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A programmatic GIS approach to analyzing wildlife habitat change in New Jersey
NEARC 2015
Prepared by:
Patr ick Woerner, GIS Specia l ist , NJ Div is ion of F ish and Wi ldl i fe
John Reiser, GISP, Business Intel l igence Analyst , Rowan Univers i ty
Sharon Petz inger, Senior Zoologist , NJ Div is ion of F ish and Wi ldl i fe
• Rapid Urbanization/Suburban Sprawl* • Roughly ~15,000 acres per year • Rate of sprawl development gained momentum
(~7% increase) over last two decades (while less land available)
• Urban surpassed upland forest as dominant land type as of 2007
• Increased impervious surface by nearly nine football fields per day (2002-‐2007)
• Myth of Population Growth as Driver • Residential land grew nearly twice as fast as
population during 1986-‐2007 period (4x the rate of population growth in the 2002-‐2007 period)*
• Habitat Loss • Habitat Destruction • Habitat Fragmentation (loss of habitat functionality)
NJ Landscape Context
* Hasse and Lathrop (2010) Changing Landscapes in the Garden State: Urban Growth and Open Space Loss in NJ 1986 thru 2007.
NJDEP Land Use/Land Cover Data (LULC) • Statewide aerial photo
interpreted • Modified Anderson (USGS)
Classification System • Hierarchical, 86 unique codes • Available for 1986, 1995, 2002,
2007, 2012,… 2015? • Multiple uses, but intended as a
resource for change analysis
Landscape Project • Habitat mapping for E, T, SC
wildlife based on occurrences and LULC-‐derived habitat data
• Associates each species with specific set of LULC classes according to habitat requirements
• Used for conservation planning, environmental review, habitat management , acquisitions, land use regulation
Urban Growth and Open Space Loss in NJ 1986-‐2007 • Ongoing studies based on LULC
examining NJ urban growth and land use change
• Provides “report card” on urban growth and open space loss looking at time periods 1986-‐95 (t1), 1995-‐02 (t2) 2002-‐07 (t3) and 2007-‐12 (t4)
• General reporting on LULC categories used to inform policy
Basis of HCAP
• Tracking of habitat loss and fragmentation, the two greatest threats to wildlife populations
• Satisfies State Wildlife Action Plan
conservation objectives of evaluating species-‐specific and regional habitat change every five years and assessing trends in loss and conversion
• Baseline component for development of
species status assessments and recovery plans and use in Delphi Status Review process
• Tool to guide and monitor effectiveness of habitat conservation planning, land-‐use regulation and planning, land management, restoration and preservation efforts
Overview & Applications
• Programmatic approach to analysis to obtain multi-‐level estimates of habitat change
• Covers four time periods, spanning nearly
three decades (T1: 1986 – 1995, T2: 1995 – 2002, T3: 2002 – 2007 and T4: 2007-‐2012)
• Incorporates range extents for 60 species, across five taxon (birds, mammals, reptiles, amphibians, and invertebrates)
Overview & Applications Common Name
Allegheny Woodrat Least Tern
American Bi6ern Loggerhead Shrike
American Kestrel Long-‐eared Owl
Arogos Skipper Longtail Salamander
Bald Eagle Mitchell's Satyr
Banner Clubtail Northeastern Beach Tiger Beetle
Barred Owl Northern Goshawk
Black-‐crowned Night-‐heron Northern Harrier
Black Rail Northern Pine Snake
Black Skimmer Osprey
Blue-‐spo6ed Salamander Peregrine Falcon
Bobcat Pied-‐billed Grebe
Bobolink Pine Barrens Treefrog
Bog Turtle Piping Plover
Bronze Copper Red-‐headed Woodpecker
Brook Snaketail Red-‐shouldered Hawk
Ca6le Egret Red Knot
Checkered White Robust Baske6ail
Cope's Gray Treefrog Roseate Tern
Corn Snake Savannah Sparrow
Eastern Tiger Salamander Sedge Wren
Frosted Elfin Short-‐eared Owl
Golden-‐winged Warbler Silver-‐bordered FriNllary
Grasshopper Sparrow Superb Jewelwing
Gray Petaltail Timber Ra6lesnake
Harpoon Clubtail Upland Sandpiper
Henslow's Sparrow Vesper Sparrow
Horned Lark Wood Turtle
Indiana Bat Yellow-‐crowned Night-‐heron
Kennedy's Emerald
Overview & Applications
Nuanced, multi-‐dimension species-‐ and habitat-‐ specific change metrics
• Species-‐feature label specific (e.g. nesting vs. foraging)
• Not only loss/gain/net change, but also transitions between different habitat categories
• Fragmentation analysis -‐ number of patches, average, median, minimum, maximum patch size and average, median, minimum, maximum edge-‐to-‐area ratio
• % change in habitat category in relation to total area of all habitat (all categories)
• % change in habitat category in relation to all change to non-‐habitat
• % change of a habitat category in relation to total acreage of that category
• Secondary Analysis of WMAs, preservation areas, regulated areas…
• To form basis of comparative analysis, base layers created following Landscape Project method using LULC from:
• 1986
• 1995
• 2002
• 2007
• 2012
Data Development
LANDSCAPE BASE LAYER
DATA DEVELOPMENT
LULC
Major Roads
Landscape Base Layer
Water Buffer (100m) Riparian
Clipped by
Combined with
Erased by
||
Flood Prone
Hydric Soils
Wetlands
Water Buffer (50m)
• Species-‐habitat associations derived from the Landscape Project for each unique species-‐feature label (type of occurrence) combination
• Habitat selections modified to meet purpose of change analysis
• Species-‐habitat associations based on:
• peer-‐reviewed scientific literature
• occurrence-‐land use analysis to determine preferential selection of certain habitats (i.e., LULC codes used disproportionally to their availability within a species range)
• ENSP research and expert opinion
Data Development
Attribute Descriptions
Data Development
Field Name Description biopid Internal (ENSP) identification code used for individual species spcid Another internal (ENSP) identification code used for individual species spccommonn Common name of species
lusort There are 94 lucodes for each species. This number sorts each lucode for each species.
lucode
NJDEP modified Anderson system land use/land cover (lulc) code. For more information: http://www.state.nj.us/dep/gis/digidownload/metadata/lulc07/anderson2007.html
lupick whether or not the corresponding lucode was considered to be “habitat” for a given species.
type broad category of lucode label specific category of lucode
rip_only if contains “YES”, this signifies for that specific lucode, only the area that falls within the riparian layer were selected.
patchrules Not implemented in this version of the HCAP size_req Patch size thresholds for specified species core_req “Core” requirement for specified species
habcat which habitat category the lucode was categorized as for that given species
• For reporting purposes 96 Anderson codes grouped into 18 habitat categories (habcats)
Data Development
• Species range extents built by generating minimum convex hull on occurrence area data and by incorporating biologists' feedback
• Road-‐bound blocks used as consistent units of analysis across all species
Data Development
Timber Rattlesnake Range Extent and Road Blocks
Any polygons matching the location criteria and the classification criteria are included and coded for presence or absence in any time period.
Data Development
Top level view of overall habitat impacts – gains and losses
Habitat Change
Legend
Transitional
GAIN
LOSS
Stable Habitat
Detailed change based on time of habitat change
Habitat Change
Legend
Transitional
GAIN T1
GAIN T2
GAIN T3
GAIN T4
LOSS T1
LOSS T2
LOSS T3
LOSS T4
Stable Habitat
• Species range extents built by generating minimum convex hull on occurrence area data and by incorporating biologists' feedback
Golden-‐winged Warbler
Golden-‐Winged Warbler Range
Suitable habitat selected based on range extent and matching land use codes.
Golden-‐winged Warbler
Top level view of overall habitat impacts – gains and losses
Golden-‐winged Warbler
Legend
Transitional
GAIN
LOSS
Stable Habitat
Detailed change based on time of habitat change
Golden-‐winged Warbler
Legend
Transitional
GAIN T1
GAIN T2
GAIN T3
GAIN T4
LOSS T1
LOSS T2
LOSS T3
LOSS T4
Stable Habitat
-‐12,000
-‐10,000
-‐8,000
-‐6,000
-‐4,000
-‐2,000
0
2,000
4,000
Net 1986-‐1995 Net 1995-‐2002 Net 2002-‐2007 Net 2007-‐2012 Net 1986-‐2012
Timeframe
Net Acres
Net Changes (acres) in Golden-‐winged Warbler Habitat: 1986-‐2012
Golden-‐winged Warbler
-‐20,000
-‐15,000
-‐10,000
-‐5,000
0
5,000
10,000
15,000
20,000
25,000
Net 1986-‐1995 Net 1995-‐2002 Net 2002-‐2007 Net 2007-‐2012 Net 1986-‐2012
Net Acres
Timeframe
Net Changes (acres) in Golden-‐winged Warbler Non-‐habitat Types
NON-‐HABITAT AGRICULTURE
NON-‐HABITAT NATURAL
NON-‐HABITAT URBAN
HABITAT
Golden-‐winged Warbler
Non-‐habitat Urban y = -‐2592.9x + 12122
R² = 0.9394
Habitat y = 1977.7x -‐ 7426.2
R² = 0.4341 -‐8,000
-‐6,000
-‐4,000
-‐2,000
0
2,000
4,000
6,000
8,000
10,000
12,000
Net 1986-‐1995 Net 1995-‐2002 Net 2002-‐2007 Net 2007-‐2012
Net Acres
Timeframe
Net Changes (acres) in Golden-‐winged Warbler Non-‐habitat Types
NON-‐HABITAT AGRICULTURE
NON-‐HABITAT NATURAL
NON-‐HABITAT URBAN
HABITAT
Golden-‐winged Warbler
-‐15,000
-‐10,000
-‐5,000
0
5,000
10,000
15,000
20,000
Net 1986-‐1995 Net 1995-‐2002 Net 2002-‐2007 Net 2007-‐2012
Net Acres
Timeframe
Net Change in Golden-‐winged Warbler Habitat Types
SHRUB UPLAND
SHRUB WETLAND
UPLAND FOREST CON
UPLAND FOREST DEC
UPLAND FOREST MIX
WETEMERG
WETLAND FOREST CON
WETLAND FOREST DEC
WETLAND FOREST MIX
Golden-‐winged Warbler
-‐20,000
-‐15,000
-‐10,000
-‐5,000
0
5,000
10,000
15,000
20,000
25,000
Net 1986-‐1995 Net 1995-‐2002 Net 2002-‐2007 Net 2007-‐2012 Net 1986-‐2012
Net Acres
Timeframe
Net Change in Golden-‐winged Warbler Habitat Types
HABITAT PRIMARY GWWA
HABITAT SECONDARY GWWA
NON-‐HABITAT AGRICULTURE
NON-‐HABITAT NATURAL
NON-‐HABITAT URBAN
Golden-‐winged Warbler
Statewide E&T Habitat Change
-‐150000
-‐100000
-‐50000
0
50000
100000
150000
200000
T1 T2 T3 T4
Acres
Time Period
GAIN
LOSS
Net Change
Annualized Change 11,740 13,839 11,919 4,928
Gloucester County
Harrison Township
Morris County
Washington Township
• Data was prepped using ArcGIS • Union LULC layers to create base data with Anderson Level IV codes for five time
periods.
• Eliminate sliver polygons
• Data was loaded into PostgreSQL
• Custom SQL functions perform the selections and filtering necessary
• Database views provide for easy reporting and allow for access using ArcGIS software
Data-‐Driven Analysis
• PL/pgsql functions perform selections and spatial functions to produce individual species’ habitat layers.
• Polygons are selected, a bitmask is calculated for presence of habitat within a time period, and a view is created to make an ArcGIS layer.
Habitat Selection Process
• A bitmask was employed to accurately and concisely store the habitat status for a given polygon for a given species.
• Allows for quick selection of habitat meeting certain time periods.
• Allows for easy change to species habitat status • Riparian-‐specific habitat
• Core/patch size requirements
Using a Bitmask
• Some species have additional constraints, such as: • Certain land uses must be within a riparian zone to be considered habitat
• Land use patches must exceed a certain size
• “Core” (inward buffering) of a habitat patch must exceed a size threshold
• PL/pgsql functions handle these constraints in additional passes over the data.
• Wherever possible, simple value comparisons are performed instead of spatial comparisons, which are expensive.
• Riparian is a “precompiled” flag for each base polygon
• Core threshold function requires spatial analysis – all performed in SQL • SELECT newregionid, period,
ST_Multi(ST_MakeValid( ( ST_Dump(ST_Buffer(shape, -295.276)) ).geom )) as shape FROM biopid45_rd
• PostGIS has many spatial functions and operators.
Additional Habitat Constraints
• Once all of the habitat layers have been calculated, we can count the number of species that consider a given base land use polygon as potential habitat.
• Counts are performed for each time period and can be used to show change in available habitat due to increased development.
Species “Richness”
• Using Tableau with the spatial database enables interactive dashboards to be created for all of the species.
• An interactive website with graphs, maps, and other reports planned for mid-‐2016.
• Interactive demo of a habitat change dashboard.
Interactive Reporting
• Having this process in PL/pgsql and PostGIS has considerable benefits: • ArcGIS ModelBuilder could not handle multiple iterations (species, time periods).
• ArcGIS Desktop was slow to perform individual steps.
• Parameters (such as a species’ land use – habitat preferences and patch size requirements) are in tables – no need to modify the SQL.
• Time to produce a single habitat feature class ranges from 11 seconds to 7 minutes.
• Entire analysis can be recalculated in a matter of hours.
• PostgreSQL and PostGIS are free, well supported software projects.
• All of the logic is in version control.
• Potential drawbacks: • Spatial SQL may be unfamiliar and is a different approach to the data than Desktop
GIS analysis.
• Need a DBA (or become familiar with PostgreSQL)
Spatial Analysis within a Database
John Reiser
Rowan University
Analytics, Systems, and Applications
@johnjreiser
Patrick Woerner
NJ DEP
Endangered Non-‐Game Species Program
609 259-‐6967
Questions, comments?
Thank you!