基于纹理和光谱信息的奈曼旗防风遥感解译
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摘要
中药材种植面积是制定中药材生产、扶贫等政策和确定药材贸易数量的重要依据,精确掌握中药材种植的分布、面积和产量等信息是中药种植结构调整的基础。奈曼旗现为内蒙古通辽市最大的蒙中药材种植地,及时有效地获取奈曼旗的蒙中药材种植面积对后续产业发展具有重要意义。该研究选取奈曼旗大面积种植的药用植物防风为例,以融合的2 m分辨率的资源三号(ZY-3)遥感影像作为数据源,基于地面调查的数据,选取各典型地物的样本数据,得出不同地物的光谱特征曲线,获取防风光谱信息;采用基于概率统计的滤波纹理分析方法,选取5种不同纹理滤波下的纹理图像显示结果进行比较分析,最终确定基于信息熵的防风纹理特征。应用遥感影像的纹理和光谱信息提取解译奈曼旗防风的分布范围及种植面积。结果表明:防风主要分布在奈曼旗的东北地区和中南部地区,种植面积达5 336亩(1亩≈667 m~2)。野外实地验证数据与遥感解译结果吻合程度很高,差异性较小。说明采用光谱信息和纹理信息结合的方法可以实现防风的判别,解译结果可为县域制定中药材产业扶贫行动和农业产区经济发展规划提供参考。
The planting area of Chinese medicinal materials is an important basis for formulating policies such as production and poverty alleviation of Chinese medicinal materials and is determining the quantity of medicinal materials trade. Accurately mastering the information of the distribution,area and yield of Chinese medicinal materials cultivation is the basis of the adjustment of the planting structure of traditional Chinese medicine. It is now the largest planting place of Mongolian traditional Chinese medicinal materials in Naiman banner that is belonging to Tongliao city,Inner Mongolia. It is of great significance to obtain the planting area of Mongolian Chinese medicinal materials in Naiman banner in time and effectively for the development of subsequent industries. In this study,Saposhnikovia divaricata,a medicinal plant planted in Naiman banner,was selected as an example,and the fusion 2 m resolution ZY-3 remote sensing image was used as the data source. Based on the ground survey data,the sample data of each typical ground object were selected,and the spectral characteristic curves of different ground objects were obtained,and the S. divaricata spectral information was obtained. Using the filtering texture analysis method based on probability statistics,five kinds of texture image display results under different texture filtering were compared and analyzed,and finally the S. divaricata texture features based on information entropy are determined. The distribution range and planting area of S. divaricata in Naiman banner were extracted and interpreted by using the texture and spectral information of remote sensing images. The results showed that: S. divaricata was mainly distributed in the northeast and central south of Naiman banner,and the planting area was 5 336 mu( 1 mu≈667 m~2). The field verification data were in good agreement with the remote sensing interpretation results,and the difference was small. It shows that the combination of spectral information and texture information can realize the discrimination of S. divaricata,and the interpretation results can provide a reference for the county to formulate the poverty alleviation action of Chinese medicinal material industry and the economic development plan of agricultural producing areas.
The planting area of Chinese medicinal materials is an important basis for formulating policies such as production and poverty alleviation of Chinese medicinal materials and is determining the quantity of medicinal materials trade. Accurately mastering the information of the distribution,area and yield of Chinese medicinal materials cultivation is the basis of the adjustment of the planting structure of traditional Chinese medicine. It is now the largest planting place of Mongolian traditional Chinese medicinal materials in Naiman banner that is belonging to Tongliao city,Inner Mongolia. It is of great significance to obtain the planting area of Mongolian Chinese medicinal materials in Naiman banner in time and effectively for the development of subsequent industries. In this study,Saposhnikovia divaricata,a medicinal plant planted in Naiman banner,was selected as an example,and the fusion 2 m resolution ZY-3 remote sensing image was used as the data source. Based on the ground survey data,the sample data of each typical ground object were selected,and the spectral characteristic curves of different ground objects were obtained,and the S. divaricata spectral information was obtained. Using the filtering texture analysis method based on probability statistics,five kinds of texture image display results under different texture filtering were compared and analyzed,and finally the S. divaricata texture features based on information entropy are determined. The distribution range and planting area of S. divaricata in Naiman banner were extracted and interpreted by using the texture and spectral information of remote sensing images. The results showed that: S. divaricata was mainly distributed in the northeast and central south of Naiman banner,and the planting area was 5 336 mu( 1 mu≈667 m~2). The field verification data were in good agreement with the remote sensing interpretation results,and the difference was small. It shows that the combination of spectral information and texture information can realize the discrimination of S. divaricata,and the interpretation results can provide a reference for the county to formulate the poverty alleviation action of Chinese medicinal material industry and the economic development plan of agricultural producing areas.
引文
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[14]年波,杨士剑,王金亮.植被遥感信息提取的最佳波段选择———以云岭中部山区为例[J].云南地理环境研究,2004,16(2):18.
[15]宋海生,徐瑞松,马跃良,等.ETM+多光谱遥感影像彩色合成最佳波段选择研究———以顺德区土地遥感图制作为例[J].广东农业科学,2013,40(15):196.
[16]张永彬,刘佳丽,汲姣,等.露天灰岩矿纹理特征分析及面向对象的分类[J].地质与勘探,2018,54(2):348.
[17]卢远方,韩建征,杨书啟.遥感技术在农业领域的应用研究[J].技术与市场,2016,23(6):118.
[2]薛雪,王浩,贾天颖,等.不同生长方式及年限防风饮片质量的比较研究及对《中国药典》防风饮片标准的思考[J].中国中药杂志,2019,doi:10.19540/j.cnki.cjcmm.20190604. 201.
[3]金欣,卢恒,曾燕,等.基于3S技术的京津冀地区野生黄芩资源储量调查研究[J].中国中药杂志,2012,37(17):2524.
[4]史婷婷,张小波,郭兰萍,等.基于决策树模型的文山三七种植面积估算方法研究[J].中国中药杂志,2017,42(22):4358.
[5]周扬帆.基于BP神经网络方法提取马铃薯空间分布研究[D].北京:中国农业科学院,2018.
[6]张建国,李宪文,吴延磊.面向对象的冬小麦种植面积遥感估算研究[J].农业工程学报,2008,24(5):156.
[7]张超,金虹杉,刘哲,等.基于GF遥感数据纹理分析识别制种玉米[J].农业工程学报,2016,32(21):183.
[8]史婷婷,张小波,曲晓波,等.基于面向对象分类的人参种植分布信息提取研究———以抚松县为例[J].中国中药杂志,2017,42(22):4353.
[9]史婷婷,张小波,张珂,等.基于多源多时相遥感影像的宁国前胡种植面积提取研究[J].中国中药杂志,2017,42(22):4362.
[10]聂传朋,刘瑞姣,李焰焰,等.3S技术在中药材种植信息提取中的应用———以太和县桔梗种植为例[J].阜阳师范学院学报:自然科学版,2013,30(1):28.
[11]张优,王娟,张杰,等.基于遥感和GIS技术的四川省猪苓适宜性分布范围研究[J].中国中药杂志,2016,41(17):3148.
[12]卢有媛,郭盛,严辉,等.金莲花生态适宜性区划研究[J].中国中药杂志,2018,43(18):3658.
[13]李静,余意,郭兰萍,等.枸杞子品质区划研究[J].中国中药杂志,2019,44(6):1156.
[14]年波,杨士剑,王金亮.植被遥感信息提取的最佳波段选择———以云岭中部山区为例[J].云南地理环境研究,2004,16(2):18.
[15]宋海生,徐瑞松,马跃良,等.ETM+多光谱遥感影像彩色合成最佳波段选择研究———以顺德区土地遥感图制作为例[J].广东农业科学,2013,40(15):196.
[16]张永彬,刘佳丽,汲姣,等.露天灰岩矿纹理特征分析及面向对象的分类[J].地质与勘探,2018,54(2):348.
[17]卢远方,韩建征,杨书啟.遥感技术在农业领域的应用研究[J].技术与市场,2016,23(6):118.