植被的光谱吸收特征与植被生长状况密切相关,同时也受土壤湿度等外界因素影响。本文以长江口南汇湿地典型植被芦苇、互花米草和海三棱藨草为研究对象,在计算其光谱特征参数的基础上,分析了这3种植被的光谱吸收特征形变PSDI值。最后结合实测的土壤湿度,分析了3种植被的PSDI值和土壤湿度的相关性。研究结果发现:互花米草的PSDI值最大,海三棱藨草的PSDI值最小,芦苇介于两者之间,表明互花米草受外界的环境影响较大;互花米草的PSDI值与土壤湿度的相关性最大,芦苇的PSDI值与土壤湿度的相关性最小,海三棱藨草介于两者之间,表明互花米草适应湿地环境能力较强。
Abstract
The spectral absorption characteristics of vegetation are not only closely related to the growth of vegetation, but also affected by soil moisture and other factors. In this study , Phragmites australis, Spartina anglica and Scirpus mariqueter were taken as the research objects, which are the typical vegetation in Nanhui Wetland of Yangtze River Estuary. Based on the calculation of spectral characteristic parameters, their spectrum of absorption and deformation of PSDI values were analyzed. Finally, combining with the measured soil moisture, the correlation between PSDI values of three kinds of vegetation and soil moisture values were analyzed. The results show that the PSDI value of Spartina anglica is maximum, the PSDI value of Scirpus mariqueter is minimum, and that of Phragmites australis is somewhere in between. It indicates that Spartina anglica is most affected by environment. The correlation between PSDI of Spartina anglica and soil moisture is maximum, the correlation between PSDI of Phragmites australis and soil moisture is minimum, while that of Scirpus mariqueter is somewhere in between.This points that Spartina anglica has stronger adaptability to wetland environment.
关键词
光谱吸收特征 /
植被 /
湿地 /
土壤湿度
Key words
spectral absorption characteristics /
vegetation /
wetland /
soil moisture
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参考文献
[1] LI Lan-tao, REN Tao, MA Yi, et al. Evaluating chlorophyll density in winter oilseed rape (Brassica napus L.) using canopy hyperspectral red-edge parameters[J]. Computers & Electronics in Agriculture, 2016, 126:21-31.
[2] LIU N, BUDKEWITSCH P, TREITZ P. Examining spectral reflectance features related to Arctic percent vegetation cover: Implications for hyperspectral remote sensing of Arctic tundra[J]. Remote Sensing of Environment, 2017, 192(4):58-72.
[3] DEHAAN R L, TAYLOR G R. Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization[J]. Remote Sensing of Environment, 2002, 80(3):406-417.
[4] SUN Hong, LI Min-zan, LI Dao-liang. The vegetation classification in coal mine overburden dump using canopy spectral reflectance[J]. Computers & Electronics in Agriculture, 2011, 75:176-180.
[5] CHO M A, SKIDMORE A K. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method[J]. Remote Sensing of Environment, 2006, 101(2):181-193.
[6] LIU Wen-wen, MAUNG-DOUGLASS K, STRONG D R, et al. Geographical variation in vegetative growth and sexual reproduction of the invasive Spartina alterniflora in China[J]. Journal of Ecology, 2016, 104(1):173-181.
[7] KOKALY R F. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration[J]. Remote Sensing of Environment, 2001, 75(2):153-161.
[8] KOKALY R F, CLARK R N. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression[J]. Remote Sensing of Environment, 1999, 67(3):267-287.
[9] SANCHES I D, FILHO C R S, KOKALY R F. Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680nm absorption feature with continuum removal[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2014, 97(11):111-122.
[10] DJEBOU D C S, SINGH V P. Retrieving vegetation growth patterns from soil moisture, precipitation and temperature using maximum entropy[J]. Ecological Modelling, 2015, 309(8):10-21.
[11] ÖZKAN U, GÖKBULAK F. Effect of vegetation change from forest to herbaceous vegetation cover on soil moisture and temperature regimes and soil water chemistry[J]. Catena, 2017, 149(2):158-166.
[12] HAN Zhen,YUN Cai-xing. Satellite remote sensing application in the nearshore water area of the Yangtze River Estuary[M]. Beijing: China Ocean Press,2011:10,39.
韩震, 恽才兴. 长江口近岸水域卫星遥感应用技术研究[M]. 北京:海洋出版社, 2011: 10,39.
[13] ECK H V, MATHERS A C, MUSICK J T. Plant water stress at various growth stages and growth and yield of soybeans [J]. Field Crops Research, 1989, 17(1):1-16.
[14] KATERJI N, HOORN J W V, HAMDY A, et al. Comparison of corn yield response to plant water stress caused by salinity and by drought[J]. Agricultural Water Management, 2004, 65(2):95-101.
基金
国土资源部公益性行业科研专项项目资助(201211009)