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Monitoring Landscape Dynamics John Gross I&M Annual Meeting San Diego, California 7 February 2006.

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Presentation on theme: "Monitoring Landscape Dynamics John Gross I&M Annual Meeting San Diego, California 7 February 2006."— Presentation transcript:

1 Monitoring Landscape Dynamics John Gross I&M Annual Meeting San Diego, California 7 February 2006

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

3 Highlight 1 – Look what we’re 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 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:

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 – I&M and NASA (Turner, Nemani, Gross) National Phenological Network Heinz Center – terrestrial and coastal groups NDMI, 1989 to 2004

8 Draft protocols: NCCN GRYN NCRN Bandelier National Monument March 4, 1999 Networks and Landscape Dynamics

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 DATE 1 increasing canopy snow/ice/cloud broadleaf/grass/crop conifer water burn shadow soil

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 MechanismType of effectMonitoring 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) Linking landscape change to park resources

13 Public data Spatial DatasetSource 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

14 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

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) Efficiencies from a highly focused group with clearly objectives. Very rapid progress and consistency. Agencies: PCA, CCRS, CSA, Universities Lesson:

18 How can we best monitor linear park units? NETN, GLKN, HTLN Appalachian Trail & river-based parks What’s on the horizon?

19 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

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 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

22 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

23 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

24 Selected Resources Landscape dynamics Landscape dynamics web site: NASA TOPS - NASA DEVELOP Internship program - Phenology and climate change: National Phenological Network - European Phenological Network - Pacific West Region Climate Change Page - John Gross ,

25 Remote Sensing and Landscape Dynamics


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