George A. Seielstad

Zone mapping application for precision-farming: a decision support tool for variable rate application

A web-based decision support tool, zone mapping application for precision farming (ZoneMAP, http://zonemap.umac.org), has been developed to automatically determine the optimal number of management zones and delineate them using satellite imagery and field data provided by users. Application rates, such as of fertilizer, can be prescribed for each zone and downloaded in a variety of formats to ensure compatibility with GPS-enabled farming equipment. ZoneMAP is linked to Digital Northern Great Plains, a web-based application which hosts an archive of satellite imagery, as well as high resolution imagery from airborne sensors. Management zones created by ZoneMAP mapped natural variation of the soil organic matter and other nutrients relatively well and were consistent with zone maps created by traditional means. The results demonstrated that ZoneMAP can serve as an effective and easy-to-use tool for those who practice precision agriculture.

Digital Northern Great Plains: A Web-Based System Delivering Near Real Time Remote Sensing Data for Precision Agriculture

The US Northern Great Plains is one of the world’s most agriculturally productive areas. Growers in the region are eager to adopt modern technology to improve productivity and income. Use of information derived from remote sensing satellites to better manage farms and rangelands while reducing environmental impacts has gained popularity in recent years. However, prohibitive costs and non-availability of near real time remote sensing imagery has slowed the adoption of this technology for in-field decision making. Digital Northern Great Plains (DNGP), a web based remote sensing data dissemination system, was developed to address these drawbacks. It provides end users easy and free access to a variety of imagery and products in near real time. With delivery of archived and current data, DNGP has helped farmers and ranchers reduce operational costs and increase productivity through a variety of innovative applications. Moreover, negative environmental impacts were lessened.

Oil Spill Detection by MODIS Images using Fuzzy Cluster and Texture Feature Extraction

Oil spills in the ocean are one of major environmental concerns, especially in the coastal waters. Multispectral satellite sensors, such as AVHRR, MODIS and MERIS, have been used to detect oil spills which often exhibit a differing spectral reflectance than the surrounding waters. Some simple image processing methods, such as contrast enhancement, have been applied to remote sensing images to delineate the oil spills. But these methods often require subjective judgment from an operator and can not be used in an automatic manner, which is desirable when there is no a priori knowledge of occurrence or the spectral attributes of spills. In this study, we used a fuzzy C-means (FCM) cluster algorithm with a texture feature analysis to detect oil spill using MODIS images. The MODIS images of one incident, which happened near the new port of Dalian in Northeast Chinas Liaoning Province on April 3, 2005, were analyzed and the results proved the efficiency of algorithm. However, in very near shore regions, delineation of water and oil boundary is noisy due to a similar spectral signature across land-water boundary.

Empowering farmers with remote sensing knowledge: A success story from the US Upper Midwest

Satellite imagery has proven potential in farm level applications, especially in the US Upper Midwest where the farm sizes are large enough to be studied using high and medium resolutions. In order for farmers to use this technology to improve their productivity and income, it is imperative that they be sufficiently exposed to the technology so that they are able to take full advantage of it. Training of farmers and ranchers in satellite imagery use started at the University of North Dakota in 2000 and has been refined over time. The basic ‘hands on’ training involves downloading imagery from the Upper Midwest Aerospace Consortium (UMAC) website, downloading and use of free visualization software programs available on the web and an introduction to the various application possibilities. Advanced training, which involves more complex extraction of useful information from digital images, is also available to those who complete the basic training. Over 500 farmers, ranchers, crop consultants and other end users have been trained through this programme and the results are beginning to show through the success stories of cost savings and environmental benefits that have emerged.

Initial attitude estimation of tactical grade inertial measurement unit for airborne environmental camera

The initial attitude estimation of the airborne navigation system greatly affects the overall attitude accuracy. The initial alignment depends on the inertial measurement units performance. In the case of the tactical grade inertial measurement unit, even though tilt angles can be estimated relatively accurate by using accelerometer outputs, it is difficult to determine the azimuth angle with gyroscope outputs because of the uncompensated bias components. Therefore, it is necessary to use the appropriate calibration method in order to compensate biases. In this paper, we propose a new method to determine the uncompensated biases. Furthermore, the measurement error covariance is difficult to determine in the navigation error equation because of the navigation frame disturbance. This paper uses the adaptive Kalman filter method to calculate the measurement error covariance matrix.

Precision farming management via information dissemination

In the Northern Great Plains of the United States, growing seasons are short (80-120 days) but extremely productive. Farms and ranches are large (>1000 acres), so many of precision agricultures early adopters reside in the region. Management optimization depends on decisions taken based on past as well as in-season information. Spatial data is an ideal tool to answer both the long term and the short-term needs.

Comparision of Landsat and MODIS Estimates of Heat Fluxes: Effect of Surface Heterogeniety

Using remote sensing to characterize the hydrologic behavior of the land surface on a routine basis is of considerable practical interest. Technique combining information on land and atmospheric properties with remotely observed variables has improved prediction of a number of hydrological variables, such as evapotranspiration rate, which is an important parameter for water resource management especially over agricultural regions. However, spatial extent of typical field scales is not regularly resolved within the pixel resolution of satellite sensors. Therefore landscape heterogeneity will influence the estimate of surface fluxes by satellite sensors with different spatial resolutions. Data from Landsat TM/ETM+ (120/60 m) and MODIS (1 km) satellite platforms are employed to independently estimate evapotranspiration. Evapotranspiration estimates derived with SEBAL at these multiple resolutions were assessed against eddy covariance flux measurements collected at two different areas, Brookings, SD with quite flat landscape and Fort Peck, MT with mountainous terrain. Altogether, these data allow a multiple scale intercomparison of remotely sensed predictions of heat fluxes. A high degree of consistency was observed between the retrievals of the net solar radiation and soil heat flux from Landsat and MODIS, while the comparison for sensible and latent heat fluxes was poor. For comparisons between satellite prediction and flux tower measurement, Landsat performs better than MODIS. The apparent degrade of agreement for both sensible and latent heat estimates with increasing spatial scale suggests that landscape heterogeneity influence the remotely sensed predictions of heat fluxes.

The Airborne Environmental Research Observational camera (AEROcam): a case study of multidisciplinary experiential learning

The Upper Midwest Aerospace Consortium, an organization of remote sensing research groups at eight Upper Midwest universities with its headquarters located at the University of North Dakota (UND), has entered into a strategic alliance with the UND School of Engineering & Mines to design Earth imaging sensors. The first of these instruments, the Airborne Environmental Research Observational Camera (AEROCam), is a four-band multispectral sensor with extremely high image resolution designed for flight on University of North Dakota airplanes. Position and attitude data for the camera is collected by a global positioning system and an inertial navigation system, so that the images can be automatically geo-corrected after each flight and then distributed to end users via the Internet. Targeted scientific applications include precision agriculture, natural resource management, and disaster (e.g., flooding and forest fire) response. A multidisciplinary team of electrical and mechanical engineering students is developing the AEROCam system, with input from regional farmers and ranchers. Project management and funding are being provided by the John D. Odegard School of Aerospace Sciences, with the technical management team consisting of faculty within the School of Engineering & Mines. As members of the AEROCam design team, undergraduate and graduate students have received an opportunity to contribute to a real-world systems engineering project and to gain valuable hands-on experience. The university is benefiting from the project in many ways, from an increased expertise in systems engineering, to a vastly improved research and development infrastructure, to the production of an excellent public relations vehicle. A real market need exists for the digital imagery that will be gathered by the AEROCam sensor, and this has motivated both the students and the faculty to design, build, and test a professional-quality instrument.

Applications of remote sensing to precision agriculture with dual economic and environmental benefits

In the U.S. Northern Great Plains, growing seasons are short but extremely productive. Farms and ranches are large, so many of precision agricultures early adopters reside in the region. Crop yield maps at seasons end reveal sizable variations across fields. Farm management relying upon uniform chemical applications is ineffective and wasteful. We provided information about crop and range status in near- real-time, so that in-season decisions could be made to optimize final yields and minimize environmental degradation. We created learning communities, in which information is shared among scientists, farmers, ranchers, and data providers. The new information for agricultural producers was satellite and aerial imagery. Value-added information was derived from ETM+, AVHRR, IKONOS, and MIDOS sensors. The emphasis was on reducing the time between acquisition of data by a satellite and delivery of value-added products to farmers and ranchers. To distribute large spatial data sets in short times to rural users we relied upon satellite transmission (Direct PC). Results include: (1) management zone delineation, (2) variable-rate fertilizer applications, (3) weed detection, (4) irrigation efficiency determination, (5) detection of insect infestation, (6) specification of crop damage due to inadvertent chemical application, and (7) determination of livestock carrying capabilities on rangelands.

Providing Precision Crop and Range Protection in the US Northern Great Plains

Faculty, students, and staff from eight universities in the U.S. Northern Great Plains formed the Upper Midwest Aerospace Consortium (UMAC ) to lead a regional transition to sustainability . One major focus was on agriculture, an important part of the region’s economy and social structure. By forming a learning community in concert with farmers and ranchers, UMAC has made information an asset as valuable as land, labor, and capital. One primary source of information combined with traditional sources is remotely sensed imagery. UMAC has created an end-to-end operation, starting with data acquisition by airborne and orbiting sensors customized to acquire data needed to meet producer demands, proceeding to development of value-added products, and finally making them readily accessible on the WWW to non-expert users whom we also train. A specific example of the operation in action illustrates the economic and environmental benefits that result.