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Matt Bradford_Error and precision in rainforest biomass estimations
 

Matt Bradford_Error and precision in rainforest biomass estimations

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    Matt Bradford_Error and precision in rainforest biomass estimations Matt Bradford_Error and precision in rainforest biomass estimations Presentation Transcript

    • Error and accuracy of rainforestbiomass estimationMatt Bradford, Adam Mckeown, Anna Deffner19th February 2013SUSTAINABLE ECOSYSTEMS
    • Above ground biomass (AGB) Location Approx AGB (Mg/ha) Source Solomon Is. and New Caledonia 552 Keppel et al. (2007) Congo (Gilbertiodendron forest) 535 Makana et al. (2011) Wet Tropics, QLD (uplands) 500 CSIRO (unpublished) Borneo, SE Asia 457 Slik et al. (2010) Congo (mixed rainforest) 399 Makana et al. (2011) Amazon basin 288 Malhi et al. (2006) Amazon basin central and eastern 278 Baker et al. (2004) Wet Tropics, QLD (lowlands) 270 Liddell et al. (2007) Barro Colorado Is. Panama 270 Chave et al. (2004) Amazon basin north west 221 Baker et al. (2004) Amazon basin south west 207 Baker et al. (2004)2 | Rainforest biomass Matt Bradford February 2013
    • Above ground biomass (AGB) Approx AGB Location Forest type (Mg/ha) Reference Wet Tropics, QLD (uplands) Rainforest 500 CSIRO (unpublished) Victoria Tall Eucalypt 476 Tajchman et al. (1996) Batemans Bay, NSW Woodland 397 Ximenes et al. (2006) Wombat SF, Vic Woodland 309 TERN supersite Wet Tropics, QLD (lowlands) Rainforest 270 Liddell et al. (2007) New England, NSW Woodland 250 Yoa et al. (2011) SERF, Samford QLD Woodland 200 TERN supersite South west WA Mallee woodlands 150 Jonson and Freudberger (2011) South west WA Woodland 150 Jonson and Freudberger (2011) Northern Territory Woodland 55 OGrady et al. (2000)3 | Rainforest biomass Matt Bradford February 2013
    • Above ground biomass (AGB) estimations• Accuracy of AGB estimations is influenced by: • measurement error • algorithm suitability – availability of wood density and height – number of large trees • Size of measurement unit • Biomass held in understory and other life-forms How do we best collect and use our data to calculate an accurate AGB? 4 | Rainforest biomass Matt Bradford February 2013
    • AGB estimation in rainforest• Large number of trees• Large numbers of stems in understory • Approx 9500 stems/ha, 4.9% of biomass• High plant diversity • Architecture • wood density (0.2-1.1g/cm3)5 | Rainforest biomass Matt Bradford February 2013
    • Robson Creek 25ha rainforest plot6 | Rainforest biomass Matt Bradford February 2013
    • Robson Creek 25ha rainforest plot7 | Rainforest biomass Matt Bradford February 2013
    • Robson Creek 25ha rainforest plotROBSON CREEK 25HA SUPERSITE. TREES>=10CM DBHHectare Subplot TeamDateTag # DBH POM Species HT East North Codes (cm) (m) (m) (m) (m) 8 | Rainforest biomass Matt Bradford February 2013
    • Robson Creek 25ha rainforest plot • 23400 stems ≥10 cm DBH •Mean of 936 stems per hectare • 213 species from 50 families •Mean of 98 species per hectare (81-120) •Range of wood density from 0.2 – 1.0 g/cm39 | Rainforest biomass Matt Bradford February 2013
    • Robson Creek 25ha rainforest plot 10000 8000Number of observations 6000 4000 2000 2000 1.2% of stems 1500 19% of biomass Number of Observations 0 10 30 50 70 90 110 130 160 Diameter at breast height (cm) 1000 500 0 Species 10 | Rainforest biomass Matt Bradford February 2013
    • Variation in AGB Chave et al. (2005) AGB = 0.0509*DBH2*HT*wood density 600 500 Above ground biomass (Mg/ha) 400 300 200 100 0 Hectare11 | Rainforest biomass Matt Bradford February 2013
    • Variation in AGB 50 40 Above ground biomass (Mg) 30 20 10 0 20 x 20m subplot12 | Rainforest biomass Matt Bradford February 2013
    • Variation in AGB 1200 1000Above ground biomass (Mg/ha) 800 Mean = 402.0 ± 159.6 SD 600 400 Mean = 402.0 ± 56.9 SD 200 0 13 | Rainforest biomass Matt Bradford February 2013
    • Estimation error – plot size 0.25 20 x 20 m plot 0.20 Mean proportion error 0.15 0.10 0.05 0.00 0 5 10 15 20 25 Area (Ha)14 | Rainforest biomass Matt Bradford February 2013
    • Estimation error – plot size 0.25 0.20 20*20 Mean proportion error 20*50 50*100 0.15 100*100 0.10 0.05 0.00 0 5 10 15 20 25 Area (Ha)15 | Rainforest biomass Matt Bradford February 2013
    • Estimation accuracy – plot size Probability of being within 5% of true value 1.0 0.8 20*20 0.6 20*50 50*100 100*100 0.4 0.2 0.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Area (ha)16 | Rainforest biomass Matt Bradford February 2013
    • 17 | Rainforest biomass Matt Bradford February 2013
    • Collaboration - LIDAR r2 = 0.90 LIDAR height estimate (m) 40 30 20 10 0 0 10 20 30 40 Measured height (m)18 | Rainforest biomass Matt Bradford February 2013
    • Collaboration - LIDAR 73 cm DBH stem LIDAR height = 43m AGB = 8.83 Mg CSIRO height = 44m AGB = 9.03 MgMean 11 stems/ha >70 cm DBH AGB = 2.2 Mg/ha19 | Rainforest biomass Matt Bradford February 2013
    • Accuracy of AGB estimation• Measurement error – height and DBH• Choice of algorithm – wood density• Number of large trees• Biomass held in understory and other life-forms• Size of measurement unit20 | Rainforest biomass Matt Bradford February 2013
    • Thank youEcosystem SciencesMatt BradfordResearch Projects Officert +61 7 40918825e matt.bradford@csiro.auw www.csiro.au/ECOSYSTEM SCIENCES
    • 22 | Rainforest biomass Matt Bradford February 2013
    • Collaboration• Column plots of measured BA vs prism BA vs TLS BA23 | Rainforest biomass Matt Bradford February 2013
    • Collaboration• May have to use BA comparisons 2 p = 0.0000; r = 0.9246 500 400 Calculated AGB (Mg/ha) 300 200 100 0 0 10 20 30 40 50 Measured basal area (m2/ha)24 | Rainforest biomass Matt Bradford February 2013