Using Remote Sensing and a Spatial Plant Productivity Model to Assess Biomass Change

Abstract

Accounting for biomass and carbon change in forestry and agriculture under the Kyoto and other international protocols requires an assessment of the change in land cover, including afforestation, reforestation and deforestation events. Due to the time associated with soil carbon and biomass decay, the impact of an event associated with land cover change may continue over many years. Remote sensing was used to identify the location, area and time of an afforestation, reforestation or deforestation event. This time-based, activity-by-activity approach, covering all continental woody vegetation, provides a platform of land cover history. This land cover history is used in conjunction with calculations of Net Primary Productivity and estimates of pool turnover and decay to provide a first phase estimate of biomass and carbon on a spatially referenced basis. The Net Primary Productivity was calculated for Australia using a physiological model (3-PG (Spatial)) based on the relationship between the photosynthetically active radiation absorbed by plant canopies (APAR) and the (biomass) productivity of those canopies at a monthly time step. The factor converting APAR to biomass was reduced from the selected optimum value by modifiers dependent on soil fertility; atmospheric vapour pressure deficits, soil water content and temperature. Leaf Area Index, essential for the calculation of APAR, was estimated from 10-year mean values of Normalized Difference Vegetation Indices. Incoming short-wave radiation - and hence APAR - was corrected for slope and aspect using a Digital Elevation Map. The ESOCLIM package was used to generate climate surfaces for the country. Soil fertility and water holding capacity values were obtained from the (digital) soil atlas of Australia. The correlation between the first phase estimate of biomass and sites across Australia that ranged from and shrublands to tall wet sclerophyll (2 - 450 t/ha biomass) was examined. This correlation is significant and is useful for improving the efficiency of estimating biomass and carbon totals and change.