1999&#x02014;2013年中国东北植被物候信息遥感监测

doi:10.11849/zrzyxb.20160239

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自然资源学报 ›› 2017, Vol. 32 ›› Issue (2) : 321-328. DOI: 10.11849/zrzyxb.20160239

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1999—2013年中国东北植被物候信息遥感监测

李晓东^{1, 2}, 曾发梁³, 姜琦刚¹, 闫守刚^{2, *}

作者信息 +

Remote Sensing Monitoring of Vegetation Phonological Features in Songnen Plain of Northeast China during 1999-2013

LI Xiao-dong^{1, 2}, ZENG Fa-liang³, JIANG Qi-gang¹, YAN Shou-gang²

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摘要

随着3S技术的迅猛发展,用于地表遥感监测的卫星影像数据浩如烟海,而影像上地表植被长势等隐含信息的提取通常工作量大、耗费时间。论文提出了一种计算机自动提取地表植被物候信息的方法,主要用于对地表植被生长季及其年内长势进行快速提取。方法的基本原理是基于NDVI序列数据集,构建时间点对的时长跨度与该点对半方差的函数关系。选择东北松嫩平原地区作为重点试验区,计算了该区域1999—2013年地表植被生长季长度和长势特点,并选择东北地区物候观测数据进行验证分析。结果表明：1）东北地区农耕作物的生长季持续期在107~126 d左右。计算得到的结果与实测数据的最大误差在5 d值域范围内,沼泽植被在160 d以上（误差10 d左右）,草地为120~139 d;2）研究区地表植被的生长盛期峰值出现在第150天前后。结果较为真实、合理地反映了研究区域地表植被的物候信息。

Abstract

With the rapid development of 3S technology, there are vast volume of satellite image data can be used in monitoring the surface vegetation coverage, moreover, the extraction of the implied information in the images, mainly referred to the vegetation ecological features such as the annual semi-variance of surface vegetation growth and the duration length of the vegetation-growing season, are often heavy work and time-consuming. A new extraction method using the variation function is proposed to quickly and accurately extract vegetation ecological features and study the vegetation change in the growing season. The method uses normalized difference vegetation index (NDVI) dataset, and builds the relational function of the time span and the semi-variance of each point pair based on the NDVI dataset. The Songnen Plain is selected as the experimental zone, where the surface vegetation is analyzed to extract the duration and the semi-variance of the vegetation growing season. The extracted data were statistically analyzed and verified. Eventually, the calculated result was compared to the actual local phonology. The results are as follows: 1) The largest differences (the maximum of semi-variance function) of the surface vegetation growth status appeared at the time-interval of 150 d. 2) The duration of the rain-fed crops in the growing season is 107-126 d, while the deviation of the crops between the fitted values and the measured values of the duration in the growing season is less than 5 d. Similarly, for grassland the range is 120-139 d (deviation, 5-10 d), for marsh vegetation it is 160-170 d (deviation, 10 d). So the results resembled the ecological state of the surface vegetation.

导出引用

李晓东, 曾发梁, 姜琦刚, 闫守刚. 1999—2013年中国东北植被物候信息遥感监测[J]. 自然资源学报, 2017, 32(2): 321-328 https://doi.org/10.11849/zrzyxb.20160239

LI Xiao-dong, ZENG Fa-liang, JIANG Qi-gang, YAN Shou-gang. Remote Sensing Monitoring of Vegetation Phonological Features in Songnen Plain of Northeast China during 1999-2013[J]. JOURNAL OF NATURAL RESOURCES, 2017, 32(2): 321-328 https://doi.org/10.11849/zrzyxb.20160239

中图分类号： S127

参考文献

[1] 何月, 樊高峰, 张小伟, 等. 浙江省植被物候变化及其对气候变化的响应 [J]. 自然资源学报, 2013, 28(2): 220-233. [HE Y, FAN G F, ZHANG X W, et al. Vegetation phenological variation and its response to climate changes in Zhejiang Province. Journal of Natural Resources, 2013, 28(2): 220-233. ]
[2] 平跃鹏, 臧淑英. 基于MODIS时间序列及物候特征的农作物分类 [J]. 自然资源学报, 2016, 31(3): 503-513. [PING Y P, ZANG S Y. Crop identification based on MODIS NDVI time-series data and phenological characteristics. Journal of Natural Resources, 2016, 31(3): 503-513. ]
[3] 马丽, 徐新刚, 贾建华, 等. 利用多时相TM影像进行作物分类方法 [J]. 农业工程学报, 2008, 24(S2): 191-195. [MA L, XU X G, JIA J H, et al. Crop classification method using multi-temporal TM images. Transactions of the CSAE, 2008, 24(S2): 191-195. ]
[4] FISCHER A. A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters [J]. Remote Sensing of Environment, 1994, 48: 220-230. doi:10.1016/0034-4257(94)90143-0.
[5] YU F F, KEVIN P PRICE, JAMES ELLIS, et al. Response of seasonal vegetation development to climatic variations in eastern central Asia [J]. Remote Sensing of Environment, 2003, 87: 42-45. doi:10.1016/S0034-4257(03)00144-5.
[6] FISHER J I, MUSTARD J F. Cross-scalar satellite phenology from ground, Landsat and MODIS data [J]. Remote Sensing of Environment, 2007, 109(3): 261-273. doi:10.1111/j.1365-2486.2006.01311.x
[7] KROSS A, FERNANDES R, SEAQUIST J , et al. The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests [J]. Remote Sensing of Environment, 2011, 115(6): 1564-1575. doi:10.1016/j.rse.2011.02.015.
[8] DUCHEMIN B, HADRIA R, RODRIGUEZ J C, et al. Spatialisa-tion of a crop model using phenology derived from remote sensing data [J]. Geoscience and Remote Sensing Symposium, 2003, 4: 2200-2202. doi:10.1109/IGARSS.2003.1294388.
[9] GUYON D, GUILLOT M, VITASSE Y, et al. Monitoring elevation variations in leaf phenology of deciduous broadleaf forests from MODIS/VEGETATION time-series [J]. Remote Sensing of Environment, 2011, 115(2): 615-627. doi:10.1016/j.rse.2010.10.006.
[10] 吴炳方. 中国农情遥感速报系统 [J]. 遥感学报, 2004, 8(6): 481-497. [WU B F. China agriculture with remote sensing systems. Journal of Remote Sensing, 2004, 8(6): 481-497. ]
[11] 武永峰, 李茂松, 李京. 中国植被绿度期遥感监测方法研究 [J]. 遥感学报, 2008, 12(1): 92-103. [WU Y F, LI M S, LI J. Research on a detection method of Chinese terrestrial vegetation greenness periods based on remote sensing, Journal of Remote Sensing, 2008, 12(1): 92-103. ]
[12] 国志兴, 张晓宁, 王宗明, 等. 东北地区植被物候期遥感模拟与变化规律 [J]. 生态学杂志, 2010, 29(1): 165-172. [GUO Z X, ZHANG X N, WANG Z M, et al. Simulation and variation pattern of vegetation phenology in Northeast China based on remote sensing. Chinese Journal of Ecology, 2010, 29(1): 165-172. ]
[13] 周永章, 王正海, 侯卫生. 数学地球科学 [M]. 广州: 中山大学出版社, 2012: 8-59. [ZHOU Y Z, WANG Z H, HOUW S. Mathematical Geosciences. Guangzhou: Sun Yat-sen University Press, 2012: 8-59. ]
[14] 徐爱萍, 舒红. 空间数据分析与R语言实践 [M]. 北京: 清华大学出版社, 2013. [XU A P, SHU H. Applied Spatial Data Analysis with R. Beijing: Tsinghua University Press, 2013. ]
[15] 侯学会, 牛铮, 高帅. 近十年中国东北森林植被物候遥感监测 [J]. 光谱学与光谱分析, 2014, 34(2): 515-519. [ HOU X H, NIU Z, GAO S. Phenology of forest vegetation in northeast of China in ten years using remote sensing. Spectroscopy and Spectral Analysis, 2014, 34(2): 515-519. ]