|
|
Ministry of Environment Strategic Plan for the Use of Prescribed Fire to Restore Ecosystems in the Okanagan Region Submitted by: B.A. Blackwell and Associates Ltd. V7J 3B5 And R.W. Gray
Consulting Ltd. V2R 2N2 Submitted to: Robert Stewart Ecosystem Biologist V2A 7C8 March 2006 |
Executive Summary
The Okanagan Region represents one of the most biologically diverse
regions of the Province. Low levels of precipitation, hot summers and mild
winters provide a wide range of habitat for species that are unique to both
· Fire suppression has resulted in a change in fire regime (fire frequency and severity) throughout the region. Increasing tree densities and the resultant competition for moisture and nutrients have negatively impacted large areas of open grassland and forest. These changes have impacted forest health throughout the region.
· Habitats are shrinking, threatening and endangering many species. For example, only approximately 9% of the natural grasslands native to the region remain, due to roads, human development, and orchards.
· Introduction of exotic species threaten many of the native habitats.
· Overgrazing has also had a major impact on biodiversity by causing disturbance of soil and native vegetation, and by providing ideal conditions for the invasion and spread of exotic species.
While fire suppression is but one of many problems impacting biodiversity and ecosystem health in the region, it probably represents the single most important and spatially extensive issue that managers have the potential to impact. This report: 1) documents a rationale for prioritising prescribed burning as a restoration tool within the region; 2) uses GIS inventories and analysis tools to implement the rationale and spatially identify burn priorities, and; 3) outlines a five year plan to initiate the implementation of the project.
Prescribed burn priorities identified during a GIS analysis were field checked in several locations within the study area. For the most part, the results indicated that the algorithm developed to identify restoration priorities met the objectives of a coarse scale analysis. Following the field visit, identified treatment areas were summarized by priority within ownership categories, inside and outside of the Timber Harvesting Land Base (THLB), and by treatment complexity and associated costs.
Overall, a total of more than 36,000 ha have been classified as high priority for prescribed burning to facilitate restoration. However, much of this area (26,267 ha) is within the THLB on crown land and may be constrained by other land management objectives. Only 9,734 ha are outside of the THLB. Large areas, both within the THLB (334,152 ha) and outside the THLB (157,508 ha), have been classified as moderate priority for prescribed burning.
In addition to prescribed burn priority identification, the report addresses the development of a five-year burning plan, monitoring treated areas, and discusses treatment strategies.
Table of Contents
3.0 Historic Fire Regime and Fire Regime Departure Layers
4.1.1 Historic
Natural Fire Regime
4.1.2 Fire
Regime Condition Class
4.2 Treatment
Complexity Rating
4.2.1 Fire
Regime Condition Class
4.2.2 Wildland
Urban Interface
5.1 Field
checking the GIS analysis
5.2 Analysis
and Mapping Summaries
5.2.1 Distribution
of Historic Natural Fire Regimes within the study area.
5.2.2 Distribution
of Condition Class Within the Study Area
5.2.3 Distributions
of Prescribed Burn Priorities Within the Study Area.
5.2.4 Distribution
of Treatment Complexity Classes Within the Study Area
5.3.1 Setting
Appropriate Objectives
6.0 Monitoring Prescribed Fire
6.2 Vegetation
And Forest Structure Monitoring
6.3 General
Monitoring for all areas
6.3.1 Response
of red and blue listed plants
6.4 Monitoring
for specific areas
6.5.1 Douglas-fir
habitats (along slopes and crests of hills)
6.5.5 Aspen
copses, Aspen forest, Open Water, Vernal Ponds and Rock or Talus
6.5.7 Monitoring
for mule deer
6.5.8 Monitoring
for California bighorn sheep
7.0 Transition from Old Program Priorities to Newly Identified Priorities
List of Figures
Figure 2. View of the approximate study area looking
north (image sourced from Google EarthÔ)
Figure 5. Hillslope on the west side of Darke Lake.
Figure 6. Landscape view of the east side of Darke Lake.
Figure 7. Stand-level view of forest structure on the
east side of Darke Lake.
Figure 8. Historic natural fire regime within the study
area.
Figure 9. Condition class within the study area.
Figure 10. Treatment Priority Rating within the study
area.
Figure 11. Treatment Complexity Rating within the study
area.
List of Tables
Table
1. Rating scale matrix for input coverages to establish Treatment Rating
Priority.
Table 2. Final Treatment Priority Ratings
Table 3. Rating scale matrix for input coverages to
establish Treatment Complexity Ratings.
Table 4. Final Treatment Complexity Ratings
1.0 Introduction
Historically in
Currently, ER programs could be described as being “reactive” in nature with treatment units identified by immediate priority and funded out of whatever dollars can be cobbled together. Agency personnel assigned to the task of implementing ER do not have the time or resources for long-range planning, and become little more than contract administrators for a program carried out largely by outside consultants and contractors. The one exception to the rule in BC, at least when it comes to higher level strategic planning, is the Kootenay-Boundary Land-Use Plan (KBLUP), which has taken large steps towards developing a strategic ER program. The KBLUP used the assignment of natural disturbance type 4 (NDT4) ecosystems based on biogeoclimatic ecosystem (BEC) zones. This information was used to guide restoration activities within the management area. NDT4 ecosystems include grassland, shurbland and forested communities that generally experience frequent, low severity fire regimes. Unfortunately, this plan also suffers from many of the same strategic planning shortfalls experienced in other parts of the province. The KBLUP plan has identified, in a coarse way, the target ecosystems for restoration in the NDT4; this could be referred to as “top-down” direction. The “bottom-up” direction of the plan (the local-level, long-term juxtaposition of objectives) is missing. Individual projects are not explicitly linked but appear to be reactionary and “single-species” focused, funding is inconsistent, program monitoring is inadequate, and resources are insufficient. These are “strategic” issues with the plan though from both a spatial sense and an objective-attainment sense.
A solution to the strategic ER planning issue is the development of regional ER programs focused on managing for ecosystem resilience (Wade 1988, Walker 1994) as opposed to managing for a single species. These programs require long-term direction from strategic plans in order to develop activities that will meet long-term landscape level objectives. These programs also require dedicated personnel resources and operations funding. While the latter is not part of this project, addressing the former would certainly provide strong rationale for increased resources to implement the program.
The approach to strategic planning put
forward in this report comes from prior experience developing comprehensive ER
programs in both the US and BC. In the case of the
1. Purpose and Need for Ecosystem Restoration. This may come from pre-existing agency documents that have established goals and objectives for the region. This should include strong scientific rationale for the program (i.e., why, where, when and how). In the Squamish Forest District, extensive historic fire regime and stand structure studies were conducted to provide direction for the restoration program. These studies were designed to determine the historic or natural range of variation (RONV) and how it compared with current ecosystem structure and composition. Unfortunately, there are few Okanagan region studies of this nature; however, several surrogates do exist. Gray and Riccius (1999) in the Merritt area and Gray (2003) in the Sinlahekin Valley in Washington State provide some applicable data, while the coarse-scale historic natural fire regime (HNFR) and fire regime condition class (FRCC) models developed by Blackwell et al. (2003) provide further spatial direction. This foundation information is critical to enabling spatial prioritization of areas further along in the process. Defining the restoration program rationale is also critical for public education purposes. Glaring knowledge gaps in disturbance dynamics and ecosystem responses can be identified here and incorporated into program-level adaptive management and monitoring.
2. Data Collection, Collation, and Analysis. In this phase the gross area under management, past management activities, pre-existing and future management activities are investigated. Coarse- and fine-filter analysis systems (GIS-based) that will enable identification of future (5-year window) treatment priorities are built in this phase. The gross area of management needs to be reviewed in the context of biogeography (topography, physiography, vegetation characteristics), ownership (public, private), and primary land-use (timber harvesting, wildlife habitat). This becomes one of the key baseline layers for planning the feasibility and costs of various ER strategies. In addition, existing plans for future treatments are reviewed to see how they fit with the new model. There is insufficient funding in the system to discard prior work so every effort must be made to make some use of it. GIS-based filters and queries are used to help dial down to operational units once a hierarchical planning approach (agency goals, objectives and prioritization) has been developed. This stage incorporates the necessary environmental, ecological, and social values in a spatial context. Once individual treatment areas have been identified, further analysis is conducted to address treatment strategies, feasibility of success, risk, and cost. Issues such as down-wind smoke sensitive areas, adjacent area hazards (recent harvest/land clearing slash), adjacent area land-use conflicts (livestock vs native ungulates, etc.) are addressed here.
The Ministry of Environment (MOE) has not traditionally managed ecosystems.
They have managed for specific species and/or attributes within ecosystems but
they have not systematically planned for the management of ecosystem health
(defined as inherent natural diversity and resilience (Wade 1988, Walker 1994)
across their jurisdiction. Ecosystem management has been characterized by diffuse,
single-focus treatments within a general ecological framework. There is no
overarching strategy to manage all ecosystems in a resilient state starting
with those in the furthest departed condition.
The Ministry of
Forests and Range (MOFR) has not traditionally managed ecosystems either. They
have managed for a specific objective (i.e., timber and range) within
ecosystems but they have not systematically planned for the management of
ecosystem diversity and resilience across their jurisdiction. This has resulted
in a dominant objective being managed for in a diffuse way. Management focus is
scattered across the landscape in an attempt to answer to social and
environmental impacts. Ecosystem health issues abound, attesting to the
inadequacy of this approach.
The KBLUP plan could
be seen as a compromise strategy between the two. The MOFR emphasis is
de-emphasized to a certain degree (reduced stocking standards); ecosystem
health can be maintained through repeated disturbance to promote diversity and
resilience at the stand level. The MOE single species or habitat attribute
emphasis, unfortunately, still prevails as can be seen in how treatments are
prioritized. The most ecologically, socially and economically feasible areas
are not the first to be treated, instead the prioritization of treatment is
determined through the single-species/attribute approach. To make the KBLUP
truly successful, the strategy for ecosystem health should shift to a
prioritization based on landscape scale forest health factors and treatment
funding should compliment and encourage this shift. Although it would be
ecologically ideal to treat all areas that are departed from their natural
state, social and economic constraints mean that this approach may fail to
achieve landscape-level forest health objectives. With limited human and
financial recourses, it is arguably more efficient to prioritize treatments
first in areas that are less severely departed from their natural state. This
ensures that these ecosystems are maintained in a healthy state, potentially
enables more area to be treated and allows any remaining resources to be
directed to treating more departed ecosystems.
The approach of this
plan is to adopt a strategic direction focusing on landscape-scale ecosystem
health. While definitions of ecosystem health are prevalent throughout the
literature the central tenets are the creation and maintenance of naturally
diverse (meaning “native” species) and resilient (elasticity following
disturbance) ecosystems (Cairn 1988, Jordan 1988, Everett 1994, Kolb et al.
1994, DellaSala et al. 1995, Gayton 2001). This requires the ability to
quantify “health” in a GIS platform. In the dry forest and grassland ecosystems
of
2.0 Study Area
The study area
encompasses 2,966,765 ha of the Okanagan (Figure 1 and Figure 2). The study area boundary is based on the MOE’s
Region 8 (Okanagan) jurisdictional boundary. The boundary’s southern most edge
runs along the Canada-US border. The eastern boundary starts between
The study area falls
predominantly within the Southern Interior Forest Region (RSI). A small portion
of the area overlaps with the Coast Forest Region (RCO). Several forest
districts overlap with the study area. Within the RSI, these include the
Okanagan Shuswap District, the Arrow Boundary District and the Cascades Forest
District. A small portion of the RCO’s Chilliwack District overlaps the study
area’s southwestern corner.
The terrain and
climate within this area is highly diverse. The following biogeoclimatic (BEC)
zones define the study area:
· Interior Douglas-fir (861,560.7 ha [29.0%]);
· Englemann Spruce-Subalpine fir (705,588.0 ha [23.8%]);
· Interior Cedar Hemlock (560,864.5 ha [18.9%]);
· Montane Spruce (559,649.7 ha [18.9%]);
· Ponderosa Pine (128,591.2 ha [4.3 %])
· Alpine Tundra (79,750.5 ha [2.7%]);
· Bunchgrass (61,938.9 ha [2.1%]); and
· Coastal Western Hemlock (8,821.2 ha [0.3%]).
The study area contains features that were
formed by uplifting of the earth’s crust between one and two million years ago.
The mountainous areas are made up of folded sedimentary and metamorphic rock,
and form part of the Columbia Mountain Chain (consisting of the
The network of lakes and rivers left behind
when the glaciers retreated influences the local climate. Because of the
influence of these water-bodies on air currents, the
Figure 1. Study Area Boundary
Figure 2. View of the approximate study area looking north (image sourced from Google EarthÔ)
3.0 Historic Fire Regime and Fire Regime Departure Layers
The model outputs for
Historic Natural Fire Regime (HNFR) and Fire Regime Condition Class (FRCC)
(Blackwell et al. 2003) incorporate the best available knowledge of
pre-settlement fire history and the social and ecological consequences of
departures in those fire regimes. Data used in the HNFR model includes top-down
controls (regional climate), bottom-up controls (local vegetation
classification, topography, and physiography), and process metrics from
regional stand-level fire history studies. The FRCC model includes stand-level
structural elements that impact fire behavior and fire regime characteristics
and provides a qualitative assessment of potential fire-related consequences. At
their core, these two models assess risk as probability (HNFR) and consequence
(FRCC). Risk analysis should form the foundation of any ER program
prioritization strategy. Probability and consequence can be configured in any
number of ways depending on the management objectives for the land manager. In
the case of the Ministry of Environment, a strong program emphasis is placed on
managing ecosystem sustainability, diversity, and above all resilience.
The investigation of
RONV provides us with the capacity to predict ecosystem component adaptability
to the natural disturbance regime. Following a natural disturbance, the
ecosystem components that are adapted to the disturbance should exhibit
resilience. If they do not respond, it is likely that the characteristics of
the disturbance regime (timing, intensity, etc.) have exceeded RONV (Meyer et
al. 2005).
Why is RONV important?
As a society and as professionals managing on behalf of society, we need to be
able to confidently predict the impacts of our actions and the consequences of
natural disturbances. The RONV condition provides us with a documented set of
parameters based on stand and landscape patterns resulting from disturbance
processes. If we choose to manage conditions outside RONV it is our responsibility
to predict outcomes and inform managers of potential consequences.
4.0 Methods
There were three steps
undertaken to identify and prioritize unconstrained areas for ecological
restoration treatment (Figure 3):
1.
Develop a
Priority Treatment Rating using condition class, historic natural fire regime
and forest health factors.
2.
Develop a
Treatment Complexity Rating using wildland urban interface and condition class.
3.
Combine
the Treatment Complexity Rating, Timber Harvesting Land Base (THLB) and
Ownership to delineate the least constrained High Priority Treatment areas.
Figure 3. Steps undertaken to identify and prioritize
unconstrained areas for ecological restoration treatment.
4.1 Treatment Priority Rating
4.1.1 Historic Natural Fire Regime
The HNFR layer is a
combination of known and predicted fire regime metrics by BEC subzone plus
topographic variables that affect fire behavior. A detailed description of how
the HNFRs were constructed can be found in Blackwell et al. 2003. In the
predictive model HNFR is ranked according to flammability of the dry forest
types, which include the Ponderosa Pine and Interior Douglas-fir BEC zones. The
most flammable fire regime is HNFR I which includes low elevation, dry
ponderosa pine and Douglas-fir forests with grass and/or timber litter as the
primary carrier of surface fire. Upslope and on cool aspects at low elevation
from HNFR I is the HNFR II fire regimes which are characterized by slightly
cooler and moister conditions than HNFR I. Fuelbeds in HNFR II range from grass
and timber litter to compacted duff and moss. Further upslope and on cooler
aspects is HNFR IV which has a shorter fire season than HNFR I. Fuels here
typically ranged from compacted timber litter to heavy loading of timber litter
and dead downed trees.
4.1.2 Fire Regime Condition Class
FRCC is a quantitative
measure of fire regime departure from historic conditions. The more fire is
removed from a fire regime the more the fire regime changes. This modeling
system has as its basis the supposition that fire will eventually return to the
fire regime. The more removed fire is, combined with the more frequent fire
used to be within the system, the greater the environmental impact. This
supposition is in marked contrast to the opinions of some professionals that
once an historic fire regime has changed there is little value in considering a
move back to historic trends. This professional opinion has its basis in the
opinion that fire will not return to the system. The use of this model holds
its value in presenting the range of consequences that could occur once fire
returns to the system. Once again, the reader is encouraged to review Blackwell
et al. 2003 for a detailed description of FRCC.
Applying in a
predictive model FRCC can be used to derive two different metrics: a)
time-since-disturbance, or b) complexity of treatment. Time-since-disturbance
is a qualitative indicator of structural changes to the ecosystem due to the
cessation of fire (i.e., increased fuel loading, increased tree density,
increased canopy closure, etc.). In Blackwell et al. (2003) FRCC is
ranked from furthest departed (longest time-since-disturbance) to least
departed. This ranking would identify sites with fuel load, tree density, etc.,
as the highest priority for treatment.
FRCC can also be used
to gauge complexity of treatment, with complexity defined as both complex
objectives, and high unit treatment cost. Canopy closure from forest cover
inventories is an integral component of FRCC determination. FRCC 1 to 3
indicate increasing time-since-disturbance and increasing canopy closure (i.e.,
increasing stand density). For example, a stand classified as FRCC 3 in an HNFR
I fire regime has missed numerous surface fires and has likely experienced an
increase in canopy closure. Ecologically, this may be the most appropriate
stand to treat first within the framework of a prioritization model. Obviously
with fuels and stand structure significantly departed from RONV these stands
are at high risk to adverse impacts from wildfire and/or insects and diseases.
Stand-level biodiversity and resilience would be impaired should a natural
disturbance occur. However, several issues make this approach difficult to
implement. A limited restoration program budget is one significant issue but a
greater issue is the need to conduct very intensive pre-burning fuel
modification treatments.
The very issues that
make FRCC 3 stands good candidates to place at the top of prioritization make
them very complex candidates to restore. High stand density, high fuel load,
and high canopy closure make these sites very difficult and expensive to treat
with prescribed fire alone. In our prioritization model, we have reversed the
ranking order for FRCC and have identified FRCC 1 stands as the priority. This
is from the perspective of relative complexity of treatment objectives and
cost. With a limited annual budget and a small, developing burn program it was
felt by the project team that this was a more appropriate approach for this
project.
Another ideological
reason for taking this approach is to treat FRCC 1 sites as a preventative
measure, thus holding FRCC 1 sites from sliding into FRCC 2. This approach is
being widely debated amongst our colleagues in the
4.1.3
Forest Health Factors
Since 1999, the MOFR
has carried out aerial overview flights across the majority of forested land in
BC to survey forest health (JCH Forest Pest Management 2002). The survey data
has been used as a key source to document the Mountain Pine Beetle outbreak and
a number of other important forest health agents in BC. The data is composed of
pest infestation polygons and points each assigned a specific Forest Health
Factor (pest code, damage agent or condition code).
The entire length of
record (1999 to 2004) was used in this project. All point infestations were
buffered by 100 m. This buffered coverage was then merged with the polygon
infestation coverage to create a Forest Health Factor (FHF) coverage for the
entire Okanagan Region.
4.1.4 GIS Processing
Each input coverage
(FRCC, HNFR and FHF) was converted into 25 x 25 m raster cells and assigned
ratings according to Table 1. Individual ratings for each raster cell were
developed on 0 to 10 scales.
Table 1. Rating scale matrix for input coverages to
establish Treatment Rating Priority.
|
Input Coverages |
Rating |
|
|
Fire Regime Condition Class |
1 |
10 |
|
2 |
5 |
|
|
3 |
0 |
|
|
None |
0 |
|
|
Historic Natural Fire Regime |
I |
10 |
|
II |
8 |
|
|
IV |
5 |
|
|
Others/None |
0 |
|
|
|
Present |
10 |
|
Absent |
0 |
|
To create the Treatment Priority Rating, the rating for each raster cell was
calculated as a weighted sum of the three input coverages as per the following
equation:
(FRCC*0.33) + (HNFR*0.33) + (FHF*0.33) =
Treatment Priority Rating
Table 2 shows final Treatment Priority Ratings.
Table 2. Final
Treatment Priority Ratings
|
Rating |
Priority |
|
>/= 7.5 |
High |
|
>/= 5.0 < 7.5 |
Moderate |
|
>/= 2.5 < 5.0 |
Low |
|
0 < 2.5 |
Very Low |
4.2 Treatment Complexity Rating
4.2.1 Fire Regime Condition Class
FRCC 3 stands are
considered the most difficult and costly to treat. See section 4.1.1 for a overview discussion of this issue.
4.2.2 Wildland Urban Interface
The Wildland Urban
Interface (WUI) was used as an indicator of proximity to inhabited areas and
was based on structure density from TRIM. All anthological building features
were extracted from this data set and buffered such that structural classes
could be assigned based on their density on the landscape.
Interface density
classes were delineated as follows:
Undeveloped
0-1 structures/km2
Isolated
=1-10 structures/km2
Mixed
= 10-100 structures/km2
Developed
= 100-1000 structures/km2
Urban
= > 1000 structures/km2
All WUI categories,
except undeveloped, were buffered by 1 km. Difficulty to treat increases (i.e,
cost, emissions mitigation, etc.) with increasing proximity to WUI and
increasing WUI density class.
4.2.3 GIS Processing
Each input coverage
(FRCC and WUI) was converted into 25 x 25 m raster cells and assigned ratings
according to Table 3. Individual ratings for each raster cell were
developed on 0 to 10 scales.
Table 3. Rating
scale matrix for input coverages to establish Treatment Complexity Ratings.
|
Input Coverages |
Rating |
|
|
Fire Regime Condition Class |
3 |
10 |
|
2 |
8 |
|
|
1 |
5 |
|
|
None |
0 |
|
|
Wildland Urban Interface |
Urban |
10 |
|
Developed |
9 |
|
|
Mixed |
7 |
|
|
Isolated |
5 |
|
|
Buffer 1km |
5 |
|
|
Underdeveloped/None |
0 |
|
To create the Treatment Complexity Rating, the rating for each raster cell was
calculated as a weighted sum of the two input coverages as per the following
equation:
(FRCC*0.5) + (WUI*0.5) = Treatment Complexity
Rating
Table 4 shows final Treatment Complexity Ratings.
Table 4. Final
Treatment Complexity Ratings
|
Rating |
Priority |
|
>/= 7.5 |
High |
|
>/= 5.0 < 7.5 |
Moderate |
|
>/= 2.5 < 5.0 |
Low |
|
0 < 2.5 |
Very Low |
4.3 Constrained Area
The Timber Harvesting
Land Base, Ownership and Treatment Complexity result were all overlaid
spatially to net out the unconstrained High Priority Treatment areas.
5.0 Results and Discussion
5.1 Field checking the GIS analysis
Once the initial
mapping results were completed in GIS, map outputs of selected areas were
created to check the results.
Figure 4. Section of
As is described in the introduction and
methodology sections, we began with two layers: HNFR and FRCC. This area is in
FRCC 3, the most departed condition and has stand-level fuels and tree density
conditions similar to those pictured in Figure
5. This image is of the landscape at “Photo A” in Figure 4. Tree density is high and there is a high incidence of
dead trees in the canopy. It was not possible to observe surface fuels due to
snow conditions. The addition of Forest Health Factors, the black points and
polygons in Figure 4, strengthens the case for placing these areas on the
prioritization list. It is interesting to note that this area is designated as
both an OGMA and
UWR, which appears to be contradictory to sound biodiversity management
considering its current condition. Current stand structure is significantly
departed from historic structure and is jeopardizing the resilience of historic
“old forest” structures. Land-use designations such as OGMA and UWR did not
enter our prioritization model as planning elements for reasons that became
apparent in this
Figure 5. Hillslope on the west
side of
The modeling combination of HNFR, FRCC, and
FHF would suggest that the west side of
Several factors make the west side of
The second contributor to complexity on the
west side of
The east side of Darke Lake, Photo points B (Figure 6) and C (Figure 7), is modeled as FRCC 1, meaning it is least departed from historic conditions. From a landscape perspective (Figure 6) the forest is more open than the opposite side of the valley. At the stand-level (Figure 7) the rate of forest ingrowth becomes apparent.
Figure 6. Landscape view of the
east side of
Figure 7. Stand-level view of
forest structure on the east side of
This area, as a candidate for restoration, has definite advantages over the
west side forests. Project complexity is reduced by the forest structure and
fuelbed characteristics. A light spring burn, or even a fall burn, is possible
here whereas on the west side of the valley only a fall burn would be feasible
(due to aspect and fuelbed moisture). It would still be advisable to do some
fuel modification prior to prescribed burning on a site such as this to gain a
higher precision in meeting burn objectives (5.3.1 Setting
Appropriate Objectives). Project cost in this situation would be greatly
reduced compared with those across the valley but would still be high due to
proximity to the WUI.
5.2 Analysis and Mapping Summaries
5.2.1 Distribution of Historic Natural Fire Regimes within the study area
Within the total study area of 2,966,765
ha, there were 850,000 ha (29%) assigned to a Historic
Table 5. Area summary of ownership and historic natural fire regimes within and outside of timber harvesting land base for the study area.
|
|
HNFR |
|||
|
|
I |
II |
IV |
Other |
|
Inside THLB |
ha (%) |
ha (%) |
ha (%) |
ha (%) |
|
Federal Ownership |
0 |
0 |
0 |
0 |
|
Provincial Ownership |
343,025 (22) |
622,194 (39) |
455,791 (29) |
172,416 (11) |
|
Private Ownership |
0 |
0 |
0 |
0 |
|
No Data |
0 |
0 |
0 |
0 |
|
|
|
|
|
|
|
Outside THLB |
|
|
|
|
|
Federal Ownership |
27,306 (2) |
7,423 (1) |
1,442 (0) |
28,001 (2) |
|
Provincial Ownership |
210,036 (15) |
202,496 (15) |
213,424 (16) |
336,208 (25) |
|
Private Ownership |
211,878 (16) |
26,011 (2) |
14,850 (1) |
84,364 (6) |
|
No Data |
55 (0) |
32 (0) |
106 (0) |
157 (0) |
|
|
|
|
|
|
|
Ownership Totals |
|
|
|
|
|
Federal Ownership |
27,306 (1) |
7,423 (0) |
1,442 (0) |
28,001 (1) |
|
Provincial Ownership |
553,061 (19) |
824,690 (28) |
669,215 (23) |
508,623 (17) |
|
Private Ownership |
211,878 (7) |
26,011 (1) |
14,850 (1) |
84,364 (3) |
|
No Data |
55 (0) |
32 (0) |
106 (0) |
157 (0) |
Figure 8. Historic natural fire regime within the study area.
5.2.2 Distribution of Condition Class Within the Study Area
As discussed early in the report condition
class provides a good measure of ecos
|
|
Condition
Class |
|||
|
|
1 |
2 |
3 |
Other |
|
|
ha
(%) |
ha
(%) |
ha
(%) |
ha
(%) |
|
Inside
THLB |
|
|
|
|
|
Federal
Ownership |
0 |
0 |
0 |
0 |
|
Provincial
Ownership |
606,142 (38) |
359,321 (23) |
619,182 (39) |
8,781 (1) |
|
Private
Ownership |
0 |
0 |
0 |
0 |
|
No Data |
0 |
0 |
0 |
0 |
|
|
|
|
|
|
|
Outside
THLB |
|
|
|
|
|
Federal
Ownership |
40,624 (3) |
3,275 (0) |
8,371 (1) |
11,902 (1) |
|
Provincial
Ownership |
352,033 (26) |
115,229 (8) |
184,328 (14) |
310,573 (23) |
|
Private
Ownership |
141,625 (10) |
14,180 (1) |
62,169 (5) |
119,128 (9) |
|
No Data |
55 (0) |
21 (0) |
27 (0) |
247 (0) |
|
|
|
|
|
|
|
Ownership
Totals |
|
|
|
|
|
Federal
Ownership |
40,624 (1) |
3,275 (0) |
8,371 (0) |
11,902 (0) |
|
Provincial
Ownership |
958,175 (32) |
474,550 (16) |
803,510 (27) |
319,354 (11) |
|
Private
Ownership |
141,625 (5) |
14,180 (0) |
62,169 (2) |
119,128 (4) |
|
No Data |
55 (0) |
21 (0) |
27 (0) |
247 (0) |
Table 6. Area summary of ownership and condition class within and outside of the timber harvesting land base for the study area.
and Figure 9). The total area for all ownership categories in Condition Class 3 was larger (874,077 ha – 29%) when compared with Condition Class 2 (492,026 ha – 16%). Outside of the THLB, where the MOE has greater responsibilities the provincially owned area within condition class 2 and 3 was 115,229 ha (8%) and 184,328 ha (14%) respectively. The provincially managed area was significantly greater within the THLB where condition class 2 and 3 accounted for 359,321 ha (23%) and 619,182 ha (39%) of the total THLB.