16_Qingmin_Meng

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					    Modeling Biomass and
 Timber Volume by Using an
Allometric Growth Model from
     Landsat TM Images
      Qingmin Meng, Chris Cieszewski
   D. B. Warnell School of Forest Resources
            University of Georgia
            Introduction
Ground truthing vs. remote sensing data.
Remote sensing.
Direct exploration of the multispectral data.
Using vegetation index, such as VI, TVI, or NDVI.
Kth nearest neighbor estimation.
Introduction
  Uncertainty of the expansion from a pixel
 scale to a regional scale.
  Can we improve it ?
  A method provides information of pixels and
 possesses the space information.
Objectives
  Mixed-effects models will be employed and
  regional differences will be considered.
  Build new indices, surface area and volume of
  NDVI.
  Model selection.
  Analyze the spatial difference of forest biomass
  and timber volume according to the fitted
  models.
Methodology
 NDVI, NDVIsa, and NDVIvol.
 Allometric growth model

   Y  Y0 M   b
Methodology       (cont’d)

  General equation of allometric growth law
    y  ax   b



 What is the general law of allometric growth?
Methodology        (cont’d)

  Linear fixed-effects model
    Y  X  

  Linear mixed-effects model
    Y  X  Zb  
Methodology        (cont’d)
  GIS and RS techniques
  Geometric correction, data transformation,
 mask, triangular irregular network function,
 3-D model, and NDVIsa and NDVIvol
 extraction ( in Imagine and ArcView).
Study area and data
                        Georgia regions
                             Ridge and valley
                             Mountain
                             Piedmont
                             Upper coastal plain
                             Lower coastal plain




    Figure 1. Five study regions in GA.
Study area and data                (cont’d)




                                 Image boundary
                                 County boundary



    Figure 2. Study areas covered by images.
Study area and data                 (cont’d)
  The 2001 data for six county-level
 dependent vaiables.
 biomass of all, all live merchantable biomass, volume
 of all live trees, volume of growthing stock, volume of
 sawtimber, and volume of the sawlog portion.
  NDVIsa and NDVIvol are extracted from
 Landsat TM images.
Results
  Table 1. Fixed vs. mixed effects models using NDVIvol as predictor
       Model                                     Residual                        Adjusted
                                       Loglik-
                     AIC*     BIC*               standard   p-value   R-square   R-square
 Response        #                      lihood
                                                  error
                 1   108.39   125.91    -48.19     0.3174
   Biomass
                 2   106.64   118.32    -49.32     0.3229    0.325
  of all trees                                                                    0.4954
                 3   147.29   156.05    -70.65     0.3936   <.0001     0.4991
                 1   112.48   130.00    -50.24     0.3222
M erchanatable
                 2   110.54   122.22    -51.27     0.3276   0.3572
   biomass
                 3   151.88   160.63    -72.94     0.4002   <.0001     0.4887     0.4849
                 1   111.60   129.12    -49.80     0.3213
 Volume of all
                 2   109.84   121.52    -50.92     0.3272   0.3271
   live trees    3   147.73   156.49    -70.87     0.3942   <.0001     0.5012     0.4976
                 1   127.24   144.76    -57.62     0.3409
Growing stock
                 2   124.46   136.17    -58.23     0.3451   0.5451
   volume        3   164.71   173.47    -79.35     0.4194   <.0001     0.4739     0.4701
                 1   212.29   229.81   -100.15     0.4695
  Sawtimber
                 2   208.55   220.22   -100.27     0.4704   0.8849
   volume
                 3   240.55   249.31   -117.28     0.5532   <.0001     0.3061     0.3010
  Volume of      1   202.49   220.02    -95.25     0.4526
   sawlog        2   198.91   210.59    -95.45     0.4537   0.8146
   portion       3   233.94   242.70   -113.97     0.5400   <.0001     0.3142     0.3092
Results                (cont’d)
 Table 2. Fixed vs. mixed effects model using NDVIsa as predictor
      Model                                       Residual                     Adjusted
                                       Loglik-                p-        R-
                     AIC*     BIC*                standard                     R-square
 Response        #                     lihood                value    square
                                                   error
                 1   154.17   171.69     -71.09     0.3746
   Biomass
                 2   162.90   174.58     -77.45     0.3987   0.0017
  Of all trees
                 3   189.58   198.34     -91.79     0.4590   <.0001   0.3188     0.3139
                 1   157.24   174.76     -72.62     0.3789
Merchanatable    2   165.39   177.08     -78.69     0.4024   0.0023
  biomass
                 3   192.88   201.64     -93.44     0.4645   <.0001   0.3112     0.3062
                 1   157.99   175.52     -72.99     0.3803
 Volume of all   2   166.67   178.35     -79.99     0.4047   0.0018
  live trees     3   191.09   199.85     -92.55     0.4615   <.0001   0.3164     0.3114
                 1   171.75   189.27     -79.87     0.4006
Growing stock    2   177.86   189.54     -84.93     0.4215   0.0064
   volume        3   204.44   213.19     -99.22     0.4845   <.0001   0.2979     0.2928
                 1   237.75   255.26    -112.87     0.5166
  Sawtimber
                 2   234.15   245.83    -112.07     0.5176   0.8172
   volume        3   260.53   269.29    -127.27     0.5946   <.0001   0.1982     0.1924
  Volume of      1   226.13   243.65    -107.06     0.4905
   sawlog        2   226.92   238.60    -109.46     0.5037   0.0911
   portion       3   255.29   264.05    -124.65     0.5833   <.0001   0.1997     0.1938
Results
                      Table 3. The best models
                                                    Model
   Estimation             Region
                                        Intercept            Slope
                            1        -2.1443069             0.9755356
     Biomass                2        -6.9357346             1.1892055
    Of all trees            3        -9.1793452             1.2557426
                            4        -2.9403983             1.0022634
                            5        -0.8259313             0.9267044
                            1        -2.1920545             0.9695219
                            2        -6.9057884             1.1803815
  Merchanatable             3        -9.3062054             1.2531440
    biomass                 4        -3.2558101             1.0064089
                            5        -0.7418328             0.9158308
                            1        -5.698310              0.9636407
                            2        -10.031518             1.1589341
 Volume of all live         3        -13.292529             1.2665148
      trees                 4        -6.937340              1.0082550
                            5        -4.061954              0.9038428
Results              (cont’d)
           Table 3. The best models (cont’d)
                         1      -7.112829      1.0151401
                         2      -10.415131     1.1704217
   Growing stock         3      -13.253895     1.2614818
      volume             4      -8.301378      1.0560060
                         5      -5.545369      0.9558549
                         1      -7.620637       1.077952
     Sawtimber           2      -11.065563      1.245154
      volume             3      -8.614393       1.126178
                         4      -6.998769       1.047766
                         5      -7.610956       1.077485
                         1      -9.173880       1.073298
                         2      -13.618008      1.279459
  Volume of sawlog
      portion            3      -10.394362      1.129898
                         4      -8.293959       1.032473
                         5      -9.137134       1.071600
Conclusions (cont’d)
  The allometric growth model is suitable for the
  assessment of biomass and timber volume at a large
  scale.
  The linear mixed-effects models can more accurately
  estimate biomass and timber volume than the linear
  fixed-effects models.
  NDVIsa and NDVIvol both contain the pixel
  information and area information.
  NDVIvol is more suitable than NDVIsa in predictions.
Conclusions (cont’d)
 Regional characteristics of allometry of
 biomass and timber volume.
     In the ridge and valley region and the lower
 coastal plain region, the overall indices, biomass of all,
 et al. have negative allometric characteristics.
     In the mountain region and piedmont region, the
 overall indices of biomass and volume have positive
 allmoteric characteristics.
      In the upper coast plain region, however, the
 overall index of biomass and volume have neutral
 allometric characteristics
The end.


       Thank you.