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Presented by Joseph K. Berry Principal, Berry & Associates // Spatial Information Systems

GIS in Natural Resources and Agriculture. Wednesday, October 8, 2014 9:30 to 10:30am North Classroom Building, Room 5032, University of Colorado at Denver.

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Presented by Joseph K. Berry Principal, Berry & Associates // Spatial Information Systems

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  1. GIS in Natural Resources and Agriculture Wednesday, October 8, 2014 9:30 to 10:30am North Classroom Building, Room 5032, University of Colorado at Denver Premise: While Natural Resources and Production Agriculture have significant differences in their respective motivations, goals, decision environments, and technological approaches, advanced Map Analysis and GIS Modelingapplications are bridging these differences. Premise: While Natural Resources and Production Agriculture have significant differences in their respective motivations, goals, decision environments, and technological approaches, advanced Map Analysis and GIS Modelingapplications are bridging these differences. This PowerPoint with notes and online links to further reading is posted at www.innovativegis.com/basis/Present/CUdenver2014/ Presented byJoseph K. Berry Principal, Berry & Associates // Spatial Information Systems Adjunct Faculty in Geosciences, Department of Geography, University of Denver Adjunct Faculty in Natural Resources, Warner College of Natural Resources, Colorado State UniversityEmail: jberry@innovativegis.com— Website: www.innovativegis.com/basis

  2. Mapping vs. Analyzing (Processing Mapped Data) • …GISis a Technological Tool involving — • Mappingthat creates a spatial representation of an area • Displaythat generates visual renderings of a mapped area • Geo-querythat searches for map locations having a specified classification, condition or characteristic • …and an Analytical Tool involving — • Spatial Mathematics that applies scalar mathematical formulae to account for geometric positioning, scaling, measurement and transformations of mapped data • Spatial Analysis that investigates the contextual relationships within and among mapped data layers • Spatial Statistics that investigates the numerical relationships within and among mapped data layers “Map” “Analyze” Geographic Information Systems (map and analyze) (Descriptive Mapping) (Prescriptive Modeling) Global Positioning System (locate and navigate) Remote Sensing (measure and classify) GPS/GIS/RS (Biotechnology) (Nanotechnology) (Berry)

  3. A Mathematical Structure for Map Analysis/Modeling GeotechnologyRS – GIS – GPS Technological Tool Analytical Tool (Continuous, Map Surfaces) Map Analysis/Modeling Mapping/Geo-Query(Discrete, Spatial Objects) Geo-registered Analysis Frame Map Stack  Matrix of Numbers “Map-ematics” Maps as Data, not Pictures Vector & Raster — Aggregated & Disaggregated Qualitative &Quantitative …organized set of numbers Grid-based Map Analysis Toolbox Spatial Analysis Operations Spatial Statistics Operations SpatialSTEM A Map-ematical Framework Traditional math/stat procedures can be extended into geographic space to support Quantitative Analysis of Mapped Data “…thinking analytically with maps” ArcGIS Spatial Analyst operations …over 170 individual “tools” www.innovativegis.com/basis/BeyondMappingSeries/, Book IV, Topic 9 for more discussion (Berry)

  4. Comparing Natural Resources and Agriculture (a GIS perspective) vs. Analytical Tool(Prescriptive “Why, So What and What if”) “Technical Tool” (Descriptive “Where is What”) Geo-registered Analysis Frame Map Analysis/Modeling (ContinuousMap Surfaces) Mapping/Geo-Query (Discrete Spatial Objects) Map Stack  Matrix of Numbers …organized set of numbers Spatial Analysis Spatial Statistics Spatial Statisticsseeks to map the variation in a data set instead of focusing on a single typical response (central tendency), thereby providing a Statistical Frameworkfor investigating the NumericalSpatial Relationships within and among grid map layers Spatial Analysisextends the basic set of discrete map features (points, lines and polygons) to map surfaces that represent continuous geographic space (matrix), thereby providing a Mathematical Frameworkfor investigating ContextualSpatial Relationships within and among grid map layers Statistical Framework Mathematical Framework Natural Resources Mapping/Geo-query Terrain Analysis Variable-width Buffers Emergency Response Visual Exposure Shape/Patterns Consensus Building : Agriculture Navigation Yield Mapping Nutrient Surfaces Prescription Map Spatial T-test Clustering Regression : Relative Positioning within map variables Spatial Coincidence among map variables (Berry)

  5. Spatial Analysis Operations(Math Examples) Spatial Derivative Advanced Grid Math —Math, Trig, Logical Functions MapSurface 2500’ …is equivalent to the slope of thetangent plane at a location Map Calculus —Spatial Derivative, Spatial Integral 500’ The derivativeis the instantaneous “rate of change” of a function and is equivalent to the slope of thetangent line at a point Surface Fitted Plane Slope draped over MapSurface Curve SLOPE MapSurfaceFitted FOR MapSurface_slope 65% Spatial Integral Dzxy Elevation 0% ʃ Districts_AverageElevation …summarizes the values on a surface for specified map areas (Total= volume under the surface) Advanced Grid Math Surface Area COMPOSITE Districts WITH MapSurface Average FOR MapSurface_Davg …increases with increasing inclination as a Trig function of the cosine of the slope angle MapSurface_Davg S_Area= Fn(Slope) S_area= cellsize / cos(Dzxy Elevation) Surface Theintegral calculates the area under the curve for any section of a function. Curve (Berry)

  6. Spatial Analysis Operations(Distance Examples) 96.0minutes …farthest away by truck, ATV and hiking Map Geometry —(Euclidian Proximity, Effective Proximity, Narrowness) Plane Geometry Connectivity —(Optimal Path, Optimal Path Density) Solid Geometry Connectivity—(Viewshed, Visual Exposure) HQ(start) Distance Euclidean Proximity Effective Proximity Off Road Relative Barriers On Road 26.5minutes …farthest away by truck Off Road Absolute Barrier On + Off Road Travel-Time Surface Farthest (96.0 min) Plane Geometry Connectivity HQ (start) Shortest straight line between two points… …from a point to everywhere… …not necessarily straight lines (movement) Truck = 18.8 min ATV = 14.8 min Hiking = 62.4 min …like a raindrop, the “steepest downhill path” identifies the optimal route (Quickest Path) Solid Geometry Connectivity Rise Run Tan = Rise/Run …so what happens when you calculate visual connectivity from all road locations (like tossing a handful of rocks)— Seen if new tangent exceeds all previous tangents along the line of sight Visual Exposure Density Surface Splash Viewshed (Berry)

  7. Wildfire Risk/Behavior Modeling(Example of an advanced NR application) Wildfire Risk Modeling …wildfirerisk integrates numerous map layers such as weather factors, historical fire occurrence, surface and canopy fuels, terrain, and suppression effectiveness. Economic impact of wildfire is based on probability/intensity of a wildfire (Risk) times assessor data (Value). Risk Wildfire Risk Value Rebuild Value Risk times $Value= $Exposure Wildfire Behavior Modeling The consequences of wildfires have never been greater as more people move into wildfire-prone areas. There is an increasing need forrisk assessment, fuel treatments, mitigation planning, prevention awareness, wildfire behavior modeling, real-time suppression response and recovery preparedness to reduce risk and impacts to communities. Fire ignites and moves SW …analysis of wildfire spread and behavior, integrates current weather, fuel characteristics, and topography. Simulation results are in real time, providing capabilities to adjust simulations with observed data and proposed suppression activities. After Scott, Pyrologix; Buckley and Ramirez, Tecnosylva (Berry)

  8. Comparing Natural Resources and Agriculture (a GIS perspective)

  9. Spatial Statistics (Linking Data Space with Geographic Space) Roving Window (weighted average) Spatial Distribution Geo-registered Sample Data Spatial Statistics Continuous Map Surface Discrete Sample Map Non-Spatial Statistics Surface Modeling techniques are used to derive a continuous map surface from discrete point data– fits a Surface to the data (maps the variation). Standard Normal Curve Average = 22.6 …lots of NE locations exceed Mean + 1Stdev In Geographic Space, the typical value forms a horizontal plane implying the average is everywhere StDev = 26.2 (48.8) X + 1StDev = 22.6 + 26.2 = 48.8 Histogram In Data Space, a standard normal curve can be fitted to the data to identify the “typical value” (average) X= 22.6 80 0 30 40 50 60 70 10 20 Unusually high values Numeric Distribution +StDev Spatial Variation Average (Berry) (Berry)

  10. Spatial Statistics Operations(Data Mining Examples) Map Clustering: Elevation vs. Slope Scatterplot Cluster 2 “data pair” of map values …as similar as can be WITHIN a cluster …and as different as can be BETWEEN clusters “data pair” plots here in… Data Space Elevation (Feet) Geographic Space Slope draped on Elevation + + Cluster 1 Slope Slope (Percent) Elev X axis = Elevation (0-100 Normalized) Y axis = Slope (0-100 Normalized) Geographic Space Advanced Classification (Clustering) Data Space Map Correlation: Spatially Aggregated Correlation Scalar Value– one value represents the overall non-spatial relationship between the two map surfaces Roving Window r = .432 Aggregated …1 large data table with 25rows x 25 columns = 625 map values for map wide summary Map of the Correlation Entire Map Extent Elevation (Feet) r = …where x = Elevation value and y = Slope value and n = number of value pairs …625 small data tables within 5 cell reach = 81map values for localized summary Slope (Percent) Localized Correlation Map Variable– continuous quantitative surface represents the localized spatial relationship between the two map surfaces Predictive Statistics (Correlation) (Berry) (Berry)

  11. Interpolated Spatial Distribution Phosphorous (P) What spatial relationships do you see? Visualizing Spatial Relationships …do relatively high levels of P often occur with high levels of K and N? …how often? …where? Humans can only “see” broad Generalized Patterns in a single map variable… (Berry)

  12. Clustering Maps for Data Zones …but computers can “see” detailed patternsin multiple map variables (using Data Space) Geographic Space …groups of “floating balls” in data space identify locations in the field with similar data patterns–Data Zones(Data Clusters) …or a Continuous Equation precisely identifying the right action for each grid cell (Berry)

  13. Prescription Map Step 6) Derived Soil Nutrient Maps Zone 3 Zone 2 Variable Rate Application Zone 1 The Precision Ag Process As a combine moves through a field it… 1) uses GPS to check its locationevery second then 2) records the yield monitor valueat that location to Steps 1–3) 3) create a continuous Yield Map surface identifying the variation in crop yield every few feet throughout the field (dependent map variable). Step 5) “As-applied” maps On-the-Fly Yield Map Intelligent Implements 4) …soil samplesare interpolated for continuous Nutrient Map surfaces. Step 4) 5) The yield map is analyzed in combination with soil nutrient maps, terrain and other mapped factors (independent map variables) to derive a Prescription Map… 6) …that is used to adjust fertilization levelsapplied every few feet in the field (If <condition> then <action>). …more generally termed the Spatial Data Mining Process(e.g., Geo-Business application) (Berry)

  14. Precision Conservation (Landscape Focus) Precision Ag (Individual Field Focus) Wind Erosion Chemicals SoilErosion 2-dimensional Terrain Runoff Leaching Soils Leaching Leaching Yield Potassium 3-dimensional CIR Image Interconnected Perspective Isolated Perspective Precision Conservation(compared to Precision Ag) …related disciplines (Production Focus) (Stewardship Focus) https://www.sensorsandsystems.com/article/features/5662-precision-agricultures-success-yields-precision-conservation.html (Berry)

  15. Water Conservation Modeling (Conservation = “wise use”) Water Rights Historic Crop Water Allocation Alternative Water Budget Crop Water Allocation Crops City Farm Purchase all rights Sell or Lease Drought Monitor Farm Income City Water March 2014 “Buy andDry” “Win-Win-Win” To River To River Temporary Monitored Transfers Abnormally Dry Exceptional Drought Farmland Farmland Wireless Connectivity Weather Station Landsat Satellite Images Off-Farm Data Collection Solar Irradiance Evapotranspiration Monitors Weather Station Auto-Flume Adjustment Fully Irrigated Tree Crop under Drip Irrigation Soil Moisture Probes Deficit Irrigated Crop under Center Pivot Sprinkler Low Altitude Aerial Photos 100th Meridian On-Farm Instrumentation Fallowed Field Management Actions New/Expanded Data Collection /Instrumentation: Remote Sensing Weather/Climate Water Flow Evapotranspiration Soil Moisture Fully Irrigated Vegetables under Drip Irrigation Full Irrigation Deficit Irrigation www.regenmg.com/Home.aspx Sustainable Water and Innovative Irrigation Management (SWIIM) (Berry)

  16. Upshot(NR compared to Ag from a GIS perspective) Historical Setting: NR was an early adopter of geospatial technology as a direct outgrowth of its long and extensive mapping/inventory legacy for automated cartography and geoquery of an extended resource base. On the other hand, Ag had little use for mapping and spatially detailed inventories. Contemporary GIS Applications and Approaches: The bulk of GIS applications for both NR and Ag applications involve Technological Tools utilizing mapping, geo-query and display for NR and GPS navigation, implement control and data collection for Ag. • Ag’s analytical applications tend to be tightly focused on stewardship and economicsat the individual field level utilizing Spatial Statistics operations (numerical context; spatial coincidence) for analysis of the spatial relationships among factors affecting crop production and management actions. • NR’s analytical applications tend to focus more on ecology and environmental impacts at the landscape level utilizing Spatial Analysisoperations (geographical context; relative position) for analysis of the spatial relationships among factors ecosystem conditions and management actions. Future Directions: With increasing understanding of map analysis and GIS modeling capabilities and spatial reasoning skills both disciplines will be Pushed/Pulled closer together… • NR will incorporate more quantitative analysis of mapped data (Spatial Statistics) in its science, and • Ag will adopt a more ecological perspective focusing on the cycles and movements of soil and water (Spatial Analysis). (Berry)

  17. So Where to Head from Here? Online Materials (www.innovativegis.com/Basis/Courses/SpatialSTEM/) ) Website (www.innovativegis.com) For more papers and presentations on Geotechnology www.innovativegis.com This PowerPoint with notes and online links to further reading is posted at www.innovativegis.com/basis/Present/CUdenver2014/ Beyond Mapping Compilation Series …nearly 1000 pages and more than 750 figures in the Series provide a comprehensive and longitudinal perspective of the underlying concepts, considerations, issues and evolutionary development of modern geotechnology (RS, GIS, GPS). eMail Contact Joseph K. Berry jberry@innovativegis.com

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