Please wait a minute...
 
Remote Sensing for Land & Resources    2018, Vol. 30 Issue (4) : 125-131     DOI: 10.6046/gtzyyg.2018.04.19
|
Spatial-temporal characteristics of vegetation phenology in Shaanxi Province based on MODIS time series
Hongzhu HAN1,2, Jianjun BAI1,2(), Bo ZHANG3, Gao MA3
1. School of Geography and Tourism, Shaanxi Normal University, Xi’an 710062, China;
2. National Demonstration Center for Experimental Geography Education, Shaanxi Normal University, Xi’an 710062, China;
3. China Academy of Space Technology Xi’an , Xi’an 710000, China;
Download: PDF(6186 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Remote sensing has been widely used in the study of natural geographical environment as an effective method to monitor the large-scale of surface. Among them, as a regular and periodic event in nature, vegetation phenology plays an important role in the natural environment, especially climate change. With Shaanxi Province as the study area and on the basis of the time series reconstruction of the MODIS NDVI data by using the Savitzky-Golay filtering method, the authors extracted and analyzed the vegetation phenology parameters of Shaanxi Province for the spatio-temporal changes from 2001 to 2016. The conclusions are as follows: ①The spatial distribution characteristics of vegetation phenology are in good agreement with the spatial distribution of different landforms in Shaanxi Province. ②The average start of season(SOS) was on the 120th day, the average end of season(EOS) was on the 280th day, and the average length of season(LOS) was 160 days; ③ From 2001 to 2016, the change of SOS was -0.79 d/a (R 2=0.4, P<0.01) , the EOS was 0.50 d/a (R 2=0.25, P<0.05), and the LOS was 1.29 d/a(R 2=0.37,P<0.05); ④ In the period of different phenological stages, the spatial distribution of phenological change trend of vegetation in Shaanxi Province is relatively large.

Keywords NDVI      phenology      spatio-temporal variation      Shaanxi Province     
:  TP79  
Corresponding Authors: Jianjun BAI     E-mail: bjj@snnu.edu.cn
Issue Date: 07 December 2018
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Hongzhu HAN
Jianjun BAI
Bo ZHANG
Gao MA
Cite this article:   
Hongzhu HAN,Jianjun BAI,Bo ZHANG, et al. Spatial-temporal characteristics of vegetation phenology in Shaanxi Province based on MODIS time series[J]. Remote Sensing for Land & Resources, 2018, 30(4): 125-131.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.04.19     OR     https://www.gtzyyg.com/EN/Y2018/V30/I4/125
Fig.1  Spatial distribution characteristics of remote sensing phenological mean during 2001—2016
区域 物候期
SOS EOS LOS/d
毛乌素沙地陕北部分 127.3 292.5 165.2
黄土高原 134.2 289.6 155.4
关中平原 101.5 274.9 173.4
秦岭山地 113.4 270.2 156.8
汉江盆地 88.3 271.7 183.4
大巴山山地 109.5 267.1 157.6
Tab.1  Average phenological time in each different area during 2001—2016
Fig.2  Inter-annual trend variation of remote sensing phenology in Shaanxi Province during 2001—2016
Fig.3  Spatial distribution characteristics of remote sensing phenological change trend during 2001—2016
[1] 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.
url: http://www.cqvip.com/Main/Detail.aspx?id=32678611
[1] 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.
[2] 郑景云, 葛全胜, 郝志新 . 气候增暖对我国近40年植物物候变化的影响[J]. 科学通报, 2002,47(20):1582-1587.
[2] Zheng J Y, Ge Q S, Hao Z X . Effect of climate warming on plant phenology change in China in last 40 years[J]. Chinese Science Bulletin, 2002,47(20):1582-1587.
[3] Jeong S J, Ho C H, Gim H J , et al. Phenology shifts at start vs.end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982—2008[J]. Global Change Biology, 2011,17(7):2385-2399.
doi: 10.1111/j.1365-2486.2011.02397.x url: http://doi.wiley.com/10.1111/j.1365-2486.2011.02397.x
[4] 李荣平, 周广胜, 张慧玲 . 植物物候研究进展[J]. 应用生态学报, 2006,17(3):541-544.
[4] Li R P, Zhou G S, Zhang H L . Research advances in plant phenology[J]. Chinese Journal of Applied Ecology, 2006,17(3):541-544.
[5] Zhang X Y, Friedl M A, Schaaf C B , et al. Monitoring vegetation phenology using MODIS[J]. Remote Sensing of Environment, 2003,84(3):471-475.
doi: 10.1016/S0034-4257(02)00135-9 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425702001359
[6] Piao S L, Fang J Y, Zhou L M , et al. Variations in satellite-derived phenology in China’s temperate vegetation[J]. Global Change Biology, 2006,12(4):672-685.
doi: 10.1111/gcb.2006.12.issue-4 url: http://www.blackwell-synergy.com/toc/gcb/12/4
[7] Chen X, Tan Z J, Schwartz M D , et al. Determining the growing season of land vegetation on the basis of plant phenology and satellite data in Northern China[J]. International Journal of Biometeorology, 2000,44(2):97-101.
doi: 10.1007/s004840000056 url: http://link.springer.com/10.1007/s004840000056
[8] Ding M J, Zhang Y L, Sun X M , et al. Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan Plateau from 1999 to 2009[J]. Chinese Science Bulletin, 2013,58(3):396-405.
doi: 10.1007/s11434-012-5407-5 url: http://link.springer.com/10.1007/s11434-012-5407-5
[9] 侯学会, 牛铮, 高帅 , 等. 基于SPOT-VGT NDVI时间序列的农牧交错带植被物候监测[J]. 农业工程学报, 2013,29(1):142-150,294.
doi: 10.3969/j.issn.1002-6819.2013.01.019 url: http://www.cqvip.com/QK/90712X/201301/44571960.html
[9] Hou X H, Niu Z, Gao S , et al. Monitoring vegetation phenology in farming-pastoral zone using SPOT-VGT NDVI data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013,29(1):142-150,294.
[10] 俎佳星, 杨健 . 东北地区植被物候时序变化[J]. 生态学报, 2016,36(7):2015-2023.
doi: 10.5846/stxb201409231884 url: http://www.cqvip.com/QK/90772X/201607/668554009.html
[10] Zu J X, Yang J . Temporal variation of vegetation phenology in northeastern China[J]. Acta Ecologica Sinica, 2016,36(7):2015-2023.
[11] 李正国, 杨鹏, 周清波 , 等. 基于时序植被指数的华北地区作物物候期/种植制度的时空格局特征[J]. 生态学报, 2009,29(11):6216-6226.
[11] Li Z G, Yang P, Zhou Q B , et al. Research on spatiotemporal pattern of crop phenological characteristics and cropping system in North China based on NDVI time series data[J]. Acta Ecologica Sinica, 2009,29(11):6216-6226.
[12] 何月, 樊高峰, 张小伟 , 等. 近10年浙江植被物候的遥感监测及时空动态[J]. 中国农学通报, 2012,28(16):117-124.
[12] He Y, Fan G F, Zhang X W , et al. Vegetation phenology monitoring and spatio-temporal dynamics in Zhejiang Province in past 10 years[J]. Chinese Agricultural Science Bulletin, 2012,28(16):117-124.
[13] 张晗, 任志远 . 多种时序NDVI重建方法比较与应用分析[J]. 中国农业科学, 2014,47(15):2998-3008.
doi: 10.3864/j.issn.0578-1752.2014.15.010
[13] Zhang H, Ren Z Y . Comparison and application analysis of several NDVI time-series reconstruction methods[J]. Scientia Agricultura Sinica, 2014,47(15):2998-3008.
[14] 郑景云, 尹云鹤, 李炳元 . 中国气候区划新方案[J]. 地理学报, 2010,65(1):3-12.
doi: 10.11821/xb201001002 url: http://d.wanfangdata.com.cn/Periodical/dlxb201001001
[14] Zheng J Y, Yin Y H, Li B Y . A new scheme for climate regionalization in China[J]. Acta Geographica Sinica, 2010,65(1):3-12.
[15] 宋春桥, 柯灵红, 游松财 , 等. 基于TIMESAT的3种时序NDVI拟合方法比较研究——以藏北草地为例[J]. 遥感技术与应用, 2011,26(2):147-155.
url: http://d.wanfangdata.com.cn/Periodical/ygjsyyy201102003
[15] Song C Q, Ke L H, You S C , et al. Comparison of three NDVI time-series fitting methods based on TIMESAT:Taking the grassland in northern Tibet as case[J]. Remote Sensing Technology and Application, 2011,26(2):147-155.
[16] Roerink G J, Menenti M, Verhoef W . Reconstructing cloudfree NDVI composites using Fourier analysis of time series[J]. International Journal of Remote Sensing, 2000,21(9):1911-1917.
doi: 10.1080/014311600209814 url: https://www.tandfonline.com/doi/full/10.1080/014311600209814
[17] 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):332-344.
doi: 10.1016/j.rse.2004.03.014 url: http://linkinghub.elsevier.com/retrieve/pii/S003442570400080X
[18] 蔡博峰, 于嵘 . 基于遥感的植被长时序趋势特征研究进展及评价[J]. 遥感学报, 2009,13(6):1177-1186.
doi: 10.3321/j.issn:1007-4619.2009.06.014 url: http://d.wanfangdata.com.cn/Periodical/ygxb200906014
[18] Cai B F, Yu R . Advance and evaluation in the long time series vegetation trends research based on remote sensing[J]. Journal of Remote Sensing, 2009,13(6):1177-1186.
[19] 辜智慧 . 中国农作物复种指数的遥感估算方法研究——基于SPOT/VGT多时相NDVI遥感数据[D]. 北京:北京师范大学, 2003.
[19] Gu Z H . A Study of Calculating Multiple Cropping Index of Crop in China Using SPOT/VGT Multi-Temporal NDVI Data[D]. Beijing:Beijing Normal University, 2003.
[20] Zhou L M, Tucker C J, Kaufmann R K , et al. Variation in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999[J]. Journal of Geophysical Research, 2001,106:20069-20083.
doi: 10.1029/2000JD000115 url: http://doi.wiley.com/10.1029/2000JD000115
[21] Yu H Y, Luedeling E, Xu J C . Winter and spring warming result in delayed spring phenology on the Tibetan Plateau[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010,107(51):22151-22156.
doi: 10.1073/pnas.1012490107 pmid: 21115833 url: http://www.pnas.org/cgi/doi/10.1073/pnas.1012490107
[22] Kafaki S B, Mataji A, Hashemi S A . Monitoring growing season length of deciduous broad leaf forest derived from satellite data in Iran[J]. American Journal of Environmental Sciences, 2009,5(5):647-652.
doi: 10.3844/ajessp.2009.647.652 url: http://www.thescipub.com/abstract/?doi=ajessp.2009.647.652
[23] 王静璞, 刘连友, 贾凯 , 等. 毛乌素沙地植被物候时空变化特征及其影响因素[J]. 中国沙漠, 2015,35(3):624-631.
doi: 10.7522/j.issn.1000-694X.2015.00021 url: http://d.wanfangdata.com.cn/Periodical/zgsm201503014
[23] Wang J P, Liu L Y, Jia K , et al. Spatiotemporal variation of vegetation phenology and its affecting factors in the Mu Us Sandy Land[J]. Journal of Desert Research, 2015,35(3):624-631.
[24] 张少伟, 郭勇, 权红花 . 陕西省地理国(省)情监测中的基本地貌类型及划分指标[J]. 测绘标准化, 2012,28(4):13-16.
url: http://www.cqvip.com/QK/96859X/201204/1002003593.html
[24] Zhang S W, Guo Y, Quan H H . Fundamental relief types and their classification index used in provincial geographic conditions monitoring in Shaanxi[J]. Standardization of Surveying and Mapping, 2012,28(4):13-16.
[25] 夏浩铭, 李爱农, 赵伟 , 等. 2001—2010年秦岭森林物候时空变化遥感监测[J]. 地理科学进展, 2015,34(10):1297-1305.
doi: 10.18306/dlkxjz.2015.10.010 url: http://www.cqvip.com/QK/96015A/201510/68767574504849534948484948.html
[25] Xia H M, Li A N, Zhao W , et al. Spatiotemporal variations of forest phenology in the Qinling zone based on remote sensing monitoring,2001—2010[J]. Progress in Geography, 2015,34(10):1297-1305.
[26] Myneni R B, Keeling C D, Tucker C J , et al. Increased plant growth in the north high latitudes from 1981 to 1991[J]. Nature, 1997,386(6626):698-702.
doi: 10.1038/386698a0 url: http://www.nature.com/doifinder/10.1038/386698a0
[27] Julien Y, Sobrino J A . Global land surface phenology trends from GIMMS database[J]. International Journal of Remote Sensing, 2009,30(13):3495-3513.
doi: 10.1080/01431160802562255 url: https://www.tandfonline.com/doi/full/10.1080/01431160802562255
[28] 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 url: http://d.wanfangdata.com.cn/Periodical/dlkxjz201603005
[28] Fan D Q, Zhao X S, Zhu W Q , et al. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data[J]. Progress in Geography, 2016,35(3):304-319.
[1] SHI Feifei, GAO Xiaohong, XIAO Jianshe, LI Hongda, LI Runxiang, ZHANG Hao. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
[2] HU Yingying, DAI Shengpei, LUO Hongxia, LI Hailiang, LI Maofen, ZHENG Qian, YU Xuan, LI Ning. 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.
[3] LIU Yongmei, FAN Hongjian, GE Xinghua, LIU Jianhong, WANG Lei. Estimation accuracy of fractional vegetation cover based on normalized difference vegetation index and UAV hyperspectral images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 11-17.
[4] YE Wantong, CHEN Yihong, LU Yinhao, Wu Penghai. Spatio-temporal variation of land surface temperature and land cover responses in different seasons in Shengjin Lake wetland during 2000—2019 based on Google Earth Engine[J]. Remote Sensing for Land & Resources, 2021, 33(2): 228-236.
[5] CHEN Baolin, ZHANG Bincai, WU Jing, LI Chunbin, CHANG Xiuhong. Historical average method used in MODIS image pixel cloud compensation: Exemplified by Gansu Province[J]. Remote Sensing for Land & Resources, 2021, 33(2): 85-92.
[6] DU Fangzhou, SHI Yuli, SHENG Xia. Research on downscaling of TRMM precipitation products based on deep learning: Exemplified by northeast China[J]. Remote Sensing for Land & Resources, 2020, 32(4): 145-153.
[7] Biqing WANG, Wenquan HAN, Chi XU. Winter wheat planting area identification and extraction based on image segmentation and NDVI time series curve classification model[J]. Remote Sensing for Land & Resources, 2020, 32(2): 219-225.
[8] Guoce SONG, Zhi ZHANG. Remote sensing monitoring method for dust and wind accumulation in multi-metal mining area of Xin Barag Right Banner,Inner Mongolia[J]. Remote Sensing for Land & Resources, 2020, 32(2): 46-53.
[9] Linyan FENG, Bingxiang TAN, Xiaohui WANG, Xinyun CHEN, Weisheng ZENG, Zhao QI. Object-oriented rapid forest change detection based on distribution function[J]. Remote Sensing for Land & Resources, 2020, 32(2): 73-80.
[10] Jiaxin XU, Shibo FANG, Tingbin ZHANG, Yongchao ZHU, Dong WU, Guihua YI. NDVI changes and its correlation with climate factors of the Three River-Headwater region in growing seasons during 2000—2016[J]. Remote Sensing for Land & Resources, 2020, 32(1): 237-246.
[11] Dongya CHENG, Xudong LI. Comparison of change characteristics of NDVI in mountain basin before and after atmospheric correction[J]. Remote Sensing for Land & Resources, 2020, 32(1): 90-97.
[12] Xianhua YANG, Xiao XU, Lixiao XIAO, Li TIAN, Lina HAO, Peng XU. Evolution trend and driving force analysis of the Chaerhan Salt Lake[J]. Remote Sensing for Land & Resources, 2020, 32(1): 130-137.
[13] Wei WANG, Samat Alim, Abuduwaili Jilili. Geo-detector based spatio-temporal variation characteristics and driving factors analysis of NDVI in Central Asia[J]. Remote Sensing for Land & Resources, 2019, 31(4): 32-40.
[14] Chao MA, Panli CAI. Spatio-temporal changes and driving factors of environmental and ecological index in Culai-Lianhua area[J]. Remote Sensing for Land & Resources, 2019, 31(4): 199-208.
[15] Yunpeng YAN, Hui XU, Gang LIU, Jianyu LIU. Analysis of the variations of the lake ice phenology in the Pangong Lake area from 2013 to 2017: Remote sensing survey of the cryosphere in the high altitude and alpine region, West China(Ⅰ)[J]. Remote Sensing for Land & Resources, 2019, 31(3): 209-215.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech