Across the world, particularly in the tropics, the extent of forest clearance has been widespread. At present, few studies have been undertaken and little is known on the long-term effect of land use history following clearance, on forest recovery, a significant sink for atmospheric CO2. This study aimed at quantifying the capacity of regenerating forests in the Brazilian Legal Amazon (BLA) to recover carbon using a combination of Earth Observation (EO) data and the 3-PG forest growth model. Three sites were selected within the BLA, representative of diffrent clearance histories on which extensive deforestation has occurred. Land use history and forest age for these areas was obtained from time-series analysis of Landsat images (1974 - 2011). Long term trends of aboveground biomass (AGB) accumulation in secondary forests were studied using field inventory data from 52 secondary forest plots north of Manaus from 1993, 1995 and 2014. Plots were representative of different clearance histories; a combination of clearance frequency and period of active land use prior to abandonment. A variance based global sensitivity analysis (SA) was carried out on the 3-PG forest growth model to identify its most sensitive model inputs when applying it to a mixed tropical rainforest. A parameter set for mixed tropical forests was identified using a Monte-Carlo simulation and by comparing simulated outputs to field data. These parameters were used within 3-PG to provide estimates of total carbon sequestration and model sensitivity to future climate change. Results of this thesis showed that forest age derived from remote sensing time series was comparable to that derived from field observations and interviews. Sites with a higher land use intensity did not accumulate biomass at a significantly slower rate than those used less intensively. Accumulation rates predicted from the model closely matched those calculated from the forest inventory data gathered at each plot. SA results demonstrated scientifically credible behaviour of the model and allowed identification of the most responsive model inputs and interactions. Findings illustrated the suitability and potential of combining a process based model with EO data as a way to forecast the productivity of mixed secondary forest in Brazil. Comparisons with existing estimates highlight uncertainties in deriving secondary forest AGB from remote sensing using relationships fitted to primary forests. Development of these methodologies has applications to other tropical ecosystems that have experienced a similar history of disturbance and can provide invaluable information for future land-use planning and REDD+ monitoring.
|Date of Award||2017|
|Supervisor||Pete Bunting (Supervisor), George Petropoulos (Supervisor), Richard Lucas (Supervisor) & Joao Carreiras (Supervisor)|
Biomass accumulation in secondary forest of the Brazilian Amazon
Jones, J. (Author). 2017
Student thesis: Doctoral Thesis › Doctor of Philosophy