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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (1) : 210-217     DOI: 10.6046/zrzyyg.2021110
Spatio-temporal change characteristics of rubber forest phenology in Hainan Island during 2001—2015
HU Yingying1,2(), DAI Shengpei1,2(), LUO Hongxia1,2, LI Hailiang1,2, LI Maofen1,2, ZHENG Qian1,2, YU Xuan1,2, LI Ning3
1. Institute of Scientific and Technological Information, Chinese Academy of Tropical Agricultural Sciences/Key Laboratory of Applied Research on Tropical Crop Information Technology of Hainan Province, Haikou 571101, China
2. Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
3. Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Sciences/Danzhou Hainan, Tropical Agro-Ecosystem, National Observation and Research Station, Haikou 571101, China
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To analyze the phenological characteristics of the rubber forest in Hainan Island and to explore the phenological change characteristics of tropical forest vegetation, this study reconstructed the 2001—2015 MODIS NDVI time series using the Savitzky-Golay (S-G) filtering method based on the MODIS normalized difference vegetation index (NDVI) data. Then, this study extracted the phenological parameters of the rubber forest using the dynamic threshold method and typical sampling areas. Finally, this study analyzed the spatio-temporal changes in the phenological characteristics of the rubber forest. The results are as follows. During 2001—2015, the rubber forest started its foliation season mainly from mid-January to late March in spring and started its defoliation season from mid-November to late December in autumn, with the growing season lasting for about 7~10 months. On the time scale, the phenological characteristics did not significantly changed in the 15 years. Specially, the spring phenology occured about 0.94 days earlier, the autumn phenology showed an about 0.84 days delay, and the growing season was prolonged for about 1.79 days every year. On a spatial scale, the regions where the spring phenology occurred significantly earlier in the 15 years primarily included Baisha Li Autonomous County, Tunchang County, Qiongzhong Li-Miao Autonomous County, Wanning City, and Qionghai City, with a changing rate of -1.8~-0.1 d/a. The areas with a significant delay in autumn phenology included Danzhou City, Baisha Li Autonomous County, Tunchang County, Qiongzhong Li-Miao Autonomous County, Qionghai City, Wanning City, Ledong Li Autonomous County, Sanya City, and Baoting Li-Miao Autonomous County, with a changing rate of 0.5~2.7 d/a. The areas where the growing season was significantly prolonged mainly included Danzhou City and Baisha Li Autonomous County, with a changing rate of 0.2~0.8 d/a. The main characteristic of phenological changes of the rubber forest is the significant delay in the start date of the defoliation season.

Keywords rubber      phenology      MODIS NDVI      Hainan Island      spatio-temporal change     
ZTFLH:  TP79  
Corresponding Authors: DAI Shengpei     E-mail:;
Issue Date: 14 March 2022
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Yingying HU
Shengpei DAI
Hongxia LUO
Hailiang LI
Maofen LI
Xuan YU
Ning LI
Cite this article:   
Yingying HU,Shengpei DAI,Hongxia LUO, et al. Spatio-temporal change characteristics of rubber forest phenology in Hainan Island during 2001—2015[J]. Remote Sensing for Natural Resources, 2022, 34(1): 210-217.
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Fig.1  The map of study area
Fig.2  Typical sample image and fitting curve
地点 获取方法 展叶期
儋州市 地面观测数据 59 362 303
遥感监测数据 65 353 273
白沙黎族自治县 地面观测数据 62 364 302
遥感监测数据 65 353 289
琼中黎族苗族自治县 地面观测数据 59 359 300
遥感监测数据 57 353 289
Tab.1  Comparison of the phenology between different measuring methods(d)
Fig.3  Inter-annual variations of rubber forest phenology in Hainan Island from 2001 to 2015
Fig.4  Spatial distribution of phenology in Hainan Island form 2001—2015
Fig.5  Percentage of pixels in rubber plantation per year average phenology period
Fig.6  Spatial distribution of phenology change trend in Hainan Island from 2001 to 2015
[1] Morisette J T, Richardson A D, Knapp A K, et al. Tracking the rhythm of the seasons in the face of global change:Phenological research in the 21st century[J]. Frontiers in Ecology and the Environment, 2009, 7(5):253-260.
doi: 10.1890/070217 url:
[2] 张树清, 张柏, 汪爱华. 三江平原湿地消长与区域气候变化关系研究[J]. 地球科学进展, 2001, 16(6):836-841.
[2] Zhang S Q, Zhang B, Wang A H. A study on the relationship between distributive variation of wetlands and regional climate change in Sanjiang Plant[J]. Advance in Earth Sciences, 2001, 16(6):836-841.
[3] 范德芹, 赵学胜, 朱文泉, 等. 植物物候遥感监测精度影响因素研究综述[J]. 地理科学进展, 2016, 35(3):304-319.
doi: 10.18306/dlkxjz.2016.03.005
[3] Fan D Q, Zhao X S, Zhu W Q, et al. Revie od influencing factors of accuracy of plant phenology monitoring based on remote sensing data[J]. Progress in Geography, 2016, 35(3):304-319.
[4] 翟佳, 袁凤辉, 吴家兵. 植物物候变化研究进展[J]. 生态学杂志, 2015, 34(11):3237-3243.
[4] Zhai J, Yuan F H, Wu J B. Research progress on vegetation phenological changes[J]. Chinese Journal of Ecology, 2015, 34(11):3237-3243.
[5] 王连喜, 陈怀亮, 李琪, 等. 植物物候与气候研究进展[J]. 生态学报, 2010, 30(2):447-454.
[5] Wang L X, Chen H L, Li Q, et al. Research advances in plant phenology and climate[J]. Acta Ecologica Sinica, 2010, 30(2):447-454.
[6] 陆佩玲, 于强, 贺庆棠. 植物物候对气候变化的响应[J]. 生态学报, 2006, 31(3):923-929.
[6] Lu P L, Yu Q, He Q T. Responses of plant phenology to climate change[J]. Acta Ecologica Sinica, 2006, 31(3):923-929.
[7] Clerici N, Weissteniner C J, Gerard F. Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general categories[J]. Remote Sensing, 2012, 4(12):1781-1803.
doi: 10.3390/rs4061781 url:
[8] Xin Q C, Broich M, Zhu P, et al. Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics[J]. Remote Sensing of Environment, 2015, 161(5):63-77.
doi: 10.1016/j.rse.2015.02.003 url:
[9] Razak A, Shariff R, Ahmad N, et al. Mapping rubber trees based on phenological analysis of Landsat time series data-sets[J]. Geocarto International, 2018, 33(6):627-650.
[10] Kou W L, Xiao X M, Dong J W, et al. Mapping deciduous rubber plantation areas and stand ages with PALSAR and Landsat images[J]. Remote Sensing, 2015, 7(1):1048-1073.
doi: 10.3390/rs70101048 url:
[11] Zhai D L, Yu H, Chen S C, et al. Responses of rubber leaf phenology to climatic variations in Southwest China[J]. International Journal of Biometeorology, 2019, 63(5):607-616.
doi: 10.1007/s00484-017-1448-4 pmid: 29130120
[12] 陈汇林, 陈小敏, 陈珍丽, 等. 基于MODIS遥感数据提取海南橡胶信息初步研究[J]. 热带作物学报, 2010, 31(7):1181-1185.
[12] Chen H L, Chen X M, Chen Z L, et al. A primary study on rubber acreage estimation form MODIS:Based information in Hainan[J]. Chinese Journal of Tropical Crops, 2010, 31(7):1181-1185.
[13] 陈小敏, 陈汇林, 李伟光, 等. 海南岛天然橡胶林春季物候期的遥感监测[J]. 中国农业气象, 2016, 37(1):111-116.
[13] Chen X M, Chen H L, Li W G, et al. Remote sensing monitoring of spring phenophase of natural rubber forest in Hainan Province[J]. Chinese Journal of Agrometeorology, 2016, 37(1):111-116.
[14] 田光辉, 李海亮, 陈汇林. 基于物候特征参数的橡胶树种植信息遥感提取研究[J]. 中国农学通报, 2013, 29(28):46-52.
[14] Tian G H, Li H L, Chen H L. Research on remote sensing extraction of planting information for rubber trees based on phenlogical characteristic parameters[J]. Chinese Agricultural Science Bulletin, 2013, 29(28):46-52.
[15] 唐海川, 何永东. 海南统计年鉴.行政区划(2018)[Z].2019.
[15] Tang H C, He Y D. Hainan statistical yearbook.divisions of administrative areas(2018)[Z].2019.
[16] 许灿光, 蒋菊生, 石靓, 等. 中国植胶环境生态状况调查与评价[M]. 北京: 中国农业出版社, 2014:27-28.
[16] Xu C G, Jiang J S, Shi L, et al. Investigation and evaluation on the ecological status of glue planting environment in China[M]. Beijing: China Agricultural Press, 2014:27-28.
[17] 张惜珠, 黄慧德. 橡胶树栽培与利用[M]. 北京: 金盾出版社, 2006:5-7.
[17] Zhang X Z, Huang H D. Rubber tree cultivation and utilization[M]. Beijing: Jindun Press, 2006:5-7.
[18] Chen B Q, Li X P, Xiao X M, et al. Mapping tropical forests and deciduous rubber plantations in Hainan Island,China by integrating PALSAR 25-m and multi-temporal Landsat image[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 50(5):117-130.
doi: 10.1016/j.jag.2016.03.011 url:
[19] Dong J W, Xiao X X, Chen B Q, et al. Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery[J]. Remote Sensing of Environment, 2013, 134(5):392-402.
doi: 10.1016/j.rse.2013.03.014 url:
[20] Chen J, Jönsson P, Tamura M, et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote Sensing of Environment, 2004, 91(3) :332-334.
doi: 10.1016/j.rse.2004.03.014 url:
[21] Jönsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEE Transactions on Geo-science and Remote Sensing, 2002, 40(8):1824-1832.
doi: 10.1109/TGRS.2002.802519 url:
[22] Yin L C, Wang X F, Feng X M, et al. A comparison of SSEBop-model-based evapotranspiration with eight evapotranspiration products in the Yellow River basin,China[J]. Remote Sensing, 2020, 12(16):25-28.
doi: 10.3390/rs12010025 url:
[23] 徐佳, 樊海东, 倪健. 1950—2015年中国植物物候变化的集成分析[J]. 亚热带资源与环境学报, 2019, 14(2):1-11.
[23] Xu J, Fan H D, Ni J. Meta-analysis of plant phenological change in China during 1950 and 2015[J]. Journal of Subtropical Resources and Environment, 2019, 14(2):1-11.
[24] 余振, 孙鹏森, 刘世荣. 中国东部南北样带主要植被类型物候期的变化[J]. 植物生态学报, 2010, 34(3):316-329.
doi: 10.3773/j.issn.1005-264x.2010.03.009
[24] Yu Z, Sun P S, Liu S R. Phenological change of main vegetation types along a north-south transect of eastern China[J]. Chinese Journal of Plant Ecology, 2010, 34(3):316-329.
[25] 葛全胜, 郑景云, 张学霞, 等. 过去40年中国气候与物候的变化研究[J]. 自然科学进展, 2003, 13(10) :1048-1053.
[25] Ge Q S, Zheng J Y, Zhang X X, et al. Studies on climate and phenology changes in China in the past 40 years[J]. Progress in Natural Science, 2003, 13(10):1048-1053.
[26] 仲舒颖, 郑景云, 葛全胜. 近40 年中国东部木本植物秋季叶全变色期变化[J]. 中国农业气象, 2010, 31(1):1-4.
[26] Zhong S Y, Zheng J Y, Ge Q S. Change of autumnal leaf coloring of woody plants in eastern China for the last 40 years[J]. Chinese Journal of Agrometeorology, 2010, 31(1):1-4.
[27] Chen B Q, Xiao X M, Wu Z X, et al. Identifying establishment year and pre-conversion land cover of rubber plantations on Hainan Island,China using Landsat data during 1987—2015[J]. Remote Sensing, 2018, 10(8):13-19.
doi: 10.3390/rs10010013 url:
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