Accuracy of remotely sensed data: Sampling and analysis procedures

A review and update of the discrete multivariate analysis techniques used for accuracy assessment is given. A listing of the computer program written to implement these techniques is given. New work on evaluating accuracy assessment using Monte Carlo simulation with different sampling schemes is given. The results of matrices from the mapping effort of the San Juan National Forest is given. A method for estimating the sample size requirements for implementing the accuracy assessment procedures is given. A proposed method for determining the reliability of change detection between two maps of the same area produced at different times is given

Nationwide forestry applications program. Analysis of forest classification accuracy

The development of LANDSAT classification accuracy assessment techniques, and of a computerized system for assessing wildlife habitat from land cover maps are considered. A literature review on accuracy assessment techniques and an explanation for the techniques development under both projects are included along with listings of the computer programs. The presentations and discussions at the National Working Conference on LANDSAT Classification Accuracy are summarized. Two symposium papers which were published on the results of this project are appended

Update and review of accuracy assessment techniques for remotely sensed data

Research performed in the accuracy assessment of remotely sensed data is updated and reviewed. The use of discrete multivariate analysis techniques for the assessment of error matrices, the use of computer simulation for assessing various sampling strategies, and an investigation of spatial autocorrelation techniques are examined

Self-perceived relations between artistic creativity and mental illness: a study into lived experiences

AimTo explore the self-perceived relationships between experiences of creativity and mental illness and to understand the meanings behind these relationships.BackgroundThe idea that mental illness and artistic creativity are somehow related dates back to ancient times. There is some evidence for an actual correlation, but many questions remain unanswered on the nature and direction of the relationship. Qualitative contributions to the debate are scarce, and mainly focus on the potential benefits of participation in the arts for people with mental illness.DesignAn explorative, interpretive study.MethodsTwenty-four professional and semi-professional artists with self-reported experience with mental illness, were recruited purposively. Unstructured in-depth interviews were conducted and transcripts were subjected to interpretive analysis, guided by a hermeneutic phenomenological frame.ResultsParticipants experience a range of interactions between artistic creativity and mental illness. Three constitutive patterns describe what these interactions look like: “flow as a powerful force”; “ambiguous self-manifestation”; and “narrating experiences of suffering.”ConclusionThe findings show that both the concept of creativity and the concept of mental illness, as well as their interrelationships, are layered and complex phenomena that can take on different meanings in people’s lives. The findings provide starting points for further research that goes beyond the polarized academic debate. Understanding the experiences of artists with mental illness can help shape the role of art in public mental health and mental health care

Evaluating Forest Growth Models

Effective model evaluation is not a single, simple procedure, but comprises several interrelated steps that cannot be separated from each other or from the purpose and process of model construction. We draw attention to several statistical and graphical procedures that may assist in model calibration and evaluation, with special emphasis on those useful in forest growth modelling. We propose a five-step framework to examine logic and bio-logic, statistical properties, characteristics of errors, residuals, and sensitivity analyses. Empirical evaluations may be made both with data used in fitting the model, and with additional data not previously used. We emphasize that the validity of conclusions drawn from all these assessments depends on the validity of assumptions underlying both the model and the evaluation. These principles should be kept in mind throughout model construction and evaluation