Detecting Water Loss Sink Points in Shallow Flooded Agricultural Environments Using a Visual Technique Based on Infrared Thermography: Laboratory Testing
Abstract
:1. Introduction
2. Materials and Methods
2.1. Laboratory Setup
2.2. Soil and Sink Characteristics
2.3. Video Recording System
2.3.1. Flooding of the Soil Surface
2.3.2. Tracer Application
2.4. Experimental Procedure
2.4.1. Flooding of the Soil Surface
2.4.2. Tracer Application
2.5. Image Processing
2.5.1. Flooding Process
2.5.2. Sink Detection Method
- Snapshots from the thermal videos were saved from time t0, when the tracer was applied to the flooded area, with a time lapse of ΔtTR.
- The FLIR DUO R infrared camera’s ability to record dual images enabled overlaying the thermal and real images on each other to obtain a clear vision of the flume and the thermal diffusion in the water layer over the soil surface.
- For each series of thermographs, a threshold temperature (τ) was established at 1 °C above the maximum temperature of the water observed in the flume before applying the tracer at time t0. This threshold temperature is used to separate the temperatures associated with the tracer plume from its surroundings. It helps identify pixels with temperature values exceeding the threshold temperature, effectively distinguishing them from the surrounding temperatures, which include pixels with temperature values below this threshold.The selection of an appropriate threshold temperature is crucial. A threshold equal to or lower than the water temperature within the flume where the sink is located would make it difficult to distinguish the thermal plume from its surroundings, as the entire image would appear uniformly colored. Conversely, setting the threshold too high (close to the maximum tracer temperature) would prevent observing the plume when it reaches the sink, as the temperature of its leading edge would have decreased below the tracer temperature by that point. In this study, a threshold of 1 °C above the maximum water temperature in the flume was sufficient to detect the movement of the thermal plume toward the sink.
- The thermal images were recorded at vertical and horizontal distorted angles. Therefore, to precisely determine the position of the leading edge of the tracer plume within the flume, these raw images were rectified and cropped to encompass the soil flume section. A computer-vision-based image rectification method known as Homography (matrix) was used to correct perspective distortion in the raw images, transforming non-parallel lines (due to the perspective) into a straightened version where lines are parallel [36,37]. This adjustment facilitates accurate distance measurements and ensures precise spatial analysis. The coordinates of the four corners of the soil flume were used in this approach for rectifying the raw images. This method transformed the distorted coordinates of the raw images into real metric coordinates using the dimensions of the flume as a reference.
- In each rectified image, the location of the tracer’s leading edge was determined starting from the image at time t1 (t0 + ΔtTR). The leading edge of the tracer’s plume was considered the furthest pixel in the X-axis direction (see Figure 3), with a temperature value above the threshold temperature. This process continued until the location of the leading edge was roughly the same (less than 0.1 m in both X-axis and Y-axis directions) in the last two images (e.g., from time t to t + ΔtTR), indicating that the tracer had reached the sink point. It was assumed that when the leading edge of the thermal tracer plume reached the sink point, the tracer started draining (seepage); therefore, the tracer’s leading-edge location remained approximately constant over time from that moment onward. Thus, this location was considered to be the location of the sink point in the flume.Each step was individually analyzed, and all procedures in each step were carried out using the MATLAB image processing toolbox and custom codes.
3. Results and Discussion
3.1. Flooding Phase
3.2. Sink Detection Phase
4. Conclusions
- The results highlight that tracer discharge can significantly influence the ability to detect the sink location, depending on the characteristics of the sink.
- The proposed sink detection technique successfully identified sink positions within the flooded area (laboratory flume), except in the case (scenario b) where the sink was filled with low-permeability soil and subjected to high tracer discharge.
- Sink characteristics, such as permeability, and the tracer discharge rate were crucial factors in successfully detecting the sink location.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Scenario | Sink Material | Permeability | Saturated Hydraulic Conductivity (mm/s) | Tracer Discharge (L/s) |
---|---|---|---|---|
a | Sand | Low | 0.24 | 0.025 |
b | Sand | Low | 0.24 | 0.035 |
c | Gravel | High | 2.02 | 0.025 |
d | Gravel | High | 2.02 | 0.035 |
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Zehsaz, S.; de Lima, J.L.M.P.; de Lima, M.I.P. Detecting Water Loss Sink Points in Shallow Flooded Agricultural Environments Using a Visual Technique Based on Infrared Thermography: Laboratory Testing. Agriculture 2024, 14, 1366. https://doi.org/10.3390/agriculture14081366
Zehsaz S, de Lima JLMP, de Lima MIP. Detecting Water Loss Sink Points in Shallow Flooded Agricultural Environments Using a Visual Technique Based on Infrared Thermography: Laboratory Testing. Agriculture. 2024; 14(8):1366. https://doi.org/10.3390/agriculture14081366
Chicago/Turabian StyleZehsaz, Soheil, João L. M. P. de Lima, and M. Isabel P. de Lima. 2024. "Detecting Water Loss Sink Points in Shallow Flooded Agricultural Environments Using a Visual Technique Based on Infrared Thermography: Laboratory Testing" Agriculture 14, no. 8: 1366. https://doi.org/10.3390/agriculture14081366