Application of High Resolution Elevation Data (LiDAR) to Assess Natural and Anthropogenic Agricultural Features Affecting the Transport of Pesticides at Multiple Spatial Scales Josh Amos 1, Chris Holmes 1, JiSu Bang 2, Paul Hendley 2, Lucy Fish 3 1 Waterborne Environmental, Inc., Leesburg, VA, USA 2 Syngenta Crop Protection, LLC, North America 3 Syngenta Crop Protection, Int., Jealotts Hill 242nd ACS National Meeting & Exposition August 28-September 1, 2011 Denver, Colorado Credit: Christopher Crosby, SDSC. Source: San Diego Supercomputer Center, UC San Diego
What is LiDAR? LiDAR = Light Detection And Ranging Measures distance (range) to an object Records x, y, z position of laser energy returned from an object as a function of travel time Uses wavelength in infrared (terrestrial applications) or green (bathymetric applications) spectrum Multiple returns per pulse Initial returns for Digital Surface Models of vegetation, buildings & above ground features Final returns for Digital Elevation Models of bare earth surface 2
Resulting Data (raw point cloud) 3
Study Objective Assess the utility of LiDAR to identify landscape features affecting the transport of agricultural chemicals, such as: Presence of engineered features Terracing Contour farming Irrigation boom, furrow Micro-elevation changes within a field impacting hydrologic processes Sources of contributing surface flow In-field storage of flow accumulation local depressions without outlets --- indicators of drainage system inlets & standpipes Flow direction, accumulation and drainage paths leading to points of entry into streams Characterization of vegetation in the non-agricultural landscape Height, density, structure composition 4
Materials & Methods Standard LiDAR data and GIS software were used throughout the project to enhance accessibility Data source LiDAR 1.4 m spacing by Nebraska Department of Game and Natural Resources (collected winter 09) At least 22 states have their own state-wide LIDAR datasets, many more individual counties have data Most acuired to meet FEMA flood mapping standards consistent point spacing, accuracy, format Format, software, & functions 3D point cloud converted to 2D raster ArcGIS with Spatial Analyst & 3D Analyst Standard GIS operations applied 5
Study Area The study area is a watershed in southeast Nebraska 22 suare mile headwater watershed Densely cropped with corn and soybean Topography is low relief except near stream corridors and in some fields to the south Mean slope of 1.8% across all fields calculated from LiDAR 90 80 70 Average Slope Distribution per field Normal Mean 1.847 StDev 1.276 N 347 60 Freuency 50 40 30 20 10 0 0.0 1.5 3.0 4.5 6.0 Average Slope by Field all fields in study area are represented 7.5 6
Application of Remote Sensing to Reveal Management Practices Remote sensing accommodates assessments across large study areas Collection pond doubles as wetland buffer Aerial photography reveals agricultural practices at the field scale to the trained photo-interpretor water/sediment control basin Contour Farming Grassed Filter Strip Grass-Banked Ditch Process is time consuming because conducted one field at time & is subjective Terraces Risers? Are these features present in the LiDAR and can they be extracted by software for an entire watershed rather than on a field by field basis? Grassed Waterway Credit: JD Davis, Waterborne Env. 7
Terracing & Contour Farming What linear features were present in the LiDAR? Terracing & contour farming were visible in the LiDAR DEM as linear features & ualitatively assessed for each field in the watershed GIS operations (edge detection of slope raster) were used to extract linear features and uantify them by field 8
A simple way to locate irrigated fields Irrigation booms (both pivot and side-roll) remain in the vegetation class as thin, linear features The number of fields in the watershed can be uantified by overlaying them with a GIS layer of field boundaries Irrigation Note presence of irrigation boom 9
Subsurface Drains In-field depressions (sinks) may indicate surface water management practices, such as: Water/sediment control basins Collection ponds/ wetlands Inlets to stand pipes leading to subsurface drainage systems; e.g. tile drains Credit: http://www.thisland.illinois.edu/60ways/60ways_18.html Credit :http://jdappert.home.mchsi.com/2003_tile_vent_riser_on_corner_of_crawford.jpg Note inlets (green) to terrace (white) tiles Sinks identified in the LiDAR DEM were inspected for the presence of drainage stand pipes using aerial photography 10
Subsurface Drains In-field sinks were often correlated with tile drainage stand pipes Blow up of image to left 11
In-Field Flow & Buffers Targeting fields that may benefit from BMPs by combining LiDAR flow rasters with engineered features and aerial photos 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 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 High resolution hydrology derivatives are suitable for use in recommending vegetative buffers along waterbodies with the potential for exposure to concentrated runoff from fields 12
In-Field Flow & Buffers strategically placed vegetative buffers can mitigate pesticide, nutrient, & soil runoff into surface water sinks store & transfer surface water into tile drains concentrated flow from field into edge of field waterbody Water/sediment control basin 13
Vegetation Composition Digital Surface Models of vegetation (& buildings, irrigation) were generated from the initial return LiDAR points Standard vegetation metrics derived from forestry applications are readily calculated using raster math & are useful for characterizing: Vegetation metrics within riparian buffers & corridors (vegetation diversity, canopy cover, and height) Wind breaks that may restrict spray drift transport Non-target & endangered species colonization sites http://oregonstate.edu/terra/wp-content/uploads/2010/10/tall_tree160-lidar.jpg 14
Vegetation Composition % Vegetation Density # of all returns over height break total # of returns per unit area 20m 7m 2m Percent Vegetation Density Cell size must be larger then individual tree crowns. Cell sizes >=15 m produce good results Note variability within trees, 100% closure over building, and presence of a pivot irrigation boom in LIDAR % Canopy Closure # of 1 st returns over height break total # of 1 st returns per unit area Percent Canopy Closure 15
Mean Field Slope Can field scale statistics describing slope & elevation be used to identify fields that might benefit from management practices? Engineered fields are those with stand pipes or terracing (n=189) Non-Engineered fields do not have discernable stand pipes or terracing (n=157) a clear distinction between the groups exists Observations Engineered fields have a higher mean slope that Nonengineered All fields with mean slope >3% are Engineered are these the fields to prioritize for further examination? Only 8% of Non-engineered fields have mean slope > 2% While 63% of Engineered fields have a mean field slope > 2% 16
Within Field Slope Distributions Can field scale statistics describing slope & elevation be used to identify fields that might need management practices? Previous chart examined a single value (mean) for a field LiDAR provided a slope value for every 9 suare meters (resampled from 1 meter to 3 meters to reduce noise) Within field slope distribution are another way to look at the data Examining the curves of Engineered against Non-engineered field may help prioritize where to focus search for fields that need improvements Slope Distribution Normal Slope Distribution Normal Slope Distribution Normal 1000 800 Mean 2.045 StDev 1.264 N 17814 14000 12000 10000 Mean 0.7998 StDev 0.7422 N 64003 7000 6000 5000 Mean 1.831 StDev 1.536 N 60324 Freuency 600 400 Freuency 8000 6000 Freuency 4000 3000 4000 2000 200 2000 1000 0 0.0 1.5 3.0 4.5 6.0 7.5 Slope Measurements Field 328 9.0 0 0.0 2.8 5.6 8.4 11.2 14.0 16.8 Slope Measurements Field 145 19.6 0 0 3 6 9 12 15 Slope Measurements Field 334 18 21 17
Future Work - LiDAR Intensity Potential use of LiDAR Intensity to distinguish tillage practices Intensity measures the strength of the return pulses (not the time), and therefore contains different information Neighboring, similar looking fields visible in aerial photos have very different intensity values 18
Summary The following LiDAR-derived metrics were assessed in this project Engineered Feature Extraction In-Field Surface Flow Vegetation Composition Identified & extracted engineered features visible in LiDAR as linear features Summarized those features for each field within the watershed Identified in-field storage and contributing sources of surface water Located preferential flow pathways from fields to adjacent waterbodies Characterized the non-agricultural domain - riparian buffers, spray drift vegetated buffers calculated height, density, structure metrics 19
Acknowledgements LiDAR data provided by Nebraska Department of Game and Natural Resources, Lincoln Office (Gayle Follmer & Staci Par) Jay Davis, MS, Waterborne Environmental, Inc. 20
Questions? http://polargateways2008.gsfc.nasa.gov/jan25.html Thank you http://www.nlgis.com/?page_id=516 21