application of high resolution elevation data (lidar ) to ... exposure of humans and... ·...

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

9/2/2011 242nd ACS National Meeting & Exposition 2

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


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



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

9/2/2011 6242nd ACS National Meeting & Exposition

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

7.56.04.53.01.50.0

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Average Slope by Field

Freq

uenc

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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


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.


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


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


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


Subsurface DrainsIn-field sinks were often correlated with tile drainage stand pipes

Blow up of image to left


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


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


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


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


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?


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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Freq

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Mean 2.045StDev 1.264N 17814


19.616.814.011.28.45.62.80.0

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Freq

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Mean 0.7998StDev 0.7422N 64003


Potential use of LiDAR Intensity to distinguish tillage practices


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


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.


Acknowledgements


Questions?

http://polargateways2008.gsfc.nasa.gov/Jan25.html

http://www.nlgis.com/?page_id=516Thank you

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