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Advancing Water Resources Research and Management

AWRA SYMPOSIUM ON GIS AND WATER RESOURCES
Sept 22-26, 1996
Ft. Lauderdale, FL

MAPPING SUBSURFACE DRAINAGE SYSTEMS WITH COLOR INFRARED AERIAL PHOTOGRAPHS

Ashok Verma

Graduate Research Assistant

Richard Cooke

Assistant Professor

Department of Agricultural Engineering

University of Illinois at Urbana-Champaign

Urbana, IL 61801

and

Leon Wendte

District Soil Conservationist, USDA-NRCS

2110 W Park Court

Champaign, IL 61821

ABSTRACT:

Agricultural fields in East-Central Illinois, as in many other regions of the central plains, are predominantly drained by subsurface tile drains. There are very few records of the actual locations of many of these drainage systems, especially those installed more that 75 years ago. The unavailability of drainage maps makes it difficult to locate nonfunctioning tile lines, or even to determine the position of functional systems in cases where additional drains are to be installed. This paper describes the use of color infrared (CIR) aerial photographs and GIS analysis for mapping tile lines in Vermilion County, Illinois. The method appears to be a promising and cost effective tool as compared to conventional tile probe methods.

The drain (tile) mapping procedure is based on the fact that the soil over efficiently draining tile line dries faster than the soil at other locations in the field and has higher reflectance in the infrared region of the radiation spectrum. CIR aerial photographs for the study area were taken in spring, a few days after a heavy rain storm, converted to digital format, and subjected to edge enhancement to heighten the sharpness of the images. A GIS package (IDRISI) was used to overlay soil data, hydrological parameters, topography, and vegetation cover. The combination of these map layers made it possible to identify the layout of functional tile drainage systems. The accuracy of the method was evaluated by comparing the locations of some of the systems thus identified with ground-truth data.

KEY TERMS:

Color infrared aerial photographs; tile line mapping; remote sensing; GIS; subsurface drainage.

INTRODUCTION

Flooding during plant growth periods cause extensive damage to crops. Reduced growth in corn and soybean, related to excess soil moisture or high water table during the growth season, has been a common problem faced by Midwestern farmers. High water table conditions can be alleviated by using subsurface drainage. Many existing tile lines were laid more than 75 years ago, and over the years some of these have become nonfunctional. Most farmers do not have records of the layout of the drainage systems on their farms. It is difficult, therefore, for them to optimize the benefits that accrue from installing additional drain lines to improve subsurface drainage.

Improved subsurface drainage will not only improve crop production and farm income, but also help to reduce surface runoff. This reduction will result in reduced soil erosion and sediment load to streams and water bodies. The reduced sediment load will enhance wetland functions during non-crop periods, improve wildlife habitat, and reduce pollution associated with sediment-bound agricultural chemicals. Traditionally, farmers have used tile probes to locate tile lines by guessing at their locations, based on the location of dry and moist spots in the field. There is indeed a strong correlation between dry regions and the locations of tile lines if the drains are functioning properly. However, this correlation if often masked by the effects of variations soil type or topography. In addition, the visual clues are difficult to pick up in some soils, and tile probing is time consuming, labor extensive and uneconomical for large fields. The remote sensing method under discussion addresses these limitations of tile probe methods.

REMOTE SENSING

Remote sensing can be applied to drainage studies using proxies or surrogates (Campbell, 1987). Proxies play a significant role in drainage studies because features such as drainage tiles are below the ground surface and cannot be seen on remotely-sensed images. The identification of tile lines on color infrared aerial photographs or high resolution satellite images must be accomplished by means of proxies such as topography, vegetation and soil features that serves as clues for mapping tile lines.

It has been well documented in numerous research and field studies that remotely-sensed data can be used to monitor spatio-temporal distribution of land use and vegetation cover (Verma and Cooke, 1996; Tin-Seong, 1995; Betts et al., 1986; Buchan and Barnett, 1986; Essery and Wilcock, 1986; Gautam and Narayan, 1983; Kachhwaha, 1983; Diwvedi et al., 1981; Singh et al., 1979). Satellite data have been successfully used to map surface drainage patterns (Barrett and Curtis, 1992; Haralick et al., 1985; Merritt, 1982). However, in applications, such as the mapping of tile lines, where high resolution data are required, satellite data have been found to be mostly inappropriate. In this study, low-altitude CIR aerial photographs were used.

Reflectance in the infrared (IR) range of the radiation spectrum is very sensitive to soil moisture content (Hoffer, 1972). Variations in soil moisture and plant vigor show up as variations in near IR (0.7 - 1.3 mm) and mid IR (1.3 - 3 mm) reflectance (Lillesand and Kiefer; 1987). Some of the factors affecting soil surface reflectance include soil moisture content, soil texture (proportion of sand, silt and clay), surface roughness, the presence of iron oxide, and organic matter content. Soil moisture content is strongly related to soil texture. Coarse, sandy soils are well drained, resulting in low moisture content and relatively high reflectance, while poorly drained fine textured soils will generally have lower reflectance (table 1). Visual similarity of reflectance can be encountered in soils with different moisture content depending on the combination of the other factors that affect reflectance. However, this problem can be overcome by computer-assisted digital image analysis, particularly by separating out the effects of variations in soil type and ground elevation.

Table 1. Effect of texture on soil moisture and spectral reflectance from the soil surface (after Hoffer, 1978)

Soil texture	Soil moisture	Spectral reflectance
Sand	low	moderate
Silt	moderate	high
Clay	high	low

STUDY AREA

Tile maps were produced for Champaign and Ford counties, and for sections of Vermilion county, all in East-Central Illinois. The images and maps that are presented in this report are for a 259-hectare (640-acre) field in Vermilion County, Illinois. The main crops is this area are corn and soybean. The soil found on the field are described in table 2. These soils are very common in Central Illinois. The poorly-drained Drummer soil is lower on the landscape than the Flanagan soil, but is higher on the landscape than the Peotone soil (USDA-NRCS, 1996).

Table 2. Properties of soils in the study area.(after USDA-NRCS, 1996)

Soil series	Hydrologic group	Permeability (cm/hr)	Drainage class	Organic matter (%)	Depth (cm)	USDA texture
56B2: Dana	B	1.5-5 (moderately slowly permeable)	moderately slowly drained	3-5	0-17.5	Silt loam
					17.5-72.5	Silt clay loam
					72.5-97.5	Clay loam
152: Drummer	B/D	1.5-5 (moderately permeable)	moderately poorly drained	5-7	0-33	Silt clay loam
					33-119.4	Silt clay loam, Silt loam
					119.4-137.2	Loam, silt loam, Silt loam
154: Flanagan	B	1.5-5 (moderately slowly permeable)	somewhat poorly drained	4-5	0-40.6	Silt loam
					40.6-114.3	Silt clay loam
					114.3-152.6	Loam, clay loam, silt loam
171B: Catlin	B	1.5-5 (moderately permeable)	moderately well drained	3-4	0-38	Silt loam
					38-83.8	Silt clay loam
					83.8-187.96	Loam, clay loam, silt clay loam
330: Peotone	B/D	0.5-5 (very poorly permeable)	moderately slowly drained	5-7	0-38	Silt clay loam
					38-111.8	Silt clay loam, silt clay
					111.8-152.4	Silt clay loam, Silt loam, silt clay

DATA ACQUISITION AND ANALYSIS

Map Layers

The subsurface drainage (tile) mapping procedure necessitated the acquisition of color infrared, and black & white aerial photographs; soil maps; and contour maps. The black & white aerial photograph were used as a base for defining land use and vegetation cover.

A Hasselblad 500 EL/M camera and Kodak Aerochrome infrared type 2443 film was used for this study. The flight altitudes were about 2600 meter (8500 ft). The CIR aerial photographs were produced during spring (March to April) of 1984. During the period immediately following the spring thaw, tiles start to flow and moisture differences on soil surfaces can be detected in the infrared spectral range. The best photographs can be acquired on a cloud-free day, two or three days after a rainfall event that exceeds 5 cm (2 inches). However, it is rarely cloud-free in the first few days after a suitable rainfall event, as solar heating of the moist soil surfaces produces overcast skies. Typically, there are no more than two or three times each year when the conditions are suitable for producing the required CIR photographs.

Scanning of CIR Slides

Initially, several of the 70 mm CIR positive slides were scanned at densities ranging from 300 to 1200 DPI to determine the optimum density for further analysis. The scanning was done on flat bed scanner and the scanned image was stored in TIF (tagged image file) format on compact disks (CDs). Since no significant qualitative differences were found between 300 DPI and 1200 DPI images, the final images were stored at a density of 300 DPI. The 300 DPI scanned CIR digital image for study area is presented in figure 1.

Additional Geo-referenced Data

The soil map, elevation map, administrative boundaries and surface drainage map for the study area were digitized and added to the GIS database. These maps were overlaid as necessary to facilitate easier delineation of tile lines. The elevation map was derived from a digitized contour map. The sample field is extremely flat (figure 2) and consequently, there was but little variation in spectral reflectance that could be attributed to elevation effects. The soil map for the sample field is shown in figure 3. The boundaries between soil units were extracted in digital format from the 1:15,840-scale soil survey map of Vermilion county (USDA-NRCS, 1996). The Drummer and Flanagan soils have similar properties and are almost always found in association with each other. However, the Drummer soils have a distinctly darker tone than the Flanagan soils. Drummer is lower on the landscape and has a greater organic matter content.

Tile Line Mapping Criteria

CIR photographs can indicate drainage patterns either from inferences from plant stress or from soil moisture (Baber, 1982). Remotely sensed data from tile drained area will have fairly uniform texture representing good crop condition, or on bare fields, CIR aerial photographs exhibit a uniformly light gray tone (Robert and Rust, 1982). The degree of the gray tone depends on the soil moisture level; dry soils are light gray, moist soils are gray, and wet soils are dark gray (figure 4). Although the gray levels may be different for different soil types, if a tile is functioning properly, the moisture content of the soil in the immediate vicinity of a drainage tile will be less than the soil away from the drainage tiles and landscape will exhibit variations in reflectance in the infrared region of the radiation spectrum. Dry soils have higher spectral reflectance than wet or moist soils. This differential in reflectance can be used to reveal the location of tile drains if the effects of soil type and elevation are filtered out. Once the location of a tile line is known, differences in reflectance can also be used to determine if sections of the line are not functioning properly.

Geo-referencing of the Images

The scanned digital CIR images were transferred into IDRISI format using the BIPIDRISI command. The images were converted into three separate bands (red, green and blue). The images were then geometrically corrected based on ground control points from 1:24,000 topographic maps. A minimum of four control point were required for geometric correction. A linear polynomial equation with nearest neighbor interpolation was employed in this process.

Data Analysis

Images representing each band, and various combinations of the three bands were evaluated to see how well differences in moisture content would be displayed. The result of different combinations bands is presented in the table 3 and figure 5. The band 1, band 2 and band 3 correspond to the red, green and blue bands, respectively. The product of Band 2 and Band 3 yielded satisfactory results for tile line mapping. In this image, the dry, moist and wet regions in the field can be easily delineated. This delineation not only helps in identifying the location of the tile drains, but it can also be used to identify the regions of a field that would benefit the most from the installation of additional drains. Such information would have been difficult to obtain from ground truthing.

The product of the Band 2 and Band 3 images was subjected to edge enhancement to heighten the sharpness of the image. For this study, a 3x3 median filter was employed prior to classification.

Table 3. Efficacy of selected combinations of digital
CIR photograph bands in tile mapping applications

Combination	Status
Band 1 x Band 2	Poor
Band 2 x Band 3	Excellent
Band 3 x Band 1	Fair
Band 1/Band 3	Poor
Band 2 / Band 3	Good
(Band 1-Band 2) / (Band 1+Band 2)	Fair

Image Classification When the Band 2 x Band 3 image in displayed in Bipolar 16 mode, at least three distinct moisture zones are observed (figure 5 d). The images can also be reclassified to give "wet", "moist" and "drained" regions, if a relationship between moisture content and reflectance is developed for a particular soil type. Once the wet and moist regions are delineated, drainage system additions can be proposed, based on the locations of the existing tile lines and of the wet regions. The locations of partially clogged sections of the tile line can also be identified. If the correlation between moisture content and reflectance is known, the area of influence of random tile lines can be demarcated. Such a demarcation will be an objective of the next phase of this project. Currently, in the absence of the required correlation data, no supervised classification of the images were performed.

Delineation of Drainage Network

In the image shown in figure 5 (d), three distinct moisture regions are observed. The wettest regions appear black and blue, and the color graduates to reddish yellow in the driest regions. For the most part, tile lines show up as reddish yellow linear features. The on-screen digitization option in IDRISI was used to digitize these features and the results stored in vector format. The resulting vector files can be used to produce maps that can be supplied to farmers.

When the vector files were laid over the corresponding soil maps, it was observed that most of the linear features lay to the regions of the fields that have Drummer soils. Such an observation is consonant with the fact that Drummer soils tend to be darker and appear to be wetter than other soil types.

Accuracy Assessment

The accuracy of the prepared tile map was verified by probing for the drains at the locations indicated on the map. In almost every instance, the main tile lines were encountered on the first attempt. It took a little more effort, however, to locate the laterals, mainly because of their smaller size. The farmer/property owner also indicated that changes had been made to the field since the CIR photographs were taken. In some cases, tile lines have removed or repaired.

RESULTS AND DISCUSSION

Remotely-sensed CIR aerial photographs can be successfully used to reveal the locations of partially or fully draining tile lines, and to delineate wet, moist and drained regions in a field. The time of data acquisition has direct influence on the efficacy of the process. The CIR aerial photographs should be acquired two or three days after a significant rainfall event. The integration of GIS data layers for soil and topography was very useful in delineation the wet, moist and drained regions and mapping the tile lines (figure 6). Once the location of the existing tile lines are known, the cost of installation of new tile lines will be reduced and any further damage to existing line can be avoided during the installation of new tiles or pipe lines. One such example of final tile line map is presented in figure 7.

Without moisture condition maps, it is difficult to make drainage improvements in an efficient manner. The mere existence of comprehensive subsurface drainage maps tend to increase the value of farmland in flat, tiled-drained regions.

The tile line maps can also be incorporated into precision farming practices. They provide a covariate that can be considered in developing relationships between chemical application and yield. This consideration will result in reduced loss of agrochemical through subsurface drainage water.

REFERENCES

Baber, J.J, Jr., 1982. Detecting Crop Conditions with Low-Altitude Aerial Photography. In J.J Johannsen and J.L. Sanders (eds.): Remote Sensing for Resource Management. Soil Conservation Society of America, Ankeny, Iowa, 665 pp.

Barrett, E.C. and L.F. Curtis, 1992. Introduction to Environmental Remote Sensing. Chapman $ Hall, London, 426 pp.

Betts, N.L., M.M. Cruickshank and R.W. Tomlinson, 1986. An Evaluation of SPOT-Simulation Imagery for Land Use Mapping and Ecological Investigation in Upland Area in Northern Ireland, International Journal of Remote Sensing 7(6):779-790.

Buchan, G.M. and N.K. Barnett, 1986. Remote Sensing in Land Use Planning: An Application in West Central Scotland, International Journal of Remote Sensing 7(6):767-778.

Campbell, J.B., 1987. Introduction to Remote Sensing. Guilford Press, New York, NY, 551 pp.

Diwvedi, R.S., A.N. Singh and K.K Raju, 1981. Spectral Reflectance of Some Typical Indian Soil as affected by Tillage and Cover Type, International Journal of Remote Sensing 7(6):767-778.

Essery, C.I. and D.N. Wilcock, 1986. SPOT-Simulation Campaign: A Preliminary Land Use Classification for 200 sq. km River Catchment. International Journal of Remote Sensing 7(6):801-814.

Gautam, N.C. and L.R.A. Narayan, 1983. Landsat MSS Data for Land Use/Land Cover Inventory and Mapping: A Case Study of Andhra Pradesh, Photonirvachak: Journal of Indian Society of Photo-Interpretation and Remote Sensing 11(3):15-26.

Haralick, R.M., S. Wang, L.G. Shapiro and J.B. Campbell, 1985. Extraction of Drainage Networks by Using the Consistent Labeling Technique. Remote Sensing of Environment 18:163-175.

Hoffer, R.M., 1978. Biological and Physical Considerations in Applying Computer-Aided Analysis Techniques to Remote Sensing Data. In P.H. Swain and S.M. Davis: Remote Sensing: The Quantitative Approach, McGraw-Hill, New York, NY 396 pp.

Hoffer, R.M., 1972. Agricultural and Forest Resource Survey from Space. LARS #011579, LARS/Purdue University, West Lafayette, Indiana.

Kachhwaha, T.S., 1983. Spectral Signature obtained from Landsat Digital Data for Forest Vegetation and Land Use Mapping in India, Photogrammetric Engineering 49(5):685-689.

Lillesand T.M. and R.W. Kiefer. 1987. Remote Sensing and Image Interpretation, John Wily and Sons, Inc. New York, 2nd ed, 721 pp.

Merritt, E.S., 1982. The Role of Resource Information Companies: Bridges to Application. In J.J Johannsen and J.L. Sanders (eds.): Remote Sensing for Resource Management. Soil Conservation Society of America, Ankeny, Iowa, 665 pp.

Robert, P.C. and R.H. Rust, 1982. Remote Sensing for Real Time Agricultural Management in the Corn and Soybean Region of Minnesota. In J.J Johannsen and J.L. Sanders (eds.): Remote Sensing for Resource Management. Soil Conservation Society of America, Ankeny, Iowa, 665 pp.

Singh, A.N., S.J. Kristoff and M.F. Baumgardner. 1979. Delineating Salt-Affected Soils in the Ganges Plane by Digital Analysis of Landsat Data, Photonirvachak: Journal of Indian Society of Photo- Interpretation and Remote Sensing 7(1):35-39.

Tin-Seong, K. 1995. Integrating GIS and Remote Sensing Techniques for Urban Land-Cover and Land-Use Analysis, Geocarto International 20(1):39-49.

USDA-NRCS, 1996. Soil Survey of Vermilion County, Illinois. United States Department of Agriculture, Natural Resources Conservation Service, Vermilion County, Illinois.

Verma, A. and R.A. Cooke. 1996. Visual and Digital Analysis of SPOT HRV Data for Land Use and Vegetation Cover Classification in the Central Rayong Basin, Thailand. Submitted to Geocarto International.

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AWRA SYMPOSIUM ON GIS AND WATER RESOURCES Sept 22-26, 1996 Ft. Lauderdale, FL