Improving land change detection based on uncertain survey maps using fuzzy sets

In this paper we present a method for correcting inherent classification bias in historical survey maps with which subsequent land cover change analysis can be improved. We linked generalized linear modelling techniques for spatial uncertainty prediction to fuzzy set based operations. The predicted uncertainty information was used to compute fuzzy memberships of forest and non-forest classes at each location. These memberships were used to reclassify the original map based on decision rules, which take into consideration the differences in identification likelihood during the historical mapping. Since the forest area was underestimated in the original mapping, the process allows to correct this bias by favouring forest, especially where uncertainty was high. The analyses were performed in a cross-wise manner between two study areas in order to examine whether the bias correction algorithm would still hold in an independent test area. Our approach resulted in a significant improvement of the original map as indicated by an increase of the Normalized Mutual Information from 0.26 and 0.36 to 0.38 and 0.45 for the cross-wise test against reference maps in Pontresina and St. Moritz, respectively. Consequently subsequent land cover change assessments could be considerably improved by reducing the deviations from a reference change by almost 50 percent. We concluded that the use of logistic regression techniques for uncertainty modelling based on topographic gradients and fuzzy set operations are useful tools for predictively reducing uncertainty in maps and land cover change models. The procedure allows to get more reliable area estimates of crisp classes and it improves the computation of the fuzzy areas of classes. The approach has limitations when the original map shows high initial accurac

Using Core Competencies to Help Staff Remain Proficient

Problem: Implement a competency program that engages staff compared to usual computer-based training modules. Objective: Design a multi-modal competency program for bedside nursing staff.https://digitalcommons.centracare.com/nursing_posters/1053/thumbnail.jp

Using Repeated Oral Assisted Reading To Improve The Fluency Of Students With Learning Disabilities

This study explores the research question: How much does repeated reading improve the reading rate, accuracy, and prosody of students with learning disabilities in fifth grade? Three key topics: learning disabilities, fluency, and repeated reading are discussed. The author was greatly influenced by Timothy Rasinski and his work in the area of fluency. Repeated Oral Assisted Reading was administered to monitor and document accuracy, rate, and prosody utilizing both daily instructional-level reading probes and monthly grade-level reading probes as well as prosodic reading rubrics and a daily journal. The improvement in rate was compared to scores on nationally normed fluency assessments. Findings suggest that Repeated Oral Assisted Reading is an effective intervention for improving both reading rate and prosody in students with learning disabilities

Shifting the Paradigm of Classroom Nursing Orientation

Purpose Statement: The purpose of the Evidence-Based Practice Nursing Orientation Project is to find evidence to support a change in teaching strategy for newly hired RNs (both New Grads as well as experienced nurses), from the current process in order to improve outcomes of clinical competence, independence and nurse confidence, and retention.https://digitalcommons.centracare.com/nursing_posters/1050/thumbnail.jp

Creating Successful Mentoring Relationships

Literature suggests that a formal mentoring program increases nursing engagement and retention. Objective: Develop a nurse mentor program to enhance new nurse satisfaction and retention.https://digitalcommons.centracare.com/nursing_posters/1034/thumbnail.jp

Salmonella serology - which samples should be used: comparison of meatjuice and serum samples of the same pigs

Contamination of pork with Salmonella typhimurium is a potential source for fatal food born Salmonella-infections in humans. Screening programs are used in a number of countries to categorize pig farms into 3-4 Salmonella-risk-categories. A similar program will soon be implemented by the German government as well

Nursing Bed Side Report: Changing our Relationship with our Patients

To improve bed side shift reporting in order to standardize practices, to increase efficiency, to decrease RN late clock outs, and to increase patient satisfaction.https://digitalcommons.centracare.com/nursing_posters/1020/thumbnail.jp

Successful Mentoring Relationships

A poster presentation on the structure, process, and outcomes of a successful mentoring program.https://digitalcommons.centracare.com/nursing_posters/1032/thumbnail.jp

Estimating a largest eigenvector by polynomial algorithms with a random start

In 7 and 8 the power and Lanczos algorithms with random start for estimating the largest eigenvalue of an n x n large symmetric positive definite matrix were analyzed. In this paper we continue this study by estimating an eigenvector corresponding to the largest eigenvalue. We analyze polynomial algorithms using Krylov information for two error criteria the randomized error and the randomized residual error

Towards localized accuracy assessment of remote-sensing derived built-up land layers across the rural-urban continuum

The accuracy assessment of remote-sensing derived built-up land data represents a specific case of binary map comparison, where class imbalance varies considerably across rural-urban trajectories. Thus, local accuracy characterization of such datasets requires specific strategies that are robust to low sample sizes and different levels of class imbalance. Herein, we examine the suitability of commonly used spatial agreement measures for their localized accuracy characterization of built-up land layers across the rural-urban continuum, using the Global Human Settlement Layer and a reference database of built-up land derived from cadastral and building footprint data