不同盖度下伊犁绢蒿荒漠草地光谱特征及盖度反演精度研究
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摘要
基于覆盖度变化影响地面光谱特征以及导致反演精度不高的问题,本研究借助SVC HR-768便携式光谱仪,于4月28日对伊犁绢蒿荒漠草地群落特征、地面光谱进行测定,研究在18～30%、31～40%、41%～50%、51%～60%4个覆盖度下群落冠层光谱变化特征,进而采用光谱波段,NDVI、RVI、DVI和SAVI对其进行反演。结果表明:(1)植物反射光谱随群落覆盖度的增加而降低。(2)覆盖度和反射率成负相关,可见光波段的相关系数与覆盖度无明显规律,近红外的部分波段内,覆盖度越大,相关系数越大。(3)采用相关性较大的单一波段进行反演时,覆盖度越大,反演精度越好;当覆盖度<30%, DVI反演精度最高;而当覆盖度>30%,SAVI对覆盖度的反演精度最好。
The change of coverage affects the spectral characteristics of the ground and leads to the problem of low inversion accuracy. In this study, the SVC HR-768 portable spectrometer was used to measure the characteristics of the desert grassland of Seriphidium transiliense and its spectrum in April by the method of systematic sampling. The community characteristics and ground spectra of desert grassland in yili were determined on April 28, and the spectral changes of community canopy were studied under four different coverages of 18～30%, 31～40%, 41%～50%, 51%～60%. Then spectral bands, NDVI, RVI, DVI and SAVI were used for inversion. The results show:(1) The reflectance spectrum of plants decreased with the increase of community coverage.(2) Coverage and reflectance were negatively correlated, and there was no obvious regularity in the correlation coefficient between visible light band and coverage. Part of the near-infrared wave band was positively correlated with coverage.(3) When inversion was performed using a single band with a large correlation, the greater the coverage, the better the inversion accuracy. The DVI inversion accuracy was the highest when the coverage was less than 30%, while the SAVI had the best accuracy for coverage inversion when the coverage was greater than 30%.
The change of coverage affects the spectral characteristics of the ground and leads to the problem of low inversion accuracy. In this study, the SVC HR-768 portable spectrometer was used to measure the characteristics of the desert grassland of Seriphidium transiliense and its spectrum in April by the method of systematic sampling. The community characteristics and ground spectra of desert grassland in yili were determined on April 28, and the spectral changes of community canopy were studied under four different coverages of 18～30%, 31～40%, 41%～50%, 51%～60%. Then spectral bands, NDVI, RVI, DVI and SAVI were used for inversion. The results show:(1) The reflectance spectrum of plants decreased with the increase of community coverage.(2) Coverage and reflectance were negatively correlated, and there was no obvious regularity in the correlation coefficient between visible light band and coverage. Part of the near-infrared wave band was positively correlated with coverage.(3) When inversion was performed using a single band with a large correlation, the greater the coverage, the better the inversion accuracy. The DVI inversion accuracy was the highest when the coverage was less than 30%, while the SAVI had the best accuracy for coverage inversion when the coverage was greater than 30%.
引文
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[8] 李晓松.干旱地区稀疏植被覆盖度高光谱遥感定量反演研究[D].北京:中国林业科学研究院,2008.Li Xiaosong. Quantitive retrieval of sparse vegetation cover in arid regions using hyperspectral data[D]. Beijing: Chinese Academy of Forestry, 2008.
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[11] 魏秀红,靳瑰丽,范燕敏,等.基于高光谱遥感的退化伊犁绢蒿荒漠草地群落盖度估算[J].中国草地学报,2017,39,(6):33-39,46.Wei Xiuhong, Jin Guili ,Fan Yanmin, et al. Community coverage estimation of degraded Seriphidium transiliense desert grassland based on hyperspectral remote sensing[J]. Chinese Journal of Grassland,2017,39,(6):33-39,46.
[12] 王建光,吕小东,姚贵平,等.苜蓿和无芒雀麦混播草地高光谱遥感估产研究[J].中国草地学报,2013,35,(1):35-41.Wang Jianguang, Lv Xiaodong, Yao Guiping, et al. Estimation of fresh forage yield of mixed sowing pastures of alfalfa and smooth-brome with hyperspectral remote sensing[J].Chinese Journal of Grassland,2013,35(1):35-41.
[13] 何挺,王静,林宗坚,等.土壤有机质光谱特征研究[J].武汉大学学报(信息科学版),2006,12(11):975-979.Li Ting, Wang Jing, Lin Zongjian, et al. Spectral features of soil organic matter[J].Geomatics and Information Science of Wuhan University, 2006, 12(11):975-979.
[14] 柳维扬,彭杰,窦中江,等.基于冠层尺度的枣树色素含量的高光谱估算模型[J].光谱学与光谱分析,2017,37(1):156-161.Liu Weiyang, Peng Jie, Dou Zhongjiang, et al. Estimation models for jujube leaf pigment concentration with hyperspectrum data at canopy scale[J]. Spectroscopy and Spectral Analysis, 2017, 37(1):156-161.
[15] 杨凯,沈渭寿,刘波,等.那曲典型草地植被光谱特征分析[J].遥感技术与应用,2014,29(1):40-45.Yang Kai, Shen Weishou, Liu Bo, et al. Research on spectral reflectance characteristics for Naqu typical grassland [J].Remote Sensing Technology and Application, 2014, 29(1):40-45.
[16] 张春梅,张建明. 基于高光谱影像的干旱区草地光谱特征分析[J]. 光谱学与光谱分析,2012,32(2):445-448. Zhang Chunmen, Zhang Jianming. Research on the spectral characteristics of grassland in arid regions based on hyperspectral image[J]. Spectroscopy and Spectral Analysis, 2012,32(2):445-448.
[17] 靳瑰丽,魏秀红,范燕敏,等. 退化伊犁绢蒿荒漠草地群落特征与地面光谱反射率相关性分析[J].草地学报,2017,25(2):282-289.Jin Guili, Wei Xiuhong, Fan Yanmin, et al. Correlation between community characteristics and spectral reflectance of the ground on degraded Seriphdium transiliense desert grassland[J]. Acta Agrestia Sinica, 2017,25(2):282-289.
[18] Huete A R.A soil-adjusted vegetation index(SAVI)[J].Remote Sensing of Environment, 1988,25(3): 295-309.
[19] Gitelson A.A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation[J]. Journal of plant physiology, 2004,161(2):165-173.
[2] 章文波,符素华,刘宝元. 目估法测量植被覆盖度的精度分析.北京师范大学学报 (自然科学版), 2001, 37(3):402-408.Zhang Wenbo, Fu Suhua, Liu Baoyuan. Error assessment of visual estimation plant coverage[J]. Journal of Beijing Normal University (Natural Science), 2001, 37(3):402-408.
[3] Gitelson A.A, Kaufman Y J, Stark R, et al. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment, 2002, 80(1):76-87.
[4] 单贵莲,初晓辉,陈功,等. 多花黑麦草氮素营养及生长状况的高光谱监测[J]. 中国草地学报,2017,39(1):50-55.Shan Guilian,Chu Xiaohui,Chen Gong et al. Hyperspectral monitoring of nitrogen nutrition and growth status of Italian ryegrass[J]. Chinese Journal of Grassland, 2017,39(1):50-55.
[5] 杨璐璐,华开,张学霞. 不同CO2浓度及干旱胁迫下高羊茅的生理响应和光谱特征[J]. 中国草地学报,2014,36(4):73-78.Yang Lulu,Hua Kai,Zhang Xuexia. Physiological response and spectral characteristics of tall fescue under different CO2 concentrations and drought stress[J]. Chinese Journal of Grassland, 2014,36(4):73-78.
[6] 宋清洁,崔霞,张瑶瑶,等.基于小型无人机与MODIS数据的草地植被覆盖度研究——以甘南州为例[J].草业科学,2017,34(01):40-50.Song Qingjie, Cui Xia, Zhang Yaoyao, et al. Grassland fractional vegetation cover analysis using small UVAs and MODIS–a case study in Gannan prefecture[J].Pratacultural Science, 2017 ,34(01):40-50.
[7] 刘占宇,黄敬峰,吴新宏,等.天然草地植被覆盖度的高光谱遥感估算模型[J].应用生态学报,2006,(6):997-1002.Liu Zhanyu, Huang Jingfeng, Wu Xinhong, et al. Hyperspectral remote sensing estimation models on vegetation coverage of natural grassland[J].Chinese Journal of Applied Ecology, 2006, (6):997-1002.
[8] 李晓松.干旱地区稀疏植被覆盖度高光谱遥感定量反演研究[D].北京:中国林业科学研究院,2008.Li Xiaosong. Quantitive retrieval of sparse vegetation cover in arid regions using hyperspectral data[D]. Beijing: Chinese Academy of Forestry, 2008.
[9] Geneviéve R, Michael S, Fréderic B. Optimization of soil-adjusted vegetation [J]. Remote Sensing of Environment,1996,55:95-107.
[10] Myneni R B, Hall F G, Sellers P J, et al. The interpretation of spectral vegetation indexes [J]. IEEE Transactions on Geoscience and Remote Sensing,1995,33(2):481-486.
[11] 魏秀红,靳瑰丽,范燕敏,等.基于高光谱遥感的退化伊犁绢蒿荒漠草地群落盖度估算[J].中国草地学报,2017,39,(6):33-39,46.Wei Xiuhong, Jin Guili ,Fan Yanmin, et al. Community coverage estimation of degraded Seriphidium transiliense desert grassland based on hyperspectral remote sensing[J]. Chinese Journal of Grassland,2017,39,(6):33-39,46.
[12] 王建光,吕小东,姚贵平,等.苜蓿和无芒雀麦混播草地高光谱遥感估产研究[J].中国草地学报,2013,35,(1):35-41.Wang Jianguang, Lv Xiaodong, Yao Guiping, et al. Estimation of fresh forage yield of mixed sowing pastures of alfalfa and smooth-brome with hyperspectral remote sensing[J].Chinese Journal of Grassland,2013,35(1):35-41.
[13] 何挺,王静,林宗坚,等.土壤有机质光谱特征研究[J].武汉大学学报(信息科学版),2006,12(11):975-979.Li Ting, Wang Jing, Lin Zongjian, et al. Spectral features of soil organic matter[J].Geomatics and Information Science of Wuhan University, 2006, 12(11):975-979.
[14] 柳维扬,彭杰,窦中江,等.基于冠层尺度的枣树色素含量的高光谱估算模型[J].光谱学与光谱分析,2017,37(1):156-161.Liu Weiyang, Peng Jie, Dou Zhongjiang, et al. Estimation models for jujube leaf pigment concentration with hyperspectrum data at canopy scale[J]. Spectroscopy and Spectral Analysis, 2017, 37(1):156-161.
[15] 杨凯,沈渭寿,刘波,等.那曲典型草地植被光谱特征分析[J].遥感技术与应用,2014,29(1):40-45.Yang Kai, Shen Weishou, Liu Bo, et al. Research on spectral reflectance characteristics for Naqu typical grassland [J].Remote Sensing Technology and Application, 2014, 29(1):40-45.
[16] 张春梅,张建明. 基于高光谱影像的干旱区草地光谱特征分析[J]. 光谱学与光谱分析,2012,32(2):445-448. Zhang Chunmen, Zhang Jianming. Research on the spectral characteristics of grassland in arid regions based on hyperspectral image[J]. Spectroscopy and Spectral Analysis, 2012,32(2):445-448.
[17] 靳瑰丽,魏秀红,范燕敏,等. 退化伊犁绢蒿荒漠草地群落特征与地面光谱反射率相关性分析[J].草地学报,2017,25(2):282-289.Jin Guili, Wei Xiuhong, Fan Yanmin, et al. Correlation between community characteristics and spectral reflectance of the ground on degraded Seriphdium transiliense desert grassland[J]. Acta Agrestia Sinica, 2017,25(2):282-289.
[18] Huete A R.A soil-adjusted vegetation index(SAVI)[J].Remote Sensing of Environment, 1988,25(3): 295-309.
[19] Gitelson A.A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation[J]. Journal of plant physiology, 2004,161(2):165-173.
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