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Forest structure and fine root biomass influence soil CO2 efflux in temperate forests under drought / Antonios Apostolakis in Forests, vol 14 n° 2 (February 2023)
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Titre : Forest structure and fine root biomass influence soil CO2 efflux in temperate forests under drought Type de document : Article/Communication Auteurs : Antonios Apostolakis, Auteur ; Ingo Schöning, Auteur ; Beate Michalzik, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 411 Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne
[Termes IGN] biomasse forestière
[Termes IGN] forêt tempérée
[Termes IGN] puits de carbone
[Termes IGN] qualité du sol
[Termes IGN] sécheresse
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] température au sol
[Termes IGN] teneur en carbone
[Termes IGN] teneur en eau de la végétation
[Vedettes matières IGN] Végétation et changement climatiqueNuméro de notice : A2023-165 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f14020411 Date de publication en ligne : 17/12/2023 En ligne : https://doi.org/10.3390/f14020411 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102871
in Forests > vol 14 n° 2 (February 2023) . - n° 411[article]Testing the application of process-based forest growth model PREBAS to uneven-aged forests in Finland / Man Hu in Forest ecology and management, vol 529 (February-1 2023)
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Titre : Testing the application of process-based forest growth model PREBAS to uneven-aged forests in Finland Type de document : Article/Communication Auteurs : Man Hu, Auteur ; Francesco Minunno, Auteur ; Mikko Peltoniemi, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 120702 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière
[Termes IGN] croissance des arbres
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] forêt inéquienne
[Termes IGN] hauteur des arbres
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle de simulation
[Termes IGN] modélisation de la forêt
[Termes IGN] mortalité
[Termes IGN] peuplement mélangé
[Termes IGN] photosynthèse
[Termes IGN] Picea abies
[Termes IGN] structure d'un peuplement forestier
[Vedettes matières IGN] ForesterieRésumé : (auteur) The challenges of applying process-based models to uneven-aged forests are the difficulties in simulating the interactions between trees and resource allocation between size classes. In this study, we focused on a process-based forest growth model PREBAS which is a mean tree model with Reineke self-thinning mortality and was originally developed for even-aged forests. The primary aim was to test the application of PREBAS model to uneven-aged forests by introducing different diameter at breast height (DBH) size classes to better represent the forest structure. Additionally, we introduced a new mortality model to PREBAS which is developed for uneven-aged stands and compared with the current PREBAS version in which a modification Reineke rule is used. The tests were conducted in 26 old Norway spruce dominated stands in southern and central Finland with three consecutive measurements (on average a 25-year study period). To evaluate the model performance, we compared the estimations of stand averaged diameter at breast height (D), stand averaged tree height (H), stand averaged crown base height (), stand basal area (B) and density (N) with measurements. Moreover, biomass estimations of each tree component (foliage, branch and stem) were compared to estimations from empirical models. Results showed that introducing size distributions can represent better stand structure and improve the model predictions compared with data. Moreover, the new mortality model showed promise with qualitatively more realistic results especially among the largest tree size classes. However, model bias still existed in the simulation although the predictions were improved. It revealed that further calibration of the PREBAS model with size classes should be done to better extend the model applicability to uneven-aged forests. Numéro de notice : A2023-022 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120702 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120702 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102228
in Forest ecology and management > vol 529 (February-1 2023) . - n° 120702[article]Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 38 n° inconnu ([01/01/2023])
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Titre : Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis Type de document : Article/Communication Auteurs : Haifa Tamiminia, Auteur ; Bahram Salehi, Auteur ; Masoud Mahdianpari, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse d'image orientée objet
[Termes IGN] biomasse aérienne
[Termes IGN] boosting adapté
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification pixellaire
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image Landsat
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] New York (Etats-Unis ; état)
[Termes IGN] réserve naturelleRésumé : (auteur) Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (LiDAR), optical, SAR, and their combination to improve the AGB predictions, 2) examine the capability of tree-based machine learning models, and 3) compare the performance of pixel-based and object-based image analysis (OBIA). To investigate the performance of machine learning models, multiple tree-based algorithms were fitted to predictors derived from airborne LiDAR data, Landsat, Sentinel-2, Sentinel-1, and PALSAR-2/PALSAR SAR data collected within New York’s Adirondack Park. Combining remote sensing data from multiple sources improved the model accuracy (RMSE: 52.14 Mg ha−1 and R2: 0.49). There was no significant difference among gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) models. In addition, pixel-based and object-based models were compared using the airborne LiDAR-derived AGB raster as a training/testing sample. The OBIA provided the best results with the RMSE of 33.77 Mg ha−1 and R2 of 0.81 for the combination of optical and SAR data in the GBM model. Numéro de notice : A2022-331 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2071475 Date de publication en ligne : 27/04/2022 En ligne : https://doi.org/10.1080/10106049.2022.2071475 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100607
in Geocarto international > vol 38 n° inconnu [01/01/2023][article]Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data / Zhuomei Huang in Geocarto international, vol 38 n° inconnu ([01/01/2023])
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Titre : Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data Type de document : Article/Communication Auteurs : Zhuomei Huang, Auteur ; Yichao Tian, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] Chine
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] mangrove
[Termes IGN] optimisation par essaim de particulesRésumé : (auteur) Blue carbon ecosystems such as mangroves are natural barriers to resisting and alleviating the impact of storm surges and extreme catastrophic weather. Accurate and efficient determination of the aboveground biomass of mangroves is of great importance for the protection and restoration of blue carbon ecosystems and their response to climate change. This study proposes a light gradient boosting model (LGBM) based on particle swarm optimization (PSO) algorithm for feature selection. We constructed and verified the proposed model using 227 quadrat datasets from a field survey and Sentinel-1 and Sentinel-2 data. The determination coefficient (R2) and root-mean-square error (RMSE) were used to evaluate the performance of the model. Compared with random forest(RF), K-nearest neighbourhood regression(KNNR), extreme gradient boosting(XGBR), LGBM, and other machine learning algorithms, the LGBM-PSO model achieves better results (R2 = 0.7807, RMSE = 24.6864 Mg·ha−1), The predicted range of mangrove biomass is 4.623–206.975 Mg·ha−1. Therefore, the use of multisource remote sensing data combined with the LGBM-PSO model can provide better prediction results of aboveground biomass of mangroves, thereby providing a new method for estimating the aboveground biomass of large-scale mangroves. Numéro de notice : A2022-621 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2102226 Date de publication en ligne : 22/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2102226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101356
in Geocarto international > vol 38 n° inconnu [01/01/2023][article]Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model, LiDAR and digital hemispherical photography / Ihor Kozak in Urban Forestry & Urban Greening, vol 79 (January 2023)
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Titre : Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model, LiDAR and digital hemispherical photography Type de document : Article/Communication Auteurs : Ihor Kozak, Auteur ; Mikhail Popov, Auteur ; Igor Semko, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 127793 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] biomasse aérienne
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] forêt urbaine
[Termes IGN] houppier
[Termes IGN] image hémisphérique
[Termes IGN] Leaf Area Index
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de régression
[Termes IGN] modèle numérique de terrain
[Termes IGN] photographie numérique
[Termes IGN] Pinus sylvestris
[Termes IGN] Pologne
[Termes IGN] semis de points
[Termes IGN] surface terrièreRésumé : (auteur) The article proposes methods for combining Airborne Laser Scanning (ALS) with Digital Hemispherical Photography (DHP) data required by the Urban Forest Biomass (UFB) model to predict the aboveground biomass (AGB) of Scotch pine (Pinus sylvestris L.) in urban forests of Lublin (Poland). The article also demonstrates the potential of ALS and DHP data in urban AGB estimation. ALS and Leaf Area Index (LAI) data were calculated using a voxels-vector approach based on the measurements taken at eight permanent sample plots (PSPs). The research was conducted in 2014 and the prediction was made until 2030. It was found that the determination coefficients (R2) for the Basal Area (BA) of the trees are 0.97, and the BA modeling parameters have a high correlation with those observed in the field (model efficiency (ME) 0.94). 83 % growth trajectory based on the measured BA was appropriately modeled using the UFB model (P > 0.9). The results for AGB show that the degree of fitting and accuracy are greatest for the Monte Carlo (MC) simulation technique based on ALS and DHP data (UBF with ALS and DHP) where R2 = 0.98, RMSE = 2.97 t/ha, MAE = 2.35 t/ha, rRMSE = 1.28 %, which performed better than MC simulation technique without ALS and DHP (UBF without ALS and DHP) where R2 = 0.94, RMSE = 4.58 t/ha, MAE = 3.64 t/ha, rRMSE = 3.29 %. The results indicate that the proposed method based on combining the UFB model, LiDAR and DHP allows us to improve the accuracy of the AGB prediction. Numéro de notice : A2023-023 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ufug.2022.127793 Date de publication en ligne : 23/11/2022 En ligne : https://doi.org/10.1016/j.ufug.2022.127793 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102246
in Urban Forestry & Urban Greening > vol 79 (January 2023) . - n° 127793[article]Management of birch spruce mixed stands with consideration of carbon stock in biomass and harvested wood products / Jānis Vuguls in Forests, vol 14 n° 1 (January 2023)
PermalinkA new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing / Yali Zhang in GIScience and remote sensing, vol 60 n° 1 (2023)
PermalinkAbove ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)
PermalinkWall-to-wall mapping of forest biomass and wood volume increment in Italy / Francesca Giannetti in Forests, vol 13 n° 12 (December 2022)
PermalinkDevelopment and long-term dynamics of old-growth beech-fir forests in the Pyrenees: Evidence from dendroecology and dynamic vegetation modelling / Dario Martín-Benito in Forest ecology and management, vol 524 (November-15 2022)
PermalinkEstimating carbon stocks and biomass expansion factors of urban greening trees using terrestrial laser scanning / Linlin Wu in Forests, vol 13 n° 9 (september 2022)
PermalinkQuantifying the influence of plot-level uncertainty in above ground biomass up scaling using remote sensing data in central Indian dry deciduous forest / Thangavelu Mayamanikandan in Geocarto international, vol 37 n° 12 ([01/07/2022])
PermalinkManagement or climate and which one has the greatest impact on forest soil’s protective value? A case study in Romanian mountains / Cosmin Cosofret in Forests, vol 13 n° 6 (June 2022)
PermalinkUncertainty of biomass stocks in Spanish forests: a comprehensive comparison of allometric equations / Aitor Ameztegui in European Journal of Forest Research, vol 141 n° 3 (June 2022)
PermalinkAboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network / Chen Chen in Remote sensing of environment, vol 270 (March 2022)
PermalinkAre northern German Scots pine plantations climate smart? The impact of large-scale conifer planting on climate, soil and the water cycle / Christoph Leuschner in Forest ecology and management, vol 507 (March-1 2022)
PermalinkAssessing the dependencies of scots pine (Pinus sylvestris L.) structural characteristics and internal wood property variation / Ville Kankare in Forests, vol 13 n° 3 (March 2022)
PermalinkChanges of tree stem biomass in European forests since 1950 / Aleksandr Lebedev in Journal of forest science, vol 68 n° 3 (March 2022)
PermalinkEstimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds / Jiayuan Lin in Urban Forestry & Urban Greening, vol 69 (March 2022)
PermalinkAboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models / Dibyendu Deb in Geocarto international, vol 37 n° 4 ([15/02/2022])
PermalinkFive decades of ground flora changes in a temperate forest: The good, the bad and the ambiguous in biodiversity terms / K.J. Kirby in Forest ecology and management, vol 505 (February-1 2022)
PermalinkSurvival time and mortality rate of regeneration in the deep shade of a primeval beech forest / R. Petrovska in European Journal of Forest Research, vol 141 n° 1 (February 2022)
PermalinkCombined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation / Narissara Nuthammachot in Geocarto international, vol 37 n° 2 ([15/01/2022])
Permalink3D stem modelling in tropical forest: towards improved biomass and biomass change estimates / Sébastien Bauwens (2022)
PermalinkAbove-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data / Fardin Moradi in Annals of forest research, vol 65 n° 1 (January - June 2022)
PermalinkContributions of multi-temporal airborne LiDAR data to mapping carbon stocks and fluxes in tropical forests / Claudia Milena Huertas Garcia (2022)
PermalinkEstimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)
PermalinkGlobal canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)
PermalinkPermalinkInvestigating the role of wind disturbance in tropical forests through a forest dynamics model and satellite observations / E-Ping Rau (2022)
PermalinkMonitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)
PermalinkClimate warming-induced replacement of mesic beech by thermophilic oak forests will reduce the carbon storage potential in aboveground biomass and soil / Jan Kasper in Annals of Forest Science, vol 78 n° 4 (December 2021)
PermalinkEstimation of individual tree stem biomass in an uneven-aged structured coniferous forest using multispectral LiDAR data / Nikos Georgopoulos in Remote sensing, vol 13 n° 23 (December-1 2021)
PermalinkA generic information framework for decision-making in a forest-based bio-economy / Jean-Baptiste Pichancourt in Annals of Forest Science, vol 78 n° 4 (December 2021)
PermalinkModelling bark volume for six commercially important tree species in France: assessment of models and application at regional scale / Rodolphe Bauer in Annals of Forest Science, vol 78 n° 4 (December 2021)
PermalinkAbove-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat / Stefano Puliti in Remote sensing of environment, vol 265 (November 2021)
PermalinkMulti-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])
PermalinkThe effects of combining the variables in allometric biomass models on biomass estimates over large forest areas: A european beech case study / Erick O. Osewe in Forests, vol 12 n° 10 (October 2021)
PermalinkVariation in downed deadwood density, biomass, and moisture during decomposition in a natural temperate forest / Tomas Přívětivý in Forests, vol 12 n° 10 (October 2021)
PermalinkMapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches / He Zhang in Remote sensing, vol 13 n° 18 (September-2 2021)
PermalinkTarget-based automated matching of multiple terrestrial laser scans for complex forest scenes / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
PermalinkThe real potential of current passive satellite data to map aboveground biomass in tropical forests / Nidhi Jha in Remote sensing in ecology and conservation, vol 7 n° 3 (September 2021)
PermalinkCalibration of the process-based model 3-PG for major central European tree species / David I. Forrester in European Journal of Forest Research, vol 140 n° 4 (August 2021)
PermalinkSurface modelling of forest aboveground biomass based on remote sensing and forest inventory data / Xiaofang Sun in Geocarto international, vol 36 n° 14 ([01/08/2021])
PermalinkEstimation of biomass increase and CUE at a young temperate scots pine stand concerning drought occurrence by combining eddy covariance and biometric methods / Paulina Dukat in Forests, vol 12 n° 7 (July 2021)
PermalinkEstimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data / Yueting Wang in Ecological indicators, vol 126 (July 2021)
PermalinkSemantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
PermalinkSpatio-temporal-spectral observation model for urban remote sensing / Zhenfeng Shao in Geo-spatial Information Science, vol 24 n° 3 (July 2021)
PermalinkPermalinkIdentifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing / Elliott White Jr in Remote sensing of environment, vol 258 (June 2021)
PermalinkImproving tree biomass models through crown ratio patterns and incomplete data sources / Maria Menéndez-Miguélez in European Journal of Forest Research, vol 140 n° 3 (June 2021)
PermalinkModel-based estimation of forest canopy height and biomass in the Canadian boreal forest using radar, LiDAR, and optical remote sensing / Michael L. Benson in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
PermalinkWalking through the forests of the future: using data-driven virtual reality to visualize forests under climate change / Jiawei Huang in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)
PermalinkAboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model / S.M. Ghosh in Computers & geosciences, vol 150 (May 2021)
PermalinkEstimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])
PermalinkEvaluating P-Band TomoSAR for biomass retrieval in boreal forest / Erik Blomberg in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)
PermalinkEuropean beech leads to more bioactive humus forms but stronger mineral soil acidification as Norway spruce and Scots pine – Results of a repeated site assessment after 63 and 82 years of forest conversion in Central Germany / Florian Achilles in Forest ecology and management, vol 483 ([01/03/2021])
PermalinkVariations in temperate forest biomass ratio along three environmental gradients are dominated by interspecific differences in wood density / Baptiste Kerfriden in Plant ecology, vol 222 n° 3 (March 2021)
PermalinkWhat factors shape spatial distribution of biomass in riparian forests? Insights from a LiDAR survey over a large area / Leo Huylenbroeck in Forests, vol 12 n° 3 (March 2021)
PermalinkPermalinkApplications of remote sensing data in mapping of forest growing stock and biomass / Jose Aranha (2021)
PermalinkDéterminants de la composition floristique et estimations des stocks de carbone des peuplements forestiers matures de Uma (Tshopo, RDC) / John Katembo Mukirania (2021)
PermalinkEvaluation du stock de carbone aérien dans la végétation à partir de multiples observations satellites micro-ondes / Martin Cubaud (2021)
PermalinkPermalinkQualification des données LiDAR GEDI pour le suivi de l’impact climatique sur la forêt de Südharz / Iris Jeuffrard (2021)
PermalinkVariabilité environnementale et botanique de la densité du bois des espèces forestières et variabilité temporelle de la biomasse aérienne des forêts françaises : une analyse sur un échantillon systématique de l’inventaire forestier national / Baptiste Kerfriden (2021)
PermalinkExploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal / Santa Pandit in Geocarto international, vol 35 n° 16 ([01/12/2020])
PermalinkImproving aboveground biomass estimates by taking into account density variations between tree components / Antoine Billard in Annals of Forest Science, vol 77 n° 4 (December 2020)
PermalinkGround-based remote sensing of forests exploiting GNSS signals / Leila Guerriero in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)
PermalinkL-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia / Bambang H Trisasongko in Geocarto international, vol 35 n° 12 ([01/09/2020])
PermalinkCarbon stocks, partitioning, and wood composition in short-rotation forestry system under reduced planting spacing / Felipe Schwerz in Annals of Forest Science, vol 77 n° 3 (September 2020)
PermalinkPredicting biomass dynamics at the national extent from digital aerial photogrammetry / Bronwyn Price in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)
PermalinkInfluence of forest management activities on soil organic carbon stocks: A knowledge synthesis / Mathias Mayer in Forest ecology and management, Vol 466 (15 June 2020)
PermalinkImproving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass / Christoph Kleinn in Forest ecosystems, vol 7 (2020)
PermalinkMapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors / Svetlana Saarela in Forest ecosystems, vol 7 (2020)
PermalinkPotential of texture from SAR tomographic images for forest aboveground biomass estimation / Zhanmang Liao in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)
PermalinkMangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system / Minh Hai Pham in Plos one, vol 15 n° 5 (May 2020)
PermalinkAbove-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging / Bo Li in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
PermalinkAssessing forest availability for wood supply in Europe / Iciar A. Alberdi in Forest policy and economics, vol 111 (February 2020)
PermalinkCan Carbon Sequestration in Tasmanian “Wet” Eucalypt Forests Be Used to Mitigate Climate Change? Forest Succession, the Buffering Effects of Soils, and Landscape Processes Must Be Taken into Account / Peter D. McIntosh in International journal of forestry research, vol 2020 ([01/02/2020])
PermalinkArtificial 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])
PermalinkEstimation et suivi de la ressource en bois en France métropolitaine par valorisation des séries multi-temporelles à haute résolution spatiale d'images optiques (Sentinel-2) et radar (Sentinel-1, ALOS-PALSAR) / David Morin (2020)
PermalinkPermalinkInversion de données PolSAR en bande P pour l'estimation de la biomasse forestière / Colette Gelas (2020)
PermalinkPermalinkRéponses de la productivité des forêts aux fluctuations météorologiques : biais et surestimations des estimations de terrain / Olivier Bouriaud (2020)
PermalinkPhosphorus availability in relation to soil properties and forest productivity in Pinus sylvestris L. plantations / Teresa Bueis in Annals of Forest Science, Vol 76 n° 4 (December 2019)
PermalinkA 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)
PermalinkMapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece / Maria Kampouri in Geocarto international, vol 34 n° 12 ([15/09/2019])
PermalinkFree and open-source GIS technologies for the management of woody biomass / Michele Mangiameli in Applied geomatics, vol 11 n° 3 (September 2019)
PermalinkPressures and threats to nature related to human activities in European urban and suburban forests / Ewa Referowska-Chodak in Forests, vol 10 n° 9 (September 2019)
PermalinkQuantifying 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)
PermalinkEstimating 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)
PermalinkInnovations 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)
PermalinkOcclusion probability in operational forest inventory field sampling with ForeStereo / Fernando Montes in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (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)
PermalinkWood quality of black spruce and balsam fir trees defoliated by spruce budworm: A case study in the boreal forest of Quebec, Canada / Carlos Paixao in Forest ecology and management, vol 437 (1 April 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)
PermalinkEstimating net biomass production and loss from repeated measurements of trees in forests and woodlands: Formulae, biases and recommendations / Takashi S. Kohyama in Forest ecology and management, vol 433 (15 February 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)
PermalinkAnalysis and modelling of the wood density variability of the French forest species for the assessment of the forest biomass under climatic change [diaporama] / Jean-Michel Leban (2019)
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PermalinkBiomass ratio varies along soil water availability : An analysis based on wood density data collected by the French NFI / Baptiste Kerfriden (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)
PermalinkPermalinkEvaluating 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)
PermalinkRecommandations pour une récolte durable de biomasse forestière pour l’énergie / Guy Landmann (2019)
PermalinkPermalinkToward the development of total volume and biomass functions using terrestrial lidar and NFI data / Cédric Vega (2019)
PermalinkEstimating 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)
PermalinkFuture management options for cembran pine forests close to the alpine timberline / Nathalia Jandl in Annals of Forest Science, vol 75 n° 3 (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)
PermalinkEstimation of forest aboveground biomass from HJ1B imagery using a canopy reflectance model and a forest growth model / Xinyun Wang in Geocarto international, vol 33 n° 2 (February 2018)
PermalinkPermalinkPermalinkPermalinkProgresser dans la quantification de la biomasse forestière française (les premiers résultats du projet XyloDensMap, un projet INRA/IGN) [diaporama] / Jean-Michel Leban (2018)
PermalinkDigital aerial photogrammetry can efficiently support large-area forest inventories in Norway / Lars Johannes in Forestry, an international journal of forest research, vol 90 n° 5 (December 2017)
PermalinkEstimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications? / Fabian E. Fassnacht in Forestry, an international journal of forest research, vol 90 n° 5 (December 2017)
PermalinkEstimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery / Jose Alan A. Castillo in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)
PermalinkThe potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas / Emanuele Santi in Remote sensing of environment, vol 200 (October 2017)
PermalinkTree size thresholds produce biased estimates of forest biomass dynamics / Eric B. Searle in Forest ecology and management, vol 400 (15 September 2017)
PermalinkImproving the prediction of African savanna vegetation variables using time series of MODIS products / Miriam Tsalyuk in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
PermalinkCoverage of high biomass forests by the ESA BIOMASS mission under defense restrictions / João M.B. Carreiras in Remote sensing of environment, vol 196 (July 2017)
PermalinkMonitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
PermalinkEcological functions of vegetation as potentials of ecosystem services (floodplain alder forest in the Tríbeč microregion) / Pavol Eliáš in Journal of forest science, vol 63 n° 3 (October 2015)
PermalinkForest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation / Michael Schlund in International journal of applied Earth observation and geoinformation, vol 56 (April 2017)
PermalinkReconstructing forest canopy from the 3D triangulations of airborne laser scanning point data for the visualization and planning of forested landscapes / Jari Vauhkonen in Annals of Forest Science, vol 74 n° 1 (March 2017)
PermalinkTerrestrial laser scanning as a tool for assessing tree growth / Jonathan Sheppard in iForest, biogeosciences and forestry, vol 10 n° 1 (February 2017)
PermalinkFeasibility of Terrestrial laser scanning for collecting stem volume information from single trees / Ninni Saarinen in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)
PermalinkForest inventory-based projection systems for wood and biomass availability, ch. France [National woody biomass projection systems based on forest inventory - Projecting wood resources and potential wood supply of French forests: an approach to cope with forest system diversity] / Antoine Colin (2017)
PermalinkLand Surface Remote Sensing in Continental Hydrology, ch. 3. Using satellite scatterometers to monitor continental surfaces / Pierre-Louis Frison (2017)
PermalinkTélédétection pour l'observation des surfaces continentales, ch. 3. Utilisation des diffusiomètres satellitaires pour le suivi des surfaces continentales / Pierre-Louis Frison (2017)
PermalinkTélédétection pour l'observation des surfaces continentales, Volume 3. Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)
PermalinkFrom inventory to consumer biomass availability - the ITOC model / Udo Mantau in Annals of Forest Science, vol 73 n° 4 (December 2016)
PermalinkOverview of methods and tools for evaluating future woody biomass availability in European countries / Susana Barreiro in Annals of Forest Science, vol 73 n° 4 (December 2016)
PermalinkAboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data / Ibrahim Fayad in International journal of applied Earth observation and geoinformation, vol 52 (October 2016)
PermalinkProgress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space / Cletah Shoko in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)
PermalinkRelative 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)
PermalinkLidar detection of individual tree size in tropical forests / António Ferraz in Remote sensing of environment, vol 183 (15 September 2016)
PermalinkThe impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
PermalinkAirborne lidar estimation of aboveground forest biomass in the absence of field inventory / António Ferraz in Remote sensing, vol 8 n° 8 (August 2016)
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PermalinkAllometric models for estimating tree volume and aboveground biomass in lowland forests of Tanzania / Wilson Ancelm Mugasha in International journal of forestry research, vol 2016 ([01/08/2016])
PermalinkNationwide airborne laser scanning based models for volume, biomass and dominant height in Finland / Eetu Kotivuori in Silva fennica, vol 50 n° 4 (2016)
PermalinkAbove- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach / Marco Andrew Njana in Annals of Forest Science, vol 73 n° 2 (June 2016)
PermalinkEffects of experimental warming on soil respiration and biomass in Quercus variabilis Blume and Pinus densiflora Sieb. et Zucc. seedlings / Nam Jin Noh in Annals of Forest Science, vol 73 n° 2 (June 2016)
PermalinkInventory-based estimation of forest biomass in Shitai County, China: A comparison of five methods / X. Tang in Annals of forest research, vol 59 n° 2 (July - December 2016)
PermalinkGLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring / Erwan Motte in Sensors, vol 16 n° 5 (May 2016)
PermalinkICESat/GLAS canopy height sensitivity inferred from Airborne Lidar / Craig Mahoney in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 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)
PermalinkOn the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters / Cédric Vega in Remote sensing of environment, vol 175 (15 March 2016)
PermalinkMangrove forest characterization in Southeast Côte d’Ivoire / Isimemen Osemwegie in Open journal of forestry, vol 6 n° 3 (February 2016)
PermalinkStatistical rigor in LiDAR-assisted estimation of aboveground forest biomass / Timothy G. Gregoire in Remote sensing of environment, vol 173 (February 2016)
PermalinkEstimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)
PermalinkMeasurement of the annual biomass increment of the French forests, XYLODENSMAP project [diaporama] / Jean-Michel Leban (2016)
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PermalinkRadar based classification prior to biomass retrieval from P-Band SAR data / Pierre-Louis Frison (2016)
PermalinkExamining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments / Mbulisi Sibanda in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)
PermalinkA review of forest and tree plantation biomass equations in Indonesia / Kamalakumari Anitha in Annals of Forest Science, vol 72 n° 8 (December 2015)
PermalinkMultitemporal fluctuations in L-Band Backscatter from a japanese forest / Manabu Watanabe in IEEE Transactions on geoscience and remote sensing, vol 53 n° 11 (November 2015)
PermalinkInvestigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)
PermalinkModeling the above and belowground biomass of planted and coppiced Eucalytpus globulus stands in NW Spain / Daniel J. Vega-Nieva in Annals of Forest Science, vol 72 n° 7 (October 2015)
Permalinkvol 72 n° 7 - October 2015 - Monitoring European forests: results for science, policy, and society (Bulletin de Annals of Forest Science) / Pasi Rautio
PermalinkEstimation of forest biomass from two-level model inversion of single-pass InSAR data / M.J. Soja in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)
Permalinkvol 72 n° 6 - September 2015 - Wood properties: future needs, measurement and modelling (Bulletin de Annals of Forest Science) / Francis Colin
PermalinkAboveground-biomass estimation of a complex tropical forest in India using Lidar / Cédric Vega in Remote sensing, vol 7 n° 8 (August 2015)
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PermalinkModeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar / Qi Chen in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)
PermalinkAugmenter le niveau de production de biomasse des cultures ligneuses dédiées ou semi-dédiées. Principaux enseignements du projet SYLVA BIOM / Jean-Charles Bastien in Revue forestière française, vol 67 n° 3 (mai 2015)
PermalinkCirconscrire les gisements de biomasse-énergie pour protéger l'alimentation et la biodiversité : le défi intenable / Yves Poinsot in VertigO, vol 15 n° 1 (mai 2015)
PermalinkImproving forest aboveground biomass estimation using seasonal Landsat NDVI time-series / Xiaolin Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
PermalinkLidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass / Le Li in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
PermalinkEstimating forest biomass from TerraSAR-X stripmap radargrammetry / Svein Solberg in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
PermalinkSatellite data as indicators of tree biomass growth and forest dieback in a Mediterranean holm oak forest / Romà Ogaya in Annals of Forest Science, vol 72 n° 1 (January 2015)
PermalinkTropical forest structure characterization using airborne lidar data: an individual tree level approach / António Ferraz (dec 2015)
PermalinkAn inventory of the above ground biomass in the Mau Forest Ecosystem, Kenya / Mwangi James Kinyanjui in Open journal of forestry, vol 4 n° 10 (July 2014)
PermalinkForest biomass estimation using texture measurements of high-resolution dual-polarization C-band SAR data / Latifur Rahman Sarker in IEEE Transactions on geoscience and remote sensing, vol 51 n° 6 Tome 1 (June 2013)
PermalinkStem volume and above-ground biomass estimation of individual pine trees from LiDAR data: contribution of full-waveform signals / Tristan Allouis in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 6 n° 2 part 3 (April 2013)
PermalinkPermalinkCarbon Stock of European Beech Forest : A Case at M. Pizzalto, Italy / Aida Taghavi Bayat in APCBEE Procedia, vol 1 (2-20)
PermalinkComparing small-footprint lidar and forest inventory data for single strata biomass estimation : A case study over a multi-layered mediterranean forest / António Ferraz (2012)
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PermalinkModelling forest management within a global vegetation model - Part 2: Model validation from a tree to a continental scale / V. Bellassen in Ecological modelling, vol 222 n° 1 (janvier 10, 2011)
PermalinkModelling forest management within a global vegetation model - Part 1: Model structure and general behaviour / V. Bellassen in Ecological modelling, vol 221 n° 20 (octobre 2010)
PermalinkDes marchés bientôt avides de bois-énergie / Al Arter in Le Bois International : l'officiel du bois [édition verte], vol 2010 n° 17 (1er mai 2010)
PermalinkLa torréfaction, procédé d'avenir pour la biomasse ligneuse ? / Fabienne Tisserand in Le Bois International : l'officiel du bois [édition verte], vol 2010 n° 12 (27 mars 2010)
Permalink« La torréfaction de la biomasse, une alternative économiquement rentable et énergétiquement compétitive » / Hervé Chauvin (26 janvier 2010)
PermalinkLa biomasse à pleine vapeur / Juliette Talpin in Systèmes solaires, le journal des énergies renouvelables, n° 194 (novembre-décembre 2009)
PermalinkUsing cover measurements to estimate aboveground understorey biomass in Maritime pine stands / Annabel J. Porté in Annals of Forest Science, Vol 66 n° 3 (April - May 2009)
PermalinkLa Bourgogne planche sur la disponibilité de ses bois / Pascal Charoy in Le Bois International : l'officiel du bois [édition verte], vol 2009 n° 9 (7 mars 2009)
PermalinkBiomass component equations for Latin American species and groups of species / José Návar in Annals of Forest Science, Vol 66 n° 2 (march 2009)
PermalinkNuméro spécial - mars 2009 - Le bois énergie (Bulletin de Pollution atmosphérique) / Jean-Marie Rambaud
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