基于无人机遥感的高寒草原沙化模型及等级划分
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摘要
高寒草原是青藏高原草地生态系统的主要组成,在防风固沙、野生动物保育等方面具有重要作用。近年来,在全球气候变化和人为干扰加剧的背景下,高寒草原沙化加剧,基于时空尺度监测范围及程度是防治高寒草地沙化的前提。以青海三江源区玛多县的高寒草原为研究区,结合大疆"精灵3"和"经纬M100"旋翼无人机和地面调查,探讨基于无人机遥测的植被指数在草地沙化调查方面的适宜性,以此为基础制定了高寒草原沙化模型及等级划分标准。结果显示:(1)通过对VDVI(Visible-Band Difference Vegetation Index)、ENDVI(Enhanced Normalized Difference Vegetation Index)和NG RDI(Normalized Green-Red Difference Index)指数与草地沙化指数G DI(Grassland Desertification Index)的相关分析,选取出高寒草地沙化研究最优植被指数为VDVI(R=0.9055);(2) G DI与VDVI的关系模型为VDVI=0.3024GDI2-0.0335G DI+0. 0119(R2=0. 9326)。模型相对误差为1. 779%(RMSE=0. 165,R2=0.7447),拟合精度较高;(3)基于无人机遥感植被指数的聚类分析,将研究区高寒草原沙化划分为5个等级,即无明显沙化(VDVI>0.2247)、轻度沙化(0.1493 The alpine steppe is a major type of the grassland ecosystem in the Qinghai-Tibet Plateau and plays an important role in soil erosion control and wild animal conservation. In recent years,the desertification of alpine steppe is expanding because of the global climate change and human disturbance. Therefore,it is very important to monitor the area and extent of grassland desertification at a spatial-temporal scale for control. The study used two models of unmanned aerial vehicle( DJ Phantom 3 and Matrice100) and ground survey technology to investigate the desertification status of alpine steppe of Maduo County in Sanjiangyuan National Park,which located in Qinghai Province. The purpose of this study is to select the proper vegetation indices of UAV that suit to build desertification model and classification criteria for alpine steppe desertification. The results showed as following:( 1) Based on the respective correlation between Visible-Band Difference Vegetation Index( VDVI),Enhanced Normalized Difference Vegetation Index( ENDVI),Normalized Green-Red Difference Index( NG RDI) and Grassland Desertification Index( GDI),the optimal vegetation index of UAV is VDVI( R = 0.9055).( 2) Built the grassland desertification model,VDVI = 0.3024 GDI2-0.0335 GDI+0.0119( R2 = 0.9326),the relative error is 1.779%( RMSE = 0.165,R2 = 0.7447),which means the higher fitting precision.( 3) The desertification of the alpine steppe in the study area is divided into five grades,involving no obvious desertification( VDVI>0.2247),mild desertification( 0.1493
引文
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[37]冯双双.基于Landsat影像的草地退化动态监测[D].石家庄:河北师范大学,2016.
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[39]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.
[40]沙莎,郭铌,李耀辉,等.温度植被干旱指数(TVDI)在陇东土壤水分监测中的适用性[J].中国沙漠,2017,37(1):132-139.
[41] Meyer G E,Camargo N J.Verification of color vegetation indices for automated crop image application[J]. Computers and Electronics in Agriculture,2008,63:282-293.
[42] Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment,1979,8:127-150.
[43]邓继忠,任高生,兰玉彬,等.基于可见光波段的无人机超低空遥感图像处理[J].华南农业大学学报,2016,37(6):16-22.
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[46] Bulanon D M. A smart vision system for monitoring specialty crops[C]. Future Technologies Conference,2017:29-30.
[47] Strong C J,Burnside N G,Llewellyn D.The potential of smallUnmanned Aircraft Systems for the rapid detection of threatened unimproved grassland communities using an Enhanced Normalized Difference Vegetation Index[J]. Plos One,2017,12(10):e0186193.
[48]丁雷龙,李强子,杜鑫.基于无人机图像颜色指数的植被识别[J].国土资源遥感,2016,28(1):78-86.
[49] Collado A D,Chuvieco E,Camarasa A.Satellite remote sensing analysis to monitor desertification processes in the crop-rangeland boundary of Argentina[J]. Journal of Arid Environments,2002,52(1):121-133.
[50]高珍,邓甲昊,孙骥,等.微型无人机图像无线传输系统方案与关键技术[J].北京理工大学学报,2008(12):1078-1082.
[2]谢高地,鲁春霞,肖玉,等.青藏高原高寒草地生态系统服务价值评估[J].山地学报,2003,21(1):50-55.
[3]王一博,王根绪,沈永平,等.青藏高原高寒区草地生态环境系统退化研究[J].冰川冻土,2005,27(5):633-640.
[4]刘兴元,龙瑞军,尚占环.青藏高原高寒草地生态系统服务功能的互作机制[J].生态学报,2012,32(24):7688-7697.
[5]刘国华,傅伯杰,陈利顶,等.中国生态退化的主要类型特征及分布[J].生态学报,2001,21(1):13-19.
[6]周青平,杨阳.青海草地资源可持续发展道路的探索[J].青海畜牧兽医杂志,1992(2):31-34.
[7] Jorgensen S E.A Systems Approach to the Environmental Analysis of Pollution M inimization[M]. New York,USA:Lew is Pubilshers,1999:20-53.
[8]汪晓菲,何平,康文星.若尔盖县高原草地沙化成因分析[J].中南林业科技大学学报,2015,35(3):100-106.
[9]高雅,林慧龙.草业经济在国民经济中的地位、现状及其发展建议[J].草业学报,2015,24(1):141-157.
[10]张登山.青海高原沙化土地综合治理研究进展[C]//中国治沙暨沙业学会2018学术论文集.2018.
[11]陈慧,杜耘,肖飞,等.汉江中游河谷平原植被指数时空变化及其与沙化土地动态的关联关系[J].长江流域资源与环境,2013,22(9):1221-1226.
[12]王晓慧.沙化土地遥感监测机理和方法研究[D].北京:中国林业科学研究院,2007.
[13]李金亚.科尔沁沙地草原沙化时空变化特征遥感监测及驱动力分析[D].北京:中国农业科学院,2014.
[14]徐瑶.藏北草地退化遥感监测与生态安全评价[D].成都:成都理工大学,2014.
[15]刘爱霞,王长耀,王静,等.基MODIS和NOAA/AVHRR的荒漠化遥感监测方法[J].农业工程学报,2007,23(10):145-150.
[16]韩兰英,万信,方峰,等.甘肃河西地区沙漠化遥感监测评估[J].干旱区地理,2013,26(1):131-138.
[17]李清河,孙保平,孙立达.荒漠化动态监测与评价研究进展[J].北京林业大学学报,1998,20(3):67-73.
[18]郭玉川,何英,李霞.基于MODIS的干旱区植被覆盖度反演及植被指数优选[J].国土资源遥感,2011,89(2):115-118.
[19]赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003.
[20]张雷,杨波,程晓凌.干旱区发展的资源环境基础评价:以新疆为例[J].干旱区地理,2011,34(5):713-718.
[21] Hunt E R,Cavigelli M,Daughtry C S T,et al.Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status[J]. Precision Agriculture,2005,6(4):359-378.
[22] Sugiura R,Noguchi N,Ishii K.Remote-sensing technology for vegetation monitoring using an unmanned helicopter[J].Biosystems Engineering,2005,90(4):369-379.
[23] Sugiura R,Fukagawa T,Noguchi N,et al.Field information system using an agricultural helicopter tow ards precision farming[C]//IEEE ASME International Conference on Advanced Intelligent M echatronics,Kobe,Japan.2003.
[24] Yue J,Yang G,Li C,et al.Estimation of winter wheat aboveground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models[J]. Remote Sensing,2017,9(7):708.
[25] Du M,Noguchi N.Multi-temporal monitoring of wheat growth through correlation analysis of satellite images,unmanned aerial vehicle images w ith ground variable[J]. IFAC Papers Online,2016,49(16):5-9.
[26] Hodgson J C,Koh L P.Best practice for minimising unmanned aerial vehicle disturbance to w ildlife in biological field research[J].Current Biology,2016,26(10):404-405.
[27]刘佳,杨玲波.基于无人机影像的农情遥感监测应用[J].农业工程学报,2013,29(18):136-145.
[28]马泽忠,王福海,刘智华,等.低空无人飞行器遥感技术在重庆城口滑坡堰塞湖灾害监测中的应用研究[J].水土保持学报,2011,25(1):253-256.
[29]张养安,宋晓强,段怡红.水土保持规划中低空遥感数据的获取及应用[J].水土保持通报,2017,37(5):338-341.
[30]喻权刚,马安利,赵帮元.“3S”技术在黄土高原水土保持动态监测中的研究与实践[J].水土保持研究,2004,11(2):33-35.
[31]玛多县地方志编纂委员会.玛多县志1996—2010[M].西宁:青海民族出版社,2011:47-49,475-481.
[32]陈良,Michalk D,凌红波,等.浅谈监测天然草场产量和植被群落组成的新方法[J].内蒙古草业,2003(1):46-47.
[33]赵丽娅,钟韩珊,赵美玉,等.围封和放牧对科尔沁沙地群落物种多样性与地上生物量的影响[J].生态环境学报,2018,27(10):1783-1790.
[34]王立亚.黄河源区玛多县草地退化特征分析及治理模式初探[D].兰州:甘肃农业大学,2004.
[35]胡东萍.基于地理空间数据的地震诱发滑坡易感性统计模型研究[D].重庆:重庆大学,2014.
[36]吴希.三种权重赋权法的比较分析[J].中国集体经济,2016,34:73-74.
[37]冯双双.基于Landsat影像的草地退化动态监测[D].石家庄:河北师范大学,2016.
[38] Louhaichi M,Borman M M,Johnson D E.Spatially located platform and aerial photography for documentation of grazing impacts on w heat[J].Geocarto International,2001(16):65-70.
[39]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.
[40]沙莎,郭铌,李耀辉,等.温度植被干旱指数(TVDI)在陇东土壤水分监测中的适用性[J].中国沙漠,2017,37(1):132-139.
[41] Meyer G E,Camargo N J.Verification of color vegetation indices for automated crop image application[J]. Computers and Electronics in Agriculture,2008,63:282-293.
[42] Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment,1979,8:127-150.
[43]邓继忠,任高生,兰玉彬,等.基于可见光波段的无人机超低空遥感图像处理[J].华南农业大学学报,2016,37(6):16-22.
[44]龙满生,何东健.玉米苗期杂草的计算机识别技术研究[J].农业工程学报,2007,23(7):139-143.
[45] LDPLLC.“Enhanced Normalized Difference Vegetation Index(ENDVI)”[ED/OL].http://www.maxmax.com/maincamerapage/remote-sensing/enhanced-normalized-difference-vegetation-index.2017-03-21.
[46] Bulanon D M. A smart vision system for monitoring specialty crops[C]. Future Technologies Conference,2017:29-30.
[47] Strong C J,Burnside N G,Llewellyn D.The potential of smallUnmanned Aircraft Systems for the rapid detection of threatened unimproved grassland communities using an Enhanced Normalized Difference Vegetation Index[J]. Plos One,2017,12(10):e0186193.
[48]丁雷龙,李强子,杜鑫.基于无人机图像颜色指数的植被识别[J].国土资源遥感,2016,28(1):78-86.
[49] Collado A D,Chuvieco E,Camarasa A.Satellite remote sensing analysis to monitor desertification processes in the crop-rangeland boundary of Argentina[J]. Journal of Arid Environments,2002,52(1):121-133.
[50]高珍,邓甲昊,孙骥,等.微型无人机图像无线传输系统方案与关键技术[J].北京理工大学学报,2008(12):1078-1082.