干旱区生态环境知识资源中心(Arid): 基于大数据的西藏荒漠化治理植物优选与验证

Arid

	基于大数据的西藏荒漠化治理植物优选与验证
其他题名	Optimal selection and verification of plant species for desertification control in Tibet based on big data
	柳平增; 王雪; 宋成宝; 张超; 奥宝平; 吕涛; 张立欣
来源期刊	农业工程学报
ISSN	1002-6819
出版年	2020
卷号	36 期号:10 页码:166-173
中文摘要	植物种植作为荒漠化治理的重要方式之一,关系到荒漠化区域的高效可持续发展。为提高植物选择的科学性与合理性,在项目前期已建成荒漠生态治理大数据平台并实现中国主要荒漠化区域生态信息全方位采集的基础上,进行基于大数据的植物优选研究与试验验证。研究运用相关性分析、聚类分析等大数据分析方法对荒漠植物种质资源库中植物进行类别划分,初步筛选适应该地区气象条件的植物;进一步运用层次分析法、专家打分法等决策方法从土壤、地形、生态效益、经济效益和其他等5个方面进行综合分析与评价,以优化初选结果。将该方法应用于西藏地区荒漠化治理植物的选择,经大数据挖掘分析,初步筛选出了核桃、黑果枸杞、盐生草和花花柴等适宜植物;进一步优化分析得出,核桃具有经济效益高、耐储运、前期投入相对较少等优势,是该区域荒漠化治理中生态适应性与综合效益俱佳的植物。优选结果在西藏山南市扎囊县桑耶镇的荒漠化治理中得到了验证,目前核桃长势良好,预期生态与经济效益显著。基于大数据进行荒漠治理植物的优选可为荒漠化区域科学规划及高效治理提供坚实的理论与数据支撑。
英文摘要	Land desertification has posed a major hazard to the society,economy,and environment in the Tibet semi-arid areas. The scientific and rational control of desertification becomes much more important to improve the living environment of human beings and the sustainable development of ecological system. Plant planting is expected to be one of the effective means of desertification control. Previous research had conducted on the selection of plant species for the desert governance,but it is necessary to accurately optimize the specific plant species,particularly on the range of plant selection,data analysis process and consideration of comprehensive benefits. In this study,a method of plant selection was proposed based on big data. Two steps were mainly included: one step was to optimize plants that meet the climate suitability of the study area in preliminary analysis,and another step was to verify the influence factors of the selected plants in comprehensive evaluation. A platform of big data was established for desertification ecological governance and the realization of all-round collection of major desert ecological information in China. The initial conditions were then defined according to the characteristics and direction of desertification governance in the study area. A database of germplasm resources for desert restoration plant was constructed to classify and select the subsequent plant categories. It is also necessary to consider the limitations of the dominant meteorological factors,such as "light,temperature,and water",due to the relatively serious problems of water shortage,high intensity of sunshine,and low accumulated temperature in the study area. After the data was automatically collected by the Internet of Things(Iots),the meteorological characteristics and changes of the study area were used to provide theoretical support for the further matching of plant varieties with high temperature resistance,high intensity of sunlight and strong drought resistance. Plants with similar properties were classified into a group based on the threshold value of suitable meteorological conditions of plants by using cluster analysis,correlation analysis and other big data methods. The adaptability of plants was also analyzed during this time. In clustering,main plant species were grouped 5 categories based on upper limit and lower limit of threshold respectively. The correlation coefficient was calculated between the plants' own suitable environment and the meteorological conditions in the study area,and the average value was obtained by category. In the plant categories with a correlation coefficient greater than 0.95,the plants that located in both upper and lower classifications were assumed as optimum match on the meteorological conditions in the study area. The second step was to select plants with a high degree of comprehensive suitability based on the preliminary generation scheme. Taking the preliminary selected plants as the evaluation object,an expert scoring method and analytic hierarchy process were used to make a horizontal comparison and comprehensive ranking of plants,particularly on considering the influence of topography,soil,ecological benefits,economic benefits,farmers' planting preference and policy support on the growth of plants.
中文关键词	植物 ; 聚类分析 ; 相关性分析 ; 大数据 ; 植物优选 ; 荒漠化治理 ; 生态适应性
英文关键词	plants cluster analysis correlation analysis big data optimal selection for plant species desertification control ecological adaptability
类型	Article
语种	中文
收录类别	CSCD
WOS研究方向	Agriculture
CSCD记录号	CSCD:6749492
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/336623
作者单位	柳平增, 山东农业大学信息科学与工程学院, 泰安, 山东 271018, 中国.; 张超, 山东农业大学信息科学与工程学院, 泰安, 山东 271018, 中国.; 王雪, 山东农业大学信息科学与工程学院;;亿利绿土地科技有限公司, ;;, 泰安;;, ;;北京 271018;;100067.; 宋成宝, 山东农业大学机械与电子工程学院, 泰安, 山东 271018, 中国.; 奥宝平, 亿利绿土地科技有限公司;;内蒙古库布其沙漠技术研究院, ;;, ;;杭锦旗, 北京;; 100067;;017418, 中国.; 吕涛, 亿利绿土地科技有限公司;;内蒙古库布其沙漠技术研究院, ;;, ;;杭锦旗, 北京;; 100067;;017418, 中国.; 张立欣, 亿利绿土地科技有限公司;;内蒙古库布其沙漠技术研究院, ;;, ;;杭锦旗, 北京;; 100067;;017418, 中国.
推荐引用方式 GB/T 7714	柳平增,王雪,宋成宝,等. 基于大数据的西藏荒漠化治理植物优选与验证[J],2020,36(10):166-173.
APA	柳平增.,王雪.,宋成宝.,张超.,奥宝平.,...&张立欣.(2020).基于大数据的西藏荒漠化治理植物优选与验证.农业工程学报,36(10),166-173.
MLA	柳平增,et al."基于大数据的西藏荒漠化治理植物优选与验证".农业工程学报 36.10(2020):166-173.