8,432 research outputs found
Evaluation of an Inexpensive Sensor to Measure Soil Color
Soil color determination can be subjective due to environmental conditions and human error. The objectives of this study were to examine the precision of a relatively inexpensive color sensor (NixTM Pro); to compare soil color measurements using this color sensor to human determination by soil science professionals using the standard Munsell Color Chart; and to compare the accuracy of this color sensor to a laboratory standard colorimeter (Konica Minolta CR-400). Sensor measurements were compared to the soil color chart by converting the Nix Pro values to Munsell soil color codes using BabelColor conversion software. Thirty-one Cecil (Fine, kaolinitic, thermic Typic Kanhapludults) soil samples were collected and tested for color. Munsell color codes were converted into cyan, magenta, yellow, and black (CMYK) color values, and the Nix sensor\u27s scan results were tested against predetermined Munsell color values and colorimeter CMYK color values using correlation analysis for all treatments. Nix Pro Color Sensor was precise in soil color determination and it was more accurate than the Munsell Color Chart and comparable to the Konica Minolta CR-400 for both dry and moist soil. The Munsell Color Chart was accurate compared to the Konica Minolta CR-400 in dry soil, but it was less accurate in moist soil. The Nix Pro Color Sensor can be a successful tool to measure soil color in the standard Munsell color codes and this study presents a step-by-step method for converting sensor measurements to the standard Munsell color codes
Benefits from remote sensing data utilization in urban planning processes and system recommendations
The benefits of utilizing remote sensor data in the urban planning process of the Metropolitan Washington Council of Governments are investigated. An evaluation of sensor requirements, a description/ comparison of costs, benefits, levels of accuracy, ease of attainment, and frequency of update possible using sensor versus traditional data acquisition techniques are discussed
Application of Low-cost Color Sensor Technology in Soil Data Collection and Soil Science Education
Sensor technologies provide opportunities to increase the quality and quantity of soils data while introducing new techniques and tools for classrooms. Linear regression models were developed for organic carbon prediction using color data gathered with the Nix Proâ„¢ for dry (R2 = 0.7978, MSPE = 0.0819), and moist soils (R2 = 0.7254, MSPE = 0.1536). A mobile application, the Soil Scanner app, was created to allow for a more soil science dedicated interface that would allow users to create their own database consisting of GPS location and soil color data gathered using the Nix Proâ„¢. The final application produced results in multiple color systems, including Munsell, recorded GPS location, sample depth, moisture conditions, in-field or laboratory settings, and a photograph of the soil sample. All data could then be uploaded to an online database. The GPS location allows for easy integration of data into GIS mapping software for the spatial manipulation of soils data. The application was tested by generating GIS maps showing the gradient of soil color across two field surfaces. The Nix Proâ„¢ color sensor functions as a successful teaching tool and, coupled with the Soil Scanner app, offers a new means of gathering and storing reliable soils data. There is added benefit to having a soil science application that can be updated to include further analysis methods, resulting in an ever growing soils database. A laboratory exercise was developed that introduced students in an entry level soils course to the importance of soil color and the methods used to determine soil color. Students were then asked to determine the color of three soil samples using the Nix Proâ„¢ and the standard Munsell Color Chart before conducting simple statistical analysis and responding to a questionnaire. Responses indicate that the Nix Proâ„¢ was the preferred method of color analysis and students felt the sensor to be a more reliable method than traditional color books
Quantifying the Relationship between Soil Organic Carbon and Soil Color in Nebraska
Soil color is easily measured in the field and holds potential to be used as an indirect measurement of soil organic carbon (SOC). The main limitation to this approach is knowledge about the specific color-SOC relationship in a region, which often varies in relation to parent material, soil texture, climate, and land use. Furthermore, the Munsell color data is subjective in nature. The objectives of this study are: 1) to develop and evaluate the accuracy of pedotransfer functions (PTFs) for the prediction of SOC based on soil color and texture in the state of Nebraska and 2) to evaluate digital based color measurements methods as field predictors of SOC in Nebraska. To address the first objective, data were obtained from the National Soil Information System (NASIS) database. The dataset consisted of 1576 soil pedon descriptions and samples of various soil textures, Munsell color, and SOC. The second objective was addressed using digital color measurements of 50 soil samples from Kellogg Soil Survey Laboratory archive. Methods used included a portable color sensor (PCS) and smartphone cameras (SPCs). Regressions of moist Munsell value versus SOC using the NASIS data had R2 = 0.23 to 0.69 for individual MLRAs. Regression developed using the PCS for three selected MLRAs had R2 = 0.49 to 0.81. Various PTFs based on the NASIS data resulted in RMSE of prediction = 0.795 to 2.1. The results indicate the potential of using soil color as a predictor for SOC, especially when PCS are used to measure soil color.
Advisor: Judith Tur
Index to NASA Tech Briefs, 1975
This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs
Summary of the Active Microwave Workshop, chapter 1
An overview is given of the utility, feasibility, and advantages of active microwave sensors for a broad range of applications, including aerospace. In many instances, the material provides an in-depth examination of the applicability and/or the technology of microwave remote sensing, and considerable documentation is presented in support of these techniques. An assessment of the relative strengths and weaknesses of active microwave sensor data indicates that satisfactory data are obtainable for several significant applications
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