Estimating grassland yields using remote sensing and GIS technologies in China

Abstract

From green herbage yield, environment, and remote sensing (RS) data recorded in different grassland types in Fukang County, Xinjiang from 1991 to 1996, correlation analyses and grassland yield estimates were obtained using remote sensing and geographic information system (GIS) technologies. Methods of processing images, analysing information, and linking of remote sensing data with ground grassland data were explored. Results showed correlation between fresh herbage yields and ratio vegetation index (RVI) and normalised difference vegetation index (NDVI) (P < 0.01) in four grassland types with correlation coefficient (r) >0.679. Fresh herbage yields correlated better with RVI than with NDVI for lowland meadow, hill desert steppe, and mountain meadow, but not for plains desert steppe. Optimum non‐linear models for estimating yield were selected from six curves, and estimated total yields were verified by ground truth large‐plot investigations and statistical analyses. The effects of estimating green herbage yields using non‐linear models were better than those using linear models in all four grassland regions. The total accuracy of estimating yields by remote sensing was >75% over large areas in the four grassland types using a combination of remote sensing and GIS. Remote sensing, along with GIS, is a new approach to the use, development, and management of grasslands.