BC Wetland Trends Project

Transcription

CANADIAN INTERMOUNTAIN JOINT VENTURE
BC Wetland Trends Project:
Okanagan Valley Assessment
October 2013
Authors:
1. Bruce Harrison, Ducks Unlimited Canada.
Email: b_harrison@ducks.ca, phone: 250-374-8307 ext. 238
954A Laval Crescent, Kamloops, British Columbia, Canada,
V2C 5P5
2. Kathleen Moore, Environment Canada
Contributors:
Bill Tedford, Ducks Unlimited Canada
Dan Buffett, Ducks Unlimited Canada
Jenna Cook, Ducks Unlimited Canada/The Nature Trust of BC
Tammy Tam, Ducks Unlimited Canada
Tasha Sargent, Environment Canada
Jan Kirkby, Environment Canada
Josh Vest, Intermountain West Joint Venture
Executive Summary
Conservation organizations in British Columbia have long acknowledged the lack of wetland
habitat tracking as a serious deficiency in conserving wetlands at ecoregional scales in a
changing climate. To help address this, in 2010 a group of partners, including the Canadian
Intermountain Joint Venture (CIJV), came together on a multi-year initiative, the BC Wetland
Trends Project, to assess wetland trends and develop an approach for future monitoring. One of
their first steps was to commission a report to recommend opportunities for tracking wetlands in
BC. Several approaches were discussed involving different combinations of data and scales
(spatial and temporal) at four different locations, and one of those options was selected for a trial
in a transboundary region shared by the neighbouring Intermountain West Joint Venture (IWJV).
The IWJV partners were interested in a joint Canada-US project which could build on their
science foundation and provide methodological insight for the development of biological
planning tools on the US side of the border. Funding was acquired via a grant from the Great
Northern Landscape Conservation Cooperative which includes the CIJV and IWJV within its
boundaries.
The methodology to be tested involved the use of remote sensing and existing landcover
classifications to assess whether current wetland occurrence has changed in comparison to a
historic baseline from approximately 20 years ago. Principal objectives were to provide a 20year trend assessment for the chosen area, and to recommended procedures and logistics for
delivering a landscape-scale operational wetland tracking project which may be applied on either
side of the international border.
We selected the South Okanagan, a transborder valley in the Cold Deserts ecoregion, as our
study area. Our general approach was to compare remotely sensed images at time T2 (roughly
present day) to a wetland baseline generated at time T1 (1980s). We were confident that during
this time period the Okanagan had experienced a high degree of wetland change involving both
outright conversion and smaller incremental losses. For the wetland baseline, we considered a
number of datasets and ultimately selected 1:20,000 Terrain Resource Inventory Mapping
(TRIM) due to its relatively fine spatial resolution, geographic coverage (provincial scale) and
free availability for the entire province. Derived from 1988 aerial photography, TRIM includes
‘wetlands’, ‘lakes’ and ‘manmade’ polygon layers, which we combined into a single ‘wetland’
layer covering 12,755 hectares. A large proportion (57%) of wetlands in the study area were
under half a hectare in size. After consideration of several types of remotely-sensed imagery, we
selected orthorectified SPOT5 imagery from fall 2010, with 10-metre resolution colour and 5metre resolution panchromatic imagery, and a minimum mapping unit of 0.1 ha. Our metrics of
interest were the areal extent of wetlands and the number of wetlands.
We used eCognition software to conduct the analyses. TRIM wetland polygons were brought
into a project as the T1 wetland footprint, and SPOT5 images were segmented to build objects
for the T2 assessment involving Classification and Regression Tree (CART) analyses. Field
evaluations of current wetland conditions were undertaken at several dozen locations to assist in
imagery classification training. The associations between the sampled wetland objects and their
spectral signatures in the SPOT image were extrapolated to all other wetlands in the study area to
generate a final status classification of every wetland at time T2, and this became our ‘change
BC Wetland Trends: Okanagan Valley Assessment
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detection’. We evaluated change at the whole-wetland scale, and consequently could not
evaluate whether a wetland had decreased in size during the T1 to T2 time period; it was either
lost entirely, or it was intact. However, we believe this simplification enabled us to achieve a
higher degree of accuracy.
We detected a significant degree of wetland conversion and loss among freshwater wetlands in
the Okanagan over a 22-year period. Sixty-two percent of the wetlands and lakes in the study
area were categorized as ‘intact’, whereas 38 percent were categorized as ‘converted’ or
impacted in some way. This high degree of impact within a 22-year time period is tempered
somewhat by uncertainties in the wetland baseline, but we are still confident that impacted
wetlands represent 30% of the entire TRIM inventory, for an extrapolated impact rate of 1.4%
per year. Smaller wetland size classes (under 5 hectares) were disproportionately affected, since
more than half were impacted, compared to a third of the larger size classes. There is no reason
to believe that this loss rate has ended or even declined since 2010, and given the importance of
temporary or small wetlands for biodiversity in the Okanagan, this trend is alarming.
Overall the analyses performed on the SPOT imagery produced encouraging results, as there
were distinct spectral breaks between most of the various wetland conditions, and we have a
relatively high level of confidence in the ability of the software to accurately classify wetlands in
the Okanagan. If this separability proves consistent in other areas, then the object-oriented
segmentation and CART model development may prove sufficient for achieving our objective of
expanding and operationalizing the project. However, the quality of baseline inventories will
continue to be a concern.
We considered a number of errors which we believed might have significant effects, including
errors in the baseline, the satellite imagery, and the classification procedure. We approached
classification as an iterative process: after the baseline was selected, a preliminary round of
training was done with field data, and information was fed into the classification procedure, then
reviewed. Additional field assessments helped to identify errors and calibrate the system. We
compared the TRIM water polygons to the actual historic aerial imagery which was used during
the TRIM digitization process. Of the 66 sites for which we could acquire the historic
photography, 70% were deemed ‘accurate’, 12% were inconclusive, and only 8% were deemed
inaccurate. Overall, we judged that TRIM has its weaknesses, but it is still the best potential
baseline in most areas of BC and can be useful if limitations are acknowledged up-front. We
also measured errors in the classification procedure by comparing the final classified results to a
set of field ground-truthed reference sites, and found an overall classification accuracy of 87%.
Our results suggest that agriculture played a major role in wetland conversion, via drainage or
water extraction for hay production. Crop and pasture-hay classes together accounted for almost
1/5 of the total converted wetland area, and they likely contributed significantly to another 2/5 of
the converted area. Urban, commercial or industrial development accounted for 5% of the
converted area. Although climate change is believed to the most important wetland pressure
currently acting in the Okanagan (and an over-arching factor which influences other drivers), our
methods were insufficient to confidently quantify its role. We offer recommendations for
refining methods to enable this level of inference.
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Recommendations include: 1) undertaking a similar tracking project using existing datasets in a
second (larger) priority area, 2) establishing a more reliable baseline inventory that can be used
to track future trends elsewhere in the province at a cost-effective scale, 3) further investigating
the role of climate change via sub-wetland segmentation in a trial location, 4) allocating
resources to outreach efforts to enable broad communication of the results, and 5) continuing to
encourage new partnerships to help deliver the project at a wider scale.
At the onset of this project, we committed to informing other landscape-level partners as to
methods and trends applicable to cross-boundary ecoregional management of wetlands in
Canada and the USA. Expansion of the tracking approach to cross-border areas will require
further consultation and information transfer with US partners, but we believe this project
already represents a step forward in coordinating our conservation delivery for the benefit of
similar ecological communities.
Finally, since 2008, the partners have also been working with researchers at the University of BC
to predict how climate change may affect future wetland conditions by downscaling climate data
for regional wetland vulnerability assessments in the BC Interior By establishing a solid
baseline for future trend analysis, the BC Wetland Trends project could help validate those
climate predictions and inform the development of climate adaptation strategies for priority
species on both sides of the border.
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Table of Contents
Executive Summary ........................................................................................................................ ii
List of Tables ................................................................................................................................. vi
List of Figures ................................................................................................................................ vi
1.0 Introduction ................................................................................................................................1
1.1 Background / Project Explanation .........................................................................................1
1.2 Goals and Objectives .............................................................................................................3
2.0 Methods......................................................................................................................................4
2.1 Study Area and Wetland Baseline .........................................................................................4
2.2 Satellite Imagery ..................................................................................................................10
2.3 Detecting Wetland Change ..................................................................................................11
2.4 Accuracy Assessment ..........................................................................................................13
3.0 Results ......................................................................................................................................15
3.1 Change Detection .................................................................................................................15
3.2 Accuracy and Error ..............................................................................................................19
4.0 Discussion ................................................................................................................................23
4.1 Interpretation of Change ......................................................................................................23
4.2 Wetland Baseline .................................................................................................................24
4.3 Wetland Stressors.................................................................................................................26
4.4 Sub-wetland Segmentation ..................................................................................................30
5.0 Conclusions ..............................................................................................................................31
6.0 Recommendations ....................................................................................................................32
7.0 Literature Cited ........................................................................................................................34
Appendix A: Major Datasets Considered for Use as Wetland Baseline .......................................36
Appendix B: TRIM Digitizing Methodology ...............................................................................38
Appendix C: Spectrographic Characteristics of Field Assessment Sites ......................................39
Appendix D: Accuracy Assessments of Field Assessment Sites ..................................................42
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List of Tables
Table 1.
Table 2.
Table 3.
Table 4.
Dominant wetland types and pressures in Southeast Kootenay region of BC .............5
TRIM wetland and lake polygons separated into nine different wetland
categories ................................................................................................................... 17
TRIM wetland and lake polygons separated into nine different wetland
categories by wetland/lake size class. ....................................................................... 18
Comparison of the agreement between the eCognition T2 wetland
classifications and their corresponding field assessments ......................................... 22
List of Figures
Figure 1.
Location of the CIJV and IWJV in relation to the GNLCC and BCRs 9 and
10. .................................................................................................................................2
Figure 2. Transboundary Ecological Planning Units of the CIJV and IWJV. .............................3
Figure 3. Example comparison of TRIM wetlands (green) and lakes (blue) versus VRI
(red) in Southeast Kootenay region. .............................................................................6
Figure 4. Example of TRIM wetlands (light blue) and lakes (dark blue) in close
proximity. .....................................................................................................................7
Figure 5. Example of variation in wetland conditions within the potential TRIM
‘baseline time period’ in the East Kootenays. ..............................................................7
Figure 6. South Okanagan Priority Wetland Area .......................................................................8
Figure 7. SOK TRIM inventory showing the size percentage distribution of features ...............9
Figure 8. Maps showing changes in the water birch – red-osier dogwood riparian shrub
swamp wetland (BD) ecosystem between 1800 and 2005 ...........................................9
Figure 9. Area of purchased SPOT coverage in the South Okanagan .......................................10
Figure 10. Schematic overview of change detection process ......................................................12
Figure 11. Spectral characteristics of the six wetland sites which fell in Terminal Node 4. .......15
Figure 12. The CART decision tree model showing the nine terminal nodes as defined
by the 56 sample sites selected for this study. ............................................................16
Figure 13a. Example where TRIM polygon boundaries appear accurate. .....................................19
Figure 13b. Example where the accuracy of TRIM polygon boundaries is unclear......................20
Figure 13c. Example where no polygon exists and it is unclear whether a wetland was
present.........................................................................................................................20
Figure 13d. Example where no polygon exists although a wetland was present. ..........................20
Figure 14. Wetland threats in Okanagan.......................................................................................26
Figure 15. Dry wetland in the southern BC Interior, which may be an indication of
climate change. ...........................................................................................................27
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Figure 16a. Wetland converted to intensive agriculture (annual cropping)...................................28
Figure 16b. Wetlands lost to urban development. .........................................................................28
Figure 16c. Wetland mostly drained for hay production. ..............................................................28
Figure 17. An example of a wetland impact used in the monitoring evaluation. ........................29
Figure 18. Example of a recent aerial photo (left) and a sub-segmented 2010 SPOT5
image (right) ...............................................................................................................30
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1.0 Introduction
1.1 Background / Project Explanation
As land use and climate change alter the British Columbia (BC) landscape, many conservation
organizations in the province have recognized the absence of a clearly defined process to monitor
the status of BC’s wetlands. Without knowing where wetland losses and degradation are
occurring or the causes of such losses, it is difficult to determine which conservation tools would
be most effective in protecting or restoring wetlands and where to set priorities. To help address
this, a group of conservation partners came together in 2010 to develop a wetland tracking
initiative for BC.
The partners included the Canadian Intermountain Joint Venture (CIJV) and Pacific Coast Joint
Venture (PCJV), which are Habitat Joint Ventures created under the North American Waterfowl
Management Plan (NAWMP). Both JVs have identified the lack of wetland habitat tracking as a
serious deficiency in conserving wetlands at ecoregional scales and allowing for strategic
conservation planning in a changing climate. In 2010 the partners embarked on a multi-year
project to deliver an assessment of wetland trends and develop an approach for tracking wetland
trends into the future.
A report was commissioned in 2011 by Ducks Unlimited Canada and the Canadian Wildlife
Service to assemble and synthesize the available information into a set of ‘best options’ for
tracking the status of BC’s wetlands through time (Carver and McKenzie 2011). The report
outlined a number of study design questions, including which wetland metrics are most
appropriate at different scales and are most relevant for waterbird modeling, and whether to
collect new data or acquire existing information. Methodological options included using aerial
photography, single-sensor satellite (e.g. RadarSat), multi-sensor infrared and visible imaging
(e.g. LandSat, SPOT), and existing site-specific or province-wide landcover classifications. A
significant consideration in the report was how to address the lack of a single, standardized
wetland inventory for British Columbia on which a wetland tracking program could be based.
The report included recommendations to test four options involving different combinations of
data and scales (spatial and temporal), at four different locations. This report details the results
from testing one of these options.
During this time, there was an opportunity to collaborate with the Intermountain West Joint
Venture (IWJV), on a project involving transboundary wetlands and climate change. The IWJV
and CIJV had long identified the need to collaborate to inform and achieve shared conservation
priorities, given their similar ecological characteristics and priority species. Funding was
available via the Great Northern Landscape Conservation Cooperative (GNLCC), a US-led
landscape conservation initiative that includes the CIJV and IWJV within its boundaries. The
IWJV and the CIJV together encompass more than 98% of the GNLCC (Figure 1), as well as
large portions of Bird Conservation Regions (BCRs) 9 and 10. Ducks Unlimited Canada
successfully applied for funding in 2011 on behalf of the partners, and used the grant to test one
of the options in southern BC, including an assessment of wetland trends and exploration of
methodological options to operationalize the project at a broader scale on both sides of the
international border.
1
Figure 1. Location of the CIJV and IWJV in relation to the GNLCC and BCRs 9 and 10.
The option to be tested involved using SPOT5 satellite imagery to assess whether current
wetland occurrence has changed in comparison to a historic baseline from approximately 20
years ago. SPOT is medium to high-resolution optical imaging, specifically designed to assist
with detecting and forecasting changes in the environment due to human and natural
disturbances. It has been successfully used to classify and map wetlands elsewhere (e.g. Grenier
et al 2008, Poulin et al 2010). We were interested in using SPOT satellite imagery due to its
historic availability (dating back to the mid-1980s in many areas of BC) and its likely future
continuation. It also struck the best balance in terms of precision and cost, which would be
important considerations in operationalizing the method in other priority regions in the future.
The method was suitable for evaluating the pressures bearing on small and ephemeral wetlands
with a cost that was low enough to apply the method over larger areas (Carver and McKenzie
2011).
We selected the East Kootenay region of British Columbia as a location based on the availability
of local datasets (vegetation, soils and basemaps), the similarity of wetlands on both sides of the
border, and because it is a CIJV Priority Wetland Area. The region is located in BCR 10 and the
Western Cordillera Ecoregion (Figure 2).
2
East
Kootenay
Region
Figure 2. Transboundary Ecological Planning Units of the CIJV and IWJV.
The pilot project was led by Ducks Unlimited Canada (DUC) and the Canadian Wildlife Service
(CWS). DUC’s remote sensing team in Winnipeg had significant prior experience with this type
of work through their involvement with the Canadian Wetland Inventory project 1 where some
parts of Canada had wetland inventories developed through remote sensing. The IWJV
identified this project as an important component to further developing their science foundation
and informing their conservation design endeavors in the Western Cordillera Ecoregion.
Waterfowl cross the border during northward and southward migration to such a degree that the
wetlands on both sides of the border have been designated as Continental Priorities by the North
American Waterfowl Management Plan. Methodological recommendations will provide IWJV
science staff with insight to support the internal development of biological planning tools for the
US side of the border.
1.2 Goals and Objectives
While this project provided a 20-year trend assessment for the chosen area, the principal goal
was to evaluate the proposed wetland tracking approach, identify constraints, and provide
recommended procedures and logistics to deliver a landscape scale operational wetland tracking
1
http://maps.ducks.ca/cwi/
3
project that can be applied to both sides of the international border as well as in other areas of
BC. Assessments of wetland trends can inform conservation planning tools such as priority
waterfowl and waterbird decision support models, and help validate the predictions of climate
change models.
Specific objectives included:
• Evaluating the use of medium-scale satellite imagery (SPOT5) and available existing
landcover mapping datasets to assess change in the distribution and areal extent of
freshwater, non-floodplain wetlands within the pilot area.
• Evaluating the ability of SPOT5 to identify impacts on wetlands from climate change,
agriculture and urban development.
• Quantifying trends over a 20-year period in the pilot area.
• Documenting the procedure.
• Evaluating whether (or to what extent) this procedure may be transferable to other areas
with similar datasets.
2.0 Methods
The project used remote sensing and existing landcover classifications to assess whether current
wetland distribution has changed in comparison to a historic wetland baseline.
Key steps included:
1. Selection of the study area and preparation of the wetland baseline, which included
assessing the available inventories and assembling a library of digital datasets.
2. Selection, acquisition and preparation of satellite imagery.
3. Development of the methodology, including image transformation and pattern
recognition (e.g. classification of the imagery to identify wetlands).
4. Conducting the wetland change analysis.
5. Collection of field reference information from several wetlands in study area.
6. Assessment of the accuracy and measurement of error.
7. Reporting and ongoing outreach.
2.1 Study Area and Wetland Baseline
We originally chose the Cranbrook-Kimberley region of the East Kootenays in southeastern BC.
This area is characterized by broad valley bottoms and steep mountain ranges. The wetlands are
typically broad wetland complexes connected to the main channels of the Columbia and
Kootenay rivers, along with small isolated wetlands on the adjacent river benchlands. The
predominant wetland types and threats for this area are described in Table 1 (Carver and
McKenzie 2011).
4
Table 1. Dominant wetland types and pressures in Southeast Kootenay region of BC.
Dominant Wetland Types
Wetland Class
1
1. Marsh
2. Shallow water
Hydrogeomorphic
2
Classification
Wetland Cover Type
System - Subsystem 1
Element (Form - Subform )
1
General-Specific
Palustrine – Basin –Closed
(Isolated), Overflow
(Discharge), Linked, Terminal
Lacustrine–(as for Palustrine)
Graminoid - tall rush,
low rush/grass/sedge/
forb
Palustrine – Basin –Closed
Aquatic - submerged,
floating
Palustrine – Basin – Closed
Graminoid -grass/
sedge /forb
4. Fen (less
common)
Palustrine - Basin
Lacustrine - Basin
Shrub – low/mix shrub
Graminoid – sedge/low
rush/grass /forb
1. Climate change
2. Agriculture – cattle
grazing
3. Recreation
Non-vegetated
3
3. Wet Meadow
Dominant Wetland
Pressures
4. Forestry
5. Invasive plants
6. Dams for
hydroelectricity
generation & water
storage
Non-vegetated
(mudflats – fresh &
alkaline)
1 - From “The Canadian Wetland Classification System – Second Edition” by National Wetlands Working Group
(1997).
2 - From “A Classification Framework for Wetlands and Related Ecosystems in British Columbia – A Third
Approximation” by MacKenzie and Banner (2001).
We assembled and assessed several existing landcover datasets recommended as potential
wetland baselines in Carver and McKenzie (2011). Appendix A provides some details on each
dataset as well as our conclusions as to their utility for our purposes. We narrowed the search
down to two 1:20,000 scale datasets which are available for all of British Columbia and which
were judged to provide the best opportunity for tracking wetlands at multiple scales: Terrestrial
Resource Inventory Mapping (TRIM, Appendix B) and Vegetation Resource Inventory (VRI).
We compared these datasets to each other as well as with orthophotos and field data collected by
DUC and the Columbia Basin Compensation Program.
Other datasets considered but not used included Sensitive Ecosystems Inventory (SEI) and
Terrestrial Ecosystem Mapping (TEM). Both SEI and TEM involve the interpretation of aerial
photography supported by field-verification to identify sensitive sites at the watershed to
landscape level. Both provide a high degree of confidence in terms of mapping and classifying
wetlands (even those under 0.5 ha in area) and are typically done at larger scales (1:5,000 to
5
1:20,000) by government agencies and resource extraction industries for planning resource
allocation. However, they are expensive and unavailable for most of the province of BC.
Comparison of TRIM vs VRI for Wetland Baseline Condition
TRIM and VRI were created by foresters and photogrammetrists, not wetland ecologists, and this
limitation was considered. Neither product identifies small/ephemeral wetlands very reliably or
wetlands in heavily treed areas. In addition, neither dataset has classified the wetlands into
types (e.g. bog, fen). However, both datasets are available for most of the province and are free
of charge. We compared both datasets in a few locations where ground reference data and
orthophotos were available. We found TRIM generally provided a better depiction of the
wetlands than did VRI and in many areas, VRI was simply using wetland polygons copied over
from TRIM. For example Figure 3 shows two different locations where VRI wetlands are more
generalized and tend to follow forest edges rather than the wetland itself.
Figure 3. Example comparison of TRIM wetlands (green) and lakes (blue) versus VRI (red) in
Southeast Kootenay region. Black points indicate where DUC ground reference data is
available.
TRIM includes ‘wetlands’, ‘lakes’ and ‘manmade’ polygon layers, and these polygons are
sometimes closely associated (Figure 4) where wet emergent zones and wet meadows (wetland
layer) are adjacent to deeper, more permanent water bodies (lakes and manmade water body
layers). Where this occurred, we considered the adjacent wetland and lake polygons as a single
composite waterbody (termed a ‘wetland’ for the purposes of this study). This is reasonable
since a wetland ecologist might have delineated the TRIM wetlands and shallow lakes as a
wetland complex anyway. This also potentially enables the distinction of ‘sub-wetland
distinctions in trend analysis. E.g. Assess whether the ratio of open water (lake polygons) to
emergent zone (wetland polygons) changes over time for a given waterbody.
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Figure 4. Example of
TRIM wetlands (light
blue) and lakes (dark
blue) in close proximity.
Consideration of TRIM in Selection of Study Area
To use TRIM as a baseline condition for comparison with different time periods, we needed to
establish what year it represented. The original BC TRIM mapping program was based on
1:70,000 air photos from 1979 to 1988. (The more recent ongoing TRIM2 program involves
updates to individual features and mapsheets at a scale of 1:10,000 on a priority basis, but the
availability of TRIM2 is still limited, and the dates are more recent.) For the East Kootenays,
the TRIM mapping was sourced from air photos from 1979, 1981, 1984 and 1988. We assessed
that this wide range in years (and, likely in wetland water conditions) when establishing a
‘baseline condition’ would introduce too much uncertainty into the process. Figure 5 illustrates
the potential wide variation in conditions. Note the presence of a pond in 1979 and its
disappearance by 1988.
1979
1988
Figure 5. Example of variation in wetland conditions within the potential TRIM ‘baseline time
period’ in the East Kootenays.
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As an alternative, we made the decision to monitor habitats at lower elevations (<1,000 m) in the
South Okanagan (Figure 6), where most of the wetlands are situated and where the TRIM
wetland baseline dataset was based on aerial imagery with a narrower time spread (it all derived
from August 1988). Similar to the Kootenay region, the Okanagan is a transborder valley in an
ecoregion of interest (the Cold Deserts level 2 Ecoregion), and we were confident that during the
time period 1988-2010 there was a high degree of wetland change involving both complete and
partial loss. This area has been experiencing widespread urbanization and conversion of natural
grassland/desert areas to vineyards and orchards requiring extensive water withdrawals from
nearby streams, lakes and wetlands. The Okanagan is a relatively arid region of the province,
and wetlands are critical features due to their rarity and ability to support more species than other
ecosystems (Brinson et al 2008). The study area is 128,900 hectares in size.
Figure 6. South Okanagan
Priority Wetland Area (red
outline). The extent of the
study’s area of interest was
limited to an elevation limit of
1000 metres.
We combined the three TRIM layers
(wetlands, lakes and manmade
waterbodies) into a composite ‘wetland’
layer. This layer included 513 water
features covering 12,755 hectares. We
removed three very large lakes which
accounted for most of the area, resulting
in a single layer of 510 wetlands
comprising 1,003 hectares. (The large
lakes were removed because their levels
are intensively managed.) A large
proportion (57%) of wetlands are under
half a hectare in size. Figure 7 lists the
inventory characteristics by size criteria.
Figure 7. SOK TRIM inventory showing the size
percentage distribution of features.
8
Wetland conversion has been ongoing in the South Okanagan for decades. Eight-five percent of the natural valley-bottom wetlands
have been lost since European settlement (Sarell 1990) due to urban and agricultural developments which occur mostly in the valley
bottom (BC MWLAP 1998). Most recently, Lea (2008) found that from 1800 to 2005, 92% of shrubby birch/dogwood wetlands
(Figure 8) and 40% of cattail marshes had disappeared in the Okanagan valley. Overall, low elevation wetlands had declined by 84%,
and the Okanagan River, channelized and dyked in 1948, had declined by 93% from 212 ha to 15 ha.
Figure 8. Maps showing changes in the water birch – red-osier dogwood riparian shrub swamp wetland (BD) ecosystem between
1800 and 2005. (Source: Lea 2008)
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2.2 Satellite Imagery
SPOT5 imagery from August and October of 2010 was purchased from Blackbridge Geomatics
(Figure 9). The images were orthorectified using the National Road Network and the Canadian
30-metre Digital Elevation Model (DEM) data to control the spatial rectification process. The
purchased files consisted of 10 metre resolution colour and 5 metre resolution panchromatic
imagery with optical spectral bands of blue, green, red and near-infrared (NIR).
Figure 9. Area of
purchased SPOT coverage
in the South Okanagan.
We did not purchase any SPOT imagery from the 1980s because although the spectral resolution
was similar, we judged that the 1980s SPOT imagery would have had insufficient spatial
resolution for the relatively small wetlands in the Okanagan study areas. For this earlier imagery
(SPOT1 or SPOT2), the pixel resolution was 20 metres, and there are 25 of these pixels in a
hectare. A minimum of 4 pixels is typically required to detect ‘hard’ changes (e.g. roads), and if
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the transitions are more gradual, then 9 or 10 pixels may be required. The minimum mapping
unit is therefore approximately 0.4 ha. The more recent SPOT5 multispectral imagery has a
minimum mapping unit of approximately 0.1 ha, and we judged this spatial resolution was
appropriate for Okanagan wetlands. The SPOT5 imagery would be more biologically
meaningful because the partners’ habitat-species models indicate that the smallest wetlands (0-1
ha) are disproportionately productive in terms of waterfowl and other organisms.
We considered using other imagery to supplement the SPOT5 imagery, but this proved to be
beyond the scope of the study. These included Landsat 7 and 8, which are free and annual, but
with a coarser (30 metre) resolution. Radarsat showed some promise for determining water
levels within cloud cover and has been used successfully for wetland mapping elsewhere in
Canada 2, but it would have added another level of complexity and uncertainty to the project and
was beyond the recommendations of the Carver report. Finally, high costs prevented us from
using high-resolution imagery such as hyperspectral and LIDAR. They may be appropriate in
focus areas where we find that change is occurring where it’s necessary to know the source of
the change or analyzing partial loss in wetlands.
2.3 Detecting Wetland Change
The partnership was interested in the regional (landscape) scale and finer (e.g. municipality)
scales. Our primary metrics of interest were the areal extent of wetlands and the number of
wetlands. We were not able to analyze wetlands according to any existing classification system
(e.g. the Canadian Wetland Classification System) due to the lack of this type of information in
the baseline layer, and the general unreliability of the baseline distinctions between wetlands and
lakes. There is potential future possibility to look at this on a smaller scale in a TEM-supported
area. For the same reason, we could not address measures of wetland quality.
Our general approach was to use the combined TRIM (wetland, lake and man-made waterbody)
vector polygons as our wetland baseline, representing the occurrence of wetlands in the
landscape at time period T1 (1988). This baseline would then be compared to SPOT5 raster
pixels representing the current wetland footprint as of 2010. We had initially planned to use the
TRIM polygons to flag the presence of wetlands in T1, and to then use these polygons to classify
a 1980s-era SPOT image which would represent our T1 condition. This T1 SPOT image would
then be compared to the more contemporary (2010) SPOT image, representing the T2 condition,
and the difference between the two classified SPOT images would be the change which occurred
during that period.
However, as mentioned previously, we judged that the older SPOT imagery from the 1980s
would have had insufficient spatial resolution for the relatively small wetlands in the Okanagan
study area; it has a minimum mapping unit of 0.4 ha versus 0.1 ha for more recent SPOT5
imagery. Consequently, we modified our approach slightly: the TRIM polygons still served as
the (T1) wetland baseline, but they were used to flag wetland ‘objects’ for spectral analysis in the
contemporary (T2) SPOT satellite imagery. Field evaluations of current wetland conditions at
several dozen locations in the study area were then undertaken to assist in ‘imagery classification
2
www.wetlandnetwork.ca
11
training’, wherein the field information was used during the analytical procedure to assign status
values (e.g. intact, lost, converted to urban, converted to crop or pasture) to the individual
wetland objects (groups of pixels) in the T2 SPOT image 3. Figure 10 provides an overview of
the analytical process.
Figure 10. Schematic overview of
change detection process
We evaluated change at the whole-wetland scale, rather than at the sub-wetland scale, because
our procedure involved ‘object-oriented analysis’, where groups of pixels in the imagery are
partitioned into entire wetland objects. We could not evaluate whether a wetland had shrunk
during the T1 to T2 time period; it was either converted entirely, or it was intact. This relatively
simple level of decision-making also enabled us to achieve a higher accuracy because we lacked
confidence in ability of the baseline to distinguish between open water versus emergent
vegetation areas.
3
Historic data from Environment Canada weather stations indicated similar weather conditions during the baseline
generation period (1988) and the T2 SPOT image period (2010)
12
Object-oriented image analysis is a remote sensing discipline which partitions imagery into
meaningful image-objects. These objects contain mean spectral information in the form of
attributes which can be interpreted and exploited for image classification. The TRIM vector
polygons representing time period T1 were incorporated into an eCognition (Trimble Ltd 2013)
project and the SPOT5 images were segmented to build objects for the T2 wetland inventory.
Each of the wetlands defined by the TRIM vector polygons now contained spectral image
information for 2010. This SPOT5 information represents the T2 condition of the wetlands.
The pattern recognition process used a supervised classification along with a decision tree
analysis. Specifically, we used Salford Systems’ CART statistical modeling software (CART
model) to uncover hidden patterns in the data (Breiman, 2001). A total of 56 useable wetland
sites were identified as a CART training sample. Sites were not randomly chosen, but were
selected to represent a range of geographic locations, land cover types and elevations. The
process evaluated the image objects’ predictor variables for their contribution to separating the
data samples into the required classes. Predictor variables for each object consisted of its mean
value of the blue, green, red and NIR bands extracted from the spectral imagery. Additionally a
standard deviation for each band was calculated from all the individual pixels within each
segmented object. Two other variables that were calculated in the segmentation process were the
object’s overall spectral Brightness and the Maximum Difference. A Normalized Difference
Vegetation Index (NDVI: NIR–red/NIR+red) was also calculated for each wetland.
The result of the CART analysis is a hierarchical decision tree model with splitting rules which
produce an end product of the class nodes (classified categories). These Terminal Nodes should
contain only the samples from each separate class to successfully predict the required object
classification. This rule-based tree structure is easily executed in the eCognition object-oriented
environment to produce a classification of the features.
The associations between the status of the sampled wetland objects and their spectral signatures
in the SPOT image were then extrapolated to the 510 wetlands in the study area to generate a
final ‘change classification’ of every wetland at time T2. These wetland change classes were
subsequently evaluated to create our change detection.
2.4 Accuracy Assessment
We considered a number of errors in the process which we believed might have significant
effects, including errors in the wetland T1 baseline, errors in the SPOT imagery and errors in the
classification procedure.
In terms of the baseline, our change detection approach assumed that the baseline was an
accurate assessment of the lakes, wetlands and man-made waterbodies that were present in the
Okanagan study area in 1988. This assumption may be incorrect if there were errors in polygon
boundary delineation or categorization. We acquired a subset of the original airphotos which
were used during the digitization of the TRIM polygons in the late 1980s and conducted a review
exercise to assess the accuracy of the TRIM.
13
Remote sensing errors include uncontrollable errors such as incorrect spatial registration of data
and atmospheric and illumination effects. We were not able to quantify or meaningfully assess
these errors. Because the SPOT5 imagery dated from August and October 2010, ideally we
would have used atmospheric correction to adjust spectral values for the different image
collection dates, creating more consistent earth response signals for monitoring, but ‘top of
atmosphere’ information was not available from the imagery supplier.
Errors in the classification procedure might be expressed in a variety of ways. For example, the
technology might not be able to accurately detect wetland/lake boundaries, or it might not be
able to discriminate between wetland classes adequately. To test for and minimize these errors,
we approached classification as an iterative process. After the baseline was detected, a
preliminary round of training was done with data from field assessments. This was fed into the
classification procedure, then reviewed. We produced two drafts and a final product in this way,
where additional field assessments helped to identify errors and calibrate the system. An error
matrix was constructed to assess the classification errors.
14
3.0 Results
3.1 Change Detection
Our study compared vector polygons representing the wetland footprint as of 1988 and SPOT5
raster pixels representing the ‘current’ wetland footprint as of 2010 for the South Okanagan
region for several categories of change. We conducted a segmentation and extraction of T2
imagery band statistics for each of the wetlands defined by the T1 TRIM inventory (Appendix C).
A set of 56 field-truthed samples representing various wetland conditions were analyzed by the
decision tree classifier. The results showed good performance, with almost all of the wetland
conditions being extracted into nine separate Terminal Nodes (Figure 12), representing different
wetland categories. Where Terminal Nodes included more than one wetland condition, we made
the following decisions:
•
Terminal Nodes 1, 4 and 8 exhibited distinct spectral signatures between wetland
conditions, and we manually added breaks to separate each of these into two categories.
Figure 11 shows a graph of the spectral characteristics of the wetland sites in Terminal
Node 4.
•
Terminal Node 9 exhibited two different wetland conditions, but these were deemed
sufficiently similar that we grouped them into a single change class.
•
Terminal Node 7 included three different wetland conditions (crop, lost, and pasturehay). We did not separate these because without more information, we were not confident
that all of them would have been well described or singular in makeup. Therefore they
have been retained as a composite change class.
Figure 11. Spectral characteristics of the six wetland sites which fell in Terminal Node 4. The
three green lines correspond to wetlands which were subsequently classified as Pasture-hay;
the three blue lines correspond to wetlands which were classified as Intact-emergents.
15
INTACTSHRUB
EXPOSED
INTACT (a)
And
INTACTEMERGENTS (b)
CROP (a)
And
CULTURAL (b)
INTACT
INTACT
EMERGENTS (a)
and
PASTURE/HAY
(b)
Figure 12. The CART decision tree
model showing the nine Terminal Nodes
as defined by the 56 sample sites
selected for this study. The spectral
breaks are indicated by the (shaded)
predictor variable and the break value.
Each Terminal Node is labeled at the
bottom with a wetland change class.
Note that Node 1 in the CART decision
tree lists 64 samples used in the model;
the 8 sample difference was a result of
two wetland sites being comprised of
multiple polygons.
LOST
CROP
CROP/
LOST/
PASTURE-
16
Next, this CART decision tree model was applied in a predictive sense to the full set of 510
TRIM wetland and lake polygons in the study area. This involved using the SPOT5 image’s
spectral bands median for each polygon to assign it a wetland change class within eCognition.
The output was subsequently summarized in Excel to generate Tables 2 and 3.
Table 2. TRIM wetland and lake polygons separated into nine different wetland categories.
Categories have been further subdivided into ‘Intact’ or ‘Converted’ groups.
Terminal
Node
Change Class
1a, 2
Intact
3
Intact-shrub
1b, 4a
Intactemergents
Description
Intact wetland dominated by
shallow water cover
shrub vegetative cover
herbaceous (emergent
aquatic) vegetation cover
Subtotal (Intact Categories)
Agricultural - perennial hay
or pasture
Cover type is not included
among these categories
(wetland is assumed ‘lost’)
Count of
Wetlands
Wetland
Area (ha)
% of
Landscape
Area
147
442.99
44.2%
18
33.23
3.3%
91
146.45
14.6%
256
622.67
62.1%
27
26.88
2.7%
30
92.14
9.2%
4b
Pasture-hay
5
Lost
6, 8a
Crop
Agricultural – annual crop
32
36.91
3.7%
7
Crop/Lost/
Pasture-hay
Composite class
72
135.15
13.5%
8b
Cultural
32
20.59
2.1%
9
Exposed
61
68.95
6.9%
Subtotal (Converted Categories)
254
380.62
37.9%
Grand Total
510
1,003.28
100.0%
Residential or industrial
development
Exposed (usually alkaline) soil
with little to no vegetative
growth
17
Table 3. TRIM wetland and lake polygons separated into nine different wetland categories by wetland/lake size class. Categories
have been further subdivided into ‘Intact’ or ‘Converted’ groups.
Wetland Size Class
0-1 ha
Change Class
Count of
wetlands
Intact
Intact-shrub
Intact-emergents
Subtotal
(Intact Category)
% of
total
count
1-5 ha
Wetland
area (ha)
Count of
wetlands
91
25.27
7
71
169
48
% of
total
count
5-20 ha
Wetland
area (ha)
Count of
wetlands
25
54.35
4
2.50
10
22.15
18.10
13
23.09
2
45.88
48
99.59
6
45
Pasture-hay
21
8.66
4
6.32
Lost
18
7.38
9
19.29
Crop
23
7.64
7
16.13
Crop/Lost/Pasture-hay
52
21.15
15
38.60
Cultural
23
5.29
9
15.30
Exposed
44
14.90
14
24.93
% of
total
count
67
2
20-50 ha
Count of
wetlands
132.41
27
230.95
1
8.58
61.75
5
43.51
194.17
33
55.07
1
% of
total
count
Wetland
area (ha)
46.30
73
Wetland
area (ha)
283.04
2
11.90
1
10.41
2
13.12
4
29.10
3
29.13
Subtotal
(Converted Category)
181
52
65.02
58
55
120.57
3
33
101.37
12
27
93.65
Grand Total
350
100
110.89
106
100
220.16
9
100
295.54
45
100
376.69
BC Wetland Trends: Okanagan Valley Assessment 18
3.2 Accuracy and Error
Accuracy of the Baseline
We made a visual assessment of the accuracy of the TRIM baseline polygons compared to the
historic aerial imagery which was used during the TRIM digitization process. We were able to
find the corresponding images for 66 sites, including 47 of the samples used for CART training
and another 19 which were examined in the field but not used in the CART analysis. Images
were georeferenced, but we were not able to acquire stereo pairs.
Categories of baseline accuracy assessment:
1. Accurate
The TRIM polygon appears to correspond well to a wetland or lake in
the historic imagery.
2. Inaccurate
The TRIM polygon was either inaccurately digitized, or not digitized
even though it appears in the imagery.
3. Inconclusive
The TRIM polygon was digitized, but the accuracy of the digitizing is
unclear because we lack stereo vision.
4. Not in TRIM
Although the remnants of a converted or dried wetland were identified
during the field assessments, no polygon was digitized in the TRIM
dataset, and it is unclear whether the wetland actually existed at the time
of digitizing.
Of the 66 sites for which we could acquire the historic photography (Appendix D), 46 (70%)
were deemed ‘accurate’, 5 (8%) were deemed inaccurate, 8 (12%) were deemed inconclusive,
and 7 (10%) were not in TRIM. When a digital polygon was deemed inaccurate, it was typically
an under-representation of the wetland visible in the imagery. Given the low rate of apparent
inaccuracies, the error associated with TRIM baseline appears to be acceptable.
Figure 13a. Example where
TRIM polygon boundaries
appear accurate.
Figure 13b. Example where the
accuracy of TRIM polygon
boundaries is unclear.
Figure 13c. Example where no
polygon exists and it is unclear
whether a wetland was present.
Figure 13d. Example where no
polygon exists although a wetland
was present (Forbes wetland).
Accuracy of Change Analysis
We measured errors in the classification procedure by comparing the final classified results to a
set of field ground-truthed reference sites.
Categories of change analysis accuracy assessment:
1. Accurate
The change classification applied by eCognition agrees with the field
classification.
2. Marginal
The change classification applied by eCognition does not agree with the
field classification, but both are within the same category (intact or
converted).
3. Not accurate
The change classification applied by eCognition does not agree with the
field classification, and classes are in different categories.
We assessed the classification accuracy of the 56 sample sites used for CART training, as well as
another 12 locations which were examined in the field but not used in the CART analysis (Table
4, Appendix D). In total, the eCognition classification was accurate for 59 of 68 field-assessed
sites, and either accurate or marginal for 66 of the 68 sites. Of the 34 ‘intact’ classifications,
only one was misclassified as ‘converted’, although three others were misclassified within the
intact category. Similarly, of the 34 ‘converted’ classifications, only one was misclassified as
‘intact’, although four others were misclassified within the converted category.
Table 4 is an Error Matrix which shows that the overall accuracy was 87%. However, we were
limited by a relatively small sample dataset, especially in selected classes among the ‘converted’
category. Congalton and Green (1999) recommend using 50 training samples for each class
when there are fewer than 12 categories, but we would not have been able to achieve this in the
Okanagan due to the relative rarity of wetlands in the valley bottom. Overall there were
relatively high user accuracies for the classes (range 75% to 100%) except for ‘lost’ (33%). The
producer accuracy was relatively high for most classes (83% to 100%) except for the classes of
‘lost’ (50%) and ‘pasture-hay’ (50%).
We also acknowledge that the classification procedure may have been biased in a positive
direction due to a lack of independence between the field reference data used to train the CART
analysis and the field reference data used to evaluate the classification (Hammond and Verbyla
1996). Consequently, the classification accuracy is likely overestimated, since only 12 of the 68
sample locations used to assess accuracy were truly independent of the training sample set.
Table 4. Comparison of the agreement between the eCognition T2 wetland classifications and their corresponding field
assessments. Numbers correspond to the number of wetlands which fell into a particular classification.
Field Reference Sites
Intact
Intact
Intact
Intactemerg
Pasture
-hay
Lost
Crop
Crop/
Lost/
Cultural
Pasture
-hay
20
Intact-shrub
User
Accuracy
Exposed
1
95%
3
Intact-emergents
3
100%
7
Pasture-hay
70%
3
Lost
Converted
eCognition Classification
Intact
Intactshrub
Converted
1
1
Crop
100%
1
1
33%
4
80%
Crop-lost-pasture
9
Cultural
100%
6
Exposed
Total
Producer Accuracy
1
1
100%
6
24
3
7
6
2
4
9
7
6
83%
100%
100%
50%
50%
100%
100%
86%
100%
75%
87%
4.0 Discussion
4.1 Interpretation of Change
Table 2 indicates that 62% of the wetlands and lakes in the study area were categorized as
‘intact’ (in three different classes). Thirty-eight percent were categorized as impacted in some
way (in six classes). This is a striking amount of impact within a 22-year time period, although
it’s tempered slightly by the lack of sample independence in our classification accuracy
assessment (see previous section). Uncertainties re: the derivation of the 1988 TRIM wetland
baseline should also be considered. For example, the baseline may have included some
‘exposed’ wetlands within its set of polygons, since it is unclear from the metadata whether these
were excluded from consideration as wetlands. Exposed wetlands occupy the dry end of the
wetland hydrologic gradient, but they may have moist soil for part of the year and are therefore
not necessarily entirely lost as habitat, although their wetland functions are undoubtedly reduced.
Nevertheless, we are confident that the large majority of wetlands within the remaining other
‘impacted’ categories have truly changed since 1988, because it is unlikely that the
corresponding wetlands would have been captured within the TRIM wetland/lake inventory if
they had been in their impacted state back at time T1. If we remove the ‘exposed’ class from
consideration, the subset of ‘impacted wetlands’ still represents 31% of the entire TRIM
inventory, for an extrapolated impact rate of 1.4% per year. We expect this rate of loss has
continued since 2010.
Our procedure used the set of T1 TRIM wetlands (circa 1988) and lakes to ‘flag’ areas for
analysis in the T2 SPOT imagery (2010), and therefore would not have detected wetlands created
during the T1-T2 time period. This is a potential concern, because wetland gains during the time
period would counterbalance wetland losses to some extent. However, we believe that any
wetland gains during this period were extremely minimal. During our extensive field
assessments we encountered only two, and in both cases these were associated with drinking
water reservoir expansions (and therefore would not contribute to wetlands from a habitat
perspective). Identifying wetlands not contained in the TRIM data would require development
of a new image interpretation technique, and would be similar in scope to creating a wetland
inventory.
Table 3 indicates that the smaller wetland size classes were disproportionately affected by the
impact. In the smallest two size classes (0-1 ha and 1-5 ha), a majority of TRIM wetlands and
lakes were impacted (52% and 55%, respectively). In the largest two size classes (5-20 ha and
20-50 ha), a smaller proportion were impacted (33% and 27%, respectively). Given the
importance of temporary or small wetlands for biodiversity in the Okanagan (Semlitsch and
Bodie 1998, Austin et al 2008), this disproportionate impact is alarming, particularly for a
number of provincially red- or blue-listed communities and species such as Tiger Salamander
and Great Basin Spadefoot Toad (BC MWLAP 2004).
Overall the analyses performed on the SPOT imagery produced encouraging results. Among the
samples that were field-assessed and then run through the CART decision tree model, there were
distinct spectral breaks between most of the various wetland conditions. We have a relatively
high level of confidence in the ability of the CART analysis to accurately classify wetlands in the
Okanagan (we were able to classify wetlands with 87% accuracy). If this separability proves
consistent in other areas, then the object-oriented segmentation and CART model development
may prove sufficient for achieving our objective of expanding and operationalizing the BC
Wetland Trends Project.
We can make some inferences about the probable causes of wetland impacts. Some of the
impacted categories of wetlands can be linked to specific industries: pasture-hay and cropland
are clearly agricultural developments. Similarly, the cultural class is mostly a function of
expanding residential development. Climate change is less straightforward; it likely accounts for
some of the lost and exposed wetlands, but it’s unclear how much, particularly since we don’t
know whether exposed wetlands were included as wetlands in the development of the T1
inventory. The lack of separability between the crop, lost and pasture-hay classes is problematic,
but it could potentially be addressed in future projects via a time series approach, if multiple
image acquisition dates are available; this might reveal more identifiable spectral signatures for
the crop and pasture-hay classes as they change with the seasons.
Regardless, the change we detected demonstrates the ongoing need for action in the South
Okanagan, particularly for small wetlands. This information can be used to stimulate further
restoration and protection activities, and inferences re: wetland stressors can be used to set
priorities for audiences that require more education about wetland mitigation, specifically the
agricultural industry. We can also flag individual local governments such as the Regional
District of South Okanagan that could benefit from a better use of policy tools to conserve
wetlands. And despite our inability to make strong inferences about the role of climate change
on wetlands, we still include it in our program as a potential major driver of wetland condition.
4.2 Wetland Baseline
During the process of investigating the provenance of our baseline datasets, we became more
aware of the complications of relying on third-party datasets designed for a different purpose,
and given the difficulty we experienced in generating a reliable baseline for the project, we had
to moderate our expectations of what we were likely to achieve.
Due to the small scale of the original aerial photographs (1:70,000), the relatively small size of
many of the wetlands of interest (57% of the inventory features are less than 0.5 hectares; Figure
7), and the fact that the TRIM polygons were digitized by photogrammetrists, not wetland
ecologists, we have some question as to the positional accuracy of the polygon boundaries.
Unfortunately, our assessment of the accuracy was limited somewhat by our inability to locate
stereo pairs of the original photographs. However, we can make the following conclusions about
the TRIM baseline:
•
Of the wetlands and lakes identified as T1 TRIM polygons, we believe that at least 70%
(and possibly up to 92%) of the polygons were reasonably accurate based on our sample
assessment of 66 sites.
•
For smaller remnant wetlands that were identified in the field at T2, but did not appear in
the T1 TRIM dataset (about 10% of our sample), the accuracy cannot be assessed,
because it was unclear whether they were missed in the TRIM digitizing process or
whether they did not exist at T1. It is likely that some of these were simply missed in
TRIM; Machmer et al (2004) compared TRIM with aerial photography interpretation in
the Kootenays and found that TRIM missed some smaller wetlands, although there was
no consistent pattern.
•
The criteria used to distinguish between wetlands (marshes and swamps) and lakes
appears to have been subjective (Appendix B), and the designation of these wetland
classes appears to have been somewhat unreliable. Accordingly, these classes of
waterbody were lumped during this project.
•
Although the photo imagery used to derive T1 TRIM polygons for the Okanagan derived
from a single year (1988), the imagery used elsewhere in the province sometimes derived
from much larger date range. E.g. For the Kootenays, it ranged from 1979 to 1988.
Therefore, using TRIM for the baseline condition in other areas may introduce another
level of variability into a trend assessment, and the TRIM photography dates should be
examined closely during project scoping.
•
The accuracy appeared to be better for grassland-associated wetlands and worse for
forested wetlands where tree cover may have obscured actual wetland boundaries. This
has ramifications where succession and forest encroachment is a factor, and may confuse
trend assessment in these areas. We did not consider this a major factor in our study area
due to the relatively short T1-T2 time period and slower rate of forest encroachment.
•
For wetlands which were classified as ‘exposed’ in T2, we don’t know whether these
sites were in the same condition at T1, because there was no corresponding TRIM
classification.
Overall, TRIM has its weaknesses, but it is still the best potential baseline in most areas of BC
and we believe it can be useful in trend assessment if these limitations are acknowledged upfront, and inferences are consistent with our understanding of those limitations. TRIM also tends
to under-represent the true wetland composition of the landscape (errors of omission exceed
errors of commission, particularly for small and/or ephemeral waterbodies), and therefore
estimates of wetland losses are probably conservative.
TRIM2 mapping showed some promise as a potential future replacement for TRIM as a wetland
baseline, due to its finer scale (1:10,000). However, a review of several mapsheets by the
authors indicated that because of the forestry focus of the TRIM2 program, stream and road
elements were more likely to have been modified or updated compared to lakes or wetlands, and
where wetlands were updated, it is not clear what criteria were used. Furthermore, TRIM2 has
been generated using a wide range of aerial photography dates, and it is not freely-available at
present; it must be purchased at considerable cost. Consequently, even where available, TRIM2
remains unsuitable for use in this trend assessment procedure.
4.3 Wetland Stressors
Wetlands are sensitive to climate change, and we expected climate change to be the most
important wetland pressure currently acting in the Okanagan. In fact, a provincial-scale study
(Veridian Ecological Consulting 2004) identified the two main threats to wetlands in the Interior
Basin Ecozone (where the Okanagan is located) to be 1) accelerated climate change and 2)
ecosystem change caused by invasive species (Figure 14). Climate change was considered a
widespread threat, whereas invasive species were more localized. Habitat conversion and water
demand were also considered widespread threats.
There has generally been a drying trend in the Interior Basin over the last century, likely due to
warmer temperatures and declining snowpacks (based on observations from Environment
Canada weather records, 1950 to 2007). That trend is predicted to continue over the next 50-75
years (Rodenhuis et al 2007, Spittlehouse 2008). The predicted net effect on wetlands would be
negative, since predicted changes in temperature and precipitation will generally increase
wetland water losses (via evaporation and evapotranspiration), decrease flow inputs (via reduced
streamflow and snowpack recharge of wetlands) and alter hydrologic cycles. Native biodiversity
would undoubtedly suffer due to outright habitat loss from drying (Figure 15), rising water
temperatures and altered hydrologic timing. Invasive species which are better adapted to these
conditions could take advantage (and likely have so far).
Extent Rating
CC climate change
CC climate change
NS non-native species
UD habitat conversion
TC highways
GR vegetation modification
GR soil modification
RE resort development
RD habitat conversion
TC railways
FC roads
DA habitat conversion
AG cultivation
RE motorised aquatic
UD water demand
UD flood control structures
DA flow regulation
UD hydrograph changes
FP roads
OG pipelines
GR riparian/wetland
UD residential chemical use
RD sewage disposal
ME roads/trails
FC silviculture
AG fertilization
UD air pollution
RE motorised terrestrial
3
UD habitat conversion
4.5
TC highways
2
GR soil modification
3
GR vegetation modification
3
RD habitat conversion
4
RE resort development
2
AG cultivation
5
DA
FC
TC
RE
DA
habitat conversion
roads
railways
motorised aquatic
flow regulation
2
3
1
3
3
UD flood control structures
3
UD water demand
4
FP roads
2
UD hydrograph changes
3
GR riparian/wetland
3
OG
AG
FC
ME
RD
1
3
3
2
2
pipelines
fertilization
silviculture
roads/trails
sewage disposal
UD residential chemical use
0
1
2
3
4
5
6
7
8
9
10
Magnitude rating
11
12
13
5
NS non-native species
air pollution
14 UD 15
16
RE motorised terrestrial
2
4
3
Figure 14. Wetland threats in Okanagan (Source: Veridian Ecological Consulting 2004).
Figure 15. Dry
wetland in the
southern BC Interior,
which may be an
indication of climate
change.
Since 2008, the partners have also been working with researchers at the University of BC to
predict how climate change may affect future wetland conditions by downscaling climate data
for regional wetland vulnerability assessments in the BC Interior By establishing a solid
baseline for future trend analysis, the BC Wetland Trends project could help validate those
climate predictions and inform the development of climate adaptation strategies for priority
species on both sides of the border. It’s important to note that the term ‘climate change’ is
typically used in the context of the longer (e.g. multi-decade) term. Even a 20-year trend
attributed to drying cannot definitely be described as climate-change, and should more accurately
be described as a ‘drying trend’. It may indeed correspond to true climate change, but that
cannot be determined from two data points 22 years apart.
There are other potential drivers of wetland loss in the Okanagan: although people are now more
aware of the value of wetlands and some regulatory tools have been enacted, wetlands are still
threatened by drainage and conversion for industrial or urban development, and alteration of
hydrology for irrigation or hay production (Figures 16a-c). The latter includes water extraction
and/or drainage. And in some areas there is still heavy recreational use. In addition to the direct
impacts of these activities, there may be indirect effects such as nutrification and pollution.
Impacts have been mitigated somewhat by improved landowner stewardship practices, but the
pressure on wetlands is unavoidable in a valley where the population has been increasing by 23% annually (BC MWLAP 2004, Lea 2008).
In the Okanagan, Neilsen et al (2004) projected crop water demand to increase by 37% over the
next 95 years. This would lead to even greater water use in a portion of the province which is
already heavily-licensed. Ecozone-wide, 7,925 current surface water licenses were granted prior
to 1960 (Province of BC 2009). Over the next 40 years, another 11,000 were granted, but the
rate dropped by ¾ by 2000, reflecting greater regulation and full allocation of some systems (BC
MWLAP 2002).
Figure 16a. Wetland converted
to intensive agriculture (annual
cropping).
Figure 16b. Wetlands lost to
urban development.
Figure 16c. Wetland mostly
drained for hay production.
In the end, we were able to detect the cause of wetland change in some cases, but not in others,
due to limitations inherent in our chosen methodology. For example, we could reliably detect
wetlands which had been filled in during urban development (Figure 17) or which had been
converted to cropland, but a wetland drained for pasture and hay did not produce a different
spectral signal from a wetland lost to climate change-related drying which was subsequently
converted to pasture or hay. In the latter case, climate change may actually have been the most
important driver of change, but it is obscured by the direct agricultural impact. There was also
some spectral confusion between the lost, crop and pasture-hay classes which necessitated the
creation of a composite class, where the ultimate cause was unclear.
Figure 17. An example of a wetland impact used in the monitoring evaluation. The image on
the left shows a recent aerial photo view of a wetland which was filled in during the ‘Redwing’
development in Penticton. The image on the right shows the same location in a 2010 SPOT5
image.
As mentioned previously, the ‘exposed’ class may represent wetlands dewatered through climate
change, but because the TRIM metadata are inconclusive as to whether TRIM included exposed
or nearly dry wetlands, we are unable to state conclusively whether these wetlands dried during
the time period T1-T2.
Despite this uncertainty around climate change as an underlying driver, our results suggest that
agriculture played a major role in wetland conversion. Excluding ‘exposed’ wetlands, the crop
and pasture/hay classes together accounted for almost 1/5 of the total converted wetland area,
and they likely contributed significantly to the composite crop-lost-pasture/hay class, which
accounted for another 2/5 of the converted area. Urban, commercial or industrial development
accounted for about 5% of the converted area. Understanding more about the role of climate
change may be possible via a more intensive wetland sampling approach involving sub-wetland
segmentation (refer to next section).
4.4 Sub-wetland Segmentation
Although we could not evaluate whether a wetland had shrunk in size during the T1 to T2 time
period, or whether the proportion of open water or emergent zones had changed, we did
undertake some preliminary assessments to investigate the ability of the eCognition software to
separate out the sub-wetland elements (Figure 18) into the same classes and categories as used
in the whole-wetland change detection. Results were encouraging, but for reasons explained in
the methods (Section 2.3), we did not pursue this line of investigation.
Further work could be undertaken to assess internal basin changes over the T1-T2 period, but
this would require an investigation of the sub-segmentation of wetland polygons, involving an
inventory-style classification supported by targeted ground-sampling of intact and lost/converted
portions of polygons. The procedure could be simplified if only specific impacts are required for
specific questions. (E.g. How has cultural growth encroached on wetland areas?) A specific
spectral signature could be used as the predictor variable to identify just these impacts, if they are
distinct, and the complexity would be determined and driven by one’s needs. Questions around
the accuracy of the wetland baseline would still be pertinent, as the different wetland zones
would still have to be captured within the wetland and lake polygons.
Figure 18. Example of a recent aerial photo (left) and a sub-segmented 2010 SPOT5 image
(right). On the SPOT image, red lines denote whole wetland boundaries; black lines denote
sub-wetland segments.
5.0 Conclusions
Our primary objective in this project was to evaluate the use of medium-scale satellite imagery
and available existing landcover mapping datasets to assess changes in the distribution and areal
extent of freshwater, non-floodplain wetlands within a trial area over a 22-year period. We
realized this objective, and found up to 38% wetland conversion. Our overall classification
accuracy rate was 87%. Despite all of the land management agencies and ENGOs which are
active in the Okanagan Valley, wetlands are still being lost incrementally to residential and
agricultural development.
Another principal objective was to evaluate the proposed wetland tracking approach, identify
constraints, and provide recommended procedures and logistics to deliver a landscape scale
operational wetland tracking project that could be applied to cross-boundary ecoregional
landscapes. Our success here was more qualified, in that our methods show good potential for
certain other locations in BC, but the quality of baseline inventories will continue to be a
concern. Section 6 provides recommendations for modifying or refining the assessment
procedure and for improving baseline inventories so that we are better able to assess ongoing
trends from future remote sensing products.
Expansion of the tracking approach to cross-border areas will require further consultation with
US partners, and this will be addressed via our outreach activities. Nevertheless, we believe this
project already represents a step forward in coordinating our conservation delivery for the benefit
of similar ecological communities.
From a methodological perspective, we also wanted to evaluate the potential for using SPOT5
satellite imagery to identify impacts deriving from recognized drivers such as climate change,
intensive agriculture and urban development. We achieved this for intensive agriculture and
urban development, but ultimately found that our methods were insufficient to make confident
inferences about the role of climate change. Our limited investigation of sub-wetland
segmentation suggested good potential for learning more about this.
6.0 Recommendations
1. To move towards wider operational delivery throughout the CIJV, we believe that this
wetland tracking procedure (involving TRIM, SPOT and eCognition with a decision tree
analysis) could be undertaken without major alteration in a second, larger priority area of
British Columbia, using existing datasets. The best Interior BC (CIJV) option at present
is the Cariboo-Chilcotin in the central part of the province, where data availability is
similar to the Okanagan, but recent wetland impacts are believed to derive more from
climate-change related phenomena such as regional drying. We recommend a higher
field sampling rate with greater independence of samples for training and accuracy
assessment.
Because the partners also have a province-wide focus, we are considering potential
opportunities in coastal BC as well, which is part of the adjacent PCJV (and North Pacific
Landscape Conservation Cooperative). Options in coastal BC include the highly
populated Fraser Valley in the southwest corner of the province, where there is a
relatively rich library of datasets, and wetland impacts are believed to derive mainly from
urbanization and industrial agriculture. A potential project could involve researchers
from various universities developing a cost-effective baseline inventory and change
detection program suitable for the wetlands in the transboundary area of the PCJV (and
possibly the CIJV). This would build upon the extensive work to date on wetland
ecosystem resiliency in the face of climate change.
2. The necessity of a reliable wetland baseline was apparent throughout this trend
assessment project, and limitations inherent in the best existing TRIM baseline (e.g. some
missed wetlands, lack of distinction of different wetland types) have encouraged us to
pursue the establishment of an improved baseline inventory that can be used to track
future trends in one or more representative areas of the province at a cost-effective scale.
An inventory would need to consider issues such as minimum mapping unit (e.g. the need
to capture small wetlands), accuracy and precision of wetland delineation in different
land cover types, and potential characterization of wetland status and type (e.g. emergent
wetland vs lake). The frequency of monitoring should be aligned with Joint Venture
planning cycles.
In addition to aerial photography or SPOT5 satellite imagery, we also recommend
investigating other technologies in partnership with government agencies, land managers
or university researchers. For example, researchers from the University of Washington 4
were faced with similar challenges in investigating the effects of climate change on
wetlands in an area with no comprehensive wetland baseline. Using Object Based Image
Analysis in combination with other available data (colour or black and white orthophotos,
LIDAR), they were able to successfully create a baseline for change detection. A similar
approach has been used by researchers at the University of British Columbia (Morgan
and Gergel 2013) and in Alberta (Clare and Creed 2013),and hyperspectral imagery in
4
https://nccwsc.usgs.gov/display-project/5006c2c9e4b0abf7ce733f42/5006e7bae4b0abf7ce733f50
combination with LIDAR has been investigated by researchers from the University of
Victoria for use in mapping wetlands under a coniferous forest canopy 5. In more open
conditions involving larger wetlands, Radarsat imagery has been found to improve
wetland delineation when used in combination with SPOT or Landsat (Grenier et al
2007). These are all options for producing a more reliable, cost-effective baseline
inventory.
3. The role of climate change in BC wetland trends is still somewhat unclear, as our
baseline and methodology were insufficient to enable clear inferences about its role. It
remains a critical concern in our conservation programs and represents potential
validation of predictions from our interior climate change wetland modeling efforts.
Climate change effects could be investigated via sub-wetland segmentation. By offering
insight into trends among within-wetland elements, this technique could shed light on
climate change as a driver. For example, shrinkage of wetted area and/or expansion of
closed emergent cover could indicate drying over the assessed time period. We
recommend attempting sub-wetland segmentation via a smaller-scale analysis involving
protected areas, where presumably there would be fewer direct anthropogenic alterations
and climate change could be better isolated as a potential wetland stressor.
4. Outreach remains a critical component of this project, and the partners should commit
resources to these efforts to enable broad communication of the results. Outreach
activities should be coordinated through the Wetland Stewardship Partnership (WSP),
and information should be delivered with the appropriate level of detail to a variety of
targets, including the general public and other potential partners, as well as the wider
scientific community.
5. Expanding this project elsewhere in BC will necessarily involve considerable resources
(particularly for recommendations 1 and 2), and partnership is essential to that process.
No single agency or partner has the financial or knowledge resources to undertake largescale wetland tracking alone.
At the onset of this project, we committed to informing other landscape-level partners as
to methods and trends applicable to cross-boundary ecoregional management of wetlands
in Canada and the United States. We brought together the two neighboring
Intermountain JVs for the first time to quantitatively track landscape-level habitat
conditions, but this work is not complete. We should continue to collaborate with the
IWJV to share our experiences and learning, and investigate whether our methods can be
adapted and made compatible with tracking efforts on the US side of the border (e.g.
National Wetlands Inventory).
5
http://geog.uvic.ca/olaf/PUblications_files/CCRS%20January%202011.pdf
7.0 Literature Cited
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Nature's Pulse: The status of biodiversity in British Columbia. Victoria, Biodiversity BC.
B.C. Ministry of Environment, Lands and Parks. 1992. British Columbia Specifications and
Guidelines for Geomatics. Content Series Volume 3. Digital Baseline Mapping at 1:20,000.
Release 2.0.
B.C. Ministry of Water, Land and Air Protection. 1998. Habitat Atlas for Wildlife at Risk: South
Okanagan and Lower Similkameen. Penticton. 124p.
B.C. Ministry of Water, Land and Air Protection. 2002. Environmental Trends in British
Columbia 2002. State of Environment Reporting: 15-22.
B.C. Ministry of Water, Land and Air Protection. 2004. Wetlands of the Southern Interior
Valleys. Victoria.
Breiman, L. 2001. Statistical modeling: The two cultures. Statistical Science. 16(3): 199-231.
Brinson, M. M., B.E. Bedford, B. Middleton and J.T.A. Verhoeven. 2008. Temperate freshwater
wetlands: Response to gradients in moisture regime, human alterations and economic status.
Aquatic ecosystems: Trends and global prospects. N.V.C. Polunin, New York, Cambridge
University Press: 482p.
Carver, M. and McKenzie, E. 2011. Strengthening Wetland Conservation: An Assessment of
Data and Tracking Opportunities across British Columbia. Final Report for Wetland Trends
Project Steering Committee, Duck Unlimited Canada and Canadian Wildlife Service by
Aqua Environmental Associates. 105p.
Clare, S. and I.F. Creed. 2013. Tracking wetland loss to improve evidence-based wetland policy
learning and decision making. Wetlands Ecology and Management DOI: 10.1007/s11273013-9326-2.
Congalton, R.G. and K. Green. 1999. Assessing the accuracy of remotely sensed data: Principles
and practices. Lewis Publishers, Boca Raton.
Grenier, M., A-M. Demers, S. Labrecque, M. Benoit, R.A. Fournier, and B Drolet. 2007. An
object-based method to map wetlands using RADARSAT-1 and Landsat ETM images: Test
case on two sites in Quebec, Canada. Canadian Journal of Remote Sensing 33(S1): S28-S45.
Grenier, M., S. Labrecque, M. Garneau, and A. Tremblay. 2008. Object-based classification of
a SPOT-4 image for mapping wetlands in the context of greenhouse gases emissions: The
case of the Eastmain region, Quebec, Canada. Canadian Journal of Remote Sensing 34(S2):
S398-S413.
Hammond, T. O. and Verbyla, D. L. 1996. Optimistic bias in classification accuracy
assessment. International Journal of Remote Sensing 17:1261-1266.
Lea, T. 2008. Historical (pre-settlement) ecosystems of the Okanagan Valley and Lower
Similkameen Valley of British Columbia - pre-European contact to the present. Davidsonia
19(1): 3-36.
Machmer M., M. Carver and E. McKenzie. 2004. Small Wetland Literature Review and
Mapping. Report prepared for Columbia Basin Fish & Wildlife Compensation Program,
Nelson, BC.
MacKenzie, W.H. and A. Banner. 2001. A classification framework for wetlands and related
ecosystems in British Columbia: Third approximation. Research Branch, B.C. Ministry of
Forests, Victoria, B.C. Web document.
http://www.for.gov.bc.ca/hre/becweb/resources/classificationreports/wetlands/
Morgan, J. and S.E. Gergel. 2013. Automated analysis of aerial photographs and potential for
historic forest mapping. Canadian Journal of Forest Research 43(8): 699-710.
National Wetlands Working Group. 1997. The Canadian Wetland Classification System, 2nd
Edition. Warner, B.G. and C.D.A. Rubec (eds.), Wetlands Research Centre, University of
Waterloo, Canada. 68p.
Neilsen, D., C.A.S. Smith, G. Frank, W.O. Koch and P. Parchomchuk (2004). "Impact of climate
change on crop water demand in the Okanagan Valley, BC, Canada." Acta Horticulturae
(ISHS) 638: 273-278.
Poulin, B., A. Davranche, and G. Lefebvre. 2010. Ecological assessment of Phragmites
australis wetlands using multi-season SPOT-5 scenes. Remote Sensing of Environment 114:
1602–1609.
Province of British Columbia. 2009. BC Points of Diversion with Water Licence Information,
Land and Resource Data Warehouse. Digital Dataset.
Rodenhuis, D. R., Bennett, K.E., Werner, A.T., Murdock, T.Q. and D. Bronaugh. 2007. Hydroclimatology and future climate impacts in British Columbia. University of Victoria, Pacific
Climate Impacts Consortium. 132p.
Sarell, M. 1990. Survey of Relatively Natural Wetlands in the South Okanagan. BC Habitat
Conservation Trust Fund. 7p.
Semlitsch, R. D. and R. Bodie. 1998. Are small, isolated wetlands expendable? Conservation
Biology 12(5): 1129-1133.
Spittlehouse, D. L. 2008. Climate change, impacts, and adaptation scenarios: Climate change
and forest and range management in British Columbia. BC Ministry of Forests and Range:
38p.
Veridian Ecological Consulting, L. 2004. Provincial and regional threats to wetlands in BC. A
compilation of information from "Biodiversity Conservation in BC: An assessment of
threats and Gaps" - Holt et al. 2003. 18p.
Appendix A: Major Datasets Considered for Use as Wetland Baseline
Principal
Means of Data
Collection
Aerial
photography
Coverage
1993 to
present
Aerial
photography
1:20,000
19791988
Aerial
photography
Provincial
(although
quality
varies)
Provincial
1:20,000;
1:50,000
Various
Aerial
photography
Local or
Landscape
1:250,000
19922001
Landsat
Provincial
Dataset
Scale
Year
Soils
Mapping
1:50,000;
1:100,000
Various
Vegetation
Resource
Inventory
(VRI)
Terrain
Resource
Inventory
Mapping
(TRIM)
Terrestrial
Ecosystem
Mapping
(TEM)
1:20,000
Baseline
Thematic
Mapping
(BTM)
Local or
Landscape
Minimum
Wetland
Size
N/A
1-2 ha
Comments
Soil polygons are coarse;
points (field data) seemed
to avoid wetlands; data are
only available in agricultural
areas. Limited value for
helping to validate the
presence of wetlands or
their type.
Less accurate and reliable
than TRIM, particularly for
small wetlands.
<0.1 ha
Accurate for small wetlands
(although it still misses
some). Spatially-explicit
baseline applicable to the
entire province.
0.5 to 2 ha Too limited in coverage for
use as a baseline. It does,
however, have potential use
in the future for training
purposes with remote
sensing during the creation
of a new wetland inventory.
10 ha
BTM’s analytical
interpretation of satellite
imagery and other base
maps identifies a wide range
of features, but it is too
coarse for use as a wetland
baseline.
Appendix B: TRIM Digitizing Methodology (source: BC Ministry of
Environment, Lands and Parks 1992)
Appendix B: TRIM Digitizing Methodology (source: BC Ministry of
Environment, Lands and Parks 1992)
Appendix C: Spectrographic Characteristics of Field Assessment Sites
Hectares
Brightness
Maximum
Difference
crop
0.4080
26.9010
5.6991
153.3125
65.8125
103.5625
107.7292
Normalized
Difference
Vegetation
Index
0.0197
Mountain Ave
crop-lost-pasture
0.5656
32.9309
6.5426
152.9394
81.2879
77.2121
215.4545
0.4724
-0.4521
Oliver east of river
crop
0.853
25.6411
5.0782
130.2095
67.6667
97.9714
114.4095
0.0774
-0.2567
Osoyoos addition 1
crop
0.0655
24.2569
5.1486
124.8889
64.7778
97.2222
101.2222
0.0202
-0.2195
Osoyoos Haney 3
crop-lost-pasture
0.0969
28.6420
4.5800
114.0000
94.0000
119.0909
131.1818
0.0483
-0.1651
Osoyoos Haney 7
crop
0.4909
30.0179
4.4658
109.6964
106.9464
134.0536
129.5893
-0.0169
-0.0957
Redwing developmnt
cultural
1.8259
25.7702
5.2502
80.5692
106.4667
135.2974
89.9897
-0.2011
0.0839
Skaha dev addition 1
cultural
0.0594
26.4063
5.1645
86.1250
107.2500
136.3750
92.7500
-0.1904
0.0725
cultural
0.0510
26.8750
5.2879
84.3333
112.2222
142.1111
91.3333
-0.2175
0.1026
cultural
0.0348
26.3000
4.5551
94.8000
88.8000
117.4000
119.8000
0.0101
-0.1486
cultural
0.038
25.9821
5.0914
71.5714
105.2857
132.2857
106.5714
-0.1077
-0.0061
82E70
exposed
0.8445
27.4335
5.2665
65.5638
117.2979
144.4787
111.5957
-0.1284
0.0249
TNT MC 1
exposed
0.3810
47.3780
4.7299
133.0000
213.3095
224.0952
187.6429
-0.0885
0.0640
TNT MC 2
exposed
0.1844
31.0408
5.2596
71.5652
135.8261
163.2609
126.0000
-0.1288
0.0375
TNT MC 3
exposed
0.0998
30.2692
5.2656
72.2308
132.0000
159.3846
120.6923
-0.1381
0.0447
White Lake
exposed
9.6347
33.2827
5.1022
77.1011
147.2882
169.8160
138.3185
-0.1022
0.0314
82E33
intact
18.058
24.1209
5.4409
64.4163
108.3622
131.2390
81.9176
-0.2314
0.1390
Deadman Lake
intact
9.430
19.1225
6.1320
53.6180
86.3561
117.2588
48.7267
-0.4129
0.2786
East of SOWMA
intact
5.330
11.2738
6.9406
22.7814
49.2996
78.2470
30.0526
-0.4450
0.2426
East of SOWMA
intact
5.330
14.0313
5.4521
36.0000
52.5000
76.5000
59.5000
-0.1250
-0.0625
East of SOWMA
intact
5.330
13.5536
5.6917
33.1429
52.7143
77.1429
53.8571
-0.1778
-0.0107
Green Lake
intact
13.430
14.0267
7.8039
22.8690
59.4199
109.4622
32.6754
-0.5402
0.2904
North of Deadman
intact
5.2995
12.8566
6.8221
26.9874
52.2000
87.7099
38.8090
-0.3865
0.1471
Norton Pond
intact-emerg
0.1573
40.4286
2.3380
82.1905
64.0000
93.0952
82.1429
-0.0625
-0.1241
Osoyoos 2
intact
0.4407
10.3945
6.7724
23.7083
40.2292
70.3958
31.9792
-0.3753
0.1143
Site Name
Change Analysis
Classification
East of Deadman
BLUE
GREEN
RED
Near
Infrared
Normalized
Difference
Water
Index
-0.2415
Hectares
Brightness
Maximum
Difference
intact
0.4578
18.3833
5.2006
68.4528
64.9057
95.6038
65.1698
Normalized
Difference
Vegetation
Index
-0.1893
Osoyoos 5
intact
0.3160
21.8024
5.5449
79.5676
83.7297
120.8919
64.6486
-0.3031
0.1286
Osoyoos 6
intact-emerg
0.2008
19.6603
4.7259
79.2174
63.3478
92.9130
79.0870
-0.0804
-0.1105
Osoyoos Haney 6
intact-emerg
0.0964
19.9583
4.5637
83.5000
57.5833
91.0833
87.1667
-0.0220
-0.2044
Osoyoos Kambo
intact
0.3027
19.3378
4.8945
72.2162
66.0541
94.6486
76.4865
-0.1061
-0.0732
Penticton reservoir
intact
2.623
16.1540
5.2673
47.7943
60.5638
85.0887
65.0177
-0.1337
-0.0355
Schafer south
intact
0.811
15.6600
5.2840
50.9907
52.5327
82.7477
64.2897
-0.1255
-0.1006
intact
0.026
15.4063
5.4361
51.5000
52.7500
83.7500
58.5000
-0.1775
-0.0517
Skaha Park
intact
0.4876
14.8371
5.9588
43.2143
58.7500
88.4107
47.0179
-0.3056
0.1109
SOWMA oxbow
intact
5.760
16.4437
5.1390
57.6099
54.5390
84.5035
66.4468
-0.1196
-0.0984
SOWMA oxbow
intact
5.760
19.5938
4.7496
64.9375
66.0000
93.0625
89.5000
-0.0195
-0.1511
SOWMA oxbow
intact
5.760
16.8493
5.0133
65.2353
53.3529
84.4706
66.5294
-0.1188
-0.1099
SOWMA oxbow
intact
5.760
15.4483
5.3688
52.2099
51.7531
82.9383
60.2716
-0.1583
-0.0760
SOWMA oxbow
intact
5.760
16.8008
5.1076
56.1250
55.5000
85.8125
71.3750
-0.0918
-0.1251
SOWMA oxbow
intact
5.760
15.4150
5.1586
55.1200
48.1200
79.5200
63.8800
-0.1091
-0.1407
SOWMA oxbow
intact
5.760
14.7600
5.5679
46.0372
51.4833
82.1822
56.4572
-0.1856
-0.0461
Summerland resrvoir
intact-emerg
6.3909
19.4141
2.4249
30.2426
35.8497
47.0774
43.0893
-0.0442
-0.0917
Tuculnuit Lake
intact
47.051
11.5781
7.5165
20.6234
48.8179
87.0272
28.7816
-0.5029
0.2582
Vaseux DUC 1
intact
0.844
14.6998
5.5515
48.0704
54.9859
81.6056
50.5352
-0.2351
0.0422
Vaseux NWA 3
intact
0.280
13.5165
5.6728
39.7353
49.4412
76.6765
50.4118
-0.2067
-0.0097
82E120
intact-emerg
0.449
24.5529
4.8780
92.6923
76.2115
104.1731
119.7692
0.0696
-0.2223
Quintal rushes
intact-emerg
0.683
19.7244
4.5453
72.2436
65.1410
89.6538
88.5513
-0.0062
-0.1523
South of Gallagher
intact-emerg
2.125
21.4936
4.4757
91.5133
65.2301
96.1991
90.9558
-0.0280
-0.1647
Vaseux DUC 2
intact-emerg
31.587
18.9939
4.7306
74.3990
62.4158
89.8521
77.2348
-0.0755
-0.1061
Vaseux NWA 1
intact-emerg
12.009
22.9374
4.3713
96.5415
70.3987
100.2663
99.7919
-0.0024
-0.1727
SOWMA oxbow ripn
intact-shrub
8.582
20.9314
5.0087
104.8395
55.8046
88.4454
85.8122
-0.0151
-0.2119
Site Name
Change Analysis
Classification
Osoyoos 4
BLUE
GREEN
RED
Near
Infrared
Normalized
Difference
Water
Index
-0.0020
Site Name
Change Analysis
Classification
Vaseux NWA 2
intact-shrub
Vaseux NWA 4
intact-shrub
Ramage Pond
exposed
Ritchie Lake
crop-lost-pasture
South of gravel pit
89.8301
Normalized
Difference
Vegetation
Index
0.0046
Normalized
Difference
Water
Index
-0.2188
Hectares
Brightness
Maximum
Difference
2.795
21.0300
4.7584
100.0686
1.655
19.7768
4.5753
85.7345
60.6780
90.4859
79.5311
-0.0644
-0.1345
0.9648
35.8254
4.8903
95.0654
161.2710
175.1963
141.6729
-0.1058
0.0647
2.4954
31.0917
6.5112
132.8528
82.3887
79.7811
202.4453
0.4346
-0.4215
lost
0.9055
25.3886
4.7176
100.0396
79.5743
106.8317
119.7723
0.0571
-0.2016
Thomas Reservoir
lost
1.576
27.9453
4.7060
108.3988
91.9345
115.2798
131.5119
0.0658
-0.1771
82E129
crop-lost-pasture
0.8773
30.3450
5.9048
179.1800
67.0800
70.8200
168.4400
0.4080
-0.4304
North of Hwy 3
pasture-hay
5.6746
25.7299
4.7508
86.6000
89.6780
113.1627
122.2373
0.0385
-0.1536
Penticton airport 1
crop-lost-pasture
5.234
29.8838
4.6890
113.5763
102.3645
122.0754
140.1239
0.0688
-0.1557
Penticton airport 2
crop-lost-pasture
3.708
28.2996
4.9384
139.7549
81.5588
112.3922
119.0882
0.0289
-0.1870
Sleeping Waters
pasture-hay
2.2004
30.1557
5.2193
82.4914
113.2629
129.3448
157.3922
0.0978
-0.1630
South of Hwy 3
pasture-hay
6.2216
26.6684
5.0300
82.1566
93.7054
116.6899
134.1426
0.0696
-0.1775
Vaseux recon
crop-lost-pasture
9.6016
27.9287
4.8546
108.9313
90.4394
111.9051
135.5838
0.0957
-0.1997
BLUE
GREEN
RED
Near
Infrared
57.5784
89.0033
Appendix D: Accuracy Assessments of Field Assessment Sites
Site Name
Baseline Polygon
Accuracy Assessment
Field Classification
Change Analysis
Classification
Change Analysis Accuracy
Assessment
East of Deadman
Mountain Ave
Oliver east of river
Osoyoos addition 1
Osoyoos Haney 3
Osoyoos Haney 7
Redwing development
82E70
TNT MC 1
TNT MC 2
TNT MC 3
White Lake
82E33
Deadman Lake
East of SOWMA
Green Lake
North of Deadman
Norton Pond
Osoyoos 2
Osoyoos 4
Osoyoos 5
Osoyoos 6
inconclusive
accurate
inaccurate
n/a
inconclusive
accurate
check files
accurate
accurate
accurate
accurate
accurate
n/a
n/a
n/a
accurate
accurate
accurate
accurate
accurate
accurate
accurate
n/a
inconclusive
accurate
accurate
crop
crop
crop
crop
crop
crop
cultural
cultural
cultural
cultural
cultural
exposed
exposed
exposed
exposed
exposed
intact
intact
intact
intact
intact
intact
intact
intact
intact
Intact emerg
crop
crop-lost-pasture
crop
crop
crop-lost-pasture
crop
cultural
cultural
cultural
cultural
cultural
exposed
exposed
exposed
exposed
exposed
intact
intact
intact
intact
intact
intact emerg
intact
intact
intact
intact emerg
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
marginal
accurate
accurate
accurate
accurate
Site Name
Osoyoos Haney 6
Osoyoos Kambo
Penticton reservoir
Schafer south
Skaha park
SOWMA oxbow
Summerland reservoir
Tuculnuit Lake
Vaseux DUC 1
Vaseux NWA 3
82E120
Quintal rushes
South of Gallagher
Vaseux DUC 2
Vaseux NWA 1
SOWMA oxbow riparian
Vaseux NWA 2
Vaseux NWA 4
Ramage Pond
Ritchie Lake
South of gravel pit
Thomas Reservoir
82E129
North of Hwy 3
Penticton airport 1
Penticton airport 2
Sleeping Waters
Baseline Polygon
Accuracy Assessment
accurate
n/a
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
inaccurate
inaccurate
accurate
n/a
inconclusive
n/a
n/a
accurate
inaccurate
accurate
accurate
intact
intact
intact
intact
intact
intact
intact
intact
intact
intact
intact
intact emerg
intact emerg
intact emerg
intact emerg
intact emerg
intact shrub
intact shrub
intact shrub
lost
pasture-hay
lost
intact
pasture/hay
pasture/hay
pasture/hay
pasture-hay
pasture/hay
Change Analysis
Classification
intact emerg
intact
intact
intact
intact
intact
intact
intact emerg
intact
intact
intact
intact emerg
intact emerg
intact emerg
intact emerg
intact emerg
intact shrub
intact shrub
intact shrub
exposed
crop-lost-pasture
lost
lost
crop-lost-pasture
pasture/hay
crop-lost-pasture
crop-lost-pasture
pasture/hay
Assessment
marginal
accurate
accurate
accurate
accurate
accurate
accurate
marginal
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
accurate
marginal
accurate
accurate
not accurate
accurate
accurate
accurate
accurate
accurate
Site Name
South of Hwy 3
Vaseux recon
Baseline Polygon
Accuracy Assessment
accurate
accurate
pasture-hay
pasture-hay
Change Analysis
Classification
pasture-hay
crop-lost-pasture
Assessment
accurate
accurate
pasture-hay
pasture-hay
pasture-hay
cultural
intact
intact
intact
pasture-hay
exposed
cultural
intact emerg
pasture-hay
n/a
n/a
n/a
n/a
n/a
n/a
n/a
crop
crop-lost-pasture
crop-lost-pasture
cultural
intact
intact
intact
exposed
exposed
intact
intact emerg
lost
not in TRIM
not in TRIM
not in TRIM
not in TRIM
not in TRIM
not in TRIM
not in TRIM
marginal
accurate
accurate
accurate
accurate
accurate
accurate
marginal
accurate
not accurate
accurate
marginal
n/a
n/a
n/a
n/a
n/a
n/a
n/a
Additional sites not used in CART analysis
Quintal oxbows
Harfman property
Vaseux north
V Line oxbow
82E8
Oliver treatment plant
Osoyoos reservoir
East of Twin Lakes
Spotted Lake
Skaha development
Max Lake
East of Quintal
82E159
Forbes Wetland
Lewes Ave wetland
Oliver developed
Oliver Haney 11
Oliver Haney 15
Osoyoos Haney 5
inaccurate
inconclusive
inconclusive
accurate
accurate
accurate
accurate
inconclusive
accurate
accurate
accurate
inaccurate
not in TRIM
not in TRIM
not in TRIM
not in TRIM
not in TRIM
not in TRIM
not in TRIM

BC Wetland Trends Project

Transcription

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