Question and Answers: Automated identification of tile drainage from remotely sensed data Bibi Naz, Srinivasulu Ale, Laura Bowling and Chris Johannsen Introduction: Subsurface drainage (popularly known as tile drainage ) systems have been a common practice for decades in the Midwestern U.S. to transform the poorly drained soils into productive cropland. Although subsurface drainage provides many agronomic and environmental benefits, extensive subsurface drainage systems have important implications for surface water quality and hydrology. Due to limited information on subsurface drainage extent, it is difficult to understand the hydrology of intensively tile-drained watersheds. Despite this fact, very little effort has been expended to investigate the consequences of subsurface drainage on streamflow response at watershed scales. The analysis of hydrological effects of subsurface drainage systems is complicated by limited information on the locations of historic tile drains. Subsurface drainage tile lines that have been installed 50 or more years ago are still working fully or partially. In most cases, information on these old tile lines has been lost or does not exist. A growing emphasis on the water quality impacts of subsurface tile drainage has also increased the interest of estimating tile-drained landscapes at the national level. It is not possible to identify the tile-drained areas on such a large scale using traditional methods such as manual tile-probing techniques. Recent studies have shown that remote sensing can be an effective tool to accurately map tile lines (Verma et al., 1996; Varner et al., 2002; Northcott et al., 2000). Using remotely sensed data, two studies have been done at the Agronomy Center for Research and Education (ACRE) and the Hoagland watershed in west central Indiana (figure 1). A methodology of automated identification of tile lines was first developed at field scale in ACRE. The image processing techniques were then applied to aerial image acquired for the Hoagland watershed in order to create a tile line map at a lager scale (Naz et al., 2009, Naz and Bowling, 2008). Answers given here are based on the results from these two studies. 1. What kind of imagery is necessary to identify drainage tiles? Imagery with one meter resolution can be used to identify subsurface tile drainage systems. The most viable source for detecting tile drainage is aerial photographs such as panchromatic or 3 bands multispectral imagery (a green, red and near infrared band). High resolution imagery from satellite such as one-meter IKONOS images can also be used to identify tile lines. The imagery used in tile lines identification should be processed in digital format. 2. When should the imagery be collected? Identification of the tile lines from the imagery is based on the fact that after a rain event, the soil above the working tile line dries faster than the soil at other locations in the field and has higher reflectance in the infrared region of the spectrum. The optimum time for image acquisition should be 2-3 days after a significant rain event (at least one inch of rain) which provides best contrast between dry soil above tile lines and surrounding wet soil (figure 2). Figure 2: An example of aerial image showing installed tile lines in agricultural fields. The tile lines appear brighter on the image than surrounding areas. 1
3. Under what filed condition the imagery should be collected? Due to higher growth of vegetation at the time of image, the soil moisture differences will be not visible enough to use the image for tile identification. It is recommended that the imagery should be collected in spring or fall in the Midwest i.e. the field of interest should be under bare soil conditions. The less amount of residue on the field the better chances are of identifying drainage tiles. Examples of images taken under different condition are shown below: Infrared image taken in leaf-on condition. The vegetation appears red in the image. Image taken after a significant rainfall event and under bare soil condition. 4. How can I identify tile lines using aerial image which has been take after a significant rainfall event and under bare soil condition? Working tile lines on aerial imagery appear brighter linear features either in vertical, horizontal or in diagonal pattern. All available knowledge such as spacing between tile lines can help to distinguish tile lines from other linear feature. For example plow tracks due to tillage practices appear similar as tile lines but with narrow spacing between two tracks (figure 3). Other available data such as elevation data and landowner knowledge can also help to identify tile lines using imagery. Figure 3: The linear feature appears in the Image taken under field inside the bare soil condition but dashed line are due to there was no rainfall tillage practices. event. 5. Can I use already available aerial images for tile identification? If there is significant amount of rain event 2-3 days before the image acquisition and the imagery is taken after crop harvesting or during leaf-off canopy condition, the imagery can be used for tile 2
identification. The work done at ACRE used aerial images of 1976, 1998, and 2002 to identify individual tile lines installed in different parts of ACRE. Upon viewing each of the images, it was noted that some portion of the images can be useful to identify most of the tile lines. The areas with most information were merged to create a final data product of higher quality than could be produced with only a single image. The other areas can be classified as potential undrained areas. The decision tree classification tool was used to estimate potential tile drained areas in ACRE and Hoagland watershed (figure 4). 6. Where can I get the available images? The following websites provides high resolution aerial and satellite images at no cost for Indiana. Google earth (2005 leaf off images for IN) - earth.google.com Indiana Spatial Data Portal Repository of 2005 1 foot aerial images for the state (www.indiana.edu/ ~gisdata) Indiana View (www.indianaview.or) - Repository of Indiana satellite imagery 7. What image pre-processing steps are required? The images used to identify tile lines should be in digital format. If the image is not available in digital format, it needs to be scanned at a resolution of 400 DPI and save in a tagged image format (TIF). In order to convert the scanned image from pixel units to real world coordinates, the scanned image need to be georeferenced using ground control points either selected from another existing geo-referenced image of the same area or collected through GPS survey in the field. The images should be geo-referenced as correctly as possible. Due to geo-referencing errors the distance of the predicted tile line may vary from the true location of installed tile line. 8. Can I estimate tile drainage areas based on the land-cover and soil properties information without using any imagery? Yes, the potential drainage areas can be identified based on the assumption that subsurface tiles are most likely to be installed and functional in poorly drained soils that were recently in production. The information on cropland extent and soil properties can be extracted from the available data sets such as National Agricultural and Statistics Service (NASS) land-cover dataset and Soil Survey Geographic (SSURGO) digital soil maps. Using these datasets, the areas within cropland which have very poorly, poorly and somewhat poorly drainage class and surface slope of 1-2 % can be selected and classified as potential tile drained areas. Potential Undrained Area Potential Tile Drained Area Figure 4: GIS data layers used to estimate potentially tile drained areas: (a) land cover layer (NASS data set), (b) surface slope layer (SSURGO data set), (c) soil drainage class layer (SSURGO data set), and (d) potentially tile drained and undrained areas estimated from three GIS layers. 9. How accurate are the potential tile drainage estimate? Based on the work done at Hoagland watershed, the difference between predicted image-based tile drained area in the entire watershed and potentially tile drained 3
areas through GIS analysis is about 29%. The overestimate of potentially tile drainage area is due to the assumption that artificial tile drainage exists on all poorly drained soils. However, the actual extent of subsurface drainage may depend on other factors such as extent and type of tile lines, cost and level of knowledge of farmers about the benefits of the subsurface drainage or the presence of surface drainage. The techniques using in estimation of potentially tile drained areas can be a very effective tool in quantifying an approximate estimate of tile drained agricultural fields over a large area, which could be used as a preliminary classification in the studies where a more detailed map of individual tile lines is required (Naz and Bowling, 2008). 10. What are the image processing steps that can be used to identify the individual tile lines? Various image processing steps can be used to automate the process of identifying the individual tile lines from aerial images such as sharpening the edges of the tile lines from the surrounding areas by applying edge enhance filters. The edge enhance filtering techniques enhance the edges of the linear features using the pixel brightness values based on the average difference of adjacent pixels. Once these edges are enhanced, the areas with enhanced edges can be extracted and classify as tile line and, other areas can be selected as non-tile. The individual tile lines can then be digitized using digitizing tools available in the GIS software to create a tile line map. The detailed description of these steps is available in Naz and Bowling (2008). The example of these steps is shown in the figure 5. 11. How accurate are image-based individual tile line map? The map created for ACRE by Naz and Bowling (2008) were compared with observed tile line location map. The observed tile line location map was created from the historic design diagrams which are digitized, georeferenced and converted to tile lines. In general, the tile map had an overall accuracy of 84 % with 58% of producer accuracy for the tile class. This means that the imagery was able to identify about half of the actual drainage tiles in a field. Figure 5: Automated mapping of individual tile lines using image processing techniques: (a) digital aerial image, (b) digital aerial image after masking out undrained areas identified from decision tree classification, (c) image after directional first-difference horizontal filtering, (d) image after classification, (e) vector tile line map, and (f) final tile line map after removing erroneous lines. 12. What are some potential image-based tile identification products? 1) Image-based tile lines map The first possible product could be a classified image with two classes: 1) individual tile lines and 2) areas with no tile lines. The class which represents the tile lines can be converted into line format using either manual digitization or automated digitization in GIS software (figure 6). The manual digitization will produce an accurate map but will be more labor intensive. The automated digitization tools available in GIS software can be used to automatically digitize the identified tiles from imagery into line format but the digitized lines may not be fully connected and other linear features that appear similar as tile lines can also be digitized. At larger scale such as at multiple-county level, automated-digitization is the most appropriate method to create a final tile line map. 2) Drainage Spacing map: The drainage spacing map is another potential product that can be developed by first dividing the study area into a vector grid with cell size of 200 x 2000 m. The drainage spacing can then be calculated within each cell by dividing the area of the cell by the sum of the lengths of all tiles identified in the cell as shown in 4
figure 7a. The drainage spacing map created for the Hoagland watershed is shown in figure 7b. White County Jasper County 0 1.5 3 Benton County Figure 6: Final tile line map of the Hoagland watershed. 30 47 33 27 47 33 29 50 58 30 45 37 31 32 25 27 51 65 44 41 (a) 6 Miles variations. At field scale, these lines can be avoided during manual digitization step in order to create a final tile line map. Detailed description on how the erroneous lines can be eliminated from the tile line map at large scale is give in Naz et al. (2009) 14. At what extent the crop residue in the fields can impact the identification of the tile lines? Crop residues and soils are often spectrally similar for visible and near-infrared wavelengths (Baird and Baret, 1997; Streck et al., 2002) specifically in low spectral resolution data such as aerial images, which makes discriminating between crop residues and soil condition difficult using reflectance techniques (Daughtry, 2001). If data with a higher spectral resolution were used such as hyperspectral data, it might be possible to discriminate the underlying soil mineralogy below some threshold of residue coverage. Due to limited information on tillage practices for the work done at ACRE, their effect on tile identification was difficult to quantify. However, it was verified that some of the fields were no-till corn stubble fields at the time of the image acquisition (Beaty, 2005). No tile lines were identified in those fields due to the crop residue cover, as shown in figure 8. Figure 8: The no-till corn stubble fields appear very bright on the image are due to presence of corn residue on the soil surface. (b) Figure 7: (a) Example of some gird cells showing predicted tile lines and estimated drain spacing in each cell, (b) distribution of predicted tile drain spacing in 15. How to increase the accuracy of the tile line the Hoagland watershed. map? The problem of acquiring the right images at the right 13. What are some potential errors in drainage time complicates the issue of creating an image-based tile line map, particularly for a large area. However, tile identification? The first potential error that can affect the accuracy of selecting images from multiple previous years that the tile line map is the identification of erroneous lines were taken two to three days after a significant rainfall event or soon after the tile line installation with no as tile lines due to presence of other spatial features residue cover can increase the probability of similar as tile lines or surface color differences as a identifying tile lines from remotely sensed data. result of surface wetness or local topographic 5
References: Beaty, J. 2005. Personal communication. Superintendent, Agronomy Center for Research and Education, Purdue University, West Lafayette, Ind. Baird, F., Baret, F., 1997. Crop residue estimation using multiband reflectance. Remote Sens. Environ. 59, 530 536. Daughtry, C.S.T., 2001. Discriminating crop residues from soil by shortwave infrared reflectance. Agron. J. 93 (1), 125 131. Naz, B.S., Bowling, L.C., 2008. Automated identification of tile lines from remotely sensed data. Trans. ASABE 51 (6). Naz, B.S. Ale, S. Bowling, L. C.,2009. Detecting subsurface drainage systems and estimating drain spacing in intensively managed agricultural landscapes. agricultural water management 96, 627 637. Northcott, W.J., Verma, A.K., Cooke, R.A., 2000. Mapping subsurface drainage systems using remote sensing and GIS. In: ASABE Paper No. 002113, ASABE, St. Joseph, Mich. Streck, N.A., Rundquist, D., Connot, J., 2002. Estimating residual wheat dry matter from remote sensing measurements. Photogram. Eng. Remote Sens. 68 (11), 1193 1201. Varner B.L., Gress, T., Copenhaver, K., White, S., 2002. The effectiveness and economic feasibility of image based agricultural tile maps. Inst. of Tech., Champaign, IL. Final Report to NASA ESAD 2002. Verma, A.K., Cooke, R.A., Wendte, L., 1996. Mapping subsurface drainage systems with color infrared aerial photographs. In: American Water Resource Association s 32nd Annual Conference and Symposium GIS and Water Resources, September 22 26, Ft. Lauderdale, Florida. 6