Procedure B: A multisegment training selection and proportion estimation procedure for processing LANDSAT agricultural data

There are no author-identified significant results in this report

System for analysis of LANDSAT agricultural data: Automatic computer-assisted proportion estimation of local areas

The author has identified the following significant results. A conceptual man machine system framework was created for a large scale agricultural remote sensing system. The system is based on and can grow out of the local recognition mode of LACIE, through a gradual transition wherein computer support functions supplement and replace AI functions. Local proportion estimation functions are broken into two broad classes: (1) organization of the data within the sample segment; and (2) identification of the fields or groups of fields in the sample segment

Investigations of vegetation and soils information contained in LANDSAT Thematic Mapper and Multispectral Scanner data

An extension of the TM tasseled cap transformation to reflectance factor data is presented, and the basic concepts underlying the tasseled cap transformations are described. The ratio of TM bands 5 and 7, and TM tasseled cap wetness, are both shown to offer promise of direct detection of available soil moisture. Some effects of organic matter and other soil characteristics or constituents on TM tasseled cap spectral response are also considered

Analysis of scanner data for crop inventories

There are no author-identified significant results in this report

The analysis of scanner data for crop inventories

There are no author-identified significant results in this report

Development of Landsat-based Technology for Crop Inventories: Appendices

There are no author-identified significant results in this report

Analysis of scanner data for crop inventories

There are no author-identified significant results in this report

Analysis of scanner data for crop inventories

Progress and technical issues are reported in the development of corn/soybeans area estimation procedures for use on data from South America, with particular emphasis on Argentina. Aspects related to the supporting research section of the AgRISTARS Project discussed include: (1) multisegment corn/soybean estimation; (2) through the season separability of corn and soybeans within the U.S. corn belt; (3) TTS estimation; (4) insights derived from the baseline corn and soybean procedure; (5) small fields research; and (6) simulating the spectral appearance of wheat as a function of its growth and development. To assist the foreign commodity production forecasting, the performance of the baseline corn/soybean procedure was analyzed and the procedure modified. Fundamental limitations were found in the existing guidelines for discriminating these two crops. The temporal and spectral characteristics of corn and soybeans must be determined because other crops grow with them in Argentina. The state of software technology is assessed and the use of profile techniques for estimation is considered

Analysis of scanner data for crop inventories

There are no author-identified significant results in this report

Brief cognitive behavioral therapy in primary care: a hybrid type 2 patient-randomized effectiveness-implementation design

BACKGROUND: Despite the availability of evidence-based psychotherapies for depression and anxiety, they are underused in non-mental health specialty settings such as primary care. Hybrid effectiveness-implementation designs have the potential to evaluate clinical and implementation outcomes of evidence-based psychotherapies to improve their translation into routine clinical care practices. METHODS: This protocol article discusses the study methodology and implementation strategies employed in an ongoing, hybrid, type 2 randomized controlled trial with two primary aims: (1) to determine whether a brief, manualized cognitive behavioral therapy administered by Veterans Affairs Primary Care Mental Health Integration program clinicians is effective in treating depression and anxiety in a sample of medically ill (chronic cardiopulmonary diseases) primary care patients and (2) to examine the acceptability, feasibility, and preliminary outcomes of a focused implementation strategy on improving adoption and fidelity of brief cognitive behavioral therapy at two Primary Care-Mental Health Integration clinics. The study uses a hybrid type 2 effectiveness/implementation design to simultaneously test clinical effectiveness and to collect pilot data on a multifaceted implementation strategy that includes an online training program, audit and feedback of session content, and internal and external facilitation. Additionally, the study engages the participation of an advisory council consisting of stakeholders from Primary Care-Mental Health Integration, as well as regional and national mental health leaders within the Veterans Administration. It targets recruitment of 320 participants randomized to brief cognitive behavioral therapy (n = 200) or usual care (n = 120). Both effectiveness and implementation outcomes are being assessed using mixed methods, including quantitative evaluation (e.g., intent-to-treat analyses across multiple time points) and qualitative methods (e.g., focus interviews and surveys from patients and providers). Patient-effectiveness outcomes include measures of depression, anxiety, and physical health functioning using blinded independent evaluators. Implementation outcomes include patient engagement and adherence and clinician brief cognitive behavioral therapy adoption and fidelity. CONCLUSIONS: Hybrid designs are needed to advance clinical effectiveness and implementation knowledge to improve healthcare practices. The current article describes the rationale and challenges associated with the use of a hybrid design for the study of brief cognitive behavioral therapy in primary care. Although trade-offs exist between scientific control and external validity, hybrid designs are part of an emerging approach that has the potential to rapidly advance both science and practice. TRIAL REGISTRATION: NCT01149772 at http://www.clinicaltrials.gov/ct2/show/NCT0114977