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Vehicle‐Mounted Optical Sensing: An Objective Means for Evaluating Turf Quality
Author(s) -
Bell G. E.,
Martin D. L.,
Wiese S. G.,
Dobson D. D.,
Smith M. W.,
Stone M. L.,
Solie J. B.
Publication year - 2002
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2002.1970
Subject(s) - festuca arundinacea , normalized difference vegetation index , remote sensing , agrostis , agrostis stolonifera , reflectivity , consistency (knowledge bases) , environmental science , agronomy , mathematics , biology , poaceae , computer science , artificial intelligence , optics , physics , leaf area index , geography
Visual evaluation of turfgrass quality is a subjective process that requires experienced personnel. Optical sensing of plant reflectance provides objective, quantitative turf quality evaluation and requires no turf experience. This study was conducted to assess the accuracy of optical sensing for evaluating turf quality, to compare the rating consistency among human evaluators and optical sensing, and to develop a model that describes a relationship between optically sensed measurements and visual turf quality. Visual evaluations for turf color, texture, percent live cover (PLC), and optically sensed measurements were collected on the National Turfgrass Evaluation Program (NTEP) tall fescue ( Festuca arundinacea Schreb) and creeping bentgrass ( Agrostis palustris Huds.) trials at Stillwater, OK. Measurements were made monthly for 12 consecutive months from June 1999 through May 2000. Red (R) and near infrared (NIR) reflectance were collected with sensors and converted to normalized difference vegetative indices (NDVI). The NDVI were closely correlated with visual evaluations for turf color, moderately correlated with percent live cover (PLC), and independent of texture. Measurements of turf color and PLC were evaluated more consistently with optical sensors than by visual ratings. Normalized difference vegetation index ( Y ) could be reliably predicted by the following generalized model for turf color ( X ) and PLC ( Z ):Y = B 0 + B 1 log 10X + B 2 Z 3Optical sensing provided fast, reliable turf assessment and deserves consideration as a supplemental or replacement technique for evaluating turf quality.