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基于 MaxEnt 模型和遥感技术的奈曼旗甘草种植规划研究。

Research on planting planning of Glycyrrhiza uralensis in Naiman Banner based on MaxEnt model and remote sensing technology.

机构信息

Baotou Medical College, Baotou, 014040, China.

Inner Mongolia University, Hohhot, 010070, China.

出版信息

Sci Rep. 2024 Oct 9;14(1):23601. doi: 10.1038/s41598-024-74987-0.

Abstract

Benefits of Glycyrrhiza uralensis include removing heat, detoxifying, and moistening the lungs, easing coughs, refueling the spleen, and balancing medications. In addition to providing theoretical guidance for the development of the G. uralensis industry and rural revitalization plan, it is anticipated that this paper will also provide basic data for the formulation of production layout of the G. uralensis industry at the county level, the control of cultivation industry direction, the establishment of high-quality G. uralensis cultivation technology system. The Maximum Entropy (MaxEnt) model was used to simulate the potential distribution of G. uralensis, a Chinese medicine resource, in Naiman Banner. By conducting a field inquiry and a broad assessment of the available Chinese medicine resources, the distribution information was acquired. The random forest technique was used to classify G. uralensis. The phenological cycle and development mode of vegetation, which exhibits diverse temporal traits and aids in identification, were elucidated through long-term series analysis. The random forest classification algorithm based on multiple features showed high accuracy in remote sensing (RS) recognition of G. uralensis. Comparative analysis of the MaxEnt and RS results showed that the planting area of G. uralensis was smaller than that of its potential distribution. The expansion to high-suitability areas planting should be prioritized. Based on the dual analysis of regional and remote sensing, it not only proved the great potential of using geographic information to predict the distribution of G. uralensis, but also verified the great potential of extracting the distribution of G. uralensis from GF-6 images. These results will guide the planting and development of G. uralensis in Naiman Banner and a scientific basis for the development of G. uralensis economy, conducive to optimizing the ecological environment and promoting rural revitalization programs.

摘要

甘草的功效包括清热解毒、润肺止咳、补脾益气、调和诸药。本研究为甘草产业和乡村振兴规划的发展提供了理论指导,为县级甘草产业生产布局规划、种植产业方向调控、优质甘草栽培技术体系建立提供了基础数据。利用最大熵(MaxEnt)模型模拟奈曼旗药用植物资源甘草的潜在分布。通过野外调查和广泛评估现有中药资源,获取了分布信息。采用随机森林技术对甘草进行分类。通过长期序列分析,揭示了植被的物候周期和发育模式,具有多样化的时间特征,有助于识别。基于多特征的随机森林分类算法在甘草的遥感(RS)识别中具有较高的精度。MaxEnt 和 RS 结果的比较分析表明,甘草的种植面积小于其潜在分布面积。应优先考虑向高适宜性地区种植扩展。基于区域和遥感的双重分析,不仅证明了利用地理信息预测甘草分布的巨大潜力,还验证了从 GF-6 图像中提取甘草分布的巨大潜力。这些结果将指导奈曼旗甘草的种植和发展,为甘草经济的发展提供科学依据,有利于优化生态环境,促进乡村振兴计划。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/672c/11464684/329f20c7f8f9/41598_2024_74987_Fig1_HTML.jpg

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