Detailed physiological and micrometeorological studies have provided new insights that greatly simplify the prediction of gross photosynthesis (P-G) and the fraction of production that goes into above-ground net primary production (NPPA). These simplifications have been incorporated into a process-based forest growth model called 3-PGS ( Physiological Principles Predicting Growth with Satellite Data). Running the model requires only monthly weather data, an estimate of soil texture and rooting depth, quantum efficiency (alpha), and a satellite-derived Normalized Difference Vegetation Index (NDVI) correlated with the fraction of visible light intercepted by foliage. The model was originally tested in Australia where seasonal variation in NDVI is extreme. In Oregon, NDVI varies much less seasonally and fully stocked coniferous stands maintain nearly constant canopy greenness throughout the year. We compared 3-PGS estimates of P-G and NPPA across a steep environmental gradient in western Oregon where groundbased measurements at six sites were available from previous studies. We first tested the simplification in data acquisition of assigning the same quantum efficiency (alpha =0.04 mol C/MJ APAR) and available soil water storage capacity (theta =226 mm) to all sites. With these two variables fixed, the linear relation between predicted and measured P-G was y=1.45x+2.4 with an r(2)=0.85. When values of theta were adjusted to match seasonal measurements of predawn water potentials more closely, and the quantum efficiency was increased to 0.05 mol C/MJ absorbed photosynthetically active radiation (APAR) on the most productive site, predicted and observed values of P-G and NPPA were in near 1:1 agreement with r(2)=0.92. Because maximum greenness (NDVI) reflects the seasonal availability of water, limits on soil water storage capacity can be inferred from calculated water balances derived following the onset of summer drought. The simplifications embedded in the 3-PGS model, along with the need to acquire only one midsummer estimate of maximum greenness, make the approach well suited for assessing the productive capacity of forest lands throughout the Pacific Northwest, USA.
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School of Environmental Science, Charles Sturt UniversitySchool of Environmental Science, Charles Sturt University
Mobushir Riaz KHAN
Iftikhar Ahmad KHAN
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Institute of Geo-information & Earth Observation, Arid Agriculture University Rawalpindi, Pakistan & (ii) Department of Forest,Azad Jammu & KashmirSchool of Environmental Science, Charles Sturt University
Iftikhar Ahmad KHAN
Muhammad Hasan Ali BAIG
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Institute of Geo-information & Earth Observation (IGEO), PMAS Arid Agriculture UniversitySchool of Environmental Science, Charles Sturt University
Muhammad Hasan Ali BAIG
LIU Zheng-jia
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Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesSchool of Environmental Science, Charles Sturt University
LIU Zheng-jia
Muhammad Irfan ASHRAF
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Department of Forestry & Range Management, PMAS Arid Agriculture UniversitySchool of Environmental Science, Charles Sturt University
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US Forest Serv, Rocky Mt Res Stn Fire Sci Lab, Missoula, MT 59808 USAUS Forest Serv, Rocky Mt Res Stn Fire Sci Lab, Missoula, MT 59808 USA
Loehman, Rachel A.
Elias, Joran
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Univ Montana, Dept Math Sci, Missoula, MT 59801 USAUS Forest Serv, Rocky Mt Res Stn Fire Sci Lab, Missoula, MT 59808 USA
Elias, Joran
Douglass, Richard J.
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Univ Montana, Dept Biol, Butte, MT 59701 USAUS Forest Serv, Rocky Mt Res Stn Fire Sci Lab, Missoula, MT 59808 USA
Douglass, Richard J.
Kuenzi, Amy J.
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Univ Montana, Dept Biol, Butte, MT 59701 USAUS Forest Serv, Rocky Mt Res Stn Fire Sci Lab, Missoula, MT 59808 USA
Kuenzi, Amy J.
Mills, James N.
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Ctr Dis Control & Prevent, Div High Consequence Pathogens & Pathogenesis, Atlanta, GA 30333 USAUS Forest Serv, Rocky Mt Res Stn Fire Sci Lab, Missoula, MT 59808 USA
Mills, James N.
Wagoner, Kent
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Univ Tennessee, Off Inst Res & Assessment, Knoxville, TN 37996 USAUS Forest Serv, Rocky Mt Res Stn Fire Sci Lab, Missoula, MT 59808 USA