Remote Sensing Image Classification for Spatial Information Extraction of Panax notoginseng Fields

Abstract

Chinese herbal medicine has played an important role in the treatment of the novel coronavirus patients. Machine learning-based remote-sensing techniques play a significant role in the quantitative resource inventory of materia medica resources, particularly to explore the monitoring abilities for sustainable utilization and biodiversity protection of the cultivated medicinal plant Panax notoginseng in the macrocosm. Until now, to the best knowledge, concrete planting patterns of Panax notoginseng are still poorly known. In this study, two popular supervised classifiers, i.e., support vector machine and artificial neural network, are employed to conduct mapping Panax notoginseng fields in Wenshan city, Yunnan province, China. It only targets a single class of interest, and there are 8,072 and 8,749 polygons extracted, whilst the planting areas of Panax notoginseng are estimated as 35.45 and 32.47 square kilometers achieved by SVM and ANN, respectively. Meanwhile, special concerns raised in terms of remote sensing mapping for modeling planting patterns and monitoring plant rotations of perennial Panax notoginseng cultivated under shade-net structures based on the experiments.