高光谱信息的农林植被种类区分
|
摘要
为了能够更加快速、准确对粮食主产区的作物与树木进行种类区分,以黄淮海地区三种主要植被(玉米、小麦和杨树)为研究对象,获取该三种植被原始反射率光谱,并对原始光谱进行特征点提取、一阶微分变换、二阶微分变换以及植被指数计算四种方法的分析处理,提取三种植被各自的光谱特征点、特征波段、蓝黄红边微分值和、位置、振幅以及面积四个特征指标以及植被指数的数值区间。基于特征值在不同植被种类间数值重叠范围越小区分精度越高的原理,比较分析植被光谱在不同处理方法下的植被区分精度,并且最终选取重叠范围最小的特征指标作为区分不同植被的识别指标。结果显示:相较于原始光谱特征点提取、二阶微分变换以及植被指数计算,一阶微分变换对于玉米、小麦和杨树的识别分类具有较高的精度,其中黄边振幅、黄边面积以及黄边微分值和具有较高的识别精度,黄边振幅的识别精度达到97. 5%,黄边面积以及黄边微分值识别精度达98. 1%,用另外167组数据对该结果进行验证,显示黄边振幅的识别精度达96. 4%,黄边面积以及黄边微分值和的识别精度达97. 6%。该结果与用平均光谱曲线区分单种植被不同生长状态选取的特征值结果不同,这种方法能有效的保留个体光谱反射曲线的差异,从结果可见通过一阶微分变换提取黄边参数的方法能有效的用于树木和粮食作物共同种植区域的植被区分,并且黄边面积以及黄边微分值和的识别精度最高。
In order to distinguish between the crops and trees in the main grain producing areas more quickly and accurately,maize,wheat and poplar which are the main vegetation planted in the Huang-Huai-Hai Plain are used as the research object. Obtain the original spectral reflectance and calculate the data by using the original spectral feature point extraction,first derivation,second derivation and vegetation index. Extract the range of feature points,characteristic bands obtained by analyzing the original spectral reflectance,position,amplitude,area and differential value sum in blue,red and yellow edges by first derivation,and vegetation index by empirical formulas. Compare the accuracy of four methods in distinguishing between the three vegetable types based on the principle that the smaller the overlapping range is,the higher the accuracy of parameter will be,and choose the most suitable characteristic index as the identify indicator which has the smallest overlap in different vegetation types. The results showed that among the four methods of manipulation spectral data,first derivation had the highest accuracy in identifying corn,wheat and poplar compared with the original spectral feature point extraction,second derivation and vegetation index. Among the indexes obtained by the first derivation,the amplitude,area and differential value sum in yellow edge region had the higher recognition accuracy. The recognition accuracy of the amplitude in yellow edge was up to 97. 5%,and the area and differential value sum in yellow edge was up to 98. 1%. The results were verified with 167 other sets of data,and the verification results showed that the recognition accuracy of the amplitude in yellow edge was up to 96. 4%,and the area and differential value sum in yellow edge was up to 97. 6%. The result was different from that result which was obtained by average reflectance curve of spectrum from the single plant in different growth state,and this method could effectively preserve the difference between individual spectral reflectance curves. Thus,it could be seen that extracting the yellow edge parameters through the first derivation was effectively used in distiguishing vegetation where the crops and trees were planted in the same place,and among all the parameters,area and differential value sum in yellow edge had the highest recognition accuracy.
In order to distinguish between the crops and trees in the main grain producing areas more quickly and accurately,maize,wheat and poplar which are the main vegetation planted in the Huang-Huai-Hai Plain are used as the research object. Obtain the original spectral reflectance and calculate the data by using the original spectral feature point extraction,first derivation,second derivation and vegetation index. Extract the range of feature points,characteristic bands obtained by analyzing the original spectral reflectance,position,amplitude,area and differential value sum in blue,red and yellow edges by first derivation,and vegetation index by empirical formulas. Compare the accuracy of four methods in distinguishing between the three vegetable types based on the principle that the smaller the overlapping range is,the higher the accuracy of parameter will be,and choose the most suitable characteristic index as the identify indicator which has the smallest overlap in different vegetation types. The results showed that among the four methods of manipulation spectral data,first derivation had the highest accuracy in identifying corn,wheat and poplar compared with the original spectral feature point extraction,second derivation and vegetation index. Among the indexes obtained by the first derivation,the amplitude,area and differential value sum in yellow edge region had the higher recognition accuracy. The recognition accuracy of the amplitude in yellow edge was up to 97. 5%,and the area and differential value sum in yellow edge was up to 98. 1%. The results were verified with 167 other sets of data,and the verification results showed that the recognition accuracy of the amplitude in yellow edge was up to 96. 4%,and the area and differential value sum in yellow edge was up to 97. 6%. The result was different from that result which was obtained by average reflectance curve of spectrum from the single plant in different growth state,and this method could effectively preserve the difference between individual spectral reflectance curves. Thus,it could be seen that extracting the yellow edge parameters through the first derivation was effectively used in distiguishing vegetation where the crops and trees were planted in the same place,and among all the parameters,area and differential value sum in yellow edge had the highest recognition accuracy.
引文
[1] SUI Peng,XU Cui,QI Fan,et al(隋鹏,许翠,齐帆,等). Chinese Agricultural Science Bulletin(中国农学通报),2007,10:66.
[2] SU Xiu,GENG Jie,MA Xiao-rui,et al(苏岫,耿杰,马晓瑞,等). Marine Environmental Science(海洋环境科学),2017,(1):114.
[3] CHEN Shu-peng,TONG Qing-xi,GUO Hua-dong(陈述澎,童庆喜,郭华东). Information Mechanism Study of Remote Sensing(遥感信息机理研究). Beijing:Sciences Press(北京:科学出版社),1998. 32.
[4] YANG Guo-peng,YU Xu-chu,FENG Wu-fa,et al(杨国鹏,余旭初,冯伍法,等). Bulletin of Surveying&Mapping(测绘通报),2008,(10):1.
[5] ZANG Zhuo,LIN Hui,YANG Min-hua(臧卓,林辉,杨敏华). Journal of Central South University of Forestry&Technology(中南林业科技大学学报),2013,33(1):35.
[6] Pontinus J,Hallett R,Martin M. Remote Sensing of Environment,2005,97(2):163.
[7] ZHANG Bo,NIU Ting,FANG Shi-feng,et al(张波,牛婷,房世峰,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016,36(4):1104.
[8] YAN Xiao-yong,WANG Zhen-xi,YUE Jun(闫晓勇,王振锡,岳俊). Tianjin Agricultural Sciences(天津农业科学),2014,20(9):28.
[9] ZHANG Ying,ZHANG Xiao-li,WANG Shu-han,et al(张莹,张晓丽,王书涵,等). Journal of Northwest A&F Univcrsity·Nat. Sci. Ed.(西北农林科技大学学报·自然科学版),2016,44(2):83.
[10] Calvi1o-Cancela M,Martín-Herrero J. Remote Sensing,2016,8(856):rs8100856.
[11] SHU Tian,YUE Yan-bin,LI Li-jie,et al(舒田,岳延滨,李莉婕,等). Jiangsu Journal of Agricultural Sciences(江苏农业学报),2016,32(6):1310.
[12] LIN Chuan,GONG Zhao-ning,ZHAO Wen-ji,et al(林川,宫兆宁,赵文吉,等). Acta Ecologica Sinica(生态学报),2013,33(4):1172.
[13] CHAI Ying,RUAN Ren-zong,CHAI Guo-wu(柴颖,阮仁宗,柴国武). Remote Sensing for Land&Resources(国土资源遥感),2016,28(3):86.
[14] WANG Dong,WU Jian(王岽,吴见). Geography and Geo-Information Science(地理与地理信息科学),2015,(2):29.
[15] WAN Hong,LI Xi-can,WAN Jian-hua,et al(万红,李希灿,万剑华,等). Journal of Arid Land Resources&Environment(干旱区资源与环境),2013,27(11):39.
[16] FENG Wei,ZHU Yan,YAO Xia,et al(冯伟,朱艳,姚霞,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2009,25(11):194.
[17] LIU Jia,WANG Li-min,TENG Fei,et al(刘佳,王利民,滕飞,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2016,(13):140.
[18] ZOU Hong-yu(邹红玉). Remote Sensing Information(遥感信息),2010,2010(4):112.
[19] Vogelmann J E,Rock B N,Moss D M. International Journal of Remote Sensing,1993,14:1563.
[20] Zarco-Tejada P J,Miller J R,Mohammed G H,et al. IEEE Transactions on Geoscience and Remote Sensing,2001,39(7):1491.
[21] Carter G A,Dell T R,Cibula W G. Canadian Journal of Forest Research,1996,26:402.
[22] Gitelson A A,Merzlyak M N. Internal Journal of Remote Sensing,1997,18:2691.
[23] Gitelson A A,Merzlyak M N. Journal of Plant Physiology,1994,143:286.
[24] Sims D A,Gamon J A. Remote Sensing of Environment,2002,81:331.
[25] Gupta R K,Vijayan D,Prasad T S. Advances in Space Research,2003,32:2217.
[26] XU Ping,LIU Jun-feng,ZHANG Jing-cheng,et al(徐平,刘俊峰,张竞成,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2016,32(23):137.
[27] LIU Xiu-ying,LIN Hui,XIONG Jian-li,et al(刘秀英,林辉,熊建利,等). Remote Sensing Information(遥感信息),2005,(4):41.
[2] SU Xiu,GENG Jie,MA Xiao-rui,et al(苏岫,耿杰,马晓瑞,等). Marine Environmental Science(海洋环境科学),2017,(1):114.
[3] CHEN Shu-peng,TONG Qing-xi,GUO Hua-dong(陈述澎,童庆喜,郭华东). Information Mechanism Study of Remote Sensing(遥感信息机理研究). Beijing:Sciences Press(北京:科学出版社),1998. 32.
[4] YANG Guo-peng,YU Xu-chu,FENG Wu-fa,et al(杨国鹏,余旭初,冯伍法,等). Bulletin of Surveying&Mapping(测绘通报),2008,(10):1.
[5] ZANG Zhuo,LIN Hui,YANG Min-hua(臧卓,林辉,杨敏华). Journal of Central South University of Forestry&Technology(中南林业科技大学学报),2013,33(1):35.
[6] Pontinus J,Hallett R,Martin M. Remote Sensing of Environment,2005,97(2):163.
[7] ZHANG Bo,NIU Ting,FANG Shi-feng,et al(张波,牛婷,房世峰,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016,36(4):1104.
[8] YAN Xiao-yong,WANG Zhen-xi,YUE Jun(闫晓勇,王振锡,岳俊). Tianjin Agricultural Sciences(天津农业科学),2014,20(9):28.
[9] ZHANG Ying,ZHANG Xiao-li,WANG Shu-han,et al(张莹,张晓丽,王书涵,等). Journal of Northwest A&F Univcrsity·Nat. Sci. Ed.(西北农林科技大学学报·自然科学版),2016,44(2):83.
[10] Calvi1o-Cancela M,Martín-Herrero J. Remote Sensing,2016,8(856):rs8100856.
[11] SHU Tian,YUE Yan-bin,LI Li-jie,et al(舒田,岳延滨,李莉婕,等). Jiangsu Journal of Agricultural Sciences(江苏农业学报),2016,32(6):1310.
[12] LIN Chuan,GONG Zhao-ning,ZHAO Wen-ji,et al(林川,宫兆宁,赵文吉,等). Acta Ecologica Sinica(生态学报),2013,33(4):1172.
[13] CHAI Ying,RUAN Ren-zong,CHAI Guo-wu(柴颖,阮仁宗,柴国武). Remote Sensing for Land&Resources(国土资源遥感),2016,28(3):86.
[14] WANG Dong,WU Jian(王岽,吴见). Geography and Geo-Information Science(地理与地理信息科学),2015,(2):29.
[15] WAN Hong,LI Xi-can,WAN Jian-hua,et al(万红,李希灿,万剑华,等). Journal of Arid Land Resources&Environment(干旱区资源与环境),2013,27(11):39.
[16] FENG Wei,ZHU Yan,YAO Xia,et al(冯伟,朱艳,姚霞,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2009,25(11):194.
[17] LIU Jia,WANG Li-min,TENG Fei,et al(刘佳,王利民,滕飞,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2016,(13):140.
[18] ZOU Hong-yu(邹红玉). Remote Sensing Information(遥感信息),2010,2010(4):112.
[19] Vogelmann J E,Rock B N,Moss D M. International Journal of Remote Sensing,1993,14:1563.
[20] Zarco-Tejada P J,Miller J R,Mohammed G H,et al. IEEE Transactions on Geoscience and Remote Sensing,2001,39(7):1491.
[21] Carter G A,Dell T R,Cibula W G. Canadian Journal of Forest Research,1996,26:402.
[22] Gitelson A A,Merzlyak M N. Internal Journal of Remote Sensing,1997,18:2691.
[23] Gitelson A A,Merzlyak M N. Journal of Plant Physiology,1994,143:286.
[24] Sims D A,Gamon J A. Remote Sensing of Environment,2002,81:331.
[25] Gupta R K,Vijayan D,Prasad T S. Advances in Space Research,2003,32:2217.
[26] XU Ping,LIU Jun-feng,ZHANG Jing-cheng,et al(徐平,刘俊峰,张竞成,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2016,32(23):137.
[27] LIU Xiu-ying,LIN Hui,XIONG Jian-li,et al(刘秀英,林辉,熊建利,等). Remote Sensing Information(遥感信息),2005,(4):41.