Estimating net forest carbon fluxes by the integration of ground and remote sensing data

2009 ◽  
pp. 97-108
Author(s):  
Fabio Maselli ◽  
Marta Chiesi ◽  
Luca Fibbi ◽  
Marco Moriondo ◽  
Marco Bindi
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Carbon Fluxes ◽  
Forest Carbon ◽  
Sensing Data

Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances

2008 ◽  
Vol 255 (12) ◽  
pp. 3985-3994 ◽  
Author(s):  
Benjamin Smith ◽  
Wolfgang Knorr ◽  
Jean-Luc Widlowski ◽  
Bernard Pinty ◽  
Nadine Gobron
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Forest Carbon ◽  
Process Modelling ◽  
Conifer Forest ◽  
Sensing Data ◽  
Carbon Balances

scholarly journals Impact of Disturbances on the Carbon Cycle of Forest Ecosystems in Ukrainian Polissya

Forests ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 337 ◽  
Author(s):  
Petro Lakyda ◽  
Anatoly Shvidenko ◽  
Andrii Bilous ◽  
Viktor Myroniuk ◽  
Maksym Matsala ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Ecosystem Services ◽  
Land Cover ◽  
Forest Ecosystems ◽  
Remote Sensing Data ◽  
Forest Carbon ◽  
Land Cover Changes ◽  
Salvage Logging ◽  
Sensing Data ◽  
The Impact

Climate change continues to threaten forests and their ecosystem services while substantially altering natural disturbance regimes. Land cover changes and consequent management entail discrepancies in carbon sequestration provided by forest ecosystems and its accounting. Currently there is a lack of sufficient and harmonized data for Ukraine that can be used for the robust and spatially explicit assessment of forest provisioning and regulation of ecosystem services. In the frame of this research, we established an experimental polygon (area 45 km2) in Northern Ukraine aiming at estimating main forest carbon stocks and fluxes and determining the impact caused by natural disturbances and harvest for the study period of 2010–2015. Coupled field inventory and remote sensing data (RapidEye image for 2010 and SPOT 6 image for 2015) were used. Land cover classification and estimation of biomass and carbon pools were carried out using Random Forest and k-Nearest Neighbors (k-NN) method, respectively. Remote sensing data indicates a ca. 16% increase of carbon stock, while ground-based computations have shown only a ca. 1% increase. Net carbon fluxes for the study period are relatively even: 5.4 Gg C·year−1 and 5.6 Gg C C·year−1 for field and remote sensing data, respectively. Stand-replacing wildfires, as well as insect outbreaks and wind damage followed by salvage logging, and timber harvest have caused 21% of carbon emissions among all C sources within the experimental polygon during the study period. Hence, remote sensing data and non-parametric methods coupled with field data can serve as reliable tools for the precise estimation of forest carbon cycles on a regional spatial scale. However, featured land cover changes lead to unexpected biases in consistent assessment of forest biophysical parameters, while current management practices neglect natural forest dynamics and amplify negative impact of disturbances on ecosystem services.

scholarly journals Carbon fluxes in ecosystems of Yellowstone National Park predicted from remote sensing data and simulation modeling

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Christopher Potter ◽  
Steven Klooster ◽  
Robert Crabtree ◽  
Shengli Huang ◽  
Peggy Gross ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellowstone National Park ◽  
Simulation Modeling ◽  
National Park ◽  
Remote Sensing Data ◽  
Carbon Fluxes ◽  
Sensing Data
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