Improvement in predicting stand growth of Pinus radiata (D. Don) across landscapes using NOAA AVHRR and Landsat MSS imagery combined with a forest growth process model (3-PGS)

Recent detailed physiological and micro-meteorological studies of forest ec osystems have lead to new insights that greatly simplify the prediction of gross primary production (P-G) and above-ground net primary production (NPP A) which ore key variables related to conventional measures of forest growt h, such as mean annual increment (MAI) of stemwood. These simplifications w ere applied in a monthly time-step model (Physiological Principles Predicti ng Growth using Satellite data (3-PGS)) which requires monthly weather data (average minimum and maximum temperatures and precipitation), an estimate of soil texture, rooting depth, and the fraction of photosythetically activ e radiation absorbed by the forest canopies (fPAR) which is estimated from a satellite-derived normalized difference vegetation index (NDVI). The model was originally tested at sites in Australia and New Zealand using coarse spatial resolution AVHRR Pathfinder data which effectively limited the predictions of Npp, to broad areas. In this paper, AVHRR and Landsat Ms s data are both used by the model, allowing 3-PGS predictions to be applied at a more refined landscape scale. Accumulated above ground biomass predicted by the model was compared with b iomass data from discrete stands in a 2,265-ha Pinus radiata (D. Don) plant ation in southern New South Wales, Australia. There was a linear relation b etween predicted and measured wood production (r(2) = 0.4). Additionally, a nalysis of the results indicated the incorporation of MSS and AVHRR data al lowed a variety of stand-specific disturbances to be accounted for, such as thinning.