Mosquitos spread many diseases globally; Zika Virus and West Nile Virus are two commonly spread in the US. Mosquito abatement is often done through spraying chemicals on standing water breeding grounds to kill mosquito larvae. Identifying breeding grounds is critical work and deploying field workers is the most common way to do so. However, some geographic areas are too heavily vegetated, inaccessible, too large, or too dangerous to efficiently and effectively deploy field crews to complete this work. New methods are needed to assess these difficult to reach areas and identify mosquito breeding grounds. Entomology researchers at North Dakota State University (NDSU) set out to identify a new method using interferometric synthetic aperture radar (IfSAR) data and modelling.

IfSAR is a method that uses two or more synthetic aperture radar (SAR) images to generate maps of surface deformation or digital elevation, using differences in the phase of the waves returning to the satellite or aircraft. ifSAR data is excellent source data for deriving digital terrain models (DTM) in heavily vegetated areas. However, to support this research, the data quality, fitness of use, and accuracy of the DTM had to be assessed and validated to ensure the model’s results were accurate. Continental Mapping was called upon to support the NDSU researchers by performing that data quality and accuracy assessment.

IfSAR data over Williston, ND was collected and used as source material to derive a DTM. From there, over 200 control points were collected using real-time kinematic GPS methodologies across 6 land cover types. With that data in hand, positional accuracies were compared against those from the IfSAR-derived DTM. Accuracy statistics for the full set of control point error observations at both the 95% confidence level and 95th percentile were computed along with a histogram of control point error observations. RMSEz computed for the control point error observations was 1.07 meters, which was consistent with the stated 1 meter vertical RMSE. Control point error observations exhibited a normal distribution and validated the use of NSSDA 95% Confidence Level Accuracy calculations.

The data assessment Continental Mapping provided was critical to the project, providing researchers clarity on the quality and accuracy of the DTM to ensure that the model’s predictions were accurate and reliable.

Special thanks to Jacquelin Stenehjem for engaging Continental Mapping on her PhD dissertation research. Her full PhD is available at the NDSU Repository here.