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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 (April 2022)  

Public [article]  inGeocarto international > vol 37 n° 4 (April 2022) . - pp 1043 - 1058 Titre : 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 Type de document : Article/Communication Auteurs : Dibyendu Deb, Auteur ; Shovik Deb, Auteur ; Debasis Chakraborty, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1043 - 1058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] distribution spatiale [Termes IGN] image Landsat-8 [Termes IGN] Inde [Termes IGN] indice de végétation [Termes IGN] modèle de régression [Termes IGN] point d'appui [Termes IGN] régression linéaire [Termes IGN] régression multiple [Termes IGN] séparateur à vaste marge [Termes IGN] zone semi-aride Résumé : (auteur) This study compared the traditional regression models and support vector machine (SVM) for estimation of aboveground biomass (ABG) of an agro-pastoral ecology using vegetation indices derived from Landsat 8 satellite data as explanatory variables . The area falls in the Shivpuri Tehsil of Madhya Pradesh, India, which is predominantly a semi-arid tract of the Bundelkhand region. The Enhanced Vegetation Index-1 (EVI-1) was identified as the most suitable input variable for the regression models, although the collective effect of a number of the vegetation indices was evident. The EVI-1 was also the most suitable input variable to SVM, due to its capacity to distinctly differentiate diverse vegetation classes. The performance of SVM was better over regression models for estimation of the AGB. Based on the SVM-derived and the ground observations, the AGB of the area was precisely mapped for croplands, grassland and rangelands over the entire region. Numéro de notice : A2022-394 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1756461 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1756461 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100688 [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 (1 March 2022)  

Public [article]  inForest ecology and management > vol 507 (1 March 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)  

Public [article]  inUrban Forestry & Urban Greening > vol 69 (March 2022) . - n° 127521 Titre : Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds Type de document : Article/Communication Auteurs : Jiayuan Lin, Auteur ; Decao Chen, Auteur ; Wenjian Wu, Auteur ; Xiaohan Liao, Auteur Année de publication : 2022 Article en page(s) : n° 127521 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] allométrie [Termes IGN] biomasse aérienne [Termes IGN] Chine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt urbaine [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] structure-from-motion Résumé : (auteur) Urban forest is a crucial part of urban ecological environment. The accurate estimation of its tree aboveground biomass (AGB) is of significant value to evaluate urban ecological functions and estimate urban forest carbon storage. It has a high accuracy to estimate the forest AGB with field measured canopy structure parameters, but unsuitable for large-scale operations. Limited by low spatial resolution or spectral saturation, the estimated forest AGBs based on various satellite remotely sensed data have relatively low accuracies. In contrast, Unmanned Aerial Vehicle (UAV) remote sensing provides a promising way to accurately estimate the tree AGB of fragmented urban forest. In this study, taking an artificial urban forest in Ma'anxi Wetland Park in Chongqing City, China as an example, we used UAVs equipped with a digital camera and a LiDAR to acquire two point cloud data. One was produced from overlapping images using Structure from Motion (SfM) photogrammetry, and the other was resolved from laser scanned raw data. The dual point clouds were combined to extract individual tree height (H) and canopy radius (Rc), which were then input to the newly established allometric equation with tree H and Rc as predictor variables to obtain the AGBs of all dawn redwood trees in study area. In accuracy assessment, the coefficient of determination (R2) and Root Mean Square Error (RMSE) of extracted H were 0.9341 and 0.59 m; the R2 and RMSE of extracted Rc were 0.9006 and 0.28 m; the R2 and RMSE of estimated AGB were 0.9452 and 17.59 kg. These results proved the feasibility and effectiveness of applying dual-source UAV point cloud data and the new allometric equation on H and Rc to accurate AGB estimation of urban forest trees. Numéro de notice : A2022-319 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ufug.2022.127521 Date de publication en ligne : 24/02/2022 En ligne : https://doi.org/10.1016/j.ufug.2022.127521 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100425 [article]

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)  

Public [article]  inEuropean Journal of Forest Research > vol 141 n° 1 (February 2022) . - pp 43 - 58 Titre : Survival time and mortality rate of regeneration in the deep shade of a primeval beech forest Type de document : Article/Communication Auteurs : R. Petrovska, Auteur ; Harald Bugmann, Auteur ; Martina Lena Hobi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 43 - 58 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer platanoïdes [Termes IGN] Acer pseudoplatanus [Termes IGN] analyse de données [Termes IGN] arbre mort [Termes IGN] biomasse aérienne [Termes IGN] croissance des arbres [Termes IGN] dendrochronologie [Termes IGN] échantillonnage [Termes IGN] Fagus sylvatica [Termes IGN] forêt primaire [Termes IGN] Leaf Mass per Area [Termes IGN] mortalité [Termes IGN] ombre [Termes IGN] régénération (sylviculture) [Termes IGN] Ukraine [Vedettes matières IGN] Sylviculture Résumé : (auteur) Low mortality rates and slow growth differentiate shade-tolerant from shade-intolerant species and define the survival strategy of juvenile trees growing in deep shade. While radial stem growth has been widely used to explain mortality in juvenile trees, the leaf area ratio (LAR), known to be a key component of shade tolerance, has been neglected so far. We assessed the effects of LAR, radial stem growth and tree height on survival time and the age-specific mortality rate of juvenile Fagus sylvatica L. (European beech), Acer pseudoplatanus L. (sycamore maple) and Acer platanoides L. (Norway maple) in a primeval beech forest (Ukraine). Aboveground and belowground biomass and radial stem growth were analysed for 289 living and 179 dead seedlings and saplings. Compared with the other species, F. sylvatica featured higher LAR, slower growth and a lower mortality rate. The average survival time of F. sylvatica juveniles (72 years) allows it to reach the canopy more often than its competitors in forests with low canopy turnover rate. In contrast, a combination of lower LAR, higher growth rate and higher age-specific mortality rate of the two Acer species resulted in their shorter survival times and thus render their presence in the canopy a rare event. Overall, this study suggests that shade tolerance, commonly defined as a relationship between sapling mortality and growth, can alternatively be formulated as a relationship between survival time and the interplay of growth and LAR. Numéro de notice : A2022-199 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-021-01427-3 Date de publication en ligne : 05/11/2021 En ligne : https://doi.org/10.1007/s10342-021-01427-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100000 [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 37 n° inconnu ([25/01/2022])  

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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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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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Climate 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 [en ligne], vol 78 n° 4 (December 2021)  

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Estimation 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)  

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Above-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)  

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Multi-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])  

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The 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)  

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