2001-2021年四川省森林草原火灾时空特征遥感分析
Remote sensing-based spatial-temporal characteristics of forest grassland fires in Sichuan Province from 2001 to 2021
- 2024年 页码：1-16
网络出版日期： 2024-04-03
DOI： 10.11834/jrs.20243082
扫 描 看 全 文
浏览全部资源
扫码关注微信
摘要
近年来，四川省冬春季森林草原火灾频发，对当地生态、百姓和消防人员的生命财产安全造成巨大威胁。本研究旨在基于多源遥感观测数据探究2001-2021年四川省森林草原火灾的时空特征，服务于该区域未来火灾预警防控等需求。该研究基于MCD64A1、Fire_CCI51和MCD14ML等多源遥感数据提取研究区域有效火点数据，运用地理信息系统探究森林草原火灾时序趋势及空间分布，并采用数理统计、自适应模糊神经网络两种方式探究气象、可燃物和地形等影响因子与火灾的相关性。结果显示，该区域2001-2014年火灾频率及过火面积呈上升趋势，1~5月为火灾高发期；火灾空间分布具有异质性，主要集中于四川省西南部，而近期东北部的草原火灾明显增加；森林火灾发生与可燃物含水率的相关性较高，草原火灾发生与相对湿度的相关性较高，同时推测人为因素对草原火灾的诱发存在较大影响。该项研究基于多源遥感观测数据对2001-2021年四川省森林草原火灾时空特征进行了定性与定量的分析，为该区域森林草原火灾防控预警等需求提供有效先验信息。
Abstract
Objective Forest wildfires have occurred frequently in Sichuan province in recent years
posing a significant threat to local ecological security as well as the lives and property of people and rescue workers. The purpose of this study was to investigate the temporal and spatial characteristics of forest grassland fires in Sichuan Province from 2001 to 2021
and to provide useful information for fire prevention and control decisions.Method Based on MCD64A1
Fire_CCI51 and MCD14ML multi-source remote sensing fire products
extracted effective fire points
acquired regional fire data
and explored the temporal trend and spatial distribution of forest and grassland fires by using geographic information system. The relationship between climatic
combustible
and topography environment factors and fire was examined using mathematical statistics and an adaptable fuzzy neural network.Result The results showed that the fire frequency and fire area increased from 2001 to 2014
and the fire occurred frequently from January to May. The spatial distribution of grassland fires is heterogeneous
mainly concentrated in the southwest of Sichuan Province
while the northeast of China has increased significantly recently. In the correlation analysis of various influencing factors
the correlation between forest fire and fuel water content is high
and environmental variables are the main driving factors of temporal and spatial characteristics of forest fire. The correlation between grassland fire and meteorological factors is high
but it is speculated that human factors have great influence on grassland fire characteristics.Conclusion Based on the analysis of the temporal and spatial characteristics of fire in Sichuan province
provides a decision basis for forest grassland fire prevention and control policy
early warning and monitoring in this region.
关键词
遥感四川省MCD64A1Fire_CCI51MCD14ML森林草原火灾空间分布时间趋势时空特征
Keywords
Remote sensingSichuan ProvinceMCD64A1Fire_CCI51MCD14MLforest grassland firespatial distributiontime trendspatio-temporal characteristics
references
Abatzoglou J T, Williams A P. 2016. Impact of anthropogenic climate change on wildfire across western US forests [DOI:10.1073/pnas. 1607171113http://dx.doi.org/10.1073/pnas.1607171113]
Athmania D and Achour H. 2014. External validation of the ASTER GDEM2, GMTED2010 and CGIAR-CSI-SRTM v4. 1 free access digital elevation models (DEMs) in Tunisia and Algeria [DOI:10.3390/ rs6054600http://dx.doi.org/10.3390/rs6054600]
Benali A A and Pereira J M C. Monitoring and extracting relevant parameters of wild fire spread using remote sensing data. Anais XVI Simpósio Brasileiro de Sensoriamento Remoto-SBSR, Foz do Iguaçu, PR, Brasil, 2725–2731.
Birant D and Kut A. ST-DBSCAN: An algorithm for clustering spatial–temporal data [DOI: 10.1016/j.datak.2006.01.013http://dx.doi.org/10.1016/j.datak.2006.01.013]
Boschetti L, Roy D P, Giglio L, Huang H, Zubkova M and Humber M L. Global validation of the collection 6 MODIS burned area product [DOI:10.1016/j.rse.2019.111490http://dx.doi.org/10.1016/j.rse.2019.111490]
Carabajal C C, Harding D J, Boy J P, Danielson J J, Gesch D B and Suchdeo V P. Evaluation of the Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010) using ICESat geodetic control [DOI:10.1117/12.912776http://dx.doi.org/10.1117/12.912776]
Chen T, Bao A M and He D M. 2018. Analyzing dynamic vegetation change and its driving factors in Panzhihua City using MODIS NDVI data. Resources and Environment in The Yangtze Basin, 27(8): 1847-1857
陈桃, 包安明, 何大明. 2018. 基于MODIS NDVI的攀枝花市植被覆盖变化及其驱动力.长江流域资源与环境, 27(8): 1847-1857 [DOI: 10.11870/cjlyzyyhj201808021http://dx.doi.org/10.11870/cjlyzyyhj201808021]
Chen X F, Liu L, Li J G, Ou W H and Zhang Y H. 2020. Application and research progress of fire monitoring using satellite remote sensing. Journal of Remote Sensing, 24(5): 531-542
陈兴峰, 刘李, 李家国, 欧文浩, 张玉环. 2020. 卫星遥感火点监测应用和研究进展. 遥感学报, 24(5): 531-542 [DOI:10.11834/jrs. 20209118http://dx.doi.org/10.11834/jrs.20209118]
Chuvieco E, Aguado I, Jurdao S, Pettinari M L, Yebra M, Salas J, Hantson S, de la Riva J, Ibarra P, Rodrigues M, Echeverría M, Azqueta D, Román M V, Bastarrika A, Martínez S, Recondo C, Zapico E and Martínez-Vega F J. Integrating geospatial information into fire risk assessment. [DOI: 10.1071/WF12052http://dx.doi.org/10.1071/WF12052]
Dragozi E. Meteorological Analysis of the 2021 Extreme Wildfires in Greece: Lessons Learned and Implications for Early Warning of the Potential for Pyroconvection. [DOI:10.3390/atmos13030475http://dx.doi.org/10.3390/atmos13030475]
Fornacca D, Ren Ga and Xiao W. Performance of three MODIS fire products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a mountainous area of Northwest Yunnan, China, characterized by frequent small fires. [DOI:10.3390/rs9111131http://dx.doi.org/10.3390/rs9111131]
Gao J. 2017. Improvement and application of fuzzy neural network prediction algorithm.
高君. 2017. 模糊神经网络预测算法改进及应用. [DOI: CNKI:CDMD:2.1017.193309http://dx.doi.org/CNKI:CDMD:2.1017.193309]
Giglio L, Schroeder W, Hall J V and Justice C O. 2015. Modis collection 6 active fire product user’s guide revision A. Department of Geographical Sciences. University of Maryland, 9.
Giglio L, Van der Werf G R, Randerson J T, Collatz G J, and Kasibhatla P. Global estimation of burned area using MODIS active fire observations. Atmospheric Chemistry and Physics, 6(4): 957-974. [DOI:10.5194/acp-6-957-2006http://dx.doi.org/10.5194/acp-6-957-2006]
Guo F, Zhang L, Jin S, Tigabu M, Su Z and Wang W. Modeling anthropogenic fire occurrence in the boreal forest of China using logistic regression and random forests. [DOI:10.3390/f7110250http://dx.doi.org/10.3390/f7110250]
Jaafari A, Zenner E K, Panahi M and Shahabi H. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. [DOI:10.1016/j.agrformet.2018.12.015http://dx.doi.org/10.1016/j.agrformet.2018.12.015]
Jang J S R. ANFIS: adaptive-network-based fuzzy inference system. [DOI:10.1109/21.256541http://dx.doi.org/10.1109/21.256541]
Jiao M, Quan X, Yao J. Evaluation of Four Satellite-Derived Fire Products in the Fire-Prone, Cloudy, and Mountainous Area Over Subtropical China. [DOI: 10.1109/LGRS.2022.3188259http://dx.doi.org/10.1109/LGRS.2022.3188259]
Katarzyna G and Cooke W H. Spatial and temporal characteristics of wildfires in Mississippi, USA. [DOI:10.1071/WF08104http://dx.doi.org/10.1071/WF08104]
Li B, Zhou S, Wang T, Sui X, Jia Z, Li Y, Wang J and Wu S. An improved gridded polycyclic aromatic hydrocarbon emission inventory for the lower reaches of the Yangtze River Delta region from 2001 to 2015 using satellite data[J]. Journal of hazardous materials, 360: 329-339. [DOI:10.1016/j.jhazmat.2018.08.011http://dx.doi.org/10.1016/j.jhazmat.2018.08.011]
Li S J, Zheng K, Tang P, Huo L Z and Yuan Y. 2022. Forest burned area detection with time series data based on Stacked ConvLSTM. Journal of Remote Sensing, 26(10):1976-1987
李淑君, 郑柯, 唐娉, 霍连志, 袁媛. 2022. 基于Stacked ConvLSTM的时间序列森林火烧迹地检测. 遥感学报, 26(10): 1976-1987 [DOI:10.11834/jrs.20210471http://dx.doi.org/10.11834/jrs.20210471]
Li X T, Liu Q, Qin X L, Liu S C and Wang C Y. 2022. Method for national fuel types classification based on multi-source data. Journal of Remote Sensing, 26(3): 480-492
李晓彤, 刘倩, 覃先林, 刘树超, 王崇阳. 2022. 基于多源数据的全国可燃物类型划分方法. 遥感学报26(3): 480-492 [DOI:10.11834/jrs.20219208http://dx.doi.org/10.11834/jrs.20219208]
People's Daily. 2020. The March 30 forest fire in Xichang City killed 19 people and many people were held accountable. (人民日报. 2020. 西昌市“3·30”森林火灾事件致19人牺牲 多人被追责问责) [2023-4-18] https://baijiahao.baidu.com/s?id=16867409508 29965240&wfr=spider&for=pchttps://baijiahao.baidu.com/s?id=1686740950829965240&wfr=spider&for=pc
Liu S C, Li X T, Qin X L, Sun G F and Liu Q. 2020. Adaptive threshold method for active fire identification based on GF-4 PMI data. Journal of Remote Sensing, 24(3):215-225
刘树超, 李晓彤, 覃先林, 孙桂芬, 刘倩. 2020. GF-4 PMI影像着火点自适应阈值分割. 遥感学报, 24(3): 215-225 [DOI:10.11834/jrs.20208297http://dx.doi.org/10.11834/jrs.20208297]
Loboda T V and Csiszar I A. Reconstruction of fire spread within wildland fire events in Northern Eurasia from the MODIS active fire product. [DOI:10.1016/j.gloplacha.2006.07.015http://dx.doi.org/10.1016/j.gloplacha.2006.07.015]
Long X X. 2001. Study on the Index System of Integrated Benefit Evaluation of Protection Foreet System on the Mid-upper Reaches of the Changjiang RiVer. Journal of Sichuan Forestry Science and Technology, 22(4): 54-58
隆孝雄. 2001. 四川立地分区及适生树种.四川林业科技, 22(4): 54-58 [DOI:10.3969/j.issn.1003- 5508.2001. 04.008http://dx.doi.org/10.3969/j.issn.1003-5508.2001.04.008]
Muñoz-Sabater J, Dutra E, Agustí-Panareda A, Albergel C, Arduini G, Balsamo G, Boussetta S, Choulga M, Harrigan S and Hersbach H. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. [DOI:10.5194/essd-13-4349-2021http://dx.doi.org/10.5194/essd-13-4349-2021]
Myneni R, Knyazikhin Y and Park T. 2015 MOD15A2H MODIS/Terra leaf area Index/FPAR 8-Day L4 global 500m SIN grid V006. NASA EOSDIS Land Processes DAAC.
Núñez-Casillas L, García Lázaro J R, Moreno-Ruiz J A and Arbelo M. A comparative analysis of burned area datasets in Canadian boreal forest in 2000. [DOI:10.1155/2013/289056http://dx.doi.org/10.1155/2013/289056]
Padilla M, Stehman S V, Ramo R, Corti D, Hantson S, Oliva P, Alonso-Canas I, Bradley A V, Tansey K and Mota B. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. [DOI: 10.1016/ j.rse.2015.01.005http://dx.doi.org/10.1016/j.rse.2015.01.005]
Pang Y, Jia W, Qin X L, Si L, Liang X J, Lin X, Li Z Y. 2020. Forest fire monitoring using airborne optical full spectrum remote sensing data. Journal of Remote Sensing, 24(10): 1280-1292
庞勇, 荚文, 覃先林, 斯林, 梁晓军, 林鑫, 李增元. 2020. 机载光学全谱段遥感林火监测. 遥感学报, 24(10): 1280-1292 [DOI:10.11834/jrs.20200290http://dx.doi.org/10.11834/jrs.20200290]
Pettinari M, Lizundia-Loiola J and Chuvieco E. ESA CCI ECV Fire Disturbance: D4. 2 Product User Guide—MODIS, Version 1.0.
Pu D C, Zhang Z M, Long T F, Niu X F, He G J, Wang G Z, Sun J Y, Tang C and Wei M Y. 2020. GABAM2010 accuracy assessment using stratified random sampling. Journal of Remote Sensing, 24(5): 550-558
蒲东川, 张兆明, 龙腾飞, 牛雪峰, 何国金, 王桂周, 孙嘉悦, 唐朝, 魏明月. 2020. 分层随机抽样下全球30 m火烧迹地产品验证. 遥感学报, 24(5): 550-558 [DOI:10.11834/ jrs.20209171http://dx.doi.org/10.11834/jrs.20209171]
Qin X L, Li X T, Liu S C, Liu Q and Li Z Y. 2020. Forest fire early warning and monitoring techniques using satellite remote sensing in China. Journal of Remote Sensing, 24(5): 511-520
覃先林, 李晓彤, 刘树超, 刘倩, 李增元. 2020. 中国林火卫星遥感预警监测技术研究进展. 遥感学报, 24(5): 511-520 [DOI:10.11834/ jrs.20209135http://dx.doi.org/10.11834/jrs.20209135]
Quan X, He B, Yebra M, Yin C, Liao Z and Li X. Retrieval of forest fuel moisture content using a coupled radiative transfer model. [DOI:10.1016/j.envsoft.2017.06.006http://dx.doi.org/10.1016/j.envsoft.2017.06.006]
Quan X W, He B B, Liu X Z, Liao Z M, Qiu S and Yin C M. 2019. Retrieval of fuel moisture content by using radiative transfer models from optical remote sensing data. Journal of Remote Sensing, 23(1): 62-77
全兴文, 何彬彬, 刘向茁, 廖展芒, 邱实, 殷长明. 2019. 多模型耦合下的植被冠层可燃物含水率遥感反演. 遥感学报, 23(1): 62-77 [DOI:10.11834/jrs.20197422http://dx.doi.org/10.11834/jrs.20197422]
Quan X, Yebra M, Riaño D, He B, Lai G and Liu X. Global fuel moisture content mapping from MODIS. [DOI:10.1016/j.jag.2021. 102354http://dx.doi.org/10.1016/j.jag.2021.102354]
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, 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]
Riley K L and Loehman R A. Mid-21st-century climate changes increase predicted fire occurrence and fire season length, Northern Rocky Mountains, United States. [DOI:10.1002/ecs2.1543http://dx.doi.org/10.1002/ecs2.1543]
Standard for Classification and Classification of soil Erosion SL 190-2007. Domestic-Industry Standard-Industry Standard-Water Conservancy CN-SL.
土壤侵蚀分类分级标准 SL 190-2007. 国内-行业标准-行业标准-水利 CN-SL.
Sun F Y, Li X S, Li Z Y and Qin X L. 2020. Near-real-time forest fire monitoring system with medium and high spatial resolutions. Journal of Remote Sensing, 24(5): 543-549
孙福洋, 李晓松, 李增元, 覃先林. 2020. 近实时中高空间分辨率森林火灾监测系统展望. 遥感学报, 24(5):543-549 [DOI:10.11834/jrs.20209137http://dx.doi.org/10.11834/jrs.20209137]
Tang Y, Wang L J, Deng C, Gan Y Q and Zhao J. 2021. Research on the emergency response of forest fires in Sichuan with the help of high-definition remote sensing technology： An example of emergency monitoring of forest fires in Mianning “4·20”. Journal of Remote Sensing, 25(9):2015-2026
唐尧, 王立娟, 邓琮, 甘玉泉, 赵娟. 2021. 高分遥感技术助力森林火灾应急扑救及隐患预判—以冕宁"4·20"森林火灾为例. 遥感学报, 25(9): 2015-2026 [DOI:10.11834/jrs.20211352http://dx.doi.org/10.11834/jrs.20211352]
Wang M Y, Sun L, Shu L F and Tian X R. 2006. Fluctuation of Forest Fire in Spatial Space and Their Regional Behavior. Scientia Silvae Sinicae, 42(5): 98-103
王明玉, 孙龙, 舒立福, 田晓瑞. 2006. 林火在空间上的波动性及其区域化行为. 林业科学, 42(5): 98-103 [DOI:10.3321/j.issn:1001-7488.2006.05.018http://dx.doi.org/10.3321/j.issn:1001-7488.2006.05.018]
Wang Z J, Wei Y B and Wei H C. 2014. Research and application of spatio-temporal cluster analysis in Web data mining. Science for Young People: Teacher Edition, 35(1): 142
王志娟, 魏一搏, 魏宏昌. 2014. 时空聚类分析在Web数据挖掘中的研究与应用.青年科学:教师版, 35(1): 142.
Wooster M J, Roberts G J, Giglio L, Roy D P, Freeborn P H, Boschetti L, Justice C, Ichoku C, Schroeder W, Davies D, Smith A M S, Setzer A, Csiszar I, Strydom T, Frost P, Zhang T, Xu W, Jong M C, Johnston J M, Ellison L, Vadrevu K, Sparks A M, Nguyen H, McCarty J, Tanpipat V, Schmidt C and San-Miguel-Ayanz J. Satellite remote sensing of active fires: History and current status, applications and future requirements. Remote Sensing of Environment, 267: 112694. [DOI:10.1016/j.rse.2021.112694http://dx.doi.org/10.1016/j.rse.2021.112694]
Wu Q C, Chen F, Wang C L, Li B, Wu W, Liu S F and Xu F. 2016. Estimationof carbon emissions from biomass burning based on parameters retrieved. Journal of Remote Sensing, 20(1):11-26
吴沁淳, 陈方, 王长林, 李斌, 吴薇, 刘三超, 徐丰. 2016. 自然火灾碳排放估算模型参数的遥感反演进展. 遥感学报, 20(1): 11-26 [DOI:10.11834/jrs.20154291http://dx.doi.org/10.11834/jrs.20154291]
Xu B B, Wang W Y, Chen L F, Tao J H, Ji X Y, Zhang C J and Fan M. 2022. Forest fire spread simulation based on VIIRS active fire data and FARSITE model. Journal of Remote Sensing, 26(8): 1575-1588
徐奔奔, 王炜烨, 陈良富, 陶金花, 纪轩禹, 张成杰, 范萌. 2022. 基于VIIRS火点数据和FARSITE系统的森林火灾蔓延模拟.遥感学报, 26(8): 1575-1588 [DOI:10.11834/jrs. 20219427http://dx.doi.org/10.11834/jrs.20219427]
Yan J J, Qu J H, Ran M N and Zhang F F. 2020. Himawari-8 AHI fire detection in clear sky based on time-phase change. Journal of Remote Sensing, 24(5): 571-577
鄢俊洁, 瞿建华, 冉茂农, 张芳芳. 2020. 基于时相变化的晴空条件下Himawari-8 AHI火点检测. 遥感学报, 24(5): 571-577 [DOI:10.11834/jrs.20209122http://dx.doi.org/10.11834/jrs.20209122]
Yang W and Jiang X L. 2018. Review on Remote Sensing Information Extraction and Application of the Burned Forest Areas. Scientia Silvae Sinicae, 54(5): 135-142
杨伟, 姜晓丽. 2018. 森林火灾火烧迹地遥感信息提取及应用. 林业科学, 54(5): 135-142 [DOI:10.11707/j.1001-7488.20180515http://dx.doi.org/10.11707/j.1001-7488.20180515]
Yebra M, Quan X, Riaño D, Larraondo P R, van Dijk A I and Cary G J. A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing. [DOI:10.1016/j.rse.2018.04.053http://dx.doi.org/10.1016/j.rse.2018.04.053]
Yebra M, Dennison P E, Chuvieco E, Riaño D, Zylstra P, Hunt E R, Danson F M, Qi Y and Jurdao S. A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products. [DOI:10.1016/j.rse.2013. 05.029http://dx.doi.org/10.1016/j.rse.2013.05.029]
Zhang J, Zhao H Y, Zhang M X, Li C Y and Chen G P. 2017. Research on generalized linear model applied to forest fire forecast. Journal of forestry Engineering, 2(1): 135-142
张洁, 赵浩彦, 张民侠, 李晨阳, 陈戈萍. 2017. 广义线性模型在林火发生预报中的应用. 林业工程学报, 2(1): 135-142 [DOI:10.13360/j.issn.2096- 1359.2017.01.024http://dx.doi.org/10.13360/j.issn.2096-1359.2017.01.024]
Zhen 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, 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 Z, Wang J, Zou B, Gao Y H, Yang S Q and Wang Y Q. 2022. Initial assessment of burn severity using the transfer learning model. Journal of Remote Sensing, 26(10):2001-2013
郑忠, Jinfei Wang, 邹滨, 高阳华, 杨世琦, 王永前. 2022. 应用迁移学习的林火烈度初始评估研究. 遥感学报, 26(10): 2001-2013 [DOI:10.11834/jrs.20210156http://dx.doi.org/10.11834/jrs.20210156]
文章被引用时，请邮件提醒。
提交
相关作者
暂无数据
相关机构
福州大学 数字中国研究院
中国科学院空天信息创新研究院 数字地球重点实验室
南方科技大学 环境科学与工程学院
利兹大学 地球与环境学院, 英国 利兹 LS29JT
南方科技大学 环境科学与工程学院
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