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Application of High Resolution Elevation Data (LiDAR) to Assess Natural and Anthropogenic Agricultural Features Affecting the
Transport of Pesticides at Multiple Spatial Scales
Josh Amos1, Chris Holmes1, JiSu Bang2, Paul Hendley2, Lucy Fish3
1Waterborne Environmental, Inc., Leesburg, VA, USA 2Syngenta Crop Protection, LLC, North America
3Syngenta Crop Protection, Int., Jealotts Hill
242nd ACS National Meeting & ExpositionAugust 28-September 1, 2011
Denver, Colorado
Credit: Christopher Crosby, SDSC. Source: San Diego Supercomputer Center, UC San Diego
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What is LiDAR?
q Measures distance (range) to an objectØ Records x, y, z position of laser energy
returned from an object as a function of travel time
Ø Uses wavelength in infrared (terrestrial applications) or green (bathymetric applications) spectrum
q Multiple returns per pulseØ Initial returns for Digital Surface Models of
vegetation, buildings & above ground features
Ø Final returns for Digital Elevation Models of bare earth surface
LiDAR = Light Detection And Ranging
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Resulting Data (raw point cloud)
Study Objective
Assess the utility of LiDAR to identify landscape features affecting the transport of agricultural chemicals, such as:
q Presence of engineered features ü Terracingü Contour farming ü Irrigation – boom, furrow
q Micro-elevation changes within a field impacting hydrologic processes ü Sources of contributing surface flowü In-field storage of flow accumulation – local depressions without
outlets --- indicators of drainage system inlets & standpipesü Flow direction, accumulation and drainage paths leading to points of
entry into streams q Characterization of vegetation in the non-agricultural landscape
ü Height, density, structure composition
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Materials & Methods
Standard LiDAR data and GIS software were used throughout the project to enhance accessibility
q Data sourceü LiDAR – 1.4 m spacing by Nebraska Department of Game and
Natural Resources (collected winter ‘09)ü At least 22 states have their own state-wide LIDAR datasets,
many more individual counties have dataü Most acquired to meet FEMA flood mapping standards –
consistent point spacing, accuracy, format
q Format, software, & functionsü 3D point cloud converted to 2D rasterü ArcGIS with Spatial Analyst & 3D Analystü Standard GIS operations applied
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Study Area
q 22 square mile headwater watershed
q Densely cropped with corn and soybean
q Topography is low relief except near stream corridors and in some fields to the south
q Mean slope of 1.8% across all fieldscalculated from LiDAR
The study area is a watershed in southeast Nebraska
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Average Slope by Field
Freq
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Mean 1.847StDev 1.276N 347
Average Slope Distribution per field
all fields in study area are represented
Normal
Application of Remote Sensing to Reveal Management Practices
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Risers?
Grass-Banked Ditch
Terraces
water/sediment control basin
Collection pond doubles as wetland buffer
Grassed Waterway
Contour Farming
Grassed Filter Strip
Remote sensing accommodates assessments across large study
areas
q Aerial photography reveals agricultural practices at the field scale to the trained photo-interpretor
q Process is time consuming because conducted one field at time & is subjective
q Are these features present in the LiDARand can they be extracted by software for an entire watershed rather than on a field by field basis?
Credit: JD Davis, Waterborne Env.
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What linear features were present in the LiDAR?
q Terracing & contour farming were visible in the LiDAR DEM as linear features & qualitatively assessed for each field in the watershed
q GIS operations (edge detection of slope raster) were used to extract linear features and quantify them by field
Terracing & Contour Farming
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Irrigation
A simple way to locate irrigated fields
q Irrigation booms (both pivot and side-roll) remain in the vegetation class as thin, linear features q The number of fields in the watershed can be quantified by overlaying them with a GIS layer of field boundaries
Note presence of irrigation boom
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Subsurface Drains
q In-field depressions (sinks) may indicate surface water management practices, such as:ü Water/sediment control
basinsü Collection ponds/ wetlandsü Inlets to stand pipes leading
to subsurface drainage systems; e.g. tile drains
q Sinks identified in the LiDAR DEM were inspected for the presence of drainage stand pipes using aerial photography
Credit :http://jdappert.home.mchsi.com/2003_Tile_Vent_riser_on_corner_of_crawford.jpg
Credit: http://www.thisland.illinois.edu/60ways/60ways_18.html
Note inlets (green) to terrace (white) tiles
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Subsurface DrainsIn-field sinks were often correlated with tile drainage stand pipes
Blow up of image to left
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In-Field Flow & Buffers
Targeting fields that may benefit from BMPs by combining LiDARflow rasters with engineered features and aerial photos
q Flow accumulation grids generated from the LiDAR DEM map surface water patterns from within a field to the edge of a field or into a waterbody
q A better view of the processes governing water movement away from a field is achievable when in-field flow is combined with engineered features, aerial photography, soils, etc
q High resolution hydrology derivatives are suitable for use in recommending vegetative buffers along waterbodies with the potential for exposure to concentrated runoff from fields
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strategically placed vegetative buffers can mitigate pesticide, nutrient, & soil runoff into surface water
In-Field Flow & Buffers
sinks store & transfer surface water into
tile drains
concentrated flow from field into edge of field waterbody
Water/sediment control basin
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Vegetation Composition
Digital Surface Models of vegetation (& buildings, irrigation) were generated from the initial return LiDAR points
http://oregonstate.edu/terra/wp-content/uploads/2010/10/tall_tree160-LiDAR.jpg
q Standard vegetation metrics derived from forestry applications are readily calculated using raster math & are useful for characterizing:ü Vegetation metrics within riparian
buffers & corridors (vegetation diversity , canopy cover, and height)
ü Wind breaks that may restrict spray drift transport
ü Non-target & endangered species colonization sites
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Vegetation Composition
% Canopy Closure# of 1st returns over height break
total # of 1st returns per unit area
Note variability within trees, 100%closure over building, and presence of a pivot irrigation boom in LIDAR
Percent Canopy Closure
% Vegetation Density# of all returns over height break
total # of returns per unit area
Cell size must be larger then individual tree crowns. Cell sizes >=15 m produce good results
2m
7m
20m
Percent Vegetation Density
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Mean Field Slope
Can field scale statistics describing slope & elevation be used to identify fields that might benefit from management practices?
Ø “Engineered” fields are those with stand pipes or terracing (n=189)Ø “Non-Engineered” fields do not have discernable stand pipes or terracing (n=157)
Ø a clear distinction between the groups exists
Observations
ØEngineered fields have a higher mean slope that Non-engineeredØ All fields with mean slope >3% are Engineered
Ø Only 8% of Non-engineered fields have mean slope > 2%Ø While 63% of Engineered fields have a mean field slope > 2%
are these the fields to prioritize for further examination?
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Within Field Slope Distributions
Can field scale statistics describing slope & elevation be used to identify fields that might need management practices?
q Previous chart examined a single value (mean) for a fieldq LiDAR provided a slope value for every 9 square meters (resampled from 1
meter to 3 meters to reduce noise)q Within field slope distribution are another way to look at the dataq Examining the curves of Engineered against Non-engineered field may help
prioritize where to focus search for fields that need improvements
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Slope Measurements Field 334
Freq
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Mean 1.831StDev 1.536N 60324
Slope DistributionNormal
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Slope Measurements Field 328
Freq
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Mean 2.045StDev 1.264N 17814
Slope DistributionNormal
19.616.814.011.28.45.62.80.0
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Slope Measurements Field 145
Freq
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Mean 0.7998StDev 0.7422N 64003
Slope DistributionNormal
Potential use of LiDAR Intensity to distinguish tillage practices
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Future Work - LiDAR Intensity
q Intensity measures the strength of the return pulses (not the time), and therefore contains different information
q Neighboring, similar looking fields visible in aerial photos have very different intensity values
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Summary
Engineered Feature
Extraction
• Identified & extracted engineered features visible in LiDAR as linear features• Summarized those features for each field within the watershed
In-Field Surface
Flow
• Identified in-field storage and contributing sources of surface water• Located preferential flow pathways from fields to adjacent waterbodies
Vegetation Compo-
sition
• Characterized the non-agricultural domain - riparian buffers, spray drift vegetated buffers
• calculated height, density, structure metrics
The following LiDAR-derived metrics were assessed in this project
• LiDAR data provided by Nebraska Department of Game and Natural Resources, Lincoln Office (Gayle Follmer & Staci Par)
• Jay Davis, MS, Waterborne Environmental, Inc.
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Acknowledgements
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Questions?
http://polargateways2008.gsfc.nasa.gov/Jan25.html
http://www.nlgis.com/?page_id=516Thank you