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-byactivity 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 arid 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.
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References
Brack, C.L. 2003. Forest Inventory Estimates Across Private and Public Tenures. Australian Forestry (in press).
Caccetta, P.A. 1997. Remote sensing, geographical information systems (GIS) and Bayesian knowledge-based methods for monitoring land condition. PhD Thesis, Curtin University of Technology, Australia.
Campbell, N.A. 1984. Canonical variate analysis–a general model formulation. Aust. J. Stat. 26 (1): 86–96.
Campbell, N.A., Atchley, W.R. 1981. The geometry of canonical variate analysis. Syst. Zool., 30 (3): 268–280.
Campbell, N.A., Furby, S.L. 1994. Variable selection along canonical vectors. Aust. J. Stat., 36 (2): 177–183.
Commonwealth of Australia 2000. Australian Greenhouse Office: International.
Coops, N.C., Waring, R.H. 2000 The use of multi-scale remote sensing imagery to derive regional estimates of forest growth capacity using 3-PGS. Remote Sens. Environ. (in press).
Coops, N.C., Waring, R.H., Brown, S., Running, S.W. 2000. Predictions of Net Primary Production and seasonal patterns in water use with forest growth models using daily and monthly time-steps in south-eastern Oregon. Ecological Modeling (in press)
Furby, S.L. 2001. Land Cover Change: Specifications for Remote Sensing Analysis. National Carbon Accounting System Technical Report No. 9 - Pre-publication draft (236 pp.), Australian Greenhouse Office, Canberra.
Furby, S.L., Campbell, N.A. 2001. Calibrating images from different dates to ‘like value’ digital counts. Remote Sensing of Environment 77: 1–11.
Landsberg, J.J. 1986. Coupling of carbon, water and nutrient interactions in woody plant soil systems. Tree Physiology 2: 102–112.
Landsberg, J.J., Kesteven, J. 2001. Spatial Estimation of Plant Productivity. Chapter 2. In National Carbon Accounting System Technical Report No. 27. Edited by G. Richards. Australian Greenhouse Office, Canberra.
Landsberg, J.J., Waring, R.H. 1997. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management 95: 209–228.
Landsberg, J.J., Prince, S.D., Jarvis, P.G., McMurtrie, R.E., Luxmoore, R., Medlyn, B.E. 1997. Energy conversion and use in forests: an analysis of forest production in terms of radiation utilisation efficiency. In The use of remote sensing in the modeling of forest productivity at scales from the stand to the globe. Edited by Gholz, H.L., Nakane, K., Shimoda, H. Dordrecht. Kluwer Acad. Publ.
Linacre, E., Geerts, B. 1997. Climates and weather explained, Routledge, London.
McKay, R.J., Campbell, N.A. 1982. Variable selection techniques in discriminant analysis. 1. Description. Brit. J Math and Stat. Psych. 35: 1–29.
McKenzie, N.J., Jacquier, D.W., Ashton, L.J., Cresswell, H.P. 2000. Estimation of Soil Properties Using the Atlas of Australian Soils. CSIRO Land and Water Technical Report 11 /00, February 2000.
Mcmahon, J.P., Hutchinson, M.F., Nix, H.A., Ord, K.D. 1995. ANUCLIM, User’s Guide. CRES, ANU, Canberra.
McMurtrie, R.E., Gholz, H.L., Linder, S., Gower, S.T. 1994. Climatic factors controlling the productivity of pine stands: a model-based analysis. Ecological Bulletins 43: 173–188.
Northcote, K.H. 1979. A factual key for the recognition of Australian soils. Relim Technical Publications: Glenside, South Australia.
Potter, C.S., Randerson, J.T., Field, C.B., Matson, P.A., Vitousek, P.M., Mooney, H.A., Klooster, S.A. 1993. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles 7: 811–841.
Ruimy, A., Saugier, B., Dedieu, G. 1994. Methodology for the estimation of terrestrial net primary production from remotely sensed data. J Geophys Res. 99: 5263–5283.
Sands, P.J. 2000. 3PGpjs–A User-Friendly Interface to 3-PG, the Landsberg and Waring Model of Forest Productivity. Technical Report No. 20. CRC for Sustainable Production Forestry and CSIRO Forestry and Forest Products, Canberra.
Tickle, P., Coops, N.C., Hafner, S. 2000. Assessing forest productivity across a native eucalypt forest using a process model, 3-PG SPATIAL. For. Ecol. Manage. (in press.)
White, J.D., Coops, N.C., Scott, N.A. 2000. Predicting broad-scale forest and scrub biomass for New Zealand: investigating the application of a physiologically-based model. Ecological Modeling 131: 175–190.
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Kesteven, J.L., Brack, C.L., Furby, S.L. (2003). Using Remote Sensing and a Spatial Plant Productivity Model to Assess Biomass Change. In: Corona, P., Köhl, M., Marchetti, M. (eds) Advances in Forest Inventory for Sustainable Forest Management and Biodiversity Monitoring. Forestry Sciences, vol 76. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0649-0_3
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DOI: https://doi.org/10.1007/978-94-017-0649-0_3
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