1. Landscape Vegetation Inventory (LVI)
Forest Inventory Geospatial Monitoring Team
Introduction
Purpose: brief on LVI and project proposal, information sharing and communication

2. Short History of Forest Inventory
The compilation inventory
The 1st complete inventory (2%)
The 2nd complete inventory (27%)
Re-inventory, (11%)
Re-inventory/VRI, (37%)
VRI: 2000 – present (23%)
(%) the area % of the province by reference year
To answer the question, we need to look back first to see where we came from and how we got here, present
The forest inventory started in 1910, mainly compiling the ground surveys for mature volume for harveting for wood industry, two world wars and the depression 1930 had impacts
The first complete inventory was done in 10 years, air photo was first used. FIZ
The second complete inventory was done during , PSYU, stratified ITG sampling
The current VRI started in late 1990’s till present, TSA,
MPB infestation of 17.5 millions ha, making inventory obsolete.
With current budget and staff/resource, and speed of doing new VRI, we just can not reach to the target of inventory cycle 20 years
We must find a new way/tool to help us to bring inventory to the current as cost effective and efficient as possible.

3. Challenges挑战 Vast forest area
Insufficient funding: aging inventory data
Aging workforce and lack of expertise/skill
Increasing public expectations
Mountain Pine Beetle
Infrastructure is not fully built: standards/specifications, design methodology, implementation, training, database, H/S tools, staffing, budget/planning

4. LVI Detail and/or accuracy Cost tree stand class province Ground plot
LiDAR
VRI
Detail and/or accuracy
class
LVI
As we all know, information costs money. Detailed and better information are proportionally associated with higher costs.
At one end of the extreme, we have tree level information that can only be obtained from ground, and it is also the most expensive. On the other end, we have provincial level of resource information reporting such as VRMP, and its continuation is a question mark.
We only have one standard product VRI, in between, which basically the timber inventory we have been always been doing, using the same techniques, for more than half century. There are reasons why it exists unchanged for so long and the most important reason is that it meets the business need. However, as I mentioned in the background, we are facing challenges and we should actively look for new ways to meet these challenges.
My belief is that inventory should provide relevant and appropriate data at lowest cost to satisfy the management decision makers. Inventory folks do not need the data and it is those who manage the forest that require the data. Inventory does not have to be one standard, one way to do it.
The argument is that inventory program should be able to provide an array of data products for meeting management goals at different levels. The LVI as strategic inventory could satisfy a number of business applications such as TSR, planning, monitoring, biomass/carbon accounting etc. This new tool could fill in the gaps.
If you look at the data sources for those inventories on the right, what can we do to not only provide inventory data but also reduce cost in the new inventory design?
Our proposal for the new inventory tool is “Landscape Vegetation Inventory”, the term is better than VRI-lite. Landscape implies that inventory detail is on classes rather than stands, at landscape level and vegetation means the product is similar like VRI product. It will use DCS and satellite data as source and the inventory is done through a valid statistical sampling and classification.
VRMP
province
Cost

5. Landscape Vegetation Inventory (LVI)
A new, innovative and alternative inventory at landscape level for strategic forest management applications
Statistically valid, low cost, and flexible design providing key tree attribute estimates at population, strata and class levels
It is based on the sample data created using interpretation of very high resolution digital image interpolated to spatial distribution through Landsat segmentation and classification
Supports strategic forest management applications, such as inventory and silviculture planning, forest health survey, timber supply analysis, climate change, monitoring and reporting
It is not a replacement of VRI and other operational inventories

6. Landsat Segmentation and Stratification Photo
LVI Methodology
Landsat Segmentation and Stratification
(spatial)
Interpolation and classification
- Maps
- Attribute
database
There are 3 basic components in the LVI methodology:
Landsat segmentation providing spatial segments. It also provides initial stratifications
DCS sampling. This is where a sampling design is required according to the management requirements (precisions/cost/detail), and population conditions (forest, terrain, area etc.). Then, DCS photo acquisition, field data for photo interp calibration, and photo interpretation are carried out. The photo interpretation is very critical because it supplies the attribute data based on the sampling. Photo measurement may be needed rather than interpretation for better accuracy, but cost is going up. Ideally, we would have a dedicated person working on the DCS photo interpretation for consistency and skill development.
The third component is the attribute extrapolation and classification in which the DCS interpreted attributes are extrapolated to the Landsat segments, which are later classified into general land cover classes.
The end product will be the VRI-like maps and attribute tables. One basic requirement for attributes is that it should allow the polygons to be projected by VDYP7. So we must ensure the basic attributes required by VDYP7 are captured.
The most critical component in the process is the DCS sampling which includes 2 things: sampling design and DCS photo interpretation.
Photo
Sampling
(attributes)

7. Landsat Segmentation
Landsat provides spectrally homogenous segments

8. Photo Sampling

9. Interpolation and Classification
Match unknown segment to know photo segment
k-nearest neighbour imputation
Attributed segment map (initial spatial map)
Generalized classification map (publish)

10. SAMPLING MAPPING photo samples MAPPINGOPTIONS
MATCHING UNKNOWN TO KNOWN
- How does it work? I use the Quesnel project as an example.
- There are basically two components: Sampling and mapping.
In sampling, we establish systematically photo samples (high resolution 5-10 cm) across the population, then photo interpret these polygons like VRI.
In mapping, we create wall to wall segmentation on Landsat imagery, spectrally homogeneous and these segments are our building blocks. Some of these segments are photo interpreted, so we know what they are.
And in the final stage we have a process called matching or extrapolation, in which we match every unknown segment to one of known segment, this is how we get a wall to wall mapping product
As you can see, there are two parts in the LVI processing that have uncertainties: photo interpretation and matching. So in the end, we classify the matched segments into general classes (or strata) and the average attribute values for the strata are computed. So the end product of LVI is wall to wall cover of classification map.
INITIAL SEGMENTS
PHOTO INTERPRETED SAMPLES

11. Characteristics of LVI
Providing estimates of key tree attributes (live and dead) at class, stratum, and population levels, and spatial maps
Low cost (estimated the operational cost at $0.1-$0.2/ha)
Flexible in scale and implementation
Statistically valid and defensible
Quick turn-around

12. Summary LVI could be applied to the area where: Applications:
strategic or interim inventory is required
forest structure is simple and uniform for which LVI is more cost effective
inventory is too old or out of date and VRI is not affordable and/or justified
Applications:
Timber Supply Analysis (Nienaber), Tree Species Strategy (Mah), Quesnel District (Pelchat), Carbon Modeling (Li)
Planning (e.g. VRI, harvesting, silviculture, FFT) monitoring and reporting

13. Williams Lake LVI Project (1)
Project area: million ha, 113 maps
Initiated in 2012
New digital photography: Sept/Oct 2012
Photo interpretation : August, 2013
Ground sampling: November 2013
Completion: May, 2014

14. WL LVI Project (2) High resolution digital photos
15cm, color, plus NIR
Corridor, 1700 m in width
Stereo and geo-referenced

15. WL LVI Project (3) Photo Sample Interpreted for tree attributes
3% of total area
provide current and statistically valid estimate of the population

16. WL LVI Project (4) Classification Map
Leading sp, age/height, live and dead volumes
By BEC zones
Clustering analysis

17. WL LVI Project (5) Ground Sampling Data (about 120 plots) Auditing LVI
Accuracy assessment
Additional information
Future improvement

18. Quesnel West LVI Project
Population Area: about 1 million ha, 74 maps
1% area was photo sampled
Completed in 2011, and will be revised for a new version
Ground assessment

19. LVI for Cassiar 13.1 millions ha Forest land: 23% THLB: 1.6%
Total TSA area: 13,127,012ha, Forest land base 23%: 3,056,634 ha, THLB 1.6%: 210,681 ha

20. Total area: 13.1 millions ha
- 5 major bec zones, majority 77% no forest
Within forested land 23%, majority species is SB (33%), B (33%) and PL (23%)
Majority is over mature 57% more than 200 years old
These figures help the initial stratification for sampling and analysis

21. Pilot 2012
Test area: 2.6 millions ha (204 maps) from 2 Landsat5 scenes
Automated segmentation
Land cover classification: BCLCS level 4

22. LVI Implementation Plan
Planning and stakeholders consultation 2013
Sampling design and photo acquisition 2014
Photo interpretation and ground sampling 2015
Ecological classification 2015
kNN imputation, accuracy assessment, and data analysis 2016
Final databases and project completion 2017
The entire project starting 2014 and completed by 2017, 3 years plan
This year we will complete planning and stakeholders consultation
Which will help us to develop a sampling design
The photo acquisition starts in 2014, followed by interpretation and ground sampling in 2015
A separate ecological classification specifically for Cassiar is planned to be done in 2015, which will provide an ecologically meaningful stratification/classification for kNN imputation.
2006 will be the year for data analysis and processing
The project will be completed by March 2017 with completed spatial and attribute databases.

23. Discussion Operational issues/requirements
Comments, suggestions, questions
Any other issues?