BIODIVERSITY CONSERVATION HABITAT ANALYSIS

Transcription

1 BIODIVERSITY CONSERVATION HABITAT ANALYSIS A GIS Comparison of Greater Vancouver Regional Habitat Mapping with Township of Langley Local Habitat Mapping

2 Preface This report was made possible through the support of Environment Canada Canadian Wildlife Service (CWS). The project was overseen by Nancy Mahony (CWS), Lonnie Prouse (LEPS), and Nichole Marples (LEPS). Special thanks to Lisa Dreves (LEPS), Alison Palmer (Township of Langley), and Caroline Astley (Madrone Environmental Services Ltd.) for their support and insight in the development of this report. Langley Environmental Partners Society (LEPS) Street Langley British Columbia V3A 3Z8 The report was written by Elaine Anderson, MCIP, P.Ag. (LEPS). January

3 EXECUTIVE SUMMARY A GIS analysis was conducted to compare the habitat mapping data for the Greater Vancouver Region with the local habitat mapping data for the Township of Langley at both broad and fine scales. The comparison found that the regional mapping is fairly accurate at the broad scale with an error of about +/- 15%. Accuracy at the finer scale of analysis ranges from 7.52% for soil to 60.95% for herbs/grasses. The regional data appears to provide enough accuracy at the broad scale for general landscape level planning, but inaccuracies at the finer scale should be addressed through the integration of site specific local knowledge in order to improve monitoring. 1.0 INTRODUCTION The Canadian Wildlife Service (CWS) has taken a lead role in the development of a Biodiversity Strategy for the Greater Vancouver Region. The Biodiversity Conservation Strategy has developed a set of habitat maps for the region based on Landsat data. In order to develop a biodiversity monitoring program based on these maps, the accuracy of the maps was evaluated using local derived data from the The Township of Langley is unique within the regional district because of its detailed habitat mapping conducted as part of the Township of Langley Wildlife Habitat Strategy. The GVRD Landsat-based habitat classifications were compared to the detailed habitat mapping completed by LEPS. The LEPS mapping is based on 1995 air photos updated with 2002 and 2004 orthophotos. LEPS has ground-truthed 25% of the area, providing a good opportunity to evaluate the accuracy of the GVRD mapping. This analysis identifies which habitat types are well delineated by the GVRD and which are not. This information will be useful in developing an accurate monitoring strategy. 2.0 DATA 2.1 Overview The habitat classifications used for the local and regional mapping differ in a number of ways. Table 1 provides a comparison of the source data and interpretation method. The key differences between the data are the level of accuracy that the data provides. Table 1 Source Data LEPS GVRD Interpretation Orthophoto Satellite Scale 1:5, metre pixel Minimum polygon size 10m 2 Data set Baseline 1995 orthophotos Updated with 2002 and 2004 orthophotos Habitat classification method SHIM MSRM land cover (base) plus ancillary datasets Timberline/GeoSpatial 2002 land cover 3

4 2.2 s The British Columbia Ministry of Sustainable Resource Management s (MSRM) land cover dataset was used as the base for identifying regional scale habitat types. This data is derived from 15 metre satellite imagery. The refined land cover classification was classified into different major habitat types based on the land cover groupings. Thirty metre buffers were added to single line streams in the TRIM dataset and combined with a 30 metre buffer of all polygonal freshwater features (lakes, rivers and wetlands) to identify potential riparian areas. These potential riparian areas were identified in this way because there is a lack of detailed stream coverage across the region. Wetlands were incorporated into a separate layer. 2.3 Local habitat classifications The local Township of Langley LEPS habitat classifications are based on the SHIM methodology available for viewing at A summary of definitions for the LEPS habitat classifications are provided in Table 2 below. Table 2 LEPS Habitat Type Definitions (derived from SHIM) Class Description forest This area has a natural tree crown cover of 20 % or more of the total polygon area, and at least 80 % of the trees are conifers. forest This area has a natural tree crown cover of 20 % or more of the total polygon area, and at least 65 % of the trees are broadleaf. forest This area has a natural tree crown cover of 20 % or more of the total polygon area, but of the total trees no more then 80 % can be conifer and no more then 65 % can be broadleaf. s The area has less than 10 % tree crown cover and natural shrubs constitute 20 % or more of the ground cover. s are defined as multi-stemmed woody perennial plants, both evergreen and deciduous. The area has less than 20 % tree cover, less than 20 % shrub cover, and 20 % or more natural herbaceous cover. Herbs for this classification are defined as grass-like vascular plants, including ferns and forbs, without a woody stem. Some dwarf woody plants may be included in this category. Exposed soil Areas where recent disturbance, either human or natural, has exposed the soil substrate, such as in development sites or soil slides. The main characteristic is exposed soil under active erosion processes. Human-made Areas covered by highly impervious man-made surfaces such as surfaces (high pavement, concrete, and buildings with total impervious area > 40 intensity urban) %. This class can include industrial, commercial, and residential areas as well as roads and greenhouses. Human-made surfaces (medium intensity urban) Human-made surfaces (low intensity urban) Row Crops Areas covered by moderately impervious man-made surfaces with total impervious area between %. This class is similar to the human made surface (high imperviousness) class but more vegetation is present. Areas of low impervious human made surfaces with total impervious area < 10 %. Such areas may include low density suburban houses, barns, horse tracks, paddocks, or gravel or packed soil parking lots. Areas of agricultural crops and farmland. Agricultural areas where rows cannot be identified are classified as with an 4

5 Planted tree farm Dug-out pond or reservoir Natural wetland agriculture qualifier. Areas used as tree farms, including Christmas tree farms, ornamental tree nurseries, and fruit orchards. Dug-out ponds, either of natural or man made origin, which have been excavated and are maintained. They are mostly cleared of vegetation and may be under sudden human induced water fluctuations. This class includes natural wetlands which are largely undisturbed by human modification and retain most of their natural characteristics. 3.0 METHODOLOGY 3.1 Overview This section describes the method used to compare the LEPS data with the GVRD data. It is laid out in a recipe card format so that, if needed, others can follow the steps that were taken to compare datasets. Lessons learned are also provided to help users anticipate possible problem areas. STEP 1 Download files from ftp site Identify desired map layer (in this case LC_Desc) Clip ArcView file to Township of Langley Overlay local data and regional data in ArcGIS Issues The regional data file is huge and caused numerous computer crashes. It was difficult to know which layer to download because there are so many layers provided and very little description of each layer. Lessons Learned The operator s computer must be capable of handling a large file. Clipping the file to the study area makes the file easier to handle, although it may still be quite large. Although the Land Cover Grid Field Descriptions provided on the ftp site does provide a brief description about each layer, additional information about what each layer provides would be helpful. STEP 2 Compare regional habitat classifications with local habitat classifications. Combine classifications as necessary. Issues The regional habitat classifications are divided into eighteen habitat types, while the local habitat classifications are divided into thirteen habitat types. It was necessary to combine some of the habitat types in each dataset in order to be able 5

6 to compare the regional data with the local data. The habitat combinations are shown in Table 3 below. Table 3 Combined Habitat Classifications Township of Langley habitat classification GVRD habitat classification Combined habitat classification open closed open Planted tree farm closed open closed Wetlands Wetlands Wetlands Exposed soil s Row crops Dry grass Grass High intensity dense Medium intensity Low intensity mixed shadow Pavement Dug-out pond Intertidal/Sandbar Shadow Highly reflective The regional data and accompanying report do not provide habitat definitions although it is identified that they are derived from the original Timberline/GeoSpatial 2002 land cover classification. Without these definitions explicitly identified in the data or in the accompanying report, it is impossible to know what sort of habitat is being described in each habitat classification. The local habitat mapping conducted by LEPS provides detailed descriptions about each habitat type based on the SHIM methodology (e.g. forest has a natural tree crown cover of 20% or more of the total polygon area, and at least 65% of the trees are broadleaf). It is not possible to ground truth the regional data without these definitions since there is no indication of what to look for in each habitat classification. Lessons learned Habitat types should be defined consistently across the region from municipality to municipality to allow for direct comparisons. Habitat types should be clearly defined in the regional data in order to facilitate this comparison. 6

7 STEP 3 Compare percent habitat types between regional and local data at a broad scale across the entire Township of Langley using GIS analysis. Identify the percent of regional habitat cover that is inconsistent with local data. Compare results in order to determine whether the area represented by each habitat type is similar between the local and regional data. Issues The regional data is pixel based, while the LEPS data is polygon based. In other words, the regional data is divided up into equal pixels across the region with each pixel representing a particular habitat type. The LEPS data is divided into polygons so that each polygon represents similar contiguous habitat. This created some issues with pixels falling along the edge of polygons. Lessons learned The pixel-polygon issue was resolved by overlaying the regional pixel data on the local polygons. All pixels were split along polygon lines using ArcGIS. STEP 4 Overlay regional data with local data having a confidence ranking of 1 (local polygons with a confidence ranking of 1 have had the habitat type confirmed either by ground-truthing or other method of observation). Query ArcGIS to determine regional habitat types within the locally confirmed polygons. Summarize and compare local confidence level 1 polygons (i.e. confirmed habitat types) with regional habitat data identified in that polygon. Identify misclassification trends in the regional data. Issues The regional data was difficult to isolate into local polygons because each regional habitat type pixel was linked to every other pixel of that habitat type across the Township. Lessons learned The regional data was split in ArcGIS in order to allow the isolation of pixels within local polygons. 7

8 4.0 RESULTS This section discusses the results of the GIS analysis performed to compare percent cover of each type of habitat across the 4.1 Broad Level Analysis The results of the broad level analysis (Tables 4 and 5) indicate that the area covered by the majority of habitat types is fairly close between the local data and the regional data. The largest discrepancies are in urban (15.02%), shrubs (-12.59%), and broadleaf (10.09%). The percent discrepancy was calculated by dividing the difference between the local and regional habitat area by the total local area. A positive number indicates that there is more habitat of that type under the local classification. For example, the local data identifies 15.02% more urban habitat than the regional mapping, but 12.59% less shrub than the regional data. Potential reasons for the discrepancies found in urban, shrub, and broadleaf are discussed below. Table 4 Difference between Local and Regional Data Habitat classification Local data Regional Data Difference (local regional data) Wetlands Total Table 5 Order of Discrepancy between Regional and Local Data Order of discrepancy (highest to lowest) Difference (local regional data) % Discrepancy (discrepancy/local data) % % % % % % % Wetlands % % % (LEPS - GVRD) Total

9 The local habitat mapping identifies 15.02% more urban habitat in the Township of Langley than the regional data. The local low intensity habitat refers primarily to rural residential properties, and is likely to have a variety of habitat types within that classification because the low intensity is defined as: Areas of low impervious human made surfaces with total impervious area < 10 %. Such areas may include low density suburban houses, barns, horse tracks, paddocks, or gravel or packed soil parking lots. Because these areas are generally rural residential properties, they may also include other habitat types in small amounts, such as shrub, broadleaf, mixed, coniferous, and herbs/grasses. Since the regional data was classified using pixels rather than polygons, these smaller areas may have been picked up as different habitat types by the regional data. Consequently, the local low intensity classification may be responsible for skewing the results for the urban classification. The regional habitat mapping identifies 12.59% more shrub habitat in the Township of Langley than the local data. There is no clear reason why shrub is over-represented by the regional habitat mapping. It may be as a result of the satellite imagery failing to discern between forest cover and shrub. In fact, the fine level analysis (Section 4.2) indicates that the regional mapping incorrectly classifies 43.25% of shrub habitat as forest cover in areas of the Township that are known to be shrub. The local habitat mapping identifies 10.09% more broadleaf habitat in the Township of Langley than the regional data. The most compelling explanation of why broadleaf is underrepresented in the regional data is shown in the fine level analysis (Section 4.2). The fine level analysis indicates that the regional mapping incorrectly classifies 40.84% of broadleaf forest habitat as coniferous and mixed forest habitat (combined) in areas of the Township that are known to be broadleaf forest. In addition, both coniferous and mixed forest are overrepresented in the regional mapping at the broad level of analysis. Combining the percent that these two habitats are overrepresented results in a number that is very close to the amount that broadleaf is underrepresented as shown in the equation below: (-6.52%) + (-5.92%) = (-12.44%) 4.2 Fine Level Analysis This section describes the results of the comparison between the local data having a confidence level of 1 and the regional data in the It is assumed that the local data confidence level 1 is the most accurate data because it has been confirmed through ground-truthing or other observation. Consequently, the comparison that is 9

10 discussed here assumes that the local data is accurate and the accuracy of the regional data is assessed relative to the local data. However, it should be noted that there may be some inaccuracies in the local data due to subjective interpretation of habitat type during ground-truthing and identification of polygon boundaries during photo-interpretation. These errors are unlikely to be large, but should be kept in mind throughout this discussion. The results of the analysis are shown in Table 6 and Figures 1-9. Table 6 shows the results of the GIS analysis comparing the regional data to the local data. Discussion about each habitat cover is provided next to each figure. The y axis shows the breakdown of each habitat type shown in the x axis. In other words, the first column, broadleaf shows that the regional habitat classification correctly classifies only 30.05% of the known broadleaf habitat. Overall, the accuracy of the regional data compared to the local data is quite low. Table 6 Comparison of Regional Data to Local Data (confidence level 1) Habitat classification Wetlands Herbs/ grasses GVRD data 30.05% 9.89% 25.49% 13.40% 11.04% 18.19% 5.60% 1.43% 2.60% 15.97% 46.01% 26.72% 11.97% 3.52% 6.93% 1.36% 6.92% 6.69% 24.87% 20.94% 26.46% 19.43% 7.87% 18.13% 3.21% 5.75% 8.11% 0.48% 2.39% 0.32% 6.95% 7.52% 9.20% 10.38% 3.26% 12.48% 7.16% 7.02% 5.14% 26.16% 19.97% 19.57% 16.06% 6.96% % 4.99% 10.36% 19.19% 36.51% 21.89% 60.95% 10.77% 18.34% 0.19% 0.35% 0.69% 0.07% 0.22% 0.01% 48.50% 0.04% 2.47% 2.37% 1.53% 0.96% 12.47% 4.25% 2.03% 8.31% 41.35% 2.27% 6.39% 3.64% 1.25% 1.03% 1.63% 0.40% 8.10% 2.34% Total 100% 100% 100% 100% 100% 100% 100% 100% 100% The regional data identifies wetlands in a separate layer from the regional habitat classification data. As a result, the wetland layer was analysed separately to determine what percentage of known wetlands were correctly identified by the regional mapping for the Township of Langley (Table 7). The regional wetlands layer identified 96.32% of the known wetlands in the Table 7 Wetlands Habitat classification No Yes Wetlands 3.68% 96.32% 10

11 FIGURE % % % % BROADLEAF FOREST 30.05% of the broadleaf forest in the A combined total of 40.84% of the broadleaf forest is identified as coniferous (15.97%) or mixed (24.87%) forest. The regional data accurately describes about three quarters of the confirmed forest cover in the Township of Langley as forest cover. FIGURE CONIFEROUS FOREST 46.01% of the coniferous forest in the A combined total of 30.83% of the coniferous forest is identified as broadleaf (9.89%) or mixed (20.94%) forest. Again, the regional data is fairly accurate at identifying forest cover, but not at the specific type of forest cover. FIGURE % % % MIXED FOREST only 26.46% of the mixed forest in the A combined total of 52.21% of the mixed forest is identified as broadleaf (25.49%) or coniferous forest (26.72%). The regional data is particularly poor at identifying mixed forest, but the combined overall forest cover indicated where mixed forest is located is high at 78.67%. 11

12 FIGURE % % % WETLANDS Wetlands were analysed separately because they are stored in a different layer in the regional data. The regional mapping identifies 96.32% of the confirmed wetlands in the Township of Langley (Table 7). This chart shows the type of habitat classified in the regional data that is actually wetland. The dominant habitat types in known wetland areas according to the regional data are shrub (19.97%), mixed forest (19.43%), and herbs/grasses (19.19%). FIGURE 4 SOIL only 7.52% of the exposed soil in the The regional data identifies 36.51% of confirmed soil areas as herbs/grasses and 19.97% shrubs. These anomalies could be due to temporal changes in land use between the local mapping and the regional mapping (e.g. farmers fields and new development areas). FIGURE % % % SHRUB only 19.57% of the shrub in the The regional data identifies 21.89% of confirmed shrub areas as herbs/grasses and 18.13% mixed forest. The regional data is very poor at identifying shrub. 12

13 FIGURE HERBS/GRASSES 60.95% of the herbs/grasses in the The regional data attributes most of the remaining habitat to shrub (16.06%) and soil (10.38%). The regional data appears to be fairly accurate at identifying herbs/grasses. FIGURE WATER only 48.50% of the water in the The regional data attributes the remaining habitat fairly evenly across the various habitat types. The regional data is reasonably accurate at identifying water, although over half of the water in the Township of Langley is misclassified. FIGURE 9 URBAN 41.35% of the urban habitat in the 45.00% % % % % A combined total of 30.82% of urban habitat is identified as herbs/grasses (18.34%) and soil (12.48%). The classification of regional urban habitat into herbs/grasses and soil may due to the fact that the local urban habitat classifications were combined to include low intensity urban which may in fact include a good deal of herbs/grasses and soil. 13

14 5.0 CONCLUSIONS This analysis has shown that, at a broad level, the regional mapping is fairly accurate. The margin of error appears to be +/-15% based on the GIS comparison of the local mapping to the regional mapping. This relatively high level of correspondence between the two mapping schemes may be due to the regional data balancing out over and under estimates of habitat type. In other words, if broadleaf (for example) was over-estimated by 10% in one area of the Township and under-estimated by 10% in another area of the Township the two estimates would balance out to zero. At a finer level of analysis the comparison shows that the level of correspondence between the two mapping schemes is quite low, ranging from 7.52% for soil to 60.95% for herbs/grasses. This may be due to the fact that the local data is polygon-based while the regional data is pixel-based. For example, an area may be identified in the local mapping as mainly broadleaf and digitized as a contiguous broadleaf polygon. However, in the same area, the regional pixel based mapping may pick up ten pixels of broadleaf and three pixels of shrub. Since the unit of measure differs between the two mapping schemes, fine level comparisons are likely to yield greater errors. The regional data accurately identifies about 75% of the confirmed forest cover in the However the regional mapping is not very accurate at discriminating between the different forest habitat types (i.e. broadleaf, coniferous, and mixed). habitat is inaccurately described at both the broad and fine levels of analysis in the regional data. This may be due to the difficulty in satellite interpretation between forest cover and shrub habitat. are reasonably accurate at both the broad and fine levels of analysis. The other habitat classifications vary in their accuracy, but these variations are likely due to the combination of habitat classifications (e.g. low intensity urban included in the urban classification) or changes in land use over a relatively short period of time (e.g. soil planted with herbs/grasses). This analysis has shown that the regional mapping does provide relatively accurate broad level habitat type statistics. This information should help local governments to identify the general types of habitat within their municipality and the potential each municipality has to contribute to a regional habitat network. However, in order to improve monitoring and site level planning, inaccuracies at the finer scale should be addressed through the integration of site specific local knowledge. 14

15 BIBILIOGRAPHY Anderson, E. (2005). Wildlife Habitat Status Report Background Technical Information. Astley, C. (2005). Personal communication. AXYS Environmental Consulting Ltd. (2005). Assessment of Regional Biodiversity and Development of a Spatial Framework for Biodiversity Conservation in the Greater Vancouver Region Final Draft Report. 15