Guide to build a FPSATLAS model v1
Cosmin Man
[email protected]
7/29/16
Table of Contents
Table of Contents ............................................................................................................... i
List of Figures.................................................................................................................... ii
List of Tables .................................................................................................................... iv
1.
Introduction ............................................................................................................. 1
2.
Data preparation ..................................................................................................... 2
2.1.
Stand group definitions...................................................................................... 2
2.2.
Curves ................................................................................................................ 3
2.2.1. Generate yield curves using VDYP ............................................................... 5
2.2.2. Generate yield curves using TIPSY ............................................................... 8
2.2.3. Yield curves for MPB .................................................................................. 11
2.3.
Silvicultural systems ........................................................................................ 13
3.
Build the FPS-ATLAS model............................................................................... 15
3.1.
Blank database ................................................................................................. 15
3.2.
Populate tables in FPS-ATLAS database ........................................................ 18
3.2.1. StandGroup_Category .................................................................................. 19
3.2.2. StandGroup................................................................................................... 19
3.2.3. Curve ............................................................................................................ 20
3.2.4. StandGroup_Curve ....................................................................................... 21
3.2.5. StandGroup_Treatment ................................................................................ 22
3.2.6. Curve_Data................................................................................................... 23
3.2.7. Polygon......................................................................................................... 23
3.3.
Link the resultant GIS shapefile ...................................................................... 26
3.4.
Enabling the spatial relationships between polygons ...................................... 27
3.5.
Identify the non-forested polygons .................................................................. 28
3.6.
Automate cliques creation ............................................................................... 30
4.
Tips and tricks ....................................................................................................... 35
4.1.
Finding the right flow ...................................................................................... 35
4.2.
Natural disturbances ........................................................................................ 42
4.3.
Non-forested area rehabilitation ...................................................................... 47
4.4.
Equivalent Clearcut Area................................................................................. 48
4.5.
Increase buffer size (majority trick) ................................................................ 51
4.6.
Constraints duration trick ................................................................................ 59
5.
Outputs................................................................................................................... 61
5.1.
Harvested volume and area.............................................................................. 61
5.2.
Growing stock.................................................................................................. 64
5.3.
Age classes ...................................................................................................... 65
5.4.
Constraints ....................................................................................................... 69
5.5.
Harvest impacts on visually sensitive areas .................................................... 72
6.
References and Other Sources ............................................................................. 76
Page i of iv
List of Figures
Figure 2-1. Generate yield curve in VDYP - Model Parameter Selection part 1 ............... 5
Figure 2-2. Generate yield curve in VDYP - Model Parameter Selection part 2 ............... 6
Figure 2-3. Generate yield curve in VDYP - Model Parameter Selection part 3 ............... 7
Figure 2-4. Generate yield curves in TIPSY - Model Parameter set-up part 1 ................... 8
Figure 2-5. Generate yield curves in TIPSY - Model Parameter set-up part 2 ................... 9
Figure 2-6. Generate yield curves in TIPSY - Model Parameter set-up part 3 ................. 10
Figure 2-7. Generate yield curves in TIPSY - Model Parameter set-up part 4 ................. 10
Figure 2-8. Generate yield curves in TIPSY – export yield table ..................................... 11
Figure 2-9. Comparing yields before and after MPB attack ............................................. 13
Figure 3-1. Create a query in MS Access ......................................................................... 17
Figure 3-2. Design an append query in MS Access .......................................................... 18
Figure 3-3. Import and link a text file in MS Access (part 1) ........................................... 24
Figure 3-4. Import and link a text file in MS Access (part 2) ........................................... 24
Figure 3-5. Query design to append records to the Polygon table .................................... 25
Figure 3-6. Change the view in the object list (MS Access database) .............................. 26
Figure 3-7. Shapefile import tool in FPS-ATLAS ............................................................ 27
Figure 3-8. Polygon Neighbors (Analysis) geoprocessing tool in ArcGIS ...................... 27
Figure 3-9. Append query to Polygon_Adjacency table ................................................... 28
Figure 3-10. Query to append non-forested polygons to Polygon_Attribute table........... 29
Figure 3-11. Select query to summarize all possible values for a particular field ............ 29
Figure 3-12. Query to determine biodiversity cliques ...................................................... 30
Figure 3-13. Query to append polygons for THLB and NTHLB cliques into
Polygon_Clique table .................................................................................................................... 32
Figure 3-14. Query to append polygons for biodiversity cliques into Polygon_Clique
table ............................................................................................................................................... 32
Figure 3-15. Query to append polygons for VQO cliques into Polygon_Clique table ..... 33
Figure 3-16. Macro design view ....................................................................................... 34
Figure 3-17. Finished macro design for queries related to cliques ................................... 34
Figure 4-1. Select query design to determine the LTSY .................................................. 37
Figure 4-2. Initial run of the LTSY (197,000 m3/year)..................................................... 38
Figure 4-3. Final run of the LTSY (262,000 m3/year) ...................................................... 39
Figure 4-4. Old seral constraint set for biodiversity objectives ........................................ 40
Figure 4-5. Young seral constraint set for visual objectives ............................................. 40
Figure 4-6. In-block retention constraint set ..................................................................... 41
Figure 4-7. Base case scenario even harvest flow (243,000 m3/year) .............................. 41
Figure 4-8. Base case scenario maximum initial harvest flow (300,000 m3/year)........... 42
Figure 4-9. Append Query design to FixedSchedule_Polygon table ................................ 44
Figure 4-10. Base case even harvest flow with the fixed schedule module activated ...... 46
Figure 4-11. Delete query design for non-forested polygons in the Polygon_Attribute
table ............................................................................................................................................... 48
Figure 4-12. Max ECA constraint set ............................................................................... 51
Page ii of iv
Figure 4-13. Example of increased buffer. The selected polygon falls entirely in the
increased buffer area. .................................................................................................................... 52
Figure 4-14. Buffer geoprocessing tool to increase stream buffers .................................. 53
Figure 4-15. Intersect geoprocessing tool for increased stream buffer ............................. 53
Figure 4-16. Delete field geoprocessing tool for the increased buffer intersected feature
class ............................................................................................................................................... 54
Figure 4-17. Join two attribute tables ............................................................................... 55
Figure 4-18. Field Calculator tool for the Ratio field ....................................................... 56
Figure 4-19. Statistics tool in the Attribute Table............................................................. 57
Figure 4-20. Select By Attributes tool .............................................................................. 57
Figure 4-21. Export selected records to a txt file .............................................................. 58
Figure 4-22. Query design to append polygons to the increased buffer clique (Clique_id =
13) ................................................................................................................................................. 59
Figure 4-23. Constraint sets design to facilitate the start of the actual target to a year
different than 1 in the planning horizon ........................................................................................ 60
Figure 4-24. Query design (append to Polygon_Clique table) to duplicate cliques ......... 60
Figure 5-1. Copy function in FPS-ATLAS run window................................................... 62
Figure 5-2. Harvest flows for the base case scenario........................................................ 63
Figure 5-3. Harvest flows for the base case scenario when fire disturbances are simulated
....................................................................................................................................................... 64
Figure 5-4. Growing stock (standing merchantable volume) on THLB ........................... 65
Figure 5-5. Select query design to summarize Polygon.txt information for age classes
graphs ............................................................................................................................................ 66
Figure 5-6. Create a pivot table for the age classes data ................................................... 67
Figure 5-7. Pivot table design to summarize age classes .................................................. 67
Figure 5-8. Grouped by 10-year age classes pivot table for period 0 ............................... 68
Figure 5-9. Area by age class in year 2016 ....................................................................... 68
Figure 5-10. Setting-up the Constraints module ............................................................... 69
Figure 5-11. Define the lookup link for constraints output table ...................................... 71
Figure 5-12. Mature (>60 years old) seral constraint for NDT3_MS_Intermediate......... 71
Figure 5-13. ECA constraint ............................................................................................. 72
Figure 5-14. Young seral (<20 years old) area for the moderate visual quality objective
(VQO_M) ...................................................................................................................................... 72
Figure 5-15. Select Query design to summarize age of each polygon at year 100 ........... 73
Figure 5-16. Export select query results to a text file ....................................................... 74
Figure 5-17. Visual impacts at year 100 (looking from the Puntzi Lake, BC) ................. 75
Page iii of iv
List of Tables
Table 2-1. Stand Groups definition..................................................................................... 2
Table 2-2. Yield curves definition ...................................................................................... 4
Table 2-3. Example of how yield curves are organized...................................................... 8
Table 2-4. Estimating MPB yield ..................................................................................... 12
Table 2-5. Silvicultural systems design ............................................................................ 14
Table 3-1. StandGroup_Category table ............................................................................ 19
Table 3-2. StandGroup table ............................................................................................. 20
Table 3-3. Curve table....................................................................................................... 21
Table 3-4. StandGroup_Curve table ................................................................................. 21
Table 3-5. StandGroup_Treatment table........................................................................... 22
Table 3-6. Results for query to determine biodiversity cliques ........................................ 31
Table 3-7. Clique table...................................................................................................... 31
Table 4-1. Sequence-age lookup table .............................................................................. 36
Table 4-2. LTSY query results.......................................................................................... 37
Table 4-3. Harvest flow request with the fixed schedule module activated ..................... 45
Table 4-4. Relation between stand height, hydrological recovery and ECA .................... 48
Table 4-5. Example to calculate ECA in Excel ................................................................ 49
Table 4-6. ECA curves to be added to the Curve table ..................................................... 50
Table 4-7. ECA curves linked to stand groups in StandGroup_Curve table .................... 50
Table 5-1. Duplicate harvest flows to produce professional looking graphs .................... 62
Table 5-2. Summary constraints set-up table .................................................................... 70
Page iv of iv
1. Introduction
The purposes of this document are to detail the process of building a working FPSATLAS model (Nelson, 2003a) and to provide guidance in using the FPS-ATLAS model to
improve the quality of the analysis and presentation of results. It is expected that the user of this
document is familiar with 4 other documents: (1) FPS-ATLAS Manual version 6 (Nelson
2003a), (2) FPS-ATLAS Database Manual version 6 (Nelson, 2003b), (3) FPS-ATLAS Tutorial
Manual (Perdue and Nelson, 2009), and (4) Guide to build a resultant GIS version 1 (Man,
2016).
There are two major steps to build a FPS-ATLAS model: data preparation and model
building. The data preparation step requires significant time because the user has to define the
stand groups (or stand types) based on the inventory data (or the resultant GIS attribute table),
develop the yield curves and assign them to each stand group, and develop the silvicultural
system(s) for each stand group including the transition rules following the disturbance events and
minimum harvest ages. In the building process step, the user has to develop a blank database
from the master database that comes with the FPS-ATLAS installation files, populate the
required tables in FPS-ATLAS MS Access database (ADB), link the polygons spatially to the
shapefile, identify the non-forested polygons and instruct the FPS-ATLAS not to grow them, and
adding the spatial relationships between the polygons (i.e., who’s neighbour’s who). Finally, the
user has to develop the cliques/zones in order to apply constraints.
Some tricks and tips discussed in this document include finding the right flow, implement
natural disturbances (fires, succession) using the fixed schedule module, rehabilitate non-forested
areas (e.g., roads), use Equivalent Clearcut Area to model hydrological recovery of sensitive
watersheds, and increase buffer size around certain areas (e.g., riparian, wildlife habitat) without
rebuilding the resultant GIS file.
The final chapter describes some of the essential modelling outputs available in FPSATLAS and how these can be used to produce professional graphs and reports. Such graphs and
reports include harvested volume and area for the entire Timber Harvesting Land Base (THLB)
and by leading species (or stand groups), growing stock by THLB and non-THLB (NTHLB), age
classes, harvest impacts on visually sensitive areas using Google Earth, and constraint reports
(target required vs. achieved).
Guide to build a FPS-ATLAS model v1
Page 1 of 77
2. Data preparation
2.1. Stand group definitions
Stand groups are the core feature of the FPS-ATLAS because they define silviculture
treatments, stand growth and yield, treatment costs, log values, and carbon storage tracking.
Stand groups can be linked to the seral stage, biogeoclimatic (BEC) zones, and raster cells
through the stand group categories. Also, constraints and harvest priorities can be set for stand
groups. Thus, defining the stand groups in FPS-ATLAS requires a lot of thought.
The definition of stand groups begins with the inventory of the forest estate in question,
more specifically with the information summarized from the resultant GIS attribute table. The
resultant GIS is typically developed from a range of datasets that include administrative,
inventory, and management guidance information. A typical stand group definition includes
information about disturbance history (natural and anthropogenic), species composition, stand
productivity (e.g., site index defined as top height in metres at age 50), BEC zones, wildlife
management, and future conditions following anthropogenic (e.g., timber harvesting) and natural
(e.g., wildfires) disturbances. The sample dataset selected for this document includes in stand
group definition information about species composition, site index (SI), natural disturbances
(Mountain Pine Beetle (MPB)), BEC zones, wildlife management (ungulates), and future
conditions following anthropogenic disturbances (e.g., timber harvesting) (Table 2-1). The
sample dataset is located in the Williams Lake Timber Supply Area (TSA) and the stand group
definition was adopted from the Timber Supply Review (TSR) Data Package document (April
2013). Note that a lot of the MPB stand groups could have been defined as per methodology
outlined by Seely et al. (2008). However, for the purpose of this document, only 3 stand groups
were defined.
Table 2-1. Stand Groups definition
SG
Leading
Species
SI
1
Fd>=40%
>=7
2
Fd>=40%
>=7
3
Fd>=40%
>=7
4
Fd>=40%
>=7
5
Fd<40, all
other species
>=7
6
All
>=7
7
All
>=7
Additional Criteria
non-MPB, MDWR, outside
of IDF, SBPS, SBS, and
ICH BEC zones
non-MPB, IDF and SBPS
BEC zones with harvest
history
non-MPB, IDF and SBPS
BEC zones, no harvest
history
non-MPB, MDWR, in SBS
and ICH BEC zones
non-MPB, MDWR
non-MPB, Caribou zone
(Itcha Ilgachuz Herd), nonResults harvest history
non-MPB, Caribou zone
(Itcha Ilgachuz Herd),
Results harvest history
Guide to build a FPS-ATLAS model v1
Status
Silv System
Description
Existing
Selection/
Uneven Age
Existing
Selection/
Uneven Age
Existing
Selection/
Uneven Age
Existing
Selection/
Uneven Age
MDWR wet belt
Existing
CC
MDWR even aged
Existing
Selection/
Uneven Age
Existing
Selection/
Uneven Age
MDWR fir stands in
Transition and Deep
Sbowpack zones
MDWR, Shallow and
Moderate Snowpack
zones
MDWR, Shallow and
Moderate Snowpack
zones
Caribou Terrestrial
lichen (80% of the
habitat)
Caribou Arboreal
lichen (20% of the
habitat)
Page 2 of 77
SG
Leading
Species
SI
8
Fd
7 to 12
9
Fd
>12
10
11
12
13
14
15
16
17
18
Cw,Hw
Cw,Hw
Cw,Hw
Sx,Bl
Sx,Bl
Sx,Bl
Pl
Pl
Pl
7 to 12
12.1 to 17
>17
7 to 12
12.1 to 17
>17
7 to 12
12.1 to 17
>17
19
All
7 to 12
20
All
12.1 to 17
21
All
>17
22
Decid
All
105
Fd<40, all
other species
>=7
108
Fd
109
Fd
Additional Criteria
Status
Silv System
Description
Existing
CC
FD poor clearcut
Existing
CC
FD med/good clearcut
Existing
Existing
Existing
Existing
Existing
Existing
Existing
Existing
Existing
CC
CC
CC
CC
CC
CC
CC
CC
CC
Cw_Hw_poor
Cw_Hw_med
Cw_Hw_good
Sx_Bl_poor
Sx_Bl_med
Sx_Bl_good
Pl_poor
Pl_med
Pl_good
Existing
CC
MPB_poor
Existing
CC
MPB_med
Existing
CC
MPB_good
Existing
CC
Deciduous
transition from SG 5
Future
CC
7 to 12
transition from SG 8
Future
CC
>12
transition from SG 9
Future
CC
Future
Future
Future
Future
Future
Future
Future
Future
Future
CC
CC
CC
CC
CC
CC
CC
CC
CC
non-MPB, Outside of IDF,
SBPS and MDWR
non-MPB, Outside of IDF,
SBPS and MDWR
non-MPB
non-MPB
non-MPB
non-MPB
non-MPB
non-MPB
non-MPB
non-MPB
non-MPB
MPB, MPB_age >0, year of
attack >2000, Age_2015>15
MPB, MPB_age >0, year of
attack >2000, Age_2015>16
MPB, MPB_age >0, year of
attack >2000, Age_2015>17
Deciduous leading species
(AC, AT, EP, etc)
110 Cw,Hw
7 to 12
transition from SG 10
111 Cw,Hw
12.1 to 17
transition from SG 11
112 Cw,Hw
>17
transition from SG 12
113 Sx,Bl
7 to 12
transition from SG 13
114 Sx,Bl
12.1 to 17
transition from SG 14
115 Sx,Bl
>17
transition from SG 15
116 Pl
7 to 12
transition from SG 16
117 Pl
12.1 to 17
transition from SG 17
118 Pl
>17
transition from SG 18
MPB, average attack rate of 38% at average stand age of 96 years
CC, Clearcut with reserves
Future MDWR even
aged
Future FD poor
clearcut
Future FD med/good
clearcut
Future Cw_Hw_poor
Future Cw_Hw_med
Future Cw_Hw_good
Future Sx_Bl_poor
Future Sx_Bl_med
Future Sx_Bl_good
Future Pl_poor
Future Pl_med
Future Pl_good
2.2. Curves
Curves are referred to a relationship between time and a forest characteristic (e.g.,
volume, carbon storage, index as percentage of something). There are 4 types of curves that FPSATLAS can support: (1) yield curve as a relationship between time and volume per area unit
(typically m3/ha), (2) carbon storage curve as a relationship between time and carbon storage per
area unit (typically metric tonnes of carbon stored per ha), (3) equivalent clearcut area (ECA)
index as a relationship between time and hydrological recovery of a stand following a clearcut
event (typically between 0 an 100, where 0 denotes fully hydrological recovery), and (4)
percentage curves from (1) (values between 0 and 100). The type (2) curves can be also used to
Guide to build a FPS-ATLAS model v1
Page 3 of 77
model standing volume by species composition or timber sorts. Similarly, the type (4) curves,
defined as species percentages curves in the FPS-ATLAS manual, can be used to model other
attributes of the stand (e.g., percentage of timber sorts/grades).
In BC, there are 3 growth & yield models that are used in forest estate modelling. The
Variable Density Yield Projection (VDYP) is used to predict yield in m3/ha over time for evenaged natural stands (i.e., stands regenerated following natural stand-replacing disturbances such
as wildfires). The Table Interpolation for Stand Yields (TIPSY) is used to predict the yield in
m3/ha over time for even-aged managed stands (i.e., stands regenerated following clearcut
disturbances). Prognosis BC is used to predict the yield in m3/ha over time for complex unevenaged stands. All these growth & yield models can be freely accessed from the BC Government
website (https://www.for.gov.bc.ca/hts/SDM.htm). For the sample dataset used to develop this
document, VDYP and TIPSY were used to develop the yield curves. The assumptions used to
generate the yield curves are shown in Table 2-2. These were adopted from the TSR Data
package for the Williams Lake TSA (April 2013), yet not all of them are present in the sample
dataset (those with blank records for BEC field in Table 2-2 are not present). Note that genetic
gains were used for future managed stands (100 series modelled in TIPSY) as follows: Fd-9.7%,
Pl-3%, and Sx-12.8%.
Table 2-2. Yield curves definition
SG
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
105
108
109
110
111
Yield
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
105
108
109
110
111
Species Comp.
FD80 PL10 AT10
FD80 PL10 AT10
FD80 PL10 AT10
FD80 PL10 AT10
FD80 PL10 AT10
SX40 FD30 PL30
SX40 FD30 PL31
PL50 FD30 AT20
PL50 FD30 SX20
CW40 SX40 PL20
CW30 SX50 PL20
CW30 SX50 PL20
SX50 PL30 BL20
SX50 PL30 BL20
SX50 PL30 BL20
PL90 AT10
PL60 SX25 AT15
PL60 SX25 AT15
PL90 AT10
PL60 SX25 AT15
PL60 SX25 AT15
AT100
FD80 PL10 AT10
PL50 FD30 AT20
PL50 FD30 SX20
CW40 SX40 PL20
CW30 SX50 PL20
GY model
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
VDYP
TIPSY
TIPSY
TIPSY
TIPSY
TIPSY
Guide to build a FPS-ATLAS model v1
BEC
Regen
Delay
IDF
IDF
IDF
IDF
SBPS
IDF
IDF
SBPS
SBPS
SBPS
SBPS
SBPS
IDF
SBPS
IDF
IDF
IDF
2
2
3
2
1
SI
11
11
11
11
11
11
11
9.5
14.5
9.5
14.5
19.5
9.5
14.5
19.5
9.5
14.5
19.5
9.5
14.5
19.5
14.5
11
9.5
14.5
9.5
14.5
Stems/
ha
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1,139
1,026
1,152
1,481
1,481
Reduction factors
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB
DWB, OAF1=0.85, OAF2=0.95
DWB, OAF1=0.85, OAF2=0.95
DWB, OAF1=0.85, OAF2=0.95
DWB, OAF1=0.85, OAF2=0.95
DWB, OAF1=0.85, OAF2=0.95
Page 4 of 77
SG
112
113
114
115
116
117
118
Yield
112
113
114
115
116
117
118
Species Comp.
CW30 SX50 PL20
SX50 PL30 BL20
SX50 PL30 BL20
SX50 PL30 BL20
PL90 AT10
PL60 SX25 AT15
PL60 SX25 AT15
GY model
TIPSY
TIPSY
TIPSY
TIPSY
TIPSY
TIPSY
TIPSY
BEC
SBPS
SBPS
SBPS
SBPS
Regen
Delay
1
2
2
2
4
2
2
SI
19.5
9.5
14.5
19.5
9.5
14.5
19.5
Stems/
ha
1,173
1,255
1,255
1,001
1,133
1,133
1,001
Reduction factors
DWB, OAF1=0.85, OAF2=0.95
DWB, OAF1=0.85, OAF2=0.95
DWB, OAF1=0.85, OAF2=0.95
DWB, OAF1=0.85, OAF2=0.95
DWB, OAF1=0.85, OAF2=0.95
DWB, OAF1=0.85, OAF2=0.95
DWB, OAF1=0.85, OAF2=0.95
DWB, Decay, Waste, and Breakage. OAF, Operational Adjustment Factor
2.2.1. Generate yield curves using VDYP
To generate a yield curve in VDYP, click the create new table button from the toolbar to
open the Model Parameter Selection window (Figure 2-1). There will be 5 such windows where
a range of parameters need to be set up. In the first Model Parameter Selection window, select
Volume radio button for the Species % Derived By option. To generate curve ID 2 (Table 2-2)
select the corresponding species and percentages:



For Species#1 field select FD – Douglas Fir, and in Species % field type in 80
For Species#2 field select PL – Lodgepole Pine and in Species % field type in 10
For Species#3 field select AT – Aspen and in Species % field type in 10
Figure 2-1. Generate yield curve in VDYP - Model Parameter Selection part 1
Guide to build a FPS-ATLAS model v1
Page 5 of 77
In the second Model Parameter Selection window, select IDF for BEC Zone, select FD for Site
Species, select Supplied radio button, and type in the Site Index value of 11 (Figure 2-2).
Figure 2-2. Generate yield curve in VDYP - Model Parameter Selection part 2
In the third Model Parameter Selection window type in 100 in the % Stockable Area field. Leave
all parameters unchanged in the fourth Model Parameter Selection window. In the fifth Model
Parameter Selection window change the Starting Age to 0, select from the Volumes Reported the
Close Utilization and Net Decay, Waste, Brkg, and select the diameter at breast height (DBH)
merchantability criteria for each of the species (12.5 cm for all species as per Williams Lake
TSA TSR data package document) (Figure 2-3). When done, click Run Model.
Guide to build a FPS-ATLAS model v1
Page 6 of 77
Figure 2-3. Generate yield curve in VDYP - Model Parameter Selection part 3
Attention is needed when selecting the correct DBH merchantability criteria. It has to be
kept in mind that one would need yield curves to analyze the carbon stocks in the Carbon Budget
Model for the Canadian Forest Sector (CBM-CFS3) (Kurz, 2009). The current version of the
CBM-CFS3 (version 1.2 (June 2016)) indicates that the yield curves should have the gross
merchantable volume (i.e., the sum between the net merchantable volume and the losses due to
decay, waste, and breakage (DWB)). In VDYP, the gross merchantable volume is called “Close
utilization”. In TIPSY, the user will have a to create a different yield table where the DWB is not
selected for the corresponding DBH merchantability criterion. Lastly, consider the CBM-CFS3
requirements for the DBH criterion. In the case of the sample dataset used to develop this
document, the BC Interior requirements need to be met, which are DBH of 17.5cm for all
conifers except pine, and DBH 12.5cm for all other species (pines and deciduous). For the
example shown in Figure 2-3, the DBH for F should be selected at 17.5cm in order to be able to
accurately use the Close Utilization yield in the CBM-CFS3.
Once the VDYP model is run, the results need to be exported to a text file and then
imported and organized into an MS Excel file. The results from all yield curves should be
organized in a table with a lot of rows (e.g., 31 x number of yields) and 4 columns: (1) curve ID,
(2) Year (10- or 5-year steps), (3) net merchantable yield value (to be used in FPS-ATLAS), and
(4) gross merchantable yield value (to be used in CBM-CFS3). An example is showed in Table
2-3.
Guide to build a FPS-ATLAS model v1
Page 7 of 77
Table 2-3. Example of how yield curves are organized
Yield
Age
2
2
2
2
2
2
2
2
…
117
117
117
117
117
117
117
117
117
Net Merch (FPS-ATLAS)
0
10
20
30
40
50
60
70
…
220
230
240
250
260
270
280
290
300
0
0
0
0
0
11
24
41
…
282
282
282
280
278
277
276
275
275
Gross Merch (CBM-CFS3)
0
0
0
0
0
11
26
44
…
348
351
353
353
354
355
356
356
356
2.2.2. Generate yield curves using TIPSY
To generate a yield curve in TIPSY, create a new file when first opening TIPSY and start
editing the parameters in the subsequent windows. In the Project Title and Stand Geography
window, type in the project title and select the Forest Region, Forest District, and BEC. These
settings can also be altered after the project was created. For yield curve 105 (Table 2-2), it was
selected Southern Interior, Central Cariboo, IDF (Figure 2-4).
Figure 2-4. Generate yield curves in TIPSY - Model Parameter set-up part 1
Guide to build a FPS-ATLAS model v1
Page 8 of 77
In the next TIPSY window, the user needs to select the species. When the species composition
includes aspen, TIPSY cannot combine 2 or more species for the same yield. Alternatively, the
user generates a TIPSY yield curve for each species and then recombine the individual yield
curves in MS Excel given the percentage of each species. The alternative will work because
TIPSY does not account for inter-species competition. For the yield curve 105 (Table 2-2), there
were 3 curves created for each species: FD, PL, and AT. In the Species Specifications window,
Lodgepole Pine shows up as default species; click Delete to remove it from the list. Then, click
Add, select Interior Douglas-fir from the species list, and type 11 as the Site Index (Figure 2-5).
Then, check the box Genetic Worth and type in 9.7 in the field Gain at Index Age 60; leave all
other values as default.
Figure 2-5. Generate yield curves in TIPSY - Model Parameter set-up part 2
In the Stand Specifications window, select Planted in the Stand Regeneration field, type 2 in the
Delay field, type 1139 in the Density field, leave OAF1 and OAF2 to the default values as they
are identical to the factors shown in Table 2-2, and check the Decay, Waste & Breakage field
and select all 3 options (Decay, Waste, and Breakage) (Figure 2-6). Note again that the DWB is
only for the FPS-ATLAS yield curves, for CBM-CFS3 yield curves, the user should not select
DWB in order to estimate the gross merchantable volume.
Guide to build a FPS-ATLAS model v1
Page 9 of 77
Figure 2-6. Generate yield curves in TIPSY - Model Parameter set-up part 3
In the Table Specifications window, select the range from 0 to 300 years in steps of 10 years
(Figure 2-7).
Figure 2-7. Generate yield curves in TIPSY - Model Parameter set-up part 4
Guide to build a FPS-ATLAS model v1
Page 10 of 77
In the Stand Description Display window leave all default values. To generate the yield curve,
click on the yield sign button in the TIPSY toolbar, then export the table to a text file that can be
later imported into an MS Excel file (Figure 2-8).
Figure 2-8. Generate yield curves in TIPSY – export yield table
To generate the yield curves for other species, repeat the steps above. Note that in the case of
Aspen, the user needs to select Clumped as the regeneration method and type in 10,000 stems per
ha as density. Attention is needed to the site index, genetic worth values, and density values as
these are different for each yield curve shown in Table 2-2.
Once all species have an individual yield curve, the user needs to import the text files into MS
Excel and calculate the yields for each yield curve shown in Table 2-2 according to the species
composition. For example, in the case of yield 105, for each 10-year time step, the user will add
80% from the FD yield curve, 10% from the PL yield curve, and 10% from AT yield curve.
Similarly, for the gross merchantable yield curves that will be used to estimate carbon stocks via
CBM-CFS3. Recall, the end result should be a table with many rows and 4 columns as shown in
Table 2-3.
2.2.3. Yield curves for MPB
The mountain pine beetle (MPB) epidemic affected a significant area occupied by
lodgepole pine leading stands in BC. Research was conducted to estimate the shelf life of pinekilled trees (i.e., the length of time for which pine wood is still usable following the MPB attack)
and the merchantable volume that can be used. Seely et al. (2008) published a comprehensive
methodology to adjust the yield curves based on pine percentage composition within the stand,
MPB attack intensity, and age of stand when the MPB attack occurred. To these factors, a more
accurate estimate would consider the ranges of other stand characteristics (e.g., site index, BEC
zone etc.). For the purpose of this document, the sample dataset used here grouped all attacked
Guide to build a FPS-ATLAS model v1
Page 11 of 77
MPB stands into one large group. It was estimated from the inventory data that the MPB attack
occurred at an average stand age of 96 years with a 38% intensity (i.e., 38% of the pine trees
were killed). It would have been more accurate to determine several groups of stands based on
the age of stands when MPB attacked occurred and the attack intensity. It could have been
resulted in tens or hundreds of aggregated MPB stands for which yield curves should have been
developed.
For the sample dataset used here, the MPB grouped stands were further grouped into 3
Stand Groups (used in FPS-ATLAS) by the site index range (i.e., Stand Group 19, 20, and 21 in
Table 2-1). For these 3 stand groups, the yields developed in VDYP were used to account for the
age of MPB attack (96 years) and the 38% attack intensity using the equation in Appendix A2
from Seely et al. (2008). In Table 2-4, an example for yield curve 19 is showed. Here, starting
from the VDYP yield for curve 16, it was added a column with the MPB age denoting the age of
stand following the MPB attack, and a column denoting the attack rate percentage. The volume
for the new MPB yield (curve 19) is identical to curve 16 until MPB age becomes positive.
When MPB age becomes positive, for the first 15 years, the merchantable volume needs to be
estimated using the equation in Appendix A2 from Seely et al. (2008) for 38% of the pine
component. Following 15 years after MPB attack, it was assumed that all attacked pine trees died
and new pine trees will emerge in the recently open space. Formulae were added in Table 2-4 to
illustrate the calculations. Final results are showed in Figure 2-9.
Table 2-4. Estimating MPB yield
7
8
9
10
11
12
13
14
15
16
17
A
B
Age
0
10
20
30
40
50
60
70
80
90
C
FPS
Volume
0
0
0
0
0
5
15
30
48
68
D
CBM
Volume
0
0
0
0
0
5
16
32
51
73
E
MPB
Age
-96
-86
-76
-66
-56
-46
-36
-26
-16
-6
F
Pct
dead
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
Yield
16
16
16
16
16
16
16
16
16
16
16
100
88
95
4
16
16
16
16
110
120
130
140
107
124
139
151
117
137
155
170
14
24
34
44
G
MPB
FPS
0
0
0
0
0
5
15
30
48
68
H
MPB
CBM
0
0
0
0
0
5
16
32
51
73
0.38
80
87
0.38
0.38
0.38
0.38
89
82
92
100
97
90
102
112
18
19
20
21
Guide to build a FPS-ATLAS model v1
Formula MPB FPS
=C7
=C8
=C9
=C10
=C11
=C12
=C13
=C14
=C15
=C16
=0.9*$F17*C160.9*$F17*C16*0.0215*EXP(0.15
7*$E17)+(1-0.9*$F17)*C17
=0.9*$F18*C160.9*$F18*C16*0.0215*EXP(0.15
7*$E18)+(1-0.9*$F18)*C18
=0.9*$F19*C7+(1-0.9*$F19)*C19
=0.9*$F20*C8+(1-0.9*$F20)*C20
=0.9*$F21*C9+(1-0.9*$F21)*C21
Page 12 of 77
200
Volume (m3/ha)
160
120
80
FPS_Volume (curve 16)
40
MPB_FPS (curve 19)
0
0
50
100
150
200
250
300
Age (years)
Figure 2-9. Comparing yields before and after MPB attack
2.3. Silvicultural systems
The stand group definitions indicate the silvicultural systems to be applied to each stand
group (Table 2-1). The FPS-ATLAS model needs information about the age at which a
disturbance occurs and what happens with each stand group following the disturbance. For the
sample data used to develop this document, there were 2 silvicultural systems: (1) selection cuts
to maintain an uneven aged stand structure (i.e., uneven aged silvicultural system) and (2) clearcuts with reserves to convert any existing stand to an even aged stand structure (i.e., even aged
silvicultural system).
The design of the uneven aged silvicultural system was adopted from the Williams Lake
TSA TSR Data Package document and the management guidance for the ungulate winter range
(UWR) (http://www.env.gov.bc.ca/wld/frpa/uwr/approved_uwr.html) for the UWR numbers
falling into the area of interest (i.e., u-5-002). The UWR numbers are present in the GIS dataset
denoting the UWR which is freely available on Data BC; this dataset was included in the GIS
resultant file and summarized for the sample dataset used to develop this document. The UWR
documents indicate a 30-40 years cutting cycles starting at an age between 120-200 years old.
For the purpose of this document, it was assumed a 30-year cycle (UWR shallow and moderate
snow pack) and minimum harvest ages (MHA) of 160 years. In the case of the Cariboo
management stand groups (i.e., stand groups 6 and 7), the shelterwood system (MHA of 140
years and 70-year cycles) was adopted for stand group 6 (terrestrial lichen) and partial cuts (33%
per entry, MHA of 240 years, 80-year cycles) was adopted for stand group 7 (arboreal lichen).
Guide to build a FPS-ATLAS model v1
Page 13 of 77
The design of the even aged silvicultural system is described in the Williams Lake TSA
TSR Data Package document. In the case of the pine leading stands, the clear-cut should occur
once a minimum volume of 80 m3/ha and a MHA of 60 years have been reached. In the case of
the non-pine leading stands, the minimum volume is 120 m3/ha and MHA is 80 years. This
design was adopted for the sample dataset used to develop this document. The silvicultural
systems for the stand groups that fall in the sample area are shown in Table 2-5.
Table 2-5. Silvicultural systems design
SG
2
3
5
6
7
8
9
13
14
16
17
19
20
22
105
108
109
113
114
116
117
Yield
ID
2
3
5
6
7
8
9
13
14
16
17
19
20
22
105
108
109
113
114
116
117
Silv System
Selection/Uneven Age
Selection/Uneven Age
Clearcut with reserves
Selection/Uneven Age
Selection/Uneven Age
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
Clearcut with reserves
MHA
160
160
120
140
240
110
70
120
80
100
60
120
96
70
170
140
70
110
70
110
60
Guide to build a FPS-ATLAS model v1
Future
SG
2
3
105
6
7
108
109
113
114
116
117
116
117
22
105
108
109
113
114
116
117
SG Description
Comments
Nat_MDWR_Selection
Man_MDWR_Selection
MDWR_Clearcut
Carboo_Terrestrial_Lichen
Carboo_Arboreal_Lichen
FD poor clearcut
FD med/good clearcut
Sx_Bl_poor
Sx_Bl_med
Pl_poor
Pl_med
MPB_poor
MPB_med
Deciduous
Future MDWR even aged
Future FD poor clearcut
Future FD med/good clearcut
Future Sx_Bl_poor
Future Sx_Bl_med
Future Pl_poor
Future Pl_med
30-yr cycles
30-yr cycles
70-yr cycles
80-yr cycles
Page 14 of 77
3. Build the FPS-ATLAS model
3.1. Blank database
The FPS-ATLAS comes with a master MS Access database which is used to build the
model. Before starting to build the model, it is recommended to develop a blank master database
to avoid annoying error messages when the proper sequence to populate the tables is misused.
The following changes (in the sequence showed below) need to be made to the master database:


Make a copy of the master_2000.mdb file, usually located under the Program
Files folder where the FPS-ATLAS files were initially installed. Rename the copy
file as Blank_database.mdb; it should be used every time when a new model is
built and back-up copy of it should be kept at all times in a secured location.
In the Blank_database.mdb file add in each table, in this sequence, the following:
o Tbl: Range
Range
Range_Id Priority Cycle_Constraints Period_Constraints Description
1
1
No
No range1
o Tbl: AccessUnit
AccessUnit
AccessUnit_Id Range_Id Priority Cycle_Constraints Period_Constraints Description
1
1
1
No
No UNIT 1
o Tbl: Zone
Zone
Zone_Id AccessUnit_Id Priority Cycle_Constraints Period_Constraints Description
1
1
1
No
No zone 1
2
1
2
No
No zone 2
3
1
3
No
No zone 3
4
1
4
No
No zone 4
o Tbl: StandGroup_Category
StandGroup_Category
StandGroup_Category_Id Description
1 category1
o Tbl: StandGroup
StandGroup
StandGroup_Id StandGroup_Category_Id LogValue Priority Cycle_Constraints Period_Constraints Description
888
1
100
1
No
No Dummy
o Tbl: Curve
Guide to build a FPS-ATLAS model v1
Page 15 of 77
Curve
Curve_Id X_Initial Delta_X
Description
888
0
10 Dummy curve for import
o Tbl: StandGroup_Curve
StandGroup_Curve
StandGroup_Id CurveType_Id Curve_Id
888
1
888
o Tbl: StandGroup_Treatment
StandGroup_Treatment
StandGrou Treatment Enabl Goto_StandGro Minimum_ Maximum_ Treatment_V Ignore_Adjac
p_Id
_Id
ed
up_Id
Age
Age
alue
ency
888
2
Yes
888
50
999
0
No
o Tbl: Curve_Data
Curve_Data
Curve_Id Sequence Y_Value
888
1
0
888
2
0
888
3
20
888
4
60
888
5
100
888
6
200
888
7
400
888
8
600
888
9
800
888
10
900
888
11
950
888
12
975
888
13
1000
888
14
1000
888
15
1000
888
16
1000
888
17
1000
888
18
1000
888
19
1000

Go to File menu and select “Compact and Repair Database”. This is the blank
database to be used in building any FPS-ATLAS model from now on
Alternatively to the manual procedure presented above, one can build append queries to
populate the tables showed here, in that strict sequence. In MS Access, select Query Design from
Guide to build a FPS-ATLAS model v1
Page 16 of 77
Create menu, then select the table Range from the list that pops up, click Add, and then close the
list (Figure 3-1). Alternatively, close the list without adding any table and drag the table(s) from
the object list to the left in the query upper window (also called Records Set window); the
window below is called Results window where the user sets up the desired query results. In the
Query Tools view (that pops up after a query is created) select the Append type query and select
the table where values will be appended (e.g., Tbl: Range). Then, in the Results window of the
query, select the fields in the Append To row and in the Field row type in the values to be
appended (Figure 3-2). Once the cursor is moved away from the Field row, the “Expr1:” pops up
automatically; this is a label and it can be edited to a more appealing name, such as “RangeID:”
(note the : is mandatory to separate between the label and the value). Repeat these steps for each
of the fields that need to be populated in the Tbl:Range. When done, run the query (Query Tools
menu, click on Run button). The query can be saved and together with other queries that append
values to the other tables showed above, can be included in a Macro (from the Create menu).
The Macro will run all queries with one click. These concepts will be addressed in more details
in the remainder of section 3.
Figure 3-1. Create a query in MS Access
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Page 17 of 77
Figure 3-2. Design an append query in MS Access
3.2. Populate tables in FPS-ATLAS database
To build a basic FPS-ATLAS model, the following tables in the blank database created in
section 3.1 need to be populated (in this order):







StandGroup_Category
StandGroup
Curve
StandGroup_Curve
StandGroup_Treatment
Curve_Data
Polygon
Each of these tables are detailed in the following sections. Except Polygon, all other
tables can be developed in an MS Excel spreadsheet and using copy/paste, they can be inserted
Guide to build a FPS-ATLAS model v1
Page 18 of 77
into the corresponding tables of the FPS-ATLAS database. Alternatively, queries can be
developed to populate these tables, yet the queries need some linked tables to pull out the
information from. Thus, for all tables except Polygon, this document will use MS Excel to
develop tables and the copy/paste approach to populate FPS-ATLAs database. To get started,
create a copy of the blank database developed earlier and rename it to a suggestive name (e.g.,
FPS_guide_model_v1.mdb).
3.2.1. StandGroup_Category
The Stand Group Category is used to organize the stand groups in various categories.
Some examples include the regeneration type or stand history (e.g., existing natural, existing
managed, future stands), stand current status (existing rods to be rehabilitated to forests, not
sufficiently restocked (NSR)), etc. The stand group category can be tracked in the Map Viewer in
FPS-ATLAS to assess the changes overtime (e.g., transition of existing category to future
category, conversion of non-forested to forested etc. For the sample data used to develop this
document the stand group categories are shown in Table 3-1. To ease the process, it is
recommended to copy the contents of existing StandGroup_Category table from the
FPS_guide_model_v1.mdb into an MS Excel spreadsheet, edit it, and then paste the edits back
into the FPS_guide_model_v1.mdb.
Table 3-1. StandGroup_Category table
StandGroup_Category_Id
1
2
3
4
5
6
Description
MDWR Selection
MDWR Clearcut
Cariboo
Existing Clearcut
Future stands
Non-Forested
3.2.2. StandGroup
The StandGroup table is one of the most essential tables in the FPS-ATLAS model. It
aggregates similar stands into groups (i.e., stand groups) and links them to inventory,
management information, and yields. For the sample data used to develop this document, the
stand group definitions from Table 2-1have been translated into the table format needed in the
FPS-ATLAS database (Table 3-2). Again, copy the contents of the StandGroup table from the
FPS_guide_model_v1.mdb into an excel spreadsheet, translate Table 2-1 into the required
format, and paste the new information back into the FPS_guide_model_v1.mdb.
Guide to build a FPS-ATLAS model v1
Page 19 of 77
Table 3-2. StandGroup table
StandGr
oup_Id
888
2
3
5
6
7
8
9
13
14
16
17
19
20
22
105
108
109
113
114
116
117
StandGroup_
Category_Id
6
1
1
2
3
3
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
LogValue
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
Priority
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Cycle_Co
nstraints
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
Period_C
onstraints
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
Description
Dummy StandGroup for import
Nat_MDWR_Selection
Man_MDWR_Selection
MDWR_Clearcut
Carboo_Terrestrial_Lichen
Carboo_Arboreal_Lichen
FD poor clearcut
FD med/good clearcut
Sx_Bl_poor
Sx_Bl_med
Pl_poor
Pl_med
MPB_poor
MPB_med
Deciduous
Future MDWR even aged
Future FD poor clearcut
Future FD med/good clearcut
Future Sx_Bl_poor
Future Sx_Bl_med
Future Pl_poor
Future Pl_med
3.2.3. Curve
The Curve table defines all the curves (except carbon storage curves) that are used to
predict the yield, ECA (section 4.4), and species percentage (not discussed in this document).
These curves are linked to the stand groups via StandGroup_Curve table (section 3.2.4). The
carbon storage curves need to be defined for each stand group in StandGroup_Carbon table as
instructed in the FPS-ATLAS manual (Nelson, 2003). To develop a simple model only the yield
curves are needed. For the sample dataset used to develop this document, the curve table is
shown in Table 3-3. Again, copy the contents of the Curve table from the
FPS_guide_model_v1.mdb into an excel spreadsheet, translate Table 2-5 into the required
format, and paste the new information back into FPS_guide_model_v1.mdb. The “X_Initial”
represents the starting age on the curve and it links to the “Sequence” field in Curve_Data table.
The “Delta_X” represents the number of years between two consecutive time sequences on the
curve. For example, if X_Initial is 0 and Delta_X is 10, Sequence 1 in Curve_Data table
represents Year 0, Sequence 2 represents year 10 and so on. If X_Initial is 10 and Delta_X is 10,
Sequence 1 in Curve_Data table represents Year10, Sequence 2 represents year 20 and so on.
Attention is needed when populating the Curve and Curve_Data tables, so X_Initial and Delta_X
(in Curve table) are aligned with Sequence (in Curve_Data table).
Guide to build a FPS-ATLAS model v1
Page 20 of 77
Table 3-3. Curve table
Curve_Id
888
2
3
5
6
7
8
9
13
14
16
17
19
20
22
105
108
109
113
114
116
117
X_Initial
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Delta_X
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Description
Dummy curve for import
Nat_MDWR_Selection
Man_MDWR_Selection
MDWR_Clearcut
Carboo_Terrestrial_Lichen
Carboo_Arboreal_Lichen
FD poor clearcut
FD med/good clearcut
Sx_Bl_poor
Sx_Bl_med
Pl_poor
Pl_med
MPB_poor
MPB_med
Deciduous
Future MDWR even aged
Future FD poor clearcut
Future FD med/good clearcut
Future Sx_Bl_poor
Future Sx_Bl_med
Future Pl_poor
Future Pl_med
3.2.4. StandGroup_Curve
The StandGroup_Curve table links the stand group IDs from the StandGroup table with
the curve IDs from the Curve table. For the sample dataset used to develop this document, the
StandGroup_Curve table is shown in Table 3-4. Note that CurveTypeId field represents one of
the curve types discussed in section 2.2. The complete list of possible Ids is found in the FPSATLAS database, tbl:CurveType; Id 1 corresponds to Age type curve (in this case volume by
age), Id 2 corresponds to ECA type curve (see section 4.4), and Id 3-12 represent the species
percentages type curves.
Table 3-4. StandGroup_Curve table
StandGroup_Id
888
2
3
5
6
7
8
9
13
14
16
17
CurveType_Id
1
1
1
1
1
1
1
1
1
1
1
1
Curve_Id
888
2
3
5
6
7
8
9
13
14
16
17
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StandGroup_Id
19
20
22
105
108
109
113
114
116
117
CurveType_Id
1
1
1
1
1
1
1
1
1
1
Curve_Id
19
20
22
105
108
109
113
114
116
117
3.2.5. StandGroup_Treatment
The StandGroup_Treatment table is in essence a translation of the silvicultural systems
discussed in section 2.3 (Table 2-5). For the sample data used to develop this document, Table
3-5 shows the StandGroup_Treatment. The Treatment_Value field refers to a special value that
each treatment has in FPS-ATLAS language. The Treatment_Id field refers to 5 possible values
associated with each of the 5 possible treatments in FPS-ATLAS (1-Thinning, treatment value is
the percentage removal (e.g., 40); 2-Clearcut, does not require any value, 0 is added by default;
3-Partial cut, treatment value is the residual growing stock (in m3/ha) – add 1 to avoid rounding
errors; 4-Rehabilitation, does not require a treatment value; 5-Succession, the treatment value
refers to the new age of stand following succession). For more discussion about the treatments,
refer to the FPS-ATLAS manual and tutorials documents.
In the case of the partial cuts used in the sample dataset (e.g., stand groups 2, 3, 6, and 7)
the residual growing stock was determined from the corresponding yield curves based on the
MHA and the cutting cycle. In the case of stand group 2, the MHA is 160 years and cutting cycle
is 30 years (Table 2-5). The residual growing stock after the partial cut is the volume (m3/ha)
corresponding to age 130 (i.e., MHA less cutting cycle) on the yield curve 2 (146 m3/ha) plus 1
to avoid rounding errors (=147 m3/ha). The Ignore_Adjacency field regulates the spatial
adjacency for the green-up constraint. If FALSE, the spatial adjacency is not ignored and the
green-up constraints following the treatment will be in effect. For partial cuts, the
Ignore_Adjacency could be TRUE to be able to treat adjacent polygons.
Table 3-5. StandGroup_Treatment table
StandGro
up_Id
888
2
3
5
6
7
8
9
13
14
Treatme
nt_Id
2
3
3
2
3
3
2
2
2
2
Enabled
FALSE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
Goto_StandGr
oup_Id
888
2
3
105
6
7
108
109
113
114
Guide to build a FPS-ATLAS model v1
Minimum
_Age
50
160
160
120
140
240
110
70
120
80
Maximum
_Age
999
9999
9999
9999
9999
9999
9999
9999
9999
9999
Treatment_
Value
0
147
147
0
32
356
0
0
0
0
Ignore_Adj
acency
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
Page 22 of 77
StandGro
up_Id
16
17
19
20
22
105
108
109
113
114
116
117
Treatme
nt_Id
2
2
2
2
2
2
2
2
2
2
2
2
Enabled
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
Goto_StandGr
oup_Id
116
117
116
117
22
105
108
109
113
114
116
117
Minimum
_Age
100
60
120
100
70
170
140
70
110
70
110
60
Maximum
_Age
9999
9999
9999
9999
9999
9999
9999
9999
9999
9999
9999
9999
Treatment_
Value
0
0
0
0
0
0
0
0
0
0
0
0
Ignore_Adj
acency
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
3.2.6. Curve_Data
The Curve_Data table needs to be populated with the data for each curve defined in the
model. For a simple model, only the yield curves are needed as the ones developed in section
2.2. Attention is needed to link correctly with the initial value for time sequence 1 and the age
interval between 2 consecutive time sequences as discussed in section 3.2.3. In Table 2-3, the
Age field is replaced by the Sequence field as follows; Age 0 becomes Sequence 1, 10-2…30031. The yield curves are then added into the Curve_Data table.
3.2.7. Polygon
To populate the Polygon table, an append query will be created in order to append the
required records from the attribute table of the resultant GIS file. Refer to the Man (2016) on
how to develop a resultant GIS and export the shapefile with a reduced number of fields and the
entire attribute table in a text format. Before creating the append query, the attribute table text
file needs to be linked to the FPS-ATLAS MS Access database. To link a text file to an MS
Access database, go to the External Data menu (in an opened MS Access database) and select
Text File from the Import & Link tab. In the Get External Data – Text File window that pops up,
select the “Link to the data source by creating a linked table” option and Browse to the location
of the exported attribute table in text format (Figure 3-3). In the next window select the option
“Delimited – characters such as comma or tab separate each field”. In the next window, select
Text Qualifier as the quote sign (“) and check the “First Row Contains Field Names” (Figure
3-4). In the next window the default settings should be in place, yet if the user experiences issues
with field types, these should be adjusted here. Then, finish the import process (here the
Resultant_v1 name was left unchanged for the Linked Table Name).
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Figure 3-3. Import and link a text file in MS Access (part 1)
Figure 3-4. Import and link a text file in MS Access (part 2)
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The next step is to create an append query to Polygon table (Create menu, Query Design,
add Resultant_v1 linked table, close the list, select Append from the Query Tools menu – Design
tab, and indicate the Polygon table to append to). Then, design the query as in Figure 3-5. Drag
field Block_ID from the Resultant_v1 table into the first column of the Results window and
append it to the Polygon_Id field in the Polygon table. Type 1in the Field row to append it to
Zone_Id for all records, and so on. In the StandGroup_Id column of the Polygon table an if
statement needs to be added for all the records where AU field in the Resultant_v1 table is 0
(these were initially blank values but are imported in MS Access as 0). The 0 values for AU have
to be 888 to align them with the StandGroup table in FPS-ATLAS. Insert the following code in
the Field row for StandGroup_Id column – SG: IIf([AU]=0,888,[AU]) – . Note that in MS
Access the if statements are defined by typing “iif”, followed by round brackets. Square brackets
are used to call for the field names in a table. In the Area column of the Polygon table insert the
following expression to convert the area from square metres in the resultant GIS attribute table to
ha and round it to 2 decimals – Area_ha: Round([SHAPE_Area]/10000,2)) –. In the Description
field add two if statements to determine what is THLB, NTHLB and excluded based on contclass
field codes (refer to the metadata file detailed in Man (2016)) –
IIf([contclass]="C","THLB",IIf([contclass]="N","NTHLB","Excluded")) –. Finally save (click
save button in the top left corner of the MS Access window) the query as
“001_Append_to_Polygon_tbl” and run it. It is recommended to organize the labelling of the
queries in sequence for easier tracking and debugging.
Figure 3-5. Query design to append records to the Polygon table
To view the results of the query, open the Polygon table in the MS Access database by
double clicking on it. To view all objects (including queries) in the left list of the MS Access
window, click on the pull-down menu at the top of the list and select All Access Objects (Figure
3-6). Before closing the MS Access database, run again the Compact and Repair Database tool
(File menu).
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Figure 3-6. Change the view in the object list (MS Access database)
3.3. Link the resultant GIS shapefile
Once the essential tables are populated accordingly (section 3.2), the next step is to link
the polygons from the Polygon table to the GIS shapefile. This link will make use of the
spatially-explicit features of the FPS-ATLAS. From the FPS-ATLAS model, open the MS
Access database that was populated (e.g., FPS_guide_model_v1.mdb). From the Tools menu,
select Data Wizard… and select ShapeFileImport_V6000 module. In the next window, browse to
the location of the shapefile (i.e., the resultant GIS exported with a reduced number of fields) and
deselect the stream and road options (Figure 3-7). Then change the map.shp to Resultant_v1.shp
(i.e., the name of the resultant shapefile) and change the “res_data_” to Block_ID (i.e., the
unique identifier for each polygon in the resultant GIS file which is identical to the Polygon_Id
field in the Polygon table of the FPS-ATLAS MS Access database). In the next window, deselect
the “Save within database, else link to original files” option and select the “Compress spatial
network” option while leaving everything else unchanged. Finish the import process, it should
take less than 1 minute for relatively small datasets. To check the import results, click on the
Map Viewer and explore the map. The path of the vector and coordinates for the shapefile is
stored in the External_Input table of the FPS-ATLAS MS Access database (Value1 field). Note
that when FPS-ATLAS datasets are exchanged between computers, this path has to be updated
as well. There is no need to run the ShapeFileImport tool, but only to update the path to the
shapefile in the External_Input table.
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Figure 3-7. Shapefile import tool in FPS-ATLAS
3.4. Enabling the spatial relationships between polygons
In order for the Green-up constraint to work properly, the FPS-ATLAS model needs
information about the spatial relationships between all polygons (i.e., who’s neighbour’s who).
The Polygon_Adjacency table in the FPS-ATLAS database needs to be properly populated so the
FPS-ATLAS model is properly set-up to implement any spatial adjacency constraints. To
populate the Polygon_Adjacency table, it is needed to run the Polygon Neighbors (Analysis)
geoprocessing tool in ArcGIS (Figure 3-8) and then create an append query in FPS-ATLAS
database to import the spatial relationships. The geoprocessing tool generates a table that
includes a neighbor field for each source field, This table is then exported as a text file (Man,
2016) and linked to the FPS-ATLAS database (see section 3.2.7). Then, create an append query
in the FPS-ATLAS database to append records to the Polygon_Adjacency table (Figure 3-9).
Remember to Compact and Repair the database.
Figure 3-8. Polygon Neighbors (Analysis) geoprocessing tool in ArcGIS
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Figure 3-9. Append query to Polygon_Adjacency table
3.5. Identify the non-forested polygons
The non-forested polygons need to be identified in FPS-ATLAS so these are not aged as
the forested polygons are. Non-forested polygons have to be added to the Polygon_Attribute
table in the FPS-ATLAS database. The Attribute_Id field for the non-forested polygons refers to
the surface type and needs to be 5, while the Attribute_Value can be 1 denoting water surfaces, 2
denoting exposed land (e.g., rocks, roads, alpine etc.), and 3 denoting desert or grassland. More
details about surface attributes can be found in the FPS-ATLAS manual (section 5.3.2).
Recall that the attribute table of the resultant GIS file has 3 levels of details to define the
landbase. All polygons with X for the field contclass are considered non-forested. The contclass
= X polygons can be further classified into water surface (Attribute_Id =5, Attribute_Value = 1)
and non-water (Attribute_Id =5, Attribute_Value = 2). To differentiate between water and nonwater within contclass=X polygons, the netdown field in the resultant GIS attribute table is used.
An append query is created to add non-forested polygons to the Polygon_Attribute table (Figure
3-10). Here, an if statement is added to differentiate between water and non-water surfaces based
on the netdown field values – IIf([netdown]="2_01_Lakes" Or [netdown]="2_02_Wetlands" Or
[netdown]="2_03_Rivers",1,2) –. Note that to determine what possible values are in the netdown
field, a select query can be created and run as in Figure 3-11.
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Figure 3-10. Query to append non-forested polygons to Polygon_Attribute table
Figure 3-11. Select query to summarize all possible values for a particular field
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3.6. Automate cliques creation
Cliques are essential tools in FPS-ATLAS to manage a wide range of constraints.
However, creating cliques is not as straight forward as it first looks. Here, a method to automate
the creation of cliques is presented. As with anything else in building forest estate models, one
needs to get organized and determine a list of cliques that are needed for the analysis. The first 2
cliques refer to the THLB and NTHLB. A no harvest constraint is typically applied to the
NTHLB clique. Other cliques (after consulting the Williams Lake TSA TSR Data Package
document) include a unique combination between Natural Disturbance Type (NDT), BEC zone,
and Biodiversity Emphasis Options in order to manage for biodiversity requirements. A certain
percentage of mature plus forest (>60 years old) need to be maintain as defined in the Data
Package document. In addition, visual quality objectives (VQO) are managed by the maximum
amount of young seral forest (<10 years old) allowed in each of the established VQO zones (e.g.,
Preservation, Retention, Partial Retention, and Modification). For all of these management
objectives, there are fields in the attribute table of the resultant GIS that can be used to generate
cliques automatically using queries in FPS-ATLAS database.
To determine the list of all possible cliques given the biodiversity objectives, a select
query is created (Figure 3-12). The results of the query indicate that only 6 out of 10 possible
combinations need cliques (Table 3-6). Similarly, using the “REC_EVQO_CODE” field in a
select query it was determined that 4 additional cliques are needed for the VQO objectives
Preservation (P-0.5% max young seral), Retention (R-max 3%), Partial Retention (PR-max
5.5%), and Modification (M-max 20.5%).
Figure 3-12. Query to determine biodiversity cliques
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Table 3-6. Results for query to determine biodiversity cliques
NATURAL_DISTURBANCE
NDT3
NDT3
NDT3
NDT3
NDT3
NDT3
NDT4
NDT4
NDT4
NDT4
BEC_ZONE_CODE
IDF
IDF
MS
MS
SBPS
SBPS
IDF
IDF
SBPS
SBPS
BIODIVERSITY_EMPHASIS_OPTION
Intermediate
Low
Intermediate
Low
Intermediate
Low
Intermediate
Low
Intermediate
Low
Mature plus
(>60years) %
None
None
26
14
23
11
43
22
None
None
Column names (except the percentage requirement) correspond to field names in Resultant_v1 table (i.e., resultant GIS attribute table)
The cliques needed for the sample data used in this document are shown in Table 3-7.
Note this is the format needed for the table Clique in the FPS-ATLAS database.
Table 3-7. Clique table
Clique_Id
1
2
3
4
5
6
7
8
9
10
11
12
Cycle_Constraints
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
Period_Constraints
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
FALSE
Description
THLB
NTHLB
NDT3_MS_Intermediate
NDT3_MS_Low
NDT3_SBPS_Intermediate
NDT3_SBPS_Low
NDT4_IDF_Intermediate
NDT4_IDF_Low
VQO_M
VQO_P
VQO_PR
VQO_R
Now that cliques are defined, polygons need to be assigned to each clique by creating
append queries to Polygon_Clique table of the FPS-ATLAS database. Clique 1 and 2 can be
populated with an append query as in Figure 3-13. For clique 3 the append query is shown in
Figure 3-14. After saving the query as “005_Append_Clq_NDT3_MS_Interm”, make a copy of it
(select the query in the object list and press paste (or CTRL+V), then rename the query to
“006_Append_Clq_NDT3_MS_Low”. Right click on the query and select Design View to open
the design window. Then, change the criteria that match for clique 4 (e.g., NDT3, MS, Low).
Remember to change the clique id from3 to 4. Save and run the query. Repeat these steps to
create queries for cliques 5to 8. For clique 9, the design query is shown in Figure 3-15. Similarly
to biodiversity cliques, repeat the steps to create queries fast for all VQO cliques (9-12). When
all queries are run remember to compact and repair the database.
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Figure 3-13. Query to append polygons for THLB and NTHLB cliques into Polygon_Clique
table
Figure 3-14. Query to append polygons for biodiversity cliques into Polygon_Clique table
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Figure 3-15. Query to append polygons for VQO cliques into Polygon_Clique table
The queries that assign polygons to cliques can be included into a macro and run all
together with one mouse click. This is useful if the model has to be rebuilt (e.g., the resultant GIS
has changed or for any other reasons). To create a macro in MS Access, open the Macro window
from the Create menu. Then press the Enable All Actions button in the Macro Tools menu,
Design tab that popped up once the Macro window is opened. Select from the Add New Action
pull-down menu the SetWarnings action and in the Warnings On field select No(Figure 3-16).
This allows the macro to run without asking the user permission for every action in the macro.
Next, from the Add New Action pull-down menu select OpenQuery action and in the Query
Name field select the query for the clique 1 (004_Append_to_PolyClq_THLB_NTHLB). Repeat
these steps to add all the queries that append polygons to the Polygon_Clique table. Press
Collapse All button to reduce the details of each action in the macro. The final macro design
should look like in Figure 3-17. Save the macro. To test the macro, open the Polygon_Clique
table, make a note of the total records, delete all records, and close the table. Then run the macro
and check the number of records in the Polygon_Clique table. The two numbers should be
identical. Remember to compact and repair the database once the queries related to cliques are
run.
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Figure 3-16. Macro design view
Figure 3-17. Finished macro design for queries related to cliques
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4. Tips and tricks
4.1. Finding the right flow
Finding the harvest flows (i.e., timber cut rate) that meet best the management objectives
involves many analytical skills and judgement. From the sustainability perspective, the timber
cut rate should be less or equal to the rate of growth while all other non-timber management
objectives are met. Given the harvest algorithm programmed into the FPS-ATLAS model (see
sections 3.4 and 3.5 of the FPS manual (Nelson, 2003)), the non-timber objectives take
precedence to the timber objectives. In other words, a polygon eligible for harvesting will be
harvested only if it does not violate other non-timber objectives. Thus, the forecasted harvest
flow and growing stock on the harvestable land base (i.e., standing volume on the THLB) need
to be assessed by the user when conducting the analysis.
The first step in finding the harvest flow is to determine the theoretical long term
sustained yield (LTSY) and to test it into the FPS-ATLAS model. The theoretical LTSY is
determined for each stand group (or yield curve) by multiplying the area occupied by each stand
group (or yield curve) with the volume (in m3/ha) corresponding to the minimum harvest age
(MHA) on the yield curve. Then, divide by the MHA to obtain the LTSY in m3/year. The results
for each stand group (or yield curve) are summed for the entire THLB to determine the
theoretical LTSY for the entire forest estate. For the sample dataset used to develop this
document, the LTSY is showed in Table 4-2. The LTSY (197,000 m3/year) was determined
using a select query in the FPS-ATLAS database:





Develop a lookup table in Excel between the sequence (1,2,3…31) and the age
corresponding to each sequence (10, 20, 30…300) (Table 4-1). This lookup table
is used to link the sequence in the curve data table to the minimum harvest age in
the StandGroup_Treatment table.
Copy the lookup table from Excel (including headers) and paste it into the FPSATLAS database. Click “Yes” to confirm the headers. Then, rename the new table
(right click on the table and select Rename) to “z_seq_age”. Adding “z” to the
name moves the table to the end of the list and makes it easier to find. It also
indicates that this is not a specific table to the FPS-ATLAS model.
Create a new select query as in Figure 4-1. Add to the select query the following
tables and link the fields as indicated in figure:
o Polygon
o StandGroup_Curve
o StandGroup_Treatment
o Curve_Data
o z_seq_age
Add the criterion for the THLB using the “Description” field in the Polygon table.
Save the query as “020_LTSY” to be in sequence with other queries developed so
far in the FPS-ATLAS database.
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
Run the query and check the results – the sum of area in the query results should
be identical to the THLB area from the Polygon table (e.g., 161,269 ha)
Table 4-1. Sequence-age lookup table
Sequence
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
26
27
28
29
30
31
Age
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
270
280
290
300
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Figure 4-1. Select query design to determine the LTSY
Table 4-2. LTSY query results
020_LTSY
StandGroup_Id Curve_Id SumOfArea MHA Vol
LTSY
2
2
992.06 160 172.00 1,066.46
3
3
3,314.88 160 172.00 3,563.50
5
5
3,317.73 120 132.00 3,649.50
6
6 31,799.42 140 289.00 65,643.09
7
7
1,590.79 240 378.00 2,505.49
8
8
3,721.41 110 89.00 3,010.96
9
9
632.43 70 106.00
957.68
13
13
221.10 120 152.00
280.06
14
14
45.49 80 159.00
90.41
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020_LTSY
StandGroup_Id Curve_Id SumOfArea MHA Vol
LTSY
16
16 46,796.28 100 88.00 41,180.73
17
17 25,060.25 60 93.00 38,843.39
19
19 39,298.25 120 82.00 26,853.80
20
20
3,724.47 100 228.00 8,491.79
22
22
755.32 70 80.00
863.22
161,269.88
2,220 197,000.09
Total
To test the LTSY in the FPS-ATLAS model, open the database in FPS-ATLAS and add a
No Harvest constraint to the clique NTHLB. Recall, the NTHLB is the forested area that is
reserved and only contributes to the non-timber management objectives. Then, set up a 350-year
flow in 10-year periods and ask for 1,970,000 m3/period. Check the Param tab to ensure that
oldest first harvest algorithm is selected (i.e., 1-Age). Then, open the run window and run the
model. The results indicate that the harvest request is achieved, yet the growing stock increases
over time denoting that a higher harvest request could be achieved by the model (Figure 4-2). A
higher harvest request can be made and through trial and error (i.e., the harvest rate does not fall
under the harvest request and the growing stock is stable (flat line or slight increase) in the last
100 years of the 350-year planning horizon). The LTSY indicated by the model is approximately
2,620,000 m3/period or 262,000 m3/year (Figure 4-3). This is called the no constraints even flow
because the harvest is constant for the entire 350-year planning horizon. The fact that the flow
projected by the FPS-ATLAS is higher than the theoretical LTSY can be explained by the
relatively high proportion of the uneven aged system (approx. 23% of THLB) and poor
productivity stands (approximately 54% of the THLB) that are converted to higher productivity
stands following the clearcut. Thus a relatively high harvest rate in the beginning can be
maintained in the long term.
Figure 4-2. Initial run of the LTSY (197,000 m3/year)
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Figure 4-3. Final run of the LTSY (262,000 m3/year)
The second step is to determine the harvest flow for the base case scenario. The base case
is usually referred to a scenario that meets a set of management objectives that are currently in
place for the entire area, or that the owner of the forest estate would like to achieve. Recall, the
sample forest estate used to develop this document is located in the Williams Lake TSA. The
management objectives outlined in the TSR data package document (April 2013) were replicated
here (e.g., the silvicultural strategies, wildlife management, and biodiversity and visual
objectives). The cliques needed to achieve the base case objectives were detailed in section 3.6.
For the biodiversity cliques, seral constraints need to be created and targets set as in Table 3-6.
Create a new ruleset and apply the No Harvest constraint set to the NTHLB (check the
Param tab to use the same settings as in the previous ruleset). Then, for each of the
biodiversity cliques, create a new constraint set and apply them to the corresponding clique
(within the new ruleset created). For example, in the case of the clique NDT3_MS_Intermediate,
create an old seral constraint that requires at least 26% of the area to be older than 60 years old
(Figure 4-4). For the visual objectives, young seral constraints need to be created and applied to
the visual cliques. For example, in the case of the VQO_M clique, create a new constraint set
that limits the % of area covered by young seral stands (i.e., younger than 20 years old) to 20.5%
(the required VQOs are detailed in section 3.6 of this document or in the TSR data package for
Williams Lake TSA) (Figure 4-5). One last constraint for the base case scenario refers to the
amount of area that is not clearcut because of the wildlife tree patches or some buffers around
unmapped streams. Such constraint is called in-block retention and for the purpose of the sample
dataset used here was set to 6%. Create a new constraint set as in Figure 4-6 and apply it to the
clique THLB.
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Figure 4-4. Old seral constraint set for biodiversity objectives
Figure 4-5. Young seral constraint set for visual objectives
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Figure 4-6. In-block retention constraint set
With all the constraints set for the base case, the model is ready to be run and determine
the harvest flow. Initially, an even flow will be determined following the same rules as in the
case of the LTSY (i.e., cut rate above the harvest request and the THLB growing stock is
relatively flat or slightly increasing in the last 100 years of the 350-year planning horizon).
Starting with the LTSY harvest request in the new ruleset created with all constraints applied, it
was determined through trial and error that the even harvest flow for the base case scenario is
approximately 2,430,000 m3/period (or 243,000 m3/year) (Figure 4-7).
Figure 4-7. Base case scenario even harvest flow (243,000 m3/year)
The final step in determining an acceptable harvest flow for the base case scenario is to
explore the options of harvesting more in the beginning of the planning horizon without
negatively impacting the long term harvest rate determined in the even flow approach. In some
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cases, the growing stock will increase towards the end of the planning horizon, indicating that
more harvest could be possible, subject to the constraints set to achieve the management
objectives. Note that management objectives can constrain the growing stock to increase over
time – in this case, it is acceptable to have an increasing growing stock over time. In the case of
the sample forest estate used to develop this document, it seems that it is possible a higher initial
harvest rate than the long term rate determined in the even flow approach. This is analyzed
through trial and error with a +/- tolerance of 10% harvest rate difference per decade (e.g., from
one decade to the next a +/- 10% harvest rate is allowed to avoid significant socio-economic
impacts). The results indicate that a 300,000 m3/year harvest rate can be achieved in the first
decade and the decreasing to the long term rate (243,000 m3/year) by year 50 of the planning
horizon (Figure 4-8).
Figure 4-8. Base case scenario maximum initial harvest flow (300,000 m3/year)
In light of the above discussion, a few recommendations can be made;




Determine the theoretical LTSY
Determine the even flow harvest rate when no constraints are applied
Apply all constraints for the scenario in question and determine the even flow
harvest rate
Explore the possibility to harvest more in the beginning (or the end) of the
planning horizon in order to make a good use of the standing volume
4.2. Natural disturbances
Natural disturbances are an integral part of the forest ecosystems. Wildfire is the natural
disturbance with the most significant impacts on the landscape. Wildfires can be simulated in
FPS-ATLAS using the fixed schedule module which clearcuts the polygons that the user wants
in the specified year (of the planning horizon). The following procedure is used;

Populate the FixedSchedule_Polygon table in the FPS-ATLAS database with the
list of polygons and the year when these need to be disturbed.
o To determine the fire cycles (i.e., mean event interval) for each NDT/BEC
consult the BC Biodiversity Guidebook
(https://www.for.gov.bc.ca/tasb/legsregs/fpc/fpcguide/biodiv/biotoc.htm).
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o
o
o
o
o
For the sample forest estate used to develop this document, the mean event
intervals are between 100 and 350 years (an average of 175 years mean
event interval is used here).
Decide where the fire disturbances are simulated (entire forested land base
(THLB and NTHLB) or only NTHLB. Arguments exist that aggressive
fire suppression can be carried on small THLB areas to prevent any timber
losses. Here, it was assumed that fires will affect the entire forested land
base. Note that the more fire disturbances are simulated, the higher
negative impacts on the harvest flows.
Design an append query to the FixedSchedule_Polygon table in the FPSATLAS database and use the Rnd function to generate random disturbance
years for each polygon id for the 175 years mean event interval (Figure
4-9). The syntax is: Int((175-1+1)*Rnd([Polygon_Id])+1), where Int
returns the integer number and [Polygon_Id] is the field of the FPSATLAS database Polygon table. Note that this query covers only the first
175 years of the planning horizon.
Run the append query just once, and compact and repair the database. If
for some reason the use is not pleased with the results of the query, all
records form the FixedSchedule_Polygon table have to be deleted before
running the append query again.
Design another query to cover the period 176-350 years of the planning
horizon by using the syntax: 175+Int((175-1+1)*Rnd([Polygon_Id])+1).
Run it just once, compact and repair the database.
The result of both queries is that on average, 1,192 ha are disturbed by fire
every year.
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Figure 4-9. Append Query design to FixedSchedule_Polygon table


Open the FPS-ATLAS model and append the FixedSched_V6000 module.
Determine a new harvest flow for the base case.
o Note that the harvest flow graph in the FPS-ATLAS model includes both,
the volume harvested by the fixed schedule module and the harvested
volume from normal logging activities. In addition, the harvest request in
the ruleset window should include both volumes (fixed schedule + normal
logging). The workaround this is to run a ruleset with 0 m3/period harvest
request and copy the results into an Excel spreadsheet. Add to the fixed
schedule volume, the volume that needs to be harvested through normal
logging in order to formulate the harvest request for the ruleset window
(Table 4-3). The new harvest request can then be pasted directly into the
FPS-ATLAS database in the table RuleSet_Param.
 Filter the table RuleSet_Param for the RuleSet_Id field with the
value of the ruleset in question. Note – each ruleset has an unique
ID. To see which ruleset, open the table RuleSet and determine the
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Ruleset_Id based on the description field – here, the RuleSet_Id is
1 referring to the BaseCase_Even scenario.
 Copy the flow request from the Excel (last column in Table 4-3
without the headings and the value for year 0).
 In the RuleSet_Param table, select the Flow field and paste
(CTRL+V) the flow request.
 In FPS-ATLAS model interface, the flow request should update to
the pasted values in the RuleSet_Param table after switching
between rulesets within the ruleset window.
 Run the model.
o Using trial and error, it was determined that the new timber harvest flow
for the base case scenario is 60,000 m3/year. Note that when fire
disturbances are implemented, it is very difficult to maintain a flat
growing stock in the last 100 years of the planning horizon (Figure 4-10).
Table 4-3. Harvest flow request with the fixed schedule module activated
Fixed Schedule
(m3)
Year
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
270
280
290
300
1
1,410,305
1,478,187
1,482,745
1,481,093
2,020,527
1,894,834
2,008,817
1,962,561
1,944,409
2,013,665
2,066,341
2,358,921
2,326,952
2,134,178
2,289,722
2,328,744
2,240,171
1,922,199
1,443,656
1,362,517
1,671,400
1,964,472
1,601,953
1,838,928
1,973,468
1,906,373
1,906,630
2,177,691
2,414,324
2,101,150
Timber
(m3)
Flow Request
(m3) (1+2)
2
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
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3
3,410,305
3,478,187
3,482,745
3,481,093
4,020,527
3,894,834
4,008,817
3,962,561
3,944,409
4,013,665
4,066,341
4,358,921
4,326,952
4,134,178
4,289,722
4,328,744
4,240,171
3,922,199
3,443,656
3,362,517
3,671,400
3,964,472
3,601,953
3,838,928
3,973,468
3,906,373
3,906,630
4,177,691
4,414,324
4,101,150
Page 45 of 77
Fixed Schedule
(m3)
Year
0
310
320
330
340
350
1
2,095,782
2,061,791
2,104,600
2,246,607
2,171,167
Timber
(m3)
Flow Request
(m3) (1+2)
2
2,000,000
2,000,000
2,000,000
2,000,000
2,000,000
3
4,095,782
4,061,791
4,104,600
4,246,607
4,171,167
Figure 4-10. Base case even harvest flow with the fixed schedule module activated
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4.3. Non-forested area rehabilitation
Rehabilitation of the non-forested areas within a forest estate is becoming an appealing
activity for a forest manager given the prospect of increasing the quality of ecosystem services
(e.g., carbon, wildlife habitat, water and air quality, biodiversity, etc.). Typically, teher are two
types of non-forested areas that can be reintroduce into the forest cycle: not sufficiently
restocked (NSR) areas (areas that failed to regenerate following a stand-replacing disturbance)
and exposed land (roads, landings, other industrial sites, etc.).
In the case of the NSRs, the polygons are identified during the model building process.
These polygons are grouped into separate stand groups for which delayed yield curves and
MHAs can be set, or the age of the polygons can be altered when populating the Polygon table in
the FPS-ATLAS database. Either approach is correct and depends on the analyst preference. For
example, a 15-year-old NSR polygon that was supposed to be a pine leading stand is attributed to
stand group 900 (NSR-Pine-Good). The analyst can take the pine leading stand yield curve from
a sufficiently restocked stand and alter the yield values (insert zeroes) for the next 10-15 years,
and then continue with the normal stand growing. This assumes that in the first 10-15 years of
the planning horizon, the forest manager will take action to restore the NSRs into the normal
forest cycle. Finally, the silvicultural system needs to be altered by increasing the MHA by 10-15
years.
In the case of the exposed land, a list of the polygons that are reforested need to be
determined. Then, in the Polygon table, proper stand group(s) need to be assigned, the age reset
to zero, and the description changed from Excluded to THLB or NTHLB. Finally, these
polygons need to be deleted from the Polygon_Attribute table so the FPS-ATLAS model can
grow them. Recall, all polygons in the Polygon_Attribute table are considered excluded from the
growing cycle. A delete query (Figure 4-11) can be set up using the following SQL code – the
code can be pasted into the SQL view of the query design, where the z_Road_Poly is the table
containing the list of polygons that are reforested with the Block_ID header:
DELETE DISTINCTROW Polygon_Attribute.*
FROM Polygon_Attribute INNER JOIN z_Road_Poly ON
Polygon_Attribute.Polygon_Id = z_Road_Poly.Block_ID;
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Figure 4-11. Delete query design for non-forested polygons in the Polygon_Attribute table
4.4. Equivalent Clearcut Area
Equivalent Clearcut Area (ECA) is an index that refers to the impact a clearcut has on the
hydrology of an area (e.g., community watershed). The ECA is the inverse of the hydrological
recovery index detailed in two BC guidebooks: The Community Watershed Guidebook –
Chapter 8.1 (https://www.for.gov.bc.ca/tasb/legsregs/fpc/fpcguide/watrshed/watertoc.htm) and
Interior Watershed Assessment Procedure Guidebook (IWAP) – Appendix 8
(https://www.for.gov.bc.ca/tasb/legsregs/fpc/fpcguide/iwap/iwap-toc.htm). The link between
stand height, Hydrological percentage recovery and ECA is shown in Table 4-4.
Table 4-4. Relation between stand height, hydrological recovery and ECA
Height
(m)
0
1
2
3
4
5
6
7
8
9+
Hydrological Recovery
(%)
0
0
0
25
25
50
50
75
75
100
ECA
(%)
100
100
100
75
75
50
50
25
25
0
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In the FPS-ATLAS model, the ECA curves need to be developed for each stand group
given the relationship between stand height and ECA in Table 4-4. Stand height can be linked to
stand age from the growth and yield models and then stand age linked to ECA for each stand
group. The ECA by age curve needs to be included in the FPS-ATLAS model.
In the case the sample forest state used to develop this document, there are 22 stand
groups that need to have ECA. Using the text files generated by the growth and yield models (see
section 2.2. of this document), the analyst has to organize the height by age (as opposed to
volume by age as in section 2.2. of this document) in an Excel file and use the VLOOKUP
function in conjunction with in Table 4-4 to automate the ECA development. Note that ECA
curves can be developed for 1-year intervals. An example is showed in Table 4-5. Here, only 3
ECA curves were developed for poor, medium, and rich site. However, a more accurate approach
is to develop an ECA curve for each stand group.
Table 4-5. Example to calculate ECA in Excel
Height
(m)
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
A
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Hydrological
Recovery
(%)
B
0
0
0
25
25
50
50
75
75
100
100
100
100
100
100
100
100
ECA
(%)
Curve
ID
C
100
100
100
75
75
50
50
25
25
0
0
0
0
0
0
0
0
E
200
200
200
200
200
200
200
201
201
201
201
201
202
202
202
202
202
Descrip
F
ECA_Poor
ECA_Poor
ECA_Poor
ECA_Poor
ECA_Poor
ECA_Poor
ECA_Poor
ECA_Med
ECA_Med
ECA_Med
ECA_Med
ECA_Med
ECA_Good
ECA_Good
ECA_Good
ECA_Good
ECA_Good
Age
Height
(m)
ECA
(%)
G
0
10
20
30
40
50
60
0
10
20
30
40
0
10
20
30
40
H
0
1.2
3
4.8
6.9
8.5
10
0
1.2
4.2
7.7
10.6
0
1.2
5
8
11
I
100
100
75
75
50
25
0
100
100
75
25
0
100
100
50
25
0
Formula
=VLOOKUP(H2,$A$2:$C$18,3,TRUE)
=VLOOKUP(H3,$A$2:$C$18,3,TRUE)
=VLOOKUP(H4,$A$2:$C$18,3,TRUE)
=VLOOKUP(H5,$A$2:$C$18,3,TRUE)
=VLOOKUP(H6,$A$2:$C$18,3,TRUE)
=VLOOKUP(H7,$A$2:$C$18,3,TRUE)
=VLOOKUP(H8,$A$2:$C$18,3,TRUE)
=VLOOKUP(H9,$A$2:$C$18,3,TRUE)
=VLOOKUP(H10,$A$2:$C$18,3,TRUE)
=VLOOKUP(H11,$A$2:$C$18,3,TRUE)
=VLOOKUP(H12,$A$2:$C$18,3,TRUE)
=VLOOKUP(H13,$A$2:$C$18,3,TRUE)
=VLOOKUP(H14,$A$2:$C$18,3,TRUE)
=VLOOKUP(H15,$A$2:$C$18,3,TRUE)
=VLOOKUP(H16,$A$2:$C$18,3,TRUE)
=VLOOKUP(H17,$A$2:$C$18,3,TRUE)
=VLOOKUP(H18,$A$2:$C$18,3,TRUE)
Then the Curve table in the FPS-ATLAS database needs to be appended with the list of the ECA
curves (Table 4-6). Then, the StandGroup_Curve table in the FPS-ATLAS database needs to be
appended with the ECA curves corresponding to each stand group as in Table 4-7. Then, the
ECA curve data organized in Excel (as in the case of yield curves) needs to be added to the
Curve_Data table in the FPS-ATLAS database (attention to sequence/age relationship).
Finally, a constraint set needs to be created for the Max ECA % (e.g., 20%) and applied to a
spatial area of interest (cliques, zones etc.) (Figure 4-12). In the case of the sample dataset used
to develop this document, the ECA constraint was applied to the entire zone 1. The model is now
ready to be run and new flows to be found with the ECA constrained applied. For the sample
model use din this document, the harvest flow with fire and ECA applied is 58,000 m3/year.
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Table 4-6. ECA curves to be added to the Curve table
Curve_Id
200
201
202
X_Initial
0
0
0
Delta_X
10
10
10
Description
ECA_Poor
ECA_Med
ECA_Good
Table 4-7. ECA curves linked to stand groups in StandGroup_Curve table
StandGroup_Id
888
2
3
5
6
7
8
9
13
14
16
17
19
20
22
105
108
109
113
114
116
117
CurveType_Id
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Curve_Id
200
201
201
201
201
201
200
201
200
201
200
201
200
201
202
201
200
201
200
201
200
201
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Figure 4-12. Max ECA constraint set
4.5. Increase buffer size (majority trick)
In many cases, the modeller wants to conduct a sensitivity analysis on the impact of
increased buffers (around streams or other protected areas) on the harvest flow or other
management objectives. The most accurate approach is to run some GIS geoprocessing tools,
rebuild the resultant GIS file, and rebuild the FPS-ATLAS model. A less accurate but efficient
approach is to add existing FPS-ATLAS polygons into a new clique representing the increased
buffers, and implement a no harvest constraint on this clique. The polygons to be added to the
increased buffer clique include the polygons that fully fall into the increased buffer areas plus
some polygons that do not (Figure 4-13), yet the sum area of these polygons is within 1% of the
increased buffer area (i.e., majority trick). This principle can be used for other spatially explicit
management objectives when rebuilding the model takes too much effort.
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Figure 4-13. Example of increased buffer. The selected polygon falls entirely in the
increased buffer area.
The following procedure is recommended to achieve the list of polygons to be included in
the increased buffers cliques (here a stream example is provided);

In a GIS application (e.g., ArcMap, ArcCatalog etc.) run a Buffer (Analysis)
geoprocessing tool for the stream buffer polygon feature used to build the GIS
resultant file for the FPS-ATLAS model. Type in the desired buffer distance
increase. In the case of the sample dataset used to develop this document, the
initial stream buffer size was 20 metres. Here, the buffer was increased to 40 m
which means that the buffered polygons need another 20 metres outside buffer
(Figure 4-14).
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Figure 4-14. Buffer geoprocessing tool to increase stream buffers

Intersect (use Intersect (Analysis) geoprocessing tool) the result of the above
buffer with the resultant GIS file (Figure 4-15). This will produce an attribute
table that contains a list of polygons that intersect (fully or not) the increased
buffer area. The attribute table contains many un-useful fields; clean the attribute
table by running the Delete Field (Data Management) geoprocessing tool and
delete all fields except Shape_Area and Block_ID (i.e., polygon id in FPSATLAS) (Figure 4-16).
Figure 4-15. Intersect geoprocessing tool for increased stream buffer
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Figure 4-16. Delete field geoprocessing tool for the increased buffer intersected feature
class

Calculate a ratio between the area of the polygon from the resultant GIS file (this
area is the actual polygon area in FPS-ATLAS) and the area of the polygon from
the intersect result of the increased buffer. This ratio should be less or equal than
1. Where the ratio is 1, it means the FPS-ATLAS polygon fell entirely in the
increased buffer area. Where is less than 1, the FPS-ATLAS polygon fell partially
inside the increased buffer area. To calculate the ratio:
o Open the Attribute Table of the intersect result of the increased buffer and
add a new field, type Double. Name the field Ratio. Close the Attribute
Table.
o Join the Attribute Table of intersect result of the increased buffer with the
GIS resultant. Drag from the Catalog view into the Layer view the
resultant GIS file (or use the Add Data… menu) and the intersect result of
the increased buffer. From the right click menu of intersect result of the
increased buffer, select Join and Relates, and then Join…. Select in pulldown field 1 the Block_ID as the field that the join will be based on, in
pull-down field 2 select the resultant GIS Attribute Table, and in pulldown field 3 also select the Block_ID (Figure 4-17). In the Join Options
tab, check Keep only matching records. Then click on Validate Join and
OK.
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Figure 4-17. Join two attribute tables
o Open the Attribute Table of intersect result of the increased buffer, now it
should be a long table because the Attribute Table of the resultant GIS file
was joined in.
o From the right click menu on the field Ratio (that was added previously),
select Field Calculator….
o In the field calculator window add (by double clicking) the polygon area
field from the intersect result of the increased buffer (e.g.,
[Increased_Stream_Buff_intersect.GEOMETRY_Area]), type in the divide
sign “/” and add the polygon area from the resultant GIS (e.g.,
[Resultant_v1.SHAPE_Area]) (Figure 4-18). Click OK and check the
results in the Attribute Table.
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Figure 4-18. Field Calculator tool for the Ratio field

o Note: in some cases, the editor mode needs to be active to calculate a field
(right click on the layer in question – in the Layer view – and select Edit
Features and then Start Editing…).
o Note: the name of the area fields in a file geodatabase are added
automatically. These names can be “Shape_Area”, “SHAPE_Area”, or
“GEOMTERY_Area”. They all refer to the geometry area (in square
metres) of the polygon shape.
Determine the polygons whose area sum up to within 1% of the increased buffer
area. The following steps are suggested:
o Determine the total area of the increased buffer. Open the Attribute Table
of intersect result of the increased buffer, it still should be a long table
because the attribute table of the resultant GIS file was joined in. Yet, the
Ratio field is populated with values between 0 and 1. Right click on the
field denoting the shape area of the polygons from the intersect result of
the increased buffer (e.g., GEOMETRY_Shape) and select Statistics…. The
result window will display the SUM among other metrics for this field
(Figure 4-19). Copy and paste the SUM into an Excel spreadsheet. This is
the total area of the increased buffer that needs to be matched by the FPSATLAS polygons.
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Figure 4-19. Statistics tool in the Attribute Table
o Select only the records for which the ratio field is greater than 0.5 (i.e.,
FPS-ATLAS polygons whose more than 50% of the area falls into the
increased buffer). In the Attribute Table open the Table Options menu and
click on Select By Attributes…. Then, double click on the
Increased_Stream_Buff_intersect.Ratio to add the field in the select
statement (Figure 4-20). Then, type in > 0.5 and click Apply. The tool will
select all records for which Increased_Stream_Buff_intersect.Ratio > 0.5.
Figure 4-20. Select By Attributes tool
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o Run Statistics… tool for the field denoting the actual polygon area in FPSATLAS from the resultant GIS Attribute Table (now joined to the intersect
result of the increased buffer Attribute Table) – this field is called
SHAPE_Area and it is usually found at the end of the joined table. Copy
and paste into the Excel spreadsheet the SUM.
o Compute a ratio between the two SUMs – they should be within 1%. If
not, continue to select polygons with a certain percentage range (e.g.,
greater than 0.4 – to select more polygons, or greater than 0.6 to select
less) until the two SUMs are within 1%.
o The final selection of the polygons that matches the two SUMs needs to be
exported to a txt file and linked to the FPS-ATLAS database. From the
Table Options menu, click on Export…, ensure that only selected records
are exported, then browse to the export location, select the text file save as
type and type in the name of the txt file to be exported (Figure 4-21).
Figure 4-21. Export selected records to a txt file
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o Import and link the exported text file to the FPS-ATLAS database – use
the External Data menu options (as in Figure 3-3 and Figure 3-4)
o Create a clique for increased buffer and then design an append query (to
the Poygon_Clique table) to append polygons to the increased buffer
clique (Figure 4-22). Apply the right constraint to the new clique (e.g., No
harvest, % Retention etc.)
Figure 4-22. Query design to append polygons to the increased buffer clique (Clique_id =
13)
4.6. Constraints duration trick
Typically, one constraint set is set for the entire planning horizon (e.g., 20% old seral
requirement set for 999 years). However, there are cases when for the same clique, the short term
targets are different than the long term targets. For example, for the first 50 years of the planning
horizon, a 10% of old seral is required while 20% is required for the remaining period of the
planning horizon. In such cases, a new constraint set is defined where no constraints are checked
and call it No constraints. Then, the clique in question is duplicated. In the case of the initial
clique, the short term target is set for the desired duration (e.g., 50 years). In the case of the
duplicated clique, the No constraints is set for the desired duration (e.g., 50 years) and the long
term target is set for the entire planning horizon (or 999 years) (Figure 4-23).
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Figure 4-23. Constraint sets design to facilitate the start of the actual target to a year
different than 1 in the planning horizon
To duplicate cliques, create a new series of clique IDs (similar to creating Stand Group
series) and an append query to the Polygon_Clique table to append all polygons from one clique
into the duplicated clique. In Figure 4-24, clique 2 was duplicated as clique 102, and all polygons
belonging to clique 2 were appended to clique 102.
Figure 4-24. Query design (append to Polygon_Clique table) to duplicate cliques
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5. Outputs
This section details some of the essential outputs that are reported when conducting
timber supply analysis. Typically, the outputs that need to be in a forest management plan are
dictated by the management objectives. It is essential to indicate how certain metrics behave
during the planning horizon. Essential metrics include the harvested volume and area, the
standing volume on the timber harvesting land base (THLB), age classes (area by age) by land
base type (THLB and NTHLB) at the beginning (initial condition), midway through, and at the
end of the planning horizon, and indicators of certain management objectives (e.g., old seral
targets). The TSR analysis report documents are good sources of information in terms of outputs
needed to be present in a forest management plan.
At this stage the FPS-ATLAS users should be familiarized with the output structure of
the model. The FPS-ATLAS creates text and bin files at each run, and OVERWRITES the files
in the output folder every time a new run is initiated. It is recommended to find the right flow for
a scenario in question. Then, select the outputs needed. The most important output files needed
are the Polygon Catenated Ascii (Polygon.txt) and Constraints Catenated Ascii (Constraints.txt).
If the constraints report is not available in the Run window, append the
ReportConstraints_V6000 module and reopen the Run window. Once the right outputs are
selected, run the model again. Then, copy the output files from the output folder and paste them
into a different output folder where the FPS-ATLAS model does not have access to. For
example, create an output folder under the general modelling folder called “02_Outputs” and
within it, create subfolders for each scenario. In the corresponding subfolders, paste the
corresponding output files. You can refer to the recommended folder structure detailed in Man
(2015). Then, create a new MS Access database and link into it the Polygon.txt file and the
Polygon table from the FPS-ATLAS database. These two tables are linked by the Polygon_Id
field.
5.1. Harvested volume and area
The harvested volume and area can be reported for the entire THLB or split by certain
forest features (e.g., species composition, wood products etc.). The simplest way to produce a
graph showing the harvested volume is to copy the results from the run window (right click on
year in the Run window and select Copy) and paste them into an Excel spreadsheet (Figure 5-1).
In Excel, the harvest flow outputs just pasted need to be duplicated in order to produce
professional looking graphs. First duplicate the periods (years) except 0 (Table 5-1). Then, for
each year, 2 values are needed – the value for the year i and the value for year i+l (where i is the
year in question and the l is the period length (e.g., 10 years)). Formulas were added in figure to
be able to replicate this approach.
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Table 5-1. Duplicate harvest flows to produce professional looking graphs
A
B
4
EvenFlow
5
Yr
6
7
8
9
10
11
12
13
14
15
C
D
E
Year
Flow
(m3)
Harvest
(m3)
Area
(ha)
G
EvenFlow
(m3/year)
0
244,252
0
0
0
0
10
244,252
10
2430000
2442518
13924
10
243,047
20
2430000
2430473
11813
20
243,047
30
2430000
2431580
13334
20
243,158
40
2430000
2431040
14469
30
243,158
50
2430000
2432491
10949
30
243,104
60
2430000
2440321
14724
40
243,104
70
2430000
2430628
19933
40
243,249
80
2430000
2432098
14301
50
243,249
90
…
2430000
2430415
12323
50
…
244,032
340
243,052
340
243,019
350
243,019
350
243,019
72
73
74
75
F
…
Formula for G column
=G6
=IF(F6=F7,VLOOKUP(F6,$A$6:$D$41,3,FALSE),V
LOOKUP(F6+10,$A$6:$D$41,3))/10
=IF(F7=F8,VLOOKUP(F7,$A$6:$D$41,3,FALSE),V
LOOKUP(F7+10,$A$6:$D$41,3))/10
=IF(F8=F9,VLOOKUP(F8,$A$6:$D$41,3,FALSE),V
LOOKUP(F8+10,$A$6:$D$41,3))/10
=IF(F9=F10,VLOOKUP(F9,$A$6:$D$41,3,FALSE),V
LOOKUP(F9+10,$A$6:$D$41,3))/10
=IF(F10=F11,VLOOKUP(F10,$A$6:$D$41,3,FALSE
),VLOOKUP(F10+10,$A$6:$D$41,3))/10
=IF(F11=F12,VLOOKUP(F11,$A$6:$D$41,3,FALSE
),VLOOKUP(F11+10,$A$6:$D$41,3))/10
=IF(F12=F13,VLOOKUP(F12,$A$6:$D$41,3,FALSE
),VLOOKUP(F12+10,$A$6:$D$41,3))/10
=IF(F13=F14,VLOOKUP(F13,$A$6:$D$41,3,FALSE
),VLOOKUP(F13+10,$A$6:$D$41,3))/10
=IF(F14=F15,VLOOKUP(F14,$A$6:$D$41,3,FALSE
),VLOOKUP(F14+10,$A$6:$D$41,3))/10
=IF(F15=F16,VLOOKUP(F15,$A$6:$D$41,3,FALSE
),VLOOKUP(F15+10,$A$6:$D$41,3))/10
=IF(F72=F73,VLOOKUP(F72,$A$6:$D$41,3,FALSE
),VLOOKUP(F72+10,$A$6:$D$41,3))/10
=IF(F73=F74,VLOOKUP(F73,$A$6:$D$41,3,FALSE
),VLOOKUP(F73+10,$A$6:$D$41,3))/10
=IF(F74=F75,VLOOKUP(F74,$A$6:$D$41,3,FALSE
),VLOOKUP(F74+10,$A$6:$D$41,3))/10
=IF(F75=F76,VLOOKUP(F75,$A$6:$D$41,3,FALSE
),VLOOKUP(F75+10,$A$6:$D$41,3))/10
Figure 5-1. Copy function in FPS-ATLAS run window
To produce the graph, select the year and value column, insert a scattered with straight
lines XY graph and format it as in Figure 5-2. Harvest flows for the base case scenario. Then add
to this graph two more lines, the even flow and the LTSY. Then paste the graph in a word
document as a picture (enhanced metafile) for best display purposes. The graph in Figure 5-2
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tells the reader the reader about the theoretical LTSY and how much volume can be harvested for
the scenario in question. The comparison between the even flow and the maximum initial
approaches is used to interpret how much volume would be left in the beginning of the planning
horizon if an even flow approach would be implemented. Many forest mangers will prefer to
harvest more in short term than the long term even flow for various reasons (e.g., higher
financial return, faster conversion of natural stands to managed stands that have higher
productivities etc.). Similarly, the area harvested can be plotted in graph.
Harvested Volume (m3/year)
350,000
300,147
300,000
243,048
250,000
200,000
197,000
150,000
Basecase_EvenFlow
100,000
Basecase_MaxInit
50,000
LTSY
0
0
50
100
150
200
250
300
350
Years from 2015
Figure 5-2. Harvest flows for the base case scenario
When fire disturbances are implemented via the fixed schedule module, care is needed
when producing harvest flow graphs. Here, the volume losses from fires need to be plotted in a
separate line. The fire volume losses are determined by running a simulation with 0 harvest
request – copy and paste the run results into an Excel spreadsheet. Then, determine the harvest
flow following the usual procedure (see section 4.2) and copy and paste the output results into an
excel spreadsheet. Compute the difference between the fire losses and the total harvested volume
to find the timber volume. Then duplicate the periods/years as instructed above and produce the
graph as in Figure 5-3.
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Harvested Volume (m3/year)
300,000
Basecase_EvenFlow
LTSY
250,000
Basecase_MaxInit
Fire Losses
200,000
197,000
150,000
100,000
75,851
60,098
50,000
0
0
50
100
150
200
250
300
350
Years from 2015
Figure 5-3. Harvest flows for the base case scenario when fire disturbances are simulated
Harvested volume (and area) can be graphed by leading species using the link between
the StandGroup_Id and Description fields in the StandGroup table of the FPS-ATLAS database.
The VolumeGrid.txt from the output folder can be used to produce such a graph. However, when
fire disturbances are implemented via the fixed schedule module, the VolumeGrid.txt file cannot
be used to differentiate between fire losses and harvested timber. The solution is to use the
Polygon.txt file and the FixedSchedule table linked to a MS Access database and design a select
query to summarize the harvested timber and fire losses by stand group. Then, a table can be
developed to link the StandGroup_Id and the leading species from the stand group Description
field in the StandGroup table. This link has to be manually developed in an Excel file and pasted
into the MS Access database summarizing the results.
5.2. Growing stock
The growing stock (or standing volume) can be graphed by copying the growing stock
outputs from the FPS-ATLAS Run window, pasting it into an Excel spreadsheet and plot it like
in Figure 5-4. In the Run window, ensure the Reserve+NoHrv button is checked and then switch
to grid view (click on Grid tab). Similarly, to the harvest flows, copy/paste the outputs. The
column Non_Rsv represents the merchantable standing volume on the THLB.
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THLB Standing Volume (m3)
16,000,000
14,297,337
12,000,000
12,832,758
8,000,000
Basecase_MaxInit
4,000,000
BaseCase_EvenFlow
0
0
50
100
150
200
250
300
350
Years from 2015
Figure 5-4. Growing stock (standing merchantable volume) on THLB
5.3. Age classes
Age by area for each land classes (THLB and NTHLB) at the beginning and end of the
planning horizon are very useful graphs in interpreting the results of the analysis. The
information in the Polygon.txt file needs to be summarized in an MS Access database, the
summarized information exported to Excel, and then using pivot tables, the age classes graphs
can be developed.
In Windows Explorer, browse to the output folder where the Polygon.txt file has been
saved (e.g., …/01_FPS_ATLAS/02_Outputs/04_Basecase_MaxInit_wFire/). From the right click
menu, create a MS Access database and call it 00_Results_Summary_scenarioName. Open the
newly created database, click Enable button to override the MS security issues and link the
Polygon.txt file which now should be present in the same folder as the newly created database.
Also, link the Polygon table from the FPS-ATLAS database. Then, create a select query (Figure
5-5).
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Figure 5-5. Select query design to summarize Polygon.txt information for age classes
graphs
Export the results of the query into an Excel spreadsheet (select all, copy, and paste) and create a
pivot table (Insert menu, PivotTable) based on the exported information (Figure 5-6). Use the
pivot table to summarize the information as in Figure 5-7. In the filter field, select 0 from the
pull-down options for period 0 or 35 for period 35. Then, right click on a cell in the Row Labels
of the pivot table and select Group…. In the box Starting at, type in 10. In the box Ending at,
type in 250. In the box By type in 10. This groups the age field in 10-year classes starting at age
10 and ending at age 250. Click OK to finish the grouping. The grouped pivot table for period 0
should look like in Figure 5-8.
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Figure 5-6. Create a pivot table for the age classes data
Figure 5-7. Pivot table design to summarize age classes
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Figure 5-8. Grouped by 10-year age classes pivot table for period 0
Then, in the PivotTable Tools – Analyze, click on PivotChart to generate a chart as in Figure
5-9which shows the area distributed by 10-year age classes and by land classes (THLB and
NTHLB). Repeat the steps to create a pivot chart for period 35 (or the last period of the planning
horizon).
25,000
Year 2016
Area (ha)
20,000
THLB
15,000
NTHLB
10,000
5,000
<10
10-19
20-29
30-39
40-49
50-59
60-69
70-79
80-89
90-99
100-109
110-119
120-129
130-139
140-149
150-159
160-169
170-179
180-189
190-199
200-209
210-219
220-229
230-239
240-250
>250
0
Age Classes (Years)
Figure 5-9. Area by age class in year 2016
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5.4. Constraints
Most professional reports or memorandums on forest estate modelling results or
management plans include information on how certain management objectives have been met by
the model over the entire planning horizon. In some cases, it can be as simple as comparing two
numbers. In most cases, graphs and tables are showed. If many scenarios are analyzed, key
constraints within the forest estate model are presented in a comparative way to indicate the
differences and discuss the results.
To develop professional-looking graphs showing the constraints results, or how some key
indicators behaved over the entire planning horizon, the user needs to export in the FPS-ATLAS
output folder the Constraints.txt file. This file is generated by the module
Report_Constraints_v6000. Add the module to the Run window, check the Catenated Ascii file
and the desired fields (Figure 5-10). Once the model is run, the output folder will contain the
Constraints.txt file.
Figure 5-10. Setting-up the Constraints module
Then, import the text file in Excel. Open a new Excel spreadsheet, select a cell where the
imported text file will start from, select From Text from the Get External Data tab under the
Data menu and follow the wizard to import the Constraints.txt file. Then, manually create a table
as in Table 5-2and populate it with records from the imported Constraints.txt file. Note that the
constraints output includes for each period and for each clique/zone defined in the model, the
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applied constraint set id, the defined (or set) value of the constraint [defn], value of the constraint
at the beginning of the period [start] and at the end of the period [end]. These values are showed
by the constraint type (e.g., seral, mature, late, growth, and ECA).
Table 5-2. Summary constraints set-up table
Clique[3]
NDT3_MS_Intermediate
Link_def
Per
Link_end
Year Target (min)
Achieved
n
iod
0Clique[
0Clique[3]e
0
0
26%
68%
3]defn
nd
1Clique[
1Clique[3]e
1
10
26%
68%
3]defn
nd
…
34Clique 34Clique[3]
34
340
26%
67%
[3]defn
end
35Clique 35Clique[3]
35
350
26%
71%
[3]defn
end
Formulae
S
T
U
V
W
X
=U6&”
=U6&$T$4
=U6
=VLOOKUP(S6,$A$5:$P$140 =VLOOKUP(T6,$A$5:$P$140
Clique[3]
0
6
&"end"
*10
8,$G$1,FALSE)/100
8,7 ,FALSE)/100
”&"defn"
=U7&”
=U7&$T$4
=U7
=VLOOKUP(S7,$A$5:$P$140 =VLOOKUP(T7,$A$5:$P$140
Clique[3]
1
7
&"end"
*10
8,$G$1,FALSE)/100
8,7 ,FALSE)/100
”&"defn"
…
=U40&”
=U40&$T$
=U4
=VLOOKUP(S40,$A$5:$P$14 =VLOOKUP(T40,$A$5:$P$14
34
40 Clique[3]
4&"end"
0*10 08,$G$1,FALSE)/100
08,7,FALSE)/100
”&"defn"
=U41&”
=U41&$T$
=U4
=VLOOKUP(S41,$A$5:$P$14 =VLOOKUP(T41,$A$5:$P$14
35
41 Clique[3]
4&"end"
1*10 08,$G$1,FALSE)/100
08,7,FALSE)/100
”&"defn"
$A$5:$P$1408 – this is the constraints data table, first column (A) is the lookup link (Period&Obj&Id)
7 – is the column id for VLOOKUP function return value
Using the VLOOKUP function in excel, one can automate this process to populate the
Table 5-2 for each constraint that needs to be tracked for the entire planning horizon. Create a
link to look for in the imported constraints data by concatenating the values of the first three
fields (Period, Obj, and Id) (e.g., 0Clique[1]defn – this denotes the defined constraint value for
clique id 3 in period 0) (Figure 5-11). Note that the field Obj values are themselves a
concatenation between the spatial unit (e.g., clique or zone) and its id (e.g., Clique[3]). Similarly,
develop a link to lookup from Table 5-2 by concatenating the Period column, the label of the
clique or zone (e.g., Clique[3]) and the string “defn” or “end”. Then develop a VLOOKUP
function to look for the concatenated value from Table 5-2 into the imported constraints data.
Use the “$” character to block rows and columns and select the appropriate column to pick up
values from (e.g., Clique[3] refers to a mature constraint set, Zone[1] has ECA constraint, etc.).
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Figure 5-11. Define the lookup link for constraints output table
Using the Table 5-2, develop professional-looking graphs as those showed in Figure
5-12, Figure 5-13, and Figure 5-14.
Area >60 years (%)
100%
Target (min)
Achieved
80%
60%
40%
20%
0%
0
50
100
150
200
250
300
350
Years from 2015
Figure 5-12. Mature (>60 years old) seral constraint for NDT3_MS_Intermediate
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30%
Target (max)
Achieved
ECA (%)
25%
20%
15%
10%
5%
0%
0
50
100
150
200
250
300
350
Years from 2015
Figure 5-13. ECA constraint
Area <20 years (%)
25%
20%
15%
10%
5%
Target (max)
Achieved
0%
0
50
100
150
200
250
300
350
Years from 2015
Figure 5-14. Young seral (<20 years old) area for the moderate visual quality objective
(VQO_M)
5.5. Harvest impacts on visually sensitive areas
Visual Quality Objectives (VQO) are sometimes difficult to interpret spatially just by
analyzing the performance of the VQO constraints from graphs (e.g., Figure 5-14). The model
outputs need to be transferred in GIS application and mapped in a 3-dimensional setting for a
better spatial representation of the disturbance (natural and/or anthropogenic) impact on the
landscape visuals. While sophisticated approaches exists in GIS (e.g., DEM models and 3D
Analysis), in this section a relatively easier approach is presented that uses the Polygon.txt output
from the FPS-ATLAS model, the ArcGIS application, and Google Earth. At any point in time
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during the planning horizon, the ages of all forested polygons from the FPS-ATLAS (i.e.,
Polygon.txt file) are transferred into a feature class within a file geodatabase in ArcGIS, mapped
by age theme (e.g., younger than 20 and older than 20 years using 2 colours), and the map
converted to a KML (KMZ) file that can be opened in Google Earth application where a
particular point of view can be analyzed for disturbance impacts at various time steps during the
planning horizon.
To transfer the ages for each polygon at a certain point in time during the planning
horizon, open the MS Access database that summarizes the results for the scenario of interest. In
this database, the Polygon.txt file and the Polygon table from the FPS-ATLAS database should
be linked (check section 5.3 for details to link these two). Deesign a select query from the
Polygon.txt file to summarize the age of each polygon in period 10 (year 100 of the planning
horizon) (Figure 5-15). Then, click View from the Query tools-Results tab, then click on External
Data tab, Export to Text File and follow the wizard to export the query results to a txt or csv file.
Ensure that the field names are included in the first row during the wizard and leave everything
else as default (Figure 5-16).
Figure 5-15. Select Query design to summarize age of each polygon at year 100
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Figure 5-16. Export select query results to a text file
Once the select query results are exported to a txt file, open the ArcMap GIS application.
In the catalog view browse to the file geodatabase where the resultant GIS file is located and
make a copy of it (right click on it, select Copy, then right click on the file geodatabase and
select Paste). Rename the copy as “Resultant_v1_Ages”. Then run the Delete Field (Data
Management) geoprocessing tool to delete all the fields except Block_ID (which the link to the
Poly field in the exported text file) and Age_2015 (which is the age of each polygon at the
beginning of the planning horizon – year 0). Some other optional fields that can be useful for
mapping can be retain (e.g., contclass, netdown, rollup, and AU). Then drag the
Resultant_v1_Ages into the layer window, open the Attribute Table, add a long integer field
called Age_year100. Then drag into the layer window the exported text file with the select query
results in it. Then, right click on the Resultant_v1_Ages and join it with the text file by Block_ID
and Poly fields. Then, open the joined Attribute Table and populate it the Age_year100 field with
the age from the text file (use field calculator tool – see section 4.5 for details on joining tables
and using field calculator tool). Then, the joined text file can be removed from the
Resultant_v1_Ages table.
To map the Resultant_v1_Ages themed by age at year 100, use the symbiology tab from
the layer properties menu (right click on the Resultant_v1_Ages while in the layer view and
select Properties…, navigate to the Definition Query tab, and query only the forested polygons
(e.g., contclass <> 'X'). Then navigate to Symbiology tab, select Quantities, Graduated colors,
and the Age_year100 for the Field Value. Then from the Classification options, select 2 in the
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Classes pull-down menu and under the Classify… edit the Break Values to 20 and 10,000. Select
a green-like colour ramp that indicate a good contrast between the two age classes (younger than
20 and older than 20). Then remove the outline colours for all symbols (i.e., the colours should
not have grey outline colours). This map can be exported as PDF for reporting purposes as well.
Create the KML(KMZ) file by using the Layer To KML (Conversion) geoprocessing tool
to export the themed by age Resultant_v1_Ages to a KML file. Open the KML (KMZ) file with
Google Earth and choose a point of view to indicate the visual impact of disturbances (Figure
5-17). Use the navigation tools in Google Earth to make the best viewing possible
Figure 5-17. Visual impacts at year 100 (looking from the Puntzi Lake, BC)
Similarly, visual impacts can be explored to other points in time during the planning
horizon (e.g., every 100 years during the planning horizon and at the end of the planning
horizon). Impact of the natural disturbances alone can also be explored and assessed in
conjunction with the anthropogenic disturbances. This is an area where creativity flourishes.
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6. References and Other Sources
Kurz, W.A., Dymond, C.C., White, T.M., Stinson, G., Shaw, C.H., Rampley, G.J., Smyth, C.,
Simpson, B.N., Neilson, E.T., Tyofymow, J.A., Metsaranta, J., Apps, M.J., 2009. CBMCFS3: A model of carbon-dynamics in forestry and land-use change implementing IPCC
standards. Ecol. Model. 220, 480-504.
Man, C., 2015. Memorandum on FRST 424 project folder structure and files naming. Faculty of
Forestry. University of British Columbia. Link:
https://1drv.ms/f/s!AqQkqmDYLxEdiCC-PlsVGx5Q8qC1
Man, C., 2016. Guide to build a resultant GIS v1. Link:
https://1drv.ms/f/s!AqQkqmDYLxEdiCC-PlsVGx5Q8qC1
Nelson J., 2003b. FPS-ATLAS Reference Manual version 6. Faculty of Forestry, University of
British Columbia. Link: http://sfmtutorials.forestry.ubc.ca/fps-atlas/
Nelson, J., 2003a. FPS-ATLAS Database Manual version 6. Faculty of Forestry, University of
British Columbia. Link: http://sfmtutorials.forestry.ubc.ca/fps-atlas/
Perdue, M. and Nelson, J., 2009. FPS-ATLAS 6.0.2.0 Tutorial Manual. Faculty of Forestry,
University of British Columbia. Link: http://sfmtutorials.forestry.ubc.ca/fps-atlas/
Seely, B., Nelson, J., Vernier, P., Wells, P., Moy, A. 2008. Exploring opportunities for
mitigating the ecological impacts of current and future mountain pine beetle outbreaks
through improved planning: A focus on northeastern British Columbia. Mountain Pine
Beetle Working paper 2008-08. Canadian Forest Service, Victoria, BC. At:
https://cfs.nrcan.gc.ca/publications?id=28783
Other Sources
FPS-ATLAS video tutorials: http://sfmtutorials.forestry.ubc.ca/fps-atlas/
Growth and Yield Models in BC: https://www.for.gov.bc.ca/hts/SDM.htm
Timber Supply Review Reports by TSA: https://www.for.gov.bc.ca/hts/tsas.htm
BC data distribution service (LRDW): https://apps.gov.bc.ca/pub/dwds/home.so
Data BC Catalogue: https://catalogue.data.gov.bc.ca/dataset
Carbon Budget Model for the Canadian Forest Sector (CBM-CFS3):
http://www.nrcan.gc.ca/forests/climate-change/carbon-accounting/13107
The Community Watershed Guidebook:
https://www.for.gov.bc.ca/tasb/legsregs/fpc/fpcguide/watrshed/watertoc.htm
BC Interior Watershed Assessment Procedure Guidebook (IWAP):
https://www.for.gov.bc.ca/tasb/legsregs/fpc/fpcguide/iwap/iwap-toc.htm
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Approved Ungulate Winter Ranges (UWR) – shapefiles and management guides:
http://www.env.gov.bc.ca/wld/frpa/uwr/approved_uwr.html
Guide to build a FPS-ATLAS model v1
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