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Monitoring Landscape Dynamics

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Title: Using Remote Sensing to Better Managing Wildlife Author: John Gross Last modified by: John Gross Created Date: 10/14/2005 2:00:59 PM Document presentation format – PowerPoint PPT presentation

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Title: Monitoring Landscape Dynamics


1
Monitoring Landscape Dynamics
  • John Gross
  • IM Annual Meeting
  • San Diego, California
  • 7 February 2006

2
  • IM / NPS Highlights
  • Lessons
  • Into the future

3
Highlight 1 Look what were doing!!!
4
Quality, quantity, breadth, relevance
  • Disturbance
  • Vegetation change
  • Land condition
  • Phenology (plants, ice, permafrost)
  • Topography (coasts, reefs, etc)
  • Pattern and context

5
Programmatic Goals
  • Wise choices
  • Consistency
  • Efficiency
  • Institutional learning

6
Shared learning among IM Networks and
collaborators
Cohen, Kennedy NCCN, SWAN, NCPN, SCPN Townsend
APHN, NCRN Reed SWAN, NCPN, SCPN Wang
NETN, NCBN Hansen HTLN, GRYN Brock GULN,
SFCN, SECN, NCBN
7
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 IM and NASA (Turner,
    Nemani, Gross)
  • National Phenological Network
  • Heinz Center terrestrial and coastal groups

NDMI, 1989 to 2004
8
Networks and Landscape Dynamics
  • Draft protocols
  • NCCN
  • GRYN
  • NCRN

Bandelier National Monument March 4, 1999
9
  • 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

10
  • 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

11
  • 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

12
Linking landscape change to park resources
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 populations
Changes 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)
13
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
Plus roads, lights, imagery archives
(modified from Hansen and Gryskiewicz 2003)
14
NCRN Protocol Townsend, Gardner, Lookingbill
  • 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)
15
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.

16
  • 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)

17
Parks Canada Approach
  • Large collaborative project with limited set of
    objectives
  • Habitat fragmentation / pattern
  • Vegetation succession / retrogression
  • Vegetation productivity
  • Biodiversity (species richness)

Lesson
Efficiencies from a highly focused group with
clearly objectives. Very rapid progress and
consistency.
Agencies PCA, CCRS, CSA, Universities
18
Whats on the horizon?
How can we best monitor linear park units?
NETN, GLKN, HTLN Appalachian Trail river-based
parks
19
Whats 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/

20
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
21
Whats 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

http//ecocast.arc.nasa.gov/
(figure from http//ecocast.arc.nasa.gov/)
22
Whats 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
23
Landscape Dynamics Breakout Session focus on
partnerships
Landfire and how its going to help us Dr.
Kevin Ryan, USFS Parks Canadas approach Dr.
Donald McLennan NASA DEVELOP interns, NPN,
modeling John Gross
24
Selected Resources
  • Landscape dynamics
  • Landscape dynamics web site http//science.nps.go
    v/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?rgn223
    lv3

John Gross 970 267-2111, johng_gross_at_nps.gov
25
http//science.nature.nps.gov/im/monitor
Remote Sensing and Landscape Dynamics
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