22 March 2001 Estimating grassland yields by projection pursuit regression (PPR) and RS, GIS, and GPS
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Abstract
Four grassland types, plain desert, saline steppe, hill desert steppe, and mountain meadow, were observed to study their production changes in space and time using traditional method, PPR and remote sensing techniques. PPR models established by observed forage yields and different environmental factors as well as satellite information in four grasslands, and their applications of estimating grassland yields were discussed in detail in this paper. The problems of non-linear, non-normalized distribution, and correlated relationships between multi- variables for statistical data were solved by PPR technology. Therefore, the precision and effects to estimate grassland yields were greatly improved versus those of traditional multi-variate linear statistical method. Because of organic combination of remote sensing data and environmental information, it could not only estimate grassland yields on large area, but also extend the results gained on small area to a large extent for monitoring grassland resources and forecasting yields in future using the established models. By use of eight factors observed in four types of grasslands, the comprehensive yield in four different types were simulated by PPR and RS, GIS, GPS technology from 1995 to 1996. Results indicate that the precision of the models in plain desert, saline steppe, hill desert steppe, and mountain meadow reached over 81.76%, 88.61%, 83.50% and 92.35% respectively. The objective of scientific estimating yields in different grassland types was realized by PPR and RS, GIS, GPS, technology.
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Jianlong Li, "Estimating grassland yields by projection pursuit regression (PPR) and RS, GIS, and GPS", Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); doi: 10.1117/12.421114; https://doi.org/10.1117/12.421114
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