Application of remote sensing and GIS in study of suitability distribution of Swertia mussotii, a Tibetan medicine in Sichuan province / 中国中药杂志
China Journal of Chinese Materia Medica
; (24): 4387-4394, 2017.
Article
ي Zh
| WPRIM
| ID: wpr-338264
المكتبة المسؤولة:
WPRO
ABSTRACT
Swertia mussotii is a kind of rare medicinal materials, the relevant researches are mainly concentrated on its medicinal efficacy and medicinal value till now, researches of adaptive distribution by applying remote sensing and GIS are relatively less. This study is to analyze the adaptive distribution of S.mussotii in Sichuan province by applying remote sensing and GIS technology, and provide scientific basis for the protection and development of wild resources, artificial cultivation and adjustment of Chinese medicine industrial distribution in Sichuan province. Based on literature review and ecological factors such as altitude, annual precipitation and annual average temperature, this study extracted ecological factors, overlay analysis in GIS, as well as combining GPS field validation data by means of remote sensing and GIS, discusses the adaptive distribution of SMF sin Sichuan province. ①The area of adaptive distribution of S. mussotii in Sichuan province is 1 543.749 km², mainly in Dege county, Ganzi county, Daofu county, Kangding county, Barkam, Jinchuan county, Xiaojin county, Danba county, Daocheng county, Xiangcheng county, Xinlong county, Aba county, Muli county and other counties and cities, accounts for about 7.25% in total area. ② Combining statistical information and field validation, this study found that S. mussotii adaptive distribution gained by remote sensing and GIS is in conformity with its actual distribution. The study shows that remote sensing and GIS technology are feasible to obtain the S. mussotii adaptive distribution, they can further be applied to studies on adaptive distributions of other rare Chinese medicinal herb.
النص الكامل:
1
الفهرس:
WPRIM
اللغة:
Zh
مجلة:
China Journal of Chinese Materia Medica
السنة:
2017
نوع:
Article