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作 者:赵管乐 刘勤[1,2] 彭培好[1,3] ZHAO Guanyue;LIU Qin;PENG Peihao(College of Earth Sciences,Chengdu University of Technology,Chengdu 610059,China;Institute of Mountain Hazards and Environment,Chinese Academy of Science,Chengdu 610041,China;Institute of Ecological Resources and Landscape,Chengdu University of Technology,Chengdu 610059,China)
机构地区:[1]成都理工大学地球科学学院,成都610059 [2]中国科学院、水利部成都山地灾害与环境研究所,成都610041 [3]成都理工大学生态资源与景观研究所,成都610059
出 处:《中国农业科技导报》2022年第3期111-119,共9页Journal of Agricultural Science and Technology
基 金:中国科学院战略性先导科技专项(A类)(XDA23090501)。
摘 要:云南省华坪县作为我国芒果的主要产地之一,近年来大力发展芒果种植业以及周边产业,促进了当地农业与旅游业的快速发展。为了更加科学高效地在空间尺度上对芒果种植区进行规划与管理,采用多时相Sentinel-2遥感影像构建多种植被指数,结合辅助地形因子与已挂果投产的芒果种植区野外调查点数据,通过MaxEnt模型对华坪县已投产芒果种植区进行分类识别,最后根据不同的阈值对预测结果进行二值化分类与精度评价。结果表明:二值化分类精度最高的阈值规则为10 percentile training presence,对应的阈值为0.257,分类总体精度为93.72%;在该阈值规则下估算已挂果投产的芒果种植区面积约为1.07万hm^(2),与研究时段内华坪县已有挂果投产的芒果种植区面积1.13万~1.20万hm^(2)相近。因此,所选取的植被指数与地形因子组合在利用MaxEnt模型进行已投产芒果种植区的识别应用中取得了较好的效果,能为其他地区类似的研究应用提供借鉴,同时能为芒果种植业的发展与规划提供数据参考与决策支持。Huaping county of Yunnan province is one of the main mangos producing areas in China,and has vigorously developed mango planting and peripheral industries in recent years,which has promoted the rapid development of local agriculture and tourism. In order to plan and control manage mango planting areas in a more scientific and efficient way on the spatial scale,this study used multi-temporal sentinel-2 remote sensing images to build a variety of vegetation indexes,combined with the auxiliary terrain factors and the point data of mango planting areas that had been put into production obtained from field survey,and MaxEnt model was used to classify and identify the mango planting areas in Huaping county. Finally,binarization classification and accuracy evaluation of the predicted results were carried out according to different thresholds. The results showed that the highest threshold rule of binarization classification accuracy was 10 percentile training presence,the corresponding threshold was0.257,and the overall classification accuracy was 93.72%. Under this threshold,the estimated area of mango planting area putting into production was about 1.07×10^(4) hm^(2),which was close to(1.13~1.20)×10^(4) hm^(2) of mango planting area putting into production in Huaping county during the research period. Therefore,the combination of vegetation indexes and terrain factors selected in this study achieved good results in the application of identification of mango planting areas by MaxEnt model,which provided some references for similar research and application in other areas,and provided some data references and decision supports for the development and planning of mango planting industry.
关 键 词:Sentinel-2影像 MaxEnt模型 芒果 遥感识别
分 类 号:S126[农业科学—农业基础科学]
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