基于无人机影像的荒漠地表类型信息提取

doi:10.13866/j.azr.2019.03.29

摘要/Abstract

摘要： 基于无人机可见光影像对古尔班通古特沙漠地表类型信息进行提取,运用面向对象的多尺度分割,在提取样本的光谱、形状、纹理、植被指数特征的基础上,建立规则提取地表类型信息。结果表明:① 荒漠地表类型不同,最佳分割尺度不同;② 不同荒漠化程度地表类型特征相似,无法运用单个特征进行区分,需选用多种特征组合提取地表类型;③ 面向对象的多尺度分割方法相对于基于像元的最大似然法分类有明显提高,面向对象轻度沙漠区总体分类精度为93.00%,中度沙漠化区为91.83%,重度沙漠化区为93.50%,较基于像元的最大似然方法分别提高了10.34%、11.86%和12.50%。表明针对无人机可见光影像,面向对象的多尺度分割方法能高精度地提取荒漠地表类型信息。

关键词: 无人机影像, 荒漠, 地表类型, 信息提取, 古尔班通古特沙漠

Abstract: Based on the images of unmanned aerial vehicle (UAV),in this study the surface type information of the Gurbantunggut Desert was extracted,and the object-oriented multi-scale segmentation was used to extract the information of the sample plots of surface types from the spectrum,shape,texture and vegetation index.The results showed that:① The best segmentation scale for the different desert surface types was different;② The features of surface types with different desertification levels were similar and could not be distinguished by single characteristics;③ Compared with the pixel-based maximum likelihood method,the object-oriented multi-scale segmentation method was improved significantly.The overall classification accuracies of the object-oriented slightly,moderately and seriously desertified areas were 93.00%,91.83% and 93.50% respectively,and they were 10.34%,11.86% and 12.50% higher than those of the pixel-based maximum likelihood method.The results revealed that the object-oriented multi-scale segmentation method could be used to extract the desert surface type information with high accuracy for the UAV visible image.

Key words: UAV image, desert, land surface type, information extraction, the Gurbantonggut desert

彭佳忆, 王新军, 朱磊, 赵成义, 徐晓龙. 基于无人机影像的荒漠地表类型信息提取[J]. 干旱区研究, 2019, 36(3): 771-780.

PENG Jia-yi, WANG Xin-jun, ZHU Lei, ZHAO Chen-yi, XU Xiao-long. Information Extraction of Desert Surface Types Based on UAV Image[J]. Arid Zone Research, 2019, 36(3): 771-780.

参考文献

[1] Li Yayun,Yang Xiouchun,Shu Xiaohua,et al.The application of remote sensing technology to land desertification monitoring[J].Progress in Geogerphy,2009,28(1):55-62.
[2] Rogan John,Chen Dongmei.Remote sensing technology for mapping and monitoring land-cover and land-use change[J].Progress in Planning,2004,61(4):301-325.
[3] 郭瑞霞,管晓丹,张艳婷.我国荒漠化主要研究进展[J].干旱气象,2015,33(3):505-514.
[Guo Ruixia,Guan Xiaodan,Zhang Yanting.Main advances in desertification research in China[J].Journal of Arid Meteorology,2015,33(3):505-514.]
[4] Kundn Arnab,Patel N R,Saha S K,et al.Monitoring the extent of desertification processes in Western Rajasthan (India) using geo-information science[J].Arabian Journal of Geosciences,2015,8(8):5727-5737.
[5] Duan Cheng,Wu Ling,He Lingyun,et al.Spatio-temporal distribution pattern of vegetation coverage in Junggar Basin,Xinjiang[J].Acta Ecologica Sinica,2016,36(2):72-76.
[6] Zhou Lei,Liu Aifeng.Investigating natural drivers of vegetation coverage variation using MODIS imagery in Qinghai,China[J].Journal of Arid Land,2016,8(1):109-124.
[7] Li Na,Zhou Deming,Zhao Kuiyi.Marsh classification mapping at a community scale using high-resolution imagery[J].Acta Ecologica Sinica,2011,31(22):6717-6726.
[8] 袁泽,丁建丽,牛增懿,等.基于GF-1遥感影像的艾比湖区域田间尺度土壤盐渍化监测方法[J].中国沙漠,2016,36(4):1070-1078.
[Yuan Ze,Ding Jianli,Niu Zengyi,et al.Soil salinization monitoring in the ebinur lake region at a field scale based on GF-1 image[J].Journal of Desert Research,2016,36(4):1070-1078.]
[9] Ke Yinghai,Quackenbush Lindi J,Im Jungho.Synergistic use of Quick Bird multispectral imagery and LIDAR data for object-based forest species classification[J].Remote Sensing of Environment,2010,114(6):1141-1154.
[10] Myint Soe W,Gober Patricia,Brazel Anthon J,et al.Per-pixel vs.object-based classification of urban land cover extraction using high spatial resolution imagery[J].Remote Sensing of Environment,2011,115(5):1145-1161.
[11] 康文平,刘树林.沙漠化遥感监测与定量评价研究综述[J].中国沙漠,2014,34(5):1222-1229.
[Kang Wenping,Liu Shulin.A review of remote sensing monitoring and quantitative assessment of aeolian desertification[J].Journal of Desert Research,2014,34(5):1222-1229.]
[12] Samecka Aleksandra,Kempers Alexander.Biomonitoring of water pollution with elodea canadensis.a case study of three small polish rivers with different levels of pollution[J].Water Air and Soil Pollution,2003,145(1):139-153.
[13] Lu Bing,He Yuhong.Species classification using unmanned aerial vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland[J].Isprs Journal of Photogrammetry and Remote Sensing,2017,128(3):73-85.
[14] Bellvert Joaquim,Zarco-Tejada Pablo J,Girona Joan,et al.Mapping crop water stress index in a ‘pinot-noir’ vineyard:Comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle[J].Precision Agriculture,2014,15(4):361-376.
[15] He Linshao,Xu Jinghua,Zhang Shaoyi.Land use classification of object-oriented multi-scale by UAV image[J].Remote Sensing for Land Resources,2013,25(2):107-112.
[16] 井然,邓磊,赵文吉,等.基于可见光植被指数的面向对象湿地水生植被提取方法[J].应用生态学报,2016,27(5):1427-1436.
[Jing Ran,Deng Lei,Zhao Wenji,et al.Object-oriented aquatic vegetation extracting approach based on visible vegetation indices[J].Chinese Journal of Applied Ecology,2016,27(5):1427-1436.]
[17] 韩文霆,张立元,张海鑫,等.基于无人机遥感与面向对象法的田间渠系分布信息提取[J].农业机械学报,2017,48(3):205-214.
[Han Wenting,Zhang Liyuan,Zhang Haixin,et al.Extraction method of sublateral canal dietribution information based on UAV remote sensing[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):205-214.]
[18] 陶冶,吴甘霖,刘耀斌,等.古尔班通古特沙漠典型灌木群落土壤化学计量特征及其影响因素[J].中国沙漠,2017,37(2):305-314.
[Tao Ye,Wu Ganlin,Liu Yaobin,et al.Soil stoichiometry and their influencing factors in typical shrub communities in the Gurbantunggut desert,China[J].Journal of Desert Research,2017,37(2):305-314.]
[19] 石亚飞,张志山,黄磊,等.古尔班通古特沙漠半固定沙丘植物群落物种组成和种群结构[J].应用生态学报,2016,27(4):1024-1030.
[Shi Yafei,Zhang Zhishan,Huang Lei,et al.Species composition and population structure of plant communities on semifixed dunes of the Gurbantongut desert,China[J].Chinese Journal of Applied Ecology,2016,27(4):1024-1030.]
[20] 吴楠,张元明,潘惠霞.古尔班通古特沙漠地衣结皮对放牧踩踏干扰的小尺度响应[J].干旱区研究,2012,29(6):1032-1038.
[Wu Nan,Zhang Yuanming,Pan Huixia.Response of fungi-algae symbiotic lichen crusts to grazed livestock disturbance in the Gurbantunggut desert[J].Arid Zone Research,2012,29(6):1032-1038.]
[21] 王红玲,张元明.古尔班通古特沙漠生物结皮中藓类植物形态解剖特征[J].干旱区研究,2008,25(3):363-369.
[Wang Hongling,Zhang Yuanming.Study on the morphological and anatomical structures of desert mosses in biotic crust in the Gurbantonggut desert[J].Arid Zone Research,2008,25(3):363-369.]
[22] 王新军,赵成义,杨瑞红,等.古尔班通古特沙漠南缘荒漠化过程演变的景观格局特征分析[J].干旱区地理,2015,38(6):1213-1225.
[Wang Xinjun,Zhao Chenyi,Yang Ruihong,et al.Landscape pattern characteristics of desertification evolution in southern Gurbantunggut desert[J].Arid Land Geography,2015,38(6):1213-1225.]
[23] 买买提·沙吾提,塔西甫拉提·特依拜,丁建丽,等.面向对象的干旱区盐渍地信息提取方法研究[J].中国沙漠,2013,33(5):1586-1592.
[Mamat Sawut,Tashpolat Tiyip,Ding Jianli,et al.Object-oriented information extraction method for soil salinization in arid area[J].Journal of Desert Research,2013,33(5):1586-1592.]
[24] 黄慧萍.面向对象影像分析中的尺度问题研究[D].北京:中国科学院研究生院,2003.
[Huang Huiping.Scale Issues in Object-oriented Image Analysis[D].Beijing:Graduate University of Chinese Academy of Sciences,2003.]
[25] 鲁恒,付萧,李龙国,等.最优分割尺度支持下高分遥感影像水土资源信息分类[J].农业机械学报,2016,47(9):327-333.
[Lu Heng,Fu Xiao,Li Longguo,et al.Soil and water resources information classification in high resolution images with optimal segmentation scale[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(9):327-333.]
[26] 李晓靖,彭道黎,王海宾.基于最优尺度和规则的高分辨率影像分类研究[J].测绘工程,2017,26(9):14-22.
[Li Xiaojing,Peng Daoli,Wang Haibin.Classification of high-resolution image based on optimal scale and rule[J].Engineering of Surveying and Mapping,2017,26(9):14-22.]
[27] 李朝奎,方文,董小姣.面向对象和规则的高分辨率影像分类研究[J].测绘通报,2015(9):9-13,35.
[Li Chaokui,Fang Wen,Dong Xiaojiao.Research on the classification of high resolution image based on object-oriented and class rule[J].Bulletin of Surveying and Mapping,2015(9):9-13,35.]
[28] 庄伟伟,张元明.生物结皮对荒漠草本植物群落结构的影响[J].干旱区研究,2017,34(6):1-9.
[Zhang Weiwei,Zhang Yuanming.Effect of soil microbiotic crust on plant community in the Gurbantunggut desert[J].Arid Zone Research,2017,34(6):1-9.]
[29] 郑毅,武法东,刘艳芳.一种面向对象分类的特征分析方法[J].地理与地理信息科学,2010,26(2):19-22,114.
[Zhen Yi,Wu Fadong,Liu Yanfang.A feature analysis approach for object-oriented classification[J].Geography and Geo-Information Science,2010,26(2):19-22,114.]
[30] 汪小钦,王苗苗,王绍强,等.基于可见光波段无人机遥感的植被信息提取[J].农业工程学报,2015,31(5):152-159.
[Wang Xiaoqin,Wang Miaomiao,Wang Shaoqiang,et al.Extraction of vegetation information from visible unmanned aerialvehicle images[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(5):152-159.]
[31] Peng Haitao,Ke Changqing.Study on object-oriented remote sensing image classification based on multi-levels segmentation[J].Remote Sensing Technology and Application,2010,25(1):149-154.
[32] 张华,张改改,吴睿.基于GF-1卫星数据的面向对象的民勤绿洲植被分类研究[J].干旱区地理,2017,40(4):831-838.
[Zhang Hua,Zhang Gaigai,Wu Rui.Object-based vegetable classification based on GF-1 imagery in Minqin oasis[J].Arid Land Geography,2017,40(4):831-838.]