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Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis / Mathias Mayer in Forest ecology and management, Vol 466 (15 June 2020)
[article]
Titre : Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis Type de document : Article/Communication Auteurs : Mathias Mayer, Auteur ; Cindy E. Prescott, Auteur ; Wafa E.A. Abaker, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 25 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] afforestation
[Termes IGN] azote
[Termes IGN] biomasse forestière
[Termes IGN] changement d'occupation du sol
[Termes IGN] déchet organique
[Termes IGN] éclaircie (sylviculture)
[Termes IGN] écosystème forestier
[Termes IGN] forêt primaire
[Termes IGN] forêt secondaire
[Termes IGN] gestion forestière
[Termes IGN] peuplement mélangé
[Termes IGN] teneur en carbone
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) Almost half of the total organic carbon (C) in terrestrial ecosystems is stored in forest soils. By altering rates of input or release of C from soils, forest management activities can influence soil C stocks in forests. In this review, we synthesize current evidence regarding the influences of 13 common forest management practices on forest soil C stocks. Afforestation of former croplands generally increases soil C stocks, whereas on former grasslands and peatlands, soil C stocks are unchanged or even reduced following afforestation. The conversion of primary forests to secondary forests generally reduces soil C stocks, particularly if the land is converted to an agricultural land-use prior to reforestation. Harvesting, particularly clear-cut harvesting, generally results in a reduction in soil C stocks, particularly in the forest floor and upper mineral soil. Removal of residues by harvesting whole-trees and stumps negatively affects soil C stocks. Soil disturbance from site preparation decreases soil C stocks, particularly in the organic top soil, however improved growth of tree seedlings may outweigh soil C losses over a rotation. Nitrogen (N) addition has an overall positive effect on soil C stocks across a wide range of forest ecosystems. Likewise, higher stocks and faster accumulation of soil C occur under tree species with N-fixing associates. Stocks and accumulation rates of soil C also differ under different tree species, with coniferous species accumulating more C in the forest floor and broadleaved species tending to store more C in the mineral soil. There is some evidence that increased tree species diversity could positively affect soil C stocks in temperate and subtropical forests, but tree species identity, particularly N-fixing species, seems to have a stronger impact on soil C stocks than tree species diversity. Management of stand density and thinning have small effects on forest soil C stocks. In forests with high populations of ungulate herbivores, reduction in herbivory levels can increase soil C stocks. Removal of plant biomass for fodder and fuel is related to a reduction in the soil C stocks. Fire management practices such as prescribed burning reduce soil C stocks, but less so than wildfires which are more intense. For each practice, we identify existing gaps in knowledge and suggest research to address the gaps. Numéro de notice : A2020-288 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2020.118127 Date de publication en ligne : 08/04/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118127 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95113
in Forest ecology and management > Vol 466 (15 June 2020) . - 25 p.[article]Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass / Christoph Kleinn in Forest ecosystems, vol 7 (2020)
[article]
Titre : Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass Type de document : Article/Communication Auteurs : Christoph Kleinn, Auteur ; Magnussen, Steen, Auteur ; Nils Nölke, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 57 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Basse-Saxe (Allemagne)
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] écologie forestière
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] parcelle forestière
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) We contrast a new continuous approach (CA) for estimating plot-level above-ground biomass (AGB) in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot, henceforth called DA (discrete approach). With the CA, the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area. Hence with the CA, the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge. Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA. If realized, this difference translates to a higher precision from field sampling, or a lower required sample size. In our case study with a target precision of 5% (i.e. relative standard error of the estimated mean AGB), the CA required a 27.1% lower sample size for small plots of 100 m2 and a 10.4% lower sample size for larger plots of 1700 m2. We examined sampling induced errors only and did not yet consider model errors. We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data. The CA is a variation on a plot design for above-ground forest biomass; as such it can be applied in combination with any forest inventory sampling design. Numéro de notice : A2020-812 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s40663-020-00268-7 Date de publication en ligne : 23/10/2020 En ligne : https://doi.org/10.1186/s40663-020-00268-7 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96985
in Forest ecosystems > vol 7 (2020) . - n° 57[article]Mapping 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)
[article]
Titre : Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors Type de document : Article/Communication Auteurs : Svetlana Saarela, Auteur ; André Wästlund, Auteur ; Emma Hölmstrom, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 43 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] biomasse aérienne
[Termes IGN] carte thématique
[Termes IGN] données allométriques
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur de modèle
[Termes IGN] inférence statistique
[Termes IGN] modèle d'incertitude
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle non linéaire
[Termes IGN] semis de points
[Termes IGN] SuèdeRésumé : (auteur) Background: The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study, we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference.
Results: Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m ×18 m map units was found to range between 9 and 447 Mg ·ha−1. The corresponding root mean square errors ranged between 10 and 162 Mg ·ha−1. For the entire study region, the mean aboveground biomass was 55 Mg ·ha−1 and the corresponding relative root mean square error 8%. At this level 75% of the mean square error was due to the uncertainty associated with tree-level models.
Conclusions: Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.Numéro de notice : A2020-814 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s40663-020-00245-0 Date de publication en ligne : 03/07/2020 En ligne : https://doi.org/10.1186/s40663-020-00245-0 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96987
in Forest ecosystems > vol 7 (2020) . - n° 43[article]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)
[article]
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 radarRésumé : (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
in International journal of applied Earth observation and geoinformation > vol 88 (June 2020) . - 15 p.[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)
[article]
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 forestièreRésumé : (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
in Plos one > vol 15 n° 5 (May 2020) . - n° 0233110[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)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)Permalink