1. Applied Geosolutions/USDA-ARS SWRC/MSU Applied Geosolutions NASA Rangeland DSS Project A Presentation of Preliminary Results for Discussion to Help Refine Our Approach November, 2007

2. Overview We are building a canopy cover dataset and decision support system (DSS) for rangeland management in Arizona. The initial version will focus on providing interagency access to a database of grassland canopy cover estimates from 2000 to the present across Arizona: a)Allowing for a better understanding of vegetation conditions over time and across the landscape b)Identifying problem areas and providing timely warning of changes in grasslands c)Improving documentation of successful management by land management agencies d)Ideally, freeing up time for on-the-ground work

3. Product: Fractional Vegetation Cover Fractional Vegetation (Canopy) Cover Map for Southeastern Arizona 0% 65%

4. Privacy Concerns Land cover imagery of Arizona is available at very high resolution and no cost through Google Earth or the Arizona Geodata Portal. Nevertheless, to allay fears of “big-brother-in-the-sky”, a password system will be used to limit access to quantitative estimates of cover to the agency that manages a parcel of land, unless the agency gives permission, or as a picture at gross resolution.

5. Strengths Trustworthy Documentation Process Understanding Resolve Differences Valid in court Weaknesses Expensive Infrequent Isolated Variability Methods Observers Interpretation Representative? Trend? The Rangeland Standard: Field Data

6. Field Data Weaknesses Expensive Infrequent Isolated Variability Methods Observers Interpretation Representative? Trend? RS Strengths Inexpensive? Frequent Extensive Variability No bias, but will depend on target Interpretation Easier, though shallower Remote Sensing’s Strengths Compensate for Field Data’s Weaknesses

7. Our Challenge An approach that takes advantage of the strengths of both on-the-ground data collection and remote sensing would be best-of-both-worlds. Is remote sensing mature enough, now, to contribute to rangeland management in Arizona? -Improved remote sensing algorithms -MODIS imagery available from mid-2000 -IT tools to manage large datasets are available -Ancillary datasets (climate, GIS) are available Clearly, remote sensing can distinguish gross differences. Our challenge is to show just what state- of-the-art remote sensing can do.

8. Your Challenge Canopy cover is only one characteristic of interest, and the accuracy or spatial scale of remotely sensed estimates may not be adequate for decision-making. Your challenge is to assess the our data and tools to determine if their application would help your agency improve its land management. Are there applications where best-of-both-world benefits justify a balance of remotely sensed, and on-the-ground data collection methods? Clearly, your agency has to determine the adequacy of our data for any particular purpose.

9. Very High Cover Irrigated Mountain San Pedro Riparian Area

10. Very Low Cover Douglas Prison Bisbee Mine Douglas – A.P.

11. Landscape Features Terrace - landfill Fencelines Border by San Pedro

12. Cover Estimation – Part 1 Total Vegetation Fractional Cover is scaled from GROUND MEASUREMENTS to LANDSAT (30 m) …. From Marsett et al. 2006.

13. Cover Estimation - Part 2 …..And then from LANDSAT scale (30 m) to the MODIS scale (250-500m). LANDSAT 30 meter resolution 16 day repeat overpass 7 spectral bands MODIS 250-500 meter resolution daily repeat overpass 7 spectral bands

14. Data Products The primary data provided is 8-day maps of canopy cover of green and senescent vegetation on grasslands, though we are optimistic about estimating cover for other vegetation types as well. a)Updated near-real-time from NASA’s MODIS sensor b)Historic data back to 2000 Vegetation cover estimates are examined within the context of other data sets: a)Weather (precipitation and temperature) b)Soil properties c)Slope and topography

15. Example: Santa Rita Pasture 12B

20. The Audubon Research Ranch Sept 30, 2000 The Audubon Research Ranch Oct 1, 2006

24. Data Delivery System This information will be provided in 3 ways: 1.A web-based, interactive environment, with no software to download or GIS experience required 2.Datalayers for use within agency information systems 3.Specialized reports by allotment or land unit Simple but comprehensive analysis and reporting tools in the web- based environment will be designed to allow end-user to: 1.Compare multiple allotments 2.Generate time series plots (8-day resolution) 3.Generate maps (30 meter and 250 meter resolution) 4.What other functions are needed?

25. Web Interface to Data Just like Google Earth, the resolution will change as the user zooms in to a particular location 1.500 m MODIS based layer 2.250 m MODIS based layer 3.30 m Landsat based layer (if available) 4.1 m aerial photo layer (if available) Users will be able to overlay additional GIS information to help navigate, such as grazing allotments, ownership, roads, etc.

26. Screenshot of Prototype Web Interface

27. Interactive Report Definition The web interface will have the ability to generate reports interactively in several formats (pdf, Word, XML): a.How well does the remotely sensed data compare to the monitoring data from the allotment? b.What are the problem areas (map included in report)? c.What are the trends over time (graph included in report)? d.How do vegetation conditions compare to vegetation from areas with similar production potential? e.How has recent precipitation affected growth?

28. Batch Report-Writer The batch report-writer would read a report definition file, and then create the specified report for each area specified in a list of vector files. This series of reports could be revised annually to include new information. Similarly, an annual report summarizing the findings on all areas could also be generated.

29. Possible Additional Products Other data layers from MODIS such as NDVI or EVI greenness indices could be added fairly easily if they would be useful. We may be able to estimate height and biomass on grasslands, but that is beyond the scope of the current project.

30. Strengths We think we can quantify cover of both green and senescent vegetation, which allows for processing a time series of cover estimates throughout the year. MODIS images are essentially available for anywhere in the world, so if data are useful for Arizona, other states could use a similar approach at low cost. Canopy cover is an important characteristic for rangelands and is influential in determining rangeland health.

31. Limitations Need to confirm the accuracy of the algorithm, especially on shrublands. Cannot estimate vegetation composition or production. 500 m images are pixellated at the allotment scale. The scale of these pixels (250 or 500m) cannot be directly compared to ecological sites, nor can they be compared directly with ground based measurements that are taken over much smaller areas. This information can complement, but not replace, on- the-ground measurements. At some point, your agency would have to pay for this information.

32. Use Scenario: Resource Management Ground measurements for monitoring are a critical part of rangeland management. This will not change any time in the near future. However, budget constraints and complicated logistics make it hard (impossible?) to visit all allotments multiple times a year. So, to which allotments do we apply our limited ground resources? How do we make difficult prioritizing decisions?

33. Use Scenario: Resource Management (cont.) Possible answers: 1. Visit politically sensitive areas 2. Visit known trouble spots 3. Visit areas undergoing abnormal change Which raises a new question: Without visiting and measuring all allotments frequently, how do we know which areas are experiencing abnormal change? Rangeland remote sensing data can help. The challenge is to develop an approach that takes advantage of the strengths of both field work and remote sensing.

34. Preparing for the Future 2007 Approach to Public Land Management (X Range Cons Y million acres) 2012 Approach to Public Land Management (X-? Range Cons Y million acres) 97?% of effort on field work 3% of effort on remote sensing 90?% of effort on field work 10% of effort on remote sensing

35. Tentative Plan 2007-Mid 2008: Develop prototype Rangeland DSS tool for Arizona using existing NASA Small Business Innovative Research (SBIR) funding. Mid 2008 to 2012: Using soft money funds yet to be found, continue to work with public land managers in Arizona and other western states to refine Rangeland DSS tools and integrate with other geospatial technologies into agency workflow to improve public land management. 2012: With mature tools and a number of years of evaluation, funds are either found to continue development/production of remotely sensed products and RDSS for public land management or not.

36. Discussion Questions How accurate do cover estimates have to be for your agency to use them? How do you see your agency using this information and set of tools (what decisions could be supported)? What would the system have to do before you would recommend adoption by your agency? What would the system have to do before your agency would pay for this information?