Deforestation and the burning of fossil fuels have increased atmospheric carbon dioxide levels in recent years but the rate of increase has been much slower than expected, suggesting that carbon is 'missing' from the existing carbon budget. To understand why the current increase of atmospheric carbon dioxide is slower than expected, necessitates investigation of the ways in which carbon is release <I>into</I> and extracted <I>from</I> the atmosphere at a regional to global scale. Regenerating tropical forests are potentially significant, but as yet unquantified absorbers of atmospheric carbon dioxide. To estimate the carbon flux of a given area over a specified interval of time it is necessary to detect any changes in the distribution of the various stages of tropical forest regeneration, and to also establish their characteristic carbon content (Mg ha<SUP>-1</SUP>). This study identifies several growth stages and the associated carbon content of the regenerating forests in the Legal Amazon and Southern Cameroon using forest inventory and remote sensing techniques. In particular, estimates of carbon content were carried out by different methods according to the character and species composition of regenerating forests. Various forests biophysical variables (LAI, tree density, basal area, <I>etc</I>.) were estimated and a method of ageing regenerating forests using time-series of Landsat sensor imagery was developed. The NOAA AVHRR was used to estimate the extent of several regenerating forest stages and relationships between NOAA AVHRR response, the estimated forest biophysical variables and carbon content were used to estimate carbon pools in the regenerating forests of the Legal Amazon and Southern Cameroon. In 1991-1993, regenerating forests covered approximately 157,973 km<SUP>2</SUP> of the Legal Amazon representing a net carbon pool of 0.73-0.25 Pg; in Southern Cameroon, they covered approximately 29,090 km<SUP>2</SUP> of the region representing a net carbon pool of 0.18-0.04 Pg.

In recent years forest ecosystems have come under increasing pressure from environmental changes such as global warming and the impacts of pollution. Recent research has indicated that computer simulation models driven by remotely sensed data may be used to assess the spatial impact of global environment changes on forest processes. This thesis outlines an investigation that examined whether a general ecosystem simulation model (FOREST-BGC), driven by remotely sensed and meteorological data, could be used to estimate forest processes for a Sitka spruce (<I>Picea sitchensis</I>) plantation in mid-Wales. The research was divided into three phases. First, plot estimates of leaf area index (LAI), leaf nitrogen concentration (LNC) and standing biomass were used to drive FOREST-BGC in test simulations. It was concluded that for mapping forest processes, the model required spatial estimates of LAI and LNC. The second phase concentrated on the derivation of predictive relationships for estimating LAI and LNC from remotely sensed data for input into FOREST-BGC. Analysis showed that estimates of LAI could be obtained from measurements of radiation recorded in both broad and narrow spectral wavebands. No relationships were found between measurements of radiation and LNC. However, spatial estimates of LNC were obtained indirectly from the remote estimates of LAI using the strong relationship that existed between LAI and LNC. The final phase of the research outlined a methodology for coupling remotely sensed data to the FOREST-BGC model. Spatial estimates of LAI and LNC, together with daily meteorological data, were used to drive the model and produce maps of photosynthesis, transpiration and stem carbon production. Mapped estimates of stem carbon production compared favourably with estimates derived from tree cores. It was concluded that ecosystem simulation models when driven by remotely sensed data can provide an important tool for monitoring environmental processes across a landscape.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.