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Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey / Alkan Günlü in Geocarto international, Vol 35 n° 1 ([02/01/2020])
[article]
Titre : Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; Ilker Erkanli, Auteur Année de publication : 2020 Article en page(s) : pp 17 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] biomasse aérienne
[Termes IGN] Fagus (genre)
[Termes IGN] image ALOS-PALSAR
[Termes IGN] peuplement forestier
[Termes IGN] puits de carbone
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificiel
[Termes IGN] TurquieRésumé : (auteur) The goal of this study was to estimate aboveground stand carbon (AGSC) of pure beech stands in Turkey with ground measurements as well as topographic information and remote sensing data. For this purpose, 153 sample plots were collected from pure beech stands in study area. The AGSC of each sample plot was computed. Eight texture images (variance, dissimilarity, homogeneity, entropy, contrast, mean, second moment and correlation) with five window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9 and 11 × 11) generated from ALOS PALSAR L-band satellite image. The AGSC models predicting the relationships between ALOS PALSAR texture values and topographic information, and sample plot AGSC were developed by using multiple linear regressions (MLR). Also, artificial neural networks (ANNs) architectures were trained by comparing various numbers of neurons and activation functions in its network types. Our results revealed the ability of ANNs was better than MLR models to predict AGSC values. Numéro de notice : A2020-017 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1499817 Date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1499817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94410
in Geocarto international > Vol 35 n° 1 [02/01/2020] . - pp 17 - 28[article]Réservation
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Titre : Remote sensing technology applications in forestry and REDD+ Type de document : Monographie Auteurs : Kim Calders, Éditeur scientifique ; Inge Jonckheere, Éditeur scientifique ; Mikko Vastaranta, Éditeur scientifique ; Joanne Nightingale, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 244 p. ISBN/ISSN/EAN : 978-3-03928-471-9 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] canopée
[Termes IGN] cartographie des risques
[Termes IGN] déboisement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image Landsat
[Termes IGN] image multibande
[Termes IGN] image Sentinel
[Termes IGN] Pinus massoniana
[Termes IGN] polarimétrie radar
[Termes IGN] Réduction des émissions dues à la déforestation et la dégradation des forêts, REDD
[Termes IGN] télémétrie laser aéroporté
[Termes IGN] télémétrie laser terrestreRésumé : (Editeur) Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion. Numéro de notice : 26296 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03928-471-9 Date de publication en ligne : 07/04/2020 En ligne : https://doi.org/10.3390/books978-3-03928-471-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95009 A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal / Upama A. Koju in Journal of Forestry Research, vol 30 n° 6 (December 2019)
[article]
Titre : A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal Type de document : Article/Communication Auteurs : Upama A. Koju, Auteur ; Jiahua Zhang, Auteur ; Shashish Maharjan, Auteur ; Sha Zhang, Auteur ; Yun Bai, Auteur ; Dinesh Babu Irulappa-Pillai-Vijayakumar , Auteur ; Fengmei Yao, Auteur Année de publication : 2019 Projets : 3-projet - voir note / Article en page(s) : pp 2119 - 2136 Note générale : bibliographie
The work was supported by the CAS Strategic Priority Research Program (No. XDA19030402), the National Key Research and Development Program of China (No. 2016YFD0300101), the Natural Science Foundation of China (Nos. 31571565, 31671585), the Key Basic Research Project of the Shandong Natural Science Foundation of China (No. ZR2017ZB0422), and Research Funding of Qingdao University (No. 41117010153).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse multiéchelle
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] Google Earth
[Termes IGN] image Geoeye
[Termes IGN] image Landsat
[Termes IGN] image optique
[Termes IGN] image Quickbird
[Termes IGN] NépalRésumé : (auteur) Forests account for 80% of the total carbon exchange between the atmosphere and terrestrial ecosystems. Thus, to better manage our responses to global warming, it is important to monitor and assess forest aboveground carbon and forest aboveground biomass (FAGB). Different levels of detail are needed to estimate FAGB at local, regional and national scales. Multi-scale remote sensing analysis from high, medium and coarse spatial resolution data, along with field sampling, is one approach often used. However, the methods developed are still time consuming, expensive, and inconvenient for systematic monitoring, especially for developing countries, as they require vast numbers of field samples for upscaling. Here, we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites. The study was conducted in the Chitwan district of Nepal using GeoEye-1 (0.5 m), Landsat (30 m) and Google Earth very high resolution (GEVHR) Quickbird (0.65 m) images. For the local scale (Kayerkhola watershed), tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images. An overall accuracy of 83% was obtained in the delineation of tree canopy cover (TCC) per plot. A TCC vs. FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots. A coefficient of determination (R2) of 0.76 was obtained in the modelling, and a value of 0.83 was obtained in the validation of the model. To upscale FAGB to the entire district, open source GEVHR images were used as virtual field plots. We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model. Using the multivariate adaptive regression splines machine learning algorithm, we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices. The model was then used to extrapolate FAGB to the entire district. This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution (30 m) and accuracy (R2 = 0.76 and 0.7) with minimal error (RMSE = 64 and 38 tons ha−1) at local and regional scales. This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time. The method is especially applicable for developing countries that have low budgets for carbon estimations, and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation (REDD +) monitoring reporting and verification processes. Numéro de notice : A2019-664 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11676-018-0743-1 Date de publication en ligne : 09/07/2018 En ligne : https://doi.org/10.1007/s11676-018-0743-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99699
in Journal of Forestry Research > vol 30 n° 6 (December 2019) . - pp 2119 - 2136[article]Quantifying intra-annual dynamics of carbon sequestration in the forming wood: a novel histologic approach / Anjy Andrianantenaina in Annals of Forest Science, Vol 76 n° 3 (September 2019)
[article]
Titre : Quantifying intra-annual dynamics of carbon sequestration in the forming wood: a novel histologic approach Type de document : Article/Communication Auteurs : Anjy Andrianantenaina, Auteur ; Cyrille B.K. Rathgeber, Auteur ; Gonzalo Péres-de-Lis, Auteur ; Henri E. Cuny , Auteur ; Julien Ruelle, Auteur Année de publication : 2019 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : n° 62 Note générale : bibliographie
This work was supported by a grant overseen by the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE), and by the National Institute of Agricultural Research (INRA Grand-Est Nancy) with a doctoral fellowship granted to the first author (CJS, “Contrat Jeune Scientifique”).Langues : Anglais (eng) Descripteur : [Termes IGN] analyse d'image numérique
[Termes IGN] Angiosperme
[Termes IGN] biomasse aérienne
[Termes IGN] croissance végétale
[Termes IGN] densité du bois
[Termes IGN] Hesse, forêt de (Meuse)
[Termes IGN] microscope électronique
[Termes IGN] Picea abies
[Termes IGN] puits de carbone
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) This study presents a novel histologic approach to quantify the intra-annual dynamics of carbon sequestration in forming wood. This innovative approach, based on repeated measurements of xylem apparent density, is more direct, and more accurate than the previously published cellular-based approach. Moreover, this new approach, which was tested here on softwoods, is also applicable to hardwoods without any modification.
Context : Forest ecosystems are key players of the terrestrial carbon cycle. Indeed, wood represents the principal carbon pool of terrestrial biomass, accumulated in trees through cambial activity.
Aims : Here, we present a novel, simple, and fast approach to accurately estimate the intra-annual dynamics of aboveground woody biomass production based on image analysis of forming xylem sections.
Methods : During the 2015 growing season, we weekly collected wood samples (microcores) containing the forming xylem on seven Norway spruces (Picea abies (L.) Karst), grown in Hesse forest (North-East France). The microcores were prepared to allow the observation of the forming tissues with an optical microscope. Xylem apparent density and radial increment were then measured directly on images of the histological sections. In order to compare our “histologic approach” with the previously published “cellular approach,” we also counted the number of tracheids in each differentiation zones, and measured the tracheid dimensions all along the last-formed tree ring.
Results : The two approaches yielded comparable meaningful results, describing xylem size increase and aboveground woody biomass production as bell-shaped curves culminating in May and June respectively. However, the histologic approach provided a shorter time lag between xylem size increase and biomass production than the cellular one.
Conclusion : Better quantification of the shift between stem growth in size and in biomass will require addressing the knowledge gap regarding lignin deposition kinetics. Nevertheless, our novel histologic approach is simpler and more direct than the cellular one, and may open the way to a first quantification of intra-annual dynamics of woody biomass production in angiosperms, where the cellular approach is hardly applicable.Numéro de notice : A2019-650 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0846-7 En ligne : https://doi.org/10.1007/s13595-019-0846-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97454
in Annals of Forest Science > Vol 76 n° 3 (September 2019) . - n° 62[article]Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images / Jie Wang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)
[article]
Titre : Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images Type de document : Article/Communication Auteurs : Jie Wang, Auteur ; Xiangming Xiao, Auteur ; Rajen Bajgain, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 189 - 201 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] Leaf Area Index
[Termes IGN] Oklahoma (Etats-Unis)
[Termes IGN] paturage
[Termes IGN] phénologie
[Termes IGN] régression multipleRésumé : (Auteur) Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10–30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI 2 m2/m2, AGB > 500 g/m2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management. Numéro de notice : A2019-269 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.007 Date de publication en ligne : 21/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93086
in ISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 189 - 201[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Innovations in ground and airborne technologies as reference and for training and validation: Terrestrial Laser Scanning (TLS) / Mathias I. Disney in Surveys in Geophysics, vol 40 n° 4 (July 2019)PermalinkObject-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment / Eduarda M.O. Silveira in International journal of applied Earth observation and geoinformation, vol 78 (June 2019)PermalinkTree and stand level estimations of Abies alba Mill. aboveground biomass / Andrzej M. Jagodzinski in Annals of Forest Science, vol 76 n° 2 (June 2019)PermalinkEstimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery / Yanan Liu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkEstimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data / Kalifa Goïta in Geocarto international, vol 34 n° 3 ([01/03/2019])PermalinkForest degradation and biomass loss along the Chocó region of Colombia / Victoria Meyer in Carbon Balance and Management, vol 14 (March 2019)PermalinkQuantifying spatiotemporal post‐disturbance recovery using field inventory, tree growth, and remote sensing / Shengli Huang in Earth and space science, vol 6 n° 3 (March 2019)PermalinkGeneration of large-scale moderate-resolution forest height mosaic with spaceborne repeat-pass SAR interferometry and lidar / Yang Lei in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)PermalinkTanDEM-X digital surface models in boreal forest above-ground biomass change detection / Kirsi Karila in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkCarDen: A software for fast measurement of wood density on increment cores by CT scanning / Philippe Jacquin in Computers and Electronics in Agriculture, vol 156 (January 2019)PermalinkEvaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)PermalinkPermalinkEstimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations / Kun Liu in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkEstimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)PermalinkStrategies for climate-smart forest management in Austria / Robert Jandl in Forests, vol 9 n° 10 (October 2018)PermalinkUnmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil / Sébastien Giordano in IEEE Transactions on geoscience and remote sensing, vol 56 n° 10 (October 2018)PermalinkDeveloping allometric equations for estimating shrub biomass in a Boreal Fen / Annie He in Forests, vol 9 n° 9 (September 2018)PermalinkUsing terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach / Stéphane Momo Takoudjou in Methods in ecology and evolution, vol 9 n° 4 (April 2018)PermalinkSeasonal time-course of the above ground biomass production efficiency in beech trees (Fagus sylvatica L.) / Laura Heid in Annals of Forest Science, vol 75 n° 1 (March 2018)PermalinkEstimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor / Timothy Dube in Geocarto international, vol 33 n° 2 (February 2018)Permalink