Soizik Laguette

Remote Sensing for Assessing Rhizoctonia Crown and Root Rot Severity in Sugar Beet

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani AG-2-2, is an increasingly important disease of sugar beet in Minnesota and North Dakota. Disease ratings are based on subjective, visual estimates of root rot severity (0-to-7 scale, where 0 = healthy and 7 = 100% rotted, foliage dead). Remote sensing was evaluated as an alternative method to assess RCRR. Field plots of sugar beet were inoculated with R. solani AG 2-2 IIIB at different inoculum densities at the 10-leaf stage in 2008 and 2009. Data were collected for (i) hyperspectral reflectance from the sugar beet canopy and (ii) visual ratings of RCRR in 2008 at 2, 4, 6, and 8 weeks after inoculation (WAI) and in 2009 at 2, 3, 5, and 9 WAI. Green, red, and near-infrared reflectance and several calculated narrowband and wideband vegetation indices (VIs) were correlated with visual RCRR ratings, and all resulted in strong nonlinear regressions. Values of VIs were constant until at least 26 to 50% of the root surface was rotted (RCRR = 4, wilting of foliage starting to develop) and then decreased significantly as RCRR ratings increased and plants began dying. RCRR also was detected using airborne, color-infrared imagery at 0.25- and 1-m resolution. Remote sensing can detect RCRR but not before initial appearance of foliar symptoms.

Development of a low-cost student-built multi-spectral sensor for the International Space Station

Built by students and faculty at the University of North Dakota (UND), the International Space Station (ISS) Agricultural Camera (ISSACTM) is a multi-spectral Earth-imaging sensor currently onboard the ISS. Capabilities include three spectral bands (green, red, near-infrared), medium (~20m) spatial resolution, and off-nadir pointing (+/-30 degrees) for episodic rapid-response imaging. We describe the low-cost electro-optical design approach, which utilizes a studentcentered design and operations team and relies on modified commercial components operating within a passive vibration isolation mounting, installed inside the Window Observational Research Facility, viewing the Earth through the US Laboratory Science Window. Interfaces, safety, and other factors unique to the human-rated operational environment of the ISS are outlined. Pre-launch sensor characterization results, including spatial distortion and radiometric measurements, indicate Earth remote sensing using such a sensor is a viable approach for demonstrative operational missions. An element of the ISS National Laboratory, ISSAC was launched on HTV-2 to the ISS in January 2011. Initial operations began in June 2011. Methods of sensor operations are described, using a student staff working within the ISS operational environment. Some initial early imaging results are shown.

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.

Helping farmers and ranchers to use remote sensing as a basic management tool: a development of an interactive remote sensing and image processing teaching process

The management of agro-ecosystems is an everyday challenge for farmers, ranchers, and land managers. Four main benefits arise from proper management: 1) increased yields, 2) reduced costs, 3) reduced environmental pollution, 4) maintenance of a quantitative history of field productivity. Satellite data can greatly assist management by providing spatial information on vegetation. Despite its enormous potential, remote sensing data and its derived products are still used on a regular basis by less than 5% of users in the farming and ranching community. The teaching process is essential in helping to grow a true interest in remote sensing data by end-users. Web-based interactive tutorials and training sessions, associated with efficient data acquisition and distribution system together with basic viewers, with simple manipulation capability, can bring end-users from simple spectators to involved actors.

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.

crop and range alert system in the U.S. northern Great Plains

The Upper Midwest Aerospace Consortium has developed a crop and range alert system to provide farmers, ranchers, land managers from the Native American Community, government agencies and non-governmental organizations with frequent and near real time remote sensing data to enable decisions that both maximize the producers income and protect the environment. The project, started in 1999, includes the establishment of a learning community network of end users, fast delivery of data to remote locations, applications development and training. More than a hundred and fifty end users and research scientists participated in this learning group in which information is shared in all directions. Over fifty end users were connected via high-bandwidth satellite link to a central distribution system at the University of North Dakota. They received and shared products derived from AVHRR, MODIS, Landsat, IKONOS and aerial platforms. A number of practical applications were developed for precision farming, such as zone-based nitrogen management, stress detection, spray drift detection, and for rangeland management, such as weed detection, livestock carrying capacity, and livestock field rotations. Several instances of cost savings and higher earnings occurred. More importantly, the imagery use resulted in lesser use of chemicals in farming and ranching, leading to environmental benefits.