Summary Report
Landscape Vegetation Inventory 2014
West Williams Lake TSA
Forest Analysis and Inventory Branch
Ministry of Forests, Lands and Natural Resource Operations
Revised May 5, 2014
Executive Summary1
Landscape Vegetation Inventory (LVI) was initiated in 2012 and completed in 2014 for the area of
1,753,913 ha in the west Williams Lake TSA. The LVI database provides low cost, current and interim
spatial and attribute inventory products that could be used to support high level strategic planning and
resource analysis applications in forest resource management decision-making.
Inventory estimates from the Landscape Vegetation Inventory (LVI) were compared to estimates from
ground samples in the Williams Lake TSA. The LVI estimates were derived through photo interpretation
of 7,398 segments (area total: 38,564 ha, a sampling intensity of approximately 3%) based on the 2012
digital aerial photography. Ground estimates from two sources – 31 CMI/NFI plots and 84, 9-plot clusters
– were combined to provide an independent ground sample. For a key inventory attribute, live net
merchantable volume, the LVI estimate was 39.0 m3/ha compared to the ground estimate of 39.4 m3/ha,
not a statistically significant difference. When the samples were stratified by BEC, the ground sample
sizes within each stratum were lower, the differences between LVI and ground estimates were higher and
the variability associated with the estimates also increased. The largest BEC zone, the SBPS, showed some
evidence of overestimation of volume by LVI that was balanced by underestimation in the remaining BEC
zones (IDF and MS zones). When stratified by leading species group, the differences between the LVI and
ground estimates were quite variable although none of the differences were statistically significant.
When stratified by age class, again, the differences were highly variable and none of the differences were
statistically significant. The biggest differences were associated with the 120+ age class. Dead trees were
significantly under estimated by the photo interpretation process by about 43%.
Over the entire project area the total live net volume was estimated at 44.9 million m3 (standard error:
+0.5 millions m3) and the total dead net volume was estimated at 50.7 million m3 (standard error: +0.6
millions m3).
It is recommended that for future LVI projects; (1) the photo interpretation be improved for accuracy and
consistency; (2) a better pre-stratification of Landsat segments is adopted; and (3) improvements be
made to the kNN and classification.
LVI is an alternative to the conventional VRI and it is recommended that it be implemented for the area
where VRI is not justified and current strategic inventory is needed.
1.
Contact Xiaoping Yuan of FAIB for more information, phone: 250-953-3626, e-mail:
Xiaoping.Yuan@gov.bc.ca
Page i
Table of Contents
EXECUTIVE SUMMARY1 ........................................................................................................................................... I
TABLE OF CONTENTS .............................................................................................................................................. II
1.
INTRODUCTION ............................................................................................................................................ 1
2.
2.1
2.2
2.3
BACKGROUND .............................................................................................................................................. 1
PROJECT AREA ..................................................................................................................................................... 1
VEGETATION RESOURCES INVENTORY 2012 .............................................................................................................. 2
PROJECT INPUT DATA ........................................................................................................................................... 4
3.
3.1
3.2
3.3
3.4
3.5
3.6
3.7
LVI METHODOLOGY ...................................................................................................................................... 5
LANDSAT SEGMENTATION AND CLASSIFICATION ......................................................................................................... 5
PHOTO SAMPLING ................................................................................................................................................ 5
THE K-NEAREST NEIGHBOR (KNN) AND FUZZY C-MEANS CLASSIFICATION ...................................................................... 5
CLASS ATTRIBUTION ............................................................................................................................................. 6
GROUND SAMPLING ............................................................................................................................................. 7
DEPLETION UPDATE .............................................................................................................................................. 8
QUALITY ASSURANCE ............................................................................................................................................ 8
4.
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
RESULTS ........................................................................................................................................................ 8
CLASSIFICATIONS AND KNN .................................................................................................................................... 8
LVI SPATIAL DATA................................................................................................................................................ 8
LVI TREE ATTRIBUTES DATA ................................................................................................................................... 9
OVERALL LIVE AND DEAD NET VOLUMES .................................................................................................................. 9
FINAL LVI DATA AREA SUMMARY ........................................................................................................................... 9
GROUND SAMPLING ASSESSMENT ......................................................................................................................... 10
GROUND ADJUSTMENT OF LIVE TREE BASAL AREA AND DEAD NET VOLUME ................................................................. 12
COST SUMMARY ................................................................................................................................................ 13
5.
SUMMARY .................................................................................................................................................. 14
6.
REFERENCES ................................................................................................................................................ 15
7.
7.1
7.2
7.3
APPENDIX ................................................................................................................................................... 16
GENERALIZED SITE SERIES CLASSES BY BEC SUBZONE ................................................................................................ 17
WILLIAMS LAKE LVI CLASSIFICATION AND DATABASES ............................................................................................... 20
LVI DATA DICTIONARY ........................................................................................................................................ 24
LVI Summary Report June 10, 2013
Page ii
1.
Introduction
The Landscape Vegetation Inventory (LVI) has been developed as a new, low cost, flexible, and statistically sound
alternative inventory tool to provide spatial and attribute products at various spatial resolutions. The primary targets for
the LVI are the areas of the province where a traditional photo interpretation inventory such as Vegetation Resources
Inventory (VRI) is not justifiable or not cost effective, areas with less forest complexity, regions outside of the timber
harvesting land base, parks, or areas heavily impacted by mountain pine beetle. The LVI data is appropriate for strategic
planning and reporting, as opposed to operational purposes.
The LVI design includes three basic components: (1) Landsat multispectral imagery for segmentation and classification; (2)
low-level Digital Camera System (DCS) image sampling and interpretation; and (3) nearest neighbor classification for
extrapolation to provide broad spatial and attribute products. Other auxiliary data layers: such as the existing Vegetation
Resources Inventory (VRI), Biogeoclimatic Ecosystem Classification (BEC), Terrain Resource Inventory Management (TRIM),
Predicted Ecosystem Mapping (PEM) and Site Productivity (SP), are also used in the classification.
Forest Analysis and Inventory Branch (FAIB) designed and pilot tested the LVI in 2010, and since then has conducted and
implemented two LVI operational projects, one in Quesnel Timber Supply Area (TSA) in 2011/2012 and the other in
Williams Lake TSA in 2012-2014. This report presents a final summary of the LVI project for Williams Lake TSA.
2.
Background
2.1
Project Area
The area of interest is the western part (Chilcotin Forest District) of the Williams Lake TSA. The total project area is
1,753,913ha, covering 113 full 1:20,000 NTS maps (Figure 1). The area was first stratified into treed and not treed areas by
stand age greater than 40 years old using the existing VRI. The treed area (1,152,631ha) was further stratified by British
Columbia Beogeoclimatic Ecosystem Classification (BEC) [Meidinger and Pojar 1991].
Figure 1. The location of the project area in the Williams Lake TSA.
The area breakdowns into 3 BEC zones: Sub-Boreal Pine Spruce (SBPS), Montane Spruce (MS), and Interior Douglas-Fir
(IDF). The biggest stratum, SBPS accounted for more than half (57%) of total treed area, followed by MS (26%) and IDF
1.
Contact Xiaoping Yuan of FAIB for more information, phone: 250-953-3626, e-mail: Xiaoping.Yuan@gov.bc.ca
Page 1
(14%). There was a small area of Engelmann Spruce Subalpine Fir (ESSF) accounted for 3% in the area of interest, and for
simplicity it was to lumped into the MS stratum.
Area Distribution by BEC Zones
700
Area (x 1000ha)
600
500
400
300
200
100
0
IDF
MS
SBPS
ESSF
BEC ZONE
Figure 2. Area distributions by BEC zones in the treed stratum of the LVI project area.
2.2
Vegetation Resources Inventory 2012
The majority of the inventory data available in the current VRI database (2012) was created during 1980-2000 using the old
FIP/FC1 standards. Figure 3 provides an inventory vintage by the project area (Figure 3) .
Area (1000 ha)
Total Area by Reference Year
800
700
600
500
400
300
200
100
0
NA
Prior 1980
1980-1989
1990-1999
2000-2011
Reference Year
Figure 3. Total areas of forest inventory by reference year (NA: Not Available).
Predominant tree species was lodgepole pine (PL: Pinus contorta Dougl.), followed by Spruce (SX: Picea A. Dietr.) , Douglas
Fir (FD: Pseudotsuga menziesii (Mirb.) Franco), and several deciduous species (A: Aspen, Birch etc.). A very small amount of
Balsam Fir (B: Abies balsamea (L.) Mill.) was lumped into the SX stratum (Figure 4).
LVI Summary Report June 10, 2013
Page 2
Area Distribution by Leading Species
1,200
Area (1000 ha)
1,000
800
600
400
200
0
NA
A
B
FD
PL
SX
Leading Species
Figure 4. Area distribution by leading species (NA: no species data available).
Figure 5 provides an area distribution by age classes (<40 years old, 41-60, 61-80, 81-100, 101-120, 121-140, 141-160 and >
160).
350
Area Distribution by Stand Age
300
250
Area (x 1000 ha)
200
150
100
50
0
1-40
41-60
61-80
81-100
101-120 121-140 141-160
>160
Age Class
Figure 5. Area distribution by stand age class.
An epidemic outbreak of Mountain Pine Beetle (MPB) occurred throughout the project area during 2000-2010 peaking in
2005, and by 2012 it had killed 60% of the mature Lodgepole Pine volume in the Williams Lake TSA according MFLNR MPB
LVI Summary Report June 10, 2013
Page 3
survey. FAIB has made an annual calculation of live and dead stand volumes based on the MPB survey and the total live and
dead volumes by BEC zones for 2012 are shown in Figure 6.
35
Total Net LIve and Dead Volumes
30
Vol;ume (Millions m3)
25
20
15
10
5
0
ESSF
IDF
MS
SBPS
BEC Zone
Live Volume
Dead Volume
Figure 6. Total live and dead volumes (source: the 2012 VRI).
This MPB infestation followed by intensive salvage logging and burns, has made the existing inventory obsolete, particularly
in pine dominant area. However, in some areas, dominated by non-pine species, the VRI may still be valid and useful.
Since a new VRI was not justified right after MPB infestation due to rapid and dynamic changes in species composition and
stand structure, an interim and current inventory product such as the LVI was required particularly for high level strategic
planning and analysis such as TSR.
2.3
Project Input Data
The following datasets were prepared and assembled for the project:
(1) The primary project input data was 30m Landsat 5 Thematic Mapper (TM) imagery. Three scenes (48/24,
September 8, 2009, 49/23, August 30, 2009, and 49/24, August 30, 2009) were selected for the project area. In
addition to raw band spectral data, various spectral indices, principal components, ratios, normalized difference
vegetation index, etc. were created.
(2) The existing Vegetation Resources Inventory (VRI) 2012 data was extracted from the VRI database and served
both for the initial stratification as well as an ancillary data layer in the LVI process.
(3) The Predictive Ecosystem Mapping (PEM) was completed for the Cariboo region in 2007 and provided a seamless,
25 m grid digital ecological and site specific data for the vegetation/trees in the region [Moon et al 2008]. For the
purpose of the LVI project, the site series values were grouped into several classes based on the similarity and
closeness of the sites in terms soil and nutrient regimes in each of BEC subzones (Appendix 6.1).
LVI Summary Report June 10, 2013
Page 4
(4) Site Productivity (SP) data was created by Ministry of Forests, Lands and Natural Resources Operations (FLNRO) in
2013. It was intended to provide site index estimate province-wide for commercial tree species for strategic
purposes. It is a raster based 100m by 100m grid cell data [FLNRO 2013].
(5) Terrain Resource Inventory Management (TRIM) was used for extracting elevation, slope, and aspect data.
(6) Disturbance data was created from several sources and it was used for updating the final LVI data. The first source
of disturbances was from the consolidated disturbance layer created by FAIB in 2013, which included all harvested
change captured in VRI, RESULTS and Landsat change detection mapping. The second source of disturbance data
was the forest fire layer created by the Wildfire Management Branch of FLNRO. All the disturbances from the
sources above were extracted if they occurred after 2000. For the year of 2013, cut and fire disturbances were
created from Landsat 8 2014 change detection.
3.
LVI Methodology
3.1
Landsat Segmentation and Classification
The Landsat 5 TM scenes were segmented into small (1-5ha) spectrally homogenous segments using object based software
Definies eCognition for the project area, resulting in a total of 340,057 segments. These segments were further stratified
into treed and no treed based on Landsat classification and other auxiliary data such as the existing VRI and PEM. The final
number of treed segments was 250,221 after masking out cuts, burns, age younger than 40 years, water, swamps,
clouds/shadows etc.
3.2
Photo Sampling
A systematic strip sampling, 20km spacing in south-north direction, was selected for the sampling design, and digital color
photography with an average of 0.15m ground sampling distance (GSD) was acquired using the LEICA ADS80 line camera on
August 5/6, 2012. The corridor of the strips covered with the photographs was about 1500m in width. The treed segments
within 1km radius at each 5km grid point along each strip were selected, resulting in a total 7,926 segments (called the
“photo sample” hereafter). Each photo sample segment was interpreted manually by certified VRI photo interpreters using
the color/stereo photography and full tree stand attributes were estimated as per VRI standards. There were 28 segments
excluded from the photo sample as they were either no tree segments or were logged or burned after the photography was
taken. The total number of the final photo sample was 7,398 (area total: 36,276 ha, about 3% of the total treed area). The
final attributes for each segment after photo interpretation were then projected to the year 2013 using the VDYP7.
A dead tree layer was created for standing dead trees. For the purpose of this project, dead trees were photo interpreted
with tree species as 100% Lodgepole Pine, basal area, crown closure, and stems per ha and then these attributes were
compiled as live trees using VDYP7 for dead volume (net and gross).
3.3
The k-Nearest Neighbor (kNN) and Fuzzy C-Means Classification
While the photo sample provided statistical estimates of the variables of interest for the population, a spatial map (i.e.
spatial distribution of the variables of interest) was also required. The LVI process began with two sources of data: (1) wallto-wall spectral homogenous segments with various spectral measures (as well as other measures from the auxiliary data
such as VRI and PEM) which we call the "target dataset", and (2) a subset of these segments that are photo interpreted with
full tree attributes to the VRI standards, we call the "reference dataset". The objective of LVI was to estimate the VRI
attributes for the target dataset by the way of "matching" (or classifying) them to most similar segments in the reference
dataset. One way to achieve this was through the k-Nearest Neighbor (kNN) method.
kNN is basically a classification process in which the missing variables in the target dataset are referred to as the Y-variables
and the variables available commonly in both the target and reference datasets are referenced to as the X-variables. For
each observation in the target dataset, one or more (k) nearest neighbors are found in the reference dataset based on the
magnitudes of differences in the X-variables. Once the minimum difference between a target observation and a given
reference observation (summed up across all X-variables in relation to each of the remaining observations in the reference
dataset) is found, this particular reference observation is identified as the nearest neighbor to the target observation. This
process may be extended to identify the next nearest neighbor, or to the k nearest neighbors as defined by the user. The
LVI Summary Report June 10, 2013
Page 5
missing Y-variables could be assigned by the nearest neighbor (i.e. k=1) or estimated as the mean values of the k nearest
neighbors.
The photo sample data was classified by tree species composition, stand structure (crown closure, basal area, trees per ha,
etc.), and dead tree attributes, using fuzzy c-means classification developed by Moss (2013). Multiple Discriminant Analysis
was used for X-variable selection and determining weights by maximize the ratio of the between to within class sum of
squares by applying weights to the X-variables dataset. These discriminant functions were then applied to the target
dataset to identify nearest neighbors.
For version 1 of the LVI data that was completed in December 2013, the Y-variables were estimated based on k=1, i.e. the
nearest neighbor method.
Various classification schemes and kNN imputations were tested and evaluated (Moss 2014a). For example, instead of
selecting the 1st neighbor, averages of the first 2, 3, 4, and 5 nearest neighbors were also generated for tree attributes.
Instead of applying a “global” classification, separate classifications and kNN assignments were done by BEC zones; or
global classifications but applying to each BEC zone separately. A post classification was developed based on spectral
properties, BEC subzones, PEM classification, and spatial distance between the target segments to reference segments.
This post classification was applied to the 50 nearest neighbors identified from the kNN process.
3.4
Class Attribution
Once the final classification of the Landsat segments was determined, polygon dissolving was applied to all the segments by
the class. All the matched segments in each class were grouped together. For the numerical variables such as basal area,
volume, age, height etc., a simple mean value of all the photo sample segments was calculated weighted by segment area.
The standard deviations of classes were stored in a separate table which could be examined later to evaluate the precision
of the final attribute estimates.
Species composition for a given class was derived based on the following formula:
𝑛
𝑚
𝑆𝑖 = 100 ∗ ∑𝑚
𝑗=1(𝑆𝑖𝑗 ∗ 𝐵𝑗 ∗ 𝐴𝑗) /[∑𝑖=1 ∑𝑗=1 (𝑆𝑖𝑗 ∗ 𝐵𝑗 ∗ 𝐴𝑗)]
(1)
Where:
m: number of photo segments in the class
n: number of all possible tree species (n = 4, i.e. the 4 highest percentage proportions)
𝑆𝑖 : percentage of species i
𝑆𝑖𝑗 : percentage of species i in the jth photo segment
𝐵𝑗: basal area in the jth photo segment
𝐴𝑗: area of the jth photo segment
The resultant percentage for a given species for a given class calculated from equation (1) is weighted by basal area. The
final species composition is calculated only for the 4 tree species (PL, SX, FD, AT) that occurred commonly in the project
area as per VRI species composition standards.
Because age, height, and site index are related to the species, they are computed based on the following formulas:
𝑚
AGEi = ∑𝑚
𝑗=1(𝐴𝐺𝐸𝑖𝑗 ∗ 𝐴𝑗) / ∑𝑗=1 (𝐴𝑗)
(2)
𝑚
HTi = ∑𝑚
𝑗=1(𝐻𝑇𝑖𝑗 ∗ 𝐴𝑗) / ∑𝑗=1 (𝐴𝑗)
(3)
𝑚
SIi = ∑𝑚
𝑗=1(𝑆𝐼𝑖𝑗 ∗ 𝐴𝑗) / ∑𝑗=1 (𝐴𝑗)
(4)
AGEi : Age for species i
HTi : Height for species i
SIi : Site Index for species i
AGEij : Age for species i in the jth photo segment
LVI Summary Report June 10, 2013
Page 6
HTij : Height for species i in the jth photo segment
SIij : Site Index for species i in the jth photo segment
Depending on the leading and the second leading species determined by equation 1, the age, height, and site index
computed from equations 2-4 were assigned to the corresponding species.
3.5
Ground Sampling
A separate ground sampling component of this project was designed and carried out to:
1)
2)
3)
4)
To assess the LVI estimates over the population area;
To assess LVI photo interpretation;
To evaluate the overall final LVI product; and
To adjust/improve the LVI estimates where appropriate.
The approach used to select ground plots locations was broken into the following three sequential steps:
1) Select as many samples on the “Treed” photo plots as possible. [Result=67]
2) Next, select as many “Treed” samples as possible on the 10km X 10km National Forest Inventory (NFI) grid and
stratify these points by BEC zone to only include SBPS and MS zones. [Result=24]
3) Lastly, select as many “Treed” samples as possible on the 5km X 5km NFI grid and stratify these points by BEC zone
to only include SBPS and MS zones. Randomly select a number of these locations to insure we have the intended
number of ground sample points. {Result=18]
This process identified 109 candidate locations to measure ground sample data for the Williams Lake LVI project. At each
candidate location, a 9-points cluster, each point is a fixed radius 5.64m radius plot with a nested 3.99m plot at 50m spacing
(3 x 3 over a 100m by 100m square), was established. All the live/dead trees greater than 4cm within the 5.64m plot were
measured using a modified timber cruising method. For small trees less than 15cm in height and 4cm in diameter, a quick
estimate of number of trees was conducted. This 9-plot cluster dataset was used for population estimation as well was
broken down by number of plots falling in corresponding segments/polygons for evaluation of LVI products.
In addition, there was another ground sampling scheme adopted from two other projects, Change Monitoring Inventory
(CMI) and NFI. These CMI/NFI plots (fixed radius 0.04ha plots at the NFI 20km grid points) falling into the LVI treed area
were combined with the 9-plot clusters (see Penner 2014).
Both the 9-plot clusters and CMI/NFI plots were compiled at FAIB using non standard procedures in regards to the taper
equation and the accounting of decay, waste and breakage. The CMI samples were compiled by the RDW compiler. The LVI
samples were compiled by a standalone compiler customer built specifically for the task. These datasets went through the
following processing steps:

A GIS query to determine the status of each plot of the 9 plot LVI cluster was done to determine the placement in
the LVI population and if so, LVI polygon.

The gross volume was calculated using the 1994 taper equation.

The DWB deductions were calculated using the BEC loss factors. The risk grouping of dead trees used the live tree
criteria.

The blowup factor used to calculate per hectare values was based on plot radius: 11.28 m for the CMI samples and
5.64 m for the 9-plot samples.

Population boundaries were not recognized for both plots, i.e. the entire plot was established regardless of
dissection by a population boundary. LVI plots were split into halves or quarters depending on excessive tree
counts.
LVI Summary Report June 10, 2013
Page 7
3.6

Heights were measured for all trees, which allowed for the calculation of the average heights for all tallied trees
and subsets of trees by crown classes.

In the 9-plot clusters, tree age was captured only for the two major species that were considered representative of
the plot. The leading species age was used as the basis for the loss factor calculation for dead trees.

For CMI, tree age was captured for the site trees. Loss factor age is a variable named age_dwb and may be
obtained from the leading species.

The compilation calculated per hectare values for each dead and live tree species separately for the 9 plot sample
and for the LVI polygon. Summaries were also produced by live vs dead for each sample and polygon.
Depletion Update
The LVI data, once completed, was updated for depletion changes caused by harvesting and wild fire. The FAIB consolidated
disturbance layer 2013 was used first to update all the cut blocks occurred between 2000 and 2012. Wild fire data was
from Wild Fire Management Branch and all the fires occurred in the same time period 2000-2012 were extracted and used
to update the LVI. For the final step, all the cut/fire changes occurred in 2012 to 2014 were automatically captured from
the Landsat 8 TM imagery acquired on September 10, 2013, and were used for updating the LVI.
3.7
Quality Assurance
Three critical components were subjected to quality assurance (QA): photo interpretation, nearest neighbour matching and
classification. About 3% of photo interpreted segments were selected for QA by another certified VRI photo interpreter.
The nearest neighbour matching was checked visually as compared to the orthophoto and Landsat. Statistical estimates
from the photo sample data were used to check the total summation of matched segments and classified polygons.
4.
Results
4.1
Classifications and kNN
A total of seven classifications were tested to the photo sample data and a detailed summary report is provided by Moss
(2014a, 2014b). One of them was deemed as the best and was selected for the final processing for generating nearest
neighbors. This classification, based on combined species, structure, and dead trees, was created from the entire photo
data set; subsequently 50 nearest neighbors was determined separately for each of the 3 BEC zones; and then a final post
classification was applied to determine the best nearest neighbor for a given unknown target segment.
4.2
LVI Spatial Data
After the best nearest neighbor was determined and photo sample data was populated to the unknown segments, a final
grouping process was applied to produce a generalized spatial coverage. The argument for doing this is that LVI process is
affected by uncertainties originated from photo interpretation and the kNN process. Generalizing from small segments to
coarser resolution will reduce these uncertainties, while still providing a reliable inventory data product for strategic and
high level planning and analysis applications.
Six generalizations were produced as described in detail in Appendix 6.2. The first 4 (WL_CLASS1 –WL_CLASS4) were
provided for the Williams Lake District Office according to the leading species and live/dead tree ratios.
The fifth (WL_CLASS5) was created based on the best classification/kNN process, i.e. fuzzy C-means classification of the
entire photo sample data and kNN was applied separately by BEC zones with a post classification.
The last generalization (LANDSAT_POLY) was created from Landsat imagery. Segments were combined into coarse
resolution big polygons based on the similarity of spectral properties. This is the dataset used to replace the old VRI in the
VRI database in order to be consistent with the structure, format, and resolution of the VRI database.
LVI Summary Report June 10, 2013
Page 8
4.3
LVI Tree Attributes Data
The LVI products consist of three basic databases (both with spatial and attribute data): (1) the segment database; (2) the
classification database; and (3) the photo sample database. The segment database is the primary LVI product for FAIB
internal, and other, power users who understand the LVI process and have the ability to re-process it into other relevant
products. The classification database is the inventory product for general public access and use. The photo sample
database is a snapshot and map-less inventory of the population of interest and it can be recompiled and re-processed for
statistical analysis and estimations.
The data dictionaries of the above three kinds of LVI attributes in the LVI databases are given in Appendix 7.3.
4.4
Overall Live and Dead Net Volumes
The overall estimates of total live net volume and dead gross volume in the LVI tree area are summarized in Table 4.
Table 1. The average and total live and dead net volumes at 12.5cm estimated from the photo sample (means or total
followed by standard errors).
Average Volume (m3/ha)
Live
Dead1
39.0 + 0.4
1.
4.5
44.0 + 0.5
Total Volume (millions m3)
Live
Dead1
44.9 + 0.5
Photo Sample
50.7 + 0.6
36,276
Area (ha)
Tree
Population
1,152,631
1,753,913
Dead volume estimates derived from the photo sample was ratio adjusted using the ground sampling data (see 4.7).
Final LVI Data Area Summary
Table 4 provides an area summary of the final LVI data. Out of a total area of 1.754 million ha, new LVI data (with Inventory
Standard as “L”) accounts for 1.419 million ha, with a breakdowns by the tree area of 1.06 million ha, new cuts of 0.039
million ha and new fires of 0.119 million ha occurred between 2000 and 2014, and the no tree (opens, roads, swamps,
water etc.) of 0.201 million ha. The remaining 0.335 million ha was filled in with the VRI 2012.
Table 2. The area summary by inventory standard code.
Inventory Standard
Total Area (x 1000 ha)
LVI generated (L)
Tree
1,419
VRI fill in (V, I, F)
335
Project total area
LVI Summary Report June 10, 2013
1,060
Cut
39
Fire
119
No tree
201
Cloud/shadow
10
Young stand
34
Other
291
1,754
Page 9
4.6
Ground Sampling Assessment
A total of 84 9-plots clusters and 31 CMI/NFI plots were resulted from the ground sampling. The detail analysis of ground
sampling data in comparison with the LVI photo estimates is given by Penner (2014).
The statistical estimates of some key attributes from the LVI photo sample, ground sample (pooled together the 9-plots
clusters and CMI/NFI plots), and VRI extracted segments are summarized in Table 4 and Figure 7.
Live net volume is a very important attribute and the estimates from all three sources are very close. Age and height from
the LVI and ground are very close, while they are much higher from VRI.
Basal area and stand density (trees per ha) from the LVI are higher than those from the ground. The differences are
attributed to some differences in utilization levels between the data sources and basal area and trees per hectare are
particularly sensitive to utilization.
Dead tree volume was significantly under estimated by photo interpreters by 43%, primarily due to underestimated basal
area and trees per ha.
Table 3. The averages of some selected attributes are given by sources (the mean followed by the standard error).
9 plot
Ground1
Attribute
LVI
CMI
clusters
Sample size
38,5643 ha
31 plots
84 plots
115 plots
Live net volume (m3/ha)
39.0 ± 0.4
47.7 ± 10.2
38.0 ± 4.2
39.4 ± 5.1
Live whole stem volume (m3/ha)
50.6 ± 0.6
62.1 ± 12.8
49.2 ± 5.2
51.1 ± 6.3
Live whole stem volume (m3/ha) Dbh > 4.0 cm
62.9 ± 0.6
93.0 ± 14.5
79.9 ± 6.2
81.8 ± 7.4
Dead net volume (m3/ha)
24.8 ± 0.3
20.0 ± 4.3
47.8 ± 4.9
43.8 ± 4.8
Leading species age (years)
104.4 ± 0.5 102.8 ± 10.4
89.0 ± 3.1
91.0 ± 4.2
Basal area (m2/ha)
12.9 ± 0.1
9.4 ± 1.8
7.5 ± 0.7
7.8 ± 0.9
Leading species Height (m)
12.3 ± 0.1
13.3 ± 0.7
12.2 ± 0.3
12.4 ± 0.4
Live trees per hectare
940.7 ± 12.9 419.6 ± 75.4 304.2 ± 26.3 320.8 ± 33.4
Dead trees per hectare
220.3 ± 2.2 206.2 ± 41.4 325.9 ± 30.1 308.6 ± 31.7
1. Combined CMI/NFI and 9 plot cluster datasets with the weights given by Penner (2014)
2. The existing VRI database 2012
3. A total of 7398 photo segments with a total area of 38,564ha
LVI Summary Report June 10, 2013
Page 10
VRI2
36,295 ha
40.3 ± 0.5
43.7 ± 0.5
124.2 ± 0.7
19.1 ± 0.1
14.8 ± 0.1
524.2 ± 7.4
551.9 ± 5.5
140
120
100
80
LVI
60
40
CMI
20
Nine plot cluster
VRI
0
Net volume
(m3/ha)
Whole stem
Whole stem Dead net volume
volume (m3/ha) volume Dbh > 4.0
(m3/ha)
cm (m3/ha)
Attribute
Age (years)
25
20
15
LVI
10
CMI
Nine plot cluster
5
VRI
0
BA (m2/ha)
Ht (m)
TPH (x 100 stems/ha)
Dead TPH (x 100
stems/ha)
Attribute
Figure 7. The data in Table 3 are plotted. Standard error bars are given.
More than half of the area is in the SBPS BEC zone. The MS and SBPS are both dominated by pine-leading polygons (> 70%
of the area) followed by spruce-leading. The IDF is dominated by Douglas-fir leading polygons (50% of the area).
Overall, the LVI and ground volumes were very close (Table 4 and Figure 8). The overestimation of net live volume by the
LVI compared to the ground plots in the SBPS zone is compensated by the overestimation in the other zone so the overall
difference is -0.5 ± 4.5 m3/ha, not a statistically significant difference (α < 0.05). Note the increase in standard error
associated with the volume/ha at the stratum level compared to the population, due to the smaller sample size.
Table 4. The average net live volume is summarized by BEC and source. The mean is followed by the standard error. The
data are graphed in Figure 8.
BEC
ESSF & MS
IDF
CMI
9-plot
N
N
12
3
Net live volume (m3/ha)
LVI
26 46.1 ± 0.8
Ground
Difference
Net live volume (millions m3)
Area
LVI
(ha)
Ground
Difference
60.7 ± 8.4 -14.6 ± 8.4
14.1 ± 0.3
22.5 ± 3.1
-4.5 ± 2.6
330,682
13 39.7 ± 1.1 43.3 ± 10.7 -3.7 ± 10.8
7.8 ± 0.2
7.3 ± 1.8
-0.7 ± 2.1
165,260
SBPS
16
45 35.4 ± 0.6
25.5 ± 3.5
9.9 ± 3.6
23.0 ± 0.4
15.7 ± 2.2
6.4 ± 2.3
656,690
All
31
84 39.0 ± 0.4
39.4 ± 3.9
-0.4 ± 3.9
44.9 ± 0.5
45.4 ± 4.5
-0.5 ± 4.5
1,152,631
LVI Summary Report June 10, 2013
Page 11
80
Live net volume (m3/ha)
70
a)
60
50
40
LVI
30
Ground
20
10
0
ESSF & MS
IDF
SBPS
Total
BEC zone
Live net volume (m3)
60,000,000
b)
50,000,000
40,000,000
30,000,000
LVI
20,000,000
Ground
10,000,000
0
ESSF & MS
IDF
SBPS
Total
BEC zone
10,000,000
Live net volume (m3)
8,000,000
c) Difference (LVI - Ground)
6,000,000
4,000,000
2,000,000
0
-2,000,000
ESSF & MS
IDF
SBPS
Total
-4,000,000
-6,000,000
-8,000,000
BEC zone
Figure 8. The average (a), total (b) and difference (c) between net live volumes are given by BEC zone and source. Standard
error bars are given
4.7
Ground Adjustment of Live Tree Basal Area and Dead Net Volume
As shown in Table 4 and Figure 8, although the overall live net volume estimate from the LVI agrees with that from the
ground data, there were some differences in BEC zones, under estimate in ESSF/MS and over estimate in SBPS. A further
analysis showed that basal area could reasonably adjusted in different BEC zones while keeping the overall population live
LVI Summary Report June 10, 2013
Page 12
volume unchanged (Moss, 2014c). Accordingly, the decision was made to adjust the photo interpreted basal area as
follows:

No adjustment in IDF and ESSF, and for SX/FD/AT leading stands

Multiply ratios of 0.5607 applied to SBPS and 1.30 to MS to the photo basal area, to the stands with PL leading, 1 <
photo basal area > 30
The ground assessment as mentioned in 4.6 clearly identified under estimation of dead volume by LVI photo interpreters.
Twenty-seven 9-plot clusters were selected (with more than 4 plots falling in the segment) to pair with segments as shown
in Figure 9 and an adjustment curve was derived and applied to the photo interpreted dead net and gross volumes.
Notmslized Dead Net Volume (Weighted 4plots+)
9
8
7
Ground
6
y = 1.8231x - 0.1081
R² = 0.5462
5
4
3
2
1
0
-1
0
0.5
1
1.5
2
2.5
3
3.5
Photo
Figure 9. Normalized dead net volumes of ground 9-plot clusters (with more than 4 plots within the segment) vs the photo
segments.
4.8
Cost Summary
The total cost and breakdowns are summarized in table 5. Average cost per ha was $0.11. The cost for kNN analysis
incorporated some research and testing components and as such, the operational cost could be much lower.
Table 5. Project cost summary.
Items
Photo acquisition
Cost
Unit Cost (/1,753,913 ha)
$4,102
$0.002
Photo interpretation
$79,992
$0.046
kNN analysis
$90,000
$0.051
Statistical analysis
$25,000
$0.014
LVI Summary Report June 10, 2013
Page 13
Subtotal
$199,094
$0.11
Ground sampling
$180,885
$0.11
Total
$379,979
$0.22
It should be pointed out that cost for internal staff working on Landsat segmentation, data preparation, data processing,
etc. was not included in Table 5. If these items were contracted out to a typical operational consultant setup, it could cost
about $18,000 (10 days for segmentation/classification and 20 days for data processing, at $600/day). So the unit cost, if
the entire project was contract out, could be $0.12 or less.
Total cost for the ground sampling is $180,885, or $0.11/ha.
5.
Summary
Landscape Vegetation Inventory (LVI) was initiated in 2012 and completed in 2014 for the area of 1,753,913 ha in west
Williams Lake TSA. The LVI database provides low cost, current and interim spatial and attribute inventory products that
could be used to support high level strategic planning and resource analysis applications in forest resource management
decision-making.
This project has demonstrated a design, execution, and implementation of a typical operational LVI project in the area
where conventional VRI is not justified and current strategic inventory is needed. LVI could and should be considered as an
alternative to VRI, and be implemented where appropriate.
The LVI data is provided to potential users through the operational VRI database, however, more LVI products can be
created and provided to specific users for specific requirements. Communications and consultations are encouraged to be
made with FAIB as the LVI data is not exactly equivalent to the conventional VRI data that most people are familiar to. The
concept of providing multi-resolution inventory data for various information needs is an important shift from traditional
“one size fits all” idea. There will be varieties of inventory products ranging from higher resolution LiDAR inventory,
operational inventory, to strategic VRI and LVI, and users should be aware of the differences so that inventory data is used
appropriately.
There are many lessons learned from this project and it is recommended that:
(1). Consistency and accuracy on tree attribution must be improved in order to minimize measurement bias. For
example, stand density (trees per ha) can be measured and basal area can be modeled instead of interpreting;
(2). Attention should be paid to utilization, projection, and compilation of both photo interpretation and ground data
collection so that consistent and relevant comparisons and adjustments can be made; and
(3). There are many aspects in the LVI segmentation, classification and kNN that can and should be improved for future LVI
projects.
LVI is an alternative to the conventional VRI and it is recommended that it be implemented for the area where VRI is not
justified and current strategic inventory is needed.
LVI Summary Report June 10, 2013
Page 14
6.
References
Meidinger, D. and Pojar, J. 1991. Ecosystems of British Colombia. B.C. Ministry of Forests, Victoria, British Columbia.
FLNRO. 2014. Williams Lake TSA Timber Supply Analysis Public Discussion Paper. Forest Analysis and Inventory Branch,
Ministry of Forests, Lands and Natural Resources Operations, Victoria, British Columbia.
Ministry of Forests, Lands and Natural Resource Operations. 2013. FLNR Provincial Site Productivity Layer. Version 4.0,
FLNRO, March 31, 2013, Victoria, British Columbia.
Moss, Ian. 2013. LVI Linear Discriminant Analyses. Prepared for Forest Analysis and Inventory Branch, BC Ministry of
Forests, Lands and Natural Resources Operations, Victoria, British Columbia.
Moss, Ian, 2014a. A Comparison of Alternative Landscape Vegetation Inventory (LVI) Nearest Neighbour Assignments,
Prepared for Forest Analysis and Inventory Branch, BC Ministry of Forests, Lands and Natural Resources Operations,
Victoria, British Columbia.
Moss, Ian, 2014b. The Landscape Vegetation Inventory (LVI) Nearest Neighbour Analysis Process. Prepared for Forest
Analysis and Inventory Branch, BC Ministry of Forests, Lands and Natural Resources Operations, Victoria, British Columbia.
Moss, Ian, 2014c. Williams Lake LVI Basal Area Adjustment Ratios. Prepared for Forest Analysis and Inventory Branch, BC
Ministry of Forests, Lands and Natural Resources Operations, Victoria, British Columbia.
Penner, Margaret. 2014. Landscape Vegetation Inventory 2014 Williams Lake Sample Results. Prepared for Forest Analysis
and Inventory Branch, BC Ministry of Forests, Lands and Natural Resources Operations, Victoria, British Columbia.
LVI Summary Report June 10, 2013
Page 15
7.
Appendix
LVI Summary Report June 10, 2013
Page 16
7.1
Generalized Site Series Classes by BEC Subzone
PEM/LVI Classification
BEC Zone
Subzone
Site Series
SBPS dc
SBPS mc
SBPS mk
SBPS xc
MS xv
Modifier
Nutrient/
Moisture
Lead
Species
LVI_ID
LVI_class
Description
01
MM
Pl
8101
101
Sub-Boreal Pine-Spruce .
02
MD
Pl
8102
102
03
MM
Pl
8103
102
04
PM
Pl
8104
101
05
MM
Sx
8105
101
Dry cold, Elev 1000-1280 South and
900-1225 North. Small portion at
north-east corner. 01 and 04 most
common, some 03 05 08, and little 02
06 07. Could combine 02 03, 01 04
05 and 06 07 08
06
RW
Sx
8106
103
07
PW
Sb
8107
103
08
MW
Sx
8108
103
01
MM
Pl
8201
201
Sub-Boreal Pine-Spruce .
02
MD
Pl
8202
202
Moist cold, Elve 900-1250
03
MM
Sb
8203
202
04
RW
Sx
8204
202
Only small portion at north-west
corner. Most common 01 and very
little 02-07
05
RW
Sx
8205
202
06
RW
Sx
8206
202
07
RW
Sb
8207
202
01
MM
Pl
8301
301
Sub-Boreal Pine-Spruce .
02
PD
Pl
8302
302
03
MD
Fd
8303
302
04
MM
Pl
8304
303
05
MM
Sx Fd
8305
303
Moist cool, Elv 1000-1350 South, 9501250 North. Small portion at the
north-east corner. Most common 01,
some 04 06 07 and little 02 03 05 08.
Could combine 02 03, 04 05, and 06
07 08.
06
RW
Sx
8306
304
07
RW
Sx
8307
304
08
PW
Sx
8308
304
01
LK
MD
Pl
8401
401
Sub-Boreal Pine-Spruce .
02
LC
PD
Pl
8402
402
03
SB
MW
Sx
8403
403
Very dry and cold, Elev 1100-1500
South and 850-1300 North. 03 and
04 could be combined
04
SF
MW
Sx
8404
403
05
SH
MW
Sx
8405
404
06
SM
RW
Sx
8406
405
01
MM
Pl
8001
801
Montane Spruce.
02
PD
Pl
8002
802
Very dry very cold. 1400-1700 in the
LVI Summary Report June 10, 2013
Page 17
IDF dk(4)
IDF xm
ESSF xv (1)
Not-treed
03
MD
Pl
8003
803
04
MM
Pl
8004
801
05
MM
Pl
8005
801
06
RW
Sx
8006
804
07
PW
Sx
8007
805
08
RW
Sx
8008
804
09
PW
Sx
8009
805
01
MM
FdPl
6601
601
02
MD
Fd
6602
602
03
PD
Fd
6603
602
04
RD
Fd
6604
602
05
RD
Fd
6605
603
06
PM
Pl
6606
603
07
MM
Fd
6607
603
08
PW
Sx
6608
601
09
RW
Sx
6609
601
10
RW
Sx
6610
604
01
MM
Fd
6901
901
02
MD
Fd
6902
902
03
PD
Fd
6903
903
04
MD
Fd
6904
902
05
MM
Fd
6905
904
06
MM
Fd
6906
905
07
RM
Fd
6907
905
08
RW
Sx
6908
905
09
RW
Sx
6909
906
01
MM
Bl
5501
501
02
MD
Bl
5502
502
03
PD
Pl
5503
503
04
MM
Bl
5504
502
05
MM
Bl
5505
502
06
PM
Bl
5506
501
07
MW
Bl
5507
504
08
MW
Bl
5508
504
09
RW
Bl
5509
504
100
10
00
OW
LVI Summary Report June 10, 2013
south and 1250-1500 in the north.
Most common 01 06 08 09, some 04
05 and little 02 03 07. Could combine
01 04 05, 06 08, and 07 09
Interior Douglas-Fir dry cool Chilcotin.
Elev 1050-1350. Most common 01
and 09. Very little 02 03 04 and 07.
Some 05 06 and 10.
Could combine 01 08 09, 02 03 04,
and 05 06 07.
Very little dk(3)
Interior Douglas-Fir very dry mild.
Elev. 800-1200. Most common 01 and
some 06. Very little others. Could
combine 06 07 08, 02 04
Engelmann Spruce-Subalpine Fir very
dry very cold. Elev. 1650-2100. Most
common 01 and some 06 07 08 and
little for the rest. Could combine 02
04 05, 01 06, and 07 08 09.
WA (Water)
Page 18
(include
Alpine
Tundra ATun and ESSF
xvp)
00
WE
101
11
WL (Wetland)
00
BR DG
FB FM
FC GL
HT ME
PA RO
SF
102
12
VNT (Vegetated and no treed)
00
AF AV
CS GB
RO RU
TA
103
13
NV (Non-vegetated)
00
DL
104
14
Disturbed (e.g. road)
Nutrient: P (poor), M (medium), R (rich)
Moisture: D (dry), M (medium), W (wet)
LVI Summary Report June 10, 2013
Page 19
7.2
Williams Lake LVI Classification and Databases
Williams Lake LVI Classification and Databases
The following 6 classification schemes were used for the LVI data for Williams Lake TSA west. The first 4 classification
schemes were defined by the staff in the Williams Lake district office, while the schemes 5 and 6 were defined by FAIB.
The attribute tables for each of the first 5 classifications and data dictionary are 7.3. Classification 6 is defined by
homogeneous spectral polygons created from Landsat 8 imagery.
All the classification maps (with the attributes) above are included in the ArcGIS personal geodatabase.
1.
Age
Williams Lake Classification 1 (WL_CLASS1)
Lead species
%
Net Volume
(m3/ha)
<60
WL_CLASS1
Comment
1
Young stands
>=60
PL
>=50
<80
2
Pl leading
>=60
PL
>=50
80-100
3
Pl leading
>=60
PL
>=50
100-120
4
Pl leading
>=60
PL
>=50
>=120
5
Pl leading
>=60
SX
>=50
<50
6
Sx leading
>=60
SX
>=50
50-100
7
Sx leading
>=60
SX
>=50
>100
8
Sx leading
>=60
FD
>=50
<50
9
Fd leading
>=60
FD
>=50
50-100
10
Fd leading
>=60
FD
>=50
>100
11
Fd leading
>=60
AT
>=50
12
At leading
0
Unclassified
LVI Summary Report June 10, 2013
Page 20
2.
Age
Williams Lake Classification 2 (WL_CLASS2)
Lead species
%
Dead Net
Volume
(m3/ha)
Net Volume
(m3/ha)
<60
WL_CLASS2
Comment
1
Young stands
>=60
PL
>70
<30
2
Pl leading
>=60
PL
>70
30-50
3
Pl leading
>=60
PL
>70
50-70
4
Pl leading
>=60
PL
>70
>70
5
Pl leading
>=60
PL
50-70
<30
6
Pl leading
>=60
PL
50-70
30-50
7
Pl leading
>=60
PL
50-70
50-70
8
Pl leading
>=60
PL
50-70
>70
9
Pl leading
>=60
SX
>=50
<50
10
Sx leading
>=60
SX
>=50
50-100
11
Sx leading
>=60
SX
>=50
>100
12
Sx leading
>=60
FD
>=50
<50
13
Fd leading
>=60
FD
>=50
50-100
14
Fd leading
>=60
FD
>=50
>100
15
Fd leading
>=60
AT
>=50
16
At leading
0
Unclassified
WL_CLASS3
Comment
1
Young stands
3.
Age
Williams Lake Classification 3 (WL_CLASS3)
Lead species
%
Dead Net
Volume %*
<60
Net Volume
(m3/ha)
>=60
PL
>70
<30
2
Pl leading
>=60
PL
>70
30-50
3
Pl leading
>=60
PL
>70
50-70
4
Pl leading
>=60
PL
>70
>70
5
Pl leading
>=60
PL
50-70
<30
6
Pl leading
>=60
PL
50-70
30-50
7
Pl leading
>=60
PL
50-70
50-70
8
Pl leading
>=60
PL
50-70
>70
9
Pl leading
LVI Summary Report June 10, 2013
Page 21
>=60
SX
>=50
<50
10
Sx leading
>=60
SX
>=50
50-100
11
Sx leading
>=60
SX
>=50
>100
12
Sx leading
>=60
FD
>=50
<50
13
Fd leading
>=60
FD
>=50
50-100
14
Fd leading
>=60
FD
>=50
>100
15
Fd leading
>=60
AT
>=50
16
At leading
0
Unclassified
* Percentage of dead net volume = 100 * dead net volume/(live net volume + dead net volume)
4.
Age
Williams Lake Classification 4 (WL_CLASS4)
Lead species
%
Dead Net
Volume
(m3/ha)
Net Volume
(m3/ha)
<60
WL_CLASS4
Comment
1
Young stands
>=60
PL
>50
<30
2
Pl leading
>=60
PL
>50
30-50
3
Pl leading
>=60
PL
>50
50-70
4
Pl leading
>=60
PL
>50
>70
5
Pl leading
>=60
SX
>=50
<50
6
Sx leading
>=60
SX
>=50
50-100
7
Sx leading
>=60
SX
>=50
>100
8
Sx leading
>=60
FD
>=50
<50
9
Fd leading
>=60
FD
>=50
50-100
10
Fd leading
>=60
FD
>=50
>100
11
Fd leading
>=60
AT
>=50
12
At leading
0
Unclassified
5.
Williams Lake Classification based on Fuzzie C-Means Classification (WL_CLASS5)
The classification was developed based on a clustering analysis of leading species, stand structure and age
attributes, resulting in a total of 21 classes.
LVI Summary Report June 10, 2013
Page 22
6.
Spectral Generalized Polygons (LANDSAT_POLY)
Unique spectrally homogeneous polygons were created from Landsat 8 segments and attribute values were
computed from all the segments within polygons weighted by segment areas.
LVI Summary Report June 10, 2013
Page 23
7.3
LVI Data Dictionary
1. The LVI Photo Database
Field Name
OBJECTID
Shape
FOREST_COVER_OBJECT_ID
SEGMENT_AREA
Class
FEATURE_ID
TEIS_ID
LANDSAT_ID
BEC_ZONE
BEC_SUBZONE
LAYER_ID
MAPSHEET_ID
POLYGON_NUMBER
CROWN_CLOSURE_PCT
TREE_SPECIES_CODE_1
SPECIES_PCT_1
TREE_SPECIES_CODE_2
SPECIES_PCT_2
TREE_SPECIES_CODE_3
SPECIES_PCT_3
TREE_SPECIES_CODE_4
SPECIES_PCT_4
TREE_SPECIES_CODE_5
SPECIES_PCT_5
TREE_SPECIES_CODE_6
SPECIES_PCT_6
PCT_check
AGE_1
HEIGHT_1
AGE_2
HEIGHT_2
BASAL_AREA
BASAL_AREA_ADJ
VRI_LIVE_STEMS_PER_HA
LVI_1
LVI_2
DATA_SOURCE
INTERPRETER
DATE_INPUT
SITE_INDEX
PROJ_HT
PROJ_AGE
PROJ_DIA
Format
integer
shape
integer
float
string
integer
integer
integer
string
string
integer
string
integer
integer
string
integer
string
integer
string
integer
string
integer
string
integer
string
integer
integer
integer
float
integer
float
float
float
integer
string
integer
string
string
date
float
float
float
float
LVI Summary Report June 10, 2013
Definition
Object ID
Shape field
Segment ID
Segment area
Landsat classification
VRI Feature ID
PEM polygond ID
Landsat ID
BGC zone ID
BGC subzone ID
Layer ID
ECORA Map ID
Polygon ID
Crown closure
The first tree species ID
The first tree species percentage
The second tree species ID
The second tree species percentage
The third tree species ID
The third tree species percentage
The fourth tree species ID
The fourth tree species percentage
The fifth tree species ID
The fifth tree species percentage
The sixth tree species ID
The sixth tree species percentage
Total percentage
Age of the first species
Height of the first species
Age of the second species
Height of the second species
Basal area
Adjusted basal area
Live trees per ha
Treed=t, no treed=n
Uniform=1, mixed=2
Data source
Name of the photo interpreter
Date of interpretation
Site index
Projected height
Projected age
Projected diameter
Comment
ArcGIS internal format
ArcGIS internal format
Unique ID
from Ann
Grid site ID
Same as FOREST_COVER_OBJECT_ID
For QA check
Original projection in 2013
Page 24
PROJ_BA
PROJ_TPH
PROJ_NET_VOL
PROJ_GROSS_VOL
D_TREE_SPECIES_CODE_1
float
float
float
float
string
D_SPECIES_PCT_1
D_TREE_SPECIES_CODE_2
integer
string
D_SPECIES_PCT_2
D_TREE_SPECIES_CODE_3
integer
string
D_SPECIES_PCT_3
D_CROWN_CLOSURE_PCT
D_AGE_1
D_HEIGHT_1
D_AGE_2
D_HEIGHT_2
D_BASAL_AREA
D_BASAL_AREA_ADJ
D_STEMS_PER_HA
D_PROJ_HT
D_PROJ_TPH
D_PROJ_NET_VOL
D_PROJ_GROSS_VOL
D_PROJ_NET_VOL_ADJ
integer
integer
integer
float
integer
float
float
float
integer
float
float
float
float
float
D_PROJ_GROSS_VOL_ADJ
R2_TREE_SPECIES_CODE_1
R2_SPECIES_PCT_1
R2_TREE_SPECIES_CODE_2
R2_SPECIES_PCT_2
R2_TREE_SPECIES_CODE_3
R2_SPECIES_PCT_3
R2_AGE_1
R2_HEIGHT_1
R2_CROWN_CLOSURE_PCT
R2_BASAL_AREA
R2_VRI_LIVE_STEMS_PER_HA
PROJ_4CM_HT
PROJ_4CM_AGE
PROJ_4CM_DIA
PROJ_4CM_BA
PROJ_4CM_TPH
PROJ_4CM_NET_VOL
PROJ_4CM_GROSS_VOL
CLASSSPL5
CLASSSTAL5
CLASSDL5
float
string
integer
string
integer
string
integer
integer
float
integer
float
integer
float
float
float
float
float
float
float
integer
integer
integer
LVI Summary Report June 10, 2013
Projected basal area
Projected stems per ha
Projected net volume
Projected gross volume
Dead the first tree species ID
Dead the first tree species
percentage
Dead the second tree species ID
Dead the second tree species
percentage
Dead the third tree species ID
Dead the third tree species
percentage
Dead tree crown closure
Dead age of the first species
Dead height of the first species
Dead age of the second species
Dead height of the second species
Original dead tree basal area
Adjusted dead tree basal area
Dead trees per ha
Dead projected height
Dead projected stems per ha
Dead projected net volume
Dead projected gross volume
Dead projected net volume adjusted
Dead projected gross volume
adjusted
Layer 2 first species ID
Layer 2 first species percentage
Layer 2 second species ID
Layer 2 second species percentage
Layer 2 third species ID
Layer 2 third species percentage
Layer 2 projected age
Layer 2 projected height
Layer 2 projected crown closure
Layer 2 basal area
Layer 2 live trees per ha
Projected height at 4cm
Projected age at 4 cm
Projected diameter at 4 cm
Projected basal area at 4 cm
Projected stems per ha at 4 cm
Projected net volume at 4 cm
Projected gross volume at 4 cm
Species classification
Structure classification
Dead tree classification
Final dead volume for reporting and analysis
Final dead volume for reporting and analysis
Page 25
SPLSTAL
WLCLASS1
WLCLASS2
WLCLASS3
WLCLASS4
WLCLASS5
integer
integer
integer
integer
integer
integer
Species and structure classification
Williams Lake classification 1
Williams Lake classification 2
Williams Lake classification 3
Williams Lake classification 4
Williams Lake classification 5
RA1_BA
RA1_PROJ_BA
RA1_NET_VOL125
float
float
float
Adjusted basal area 1
Projected basal area 1
Projected net volume 1
RA2_BA
RA2_PROJ_BA
RA2_NET_VOL125
float
float
float
Projected basal area 2
Projected basal area 2
Projected net volume 2
RA3_BA
RA3_PROJ_BA
RA3_NET_VOL125
RA4_PROJECTION_YEAR
float
float
float
integer
Projected basal area 3
Projected basal area 3
Projected net volume 3
Year of projection
RA4_BA_ADJ
RA4_PROJ_BA
RA4_PROJ_AGE
RA4_PROJ_HT
RA4_PROJ_DIA
RA4_PROJ_TPH
RA4_PROJ_NET_VOL
RA4_PROJ_GROSS_VOL
RA4_PROJ_WSV
PROJECT_NAME
PROJECT_MANAGER
INFOR_DATE
Shape_Length
Shape_Area
float
float
float
float
float
float
float
float
float
string
string
date
float
float
Adjusted basal area
Projected basal area 2014
Projected age 2014
Projected height 2014
Projected diameter 2014
Projected trees per ha 2014
Projected net volume 2014
Projected gross volume 2014
Projected whole stem volume 2014
Project name
Project manager
Information date
Length of the polygon
Area of the polygon
LVI Summary Report June 10, 2013
Refer to the classification definition table
Refer to the classification definition table
Refer to the classification definition table
Refer to the classification definition table
Refer to the classification definition table
Multiplicator 1.49 for MS, 0.401 for SBPS from
Ian
Multiplicator 1.391 for MS, 0.7204 for SBPS,
from Margaret
Multiplicator 1.6058 for MS, 0.77 for SBPS,
from Xiaoping
2014
Multiplicator 1.30 for MS, 0.5607 for SBPS, for
final
The date of creation of this data
ArcGIS internal format
ArcGIS internal format
Page 26
2. The LVI Segment Database
Field Name
OBJECTID
Shape
FOREST_COVER_OBJECT_ID
SEGMENT_AREA
Format
integer
shape
integer
float
Object ID
Shape field
Segment ID
Segment area
ALL_BEC_POST_ID
ALL_BEC_POST_NN
FEATURE_ID
TEIS_ID
BEC_ZONE
BEC_SUBZONE
DEM_MEAN
SLOPE_MEAN
ASPECT_MEAN
centX
centY
Class
LAYER_ID
CROWN_CLOSURE_PCT
TREE_SPECIES_CODE_1
SPECIES_PCT_1
TREE_SPECIES_CODE_2
SPECIES_PCT_2
TREE_SPECIES_CODE_3
SPECIES_PCT_3
TREE_SPECIES_CODE_4
SPECIES_PCT_4
TREE_SPECIES_CODE_5
SPECIES_PCT_5
TREE_SPECIES_CODE_6
SPECIES_PCT_6
AGE_1
HEIGHT_1
AGE_2
HEIGHT_2
BASAL_AREA
VRI_LIVE_STEMS_PER_HA
LVI_1
LVI_2
DATA_SOURCE
INTERPRETER
DATE_INPUT
SITE_INDEX
PROJ_HT
PROJ_AGE
PROJ_DIA
integer
string
integer
integer
string
string
float
float
float
float
float
string
integer
integer
string
integer
string
integer
string
integer
string
integer
string
integer
string
integer
integer
float
integer
float
float
integer
string
integer
string
string
date
float
float
float
float
Matched DCS ID
Nearest Neighbor ID
VRI Feature ID
PEM polygond ID
BGC zone ID
BGC subzone ID
Average elevation
Average slope
Average aspect
UTN easting of centroid
UTM northing of centroid
Landsat classification
Layer ID
Crown closure
The first tree species ID
The first tree species percentage
The second tree species ID
The second tree species percentage
The third tree species ID
The third tree species percentage
The fourth tree species ID
The fourth tree species percentage
The fifth tree species ID
The fifth tree species percentage
The sixth tree species ID
The sixth tree species percentage
Age of the first species
Height of the first species
Age of the second species
Height of the second species
Basal area
Live trees per ha
Treed=t, no treed=n
Uniform=1, mixed=2
Data source
Name of the photo interpreter
Date of interpretation
Site index
Projected height
Projected age
Projected diameter
LVI Summary Report June 10, 2013
Definition
Comment
ArcGIS internal format
ArcGIS internal format
Unique ID
Global classification, BEC zone applied with
post classification
From Ann
KNN matched, DCS interpreted, 8, 18
Original projection in 2013
Page 27
PROJ_BA
PROJ_TPH
PROJ_NET_VOL
PROJ_GROSS_VOL
D_TREE_SPECIES_CODE_1
D_SPECIES_PCT_1
D_TREE_SPECIES_CODE_2
D_SPECIES_PCT_2
D_TREE_SPECIES_CODE_3
D_SPECIES_PCT_3
D_CROWN_CLOSURE_PCT
D_AGE_1
D_HEIGHT_1
D_AGE_2
D_HEIGHT_2
D_BASAL_AREA
D_BASAL_AREA_ADJ
D_STEMS_PER_HA
D_PROJ_HT
D_PROJ_TPH
D_PROJ_NET_VOL
D_PROJ_GROSS_VOL
D_PROJ_NET_VOL_ADJ
D_PROJ_GROSS_VOL_ADJ
R2_TREE_SPECIES_CODE_1
R2_SPECIES_PCT_1
R2_TREE_SPECIES_CODE_2
R2_SPECIES_PCT_2
R2_TREE_SPECIES_CODE_3
R2_SPECIES_PCT_3
R2_AGE_1
R2_HEIGHT_1
R2_CROWN_CLOSURE_PCT
R2_BASAL_AREA
R2_VRI_LIVE_STEMS_PER_HA
PROJ_4CM_HT
PROJ_4CM_AGE
PROJ_4CM_DIA
PROJ_4CM_BA
PROJ_4CM_TPH
PROJ_4CM_NET_VOL
PROJ_4CM_GROSS_VOL
CLASSSPL5
CLASSSTAL5
CLASSDL5
SPLSTAL
WLCLASS1
WLCLASS2
float
float
float
float
string
integer
string
integer
string
integer
integer
integer
float
integer
float
float
float
integer
float
float
float
float
float
float
string
integer
string
integer
string
integer
integer
float
integer
float
integer
float
float
float
float
float
float
float
integer
integer
integer
integer
integer
integer
LVI Summary Report June 10, 2013
Projected basal area
Projected stems per ha
Projected net volume
Projected gross volume
Dead the first tree species ID
Dead the first tree species percentage
Dead the second tree species ID
Dead the second tree species percentage
Dead the third tree species ID
Dead the third tree species percentage
Dead tree crown closure
Dead age of the first species
Dead height of the first species
Dead age of the second species
Dead height of the second species
Original dead tree basal area
Adjusted dead tree basal area
Dead trees per ha
Dead projected height
Dead projected stems per ha
Dead projected net volume
Dead projected gross volume
Dead projected net volume adjusted
Dead projected gross volume adjusted
Layer 2 first species ID
Layer 2 first species percentage
Layer 2 second species ID
Layer 2 second species percentage
Layer 2 third species ID
Layer 2 third species percentage
Layer 2 projected age
Layer 2 projected height
Layer 2 projected crown closure
Layer 2 basal area
Layer 2 live trees per ha
Projected height at 4cm
Projected age at 4 cm
Projected diameter at 4 cm
Projected basal area at 4 cm
Projected stems per ha at 4 cm
Projected net volume at 4 cm
Projected gross volume at 4 cm
Species classification
Structure classification
Dead tree classification
Species and structure classification
Williams Lake classification 1
Williams Lake classification 2
*1.87
Final dead volume for reporting and analysis
Final dead volume for reporting and analysis
Refer to the classification definition table
Refer to the classification definition table
Page 28
WLCLASS3
WLCLASS4
WLCLASS5
RA4_PROJECTION_YEAR
RA4_BA_ADJ
RA4_PROJ_BA
RA4_PROJ_AGE
RA4_PROJ_HT
RA4_PROJ_DIA
RA4_PROJ_TPH
RA4_PROJ_NET_VOL
RA4_PROJ_GROSS_VOL
RA4_PROJ_WSV
PROJECT_NAME
PROJECT_MANAGER
INFOR_DATE
Shape_Length
Shape_Area
integer
integer
integer
integer
float
float
float
float
float
float
float
float
float
string
string
date
float
float
LVI Summary Report June 10, 2013
Williams Lake classification 3
Williams Lake classification 4
Williams Lake classification 5
Year of projection
Projected basal area 2014
Projected basal area 2014
Projected age 2014
Projected height 2014
Projected diameter 2014
Projected trees per ha 2014
Projected net volume 2014
Projected gross volume 2014
Projected whole stem volume 2014
Project name
Project manager
Information date
Length of the polygon
Area of the polygon
Refer to the classification definition table
Refer to the classification definition table
Refer to the classification definition table
2014
Multiplicator 1.30 for MS, 0.5607 for SBPS
The date of creation of this data
ArcGIS internal format
ArcGIS internal format
Page 29
3. The LVI Class Database
Field Name
OBJECTID
Shape
POLYGON_ID
POLYGON_AREA
MAP_ID
BEC_ZONE
BEC_SUBZONE
VRI_FEATURE_ID
Disturbance_Start_Date
Disturbance_End_Date
VRI_SP1_CD
VRI_SITE_INDEX
INVENTORY_STANDARD
REFERENCE_YEAR
PROJECTION_YEAR
DATA_SOURCE
Format
integer
shape
integer
float
string
string
string
integer
date
date
string
float
string
integer
integer
string
Definition
Object ID
Shape field
Polygon ID
Polygon area in ha
NTS20K Map ID
BGC zone ID
BGC subzone ID
VRI Feature ID
Disturbance start date
Disturbance end date
Leading species ID in VRI
Site index in VRI
Inventory Standard ID
Reference year
Projection year
Data source
CLASSID
CLASS_NO
CLASS_GROUP
CLASS_AREA
NO_DCS_SEGMENTS
string
integer
string
float
integer
Classification ID
Class ID
Class group name
Class area
Number of segments in the polygon
SP1_CD
SP1_PCT
SP2_CD
SP2_PCT
SP3_CD
SP3_PCT
SP4_CD
SP4_PCT
SP5_CD
SP5_PCT
SP6_CD
SP6_PCT
AGE1
HEIGHT1
AGE2
HEIGHT2
string
integer
string
integer
string
integer
string
integer
string
integer
string
integer
integer
float
integer
float
The first tree species ID
The first tree species percentage
The second tree species ID
The second tree species percentage
The third tree species ID
The third tree species percentage
The fourth tree species ID
The fourth tree species percentage
The fifth tree species ID
The fifth tree species percentage
The sixth tree species ID
The sixth tree species percentage
Age of the first species
Height of the first species
Age of the second species
Height of the second species
SITE_INDEX
CROWN_CLOSURE
BASAL_AREA
LIVE_STEMS_PER_HA
NET_VOLUME
float
integer
float
integer
float
Site index
Crown closure
Basal area
Live trees per ha
Net volume
LVI Summary Report June 10, 2013
Comment
ArcGIS internal format
ArcGIS internal format
unique
for no LVI tree area
Same start and end dates, for cut and fire
L, V, F, I
Year of photo or Landsat
For VDYP7 projected attributes
LVI, LANDSAT, PEM, WFMB, VRI, etc
LANDSAT_PO for Landsat polygon, WLCLASS3 (15) for classification, CUT, OPEN, FIRE(LOW,
MEDIUM, HIGH), WATER
same class ID has same attribute values
TREE, NOTREE
For LVI, only 4 species, and for VRI it could be up
to 6
Weighted average
Weighted average
Weighted average
Weighted average
Derived by leading species, age and height,
weighted average
Weighted average
Weighted average
Weighted average
Primary utilization at 12.5cm, weighted average
Page 30
WSV_4CM
DEAD_STEMS_PER_HA
DEAD_NET_VOLUME
DEAD_GROSS_VOLUME
sCROWN_CLOSURE
sBASAL_AREA
sLIVE_STEMS_PER_HA
sNET_VOLUME
float
float
float
float
float
float
float
float
sWSV_4CM
sDEAD_STEMS_PER_HA
sDEAD_NET_VOLUME
sDEAD_GROSS_VOLUME
float
float
float
float
Whole stem volume
Dead tree stems per ha
Dead tree net volume
Dead tree gross volume
Strandard Error (SE) of croan closure
SE of basal area
SE of live stems per ha
SE of net volume
SE of whole stem volume, gross at
4cm
SE of dead tree stems per ha
SE of dead tree net volume
SE of dead tree gross volume
PROJ_CLASS_AGE
PROJ_CLASS_HT
PROJ_CLASS_DIA
PROJ_CLASS_BA
PROJ_CLASS_TPH
PROJ_CLASS_NET_VOL
PROJ_CLASS_GROSS_VOL
PROJECT_NAME
PROJECT_MANAGER
INFOR_DATE
Shape_Length
Shape_Area
float
float
float
float
float
float
float
string
string
date
float
float
Projected age of the class
Projected height of the class
Projected diameter of the class
Projected basal area of the class
Projected stems per ha of the class
Projected net volume of the class
Projected gross volume of the class
Project name
Project manager
Information date
Length of the polygon
Area of the polygon
LVI Summary Report June 10, 2013
Gross volume at 4cm, weighted average
All projected are based on the attribute values at
reference year
The date of creation of this data
ArcGIS internal format
ArcGIS internal format
Page 31
4. The LVI Polygon Database
Field Name
OBJECTID
Shape
POLYGON_ID
POLYGON_AREA
PIXELS
BEC_ZONE
BEC_SUBZONE
FEATURE_ID
VRI_MAX_POLY_AREA
VRI_POLY_AREA
DATA_SOURCE
REFERENCE_YEAR
PROJECTION_YEAR
INVENTORY_STANDARD
Format
integer
shape
integer
float
integer
string
string
integer
float
float
string
integer
integer
string
Definition
Object ID
Shape field
Polygon ID
Polygon area in ha
Number of Landsat pixels
BGC zone ID
BGC subzone ID
VRI Feature ID
Area of VRI portion in the LVI polygon
VRI original polygon area
Data source
Reference year
Projection year
Inventory Standard ID
CLASSID
CLASS_NO
CLASS_GROUP
NO_SEGMENTS
string
integer
string
integer
Classification ID
Class ID
Class group name
Number of segments in the polygon
SP1_CD
SP1_PCT
SP2_CD
SP2_PCT
SP3_CD
SP3_PCT
SP4_CD
SP4_PCT
SP5_CD
SP5_PCT
SP6_CD
SP6_PCT
AGE1
HEIGHT1
AGE2
HEIGHT2
BASAL_AREA
CROWN_CLOSURE
string
integer
string
integer
string
integer
string
integer
string
integer
string
integer
integer
float
integer
float
float
integer
The first tree species ID
The first tree species percentage
The second tree species ID
The second tree species percentage
The third tree species ID
The third tree species percentage
The fourth tree species ID
The fourth tree species percentage
The fifth tree species ID
The fifth tree species percentage
The sixth tree species ID
The sixth tree species percentage
Age of the first species
Height of the first species
Age of the second species
Height of the second species
Basal area
Crown closure
SITE_INDEX
LIVE_STEMS_PER_HA
float
integer
Site index
Live trees per ha
NET_VOLUME
float
Net volume
GROSS_VOLUME
WSV_4CM
float
float
Gross volume
Whole stem volume
PROJ_AGE
float
Projected age
LVI Summary Report June 10, 2013
Comment
ArcGIS internal format
ArcGIS internal format
unique
for no LVI tree area
LVI, LANDSAT, PEM, WFMB, VRI, etc
Year of photo or Landsat
For VDYP7 projected attributes
L, V, F, I
LANDSAT_PO for LVI data, CUT, OPEN,
FIRE(LOW, MEDIUM, HIGH), WATER
TREE, NOTREE
For LVI, only 4 species, and for VRI it could be
up to 6
Weighted average
Weighted average
Weighted average
Weighted average
Weighted average
Weighted average
Derived by leading species, age and height,
weighted average
Weighted average
Primary utilization at 12.5cm, weighted
average
Primary utilization at 12.5cm, weighted
average
Gross volume at 4cm, weighted average
All projected are based on the attribute values
at reference year
Page 32
PROJ_HT
PROJ_DIA
PROJ_BA
PROJ_TPH
PROJ_NET_VOL
PROJ_GROSS_VOL
D_SP1_CD
D_SP1_PCT
D_AGE
D_HT
D_BA
D_CC
D_TPH
D_NET_VOL
D_GROSS_VOL
sBASAL_AREA
sCROWN_CLOSURE
sLIVE_STEMS_PER_HA
sNET_VOLUME
sGROSS_VOLUME
sWSV_4CM
sD_AGE
sD_HT
sD_BA
sD_CC
sD_TPH
sD_NET_VOL
sD_GROSS_VOL
PROJECT_NAME
PROJECT_MANAGER
INFOR_DATE
Disturbance_Start_Date
Disturbance_End_Date
VRI_SP1_CD
VRI_SI
EST_SITE_INDEX_SPECIES_CD
EST_SITE_INDEX
SP_SI
Shape_Length
Shape_Area
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
float
string
string
date
date
date
string
float
string
float
float
float
float
LVI Summary Report June 10, 2013
Projected height
Projected diameter
Projected basal area
Projected stems per ha
Projected net volume
Projected gross volume
Dead tree first species
Dead tree first species percentage
Dead tree age
Dead tree height
Dead tree basal area
Dead tree crown closure
Dead tree stems per ha
Dead tree net volume
Dead tree gross volume
Strandard Error (SE) of basal area
SE of crown closure
SE of live stems per ha
SE of net volume
SE of gross volume
SE of whole stem volume at 4cm
SE of dead tree age
SE of dead tree height
SE of dead tree basal area
SE of dead tree crown closure
SE of dead tree stems per ha
SE of dead tree net volume
SE of dead tree gross volume
Project name
Project manager
Information date
Disturbance start date
Disturbance end date
Leading species ID in VRI
Site index in VRI
Estimated site index species in VRI
Estimated site index in VRI
Site index from the Site Productivity 2014
Length of the polygon
Area of the polygon
PL
100
The date of creation of this data
Same start and end dates, for cut and fire
ArcGIS internal format
ArcGIS internal format
Page 33