BC Wetland Trends Project · 2015-11-04 · BC Wetland Trends: Okanagan Valley Assessment iv...

CANADIAN INTERMOUNTAIN JOINT VENTURE

BC Wetland Trends Project:

Okanagan Valley Assessment October 2013

Authors: 1. Bruce Harrison, Ducks Unlimited Canada.

Email: [email protected], 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

BC Wetland Trends: Okanagan Valley Assessment ii

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 20-year 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 5-metre 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 iii

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.

BC Wetland Trends: Okanagan Valley Assessment iv

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.

BC Wetland Trends: Okanagan Valley Assessment v

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

BC Wetland Trends: Okanagan Valley Assessment vi

List of Tables Table 1. Dominant wetland types and pressures in Southeast Kootenay region of BC .............5

Table 2. TRIM wetland and lake polygons separated into nine different wetland categories ................................................................................................................... 17

Table 3. TRIM wetland and lake polygons separated into nine different wetland categories by wetland/lake size class. ....................................................................... 18

Table 4. 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

BC Wetland Trends: Okanagan Valley Assessment vii

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

BC Wetland Trends: Okanagan Valley Assessment 1

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.


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).


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 project1

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/

East Kootenay Region

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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).


Table 1. Dominant wetland types and pressures in Southeast Kootenay region of BC.

Dominant Wetland Types

Dominant Wetland Pressures Wetland Class1

Hydrogeomorphic Classification2

System - Subsystem - Element (Form - Subform1)

Wetland Cover Type1

General-Specific

1. Marsh 2. Shallow water 3. Wet Meadow3 4. Fen (less

common)

Palustrine – Basin –Closed (Isolated), Overflow (Discharge), Linked, Terminal Lacustrine–(as for Palustrine) Palustrine – Basin –Closed (Isolated), Overflow (Discharge), Linked, Terminal Lacustrine–(as for Palustrine) Palustrine – Basin – Closed (Isolated), Overflow (Discharge), Linked, Terminal Lacustrine–(as for Palustrine) Palustrine - Basin Lacustrine - Basin

Graminoid - tall rush, low rush/grass/sedge/ forb Aquatic - submerged, floating Non-vegetated Graminoid -grass/ sedge /forb Non-vegetated (mudflats – fresh & alkaline) Shrub – low/mix shrub Graminoid – sedge/low rush/grass /forb

1. Climate change 2. Agriculture – cattle

grazing 3. Recreation 4. Forestry 5. Invasive plants 6. Dams for

hydroelectricity generation & water storage

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


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.


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.

Figure 5. Example of variation in wetland conditions within the potential TRIM ‘baseline time period’ in the East Kootenays.

1979 1988

Figure 4. Example of TRIM wetlands (light blue) and lakes (dark blue) in close proximity.


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.

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.

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.


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)


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).

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

Figure 9. Area of purchased SPOT coverage in the South Okanagan.


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 Canada2

, 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


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 image3

. Figure 10 provides an overview of the analytical 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)

Figure 10. Schematic overview of change detection process


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.


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.


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 pasture-hay). 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.


INTACT (a) And

INTACT-EMERGENTS (b)

CROP

INTACT EMERGENTS (a)

and PASTURE/HAY

(b)

EXPOSED

CROP (a) And

CULTURAL (b)

LOST

INTACT-SHRUB

INTACT

CROP/ LOST/

PASTURE-

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.


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 Description Count of

Wetlands Wetland

Area (ha)

% of Landscape

Area

1a, 2 Intact Intact wetland dominated by shallow water cover

147 442.99 44.2%

3 Intact-shrub Intact wetland dominated by shrub vegetative cover

18 33.23 3.3%

1b, 4a Intact-emergents

Intact wetland dominated by herbaceous (emergent aquatic) vegetation cover

91 146.45 14.6%

Subtotal (Intact Categories) 256 622.67 62.1%

4b Pasture-hay Agricultural - perennial hay or pasture

27 26.88 2.7%

5 Lost Cover type is not included among these categories (wetland is assumed ‘lost’)

30 92.14 9.2%

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 Residential or industrial development

32 20.59 2.1%

9 Exposed Exposed (usually alkaline) soil with little to no vegetative growth

61 68.95 6.9%

Subtotal (Converted Categories) 254 380.62 37.9%

Grand Total 510 1,003.28 100.0%


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.

Change Class

Wetland Size Class

0-1 ha 1-5 ha 5-20 ha 20-50 ha

Count of wetlands

% of total

count

Wetland area (ha)

Count of wetlands

% of total

count

Wetland area (ha)

Count of wetlands

% of total

count

Wetland area (ha)

Count of wetlands

% of total

count

Wetland area (ha)

Intact 91 25.27 25 54.35 4 132.41 27 230.95

Intact-shrub 7 2.50 10 22.15 1 8.58

Intact-emergents 71 18.10 13 23.09 2 61.75 5 43.51

Subtotal (Intact Category)

169 48 45.88 48 45 99.59 6 67 194.17 33 73 283.04

Pasture-hay 21 8.66 4 6.32 2 11.90

Lost 18 7.38 9 19.29 2 55.07 1 10.41

Crop 23 7.64 7 16.13 2 13.12

Crop/Lost/Pasture-hay 52 21.15 15 38.60 1 46.30 4 29.10

Cultural 23 5.29 9 15.30

Exposed 44 14.90 14 24.93 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


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

User Accuracy

Intact Converted

Intact Intact-shrub

Intact-emerg

Pasture-hay

Lost Crop

Crop/ Lost/

Pasture-hay

Cultural Exposed

eCog

niti

on C

lass

ifica

tion

Inta

ct Intact 20 1 95%

Intact-shrub 3 100%

Intact-emergents 3 7 70%

Conv

erte

d

Pasture-hay 3 100%

Lost 1 1 1 33%

Crop 1 4 80%

Crop-lost-pasture 9 100%

Cultural 6 100%

Exposed 1 1 6 75%

Total 24 3 7 6 2 4 9 7 6

Producer Accuracy 83% 100% 100% 50% 50% 100% 100% 86% 100% 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 up-front, 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).

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

RE motorised terrestrialUD air pollutionAG fertilizationFC silviculture

ME roads/trailsRD sewage disposal

UD residential chemical useGR riparian/wetland

OG pipelinesFP roads

UD hydrograph changesDA flow regulation

UD flood control structuresUD water demand

RE motorised aquaticAG cultivation

DA habitat conversionFC roads

TC railwaysRD habitat conversion

RE resort developmentGR soil modification

GR vegetation modificationTC highways

UD habitat conversionNS non-native species

CC climate change

Magnitude rating Figure 14. Wetland threats in Okanagan (Source: Veridian Ecological Consulting 2004).

Extent Rating CC climate change 5

NS non-native species 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 habitat conversion 2 FC roads 3 TC railways 1 RE motorised aquatic 3 DA flow regulation 3

UD flood control structures 3

UD water demand 4

FP roads 2

UD hydrograph changes 3

GR riparian/wetland 3 OG pipelines 1 AG fertilization 3 FC silviculture 3 ME roads/trails 2 RD sewage disposal 2 UD residential chemical use 2

UD air pollution 4 RE motorised terrestrial 3


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 2-3% 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 Washington4

4 https://nccwsc.usgs.gov/display-project/5006c2c9e4b0abf7ce733f42/5006e7bae4b0abf7ce733f50

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


combination with LIDAR has been investigated by researchers from the University of Victoria for use in mapping wetlands under a coniferous forest canopy5

. 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 large-scale 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

Austin, M. A., D.A. Buffett, D.J. Nicolson, G.G.E. Scudder and V. Stevens (eds.). 2008. Taking 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/s11273-013-9326-2.

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. Hydro-climatology 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.

http://www.for.gov.bc.ca/hre/becweb/resources/classificationreports/wetlands/�

http://www.portofentry.com/Wetlands.pdf�




Appendix A: Major Datasets Considered for Use as Wetland Baseline Dataset Scale Year Principal

Means of Data Collection

Coverage Minimum Wetland Size

Comments

Soils Mapping

1:50,000; 1:100,000

Various Aerial photography

Local or Landscape

N/A 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.

Vegetation Resource Inventory (VRI)

1:20,000 1993 to present

Aerial photography

Provincial (although quality varies)

1-2 ha Less accurate and reliable than TRIM, particularly for small wetlands.

Terrain Resource Inventory Mapping (TRIM)

1:20,000 1979-1988

Aerial photography

Provincial <0.1 ha Accurate for small wetlands (although it still misses some). Spatially-explicit baseline applicable to the entire province.

Terrestrial Ecosystem Mapping (TEM)

1:20,000; 1:50,000

Various Aerial photography

Local or Landscape

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.

Baseline Thematic Mapping (BTM)

1:250,000 1992-2001

Landsat Provincial 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 C: Spectrographic Characteristics of Field Assessment Sites

Site Name Change Analysis Classification

Hectares Brightness Maximum Difference

BLUE GREEN RED Near

Infrared

Normalized Difference

Vegetation Index


Water Index

East of Deadman crop 0.4080 26.9010 5.6991 153.3125 65.8125 103.5625 107.7292 0.0197 -0.2415

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

Skaha dev addition 2 cultural 0.0510 26.8750 5.2879 84.3333 112.2222 142.1111 91.3333 -0.2175 0.1026

Skaha dev addition 3 cultural 0.0348 26.3000 4.5551 94.8000 88.8000 117.4000 119.8000 0.0101 -0.1486

Skaha dev addition 4 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




BLUE GREEN RED Near

Infrared


Vegetation Index


Water Index

Osoyoos 4 intact 0.4578 18.3833 5.2006 68.4528 64.9057 95.6038 65.1698 -0.1893 -0.0020

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

Skaha dev addition 5 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







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




BLUE GREEN RED Near

Infrared


Vegetation Index


Water Index

Vaseux NWA 2 intact-shrub 2.795 21.0300 4.7584 100.0686 57.5784 89.0033 89.8301 0.0046 -0.2188

Vaseux NWA 4 intact-shrub 1.655 19.7768 4.5753 85.7345 60.6780 90.4859 79.5311 -0.0644 -0.1345

Ramage Pond exposed 0.9648 35.8254 4.8903 95.0654 161.2710 175.1963 141.6729 -0.1058 0.0647

Ritchie Lake crop-lost-pasture 2.4954 31.0917 6.5112 132.8528 82.3887 79.7811 202.4453 0.4346 -0.4215

South of gravel pit 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


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 inconclusive crop crop accurate Mountain Ave accurate crop crop-lost-pasture accurate Oliver east of river inaccurate crop crop accurate Osoyoos addition 1 n/a crop crop accurate Osoyoos Haney 3 inconclusive crop crop-lost-pasture accurate Osoyoos Haney 7 accurate crop crop accurate Redwing development check files cultural cultural accurate Skaha dev addition 1 accurate cultural cultural accurate Skaha dev addition 2 accurate cultural cultural accurate Skaha dev addition 3 accurate cultural cultural accurate Skaha dev addition 4 accurate cultural cultural accurate 82E70 accurate exposed exposed accurate TNT MC 1 n/a exposed exposed accurate TNT MC 2 n/a exposed exposed accurate TNT MC 3 n/a exposed exposed accurate White Lake accurate exposed exposed accurate 82E33 accurate intact intact accurate Deadman Lake accurate intact intact accurate East of SOWMA accurate intact intact accurate Green Lake accurate intact intact accurate North of Deadman accurate intact intact accurate Norton Pond accurate intact intact emerg marginal Osoyoos 2 n/a intact intact accurate Osoyoos 4 inconclusive intact intact accurate Osoyoos 5 accurate intact intact accurate Osoyoos 6 accurate Intact emerg intact emerg accurate





Osoyoos Haney 6 accurate intact intact emerg marginal Osoyoos Kambo n/a intact intact accurate Penticton reservoir accurate intact intact accurate Schafer south accurate intact intact accurate Skaha dev addition 5 accurate intact intact accurate Skaha park accurate intact intact accurate SOWMA oxbow accurate intact intact accurate Summerland reservoir accurate intact intact emerg marginal Tuculnuit Lake accurate intact intact accurate Vaseux DUC 1 accurate intact intact accurate Vaseux NWA 3 accurate intact intact accurate 82E120 accurate intact emerg intact emerg accurate Quintal rushes accurate intact emerg intact emerg accurate South of Gallagher accurate intact emerg intact emerg accurate Vaseux DUC 2 accurate intact emerg intact emerg accurate Vaseux NWA 1 accurate intact emerg intact emerg accurate SOWMA oxbow riparian accurate intact shrub intact shrub accurate Vaseux NWA 2 inaccurate intact shrub intact shrub accurate Vaseux NWA 4 inaccurate intact shrub intact shrub accurate Ramage Pond accurate lost exposed marginal Ritchie Lake n/a pasture-hay crop-lost-pasture accurate South of gravel pit inconclusive lost lost accurate Thomas Reservoir n/a intact lost not accurate 82E129 n/a pasture/hay crop-lost-pasture accurate North of Hwy 3 accurate pasture/hay pasture/hay accurate Penticton airport 1 inaccurate pasture/hay crop-lost-pasture accurate Penticton airport 2 accurate pasture-hay crop-lost-pasture accurate Sleeping Waters accurate pasture/hay pasture/hay accurate





South of Hwy 3 accurate pasture-hay pasture-hay accurate Vaseux recon accurate pasture-hay crop-lost-pasture accurate

Additional sites not used in CART analysis

Quintal oxbows inaccurate pasture-hay crop marginal Harfman property inconclusive pasture-hay crop-lost-pasture accurate Vaseux north inconclusive pasture-hay crop-lost-pasture accurate V Line oxbow accurate cultural cultural accurate 82E8 accurate intact intact accurate Oliver treatment plant accurate intact intact accurate Osoyoos reservoir accurate intact intact accurate East of Twin Lakes inconclusive pasture-hay exposed marginal Spotted Lake accurate exposed exposed accurate Skaha development accurate cultural intact not accurate Max Lake accurate intact emerg intact emerg accurate East of Quintal inaccurate pasture-hay lost marginal 82E159 not in TRIM n/a not in TRIM n/a Forbes Wetland not in TRIM n/a not in TRIM n/a Lewes Ave wetland not in TRIM n/a not in TRIM n/a Oliver developed not in TRIM n/a not in TRIM n/a Oliver Haney 11 not in TRIM n/a not in TRIM n/a Oliver Haney 15 not in TRIM n/a not in TRIM n/a Osoyoos Haney 5 not in TRIM n/a not in TRIM n/a

BC Wetland Trends Project · 2015-11-04 · BC Wetland Trends: Okanagan Valley Assessment iv...

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