habitat change analysis project

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.


Top level view of overall habitat impacts – gains and losses


Legend

Transitional

GAIN

LOSS

Stable Habitat

Detailed change based on time of habitat change


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


-‐20,000

-‐15,000

-‐10,000

-‐5,000

0

5,000

10,000

15,000

20,000

25,000


Net Acres

Timeframe

Net Changes (acres) in Golden-‐winged Warbler Non-‐habitat Types

NON-‐HABITAT AGRICULTURE

NON-‐HABITAT NATURAL

NON-‐HABITAT URBAN

HABITAT


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




HABITAT


-‐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


-‐20,000

-‐15,000

-‐10,000

-‐5,000

0

5,000

10,000

15,000

20,000

25,000


Net Acres

Timeframe

Net Change in Golden-‐winged Warbler Habitat Types

HABITAT PRIMARY GWWA

HABITAT SECONDARY GWWA





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

[email protected]

@johnjreiser

Patrick Woerner

NJ DEP

Endangered Non-‐Game Species Program

[email protected]

609 259-‐6967

Questions, comments?

Thank you!

habitat change analysis project

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