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A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning / Rasmus M. Houborg in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)
[article]
Titre : A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning Type de document : Article/Communication Auteurs : Rasmus M. Houborg, Auteur ; Matthew F. McCabe, Auteur Année de publication : 2018 Article en page(s) : pp 173 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image RapidEye
[Termes IGN] Leaf Area Index
[Termes IGN] réflectance de surface
[Termes IGN] régressionRésumé : (Auteur) With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory ‘predictor’ variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association with inherent extrapolation and transferability limitations. Explanatory VIs formed from bands in the near-infrared (NIR) and shortwave infrared domains (e.g., NDWI) were associated with the highest predictive ability, whereas Cubist models relying entirely on VIs based on NIR and red band combinations (e.g., NDVI) were associated with comparatively high uncertainties (i.e., rMAD ∼ 21%). The most transferable and best performing models were based on combinations of several predictor variables, which included both NDWI- and NDVI-like variables. In this process, prior screening of input VIs based on an assessment of variable relevance served as an effective mechanism for optimizing prediction accuracies from both Cubist and RF. While this study demonstrated benefit in combining data mining operations with physically based constraints via a hybrid training approach, the concept of transferability and portability warrants further investigations in order to realize the full potential of emerging machine-learning techniques for regression purposes. Numéro de notice : A2018-070 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89428
in ISPRS Journal of photogrammetry and remote sensing > vol 135 (January 2018) . - pp 173 - 188[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018011 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018012 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2018013 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Forest change detection in incomplete satellite images with deep neural networks / Salman H. Khan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Forest change detection in incomplete satellite images with deep neural networks Type de document : Article/Communication Auteurs : Salman H. Khan, Auteur ; Xuming He, Auteur ; Fatih Porikli, Auteur ; Mohammed Bennamoun, Auteur Année de publication : 2017 Article en page(s) : pp 5407 - 5423 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] analyse multirésolution
[Termes IGN] apprentissage profond
[Termes IGN] détection de changement
[Termes IGN] forêt
[Termes IGN] réflectance de surface
[Termes IGN] réseau neuronal artificiel
[Termes IGN] retouche
[Termes IGN] surveillance de la végétationRésumé : (Auteur) Land cover change monitoring is an important task from the perspective of regional resource monitoring, disaster management, land development, and environmental planning. In this paper, we analyze imagery data from remote sensing satellites to detect forest cover changes over a period of 29 years (1987-2015). Since the original data are severely incomplete and contaminated with artifacts, we first devise a spatiotemporal inpainting mechanism to recover the missing surface reflectance information. The spatial filling process makes use of the available data of the nearby temporal instances followed by a sparse encoding-based reconstruction. We formulate the change detection task as a region classification problem. We build a multiresolution profile (MRP) of the target area and generate a candidate set of bounding-box proposals that enclose potential change regions. In contrast to existing methods that use handcrafted features, we automatically learn region representations using a deep neural network in a data-driven fashion. Based on these highly discriminative representations, we determine forest changes and predict their onset and offset timings by labeling the candidate set of proposals. Our approach achieves the state-of-the-art average patch classification rate of 91.6% (an improvement of ~16%) and the mean onset/offset prediction error of 4.9 months (an error reduction of five months) compared with a strong baseline. We also qualitatively analyze the detected changes in the unlabeled image regions, which demonstrate that the proposed forest change detection approach is scalable to new regions. Numéro de notice : A2017-663 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2707528 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2707528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87105
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5407 - 5423[article]Using landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas / Cooper McCann in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Using landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas Type de document : Article/Communication Auteurs : Cooper McCann, Auteur ; Kevin S. Repasky, Auteur ; Mikindra Morin, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 5002 - 5014 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat
[Termes IGN] image multibande
[Termes IGN] mosaïquage d'images
[Termes IGN] paturage
[Termes IGN] qualité radiométrique (image)
[Termes IGN] réflectance de surfaceRésumé : (Auteur) Low-cost flight-based hyperspectral imaging systems have the potential to provide important information for ecosystem and environmental studies as well as aide in land management. To realize this potential, methods must be developed to provide large-area surface reflectance data allowing for temporal data sets at the mesoscale. This paper describes a bootstrap method of producing a large-area, radiometrically referenced hyperspectral data set using the Landsat surface reflectance (LaSRC) data product as a reference target. The bootstrap method uses standard hyperspectral processing techniques that are extended to remove uneven illumination conditions between flight passes, allowing for radiometrically self-consistent data after mosaicking. Through selective spectral and spatial resampling, LaSRC data are used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from two hyperspectral flights over the same managed agricultural and unmanaged range land covering approximately 5.8 km2 acquired on June 21, 2014 and June 24, 2015 are presented. Data from a flight over agricultural land collected on June 6, 2016 are compared with concurrently collected ground-based reflectance spectra as a means of validation. Numéro de notice : A2017-665 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2699618 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2699618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87102
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5002 - 5014[article]Angular reflectance of leaves with a dual-wavelength terrestrial lidar and its implications for leaf-bark separation and leaf moisture estimation / Steven Hancock in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)
[article]
Titre : Angular reflectance of leaves with a dual-wavelength terrestrial lidar and its implications for leaf-bark separation and leaf moisture estimation Type de document : Article/Communication Auteurs : Steven Hancock, Auteur ; Rachel Gaulton, Auteur ; F. Mark Danson, Auteur Année de publication : 2017 Article en page(s) : pp 3084 - 3090 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] angle d'incidence
[Termes IGN] données lidar
[Termes IGN] écorce
[Termes IGN] indice de diversité
[Termes IGN] longueur d'onde
[Termes IGN] réflectance de surface
[Termes IGN] réflectance végétale
[Termes IGN] teneur en eau de la végétationRésumé : (Auteur) A new generation of multiwavelength lidars offers the potential to measure the structure and biochemistry of vegetation simultaneously, using range resolved spectral indices to overcome the confounding effects in passive optical measurements. However, the reflectance of leaves depends on the angle of incidence, and if this dependence varies between wavelengths, the resulting spectral indices will also vary with the angle of incidence, complicating their use in separating structural and biochemical effects in vegetation canopies. The Salford Advanced Laser Canopy Analyser (SALCA) dual-wavelength terrestrial laser scanner was used to measure the angular dependence of reflectance for a range of leaves at the wavelengths used by the new generation of multiwavelength lidars, 1063 and 1545 nm, as used by SALCA, DWEL, and the Optech Titan. The influence of the angle of incidence on the normalized difference index (NDI) of these wavelengths was also assessed. The reflectance at both wavelengths depended on the angle of incidence and could be well modelled as a cosine. The change in the NDI with the leaf angle of incidence was small compared with the observed difference in the NDI between fresh and dry leaves and between leaf and bark. Therefore, it is concluded that angular effects will not significantly impact leaf moisture retrievals or prevent leaf/bark separation for the wavelengths used in the new generation of 1063- and 1545-nm multiwavelength lidars. Numéro de notice : A2017-474 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2652140 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2652140 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86399
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 6 (June 2017) . - pp 3084 - 3090[article]Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration / Yinghai Ke in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
[article]
Titre : Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration Type de document : Article/Communication Auteurs : Yinghai Ke, Auteur ; Jungho Im, Auteur ; Seonyoung Park, Auteur ; Huili Gong, Auteur Année de publication : 2017 Article en page(s) : pp 79 – 93 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] évapotranspiration
[Termes IGN] image à haute résolution
[Termes IGN] image Terra-MODIS
[Termes IGN] indice de végétation
[Termes IGN] réflectance de surface
[Termes IGN] ressources en eau
[Termes IGN] température au solRésumé : (auteur) Continuous monitoring of actual evapotranspiration (ET) is critical for water resources management at both regional and local scales. Although the MODIS ET product (MOD16A2) provides viable sources for ET monitoring at 8-day intervals, the spatial resolution (1 km) is too coarse for local scale applications. In this study, we propose a machine learning and spatial temporal fusion (STF)-integrated approach in order to generate 8-day 30 m ET based on both MOD16A2 and Landsat 8 data with three schemes. Random forest machine learning was used to downscale MODIS 1 km ET to 30 m resolution based on nine Landsat-derived indicators including vegetation indices (VIs) and land surface temperature (LST). STF-based models including Spatial and Temporal Adaptive Reflectance Fusion Model and Spatio-Temporal Image Fusion Model were used to derive synthetic Landsat surface reflectance (scheme 1)/VIs (scheme 2)/ET (scheme 3) on Landsat-unavailable dates. The approach was tested over two study sites in the United States. The results showed that fusion of Landsat VIs produced the best accuracy of predicted ET (R2 = 0.52–0.97, RMSE = 0.47–3.0 mm/8 days and rRMSE = 6.4–37%). High density of cloud-clear Landsat image acquisitions and low spatial heterogeneity of Landsat VIs benefit the ET prediction. The downscaled 30 m ET had good agreement with MODIS ET (RMSE = 0.42–3.4 mm/8 days, rRMSE = 3.2–26%). Comparison with the in situ ET measurements showed that the downscaled ET had higher accuracy than MODIS ET. Numéro de notice : A2017-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.006 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.02.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84509
in ISPRS Journal of photogrammetry and remote sensing > vol 126 (April 2017) . - pp 79 – 93[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017043 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017042 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Hierarchically exploring the width of spectral bands for urban material classification / Arnaud Le Bris (2017)PermalinkAutomatic detection of clouds and shadows using high resolution satellite image time series / Nicolas Champion (2016)PermalinkMeasuring the directional variations of land surface reflectance from MODIS / François-Marie Bréon in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)PermalinkLevelling co-located GNSS and tide gauge stations using GNSS reflectometry / Alvaro Santamaria Gomez in Journal of geodesy, vol 89 n° 3 (March 2015)PermalinkSeeing through shadow: Modelling surface irradiance for topographic correction of Landsat ETM+ data / Tobias Schulmann in ISPRS Journal of photogrammetry and remote sensing, vol 99 (January 2015)PermalinkLimnimétrie par réflectrométrie GNSS à faible coût / Eduardo Rodrigues in Géomatique suisse, vol 112 n° 8 (août 2014)PermalinkAn improved dark object method to retrieve 500 m-resolution AOT (Aerosol Optical Thickness) image from MODIS data: A case study in the Pearl River Delta area, China / Lili Li in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkDirectional relations and frames of reference / Eliseo Clementini in Geoinformatica, vol 17 n° 2 (April 2013)PermalinkSTARS : A new method for multitemporal remote sensing / Marcio Pupin Mello in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 1 (April 2013)PermalinkScanning geometry: Influencing factor on the quality of terrestrial laser scanning points / S. Soudarissanane in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 4 (July - August 2011)Permalink