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Potential 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)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 88 (June 2020) . - 15 p. Titre : Potential of texture from SAR tomographic images for forest aboveground biomass estimation Type de document : Article/Communication Auteurs : Zhanmang Liao, Auteur ; Binbin He, Auteur ; Xingwen Quan, Auteur Année de publication : 2020 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse texturale [Termes IGN] bande P [Termes IGN] biomasse aérienne [Termes IGN] canopée [Termes IGN] image radar moirée [Termes IGN] rétrodiffusion [Termes IGN] tomographie radar Résumé : (auteur) Synthetic Aperture Radar (SAR) texture has been demonstrated to have the potential to improve forest biomass estimation using backscatter. However, forests are 3D objects with a vertical structure. The strong penetration of SAR signals means that each pixel contains the contributions of all the scatterers inside the forest canopy, especially for the P-band. Consequently, the traditional texture derived from SAR images is affected by forest vertical heterogeneity, although the influence on texture-based biomass estimation has not yet been explicitly explored. To separate and explore the influence of forest vertical heterogeneity, we introduced the SAR tomography technique into the traditional texture analysis, aiming to explore whether TomoSAR could improve the performance of texture-based aboveground biomass (AGB) estimation and whether texture plus tomographic backscatter could further improve the TomoSAR-based AGB estimation. Based on the P-band TomoSAR dataset from TropiSAR 2009 at two different sites, the results show that ground backscatter variance dominated the texture features of the original SAR image and reduced the biomass estimation accuracy. The texture from upper vegetation layers presented a stronger correlation with forest biomass. Texture successfully improved tomographic backscatter-based biomass estimation, and the texture from upper vegetation layers made AGB models much more transferable between different sites. In addition, the correlation between texture indices varied greatly among different tomographic heights. The texture from the 10 to 30 m layers was able to provide more independent information than the other layers and the original images, which helped to improve the backscatter-based AGB estimation. Numéro de notice : A2020-447 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2020.102049 Date de publication en ligne : 12/02/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102049 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95523 [article]

Mangrove 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)  

Public [article]  inPlos one > vol 15 n° 5 (May 2020) . - n° 0233110 Titre : Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system Type de document : Article/Communication Auteurs : Minh Hai Pham, Auteur ; Thi Hoai Do, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 0233110 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne [Termes IGN] biomasse forestière [Termes IGN] changement d'occupation du sol [Termes IGN] image Sentinel-SAR [Termes IGN] image SPOT 6 [Termes IGN] Inférence floue [Termes IGN] mangrove [Termes IGN] Viet Nam [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Background : Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass (AGB). Methods : This study aimed to introduce a novel model that incorporates the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classification and AGB estimation. The Ca Mau coastal area was selected as a case study since it has been considered the most preserved mangrove forest area in Vietnam and is being investigated for the impacts of land-use change on forest quality. The model was trained and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multispectral information from the SPOT image. In addition, feature selection was also carried out to choose the optimal combination of predictor variables. The model performance was benchmarked against conventional methods, such as support vector regression, multilayer perceptron, random subspace, and random forest, by using statistical indicators, namely, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Results : The results showed that all three indicators of the proposed model were statistically better than those from the benchmarked methods. Specifically, the hybrid model ended up at RMSE = 70.882, MAE = 55.458, R2 = 0.577 for AGB estimation. Conclusion : From the experiments, such hybrid integration can be recommended for use as an alternative solution for biomass estimation. In a broader context, the fast growth of metaheuristic search algorithms has created new scientifically sound solutions for better analysis of forest cover. Numéro de notice : A2020-833 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article DOI : https://doi.org/10.1371/journal.pone.0233110 Date de publication en ligne : 21/05/2020 En ligne : https://doi.org/10.1371/journal.pone.0233110 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97667 [article]

Above-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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 161 -1 72 Titre : Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging Type de document : Article/Communication Auteurs : Bo Li, Auteur ; Xiangming Xu, Auteur ; Li Zhang, Auteur Année de publication : 2020 Article en page(s) : pp 161 -1 72 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] couvert végétal [Termes IGN] hauteur de la végétation [Termes IGN] image captée par drone [Termes IGN] image hyperspectrale [Termes IGN] image RVB [Termes IGN] indice de végétation [Termes IGN] pomme de terre [Termes IGN] régression des moindres carrés partiels [Termes IGN] rendement agricole Résumé : (auteur) Rapid and accurate biomass and yield estimation facilitates efficient plant phenotyping and site-specific crop management. A low altitude unmanned aerial vehicle (UAV) was used to acquire RGB and hyperspectral imaging data for a potato crop canopy at two growth stages to estimate the above-ground biomass and predict crop yield. Field experiments included six cultivars and multiple treatments of nitrogen, potassium, and mixed compound fertilisers. Crop height was estimated using the difference between digital surface model and digital elevation models derived from RGB imagery. Combining with two narrow-band vegetation indices selected by the RReliefF feature selection algorithm. Random Forest regression models demonstrated high prediction accuracy for both fresh and dry above-ground biomass, with a coefficient of determination (r2) > 0.90. Crop yield was predicted using four narrow-band vegetation indices and crop height (r2 = 0.63) with imagery data obtained 90 days after planting. A Partial Least Squares regression model based on the full wavelength spectra demonstrated improved yield prediction (r2 = 0.81). This study demonstrated the merits of UAV-based RGB and hyperspectral imaging for estimating the above-ground biomass and yield of potato crops, which can be used to assist in site-specific crop management. Numéro de notice : A2020-125 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.013 Date de publication en ligne : 28/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.013 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94750 [article]

Assessing forest availability for wood supply in Europe / Iciar A. Alberdi in Forest policy and economics, vol 111 (February 2020)  

Public [article]  inForest policy and economics > vol 111 (February 2020) . - n° 102032 Titre : Assessing forest availability for wood supply in Europe Type de document : Article/Communication Auteurs : Iciar A. Alberdi, Auteur ; Susann Bender, Auteur ; Thomas Riedel, Auteur ; Valerio Avitable, Auteur ; Olivier Bouriaud , Auteur ; Michal Bosela, Auteur ; Andrea Camia, Auteur ; Isabel Canellas, Auteur ; F. Castro Rego, Auteur ; Christoph Fischer, Auteur ; Alexandra Freudenschuss, Auteur ; Jonas Fridman, Auteur ; Patrizia Gasparini, Auteur ; Thomas Gschwantner, Auteur ; Silvia Guerrero, Auteur ; Bjarki Kjartansson, Auteur ; Miloš Kučera, Auteur ; Adrian Lanz, Auteur ; Gheorghe Marin, Auteur ; Sarah Mubareka, Auteur ; Monica Notarangelo, Auteur ; Leonia Nunes, Auteur ; Benoit Pesty , Auteur ; et al., Auteur Année de publication : 2020 Projets : 3-projet - voir note / Article en page(s) : n° 102032 Note générale : bibliographie This research was supported by the Specific contract n. 18 “Use of National Forest Inventories data to estimate area and above ground biomass in European forests not available for wood supply” in the context of the Framework contract for the provision of forest data and services supporting the European Forest Data Centre 2012/ S 78-127532 of 21/04/2012 of the Joint Research Centre of the European Commission; the EG-013-72 agreement of the Ministry of Agriculture, Fisheries and Food (MAPA) and the INIA belonging to the Spanish Ministry of Science and Innovation (MICINN); and the project No. APVV-15-0265 granted by the Slovak Research and Development Agency. Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne [Termes IGN] biomasse forestière [Termes IGN] inventaire forestier étranger (données) [Termes IGN] ressources forestières [Termes IGN] Union Européenne [Termes IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) The quantification of forests available for wood supply (FAWS) is essential for decision-making with regard to the maintenance and enhancement of forest resources and their contribution to the global carbon cycle. The provision of harmonized forest statistics is necessary for the development of forest associated policies and to support decision-making. Based on the National Forest Inventory (NFI) data from 13 European countries, we quantify and compare the areas and aboveground dry biomass (AGB) of FAWS and forest not available for wood supply (FNAWS) according to national and reference definitions by determining the restrictions and associated thresholds considered at country level to classify forests as FAWS or FNAWS. FAWS represent between 75 and 95 % of forest area and AGB for most of the countries in this study. Economic restrictions are the main factor limiting the availability of forests for wood supply, accounting for 67 % of the total FNAWS area and 56 % of the total FNAWS AGB, followed by environmental restrictions. Profitability, slope and accessibility as economic restrictions, and protected areas as environmental restrictions are the factors most frequently considered to distinguish between FAWS and FNAWS. With respect to the area of FNAWS associated with each type of restriction, an overlap among the restrictions of 13.7 % was identified. For most countries, the differences in the FNAWS areas and AGB estimates between national and reference definitions ranged from 0 to 5 %. These results highlight the applicability and reliability of a FAWS reference definition for most of the European countries studied, thereby facilitating a consistent approach to assess forests available for supply for the purpose of international reporting. Numéro de notice : A2020-870 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.forpol.2019.102032 Date de publication en ligne : 10/11/2019 En ligne : https://doi.org/10.1016/j.forpol.2019.102032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99487 [article]

Can 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])  

Public [article]  inInternational journal of forestry research > vol 2020 [01/02/2020] . - 16 p. Titre : Can 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 Type de document : Article/Communication Auteurs : Peter D. McIntosh, Auteur ; James L. Hardcastle, Auteur ; Tobias Klöffe, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière [Termes IGN] changement climatique [Termes IGN] écosystème forestier [Termes IGN] Eucalyptus (genre) [Termes IGN] forêt équatoriale [Termes IGN] matière organique [Termes IGN] peuplement mélangé [Termes IGN] puits de carbone [Termes IGN] Tasmanie [Termes IGN] zone humide [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Small areas of the wetter parts of southeast Australia including Tasmania support high-biomass “wet” eucalypt forests, including “mixed” forests consisting of mature eucalypts up to 100 m high with a rainforest understorey. In Tasmania, mixed forests transition to lower biomass rainforests over time. In the scientific and public debate on ways to mitigate climate change, these forests have received attention for their ability to store large amounts of carbon (C), but the contribution of soil C stocks to the total C in these two ecosystems has not been systematically researched, and consequently, the potential of wet eucalypt forests to serve as long-term C sinks is uncertain. This study compared soil C stocks to 1 m depth at paired sites under rainforest and mixed forests and found that there was no detectable difference of mean total soil C between the two forest types, and on average, both contained about 200 Mg·ha−1 of C. Some C in subsoil under rainforests is 3000 years old and retains a chemical signature of pyrogenic C, detectable in NMR spectra, indicating that soil C stocks are buffered against the effects of forest succession. The mean loss of C in biomass as mixed forests transition to rainforests is estimated to be about 260 Mg·ha−1 over a c. 400-year period, so the mature mixed forest ecosystem emits about 0.65 Mg·ha−1·yr−1 of C during its transition to rainforest. For this reason and because of the risk of forest fires, setting aside large areas of wet eucalypt forests as reserves in order to increase landscape C storage is not a sound strategy for long-term climate change mitigation. Maintaining a mosaic of managed native forests, including regenerating eucalypts, mixed forests, rainforests, and reserves, is likely to be the best strategy for maintaining landscape C stocks. Numéro de notice : A2020-627 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1155/2020/6509659 Date de publication en ligne : 30/07/2020 En ligne : https://doi.org/10.1155/2020/6509659 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96030 [article]

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])  

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Estimation 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)  

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European Forest Accounts - Années 2016-2017 : Rapport d’étude / Alexandra Niedzwiedz (2020)  

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Inversion de données PolSAR en bande P pour l'estimation de la biomasse forestière / Colette Gelas (2020)  

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Remote sensing technology applications in forestry and REDD+ / Kim Calders (2020)  

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Réponses de la productivité des forêts aux fluctuations météorologiques : biais et surestimations des estimations de terrain / Olivier Bouriaud (2020)  

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Phosphorus 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)  

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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)  

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Mapping 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])  

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Free and open-source GIS technologies for the management of woody biomass / Michele Mangiameli in Applied geomatics, vol 11 n° 3 (September 2019)  

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