Steve Thomson, Talbot Brooks, Yanbo Huang, Jason Weick, Ken Fisher, Sherri DeFauw, Claudiane Ouellet-Plamondon, Patrick English 2015-04-29 01:29:24
Agricultural aircraft are used in many parts of the world for spraying crop protection and production materials and can therefore be scheduled easily for remote sensing activities as well. Now that highly capable thermal cameras have been miniaturized, we can use these lightweight cameras in unmanned aerial vehicles (UAV). At the USDA-ARS Crop Production Systems Research Unit in Stoneville, Miss., thermal imaging has shown great potential for detecting spatial differences in crop water stress, finding faults in irrigation systems, and even monitoring the Mississippi River. During the record water levels and flooding in 2011, a thermal imaging system mounted on agricultural aircraft showed great potential for detecting problem areas on the Mississippi River levee. Thermal imagery and crop water stress Proper management of irrigation is a challenge no matter what type of irrigation system is used. Irrigation scheduling can be facilitated with soil water sensors, with model-based methods that estimate evapotranspiration using adjustments for growth stage, and with evaporimeters modified for ease of use and interpretation. However, irrigation scheduling aids have not been widely adopted because of the perceived need for intensive management effort and the difficulty of interpreting trends in crop water stress. Many commercial systems are now available for smart phone and web-based monitoring of irrigation systems and sensors to improve convenience for the farmer, but farmer-oriented procedures for smart scheduling are still lacking. In Mississippi, furrow irrigation systems account for about 74% of all irrigated acreage. At first glance, these systems may not seem easily adaptable to variable scheduling. However, furrow irrigation systems that apply water in two phases for greater efficiency can be modified to apply irrigation to selected furrows. Similarly, variable-rate center-pivot irrigation (VRI) systems can be programmed to apply water at different rates over different management zones, and banks of nozzles can be shut off along the span to avoid low-lying wet areas or overlapping pivots. Center-pivot VRI systems can also be used to control application of wastewater to cropland consistent with EPA regulations regarding 30 m (100 ft) setbacks from all conduits to surface water. This ability to vary water application within a single site implies that we are matching irrigation rates to crop needs. This is true to the extent that it uses the farmer’s knowledge of existing differences in the field, many of which tend to be static. However, a higher level of precision irrigation, based on measured or modeled crop water requirements, has so far met with only limited success. No matter how fast or accurate a crop stress sensor may be, or how accurate a crop water use model is, much of the problem lies with limitations of the irrigation system itself. Unlike drip irrigation that can be precisely timed, thus readily lending itself to precision application, center-pivot systems can take up to several days to complete a circle. State-of-the art in center-pivot scheduling has been demonstrated by USDA-ARS scientists at Bushland, Texas, who have developed a center-pivot scheduling method that uses crop canopy temperature data from infrared temperature (IRT) sensors and time-temperature thresholds. Using these thresholds, irrigation decisions are based on the length of time the canopy stays above a specified temperature. Thermal imaging is an extension of the IRT concept. In its simplest form, a thermal snapshot of a field can reveal areas that consistently experience early water stress (hot spots) or areas with poor drainage (cool areas). Variable application management of center-pivot irrigation can be appropriate if warmer or cooler areas are consistently observed at regular locations, for example because of differences in soil or topography. Problem areas can be investigated, and appropriate measures can then be taken to improve the irrigation efficiency. Thermal imaging has shown good potential for tracking crop water stress using mathematical relationships that exploit the temperature difference between the crop canopy and the surrounding air. A stressed crop has a warmer canopy, and canopy temperature measurement has shown promise for detecting both biotic and abiotic crop stresses, particularly in arid environments. Leaf temperature depends on evaporative cooling, and this temperature increases as stomata close and restrict evaporative water loss. Arid conditions allow crop cooling by evaporation, so crops in a drying cycle—on their way to stress—can exhibit large temperature differences. However, humid conditions suppress this cooling, and smaller resulting temperature differences are more difficult to resolve. For accurate analysis of thermal imagery on a temporal basis, ambient effects such as solar radiation and the altitude of image acquisition must also be considered. These factors influence both the canopy temperature and the representation of canopy temperature at the camera. The images below were obtained using a Sofradir Electrophysics PV-320T thermal imaging camera on an Air Tractor 402B agricultural aircraft. This camera was chosen for its high spectral resolution of temperature, which can be represented visually in very narrow ranges. The progression of drying in certain areas of the field is shown by blue areas in the left image turning green in the right image. A preliminary model has been proposed by the first author to determine the effects on this sensor’s representation of canopy temperature, and this information might be useful to spectrally re-scale the images, so that a more accurate representation of canopy temperature can be made for temporal comparisons. Fine temporal delineation of the canopy temperature and of the canopy-air temperature differences continues to be a challenge for irrigation scheduling in humid conditions. Fusion of sensor data Fusion of image data from thermal and multispectral cameras has been used to analyze field areas for yield potential and can reveal the relationships between many different growth parameters. This concept could be extended to indicate quality indices along with yield, as in the case of cotton. Images from the Sofradir thermal camera were composited to produce a cumulative thermal map of a cotton canopy. In addition, two color infrared (CIR) images were obtained using the Digital Sensor System (DSS) (Emerge Sensor Group, Andover, Mass.). This camera was flown at an altitude of 600 m on two different days that were more than one month apart. The resulting images were processed using an intensity normalization method followed by calculation of the normalized difference vegetation index (NDVI). The two normalized CIR images were also processed using Isodata unsupervised classification to establish two classes (coded 0 = no vegetation and 1 = vegetation). Zonal means were subsequently calculated for each image (i.e., resampled to 1 m resolution), resulting in assessments of percent cover. The canopy cover change was determined by subtracting the first classified image from the vegetation mapped in the subsequent month. Highly significant patterns of yield were linked to the thermal zones in the irrigated cotton field. Using a bivariate local indicator of spatial association (LISA) map, yield and temperature were correlated for nearly 65% of the field area at p < 0.01 and for 15% of the field area at 0.01 < p < 0.05. Another bivariate LISA map (image A below), using GeoDa (Spatial Analysis Laboratory, University of Illinois, Urbana- Champaign, Ill.) indicated significant autocorrelations (p < 0.05) between cotton canopy cover change and the cumulative thermal maps. Low canopy cover change was coupled with the lowest cumulative canopy temperatures (shown in dark color). Similarly, the adjacent areas of the field (in lighter color) showed relatively high canopy cover change paired with low canopy temperature change. A third bivariate LISA map, comparing patterns of yield with in-season canopy cover change (image B), resolved four field-scale production zones including: (1) high yielding areas with low canopy cover change, (2) a more scattered grouping of high yielding areas paired with relatively high canopy cover change, (3) stressed areas of the field with low yield and low canopy cover change, and (4) low yielding areas coupled with relatively high canopy cover change. Ground-truthing demonstrated that cotton plants subjected to consistently high temperatures from middle to late August had 50% to 90% open boll. These results indicate that composited thermal imagery combined with tracking of canopy cover change at key phenological stages can be useful for in-season prediction of yield potential, as well as early senescence promoted by heat and water stress in highly heterogeneous cotton fields. Management of irrigation is tied to phenological stage in cotton, and maps such as these could also support the development of site-specific insecticide applications to protect high-yielding areas and promote costeffective application of defoliants and harvest aids. Furrow irrigation system maintenance Thermal imaging has shown value for detecting water leakage from furrow irrigation systems. The location of a water leak is quite obvious in the image to the right. The extensive dark blue area on the right indicates a leaking irrigation riser valve. Small light green to bright red areas—hot spots—can be seen to the left. These indicate restricted water flow, which is most likely due to trash or high spots in the furrows. Field maintenance, such as cleaning out the furrows or re-grading the field, might be necessary to ensure proper irrigation. An interesting anecdote relates to this field. A student employee incorrectly began irrigating the adjacent field (on the right edge of the image) and then, realizing his error, promptly re-adjusted the system to irrigate the correct field. The supervisor was off-site when this occurred and did not notice the error when he returned a few days later, as drying had already occurred. However, while the supervisor did not see evidence of the incorrect irrigation, thermal imaging gave it away, showing the initial irrigation (in pale green) on the otherwise dry field (in red). An eye on the levee Remote sensing of the Mississippi River levee came about as a collaborative effort between many federal and state government agencies. The Yazoo Mississippi Delta Levee (YMDL) Board owns and maintains the mainline Mississippi River levee from Memphis, Tennessee, to the Bolivar/Coahoma County line. They also own and maintain the Yazoo River backwater levees from Vicksburg all the way up the Cold Water and Tallahatchie Rivers. The mainline Mississippi River levee is designed to withstand a 500-year flood event plus seven vertical feet of freeboard. A sizeable stand of trees is maintained on the river side of the levee to prevent erosion of the levee due to wave and water action. Rising water against the levee creates an increase in soil hydraulic pressure. This increase in pressure can cause the eruption of boils—similar to geysers—as far as 500 m inland from the levee. Sandy soils are particularly susceptible to boils. The erosive power of water flowing through these subterranean channels can increase rapidly and, if left unchecked, can create a blowout and failure of the levee. While relief wells (dry wells that provide a stable channel and pressure relief system) can prevent the formation of some boils, they do not eliminate the risk entirely. Locating boils has thus become the principal challenge in preventing levee failure. The traditional method of locating boils involves sending out patrols of YMDL Board and National Guard personnel to scout around historical trouble spots. In heavy vegetation and cropland, this process is quite arduous. Given the widespread flooding during April and May 2011 and the limited personnel, the YMDL Board needed a more effective method for detecting boils, and they wanted to know if spatial technologies could help. While we were well prepared with spatial data and techniques for combating known boils, detecting unknown boils was an entirely new problem. We suspected that remote sensing techniques could be used to help detect boils. We interviewed the principal engineers at YMDL and then confirmed their ideas with the principal engineer for the Mississippi River Levee District. We identified two potential remote sensing methods: hyperspectral and thermal. With respect to thermal imaging, the engineers told us that water from a boil is at least a few degrees colder than the surrounding surface water, as it comes from a greater depth in the river and passes through cooler layers of soil before emerging at the surface. The passage of water through soil channels under pressure can also introduce iron into at least the preliminary flow of water. While the river level was near its peak, we decided to evaluate the potential of using the Sofradir thermal imaging camera on an Air Tractor 402B agricultural aircraft to detect boils and observe possible temperature anomalies along each side of the levee. Boils were easily detectable in the thermal images as cool blue areas. The image above shows such a boil near Deeson, Mississippi. The temperatures were set within a relatively narrow range using Electrophysics Velocity 2.0 post-processing software and adjusted until the differences could be easily discerned. Temperature differences were also observed on both the leading edge (river side) and trailing edge of the levee. In the image at the upper right, the dark blue area on the right is the river, and the light blue areas along the leading edge of the levee indicate the presence of water. These areas most likely represent a certain amount of preferential flow or internal seepage, and the green areas of cooler temperatures on the trailing edge of the levee are most likely vegetation, although a green to blue color can also indicate weakness in the levee. The temperatures were set to a wide range, so cool water to warmer vegetation and soil could be shown in the same image. Using software, a much narrower temperature range can be set over specific portions of the image, which can reveal anomalies in greater spectral detail. Summary Thermal imaging has shown great potential for use in crop health assessment and levee surveillance. Mounting the imaging system on an agricultural aircraft allows frequent imaging concurrent with other remote sensing systems that monitor crop health. Our current studies focus on using thermal imagery to assess crop injury and the onset of stress, in concert with other remote sensing methods. Our imaging system has also been brought into service for detecting water seepage on the Mississippi River levee, in consultation with appropriate state and federal agencies. Since our original study on the levee was conducted, we have purchased an ICI 9640 radiometric thermal imaging system with image georeferencing capabilities that can be mounted on both UAV and agricultural aircraft. Referencing the images to ground position along the levee will be useful, since the scene is uniform and landmarks are difficult to discern when flying low. Agricultural aircraft have a particular advantage over UAV for this application since they can be directed by the pilot and can cover very long distances along the levee. Steve Thomson, ASABE Member, USDA-ARS Crop Production Systems Research Unit, Stoneville, Miss., USA, email@example.com; Talbot Brooks, Center for Interdisciplinary Geospatial Information Technologies, Delta State University, Cleveland, Miss., USA, firstname.lastname@example.org; Yanbo Huang, ASABE Member, USDA-ARS Crop Production Systems Research Unit, Stoneville, Miss., USA, email@example.com; Jason Weick, Coastal Waters Consortium, Chauvin, Louisiana, USA, firstname.lastname@example.org; D. Ken Fisher, USDA-ARS Crop Production Systems Research Unit, Stoneville, Miss., USA, email@example.com; Sherri DeFauw, Ascend Geospatial LLC, Cleveland, Miss., USA, firstname.lastname@example.org; Claudiane Ouellet- Plamondon, ETH Zürich/Swiss Federal Institute of Technology, Zürich Switzerland, email@example.com; Patrick English, ASABE Member, Delta Research and Extension Center, Mississippi State University, Stoneville, Miss., USA, firstname.lastname@example.org.
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