利用GF-1 WFV影像和FY-3D MERSI火点产品提取过火区的方法
Extraction method of burned area using GF-1 WFV images and FY-3D MERSI fire-point products
- 2024年28卷第2期 页码：375-384
纸质出版日期： 2024-02-07
DOI： 10.11834/jrs.20221552
移动端阅览
浏览全部资源
扫码关注微信
摘要
利用遥感技术获取过火区信息对生态环境监测具有重要意义，其中高分辨率数据更适合提取小范围过火区。目前已有多种利用国外火点产品结合遥感影像提取过火区的研究。为了增强国产遥感数据火情监测能力，提高小范围过火区的提取效率和精度，基于过火前后2幅GF-1 WFV影像和多时相FY-3D MERSI火点产品开展过火区提取研究。2处研究区分别位于四川省凉山彝族自治州木里藏族自治县和西昌市。首先根据火点与过火区形成的关系，结合火点的时间、空间和光谱特征，筛选并扩充火点像元，确定过火区粗略范围；然后确定每种地表类型的分割阈值，分类过火像元和非过火像元；最后剔除周边小斑块，得到过火区提取结果。以人机交互方式获得的过火区参考真值作验证，并与神经网络分类法提取过火区的结果作对比。结果表明本文方法的过火区提取结果精度要明显高于神经网络分类法，Kappa系数达到0.82。该方法可以充分结合GF-1 WFV影像和FY-3D MERSI火点产品数据的优势，降低样本像元选择时间成本和不确定性，快速准确地提取小范围过火区。未来可考虑通过选择更高精度的火点产品，结合实地考察验证对该方法改进完善。
Abstract
Using remote-sensing technology to obtain information about burned areas is important for ecological environment monitoring. High-resolution data are more suitable for extracting small-scale burned areas. To develop the fire monitoring ability of domestic remote-sensing data and improve the extraction efficiency and accuracy of a small-scale burned area
two GF-1 WFV images (before and after fires) and multi temporal FY-3D MERSI fire products are used to extract burned areas for two study areas
respectively
located in the Tibetan Autonomous County of Muli and Xichang City
Sichuan Province.
The reference true values are obtained by human-computer interaction for verification. The results of burned areas extracted by neural network classification are compared with the result of the proposed method. Our results show that the accuracy of burned areas detected by the proposed method is higher than that by neural network classification
and the Kappa coefficients in two study areas are 0.82 and 0.87
respectively. The regions of commission and omission are usually distributed at the edge of the burned area patch. The distribution of burned area in Xichang is more compact than that in Muli
so the accuracy of burned area mapping in Xichang is higher.
The method can fully combine the advantages of the two kinds of data
reduce the uncertainty and time cost caused by sample selection
and extract the small-scale burned area quickly and accurately. Fully exploiting the temporal
spatial
and spectral characteristics of fire points and burned areas can compensate for the shortcomings of GF-1 WFV images in temporal and spectral resolution. Meanwhile
the method can fully combine the two kinds of data and minimize the impact of the difference of spatial resolution. In the future
the method can be improved using a higher accuracy of fire-point products. The accuracy of the reference true value of the burned area can be improved through field investigation.
The method is primarily divided into two parts
2
rough extraction and fine extraction. In rough extraction
according to the relationship between fire points and the formation of burned areas
the fire-point pixels are selected and expanded into the rough range of burned areas by combining temporal
spatial
and spectral characteristics. Temporal characteristic refers to fire points with concentrated occurrence time that easily form burned areas; spatial characteristic refers to fire points with concentrated location that easily form burned areas
and burned pixels are usually adjacent to fire-point pixels; spectral characteristics refer to pixels with higher NDVI difference before and after fire
which may be burned pixels. In fine extraction
the land-cover types included in the burned area are determined according to the number of fire-point pixels. The segmentation threshold is determined using the iterative-threshold method for each land-cover type. Burned pixels and unburned pixels in each land-cover type are classified using the segmentation threshold. The small patches are removed to obtain the result of burned-area extraction.
关键词
遥感过火区火点监测产品FY-3D MERSIGF-1 WFVNDVI阈值分割
Keywords
remote sensingburned areafire point productFY-3D MERSIGF-1 WFVNDVIsegmentation threshold
references
Alonso-Canas I and Chuvieco E. 2015. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. Remote Sensing of Environment, 163: 140-152 [DOI: 10.1016/j.rse.2015.03.011http://dx.doi.org/10.1016/j.rse.2015.03.011]
Boschetti L, Roy D P, Justice C O and Humber M L. 2015. MODIS-landsat fusion for large area 30 m burned area mapping. Remote Sensing of Environment, 161: 27-42 [DOI: 10.1016/j.rse.2015.01.022http://dx.doi.org/10.1016/j.rse.2015.01.022]
Dong X R and Wei Y. 2014. FY-3 Satellite’s support for forest fire remote sensing monitoring in Heilongjiang Province. Heilongjiang Meteorology, 31(2): 23, 32
董晓锐, 魏迎. 2014. FY-3卫星对黑龙江省林火遥感监测业务的支撑. 黑龙江气象, 31(2): 23, 32 [DOI: 10.3969/j.issn.1002-252X.2014.02.010http://dx.doi.org/10.3969/j.issn.1002-252X.2014.02.010]
Giglio L, Boschetti L, Roy D P, Humber M L and Justice C O. 2018. The collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, 217: 72-85 [DOI: 10.1016/j.rse.2018.08.005http://dx.doi.org/10.1016/j.rse.2018.08.005]
Giglio L, Loboda T, Roy D P, Quayle B and Justice C O. 2009. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing of Environment, 113(2): 408-420 [DOI: 10.1016/j.rse.2008.10.006http://dx.doi.org/10.1016/j.rse.2008.10.006]
Hu X W, Jin T, Yin W Q, Huo Z B, Song Y P, Zhang S K, Wang Y and Yang Y. 2020. The characteristics of forest fire burned area and sus-ceptibility assessment of post-fire debris flow in Jingjiu Township, Xichang City. Journal of Engineering Geology, 28(4): 762-771
胡卸文, 金涛, 殷万清, 霍柱北, 宋钰朋, 张绍科, 王严, 杨瀛. 2020. 西昌市经久乡森林火灾火烧区特点及火后泥石流易发性评价. 工程地质学报, 28(4): 762-771 [DOI: 10.13544/j.cnki.jeg.2020-224http://dx.doi.org/10.13544/j.cnki.jeg.2020-224]
Li J, Gong A D, Chen Y L, Wang J M and Zeng T T. 2018. Analysis on the characteristics of change and recovery of vegetation indices for forests in burned area. Journal of Geo-information Science, 20(3): 368-376
李静, 宫阿都, 陈艳玲, 王静梅, 曾婷婷. 2018. 森林过火区植被遥感参数的变化与恢复特征分析. 地球信息科学学报, 20(3): 368-376 [DOI: 10.12082/dqxxkx.2018.170464http://dx.doi.org/10.12082/dqxxkx.2018.170464]
Li J J and Fu Q Y. 2020. Automatic cloud detection of GF-7 satellite imagery. Spacecraft Recovery and Remote Sensing, 41(2): 108-115
李俊杰, 傅俏燕. 2020. “高分七号”卫星遥感影像自动云检测. 航天返回与遥感, 41(2): 108-115 [DOI: 10.3969/j.issn.1009-8518.2020.02.012http://dx.doi.org/10.3969/j.issn.1009-8518.2020.02.012]
Li L L. 2012. A Study on Auto-Thresholding Selection Methods for Image Segmentation. Lanzhou: Lanzhou University
李琳琳. 2012. 遥感图像分割中阈值的自动选取技术研究. 兰州: 兰州大学
Lu D L. 2019. Discussion on forest resource protection and forest fire management. Forestry Science and Technology Information, 51(1): 18-19
鹿德林. 2019. 谈森林资源保护与森林火灾管理. 林业科技情报, 51(1): 18-19 [DOI: 10.3969/j.issn.1009-3303.2019.01.007http://dx.doi.org/10.3969/j.issn.1009-3303.2019.01.007]
McFeeters S K. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432 [DOI: 10.1080/01431169608948714http://dx.doi.org/10.1080/01431169608948714]
Merino-de-Miguel S, Huesca M and González-Alonso F. 2010. MODIS reflectance and active fire data for burn mapping and assessment at regional level. Ecological Modelling, 221(1): 67-74 [DOI: 10.1016/j.ecolmodel.2009.09.015http://dx.doi.org/10.1016/j.ecolmodel.2009.09.015]
Rao Y M, Wang C and Huang H G. 2020. Forest fire monitoring based on multisensor remote sensing techniques in Muli County, Sichuan Province. Journal of Remote Sensing (Chinese), 24(5): 559-570
饶月明, 王川, 黄华国. 2020. 联合多源遥感数据监测四川木里县森林火灾. 遥感学报, 24(5): 559-570 [DOI: 10.11834/jrs.20209125http://dx.doi.org/10.11834/jrs.20209125]
Sun G F, Qin X L, Liu S C, Li X T, Chen X Z and Zhong X Q. 2019. Potential analysis of typical vegetation index for identifying burned area. Remote Sensing for Land and Resources, 31(1): 204-211
孙桂芬, 覃先林, 刘树超, 李晓彤, 陈小中, 钟祥清. 2019. 典型植被指数识别火烧迹地潜力分析. 国土资源遥感, 31(1): 204-211 [DOI: 10.6046/gtzyyg.2019.01.27http://dx.doi.org/10.6046/gtzyyg.2019.01.27]
Tan M Y, Chen Z X, Cao X, Chen J, Yang W and Gu Z H. 2007. Burn scar mapping in steppe using MODIS data. Journal of Remote Sensing, 11(3): 340-349
谭明艳, 陈仲新, 曹鑫, 陈晋, 杨伟, 辜智慧. 2007. 利用MODIS识别草原火灾迹地方法的研究. 遥感学报, 11(3): 340-349 [DOI: 10.11834/jrs.20070347http://dx.doi.org/10.11834/jrs.20070347]
Tong L X, Yan Q, Luo C F and Du Y K. 2017. Water body information extraction based on NDWI segmentation and object-oriented method. Geospatial Information, 15(5): 57-59
童李霞, 燕琴, 骆成凤, 杜英坤. 2017. 基于NDWI分割与面向对象的水体信息提取. 地理空间信息, 15(5): 57-59 [DOI: 10.3969/j.issn.1672-4623.2017.05.018http://dx.doi.org/10.3969/j.issn.1672-4623.2017.05.018]
Wang Z. 2011. Research and evaluation of the algorithm of land surface fire detection based on FY3-VIRR data. Fire Safety Science, 20(3): 140-145
王钊. 2011. 新一代极轨气象卫星FY3A-VIRR数据的地表火监测算法研究与评价. 火灾科学, 20(3): 140-145 [DOI: 10.3969/j.issn.1004-5309.2011.03.003http://dx.doi.org/10.3969/j.issn.1004-5309.2011.03.003]
Wu J W, Sun L Y, Ji R P, Feng R, Yu W Y and Zhang Y S. 2020. Research progress and prospect of remote sensing on extracting burned areas information. Journal of Catastrophology, 35(4): 151-156
武晋雯, 孙龙彧, 纪瑞鹏, 冯锐, 于文颖, 张玉书. 2020. 火烧迹地信息遥感提取研究进展与展望. 灾害学, 35(4): 151-156 [DOI: 10.3969/j.issn.1000-811X.2020.04.028http://dx.doi.org/10.3969/j.issn.1000-811X.2020.04.028]
Xu W N, Wang P X, Han P, Yan T L and Zhang S Y. 2011. Application of Kappa coefficient to accuracy assessments of drought forecasting model: a case study of Guanzhong plain. Journal of Natural Disasters, 20(6): 81-86
许文宁, 王鹏新, 韩萍, 严泰来, 张树誉. 2011. Kappa系数在干旱预测模型精度评价中的应用——以关中平原的干旱预测为例. 自然灾害学报, 20(6): 81-86
Xu X L, Liu J Y, Zhang S W, Li R D, Yan C Z and Wu S X. 2018. China land use/land cover change (CNLUCC). Registration and Publishing System of Resource and Environment Science and Data Center Data, China Academy of Science
徐新良, 刘纪远, 张树文, 李仁东, 颜长珍, 吴世新. 2018. 中国多时期土地利用遥感监测数据集(CNLUCC). 中国科学院资源环境科学数据中心数据注册与出版系统 [DOI: 10.12078/2018070201http://dx.doi.org/10.12078/2018070201]
Yang L, Wang J, Gao Y H, Zhang Z J and Dong Z X. 2019. Case application of forest fire monitoring based on Fengyun-3 meteorological satellite remote sensing. Environment and Development, 31(1): 129, 131
杨蕾, 王健, 高玉宏, 张作君, 战冬雪. 2019. 基于风云三号气象卫星遥感在森林火灾监测的实例应用. 环境与发展, 31(1): 129, 131 [DOI: 10.16647/j.cnki.cn15-1369/X.2019.01.073http://dx.doi.org/10.16647/j.cnki.cn15-1369/X.2019.01.073]
Yang W, Zhang S W, Tang J M, Bu K, Yang J C and Chang L P. 2013. A MODIS time series data based algorithm for mapping forest fire burned area. Chinese Geographical Science, 23(3): 344-352 [DOI: 10.1007/s11769-013-0597-6http://dx.doi.org/10.1007/s11769-013-0597-6]
Zhang J, Zhang W Y, Feng J D, Wang H Y, Yu Z and Song W. 2016. An improved algorithm for forest fire detection: a study based on brightness temperature, vegetation index and AOD. Remote Sensing Technology and Application, 31(5): 886-892
张婕, 张文煜, 冯建东, 王宏义, 于泽, 宋玮. 2016. 基于亮温—植被指数—气溶胶光学厚度的MODIS火点监测算法研究. 遥感技术与应用, 31(5): 886-892 [DOI: 10.11873/j.issn.1004-0323.2016.5.0886http://dx.doi.org/10.11873/j.issn.1004-0323.2016.5.0886]
Zhao L C. 2004. The operation and application of FY-1C/D polar orbiting meteorological satellite. Aerospace Shanghai, 21(4): 12-15
赵立成. 2004. FY-1C/D极轨气象卫星的运行和应用. 上海航天, 21(4): 12-15 [DOI: 10.3969/j.issn.1006-1630.2004.04.002http://dx.doi.org/10.3969/j.issn.1006-1630.2004.04.002]
Zheng W, Chen J, Yan H, Liu C and Tang S H. 2020. Global fire monitoring products of FY-3D/MERSI-II and their applications. Journal of Remote Sensing (Chinese), 24(5): 521-530
郑伟, 陈洁, 闫华, 刘诚, 唐世浩. 2020. FY-3D/MERSI-II全球火点监测产品及其应用. 遥感学报, 24(5): 521-530 [DOI: 10.11834/jrs.20209177http://dx.doi.org/10.11834/jrs.20209177]
Zheng W, Li Y J, Liu C and Wang M. 2011. Extracting forest burned scar region area based on multi-source remote sensing data. Scientia Silvae Sinicae, 47(8): 192-195
郑伟, 李亚君, 刘诚, 王萌. 2011. 基于多源卫星遥感数据的森林过火区面积估算方法. 林业科学, 47(8): 192-195 [DOI: 10.11707/j.1001-7488.20110830http://dx.doi.org/10.11707/j.1001-7488.20110830]
Zu X F, Qin X L, Yin L Y, Chen X Z and Zhong X Q. 2015. Decision tree method for burned area identification based on the spectral index of GF-1 WFV image. Forest Resources Management, 4: 73-78, 83
祖笑锋, 覃先林, 尹凌宇, 陈小中, 钟祥清. 2015. 基于高分一号影像光谱指数识别火烧迹地的决策树方法. 林业资源管理, (4): 73-78, 83 [DOI: 10.13466/j.cnki.lyzygl.2015.04.013http://dx.doi.org/10.13466/j.cnki.lyzygl.2015.04.013]
文章被引用时，请邮件提醒。
提交
相关作者
佘文庆 中国科学院空天信息创新研究院;中国科学院大学
张兆明 中国科学院空天信息创新研究院;中国科学院大学;海南空天信息研究院海南省地球观测重点实验室
彭燕 中国科学院空天信息创新研究院;海南空天信息研究院海南省地球观测重点实验室
何国金 中国科学院空天信息创新研究院;中国科学院大学;海南空天信息研究院海南省地球观测重点实验室
龙腾飞 中国科学院空天信息创新研究院;中国科学院大学;海南空天信息研究院海南省地球观测重点实验室
王桂周 中国科学院空天信息创新研究院;海南空天信息研究院海南省地球观测重点实验室
朱炯 中国科学院空天信息创新研究院 遥感卫星应用国家工程实验室;中国科学院大学
杜鑫 中国科学院空天信息创新研究院 遥感卫星应用国家工程实验室
相关机构
中国科学院空天信息创新研究院
中国科学院大学
海南空天信息研究院海南省地球观测重点实验室
中国科学院空天信息创新研究院 遥感卫星应用国家工程实验室
南京大学国际地球系统科学研究所
选择翻译语言
- 地址：北京市海淀区北四环西路19号 《遥感学报》编辑部 邮编：100190
- 联系电话：010-58887052 Email：nrsb@aircas.ac.cn
- 技术支持由北京北大方正电子有限公司提供 京ICP备20021838号-9
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
0
回到顶部