浏览全部资源
扫码关注微信
Published： 07 March 2021 ，
扫 描 看 全 文
陈帮乾，云挺，安锋，寇卫利，李海亮，罗红霞，杨川，王琴飞，孙瑞，吴志祥.2021.基于Landsat和Sentinel-2时间序列影像的海南西部橡胶林龙卷风灾情评估.遥感学报，25（3）： 816-829
Chen B Q，Yun T，An F，Kou W L，Li H L，Luo H X，Yang C，Wang Q F，Sun R and Wu Z X. 2021. Assessment of tornado disaster in rubber plantation in western Hainan using Landsat and Sentinel-2 time series images. National Remote Sensing Bulletin， 25（3）：816-829
风害是影响中国天然橡胶产业发展最严重的自然灾害之一，它短时间内对橡胶林造成严重的物理伤害（大量落叶、枝条和主干折断等），严重影响后期生长和产量。传统地面调查虽然精度高，但耗时费力、经济成本高，利用遥感快速评估对指导灾后生产恢复、保险赔付和科学研究等具有重要意义。本研究以2019年8月1912号热带风暴“杨柳”在海南西部诱发的龙卷风为例，从影像数据覆盖的时效性、合成方法和灾害评估指标筛选等角度出发，探讨了联合Landsat 7/8和Sentinel-2时间序列遥感影像和影像差异法评估橡胶林风害损失的潜力。结果表明：（1）无云的Landsat 7/8和Sentinel-2影像在灾害发生前后的20 d内能对90%以上的研究区域实现至少一次覆盖，30 d内基本实现完全覆盖，像元尺度平均覆盖3次，60 d平均覆盖6次；（2）风灾前采用最大值合成，灾后采用中值法合成，获得的影像差异显著且监测指标最为稳定；（3）在时间窗口方面，灾害前后40 d窗口内合成影像的监测效果趋于稳定，与90 d窗口合成影像的结果差异不明显，保守的建议采用灾害前后60 d窗口内的影像进行风灾监测；（4）受损橡胶林风灾前后EVI数值变化最大，40 d窗口之后平均下降超过0.5，NBR次之，其后是LSWI、NDVI、近红外（NIR）和短波红外（SWIR1和SWIR2）波段。从变化的百分比值看，风灾后LSWI和SWIR2的变化幅度最高，降幅超过120%，明显高于NBR、EVI、NIR和SWIR1。相比之下，LSWI的空间变异（60 d合成时标准差约27%）明显低于SWIR2（60 d合成标准差约83%）；（5）此次龙卷风造成海南西部约645公顷的橡胶林报废，作为30年经济寿命周期的作物，损失非常严重。本研究展示了联合Landsat和Sentinel-2监测橡胶林风害损失的巨大潜力，为今后橡胶林和其他作物的台风灾害及时监测提供了重要的方法参考。
Wind damage is one of the most serious natural disasters affecting the development of China’s natural rubber industry. It caused huge physical damage (e.g.
a large number of fallen leaves
branches and trunk breakage) to rubber plantation in a short period of time
which seriously affected the subsequent growth and latex production. Although traditional ground surveys have high accuracy
they are time-consuming
labor-intensive
and economical. The rapid assessment using remote sensing is of great significance for guiding post-disaster production recovery
insurance compensation
and scientific research.
Taking the tornado induced by tropical storm Yangliu in 2019 in western Hainan Island as a case study
this study explores the potential of combining Landsat and Sentinel-2 time series images to assess tornado damage of rubber plantations from the perspectives of data availability
image composite methods
and disaster assessment indicators. The image difference method was used to detect differences before and after the tornado.
The results showed that: (1) cloudless Landsat 7/8 and Sentinel-2 images can cover more than 90% of the study area at least once within 20 days before and after the disaster
and almost cover the whole area within 30 days. The average coverage at the pixel scale is three times in 30 days and six times in 60 days. (2) The image difference generated by the maximum value composite of images acquired before tornado and the medium value composite of image acquired after tornado is the most significant and stable. (3) In term of time window
the monitoring effect from images acquired within 40 days before and after tornado tends to be stable
and there is no significant difference when compared with the results generated from a 90-days time window. It is conservatively recommended to use images in a 60-day window before and after the tornado for disaster assessment. (4) The EVI of the damaged rubber plantation before and after the tornado changed the most
followed by NBR
LSWI
NDVI
near infrared (NIR)
and shortwave infrared (SWIR1 and SWIR2) bands. In terms of percentage changes
LSWI and SWIR2 showed the highest changes after the tornado
followed by NBR
EVI
NIR
and SWIR1. However
the spatial variation of LSWI was significantly lower than that of SWIR2. (5) The tornado completely destroyed about 645 ha of rubber plantation in western Hainan Island. As a crop with a 30-years economic life cycle
the loss is very serious.
This study demonstrates the great potential of combining Landsat and Sentinel-2 images to access tornado damage of rubber plantation
and provides important insights for timely evaluating typhoon disaster of rubber plantations and other crops in future.
橡胶林时间序列影像影像合成时间窗口风害评估指标
rubber plantationlong time series imagesimage composite methodtime windowdamage assessment index
Aosier B, Kaneko M and Takada M. 2007. Evaluation of the forest damage by typhoon using remote sensing technique//2007 IEEE International Geoscience and Remote Sensing Symposium. Barcelona: IEEE: 3022-3026 [DOI: 10.1109/IGARSS.2007.4423481http://dx.doi.org/10.1109/IGARSS.2007.4423481]
Chastain R, Housman I, Goldstein J, Finco M and Tenneson K. 2019. Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States. Remote Sensing of Environment, 221: 274-285 [DOI: 10.1016/j.rse.2018.11.012http://dx.doi.org/10.1016/j.rse.2018.11.012]
Chen B Q, Cao J H, Wang J K, Wu Z X, Tao Z L, Chen J M, Yang C and Xie G S. 2012. Estimation of rubber stand age in typhoon and chilling injury afflicted area with Landsat TM data: a case study in Hainan Island, China. Forest Ecology and Management, 274: 222-230 [DOI: 10.1016/j.foreco.2012.01.033http://dx.doi.org/10.1016/j.foreco.2012.01.033]
Chen B Q, Li X P, Xiao X M, Zhao B, Dong J W, Kou W L, Qin Y W, Yang C, Wu Z X, Sun R, Lan G Y and Xie G S. 2016. Mapping tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images. International Journal of Applied Earth Observation and Geoinformation, 50: 117-130 [DOI: 10.1016/j.jag.2016.03.011http://dx.doi.org/10.1016/j.jag.2016.03.011]
Chen B Q, Xiao X M, Wu Z X, Yun T, Kou W L, Ye H C, Lin Q H, Doughty R, Dong J W, Ma J, Luo W, Xie G S and Cao J H. 2018. Identifying establishment year and pre-conversion land cover of rubber plantations on Hainan Island, China using Landsat data during 1987-2015. Remote Sensing, 10(8): 1240 [DOI: 10.3390/rs10081240http://dx.doi.org/10.3390/rs10081240]
Delphin S, Escobedo F J, Abd-Elrahman A and Cropper Jr W. 2013. Mapping potential carbon and timber losses from hurricanes using a decision tree and ecosystem services driver model. Journal of Environmental Management, 129: 599-607 [DOI: 10.1016/j.jenvman.2013.08.029http://dx.doi.org/10.1016/j.jenvman.2013.08.029]
Fu F X. 2009. Analysis of wind disaster insurance of rubber trees in Hainan State Farm. China Tropical Agriculture, (5): 16-17
符方雄. 2009. 海南农垦橡胶树风灾保险分析. 中国热带农业, (5): 16-17 [DOI: 10.3969/j.issn.1673-0658.2009.05.005]
Gao B C. 1996. NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257-266 [DOI: 10.1016/S0034-4257(96)00067-3http://dx.doi.org/10.1016/S0034-4257(96)00067-3]
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27 [DOI: 10.1016/j.rse.2017.06.031http://dx.doi.org/10.1016/j.rse.2017.06.031]
Hainan Provincial Bureau of Statistics and Hainan Investigation Team of National Bureau of Statistics. 2018. Hainan Statistical Yearbook 2018. China Statistics Press
海南省统计局, 国家统计局海南调查总队. 2018. 海南统计年鉴2018. 北京: 中国统计出版社
Han W D, Huang J J and Liu S Q. 2013. Study on forest spatial structure changes after a strong typhoon disturbance. Journal of Central South University of Forestry and Technology, 33(7): 8-13
韩维栋, 黄剑坚, 刘素青. 2013. 强台风干扰后林分空间结构变化的研究. 中南林业科技大学学报, 33(7): 8-13 [DOI: 10.14067/j.cnki.1673-923x.2013.07.007http://dx.doi.org/10.14067/j.cnki.1673-923x.2013.07.007]
Holben B N. 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7(11): 1417-1434 [DOI: 10.1080/01431168608948945http://dx.doi.org/10.1080/01431168608948945]
HSF. 2007. Technique Manual of Rubber Cultivation for Hainan State Farm (HSF). Hainan: HSF: 156-160
海胶集团. 2007. 海南农垦橡胶栽培技术手册. 海南: 海胶集团: 156-160
Hu T G and Smith R B. 2018. The impact of hurricane Maria on the vegetation of Dominica and Puerto Rico using multispectral remote sensing. Remote Sensing, 10(6): 827 [DOI: 10.3390/rs10060827http://dx.doi.org/10.3390/rs10060827]
Huete A, Didan K, Miura T, Rodriguez E P, Gao X and Ferreira L G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1/2): 195-213 [DOI: 10.1016/S0034-4257(02)00096-2http://dx.doi.org/10.1016/S0034-4257(02)00096-2]
Lee M F, Lin T C, Vadeboncoeur M A and Hwong J L. 2008. Remote sensing assessment of forest damage in relation to the 1996 strong typhoon Herb at Lienhuachi Experimental Forest, Taiwan. Forest Ecology and Management, 255(8/9): 3297-3306 [DOI: 10.1016/j.foreco.2008.02.010http://dx.doi.org/10.1016/j.foreco.2008.02.010]
Li W M, Tan Z H, Li W J and Yang Q. 2010. Comparison and analysis of MODIS NDVI and MODIS EVI. Remote Sensing Information, (6): 73-78
李文梅, 覃志豪, 李文娟, 杨强. 2010. MODIS NDVI与MODIS EVI的比较分析. 遥感信息, (6): 73-78 [DOI: 10.3969/j.issn.1000-3177.2010.06.016]
Lin W F. 2007. Discussion on the improvement of the measures of resistance to wind and the reduction of cultivation of rubber trees. China Tropical Agriculture, (3): 7-9
林位夫. 2007. 橡胶树抗风减灾栽培措施改进的探讨. 中国热带农业, (3): 7-9 [DOI: 10.3969/j.issn.1673-0658.2007.03.004]
Liu S J, Zhang J H, Cai D X, Tian G H, Zhang G F and Zou H P. 2016. Application of Landsat 8 in monitoring of rubber plantation typhoon disaster. Journal of Natural Disasters, 25(2): 53-58
刘少军, 张京红, 蔡大鑫, 田光辉, 张国峰, 邹海平. 2016. Landsat 8在橡胶林台风灾害监测中的应用. 自然灾害学报, 25(2): 53-58 [DOI: 10.13577/j.jnd.2016.0206http://dx.doi.org/10.13577/j.jnd.2016.0206]
Luo H X, Cao J H, Wang L L, Zhang Y S, Dai S P and Li H L. 2013. Studying NDVI Change of typhoon NESAT in Hainan with HJ-1CCD satellite images. Remote Sensing Technology and Application, 28(6): 1076-1082
罗红霞, 曹建华, 王玲玲, 张以山, 戴声佩, 李海亮. 2013. 基于HJ-1CCD的“纳沙”台风NDVI变化研究——以海南省为例. 遥感技术与应用, 28(6): 1076-1082
Marra D M, Chambers J Q, Higuchi N, Trumbore S E, Ribeiro G H P M, Dos Santos J, Negrón-Juárez R I, Reu B and Wirth C. 2014. Large-scale wind disturbances promote tree diversity in a Central Amazon forest. PLoS One, 8(9): e103710 [DOI: 10.1371/journal.pone.0103711http://dx.doi.org/10.1371/journal.pone.0103711]
Mitchell S J. 2013. Wind as a natural disturbance agent in forests: a synthesis. Forestry: An International Journal of Forest Research, 86(2): 147-157 [DOI: 10.1093/forestry/cps058http://dx.doi.org/10.1093/forestry/cps058]
Mo Y Y and Yang L. 2018. 2017 domestic and foreign natural rubber industry development situation. World Tropical Agriculture Information, (2): 1-3 (莫业勇, 杨琳. 2018. 2017年国内外天然橡胶产业发展形势. 世界热带农业信息, (2): 1-3) [DOI: 10.3969/j.issn.1009-1726.2018.02.002]
Nielsen E M. 2006. Rapid mapping of hurricane damage to forests//2006 Proceedings of the Eighth Annual Forest Inventory and Analysis Symposium. Washington, DC: US Department of Agriculture, Forest Service: 307-316
Tucker C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150 [DOI: 10.1016/0034-4257(79)90013-0http://dx.doi.org/10.1016/0034-4257(79)90013-0]
Laurin G V, Liesenberg V, Chen Q, Guerriero L, Del Frate F, Bartolini A, Coomes D, Wilebore B, Lindsell J and Valentini R. 2013. Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa. International Journal of Applied Earth Observation and Geoinformation, 21: 7-16 [DOI: 10.1016/j.jag.2012.08.002http://dx.doi.org/10.1016/j.jag.2012.08.002]
Vogt J, Piou C and Berger U. 2014. Comparing the influence of large- and small-scale disturbances on forest heterogeneity: a simulation study for mangroves. Ecological Complexity, 20: 107-115 [DOI: 10.1016/j.ecocom.2014.09.008http://dx.doi.org/10.1016/j.ecocom.2014.09.008]
Vogt J, Skóra A, Feller I C, Piou C, Coldren G and Berger U. 2012. Investigating the role of impoundment and forest structure on the resistance and resilience of mangrove forests to hurricanes. Aquatic Botany, 97(1): 24-29 [DOI: 10.1016/j.aquabot.2011.10.006http://dx.doi.org/10.1016/j.aquabot.2011.10.006]
Wang W T, Qu J J, Hao X J, Liu Y Q and Stanturf J A. 2010. Post-hurricane forest damage assessment using satellite remote sensing. Agricultural and Forest Meteorology, 150(1): 122-132 [DOI: 10.1016/j.agrformet.2009.09.009http://dx.doi.org/10.1016/j.agrformet.2009.09.009]
Xia H. 2019. Study on the implementation of natural rubber income insurance in Hainan Province. Jiangxinongye, (8): 139
夏欢. 2019. 海南省天然橡胶收入保险实施现状研究. 江西农业, (8): 139[DOI: 10.19394/j.cnki.issn1674-4179.2019.08.123]
Xiao X M, Hollinger D, Aber J, Goltz M, Davidson E A, Zhang Q Y and Moore III B. 2004. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sensing of Environment, 89(4): 519-534 [DOI: 10.1016/j.rse.2003.11.008http://dx.doi.org/10.1016/j.rse.2003.11.008]
Yu W, Zhang M L, Mai Q F and Jiang J S. 2006. Damage of typhoon Damrey to the rubber industry in Hainan state farm bureau and its countermeasures for future development. Chinese Journal of Tropical Agriculture, 26(4): 41-43
余伟, 张木兰, 麦全法, 蒋菊生. 2006. 台风“达维”对海南农垦橡胶产业的损害及所引发的对今后产业发展的思考. 热带农业科学, 26(4): 41-43 [DOI: 10.3969/j.issn.1009-2196.2006.04.016http://dx.doi.org/10.3969/j.issn.1009-2196.2006.04.016]
Yun T, Zhang Y X, Wang J M, Hu C H, Chen B Q, Xue L F and Chen F D. 2018. Quantitative inversion for wind injury assessment of rubber trees by using mobile laser scanning. Spectroscopy and Spectral Analysis, 38(11): 3452-3463
云挺, 张艳侠, 王佳敏, 胡春华, 陈邦乾, 薛联凤, 陈凡迪. 2018. 基于移动激光扫描的橡胶林风害相关参数精准反演. 光谱学与光谱分析, 38(11): 3452-3463) [DOI: 10.3964/j.issn.1000-0593(201811-3452-12]
Zhai D L, Dong J W, Cadisch G, Wang M C, Kou W L, Xu J C, Xiao X M and Abbas S. 2018. Comparison of pixel- and object-based approaches in phenology-based rubber plantation mapping in fragmented landscapes. Remote Sensing, 10(1): 44 [DOI: 10.3390/rs10010044http://dx.doi.org/10.3390/rs10010044]
Zhang J H, Zhang M J, Liu S J and Che X F. 2014. Application of FY-3 meteorological satellite in monitoring remote sensing of rubber plantation in Hainan Island. Chinese Journal of Tropical Crops, 35(10): 2059-2065
张京红, 张明洁, 刘少军, 车秀芬. 2014. 风云三号气象卫星在海南橡胶林遥感监测中的应用. 热带作物学报, 35(10): 2059-2065 [DOI: 10.3969/j.issn.1000-2561.2014.10.029http://dx.doi.org/10.3969/j.issn.1000-2561.2014.10.029]
Zhang M J, Zhang J H, Liu S J, Du H H and Che X F. 2014. TY-3A-based remote sensing monitoring of “Nesat” typhoon damage to rubber plantations in Hainan Island. Journal of Natural Disasters, 23(3): 86-92
张明洁, 张京红, 刘少军, 杜函函, 车秀芬. 2014. 基于FY-3A的海南岛橡胶林台风灾害遥感监测——以“纳沙”台风为例. 自然灾害学报, 23(3): 86-92 [DOI: 10.13577/j.jnd.2014.0311http://dx.doi.org/10.13577/j.jnd.2014.0311]
Zhu Z, Wang S X and Woodcock C E. 2015. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sensing of Environment, 159: 269-277 [DOI: 10.1016/j.rse.2014.12.014http://dx.doi.org/10.1016/j.rse.2014.12.014]
相关文章
相关作者
相关机构
京公网安备11010802024621