Monitoring Grassland Growth Based on Consistency-Corrected Remote Sensing Image

Abstract

Monitoring grassland growth in large areas usually needs multiple images from different sensors or on different dates to cover the study area completely. Images from different sensors or on different dates need consistency correction to eliminate the sharp differences between images. The main contribution of this study is to promote a method for consistency correction of images on different days by constructing a linear regression equation of land cover types and the classification pixel mean. Taking a prefecture-level area in China as a test area, the consistency corrected images were applied for monitoring grassland growth. The results showed the following. First, compared with the normal correction equation constructed for two images, taking all features into account, the coefficient of determination of the equation corrected by the land cover types was improved, and the root mean square error was also significantly reduced. Secondly, the areas of consistency in the corrected image were improved compared with the original image, with an improvement rate of 21% for images from the same sensor and 25% for images from different sensors. The pixel average was much closer to the benchmark images, indicating that the corrected image was more consistent than the original image. Thirdly, when applied for monitoring grassland growth, consistency correction can solve the problem of misjudging grassland degradation. Grassland that was judged to be degraded using direct imagery, in fact, showed stable growth after consistency correction, and this type accounted for 7.33% of the regional grassland area. The seasonal characteristics of grass growth in the region were also obtained by monitoring the growth of grass in the region throughout the year. The application test showed that an effective image consistency correction method can improve the accuracy of grassland growth monitoring across a large area.