摘要
以河南省漯河市、周口市和驻马店市交界处为研究区,基于2000年10月~2011年12月间的101幅图像,采用谐波模型和断点识别算法拟合Landsat时间序列实现对过火像元的检测,并将结果与目视解译结果、MODIS火烧迹地产品MCD64A1检测结果对比进行精度分析。结果表明:①随着燃烧面积指数BAI(Burned Area Index)异常值阈值增大,焚烧火点误判误差减小,漏判误差增大,火烧迹地制图的总体精度先增大后减小;②当BAI异常值阈值2.9×RMSE(Root Mean Square Error)时,该方法总体精度达到最高93.25%,MCD64A1产品总体精度为70.25%;③本文算法的漏判误差和误判误差相对平衡,而MCD64A1产品的漏判误差远大于误判误差。研究表明,相比MODIS火烧迹地产品数据,Landsat时间序列火烧迹地法可更有效地检测农田火烧迹地。
Straw burning releases large qualities of gases and aerosol particles threatening the human health and air quality. Remote sensing plays a more and more important role in monitoring burned area for croplands either at regional scales or at global scales in recent years. The algorithm was based on harmonic model and break point identification to detect annual burned area using Landsat time series and then the algorithm was tested in the border area of Luohe,Zhoukou and Zhumadian in Henan Province using all available Landsat imagery between year 2000 and year 2011. The results were then compared with visual interpretation results and MODIS product MCD64A1 respectively for accuracy analysis and assessment,which show that: 1) As Burned Area Index(BAI) threshold increases,both the commission errors decrease and the omission errors increase.The overall accuracy of mapping cropland burned areas first increases and then decreases. 2) When BAI threshold is 2.9 times root mean square error(RMSE), the overall accuracy reached at the peak value,which is 93.25%. This represents a significant improvement in comparison with MCD64A1 product(The overall accuracy of MCD64A1 product is 70.25%). 3) The algorithm in this paper had balanced omission and commission errors,while MODIS product had more omission errors than commission errors. The results sufficiently show that,the Landsat time series based method in this paper can detect cropland burned area more effectively in comparison with MODIS burned area product.
作者
张素梅
杜惠琳
刘良
白翔宇
冯凯东
赵红梅
Zhang Sumei;Du Huilin;Liu Liang;Bai Xiangyu;Feng Kaidong;Zhao Hongmei(College of Mining and Engineering,Taiyuan University of Technology,Taiyuan 030024,Shanxi,China;School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,Hubei,China;Key Laboratory of Wetland Ecology and Environment,Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,Jilin,China)
出处
《地理科学》
CSSCI
CSCD
北大核心
2020年第1期149-157,共9页
Scientia Geographica Sinica
基金
国家自然科学基金项目(41771504)
国家重点研发计划(2017YFC0212303)资助。
作者简介
张素梅(1982-),女,河南巩义人,讲师,主要从事资源环境遥感研究。E-mail:zsm_2004@l26.com;通讯作者:赵红梅,副研究员。E-mail:zhaohongmei@iga.ac.cn。
