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Title: Coupling remotely sensed data to a forest ecosystem simulation model
Author: Lucas, N. S.
Awarding Body: University College of Swansea
Current Institution: Swansea University
Date of Award: 1995
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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 (Picea sitchensis) 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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available