Monitoring Landscape DynamicsJohn Gross

I&M Annual MeetingSan Diego, California

7 February 2006

• I&M / NPS Highlights• Lessons• Into the future …

Highlight 1 – Look what we’re doing!!!

Quality, quantity, breadth, relevance

• Disturbance

• Vegetation change

• Land condition

• Phenology (plants, ice, permafrost)

• Topography (coasts, reefs, etc)

• Pattern and context

Programmatic Goals

• Wise choices

• Consistency

• Efficiency

• Institutional learning

Cohen, Kennedy – NCCN, SWAN, NCPN, SCPN

Townsend – APHN, NCRN

Reed – SWAN, NCPN, SCPN

Wang – NETN, NCBN

Hansen – HTLN, GRYN

Brock – GULN, SFCN, SECN, NCBN

Shared learning among I&M Networks and collaborators:

With External Partners:

• Workshop – NASA, PCA, CCRS, CSA, NPS

• Ecosystem modeling – NASA (SIEN – YOSE)

• NASA internship program (SIEN, Fire, USFS)

• NASA Proposals and grants:• Invasive species & fire (Welch, Paintner, Benson, Morrisette)• Monitoring proposal (Hansen et al.)• Land use and climate effects on biodiversity in 70 large parks

(Hansen & Running)• Park Science paper – I&M and NASA (Turner, Nemani, Gross)• National Phenological Network • Heinz Center – terrestrial and coastal groups

NDMI, 1989 to 2004

Draft protocols: • NCCN • GRYN • NCRN

Bandelier National Monument March 4, 1999

Networks and Landscape Dynamics

NCCN Protocol – Warren Cohen and Robert Kennedy • Very large and remote parks

• Landsat focus: cheap, consistent, historical, good near short-wave

• Track changes in broad physiognomic classes• Many changes described as proportional mixture changes

DATE 1

increasing canopy

snow/ice/cloud

broadleaf/grass/crop

conifer

waterburn

shadow

soil

NCCN Protocol – Warren Cohen and Robert Kennedy • Very large and remote parks

• Landsat is core of effort• Cheap, consistent, historical, good near short-wave sensor

• Track changes in broad physiognomic classes• Many changes described as proportional mixture changes

• Multi-tiered validation approach

• Focus on pixel-based products• Solid foundation for post-map analysis• Facilitate patch or super-pixel pattern analyses

GRYN Protocol – Hansen / Jones

• Large area

• Land use intensification in critical habitats

• Excellent conceptual models linking landscape change to resources

• Extensive use of remotely sensed and ancillary data

Mechanism Type of effect Monitoring

Change in effective size of reserve

Species area effect

Minimum dynamic area

Tropic structure

Land use and habitat area

Disturbance patterns

Wildlife populationsChanges in ecological flows into and out of reserve

Disturbance initiation and runout zones

Placement in watershed or airshed

Disturbance patterns

Water & air quality

Impoundments / hydrology

Loss of crucial habitat outside of reserve

Ephemeral habitats

Dispersal or migration habitats

Population source / sink habitats

Land use and habitat location

Animal movements

Animal demography

Increased exposure to human activity at reserve edge

Poaching

Displacement

Exotics / disease

Human density

Human activity

Exotics / disease

(modified from Hansen and DeFries in prep)

Linking landscape change to park resources

Public data

Spatial Dataset Source

Housing and population density U.S. Census Bureau (2000)

Water discharge permit records State Department of Environmental Quality; U.S. EPA

Land cover USGS, NLCD; NOAA CCAP, LandFire

Conventional water pollution EPA National Watershed Characterization

Hydrologic modification EPA National Watershed Characterization; NPS impoundments database

Cities National Atlas of the United States

Overall population change U.S. Census Bureau

Change in farmland acreage U.S. Census of Agriculture; State Agriculture Statistics Services

Trends in major dam construction U.S. Army Corp of Engineers and FEMA, National Inventory of Dams

Changes in housing density U.S. Census Bureau, “Profile of Selected Housing Characteristics”

(modified from Hansen and Gryskiewicz 2003)

Plus: roads, lights, imagery archives

• Many small parks in rapidly urbanizing landscape

• Effects of imagery resolution

• Pattern analysis based on graph theory

• Comprehensive testing and review of protocol

(Figure: Townsend et al. draft protocol)

NCRN Protocol – Townsend, Gardner, & Lookingbill

Lessons leaned

Many opportunities for broad-scale analyses

• Core vital signs,

• Major potential to use inexpensive, widely-available data,

• Change detection - use of inexpensive high-frequency, coarse-resolution data to strategically acquire expensive data,

• Scale of objectives consistent with USGS, EPA, NOAA, PCA,

• Potential for program-wide efficiencies in data processing and analysis,

• Potential collaborations at local to international scales.

Finer-scale landscape dynamics (often vegetation change)

• Partnership opportunities at regional, network or biome scale

• Many more network- or park-specific issues

• Change detection is a very big issue (resolution, cost)

Parks Canada Approach

Large collaborative project with limited set of objectives:

• Habitat fragmentation / pattern

• Vegetation succession / retrogression

• Vegetation productivity

• Biodiversity (species richness)

Efficiencies from a highly focused group with clearly objectives. Very rapid progress and consistency.

Agencies: PCA, CCRS, CSA, Universities

Lesson:

How can we best monitor linear park units?

NETN, GLKN, HTLN

Appalachian Trail & river-based parks

What’s on the horizon?

What’s in the future

Emergence of a National Phenological Network

• Seasonal changes are one of the most pervasive environmental variations on Earth

• Effects seen in agriculture, transportation, health, hydrology, etc.• Direct link between monitoring results and broader social values

• http://www.uwm.edu/Dept/Geography/npn/

Why we want a National Phenological Network

• Priority vital sign for multiple networks,

• Standardized protocols,

• Ability to use and contribute to broader context,

• Leverage activities by others,

• Excellent means to link and add value to other measures.

Implementation team meeting – March 22-23, 2006

Involves USGS, USFS, EPA, NOAA, NASA, NPS, universities

What’s in the future

Greater use of ecosystem modeling for monitoring and management

• Rama Nemani, NASA Ames – Terrestrial Observation and Prediction System (TOPS).

• Current link to NASA internship program• Pilot project with SIEN – Yosemite NP• Hope to expand to Island Royale• Educational process

(figure from http://ecocast.arc.nasa.gov/)

http://ecocast.arc.nasa.gov/

What’s in the future

Coordinated acquisition of regional to national data?

• Focus on broad-scale data sets:• Land cover, roads, population, agricultural records, pollution, etc.• Linkages to MRLC, Landfire

• Consistent evaluation, system-wide context

MRLC land cover zones

Landfire and how it’s going to help us – Dr. Kevin Ryan, USFS

Parks Canada’s approach – Dr. Donald McLennan

NASA DEVELOP interns, NPN, modeling – John Gross

Landscape Dynamics Breakout Session – focus on partnerships

Selected Resources

Landscape dynamics

• Landscape dynamics web site: http://science.nps.gov/im/monitor/lulc/LULC.cfm

• NASA TOPS - http://ecocast.arc.nasa.gov/

• NASA DEVELOP Internship program - http://develop.larc.nasa.gov/

Phenology and climate change:

• National Phenological Network - http://www.uwm.edu/Dept/Geography/npn/

• European Phenological Network - http://www.dow.wau.nl/msa/epn/index.asp

• Pacific West Region Climate Change Page -

http://inside.nps.gov/regions/region.cfm?rgn=223&lv=3

John Gross 970 267-2111, johng_gross@nps.gov

http://science.nature.nps.gov/im/monitor

Remote Sensing and Landscape Dynamics