| 4,646 | 72 | 37 |
| 下载次数 | 被引频次 | 阅读次数 |
在介绍了无人机平台及携带的多种传感器的基础上,总结了无人机遥感提取植被覆盖度的算法,并将算法分成了三大类:颜色空间法、植被指数法和机器学习分类法;针对无人机遥感提取植被覆盖度所存在的问题及发展趋势进行了讨论.结果表明:颜色空间法能有效消除无人机图像中亮度和饱和度等因素的干扰;植被指数法原理简单,且具有良好的精度,是应用最广泛的方法;机器学习分类法适用于不同传感器获取的图像且提取精度高.
Abstract：Based on the introduction of the UAV platforms and various sensors, this paper mainly summarizes the method used to extract fractional vegetation cover from UAV remote sensing data, which are divided into three categories: color space method, vegetation index method and machine learning classification.The issues and challenges of the retrieval of fractional vegetation cover from UAV are discussed.The results show that color space method can eliminate the influence of brightness, saturation and other factors in UAV images.Vegetation index method is most widely used at present which is simple in principle and has good accuracy.Machine learning classification method is suitable for images acquired by various sensors and has relatively high accuracy in vegetation extraction.
[1] 贾坤，姚云军，魏香琴，等.植被覆盖度遥感估算研究进展[J].地球科学进展，2013,28(7):774-782.
[2] 汪明霞，王卫东.植被覆盖度的提取方法研究综述[J].黄河水利职业技术学院学报，2013,25(2):23-26.
[3] CHEN J J,YI S H,QIN Y,et al.Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai-Tibetan Plateau[J].International Journal of Remote Sensing,2016,37(8):1922-1936.
[4] 王晓静，卢霞，陶坤旺.无人机遥感技术与应用[J].科学技术创新，2019(1):1-5.
[5] 李德仁，李明.无人机遥感系统的研究进展与应用前景[J].武汉大学学报(信息科学版),2014,39(5):505-513.
[6] RIIHIM?KI H,LUOTO M,HEISKANEN J.Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data [J].Remote Sensing of Environment,2019,224:119-132.
[7] 牛亚晓，张立元，韩文霆，等.基于无人机遥感与植被指数的冬小麦覆盖度提取方法[J].农业机械学报，2018,49(4):212-221.
[8] YAN G J,LI L Y,COY A,et al.Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing[J].ISPRS Journal of Photogrammetry and Remote Sensing,2019,158:23-34.
[9] CHOI S K,LEE S K,JUNG S H,et al.Estimation of fractional vegetation cover in sand dunes using multi-spectral images from fixed-wing UAV[J].Journal of the Korean Society of Surveying,Geodesy,Photogrammetry and Cartography,2016,34(4):431-441.
[10] 宋晨晖.民用无人机应用进展[J].机电工程技术，2018,47(11):149-152.
[11] YUE J B,YANG G J,TIAN Q J,et al.Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices[J].ISPRS Journal of Photogrammetry and Remote Sensing,2019,150:226-244.
[12] 裴信彪，吴和龙，马萍，等.基于无人机遥感的不同施氮水稻光谱与植被指数分析[J].中国光学，2018,11(5):832-840.
[13] 刘忠，万炜，黄晋宇，等.基于无人机遥感的农作物长势关键参数反演研究进展[J].农业工程学报，2018,34(24):60-71.
[14] AKAR O.The rotation forest algorithm and object-based classification method for land use mapping through UAV images[J].Geocarto International,2018,33(5):538-553.
[15] FRASER B T,CONGALTON R G.Evaluating the effectiveness of unmanned aerial systems (UAS) for collecting thematic map accuracy assessment reference data in New England forests[J].Forests,2019,10(1):24-38.
[16] 朱孟，周忠发，蒋翼，等.基于可见光波段无人机遥感的火龙果精准识别方法[J].激光与光电子学进展，2020,57(14):359-368.
[17] 陶惠林，徐良骥，冯海宽，等.基于无人机数码影像的冬小麦株高和生物量估算[J].农业工程学报，2019,35(19):107-116.
[18] 魏鹏飞，徐新刚，李中元，等.基于无人机多光谱影像的夏玉米叶片氮含量遥感估测[J].农业工程学报，2019,35(8):126-133.
[19] DENG L,MAO Z H,LI X J,et al.UAV-based multispectral remote sensing for precision agriculture:a comparison between different cameras[J].ISPRS Journal of Photogrammetry and Remote Sensing,2018,146:124-136.
[20] 陈鹏飞，李刚，石雅娇，等.一款无人机高光谱传感器的验证及其在玉米叶面积指数反演中的应用[J].中国农业科学，2018,51(8):1464-1474.
[21] 陈震，马春芽，孙浩，等.基于无人机影像的作物土壤水分亏缺反演方法[J].中国农业信息，2019,31(4):23-35.
[22] YIN D M,WANG L.Individual mangrove tree measurement using UAV-based LiDAR data:possibilities and challenges[J].Remote Sensing of Environment,2019,223:34-49.
[23] TEN HARKEL J,BARTHOLOMEUS H,KOOISTRA L.Biomass and crop height estimation of different crops using UAV-based Lidar[J].Remote Sensing,2020,12(1):17-21.
[24] 张学习，杨宜民.彩色图像工程中常用颜色空间及其转换[J].计算机工程与设计，2008,29(5):1210-1212.
[25] 董晓辉.基于多彩色空间的麦田监控图像分割技术研究[D].郑州：河南农业大学，2015.
[26] 牛亚晓，张立元，韩文霆.基于Lab颜色空间的棉花覆盖度提取方法研究[J].农业机械学报，2018,49(10):240-249.
[27] LI L,MU X,MACFARLANE C,et al.A half-gaussian fitting method for estimating fractional vegetation cover of corn crops using unmanned aerial vehicle images[J].Agricultural and Forest Meteorology,2018,262:379-390.
[28] 周在明，杨燕明，陈本清.基于无人机影像的滩涂入侵种互花米草植被信息提取与覆盖度研究[J].遥感技术与应用，2017,32(4):714-720.
[29] 王玮，冯琦胜，郭铌，等.基于长时间序列NDVI资料的我国西北干旱区植被覆盖动态监测[J].草业科学，2015,32(12):1969-1979.
[30] 郭铌.植被指数及其研究进展[J].干旱气象，2003,21(4):71-75.
[31] 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.
[32] 赵静，杨焕波，兰玉彬，等.基于无人机可见光图像的夏季玉米植被覆盖度提取方法[J].农业机械学报，2019,50(5):232-240.
[33] PUREVDORJ T,TATEISHI R,ISHIYAMA T,et al.Relationships between percent vegetation cover and vegetation indices[J].International Journal of Remote Sensing,1998,19(18):3519-3535.
[34] 冯露，岳德鹏，郭祥.植被指数的应用研究综述[J].林业调查规划，2009,34(2):48-52.
[35] JAY S,BARET F,DUTARTRE D,et al.Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops[J].Remote Sensing of Environment,2019,231:110898.
[36] 于全波.金寨县植被覆盖度时空变化特征及其驱动力因素[D].泰安：山东农业大学，2016.
[37] 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.
[38] 邹洪坤.湖北省植被覆盖度动态变化及其对气候变化的响应[D].武汉：武汉大学，2018.
[39] 周在明，杨燕明，陈本清.基于可见光波段无人机影像的入侵物种互花米草提取研究[J].亚热带资源与环境学报，2017,12(2):90-95.
[40] 王成波.基于无人机可视光谱的玉米植被覆盖度研究[D].昆明：昆明理工大学，2016.
[41] ASHAPURE A,JUNG J,CHANG A,et al.A comparative study of RGB and multispectral sensor-based cotton canopy cover modelling using multi-temporal UAS data[J].Remote Sensing,2019,11(23):2757.
[42] 谢兵，杨武年，王芳.无人机可见光光谱的植被覆盖度估算新方法[J].测绘科学，2020,45(9):72-77.
[43] MARCIAL-PABLO M D J,GONZALEZ-SANCHEZ A,JIMENEZ-JIMENEZ S I,et al.Estimation of vegetation fraction using RGB and multispectral images from UAV[J].International Journal of Remote Sensing,2019,40(2):420-438.
[44] RICHARDSON A J,WIEGAND C L.Distinguishing vegetation from soil background information[J].Photogrammetric Engineering and Remote Sensing,1977,43(12):1541-1552.
[45] BIRTH G S,MCVEY G R.Measuring the color of growing turf with a reflectance spectrophotometer 1[J].Agronomy Journal,1968,60(6):640-643.
[46] ROUSE JR J W,HAAS R H,SCHELL J A,et al.Monitoring vegetation systems in the Great Plains with ERTS[J].NASA Special Publication,1974,351:309-317.
[47] GITELSON A A,KAUFMAN Y J,MERZLYAK M N.Use of a green channel in remote sensing of global vegetation from Eos-modis[J].Remote Sensing of Environment,1996,58(3):289-298.
[48] HUETE A R.A soil-adjusted vegetation index (SAVI)[J].Remote Sensing of Environment,1988,25(3):295-309.
[49] GITELSON A,MERZLYAK M N.Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L.and Acer platanoides L.leaves.Spectral features and relation to chlorophyll estimation[J].Journal of Plant Physiology,1994,143(3):286-292.
[50] CRUDEN B A,PRABHU D,MARTINEZ R.Absolute radiation measurement in Venus and Mars entry conditions[J].Journal of Spacecraft and Rockets,2012,49(6):1069-1079.
[51] BARET F,GUYOT G.Potentials and limits of vegetation indices for LAI and APAR assessment[J].Remote Sensing of Environment,1991,35(2):161-173.
[52] VOGELMANN J E,ROCK B N,MOSS D M.Red edge spectral measurements from sugar maple leaves[J].International Journal of Remote Sensing,1993,14(8):1563-1575.
[53] 梁华为.直接从双峰直方图确定二值化阈值[J].模式识别与人工智能，2002,15(2):253-256.
[54] 卞雪.基于无人机低空遥感影像可见光波段的植被覆盖率研究[D].南京：南京农业大学，2017.
[55] 李冰，刘镕源，刘素红，等.基于低空无人机遥感的冬小麦覆盖度变化监测[J].农业工程学报，2012,28(13):160-165.
[56] 刘艳慧，蔡宗磊，包妮沙，等.基于无人机大样方草地植被覆盖度及生物量估算方法研究[J].生态环境学报，2018,27(11):2023-2032.
[57] LIMA-CUETO F J,BLANCO-SEPúLVEDA R,GóMEZ-MORENO L M,et al.Using vegetation indices and a UAV imaging platform to quantify the density of vegetation ground cover in olive groves (Olea Europaea L.) in Southern Spain[J].Remote Sensing,2019,11(21):2564.
[58] IIZUKA K,KATO T,SILSIGIA S,et al.Estimating and examining the sensitivity of different vegetation indices to fractions of vegetation cover at different scaling grids for early stage acacia plantation forests using a fixed-wing UAS[J].Remote Sensing,2019,11(15):1816.
[59] 谷晓天.基于机器学习的湟水流域土地利用/土地覆被分类研究[D].西宁：青海师范大学，2018.
[60] QUINLAN J R.Induction of decision trees[J].Machine Learning,1986,1(1):81-106.
[61] BREIMAN L.Random forests[J].Machine Learning,2001,45(1):5-32.
[62] 韩东，王浩舟，郑邦友，等.基于无人机和决策树算法的榆树疏林草原植被类型划分和覆盖度生长季动态估计[J].生态学报，2018,38(18):6655-6663.
[63] WANG H,HAN D,MU Y,et al.Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform[J].Agricultural and Forest Meteorology,2019,278:107665.
[64] ZHOU C Q,YE H B,XU Z F,et al.Estimating maize-leaf coverage in field conditions by applying a machine learning algorithm to UAV remote sensing images[J].Applied Sciences,2019,9(11):2389.
[65] 晏磊，廖小罕，周成虎，等.中国无人机遥感技术突破与产业发展综述[J].地球信息科学学报，2019,21(4):476-495.
[66] AD?O T,HRU?KA J,PáDUA L,et al.Hyperspectral imaging:a review on UAV-based sensors,data processing and applications for agriculture and forestry[J].Remote Sensing,2017,9(11):1110.
基本信息:
DOI：10.16163/j.cnki.22-1123/n.2021.04.023
中图分类号:Q948;TP751
引用信息:
[1]刘琳,郑兴明,姜涛,等.无人机遥感植被覆盖度提取方法研究综述[J].东北师大学报(自然科学版),2021,53(04):151-160.DOI:10.16163/j.cnki.22-1123/n.2021.04.023.
基金信息:
中央高校基本科研业务费专项资金资助项目(2412020FZ004); 吉林省科技发展计划项目(20180201012GX,20180623058)
2021-12-14
2021-12-14