浏览全部资源
扫码关注微信
纸质出版日期： 2020-10-07 ，
扫 描 看 全 文
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
Pang Y，Jia W，Qin X L，Si L，Liang X J，Lin X and Li Z Y. 2020. Forest fire monitoring using airborne optical full spectrum remote sensing data. Journal of Remote Sensing（Chinese）， 24（10）：1280-1292［DOI：10.11834/jrs.20200290］
森林火灾是一种危害极大的自然灾害，是森林扰动的主要类型之一，直接影响森林生态系统结构、碳循环甚至全球气候的变化。近年来，航空平台和传感器的技术进步有效地提升了机载遥感系统探测和监测森林火灾的能力，推动了机载遥感在森林可燃物调查及载量评估、火险测报预测、火场态势及火情监测、灾害损失评估以及火烧迹地生态修复治理等方面的应用。本文首先介绍了中国林业科学研究院机载光学全谱段遥感系统CAF-LiTCHy（Chinese Academy of Forestry’s LiDAR，Thermal，CCD and Hyperspectral airborne observation system），描述了激光雷达扫描仪、热红外相机、CCD相机和高光谱传感器等传感器的参数；然后，阐明了集成方案和观测数据的处理方法；最后，以四川省西昌市“3.30森林火灾”作为该系统火后灾情遥感调查和灾情评估应用示例，综合多传感器数据特征，进行森林火烧程度评价，分析该系统采集的正射影像、冠层高度模型、高光谱影像、热红外影像在森林火灾监测评价中的潜力。研究结果表明CAF-LiTCHy机载遥感观测系统能有效获取森林火灾的灾情信息、火场及火环境参数，可为预防、预报预警、扑救指挥、灾害评估和生态修复提供支持。
Forest fire is one of the major forest disturbances
which directly affects forest ecosystem structure
carbon cycle
even global climate change. In recent years
the technical progresses of aerial platforms and onboard sensors have effectively improved the capability of airborne remote sensing to detect and monitor forest fires. As a result
many forest fire applications have been carried out with airborne data
including the forest fuel assessment
evaluation of forest fire potential and prevention
forest fire monitoring and situation evaluation during firefighting
post-fire damage assessment
ecosystem management and restoration.
This paper introduces an integrated airborne remote sensing system
i.e.
Chinese Academy of Forestry's LiDAR
Thermal
CCD and Hyperspectral (CAF-LiTCHy) airborne observation system. The CAF-LiTCHy airborne observation system is equipped with Light Detection and Ranging (LiDAR) scanner
thermal infrared camera
CCD camera and hyperspectral scanner and can obtain the corresponding data
simultaneously. The parameters of these sensors and the data processing scheme were described to show the advanced and scientific nature of CAF-LiTCHy airborne observation system. Moreover
the detailed “image - spectral - temperature - height” information of forest benefits the aforementioned applications. Here
the Xichang ‘3.30 Forest Fire’ in Sichuan Province was selected as a case study to test the capability of CAF-LiTCHy airborne observation system in post-fire survey and disaster assessment.
The burn severity levels can be interpreted from the high spatial resolution CCD image directly. Meanwhile
the LiDAR-derived Canopy Height Model (CHM) shows the different burn severities of the canopy in spatial and vertical structural level. The vegetation indices calculated from hyperspectral image indicate that the pigment content and water content of canopy decrease simultaneously. The thermal information shows a higher temperature in high burn severity areas than other areas due to lots of charcoals from fire. Finally
with the combination of the CCD
CHM
spectral and thermal temperature information
the distribution of burn severity levels of the study area was mapped. The CAF-LiTCHy airborne observation system can obtain forest fire information and offer fire-related parameters effectively. The design of CAF-LiTCHy airborne observation system is advanced and scientific
and the acquired data are in high resolution
multiple dimensions and good geo-location consistency. The efficiency and stability for airborne data collection provide rapid response for fire events. Furthermore
the CAF-LiTCHy airborne observation system has the potential to provide support for fire prevention
fire risk forecast and early warning
firefighting decision-making
post-fire damage assessment and ecological restoration.
遥感机载观测系统光学全谱段遥感西昌“3.30森林火灾”林火烈度热红外影像高光谱影像
remote sensingairborne observation systemoptical full spectrum remote sensingCAF-LiTCHyXichang ‘3.30 Forest Fire’burn severitythermal imagehyperspectral image
AEROcontrol-GNSS/IMU Navigation System. Available online. https:// www.igi-systems.com/aerocontrol.htmlhttps://www.igi-systems.com/aerocontrol.html [2020-09-15]
Agee, James K. 1997. Fire Ecology of Pacific Northwest Forests. Island Press, 4(7): 195-200[DOI: 10.1071/WF9940195http://dx.doi.org/10.1071/WF9940195]
Agisoft LLC. 2018. Agisoft PhotoScan Professional, Version 1.4.3; Agisoft LLC: St. Petersburg, Russia
AisaFENIX 1K. Available online. https://www.specim.fi/products/aisafenix-1k/[2020-09-15]
Allison R S, Johnston J M, Craig G, Jennings S. 2016. Airborne optical and thermal remote sensing for wildfire detection and monitoring. Sensors, 16(8): 1310[DOI: 10.3390/s16081310http://dx.doi.org/10.3390/s16081310]
Alonzo M, Morton D C, Cook B D, Andersen H E, Babcock C, Pattison R. 2017. Patterns of canopy and surface layer consumption in a boreal forest fire from repeat airborne lidar. Environmental Research Letters, 12(6): 065004[DOI: 10.1088/1748-9326/aa6adehttp://dx.doi.org/10.1088/1748-9326/aa6ade]
Asner G P, Knapp D E, Kennedy-Bowdoin T, Jones M O, Martin R E, Boardman J W, Field C B. 2007. Carnegie Airborne Observatory:in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems. Journal of Applied Remote Sensing, 1(013536): 1-21[DOI: 10.1117/1.2794018http://dx.doi.org/10.1117/1.2794018]
Cook B D, Corp L A, Nelson R F, Middleton E M, Morton D C, McCorkel J T, Masek J G, Ranson K J, Ly V and Mountesano P M. 2013. NASA Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) airborne imager. Remote Sensing, 5(8): 4045-4066[ DOI:10.3390/rs50 84045]
GNSS-303L-A USER GUIDE V2. 2018.03. https://www.gnss.ca/brochures/GNSS-303L-A-User-Guide.html [2020-09-15]
González-Olabarria J R, Rodríguez F, Fernández-Landa A, Mola-Yudego B. 2012. Mapping fire risk in the Model Forest of Urbión (Spain) based on airborne LiDAR measurements. Forest Ecology and Management, 282: 149-156[DOI: 10.1016/j.foreco.2012.06.056http://dx.doi.org/10.1016/j.foreco.2012.06.056]
Haboudane D, Tremblay N, Miller J R and Vigneault P, 2008. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 46(2), pp.423-437[DOI: 10.1109/TGRS.2007.904836http://dx.doi.org/10.1109/TGRS.2007.904836]
Huesca M, Merino-de-Miguel S, González-Alonso F, Martínez S, Miguel Cuevas J and Calle A. 2013. Using AHS hyper-spectral images to study forest vegetation recovery after a fire. International journal of remote sensing, 34(11), pp.4025-4048[DOI: 10.1080/01431161.2013.772313http://dx.doi.org/10.1080/01431161.2013.772313]
iXM-RS150F/iXM-RS100F. Availableonline. https://industrial.phaseone. com/iXM-RS_150F_Camera.aspx (accessed on 2020.09.15https://industrial.phaseone.com/iXM-RS_150F_Camera.aspx(accessedon2020.09.15)
IRBIS 3 Analysis Software. Available online. https://www.infratec.eu/thermography/thermographic-software/irbis3/ [2020-09-15]
Jia G J, Burke I C, Goetz A F H, Kaufmann M R and Kindel B C. 2006. Assessing spatial patterns of forest fuel using AVIRIS data. Remote Sensing of Environment, 102(3-4): 318-327[DOI: 10.1016/j.rse.2006.02.025http://dx.doi.org/10.1016/j.rse.2006.02.025]
Jia W, Coops N C, Tortini R, Pang Y and Black T A. 2018. Remote sensing of variation of light use efficiency in two age classes of Douglas-fir. Remote Sensing of Environment, 219: 284-297[DOI: 10.1016/j.rse.2018.10.017http://dx.doi.org/10.1016/j.rse.2018.10.017]
Jia W, Pang Y, Tortini R, Schläpfer D, Li Z and Roujean J L. 2020. A kernel-driven BRDF approach to correct airborne hyperspectral imagery over forested areas with rugged topography. Remote Sensing, 12(3): 432[DOI: 10.3390/rs12030432http://dx.doi.org/10.3390/rs12030432]
Jia W, Pang Y, Yue C R, Li Z Y, Che T, Ma M G. 2016. The Processing of airborne AISA Eagle II data in EJina Banner study area. Remote Sensing Technology and Application, 31 (3): 504-510
荚文, 庞勇, 岳彩荣, 李增元, 车涛, 马明国. 2016. 机载AISA Eagle Ⅱ高光谱数据处理—以额济纳旗试验区为例. 遥感技术与应用, 31(3), 504-510 [DOI: 10.11873j.issn.1004-0323.2016.3.0504http://dx.doi.org/10.11873j.issn.1004-0323.2016.3.0504]
Junttila S, Vastaranta M, Hämäläinen J, Latva-Käyrä P, Holopainen M, Hernández Clemente R, Hyyppä H and Navarro-Cerrillo R M. 2017. Effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery-Case study in Norway Spruce-dominated stands in Southern Finland. Scandinavian Journal of Forest Research, 32(2): 154-165[DOI: 10.1080/02827581.2016.1207800http://dx.doi.org/10.1080/02827581.2016.1207800]
Kampe T U, Johnson B R, Kuester M A and Keller M. 2010. NEON: The first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure. JOURNAL OF APPLIED REMOTE SENSING, 4(1): 043510[DOI: 10.1117/1.3361375http://dx.doi.org/10.1117/1.3361375]
Kaufman Y J, Hobbs P V, Kirchhoff V W J, Artaxo P, Remer L A, Holben B N, King M D, Ward D E, Prins E M, Longo K M, Mattos L F, Nobre C A and Spinhirne J D, Ji Q, Thompson A M, Gleason J F, Christopher S A and Tsay S C. 1998. Smoke, cloud and radiation-Brazil SCAR-B experiment. Journal of Geophysical Research, 103(D24): 31,783-31,808[DOI: 10.1029/98JD02281http://dx.doi.org/10.1029/98JD02281]
Keeley J E. 2009. Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire, 18(1): 116-126[DOI: 10.1071/WF07049http://dx.doi.org/10.1071/WF07049]
Lentile L B, Holden Z A, Smith A M S, Falkowski M J, Hudak A T, Morgan P, Gessler P E and Benson N C. 2006. Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, 15(3): 319-345[DOI: 10.1071/WF05097http://dx.doi.org/10.1071/WF05097]
Li Z Y, Qin X L, Gao Z H, Deng G, Yin L Y, Sun G F and Zu X F. 2016. Analysision monitoring burning status of forest fire using GF-4 satellite images. Spacecraft Engineering, 25(S1): 201-205
李增元, 覃先林, 高志海, 邓广, 尹凌宇, 孙桂芬, 祖笑锋. 2016 高分四号卫星监测森林火灾燃烧状态分析. 航天器工程, 25(S1): 201-205
Ma Z Y, Chen B W, Pang Y, Liao S X, Qin X L and Zhang H Q. 2020. Forest Fire potential forecast based on FCCS model. Remote Sensing for Land and Resources, 32(1) : 43-50
马振宇，陈博伟，庞勇，廖声熙，覃先林，张怀清. 2020. 基于林火特征分类模型的森林火情等级制图．国土资源遥感, 32(1): 43-50 [DOI: 10.6046/gtzyyg.2020.01.07http://dx.doi.org/10.6046/gtzyyg.2020.01.07]
McCarley T R, Kolden C A, Vaillant N M, Hudak A T, Smith A M S, Wing B M , Kellogg B S and Kreitler J. 2017. Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure. Remote Sensing of Environment, 191: 419-432[DOI: 10.1016/j.rse.2016.12.022http://dx.doi.org/10.1016/j.rse.2016.12.022]
Meng R, Wu J, Schwager K L, Zhao F, Dennison P E, Cook B D, Brewster K, Green T M and Serbin S P. 2017. Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem[J].Remote Sensing of Environment, 191: 95-109[DOI: 10.1016/j.rse.2017.01.016http://dx.doi.org/10.1016/j.rse.2017.01.016]
Pang Y. 2014. Frontiers of Forest parameters estimation from waveform lidar data. China Science and Technology Achievements, 2014, 3: 23&28
庞勇. 2014. 波形激光雷达森林参数反演前沿技术研究. 中国科技成果, 2014, 3:23&28 [DOI: 10.3772/j.issn.1009-5659.2014.03.011http://dx.doi.org/10.3772/j.issn.1009-5659.2014.03.011]
Pang Y, Li Z Y, Ju H B, Lu H, Jia W, Si L, Guo Y, Liu Q W, Li S M, Liu L X, Xie B B, Tan B X and Dian Y Y. 2016. LiCHy: The CAF’s LiDAR, CCD and hyperspectral integrated airborne observation system. Remote Sensing, 8(5): 398 [DOI: 10.3390/rs80 50398http://dx.doi.org/10.3390/rs8050398]
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］
Quintano C, Fernández-Manso A, Calvo L, Marcos E and Valbuena L. 2015. Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems. International Journal of Applied Earth Observation and Geoinformation, 2015 (36) : 1-12[DOI: 10.1016/j.jag.2014.10.015http://dx.doi.org/10.1016/j.jag.2014.10.015]
Quintano C, Fernandez-Manso A and Roberts D A. 2017. Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature. Remote Sensing of Environment, 2017(190): 83-95[DOI:10.1016/j.rse.2016.12.009http://dx.doi.org/10.1016/j.rse.2016.12.009]
Ramirez F J R, Navarro-Cerrillo R M, Varo-Martínez M Á, Quero J L, Doerr S and Hernández-Clemente R. 2018. Determination of forest fuels characteristics in mortality-affected Pinus forests using integrated hyperspectral and ALS data. International journal of applied earth observation and geoinformation, 68：157-167[DOI: 10.1016/j.jag.2018.01.003http://dx.doi.org/10.1016/j.jag.2018.01.003]
Schläpfer D, Richter R and Feingersh T. 2014. Operational BRDF effects correction for wide-field-of-view optical scanners (BREFCOR). IEEE Transactions on Geoscience and Remote Sensing, 53(4): 1855-1864[DOI: 10.1109/TGRS.2014.2349946http://dx.doi.org/10.1109/TGRS.2014.2349946]
Tao C S, Niu S K, Chen L and Li L Q. 2018. Canopy characteristics and potential crown fire behavior of main coniferous forest in Miaofeng Mountain Forest Farm in Beijing. JOUＲNAL OF BEIJING FOＲESTＲY UNIVEＲSITY, 40(5): 82-89
陶长森, 牛树奎, 陈羚, 李连强. 2018. 妙峰山林场主要针叶林冠层特征及潜在火行为. 北京林业大学学报, 40(5): 82-89 [DOI: 10.13332/j.1000-1522.20170408http://dx.doi.org/10.13332/j.1000-1522.20170408]
VarioCAM® HD research 900. Available online. https://www.infratec-infrared<math id="M5"><mo>.</mo></math>http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20248652&type=http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=20248650&type=0.761999962.03200006com / thermography/infrared-camera / variocam-hd-research-900 [2020 -09-15]
Veraverbeke S, Dennison P, Gitas I, Hulley G, Kalashnikova O, Katagis T, Kuai L, Meng R, Roberts D and Stavros N. 2018. Hyperspectral remote sensing of fire: State-of-the-art and future perspectives. Remote Sensing of Environment, 216: 105-121[DOI: 10.1016/j.rse.2018.06.020http://dx.doi.org/10.1016/j.rse.2018.06.020]
Veraverbeke S, Hook S and Hulley G. 2012. An alternative spectral index for rapid fire severity assessments. Remote Sensing of Environment, 123: 72-80[DOI: 10.1016/j.rse.2012.02.025http://dx.doi.org/10.1016/j.rse.2012.02.025]
相关作者
相关机构
京公网安备11010802024621