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Assessment of tornado disaster in rubber plantation in western Hainan using Landsat and Sentinel-2 time series images

  • role： First author第一作者
  • Affiliation：

    Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS)/Danzhou Investigation & Experiment Station of Tropical Cops, Ministry of Agriculture and Rural/ State Key Laboratory Incubation Base for Cultivation & Physiology of Tropical Crops, Haikou 571101, China

  • Email：chbq40@163.com
  • Introduction：陈帮乾，1982年生,男，副研究员，研究方向为热带农林遥感。E-mail:chbq40@163.com
CHEN Bangqian1,  
  • Affiliation：

    School of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China

YUN Ting2,  
  • Affiliation：

    Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS)/Danzhou Investigation & Experiment Station of Tropical Cops, Ministry of Agriculture and Rural/ State Key Laboratory Incubation Base for Cultivation & Physiology of Tropical Crops, Haikou 571101, China

AN Feng1,  
  • Affiliation：

    College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming 650224, China

KOU Weili3,  
  • Affiliation：

    Institute of Scientific and Technical Information, CATAS, Haikou 571101, China

LI Hailiang4,  
  • Affiliation：

    Institute of Scientific and Technical Information, CATAS, Haikou 571101, China

LUO Hongxia4,  
  • Affiliation：

    Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS)/Danzhou Investigation & Experiment Station of Tropical Cops, Ministry of Agriculture and Rural/ State Key Laboratory Incubation Base for Cultivation & Physiology of Tropical Crops, Haikou 571101, China

YANG Chuan1,  
  • Affiliation：

    Tropical Crops Genetic Resources Institute, CATAS, Haikou 571101, China

WANG Qinfei5,  
  • Affiliation：

    Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS)/Danzhou Investigation & Experiment Station of Tropical Cops, Ministry of Agriculture and Rural/ State Key Laboratory Incubation Base for Cultivation & Physiology of Tropical Crops, Haikou 571101, China

SUN Rui1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS)/Danzhou Investigation & Experiment Station of Tropical Cops, Ministry of Agriculture and Rural/ State Key Laboratory Incubation Base for Cultivation & Physiology of Tropical Crops, Haikou 571101, China

  • Email：wzxrri@163.com
  • Introduction：吴志祥，1970年生，男，研究员，研究方向为橡胶林碳循环等。E-mail:wzxrri@163.com
WU Zhixiang1*

résumé

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.

mots-clés

rubber plantation;long time series images;image composite method;time window;damage assessment index

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    Fu F X. 2009. Analysis of wind disaster insurance of rubber trees in Hainan State Farm. China Tropical Agriculture, (5): 16-17
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Hainan Provincial Bureau of Statistics and Hainan Investigation Team of National Bureau of Statistics. 2018. Hainan Statistical Yearbook 2018. China Statistics Press
  11. 11.
    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
  12. 12.
    Holben B N. 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7(11): 1417-1434
  13. 13.
    HSF. 2007. Technique Manual of Rubber Cultivation for Hainan State Farm (HSF). Hainan: HSF: 156-160
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    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]
  24. 24.
    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
  25. 25.
    Tucker C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    Xia H. 2019. Study on the implementation of natural rubber income insurance in Hainan Province. Jiangxinongye, (8): 139
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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

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