[1]李亚妮,曹建君,杨树文,等.基于决策树的大尺度复杂地区夏收作物遥感提取与分析[J].江苏农业学报,2022,38(05):1257-1264.[doi:doi:10.3969/j.issn.1000-4440.2022.05.012]
 LI Ya-ni,CAO Jian-jun,YANG Shu-wen,et al.Extraction and analysis of summer crops in large-scale complex areas based on decision tree[J].,2022,38(05):1257-1264.[doi:doi:10.3969/j.issn.1000-4440.2022.05.012]
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基于决策树的大尺度复杂地区夏收作物遥感提取与分析()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
38
期数:
2022年05期
页码:
1257-1264
栏目:
农业信息工程
出版日期:
2022-10-31

文章信息/Info

Title:
Extraction and analysis of summer crops in large-scale complex areas based on decision tree
作者:
李亚妮123曹建君14杨树文123李霞5刘尚钦123
(1.兰州交通大学测绘与地理信息学院,甘肃兰州730070;2.地理国情监测技术应用国家地方联合工程研究中心,甘肃兰州730070;3.甘肃省地理国情监测工程实验室,甘肃兰州730070;4.甘肃省自然资源技能鉴定指导中心,甘肃兰州730070;5.甘肃省基础地理信息中心,甘肃兰州730070)
Author(s):
LI Ya-ni123CAO Jian-jun14YANG Shu-wen123LI Xia5LIU Shang-qin123
(1.Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China;2.National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China;3.Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China;4.Gansu Province Natural Resources Skill Appraisal Guidance Center, Lanzhou 730070, China;5.Geomatics Center of Gansu Province, Lanzhou 730070, China)
关键词:
大尺度作物监测作物分类决策树Sentinel-2
Keywords:
large-scale crop monitoringcrop classificationdecision treeSentinel-2
分类号:
S127;TP79
DOI:
doi:10.3969/j.issn.1000-4440.2022.05.012
文献标志码:
A
摘要:
以甘肃省为研究区,基于时序Sentinel-2卫星影像数据,计算归一化植被指数(NDVI)和黄度值,采用决策树(Decision trees,DTs)方法提取研究区的主要夏收作物小麦和油菜种植面积,并对比分析其精度。利用此方法得到甘肃省小麦和油菜作物识别的平均总体精度达到87.4%,其中,甘肃省中部地区平均总体精度为92.4%,河西地区平均总体精度为87.7%,东南部地区平均总体精度为82.0%。研究结果表明,基于Sentinel-2卫星影像数据采用决策树方法进行大尺度复杂区域、高分辨率作物种植面积提取具有可行性。
Abstract:
Taking Gansu province as the research area, the normalized difference vegetation index (NDVI) and yellowness value were calculated based on the time series Sentinel-2 satellite image data. The decision trees (DTs) method was used to extract the planting area of wheat and rape in the study area, and the accuracy was compared and analyzed. Using this method, the average overall accuracy of wheat and rape identification in Gansu province was 87.4%. The average overall accuracy was 92.4% in the central region of Gansu province, 87.7% in Hexi region, and 82.0% in the southeastern region. The results show that it is feasible to extract the planting area of high-resolution crops in large-scale complex areas by using decision tree method based on Sentinel-2 satellite image data.

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备注/Memo

备注/Memo:
收稿日期:2022-03-09基金项目:国家自然科学基金项目(41761082);国家重点研发计划项目(2017YFB0504201);兰州交通大学优秀平台项目(201806);甘肃省重点人才项目(2021RCXM073)作者简介:李亚妮(1997-),女,陕西省榆林人,硕士研究生,主要从事农业遥感研究。(E-mail)1262185031@qq.com通讯作者:曹建君,(E-mail)369745844@qq.com
更新日期/Last Update: 2022-11-07