A Comparison of Techniques for Estimating NDVI for Agricultural Intervention Impact Assessment

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Flood Mapping of Recent Major Hurricane Events with Synthetic Aperture Radar, Commercial Imaging, and Aerial Observations

Floodwater mapping is an important remote sensing process that is used for disaster response, recovery, and damage assessment practices. Developing a system to read in Synthetic Aperture Radar (SAR) data and perform land cover classification will allow for the production of near real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of the situation. SAR is a unique remote sensing tool. Data in this project was obtained by NASA Jet Propulsion Laboratorys Uninhabited Aerial Vehicle SAR (UAVSAR), an L-band radar mounted to a Gulfstream III jet. Data collected by UAVSAR is similar to what will be available from the NASA-Indian Space Research Organization (NISAR) mission starting in early 2022. Using Python and ArcGIS applications, a model was developed using training samples taken from NOAA post-event aerial photography and UAVSAR data gathered in the aftermath of Hurricane Florence in September 2018

Interpreting the Interpretations: The Use of Structured Reporting Improves Referring Clinicians' Comprehension of Coronary CT Angiography Reports

BACKGROUND: Efficiency of coronary computed tomography angiography (CCTA) in clinical practice depends on precise reporting and accurate result interpretation. OBJECTIVE: We sought to assess referring clinicians’ understanding of patient’s coronary artery disease (CAD) severity and to compare satisfactions with free-form impression (FFI) vs. structured impression (SI) section of CCTA reports. MATERIALS AND METHODS: 50 clinical CCTA reports from May 2011 to April 2012 were retrospectively selected (25 FFI and 25 SI), to include cases with the entire spectrum of CAD (6 categories comprised of normal, minimal, mild, moderate, severe stenosis, and occlusion). A survey containing randomized blinded impressions only was distributed to 4 cardiologists and 2 cardiac imaging specialists. Clinician interpretation was examined regarding Q1) worst stenosis severity, Q2) number of vessels with significant stenosis, and Q3) the presence of non-evaluable segments. Agreement proportions and Cohen’s kappa were evaluated between FFI vs. SI. Satisfactions were measured with respect to content, clarity, and clinical effectiveness. RESULTS: Q1 agreement was excellent for both FFI and SI (by six categories: 80% vs. 85%, p>0.05; kappa: 0.87 vs. 0.89; by no CAD vs. non-significant vs. significant CAD: 99% vs.97%; p>0.05; kappa: 0.99 vs. 0.94). Q2 agreement improved from fair to moderate (53% vs. 68%, p=0.04; kappa 0.31 vs. 0.52). Q3 agreement was moderate (90% vs. 87%, p>0.05; kappa 0.57 vs. 0.58). Satisfactions with impressions were high and similar with FFI vs. SI for clinicians. CONCLUSION: Structured impressions were shown to improve result interpretation agreement from fair to moderate with regard to the number of vessels with significant stenosis