 10,000 × 10,000 30 m pixels was defined for CONUS and adjusted to consolidate smaller tiles along national borders, resulting in 98 non-overlapping tiles. Data from Landsat-5,-7, and -8 were reprojected to the tile extents, masked to remove clouds, shadows, water, and snow/ice, then composited using a cosine similarity approach. The resultant images were used in a change detection algorithm to determine areas of vegetation change. This approach enabled more efficient processing compared to using single Landsat scenes, by taking advantage of overlap between adjacent paths, and allowed an automated system to be developed for the entire process. Numéro de notice : A2015-965 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.7.573 En ligne : https://doi.org/10.14358/PERS.81.7.573 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80023
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 7 (July 2015) . - pp 573 - 586[article]Operationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery / K. Dons in International journal of applied Earth observation and geoinformation, vol 39 (July 2015)
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Titre : Operationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery Type de document : Article/Communication Auteurs : K. Dons, Auteur ; C. Smith-Hall, Auteur ; H. Meilby, Auteur ; Rasmus Fensholt, Auteur Année de publication : 2015 Article en page(s) : pp 18 - 27 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse (combustible)
[Termes IGN] charbon de bois
[Termes IGN] classification dirigée
[Termes IGN] déboisement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Quickbird
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] sous-bois
[Termes IGN] TanzanieRésumé : (auteur) Quantification of forest degradation in monitoring and reporting as well as in historic baselines is among the most challenging tasks in national REDD+ strategies. However, a recently introduced option is to base monitoring systems on subnational conditions such as prevalent degradation activities. In Tanzania, charcoal production is considered a major cause of forest degradation, but is challenging to quantify due to sub-canopy biomass loss, remote production sites and illegal trade. We studied two charcoal production sites in dry Miombo woodland representing open woodland conditions near human settlements and remote forest with nearly closed canopies. Supervised classification and adaptive thresholding were applied on a pansharpened QuickBird (QB) image to detect kiln burn marks (KBMs). Supervised classification showed reasonable detection accuracy in the remote forest site only, while adaptive thresholding was found acceptable at both locations. We used supervised classification and manual digitizing for KBM delineation and found acceptable delineation accuracy at both sites with RMSEs of 25–32% compared to ground measurements. Regression of charcoal production on KBM area delineated from QB resulted in R2s of 0.86–0.88 with cross-validation RMSE ranging from 2.22 to 2.29 Mg charcoal per kiln. This study demonstrates, how locally calibrated remote sensing techniques may be used to identify and delineate charcoal production sites for estimation of charcoal production and associated extraction of woody biomass. Numéro de notice : A2015-299 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2015.02.001 En ligne : http://www.sciencedirect.com/science/article/pii/S0303243415000331 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76475
in International journal of applied Earth observation and geoinformation > vol 39 (July 2015) . - pp 18 - 27[article]Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features / Peijun Du in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)
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Titre : Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features Type de document : Article/Communication Auteurs : Peijun Du, Auteur ; Alim Samat, Auteur ; Björn Waske, Auteur ; Sicong Liu, Auteur ; Zhenhong Li, Auteur Année de publication : 2015 Article en page(s) : pp 38 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données polarimétriques
[Termes IGN] image Radarsat
[Termes IGN] polarimétrie radar
[Termes IGN] Rotation Forest classification
[Termes IGN] texture d'imageRésumé : (auteur) Fully Polarimetric Synthetic Aperture Radar (PolSAR) has the advantages of all-weather, day and night observation and high resolution capabilities. The collected data are usually sorted in Sinclair matrix, coherence or covariance matrices which are directly related to physical properties of natural media and backscattering mechanism. Additional information related to the nature of scattering medium can be exploited through polarimetric decomposition theorems. Accordingly, PolSAR image classification gains increasing attentions from remote sensing communities in recent years. However, the above polarimetric measurements or parameters cannot provide sufficient information for accurate PolSAR image classification in some scenarios, e.g. in complex urban areas where different scattering mediums may exhibit similar PolSAR response due to couples of unavoidable reasons. Inspired by the complementarity between spectral and spatial features bringing remarkable improvements in optical image classification, the complementary information between polarimetric and spatial features may also contribute to PolSAR image classification. Therefore, the roles of textural features such as contrast, dissimilarity, homogeneity and local range, morphological profiles (MPs) in PolSAR image classification are investigated using two advanced ensemble learning (EL) classifiers: Random Forest and Rotation Forest. Supervised Wishart classifier and support vector machines (SVMs) are used as benchmark classifiers for the evaluation and comparison purposes. Experimental results with three Radarsat-2 images in quad polarization mode indicate that classification accuracies could be significantly increased by integrating spatial and polarimetric features using ensemble learning strategies. Rotation Forest can get better accuracy than SVM and Random Forest, in the meantime, Random Forest is much faster than Rotation Forest. Numéro de notice : A2015-706 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.03.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.03.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78342
in ISPRS Journal of photogrammetry and remote sensing > vol 105 (July 2015) . - pp 38 - 53[article]Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data / Laven Naidoo in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)
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Titre : Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data Type de document : Article/Communication Auteurs : Laven Naidoo, Auteur ; Renaud Mathieu, Auteur ; Russell Main, Auteur ; Waldo Kleynhans, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 234 - 250 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Afrique du sud (état)
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] bande X
[Termes IGN] biomasse
[Termes IGN] canopée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] image TerraSAR-X
[Termes IGN] savaneRésumé : (auteur) Structural parameters of the woody component in African savannahs provide estimates of carbon stocks that are vital to the understanding of fuelwood reserves, which is the primary source of energy for 90% of households in South Africa (80% in Sub-Saharan Africa) and are at risk of over utilisation. The woody component can be characterised by various quantifiable woody structural parameters, such as tree cover, tree height, above ground biomass (AGB) or canopy volume, each been useful for different purposes. In contrast to the limited spatial coverage of ground-based approaches, remote sensing has the ability to sense the high spatio-temporal variability of e.g. woody canopy height, cover and biomass, as well as species diversity and phenological status – a defining but challenging set of characteristics typical of African savannahs. Active remote sensing systems (e.g. Light Detection and Ranging – LiDAR; Synthetic Aperture Radar – SAR), on the other hand, may be more effective in quantifying the savannah woody component because of their ability to sense within-canopy properties of the vegetation and its insensitivity to atmosphere and clouds and shadows. Additionally, the various components of a particular target’s structure can be sensed differently with SAR depending on the frequency or wavelength of the sensor being utilised. This study sought to test and compare the accuracy of modelling, in a Random Forest machine learning environment, woody above ground biomass (AGB), canopy cover (CC) and total canopy volume (TCV) in South African savannahs using a combination of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) radar datasets. Training and validation data were derived from airborne LiDAR data to evaluate the SAR modelling accuracies. It was concluded that the L-band SAR frequency was more effective in the modelling of the CC (coefficient of determination or R2 of 0.77), TCV (R2 of 0.79) and AGB (R2 of 0.78) metrics in Southern African savannahs than the shorter wavelengths (X- and C-band) both as individual and combined (X + C-band) datasets. The addition of the shortest wavelengths also did not assist in the overall reduction of prediction error across different vegetation conditions (e.g. dense forested conditions, the dense shrubby layer and sparsely vegetated conditions). Although the integration of all three frequencies (X + C + L-band) yielded the best overall results for all three metrics (R2 = 0.83 for CC and AGB and R2 = 0.85 for TCV), the improvements were noticeable but marginal in comparison to the L-band alone. The results, thus, do not warrant the acquisition of all three SAR frequency datasets for tree structure monitoring in this environment. Numéro de notice : A2015-713 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.04.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.04.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78353
in ISPRS Journal of photogrammetry and remote sensing > vol 105 (July 2015) . - pp 234 - 250[article]Subsidence monitoring in coal area using time-series InSAR combining persistent scatterers and distributed scatterers / Zhengjia Zhang in International journal of applied Earth observation and geoinformation, vol 39 (July 2015)
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Titre : Subsidence monitoring in coal area using time-series InSAR combining persistent scatterers and distributed scatterers Type de document : Article/Communication Auteurs : Zhengjia Zhang, Auteur ; Chao Wang, Auteur ; Yixian Tang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 49 - 55 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Chine
[Termes IGN] effondrement de terrain
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] mine de charbon
[Termes IGN] subsidence
[Termes IGN] surveillance géologiqueRésumé : (auteur) In coal mining areas, ground subsidence persistently happens, which produces serious environmental issues and affects the development of cities. To monitor the ground deformation due to coal mining, a modified time-series InSAR technique combining persistent scatterers (PSs) and distributed scatterers (DSs) is presented in this paper. In particular, DSs are efficiently identified using classified information and statistical characteristics. Furthermore, a two-scale network is introduced into traditional PSI to deal with PSs and DSs in a multi-layer framework by taking the advantage of the robust of PSs and the widely distribution of DSs. The proposed method is performed to investigate the subsidence of Huainan City, Anhui province (China), during 2012–2013 using 14 scenes of Radarsat-2 images. Experimental results show that the proposed method can ease the estimation complexity and significantly increase the spatial density of measurement points, which can provide more detailed deformation information. Result shows that there are obvious subsidence areas detected in the test site with subsidence velocity larger than 5 cm/year. The proposed method brings practical applications for non-urban area deformation monitoring. Numéro de notice : A2015-213 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2015.02.007 En ligne : http://www.sciencedirect.com/science/article/pii/S0303243415000392 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76476
in International journal of applied Earth observation and geoinformation > vol 39 (July 2015) . - pp 49 - 55[article]Toward evaluating multiscale segmentations of high spatial resolution remote sensing images / Xueliang Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
PermalinkCompilation de données radar et optiques pour la cartographie des classes d'occupation du sol aux environs du système lacustre de Bizerte (Tunisie du Nord) / Ibtissem Amri in Photo interprétation, European journal of applied remote sensing, vol 51 n° 2 (juin 2015)
PermalinkA fuzzy spatial reasoner for multi-scale GEOBIA ontologies / Argyros Argyridis in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)
PermalinkIn-orbit geometric calibration and validation of ZY-3 three-line cameras based on CCD-detector look angles / Jinshan Cao in Photogrammetric record, vol 30 n° 150 (June - August 2015)
PermalinkMangrove tree crown delineation from high-resolution imagery / Muditha K. Heenkenda in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)
PermalinkMTF-adjusted pansharpening approach based on coupled multiresolution decompositions / Abdelaziz Kallel in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
PermalinkPotentialités des images Landsat pour l'identification et la délimitation de zones humides à l'échelle régionale : l'exemple de l'Est de la France / Sébastien Lebaut in Physio-Géo, vol 9 (juin 2015)
PermalinkUtilisation des données des capteurs MODIS et SPOT-VGT pour l'analyse de la dynamique des feux dans deux territoires (réserve protégée et unités pastorales) au Ferlo (Sénégal) / Mamadou Adama Sarr in Photo interprétation, European journal of applied remote sensing, vol 51 n° 2 (juin 2015)
PermalinkVery high resolution image matching based on local features and k-means clustering / Amin Sedaghat in Photogrammetric record, vol 30 n° 150 (June - August 2015)
PermalinkBuilding a hybrid land cover map with crowdsourcing and geographically weighted regression / Linda M. See in ISPRS Journal of photogrammetry and remote sensing, vol 103 (May 2015)
PermalinkA critical comparison among pansharpening algorithms / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
PermalinkDétermination de la précision planimétrique des images Google Earth haute résolution de Rome (1ère partie) / Guiseppe Pulighe in Géomatique expert, n° 104 (mai - juin 2015)
PermalinkPermalinkMultispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data / Prabir Das in Geocarto international, vol 30 n° 5 - 6 (May - July 2015)
PermalinkSpatial analysis of high-resolution urban thermal patterns in Vojvodina, Serbia / Dusan Jovanovic in Geocarto international, vol 30 n° 5 - 6 (May - July 2015)
PermalinkUse of Landsat and Corona data for mapping forest cover change from the mid-1960s to 2000s: Case studies from the Eastern United States and Central Brazil / Dan-Xia Song in ISPRS Journal of photogrammetry and remote sensing, vol 103 (May 2015)
PermalinkApport du LiDAR dans le géoréférencement d'images hyperspectrales en vue d'un couplage LiDAR/hyperspectral / Antoine Ba in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)
PermalinkL'approche détection des changements pour estimer l'humidité du sol en milieu semi-aride à partir d'images ASAR, cas des hautes plaines de l'Est de l'Algérie / Mokhtar Guerfi in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)
PermalinkCAESAR: an approach based on covariance matrix decomposition to improve multibaseline–multitemporal interferometric SAR processing / Gianfranco Fornaro in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkEvaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework / H. Croft in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
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