Gaia Vaglio Laurin

Dr. Gaia Vaglio Laurin

orcid

Research Focus & Metrics

Academic Career Length

28Years (1999-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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10 January 2016 in Global Change Biology
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VAValerio Avitabile
Valerio Avitabile
MHMartin Herold
Martin Herold
GHGerard B. M. Heuvelink
Gerard B. M. Heuvelink
SLSimon L. Lewis
Simon L. Lewis
OPOliver L. Phillips
Oliver L. Phillips
PAP Asthon
P Asthon

1 March 2014 in ISPRS Journal of Photogrammetry and Remote Sensing
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Journal Article
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Gaia Vaglio LaurinGaia Vaglio Laurin
Gaia Vaglio Laurin
Qi ChenQi Chen
Qi Chen
JLJeremy Lindsell
Jeremy Lindsell
DCDavid A. Coomes
David A. Coomes
FFFabio Del Frate
Fabio Del Frate
Riccardo ValentiniRiccardo Valentini
Riccardo Valentini

28 January 2014 in Biogeosciences
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Riccardo ValentiniRiccardo Valentini
Riccardo Valentini
AAA. Arneth
A. Arneth
ABA. Bombelli
A. Bombelli
SCSimona Castaldi
Simona Castaldi
Roberto Cazzolla GattiRoberto Cazzolla Gatti
Roberto Cazzolla Gatti
RSRobert Scholes
Robert Scholes
This paper, developed under the framework of the RECCAP initiative, aims at providing improved estimates of the carbon and GHG (CO2, CH4 and N2O) balance of continental Africa. The various components and processes of the African carbon and GHG budget are considered, existing data reviewed, and new data from different methodologies (inventories, ecosystem flux measurements, models, and atmospheric inversions) presented. Uncertainties are quantified and current gaps and weaknesses in knowledge and monitoring systems described in order to guide future requirements. The majority of results agree that Africa is a small sink of carbon on an annual scale, with an average value of −0.61 ± 0.58 Pg C yr−1. Nevertheless, the emissions of CH4 and N2O may turn Africa into a net source of radiative forcing in CO2 equivalent terms. At sub-regional level, there is significant spatial variability in both sources and sinks, due to the diversity of biomes represented and differences in the degree of anthropic impacts. Southern Africa is the main source region; while central Africa, with its evergreen tropical forests, is the main sink. Emissions from land-use change in Africa are significant (around 0.32 ± 0.05 Pg C yr−1), even higher than the fossil fuel emissions: this is a unique feature among all the continents. There could be significant carbon losses from forest land even without deforestation, resulting from the impact of selective logging. Fires play a significant role in the African carbon cycle, with 1.03 ± 0.22 Pg C yr−1 of carbon emissions, and 90% originating in savannas and dry woodlands. A large portion of the wild fire emissions are compensated by CO2 uptake during the growing season, but an uncertain fraction of the emission from wood harvested for domestic use is not. Most of these fluxes have large interannual variability, on the order of ±0.5 Pg C yr−1 in standard deviation, accounting for around 25% of the year-to-year variation in the global carbon budget. Despite the high uncertainty, the estimates provided in this paper show the important role that Africa plays in the global carbon cycle, both in terms of absolute contribution, and as a key source of interannual variability.

1 January 2012 in International Journal of Forestry Research
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DLDengsheng Lu
Dengsheng Lu
Qi ChenQi Chen
Qi Chen
GWGuangxing Wang
Guangxing Wang
EMEmilio Moran
Emilio Moran
MBMateus Batistella
Mateus Batistella
DSDavid Saah
David Saah
Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

1 April 2016 in Remote Sensing of Environment
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Journal Article
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Gaia Vaglio LaurinGaia Vaglio Laurin
Gaia Vaglio Laurin
Nicola PulettiNicola Puletti
Nicola Puletti
WHWilliam Hawthorne
William Hawthorne
Veraldo LiesenbergVeraldo Liesenberg
Veraldo Liesenberg
PCPiermaria Corona
Piermaria Corona
RVRiccardo Valentini
Riccardo Valentini