File Geodatabase Feature Class
transition zone, baseline, SFBJV, wetland, estuary, sea level rise, climate change, conservation, marsh, ecotone
As a component of its Inventory and Monitoring program, the San Francisco Bay Joint Venture (SFBJV) has a need to identify the baseline acreage of transition zone within its area of responsibility . The SFBJV requires this data as a foundation for tracking changes to the distribution and acreage of transition zone as a means for evaluating progress towards meeting habitat goals. This shapefile represents the "baseline" distribution and extent of tidal marsh-upland transitional zone elevations (aka ecotones) within San Francisco Bay (South and Central), San Pablo Bay and Suisun Bay.
The GIS dataset represents the ‘baseline’ distribution and acreage of estuarine-upland transition zone elevations that are both (a) exposed to tides; and (b) adjacent to a saline marsh. The baseline data can be used to track changes to transition zone distribution and acreage over time. The GIS dataset not only represents the current baseline distribution of transition zone, but also distinguishes between transition zone that is either in the ‘backshore’ (adjacent to both a tidal wetland and an upland) or in the ‘mid marsh’ (only adjacent to a tidal wetland).
The SFBJV baseline transition zone GIS dataset is derived from the ‘Bay Margin’ estuarine-upland transition zone decision support system (DSS) funded by the USFWS and California Coastal Conservancy. See http://climate.calcommons.org/dataset/san-francisco-bay-estuarine-terrestrial-transitional-zone-decision-support-system for more information on the DSS.
Included below is an overview of the steps taken to modify and improve the current transition zone distribution (and rankings) layer in order to establish a ‘baseline’ for transition zone acreage through the SFBJV area of responsibility. The SFBJV baseline transition zone GIS layer differs from the original dataset in that it contains only transition zone, that: (1) is exposed to tides (“actual” transition zone); and (2) is adjacent to a saline emergent wetland. The resulting SFBJV baseline transition zone GIS layer is considered to represent ‘actual’ existing transition zone distributions (ie transitional elevations exposed to tidal action and adjacent to saline marsh) in SF, San Pablo and Suisun bays.
Although the SFBJV 'baseline' transition zone is considered to represent "current conditions" (July, 2018) it utilizes GIS datasets from a wide range of dates. These datsets include: NOAA (C-CAP, 2010), CDFW (CalVeg, 2007/14), NOAA/USGS (2m BathyTopo Lidar, ~2010), NOAA (tides and currents), Point Blue (tidal/non-tidal mask, ~2010), and GreenInfo (CPAD). The distribution and quality of transition zone are based on GIS models that use best available (and the most recent) tidal and habitat datasets. As a result, the accuracy of the baseline transition zone layer produced from this process is only as good as the underlying datasets used in our model.
Brian Fulfrost, Brian Fulfrost and Associates (BFA) - firstname.lastname@example.org David Thomson, San Francisco Bay Observatory (SFBBO)
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|Minimum (zoomed out)||1:150,000,000|
Brian Fulfrost, Brian Fulfrost and Associates (BFA) - email@example.com David Thomson, San Francisco Bay Observatory (SFBBO)
‘Bay Margin’ Transition Zone Our first step was to develop a technical report (for a complete discussion of the products and methods used to develop these original GIS datasets see the ‘Bay Margin’ website on the California Climate Commons) containing a detailed characterization of the physical and biological properties of transition zones with respect to the functions of the tidal marsh ecosystem, including resilience to sea level rise, and the needs for obligate fauna. A list of indicators were developed based on these functions and utilized to map their distribution and assess their quality. For the purposes of tidal marsh ecosystem recovery in San Francisco Bay, critical habitats at the bay’s margin are defined as those occurring between the marsh “plain” or the zone of regular flooding, through estuarine-terrestrial transitional habitats (“Transitions”), and into some portion of adjacent terrestrial habitats. Our second step, was to map the distribution of transitional zone distributions via tidal elevation modeling based on tidal elevation ranges documented in the Transitional Zone Definition Report. The resulting GIS data includes both ‘actual’ (exposed to tides and within potential tidal elevation range) and ‘potential’ (not exposed to tides but within potential tidal elevation range) transition zone distributions. Finally, current transition zone distributions (actual and potential) were then ranked according to a range of mappable metrics of habitat function, including linkages to both wetlands and uplands, landscape metrics (width, size and shape), and adjacency to development. Rankings are based on indicators of habitat function described in the Transitional Zone Definition Report. Transition Zone Distributions Once we had characterized transition zone habitat with the help of experts, we mapped the distribution of transitional zone based on tidal and elevation constraints. The width of transitional habitat is largely determined by the extent of the irregularly-flooded tidal zone, which modifies the salinity of the soil, and the consequent distribution of flora. High resolution Lidar (1 meter) was combined with tidal surfaces created from NOAA tidal gauges to identify and map the range of tidal elevations corresponding with transition zone habitat. Two tidal rasters (converted to NAVD88) were generated from the tidal gauge data to assist with mapping the lower and upper limits of Transitions. The first was an interpolated surface of MHHW (using ~ 40 tidal gauges) and the second was a trend surface of the difference between MHHW and HOWL (using around ~16 tidal gauges) to account for tidal variability at the upper limit of the estuary. The Lidar elevation data was merged with the MHHW surface in ArcGIS so elevation represented elevation relative to MHHW for the entire SF Bay. The “range” of potential Transitions was identified as .31 meters above MHHW as the lower limit to HOWL + .27 meters as the upper limit. Raster output from the first order model was converted to vector polygons, simplified and adjacent polygons (w/i 1 meter) were merged. The final transitional zone patches were categorized as either ‘tidal’ or ‘non-tidal’ based on boundaries provided by Point Blue. Ranking Transition Zone Habitat Quality Each transition zone polygon identified was then ranked according to a series of 9 metrics representing the indicators of habitat quality. Index values (positive and negative) were created for each metrics in order to rank each transition polygon according to habitat quality. Indicators values were summed into a combined index representing potential value to tidal marsh ecosystem management. Both actual transitional elevations (“levee on”) and potential transitional elevations (“levee off”) were assigned index values. A major use of the characterization report was to identify indicators of habitat “quality” and function, that could be used to map and rank the restoration potential of Transition. We identified the 3 most salient (and practical) indicators to be used in our GIS based suitability model. These included: (a) Transition width - 30 meters was identified as a minimum width for functional habitat; (b) Transition Shape and Size - areas with more core area were determined to provide better overall habitat function (especially for wildlife) and a minimum area of 900 m2 (based on the 30 m width) was used as threshold of adequate habitat size ; and (c) adjacent habitat (or development) - habitat connectivity was identified as a major influence on habitat function and as a result we ranked transition zones positively according to their adjacency to wetlands (tidal and freshwater) and uplands. We ranked transition zones patches (actual and potential) using the following nine metrics (max ranking value = 200+): Mean Width (-10 to 30) Adjacent Urban (-30 to 0) Shape (-10 to 10) Adjacent Agriculture (-15 to 0) Adjacent Tidal Wetland (0 to 30) % developed (0 to 50) Adjacent Freshwater Wetland (0 to 30) tidal/non-tidal (50/25) Adjacent Upland (0 to 30) Data Sources: NOAA (C-CAP 2010), CDFW (CalVeg 2007/2014), NOAA/USGS (2m BathyTopo Lidar ~ 2010), NOAA (tides and currents ), Point Blue (tidal/non-tidal mask, ~2010), and GreenInfo (CPAD). SFBJV Baseline Transition Zone The SFBJV baseline transition zone layer was derived from the current transition zone distribution (and rankings) GIS layer created for the Bay Margin DSS. SFBJV Science Steering Committee (SSC) After a year long discussion, the SSC made a decision to adopt the use of the bay margin GIS data for calculating baseline transition zone - with some key modifications (and eventually some additional improvements). Since the function of the baseline data was to be able to track changes to current conditions, the committee decided that to be included in the ‘baseline’, transition zone elevations needed to meet the following conditions: exposed to tidal action (‘actual transition zone), and adjacent to a saline wetland. GIS Processing We utilized the attributes already available in the Bay Margin GIS to remove transitional elevations not meeting the criteria identified by the SSC. As part of this process, and with the help of the SSC, we also identified and removed a significant amount of error in Suisun bay and to a much lesser degree in San Pablo bay. Step 1a: Identify Tidal Only We queried the Bay Margin GIS layer for transition zone distributions that were tidal. As part of Transitional elevations had already been categorized as either tidal/non-tidal using a mask provided by Point Blue. Polygons meeting these criteria represent ‘actual’ transition zone elevations since they are currently exposed to tidal action. The accuracy of this query depends largely on the tidal/non-tidal mask developed by Point Blue for their predictive tidal marsh planning tool (Veloz et al, 2014), which we used to differentiate transition zone elevations. After some QA/QC (and edits) early on in the Bay Margin project, we found the mask more than adequate to meet our needs, especially at the landscape scale. Step 1b: Identify Adjacent to Saline Wetland We queried the results of Step 1a to only include tidal transitional elevations that were also adjacent to a saline wetland. We used the index value representing the size (and length of shared boundary) of adjacent tidal wetland. This attribute was part of the metrics used for ranking restoration potential of transitions for the Bay Margin DSS. It was calculated using vegetation data from CalVeg. Transition zone elevations were selected for any value (representing saline wetland adjacency) over zero. As a result, transitions included in the baseline dataset can be adjacent to a saline wetland of any size (and length of shared boundary). Step 2: Identify and Remove Error from Lidar We reviewed the transitional elevations resulting from Step 1 with the help of the SSC. During this process we identified errors in the Lidar we used to model tidal elevations. The “bare earth” elevations in locations of freshwater or brackish marshes were not actually ‘bare earth’ but instead top of vegetation. As a result, we falsely identified a significant amount of potential Transitional elevations, mostly in Suisun Bay . We removed areas identified as Transition zone within Suisun (~2300 acres) and San Pablo (~40 acres) bays, that were also identified as fresh or brackish vegetation using recent vegetation datasets. Two major techniques for removing locations with these errors were suggested (see below). These techniques included, either: reprocessing lidar with offsets; or using available vegetation datasets to remove transition zone elevations in locations of freshwater or brackish vegetation. Although reprocessing Lidar has been used in other places to mitigate this bare earth issue in wetlands, we determined the work involved was potentially too time intensive and costly. At the same time, GIS datasets of vegetation in Suisun Bay, where most of the errors existed, are readily available. As a result, we chose to use using available vegetation datasets to remove these underlying lidar issues. Removing Errors using Vegetation Datasets We identified all the brackish and freshwater vegetation from the most recent GIS datasets from Napa, Sonoma and Solano counties. We then removed the areas of brackish and freshwater vegetation that overlapped with the remaining transition zone elevations (tidal and adjacent to wetland). CDFW Suisun Bay vegetation (2015) - Solano county only Sonoma Vegetation (2017) CDFW Napa vegetation (2002) SF Bay NERR Rush Rush vegetation data (2014) Any multipart polygons that remained after removing the area of overlap were ‘exploded’ into single part polygons so habitat adjacency could be recalculated. Also, any remaining polygons smaller than 8 square meter were removed. North Suisun Bay We evaluated two other approaches to remove errors in north Suisun Bay (where most of the error existed) based on suggestions from the SSC members and project partners. These included: Only removing errors where fresh or brackish vegetation persisted over time within these marshes (using CDFW vegetation data from 2012 and 2015) in order to account for the shifting salinity of marshes within Suisun Bay Using an “offset” raster provided by Point Blue that accounted for the underlying Lidar issue with bare earth heights in locations of fresh and brackish marshes After a significant amount of QA/QC of the results of these approaches using high resolution aerial photography, neither adequately removed enough of the obvious error, especially when compared to the results when we used the most recent vegetation data of north Suisun bay (2015). South Suisun bay In south Suisun bay (Contra Costa County) we manually removed vegetation based on an online literature search focusing on the vegetation composition of relevant marshes and shoreline. We reviewed online materials with a focus on identifying if marshes in south suisun bay were either saline or brackish/freshwater. We removed approximately 300 acres as a result of this process. Pacheco creek (lower walnut creek restoration project) existing habitats draft from May 2017 appears to be salt marsh / ruderal https://www.jmlt.org/pacheco_marsh.html http://www.co.contra-costa.ca.us/5792/Reports-Documents Point Edith Wildlife Area / Hastings Slough From Adapting to Rising Tides Report: “Dominant species in Point Edith Marsh are alkali bulrush, pickleweed, Olney’s bulrush, tule, broadleaf cattail, rush, saltgrass, and sedges.” http://www.adaptingtorisingtides.org/wp-content/uploads/2015/09/Natural-Areas-Chapter_draft-Feb-2016.pdf A review of areas west of hastings slough identified as transition zone (and uplands by Point Blue) appear to be brackish or freshwater veg (or invasives like lepidium). Used bing maps birds eye for extra area of review (see HastingsSlough_BingMaps_birdseye.jpg as example). Bay Point Regional shoreline / wetlands From Bay Point Regional Shoreline Map (brochure): “The brackish seasonal wetlands are man-made ponds, with slightly brackish to strongly saline soil conditions that promote dominant species such as alkali bulrush, pickleweed, saltgrass, Italian ryegrass, rush, rabbits foot grass, and lamb’s quarters. Intermixed with these species are Mexican rush, heliotrope, umbrella sedge, and others. The tidal marshlands support brackish, tidal marsh species such as bulrush, alkali bulrush, broadleaf cattail, and narrowleaf cattail. Other less dominant species include Mexican rush and annual saltmarsh aster.” Picture of site accessed online (https://www.ebparks.org/parks/bay_point/default.htm#trailmap) looks like brackish marsh Concord Naval Weapons Station Removed vegetation using high resolution oblique aerial imagery from Microsoft Bing Maps (accessed May and June 2018) Step 3: Recalculate Saline Wetland and Upland Habitat Adjacency After the areas of freshwater and brackish vegetation were removed from the preliminary baseline transition zone polygons, we recalculated (using CalVeg) the index values of any polygons modified by previous steps. Index values that were recalculated included upindex, the metric of upland adjacency, and tideidx, the metric of salt marsh adjacency. Step 4: Backshore vs. Mid-Marsh ‘Transition Zone’ Although we removed a significant amount of error (~2650 acres), the resulting transition zone datasets continued to contain locations of transitional zone elevations within the ‘mid marsh’. In some locations (e.g. Rush ranch) these were identified by members of the SSC as error (at the same time the Rush ranch vegetation dataset provided by NERR identified these ‘errors’ as locations of salt marsh vegetation). As a result of this concern, we added an attribute to the SFBJV baseline transition zone that distinguishes transition zone within the ‘mid-marsh’ from that at the ‘backshore’. In order to do this efficiently (without the need of additional analyses), we used the underling bay margin attribute data used for ranking restoration potential. Backshore Transitional elevations that were (tidal and) adjacent to both a saline wetland and an upland (both metrics values greater than zero) were tagged as ‘backshore’. Mid-marsh Transitional elevations that were (tidal and) adjacent to a saline wetland (metric value greater than zero) were tagged as ‘mid-marsh’. Transition zone elevations that have been categorized as being within the mid marsh plain, are either one of the following: remaining error from the underlying Lidar and/or in our tidal modeling (not captured in the vegetation datasets used), or potential transitional zone ground elevations. The ”mid marsh” as it is used here includes not only locations that are actually within the marsh but also could include: levee flanks, areas along the periphery of the marsh but not adjacent to uplands (or not identified as adjacent to uplands by the Calveg datasets), and areas adjacent to sloughs - all of which could be potential transitional elevations. Although we are confident that our approach enhances the product at the landscape level, our ability to distinguish the two types of transitions our method is is only as good as the underlying data we used to identify tidal wetlands and uplands (CalVeg). As a result, we do not expect the additional attribute distinguishing the two types of transition zone (backshore and mid marsh) to be 100% accurate. Processing Steps (Bay Margin DSS) - included here as a addiotnal reference Step #1: Tidal Elevations We first mapped the distribution of potential transition zone throughout the study area based on tidal elevations associated with functioning transitional zone habitat. The mapped distribution of potential transition zone was based on combining (a) high resolution Lidar with (b) spatial models of MHHW and HOWL and then identifying tidal elevations associated with transitional zone habitat (>= .3 m above MHHW to HOWL + .27 m). Step #2: Habitat Quality Each transition zone polygon identified was then ranked according to a series of 7 metrics representing the indicators of habitat quality ( see Thomson 2013 and Fulfrost and Thomson 2015). Metrics include: 1. Width - measured as mean width of the polygon (30 meters was considered threshold) and weighted by size of polygon (index values can be both positive or negative) 2. Size and Shape - measured by minimum bounding circle and weighted by area of polygon (Index values can be both positive and negative) 3. Adjacent Tidal Wetland - measured as the size of adjacent tidal wetlands and weighted by the length of the shared boundary 4. Adjacent Freshwater Wetland - measured as the size of adjacent non-tidal wetlands and weighted by the length of the shared boundary 5. Adjacent Upland - measured as the size of adjacent upland land cover types and weighted by the length of the shared boundary 6. Adjacent Urban Area- measured as the size of adjacent urbanized land cover and weighted by the length of the shared boundary (index values are negative) 7. Adjacent Agricultural - measured as the size of adjacent agricultural land cover and weighted by the length of the shared boundary (Index values are negative) Step #3: Development (within polygon) The percentage of each transition zone polygon that was “undeveloped”was measured using urban land use classes at the parcel level . Polygons with larger undeveloped areas were given higher values for this metric. Step #4: Tidal or Non Tidal Each transition zone polygon was identified as either being within a tidal (50 points) or non tidal (25 points) location using a mask provided by Point Blue. Non tidal areas were also mapped and ranked in order to account of potential marsh migration and restoration potential. Step #5 Rankings of restoration value (and categories from extremely low to high) are assigned relative to all the potential transition sites mapped within the study area. The final restoration index values (restidx) was calculated as: (a) sum of the 10 metric indices (100+ points) + + (b) development index (50 points) + (c) tidal (50 points) or non-tidal (25 points) index GIS users can filter sites based not only on the sum total ranking but also user defined criteria (eg undeveloped, tidal, habitat adjacency, etc.) for individual metrics or sets of metrics. The 10 fields within the attribute table that represent habitat metrics (steps 2-4) and final restoration index (step 5) contain index values. Please email Brian Fulfrost (firstname.lastname@example.org) if you would also like the measurement values associated with each of these metrics (I’m happy to provide them).
Internal feature number.
Sequential unique whole numbers that are automatically generated.
Coordinates defining the features.
Length of feature in internal units.
Positive real numbers that are automatically generated.
Area of feature in internal units squared.
Positive real numbers that are automatically generated.
Index Value representing measurement of the size of adjacent upland land cover types weighted by the length of the shared boundary. values range from 0 to 30
Index Value representing measurement of the size of adjacent upland urbanized land cover types weighted by the length of the shared boundary. Urbanized land cover types tend to degrade habitat and serve as a source of higher predation on obligate fauna. Values are negative (or zero). values range from -30 to 0
Index Value representing measurement of mean width of the polygon (30 meters was considered threshold) weighted by size of polygon values range from -10 to 30
Index Value representing measurement of the size of adjacent tidal wetland habitat weighted by the length of the shared boundary. Values range from 0 to 40
Index Value representing measurement of the size of adjacent freshwater wetland habitat weighted by the length of the shared boundary. values range from 0 to 10
Index Value representing measured by minimum bounding circle weighted by area of polygon values range from -10 to 10
Index Value representing measurement of the size of adjacent agriculture land cover types weighted by the length of the shared boundary. values range from -15 to 0
The percentage of the area (as a fraction of 1) that is within a protected area.
Index value if it is within (50) a tidal area or withina nontidal area (25).
Index Value representing measurement of the percentage of undeveloped (vs open space/undeveloped) land withim the polygon. values range from 0 to 50
Indicates whether the polygon si within a tidal or nontidal location.
The sum of all metrics index values represnting Total Restoration Potential of each polygon. Values range from 0 to 200 (+/-). Values from 0 to 100 represent low restoration potential. Values from 100 to 200 represent moderate to high restoration potential.