Presented at the:
Australasian Forest & Wood Products Conferences:
Residues to Revenues.
Rotorua, October 12-13 and Melbourne, October 17-18, 2005.
Quantifying the
availability and
volume of the forest
resides resource
B.Hock, P.Nielsen, S.Grigolato,
J.Firth, B.Moeller, T.Evanson
Scion, Rotorua, New Zealand
Dept of Land and Agricultural and Forest Systems, University of Padua, Italy
Dept of Development and Planning, Aalborg University, Denmark
Logging residues for
energy production
Energy prices are increasing
Interest is growing in the use
of in-forest residues as a
sustainable energy resource
Consider woody biofuel as a forest product
• Assess the volume available
• Optimise the logistic of the supply chain
• Minimise the supply cost
Biomass supply from
forest plantations
Two models are being developed
 National availability and cost supply model
 Within-forest ”
”
”
”
”
National availability and
cost supply model
Model overview
The location of forests, the
transportation network, possible
cogen plant locations and other
spatial issues are mapped.
The information is analysed within raster
GIS.
Techniques include cell-to-cell functions,
neighborhood statistics and zonal
geometry.
The results are intensity maps or
distributions of site-specific costs.
National availability and
cost supply model
Calculating the transport cost
 The accumulated travel
distance from a point location
determines the transportation
costs along the road network
to that point.
 This example visualizes the
cost of transportation across
a region.
Estimated annual forest
residue availability
TLA
National availability and
cost supply model
Costs of biomass at site
 The site-specific amount
and cost of biomass are
calculated by overlaying
in-forest residues and
transport costs.
 The result is a distribution
of biomass amounts and
costs, which is unique for
each location relative to a
planned bioenergy plant.
Availability and cost of
residues at 4 locations
Within forest availability
and cost supply model
A model was developed
in collaboration with
Carter Holt Harvey
Forests Ltd.
The case study was based
on the Kinleith Forest, in
the North Island of New
Zealand, complimented by
National Exotic Forest
Description (NEFD)
regional yield tables
Biofuel as a product:
some issues
Logging residues are unevenly distributed
geographically and in time
Volume of residues at landings is influenced
by the characteristics of the logging
operation (eg. harvesting methods,
equipment capacity, terrain characteristics)
Extraction of residues is affected by road
types and density
The within-forest chain
Volume at
harvest
Residue at
landings
Transportation
of residue to
hogger
Chipping
by hogger
Transportation
of chips to
cogen
Volume and cost
at cogen plant
Methodology
The within-forest availability and cost supply model
The components:
Calculate potential amount of logging residue
Assign logging residue to landings
Select hogger site locations
Determine transportation network
Minimise overall costs
Logging residue availability
Forest Database
Investigate variables that affect availability
forest stand data
topography
NEWLAND_CU REGIME_ID TENDING_OP
NL
2004
:PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104795
NL
2004
:PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104796
NL
2004
:PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104795
NL
2004
:PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236743:CF,28,3104794
NL
2004
:PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236743:CF,26,3239770
NL
2004
:PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104795
NL
2004
:PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104795
NL
2004
:PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104796
NL
2004
:PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104796
NL
2004
:PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236777:CF,28,3104794
NL
2004
:PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236777:CF,27,3104793
NL
2004
:PL,1983,.,3004192,P.RAD:WT,5,287,3004934:PR,6,300,3.0,.,3004611:PR,7,291,5.0,.,3004610:MS,20,3281268:CF,27,3104796
NL
2004
:PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236777:CF,26,3239767
NL
2004
:PL,1983,.,3004191,P.RAD:WT,5,300,3004933:PR,6,300,3.0,.,3004609:PR,7,293,5.0,.,3004608:MS,20,3281268:CF,27,3104790
NL
2004
:PL,1983,.,3004191,P.RAD:WT,5,300,3004933:PR,6,300,3.0,.,3004609:PR,7,293,5.0,.,3004608:MS,20,3281268:CF,27,3104790
NL
2004
:PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236777:CF,26,3239767
NL
2004
:PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236778:CF,26,3239767
NL
2004
:PL,1978,.,3004190,P.RAD:PR,5,500,2.0,.,3004607:PR,7,350,4.0,.,3004606:WT,8,367,3004932:PR,8,272,6.0,.,3004605:MS,20,3036797:MS,24,3236778:CF,27,3104793
NL
2004
:PL,1976,.,3004196,P.RAD:PR,5,500,2.0,.,3004614:PR,7,350,4.0,.,3004613:PR,8,252,6.0,.,3004612:PT,13,375,3004939:PT,14,375,3004938:MS,26,3222280:CF,28,3035630
1>>>
NewField as integer (long) = TSV_mc_ha
NEFD Database
2>>> VBA function
dim TSV_mc_ha as integer
If [cf] = "40" Then TSV_mc_ha = 993
if [cf] = "39" Then TSV_mc_ha = 908
If [cf] = "38" Then TSV_mc_ha = 883
If [cf] = "37" Then TSV_mc_ha = 856
if [cf] = "36" Then TSV_mc_ha = 830
If [cf] = "35" Then TSV_mc_ha = 799
If [cf] = "34" Then TSV_mc_ha = 774
if [cf] = "33" Then TSV_mc_ha = 745
If [cf] = "32" Then TSV_mc_ha = 715
If [cf] = "31" Then TSV_mc_ha = 688
if [cf] = "30" Then TSV_mc_ha = 656
If [cf] = "29" Then TSV_mc_ha = 626
If [cf] = "28" Then TSV_mc_ha = 592
if [cf] = "27" Then TSV_mc_ha = 562
If [cf] = "26" Then TSV_mc_ha = 530
If [cf] = "25" Then TSV_mc_ha = 495
if [cf] = "24" Then TSV_mc_ha = 463
If [cf] = "23" Then TSV_mc_ha = 428
If [cf] = "22" Then TSV_mc_ha = 394
if [cf] = "21" Then TSV_mc_ha = 360
If [cf] = "20" Then TSV_mc_ha = 326
If [cf] = "19" Then TSV_mc_ha = 290
if [cf] = "18" Then TSV_mc_ha = 256
If [cf] = "17" Then TSV_mc_ha = 241
If [cf] = "16" Then TSV_mc_ha = 218
if [cf] = "15" Then TSV_mc_ha = 196
If [cf] = "14" Then TSV_mc_ha = 304
If [cf] = "13" Then TSV_mc_ha = 278
if [cf] = "12" Then TSV_mc_ha = 252
If [cf] = "11" Then TSV_mc_ha = 226
If [cf] > "40" Then TSV_mc_ha = 995
if [cf] < "11" Then TSV_mc_ha = 0
Approximate the volume
of logging residue
for the next 17 years.
forest productivity data
Logging residue availability
NEFD Database
Kinleith Database
Forest stand data calculation
Silvicultural Regime
• analysis
• only radiata pine considered
Area
• year of establishment
• tending history
• proposed felling year
Total Recoverable Volume (TRV)
•
•
•
•
import yield tables to GIS
calculate block area
evaluate the TRV for each block
determine the logging residue for
each block
Logging residue availability
TRV
m3/ha
Logging residues Volume
m3/ha
Drying period
1 year
Logging residues Weight
tonne/ha
Residue calculation
As percentage
of TRV
(Depends on logging
method)
Volume (m3) *
0.75 t/m3 =
weight (tonnes)
Logging residue availability
Total Recoverable Volume
(m3/year)
Yearly average: 943 000 m3
TRV
1600000
volume, cubic meter/year
Results
1800000
1400000
1200000
1000000
800000
600000
400000
200000
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
year
Logging residue availability
(tonnes/year)
50000
45000
3%
4%
40000
35000
tonnes/year
Yearly average:
21 500 - 28 200 tonnes
2006
2005
0
30000
25000
20000
15000
5000
year
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
0
2005
Yearly average per hectare:
0.6 tonnes/ha - 0.8 tonnes/ha
10000
Logging residue availability
Results
The graph shows
how availability
varies over time.
45000
4%
3%
40000
35000
30000
25000
20000
15000
10000
5000
year
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
0
2005
tonnes/year
For example,
there are two
periods when
supply falls below
10,000 tonnes per
year.
50000
Assigning logging residue
to landings
To calculate logging residue at each
landing:
•locate landings (12 700)
•define the catchment area for
each landing
•overlay the logging residue
•sum the logging residue for each
landing
•repeat for each year
Assigning logging residue
to landings
Location of landings with assigned residues
2006
2007
2008
Residues (red dots) vary over time and across the forest
Location of hogger sites
GIS – based analysis
Reclassify roads
according to their
carrying capacity
Road type
Capability
Hogger
site
Public
Chips
No
Forest
sealed or
unsealed
Residue or
chips
Yes
Forest stub
or track
Residue
No
Location of hogger sites
Selection criteria:
•Must be associated
with roads suitable
for chip trucks
•Must have a
minimum area of
5000 m2
Location of hogger sites
Selection criteria
•Must be no closer
than 20km to
adjacent hogger
sites
Superskid sites - 40
Superskid sites - 15
Transportation network
 Network analysis to
determine the minimum
cost route between
each landing and the
hogger sites
 Similarly for the routes
between hogger sites
and cogen plant
Minimum cost calculations
 Define variables:
Maximum distance
between landing and
hogger site
Minimum residues at
landing
 Run minimum cost
calculation
Insert data
Define
scenarios
Perform
calculation
results
Results
Supply distance
Logging residue
Year 2006
Logging residue
Llandings
Distance
Supply Cost
Year 2007
Logging residue
Llandings
Distance
Supply Cost
Year 2008
Logging residue
Llandings
Distance
Supply Cost
Variables:
maximum distance
8000 m – 9000 m
residue at landing
>0 in intervals of
12.5 tonne
32.0
31.9
31.8
31.7
31.6
31.5
31.4
31.3
31.2
31.1
31.0
30.9
100
18122
296
1749
31.845
13832
149
1056
31.826
17002
374
2069
31.646
10954
220
1479
31.086
cost, $/tonne
cost, $/tonne
legend
0
8000
50
0
20
40
60
min. logging residues, tonne
80
100
0
9000
50
m
tonnes
100
6304
47
575
31.856
22820
510
3006
31.917
17044
172
1250
31.930
8508
58
668
32.100
tonnes
n°
km
$/tonnes
12568
136
977
31.706
5749
41
532
31.826
20554
432
2562
31.736
15297
154
1132
31.771
7384
47
585
31.848
tonnes
n°
km
$/tonnes
8504
85
769
30.965
3855
25
480
31.266
13638
258
1803
31.128
10605
102
914
31.067
5180
32
540
31.189
tonnes
n°
km
$/tonnes
32.2
32.1
32.0
31.9
31.8
31.7
31.6
31.5
31.4
31.3
31.2
31.1
31.0
0
20
40
60
min. logging residues, tonne
80
100
Conclusions
• the availability of residue depends not only on volume,
but also on the transportation cost to the power plant
• a large number of variables need to be considered
including drying, in–forest logging distribution, transport
and chipping techniques
• GIS based models are effective tools for Decision Support
Systems (DSS)