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Monitoring Landscape Dynamics John Gross I&M Annual Meeting San 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 Shared learning among I&M 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 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 Networks and Landscape Dynamics Draft protocols: • NCCN • GRYN • NCRN Bandelier National Monument March 4, 1999 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 broadleaf/grass/crop increasing canopy conifer shadow water burn DATE 1 soil snow/ice/cloud 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 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 Loss of crucial habitat outside of reserve Ephemeral habitats Dispersal or migration habitats Population source / sink habitats Land use and habitat location Animal movements Water & air quality Impoundments / hydrology Animal demography Increased exposure to human activity at reserve edge Poaching Displacement Exotics / disease (modified from Hansen and DeFries in prep) Human density Human activity Exotics / disease 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 Conventional water pollution Hydrologic modification Cities Overall population change Change in farmland acreage USGS, NLCD; NOAA CCAP, LandFire EPA National Watershed Characterization EPA National Watershed Characterization; NPS impoundments database National Atlas of the United States U.S. Census Bureau 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) 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) 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, coarseresolution 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) Lesson: Efficiencies from a highly focused group with clearly objectives. Very rapid progress and consistency. Agencies: PCA, CCRS, CSA, Universities What’s on the horizon? How can we best monitor linear park units? NETN, GLKN, HTLN Appalachian Trail & river-based parks 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 http://ecocast.arc.nasa.gov/ (figure from 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 Landscape Dynamics Breakout Session – focus on partnerships 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 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, [email protected] http://science.nature.nps.gov/im/monitor Remote Sensing and Landscape Dynamics