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Wall-to-wall mapping of forest biomass and wood volume increment in Italy / Francesca Giannetti in Forests, vol 13 n° 12 (December 2022)  

Public [article]  inForests > vol 13 n° 12 (December 2022) . - n° 1989 Titre : Wall-to-wall mapping of forest biomass and wood volume increment in Italy Type de document : Article/Communication Auteurs : Francesca Giannetti, Auteur ; Gherardo Chirici, Auteur ; Elia Vangi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1989 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne [Termes IGN] carte thématique [Termes IGN] écosystème forestier [Termes IGN] forêt méditerranéenne [Termes IGN] gestion forestière [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Italie [Termes IGN] puits de carbone [Termes IGN] volume en bois [Vedettes matières IGN] Foresterie Résumé : (auteur) Several political initiatives aim to achieve net-zero emissions by the middle of the twenty-first century. In this context, forests are crucial as a carbon sink to store unavoidable emissions. Assessing the carbon sequestration potential of forest ecosystems is pivotal to the availability of accurate forest variable estimates for supporting international reporting and appropriate forest management strategies. Spatially explicit estimates are even more important for Mediterranean countries such as Italy, where the capacity of forests to act as sinks is decreasing due to climate change. This study aimed to develop a spatial approach to obtain high-resolution maps of Italian forest above-ground biomass (ITA-BIO) and current annual volume increment (ITA-CAI), based on remotely sensed and meteorological data. The ITA-BIO estimates were compared with those obtained with two available biomass maps developed in the framework of two international projects (i.e., the Joint Research Center and the European Space Agency biomass maps, namely, JRC-BIO and ESA-BIO). The estimates from ITA-BIO, JRC-BIO, ESA-BIO, and ITA-CAI were compared with the 2nd Italian NFI (INFC) official estimates at regional level (NUT2). The estimates from ITA-BIO are in good agreement with the INFC estimates (R2 = 0.95, mean difference = 3.8 t ha−1), while for JRC-BIO and ESA-BIO, the estimates show R2 of 0.90 and 0.70, respectively, and mean differences of 13.5 and of 21.8 t ha−1 with respect to the INFC estimates. ITA-CAI estimates are also in good agreement with the INFC estimates (R2 = 0.93), even if they tend to be slightly biased. The produced maps are hosted on a web-based forest resources management Decision Support System developed under the project AGRIDIGIT (ForestView) and represent a key element in supporting the new Green Deal in Italy, the European Forest Strategy 2030 and the Italian Forest Strategy. Numéro de notice : A2022-864 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.3390/f13121989 Date de publication en ligne : 24/11/2022 En ligne : https://doi.org/10.3390/f13121989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102156 [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 inconnu n° inconnu ([01/08/2022])  

Public [article]  inGeocarto international > vol inconnu n° inconnu [01/08/2022] 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 : 2022 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 particules Résumé : (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 [article]

Quantifying 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])  

Public [article]  inGeocarto international > vol 37 n° 12 [01/07/2022] . - pp 3489 - 3503 Titre : Quantifying the influence of plot-level uncertainty in above ground biomass up scaling using remote sensing data in central Indian dry deciduous forest Type de document : Article/Communication Auteurs : Thangavelu Mayamanikandan, Auteur ; Suraj Reddy, Auteur ; Rakesh Fararoda, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3489 - 3503 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] forêt de feuillus [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] incertitude des données [Termes IGN] Inde [Termes IGN] placette d'échantillonnage [Termes IGN] superposition d'images Résumé : (auteur) Accurate and reliable estimation of Above Ground Biomass (AGB) in tropical forests is much needed for net carbon assessments. The aim of the study is to determine the uncertainty in biomass estimation in terms of field plot size, shape and location error using field plot and remote sensing data in tropical dry deciduous forests of India. Detailed tree measurements and location mapping are performed in 13 (1 ha) plots and 1 a very large permanent plot of 32 ha and AGB is estimated using local volume equations. Remote sensing-based AGB estimated using a multiple linear regression model between the reflectance (Sentinel-2) and backscatter (Sentinel-1) with field AGB. The result shows relative root mean square error of the model decreased by approximately 50% with a plot size increase from 0.01 ha (64%) to 0.64 ha (14%). Furthermore, we also observed that the effect of global positioning system location errors in AGB modelling would be negated by increasing plot size. Numéro de notice : A2022-587 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1864029 Date de publication en ligne : 29/12/2020 En ligne : https://doi.org/10.1080/10106049.2020.1864029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101361 [article]

Aboveground 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)  

Public [article]  inRemote sensing of environment > vol 270 (March 2022) . - n° 112885 Titre : Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network Type de document : Article/Communication Auteurs : Chen Chen, Auteur ; Yi Ma, Auteur ; Guangbo Ren, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112885 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] carte d'occupation du sol [Termes IGN] carte thématique [Termes IGN] image hyperspectrale [Termes IGN] image Sentinel-MSI [Termes IGN] littoral [Termes IGN] marais salant [Termes IGN] réseau antagoniste génératif Résumé : (auteur) Coastal wetlands are main components of the “blue carbon” ecosystems in coastal zones. Salt-marsh biomass is especially important regarding climate-change mitigation. Generating high precision biomass maps for evaluating the ecological functions of coastal wetlands is essential; however, conducting accurate biomass inversions with limited in situ observations from coastal wetlands is challenging. We propose a generative adversarial network with a constrained factor model (GAN-CF) for expanding limited in situ salt-marsh biomass observations. We used Sentinel-2 images and a deep belief network based on the conjugate gradient method (CG-DBN) for obtaining land-cover maps and the salt-marsh distribution (species: Phragmites australis, Suaeda glauca, Spartina alterniflora, and mixed species dominated by Tamarix chinensis) in the study area. This study bridges in situ hyperspectral and Sentinel-2 multispectral data by a satellite-band equivalent conversion model. The biomass and multispectral data derived from Sentinel-2 were used as input for the proposed GAN-CF model, which produced and constrained the generated samples based on the features (i.e., spectra, vegetation index, and biomass) of the in situ observations. Aboveground biomass (AGB) maps at 10-m spatial resolution were produced by constructing multiple linear regression models (MLRMs) based on the generated samples of each salt-marsh type using Sentinel-2 images. The quantity and richness of the generated samples improved the AGB estimations in the study area. The inversion accuracy of S. alterniflora was significantly improved (RMSE = 3.71 Mg/ha); the estimated AGB was strongly related to the in situ observations (R = 0.923). The estimated AGB was validated using in situ observations. The total amount of salt-marsh AGB in the study area in 2019 was estimated at 2.36 × 105 Mg, with 7.95 Mg/ha average. The salt-marsh biomass in decreasing order was as follows: P. australis (12.7 Mg/ha) > S. alterniflora (11.5 Mg/ha) > mixed species (8.97 Mg/ha) > S. glauca (2.18 Mg/ha). The salt-marsh area in decreasing order was as follows: S. glauca (10,410 ha) > P. australis (7320 ha) > mixed species (6740 ha) > S. alterniflora (5240 ha). By a feasibility analysis we estimated the biomass based on the Sentinel-2 data covering the Yellow River delta wetland in May, July, and September 2019 and the Jiaozhou Bay wetland in September 2019 by using the generated samples. The generated samples based on the 2013–2019 in situ observations constitute a salt-marsh biomass database, which can be useful for quantifying the regional carbon storage and ecological restoration monitoring. Numéro de notice : A2022-128 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112885 Date de publication en ligne : 07/01/2022 En ligne : https://doi.org/10.1016/j.rse.2021.112885 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99710 [article]

Are 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)  

Public [article]  inForest ecology and management > vol 507 (March-1 2022) . - n° 120013 Titre : Are northern German Scots pine plantations climate smart? The impact of large-scale conifer planting on climate, soil and the water cycle Type de document : Article/Communication Auteurs : Christoph Leuschner, Auteur ; Agnes Förster, Auteur ; Marco Diers, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120013 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] acidification des sols [Termes IGN] albedo [Termes IGN] Allemagne [Termes IGN] biomasse aérienne [Termes IGN] canopée [Termes IGN] changement climatique [Termes IGN] évapotranspiration [Termes IGN] Fagus sylvatica [Termes IGN] foresterie [Termes IGN] Pinus sylvestris [Termes IGN] puits de carbone [Termes IGN] résilience écologique [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Increasing temperatures and rising atmospheric vapor pressure deficits are exposing forests around the globe to increasing drought and heat stress, demanding a shift to climate-smart forestry for increasing the stress resistance and resilience of production forests and to enhance their climate change mitigation potential. Based on measurements in paired pine and beech forests and the review of literature data, we analyse the biophysical consequences and the carbon cycle impact of large-scale Scots pine (Pinus sylvestris L.) plantations in northern Germany in the face of a warming and aridifying climate. We quantified canopy surface albedo and surface temperature, evapotranspiration and deep seepage, carbon (C) storage in biomass and soil and annual C sequestration, and soil acidification of pine plantations in comparison to beech forests (Fagus sylvatica L.), the natural forest vegetation. We find that near-infrared (NIR, 700–3000 mn) canopy surface albedo is higher by 5.2 percentage points during summer over beech as compared to pine forest, resulting in a 9 % higher net radiation and a 0.6 K higher surface temperature of the pine canopy. Deep seepage is on average by 68 mm yr−1 smaller under pine than beech forest (66 mm yr−1 vs. 134 mm yr−1) due to the higher evapotranspiration of pine. C storage in biomass and soil is by ∼ 48 Mg C ha−1 higher in beech than pine forests, reflecting the higher productivity of beech, demonstrating an unfavorably low C sequestration potential of Scots pine plantations. We conclude that the large-scale Scots pine plantations in northern Germany (>1.7 million ha) are neither environmental-friendly nor climate smart, given their enhancement of climate-warming, low climate change mitigation potential, and negative effect on groundwater recharge. Replacing pine plantations by beech (or other hardwood) forests in northern Germany and adjacent regions is urgently needed for achieving the goals of climate-smart forestry. Numéro de notice : A2022-136 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120013 Date de publication en ligne : 24/01/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99742 [article]

Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds / Jiayuan Lin in Urban Forestry & Urban Greening, vol 69 (March 2022)  

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Aboveground 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])  

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Survival 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)  

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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 37 n° inconnu ([30/01/2022])  

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Combined 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])  

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3D stem modelling in tropical forest: towards improved biomass and biomass change estimates / Sébastien Bauwens (2022)  

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Above-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 (2022)  

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Estimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)  

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Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)  

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Monitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)  

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