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The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass / Ronald E. McRoberts in Annals of Forest Science, vol 73 n° 4 (December 2016)
[article]
Titre : The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass Type de document : Article/Communication Auteurs : Ronald E. McRoberts, Auteur ; Erik Naesset, Auteur ; Terje Gobakken, Auteur Année de publication : 2016 Article en page(s) : pp 839 - 847 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique
[Termes IGN] biomasse
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] forêt
[Termes IGN] image Landsat
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Minnesota (Etats-Unis)
[Termes IGN] Norvège
[Termes IGN] régression
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Key message : When areas of interest experience little change, remote sensing-based maps whose dates deviate from ground data can still substantially enhance precision. However, when change is substantial, deviations in dates reduce the utility of such maps for this purpose.
Context : Remote sensing-based maps are well-established as means of increasing the precision of estimates of forest inventory parameters. The general practice is to use maps whose dates correspond closely to the dates of ground data. However, as national forest inventories move to continuous inventories, deviations between map and ground data dates increase.
Aims : The aim was to assess the degree to which remote sensing-based maps can be used to increase the precision of estimates despite differences between map and ground data dates.
Methods : For study areas in the USA and Norway, maps were constructed for each of two dates, and model-assisted regression estimators were used to estimate inventory parameters using ground data whose dates differed by as much as 11 years from the map dates.
Results : For the Minnesota study area that had little change, 7-year differences in dates had little effect on the precision of estimates of proportion forest area. For the Norwegian study area that experienced considerable change, 11-year differences in dates had a detrimental effect on the precision of estimates of mean biomass per unit area.
Conclusions : The effects of differences in map and ground data dates were less important than temporal change in the study area.Numéro de notice : A2016--168 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-015-0485-6 Date de publication en ligne : 12/05/2015 En ligne : https://doi.org/10.1007/s13595-015-0485-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87210
in Annals of Forest Science > vol 73 n° 4 (December 2016) . - pp 839 - 847[article]Wave period and coastal bathymetry using wave propagation on optical images / Céline Danilo in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
[article]
Titre : Wave period and coastal bathymetry using wave propagation on optical images Type de document : Article/Communication Auteurs : Céline Danilo, Auteur ; Farid Melgani, Auteur Année de publication : 2016 Article en page(s) : pp 6307 - 6319 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bathymétrie
[Termes IGN] fréquence
[Termes IGN] Hawaii (Etats-Unis)
[Termes IGN] image Landsat-8
[Termes IGN] image optique
[Termes IGN] lever bathymétrique
[Termes IGN] littoral
[Termes IGN] rayonnement électromagnétique
[Termes IGN] vagueRésumé : (Auteur) We propose a method based on combining wave tracing and linear wave theory for the estimation of wave period and bathymetry in coastal areas from satellite images. The method depends on several parameters for which we provide ranges of variations adapted to the instrument. Experimental results are conducted on several sites located around the Hawaiian island of Oahu, using 13 Landsat-8 images. Results show that wave period estimations are compatible with the wave buoy measurements in all cases. In addition, bathymetry estimation results show a standard deviation of less than 15% of the observed depth out of the surf zone until 20 m for sites with a direct exposure to the swell and with an absence of clouds. The proposed method, which does not rely on ancillary data, represents a promising tool for bathymetry estimation using satellite images in which waves are present. Numéro de notice : A2016-912 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2579266 En ligne : https://doi.org/10.1109/TGRS.2016.2579266 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83134
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6307 - 6319[article]Relative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation / Alyssa Endres in European journal of remote sensing, vol 49 n° 1 (2016)
[article]
Titre : Relative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation Type de document : Article/Communication Auteurs : Alyssa Endres, Auteur ; Giorgos Mountrakis, Auteur ; Huiran Jin, Auteur ; Wei Zhuang, Auteur ; Ioannis Manakos, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 795 - 807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] biomasse aérienne
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] feuillu
[Termes IGN] fusion de données
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image LandsatRésumé : (auteur) Aboveground forest biomass estimation is an integral component for climate change, carbon stocks assessment, biodiversity and forest health. LiDAR (Light Detection And Ranging), specifically NASA’s Laser Vegetation Imaging Sensor (LVIS), PALSAR (Phased Array type L-band Synthetic Aperture Radar), and Landsat data have been previously used in biomass estimation with promising results when used individually. In this manuscript, all three products are jointly utilized for the first time to assess their importance for deciduous biomass estimation. Results indicate that LVIS inputs are ranked as most important followed by PALSAR inputs. Particularly for PALSAR, scenes acquired in May and August were ranked higher compared to other months. Numéro de notice : A2016-827 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164942 En ligne : http://dx.doi.org/10.5721/EuJRS20164942 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82707
in European journal of remote sensing > vol 49 n° 1 (2016) . - pp 795 - 807[article]Accuracy assessment of NOAA coastal change analysis program 2006 - 2010 land cover and land cover change data / John W. McCombs in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 9 (September 2016)
[article]
Titre : Accuracy assessment of NOAA coastal change analysis program 2006 - 2010 land cover and land cover change data Type de document : Article/Communication Auteurs : John W. McCombs, Auteur ; Shan G. Burkhalter, Auteur ; Christopher D. Robinson, Auteur Année de publication : 2016 Article en page(s) : pp 711 - 718 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photo-interprétation
[Termes IGN] analyse diachronique
[Termes IGN] couvert végétal
[Termes IGN] détection de changement
[Termes IGN] Etats-Unis
[Termes IGN] image Landsat
[Termes IGN] littoral
[Termes IGN] niveau d'analyseRésumé : (Auteur) A new approach to locating accuracy assessment sample units was used to quantify 2010 land cover accuracy, in addition to being able to make statements about 2006-2010 land cover change mapping accuracy for National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) data. Three customized tiers of sampling strata were created, as discussed, to meet these goals. Stratified random sampling was employed in each stratum with a six out of nine pixel-homogeneity criteria (different from the final minimum mapping unit) required for each sampling unit. Accuracy was assessed for nine regions in the coastal United States with overall accuracy ranging from 82.3 percent to 85.6 percent. Binary change was mapped with 88.7 percent accuracy, with the largest error being errors of commission (71.2 percent user accuracy). This sampling design also allowed for the identification of 137 locations where true change was not mapped, allowing for statements to be made about missed change. Numéro de notice : A2016-741 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.82.9.711 En ligne : https://doi.org/10.14358/PERS.82.9.711 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82277
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 9 (September 2016) . - pp 711 - 718[article]Estimating forest species abundance through linear unmixing of CHRIS/PROBA imagery / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
[article]
Titre : Estimating forest species abundance through linear unmixing of CHRIS/PROBA imagery Type de document : Article/Communication Auteurs : S. Stagakis, Auteur ; Theofilos Vanikiotis, Auteur ; Olga Sykioti, Auteur Année de publication : 2016 Article en page(s) : pp 79 - 89 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des mélanges spectraux
[Termes IGN] carte de la végétation
[Termes IGN] classification bayesienne
[Termes IGN] effet d'ombre
[Termes IGN] espèce végétale
[Termes IGN] Fagus sylvatica
[Termes IGN] Grèce
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-8
[Termes IGN] image PROBA-CHRIS
[Termes IGN] orthoimage
[Termes IGN] parc naturel national
[Termes IGN] partition d'image
[Termes IGN] Pinus nigra
[Termes IGN] richesse floristique
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) The advancing technology of hyperspectral remote sensing offers the opportunity of accurate land cover characterization of complex natural environments. In this study, a linear spectral unmixing algorithm that incorporates a novel hierarchical Bayesian approach (BI-ICE) was applied on two spatially and temporally adjacent CHRIS/PROBA images over a forest in North Pindos National Park (Epirus, Greece). The scope is to investigate the potential of this algorithm to discriminate two different forest species (i.e. beech – Fagus sylvatica, pine – Pinus nigra) and produce accurate species-specific abundance maps. The unmixing results were evaluated in uniformly distributed plots across the test site using measured fractions of each species derived by very high resolution aerial orthophotos. Landsat-8 images were also used to produce a conventional discrete-type classification map of the test site. This map was used to define the exact borders of the test site and compare the thematic information of the two mapping approaches (discrete vs abundance mapping). The required ground truth information, regarding training and validation of the applied mapping methodologies, was collected during a field campaign across the study site. Abundance estimates reached very good overall accuracy (R2 = 0.98, RMSE = 0.06). The most significant source of error in our results was due to the shadowing effects that were very intense in some areas of the test site due to the low solar elevation during CHRIS acquisitions. It is also demonstrated that the two mapping approaches are in accordance across pure and dense forest areas, but the conventional classification map fails to describe the natural spatial gradients of each species and the actual species mixture across the test site. Overall, the BI-ICE algorithm presented increased potential to unmix challenging objects with high spectral similarity, such as different vegetation species, under real and not optimum acquisition conditions. Its full potential remains to be investigated in further and more complex study sites in view of the upcoming satellite hyperspectral missions. Numéro de notice : A2016-778 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.05.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.05.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82473
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 79 - 89[article]Floristic composition and across-track reflectance gradient in Landsat images over Amazonian forests / Javier Muro in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkSpatiotemporal subpixel mapping of time-series images / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkSuivi spatiotemporel de la tache urbaine à l'aide de cartes anciennes, d'images satellitaires et de SIG. La cas de Blida en Algérie (de 1936 à 2015) / Elodie Ruch in Géomatique expert, n° 112 (septembre - octobre 2016)PermalinkAssessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm, India / Anju Bala in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)PermalinkA superresolution land-cover change detection method using remotely sensed images with different spatial resolutions / Xiaodong Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkOptical remotely sensed time series data for land cover classification: A review / Cristina Gómez in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)PermalinkMonitoring of water stress in wheat using multispectral indices derived from Landsat-TM / Nitika Dangwal in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)PermalinkEstimating forest and woodland aboveground biomass using active and passive remote sensing / Zhuoting Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 4 (April 2016)PermalinkForest above ground biomass inversion by fusing GLAS with optical remote sensing data / Xiaohuan Xi in ISPRS International journal of geo-information, vol 5 n° 4 (April 2016)PermalinkComparison of three Landsat TM compositing methods: A case study using modeled tree canopy cover / Bonnie Ruefenacht in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)Permalink