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.
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.
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|
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).
|Soil series||Hydrologic group||Permeability
|Drainage class||Organic matter (%)||Depth (cm)||USDA texture|
(moderately slowly permeable)
|moderately slowly drained||3-5||0-17.5||Silt loam|
|17.5-72.5||Silt clay loam|
|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|
(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|
|38-83.8||Silt clay loam|
|83.8-187.96||Loam, clay loam, silt clay loam|
(very poorly permeable)
|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|
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.
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.
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.
|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)
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.
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.
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.
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