Optimal spatial resolution of remote-sensing imagery for monitoring cantaloupe greenhouses

Abstract

Plastic greenhouses are vital agricultural facilities to protect cash crops from disease and insects, especially in the Hainan region of China, which has high temperature and high humidity. Remote-sensing technology is an efficient means to quickly determine the spatial distribution of plastic greenhouses on the regional scale. With the rapid development of remote-sensing technology, and especially the increasing types of high-spatial-resolution remote-sensing imagery, many studies have obtained good results by using remote-sensing technology to monitor plastic greenhouses. However, the best spatial resolution of images for monitoring plastic greenhouses has yet to be studied. To address this issue, we use cantaloupe greenhouses as the research object and GF-2 images with 1m spatial resolution as data source. We then use the re-sampling method to generate images from these data with spatial resolutions of 0.5, 2, 3, and 5 m. The details of the spatial distribution (texture features and shape features) and the spectral features of the plastic greenhouses were then extracted from images of varying spatial resolution, and a remote-sensing monitoring method for cantaloupe greenhouses was constructed based on the object-oriented random forest algorithm, which combines spectral, texture and shape features, and the monitoring results are compared. The results show that the use of 2 m spatial resolution provides the highest monitoring accuracy of cantaloupe greenhouses (overall accuracy = 94.85% and KIA = 0.92). This study thus provides a theoretical basis for remote-sensing monitoring of greenhouse cantaloupes that satisfies the current demands of production accuracy.