引用本文: 伏帅，冯琦胜，党菁阳，雷可欣，乔万鑫，梁天刚，潘冬荣，孙斌，姜佳昌. 基于无人机图像的草地植被盖度估算方法比较. 草业科学, 2022, 39(3): 455-464 doi: shu

Citation: FU S, FENG Q S, DANG J Y, LEI K X, QIAO W X, LIANG T G, PAN D R, SUN B, JIANG J C. Comparison of grassland vegetation coverage extraction algorithms from UAV technology. Pratacultural Science, 2022, 39(3): 455-464 doi: shu

基于无人机图像的草地植被盖度估算方法比较

1.
草地农业生态系统国家重点实验室 / 农业农村部草牧业创新重点实验室 / 草地农业教育部工程研究中心 / 兰州大学草地农业科技学院, 甘肃兰州 730020
2.
甘肃省草原技术推广总站, 甘肃兰州 730000

作者简介: 伏帅(1999-)，男，陕西渭南人，在读硕士生，研究方向为草地遥感。E-mail: fush21@onlabwinterschool.com

通讯作者: 冯琦胜(1983-)，男，甘肃镇原人，高级实验师，博士，研究方向为草地遥感。E-mail: fengqsh@onlabwinterschool.com

基金项目: 国家自然科学基金项目(41801191、41805086)；甘肃省农业财政项目(2016276)；现代农业产业技术体系建设专项资金(CARS-34)；中国工程院重点咨询项目(2021-HZ-5、2020-XZ-29)；长江学者和创新团队发展计划(IRT17R50)；兰州大学中央高校基本科研业务费专项资金(lzujbky2021-kb13)

摘要: 植被盖度是反映植被基本情况的客观指标和重要参数。本研究在对比8种常用的可见光植被指数计算草地盖度精度的基础上，发现这些植被指数对荒漠草地的植被盖度估测效果较差，因此提出一种适用于荒漠草地植被盖度估测的荒漠植被指数(DVI)，并评价了不同植被指数对不同草地类型的植被盖度估测效果，分析了不同草地类型阈值取值的变化情况。结果表明：1)所选植被指数对草甸草地和典型草地的盖度估测效果均较好，精度较高(准确率 > 90%，F₁得分 > 0.9)。草甸草地中超绿指数(ExG)的盖度估测效果最好(准确率 > 93%，F₁得分 > 0.95)，典型草地中各植被指数无明显差异，但对荒漠草地植被盖度估测效果较差，精度较低(F₁得分 ≤ 0.6)。2) DVI对荒漠草地植被盖度估测精度较高(准确度 > 93%，F₁得分达到0.71)，能够有效弥补上述植被指数的缺陷。3)绿叶指数(GLI)和植被颜色指数(CIVE)的阈值对草地类型敏感性最弱；ExG、超绿超红差分指数(ExGR)、植被因子指数(VEG)、Woebbecke指数(WI)等植被指数的阈值对草甸草地和典型草地的敏感性较弱，但对荒漠草地的敏感性较强；组合指数(COM)和Lab指数(Lab)对草地类型的敏感性最强。

关键词:

English

Comparison of grassland vegetation coverage extraction algorithms from UAV technology

1.
State Key Laboratory of Grassland Agro-ecosystems / Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs / Engineering Research Center of Grassland Industry, Ministry of Education / College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, Gansu, China
2.
Gansu Grassland Technical Extension Station, Lanzhou 730000, Gansu, China

Corresponding author: FENG Qisheng　E-mail: fengqsh@onlabwinterschool.com

Received Date: 2021-06-16
Accepted Date: 2021-09-15
Available Online: 2022-01-22
Publish Date: 2022-03-15

Abstract: Vegetation coverage is an objective index and an important parameter that reflects the basic extent of vegetation. In this study, we evaluated and analyzed the effectiveness of eight different visible-light-based vegetation indices for estimating the vegetation cover of different grassland types. Upon comparison of accuracy of these vegetation indices, we found that these vegetation indices were less effective in estimating the vegetation cover of desert grasslands. Therefore, we have proposed a desert vegetation index (DVI) to estimate the vegetation cover of desert grasslands. The effects of different vegetation indices on the vegetation cover estimation of different grassland types were evaluated, and the changes in the threshold values of different grassland types were analyzed. The results showed that: 1) The common vegetation indices could estimate the vegetation coverage in meadow grasslands and typical grasslands with a high accuracy (accuracy > 90%, F₁ > 0.9). The ExG (excess green index) was the best in estimating the vegetation coverage in meadow grasslands (accuracy > 93%, F₁ > 0.95), and there was no significant difference among the estimation power of vegetation indices when estimating the coverage in typical grasslands. However, the common vegetation indices exhibited low accuracy of vegetation coverage estimation (F₁ ≤ 0.6) in desert grasslands. 2) The DVI proposed in this study has a high estimation accuracy of vegetation coverage in desert grasslands (accuracy > 93%, F₁ score reached 0.71), which can effectively compensate for the defects of the above-mentioned vegetation indices. 3) The thresholds of GLI (green leaf index) and CIVE (color index of vegetation extraction) were the least sensitive to grassland types; the thresholds of ExG, ExGR (excess green minus excess red index), VEG (vegetative index), and WI (woebbecke index) were less sensitive to the effects of meadow grasslands and typical grasslands, but more sensitive to the effect of desert grasslands; and COM (combination index) and Lab (lab index) were the most sensitive to all the grassland types considered in this study.

Key words:

1. [1]
  贾坤, 姚云军, 魏香琴, 高帅, 江波, 赵祥. 植被覆盖度遥感估算研究进展[J]. 地球科学进展地球科学进展, 2013, 28(7): 774-782. doi:
  JIA K, YAO Y J, WEI X Q, GAO S, JIANG B, ZHAO X. A review on fractional vegetation cover estimation using remote sensing[J]. Advances in Earth ScienceAdvances in Earth Science, 2013, 28(7): 774-782. doi:
2. [2]
  赵威成, 张红华, 马福义, 叶欣. 基于像元二分模型的地表植被覆盖度估算模块化设计与实现[J]. 北京测绘北京测绘, 2020, 34(3): 285-288.
  ZHAO W C, ZHANG H H, MA F Y, YE X. Modular design and implementation of vegetation coverage estimation based on dimidiate pixel model[J]. Beijing Surveying and MappingBeijing Surveying and Mapping, 2020, 34(3): 285-288.
3. [3]
  葛静, 孟宝平, 杨淑霞, 高金龙, 冯琦胜, 梁天刚, 黄晓东, 高新华, 李文龙, 张仁平, 王云龙. 基于UAV技术和MODIS遥感数据的高寒草地盖度动态变化监测研究: 以黄河源东部地区为例[J]. 草业学报草业学报, 2017, 26(3): 1-12.
  GE J, MENG B P, YANG S X, GAO J L, FENG Q S, LIANG T G, HUANG X D, GAO X H, LI W L, ZHANG R P, WANG Y L. Dynamic monitoring of alpine grassland coverage based on UAV technology and MODIS remote sensing data: A case study in the headwaters of the Yellow River[J]. Acta Prataculturae SinicaActa Prataculturae Sinica, 2017, 26(3): 1-12.
4. [4]
  冯家莉, 刘凯, 朱远辉, 李勇, 柳林, 蒙琳. 无人机遥感在红树林资源调查中的应用[J]. 热带地理热带地理, 2015, 35(1): 35-42.
  FENG J L, LIU K, ZHU Y H, LI Y, LIU L, MENG L. Application of unmanned aerial vehicles to mangrove resources monitoring[J]. Tropical GeographyTropical Geography, 2015, 35(1): 35-42.
5. [5]
  王光彦, 姚坚, 李登富, 赵培. 低空无人机遥感在水利工程测绘中的应用研究[J]. 测绘与空间地理信息测绘与空间地理信息, 2016, 39(5): 113-115, 118.
  WANG G Y, YAO J, LI D F, ZHAO P. Study on application of remote sensing by unmanned aerial vehicle in hydraulic engineering survey[J]. Geomatics & Spatial Information Technology, 2016, 39(5): 113-115, 118.
6. [6]
  纪景纯, 赵原, 邹晓娟, 宣可凡, 王伟鹏, 刘建立, 李晓鹏. 无人机遥感在农田信息监测中的应用进展[J]. 土壤学报土壤学报, 2019, 56(4): 773-784.
  JI J C, ZHAO Y, ZOU X J, XUAN K F, WANG W P, LIU J L, LI X P. Advancement in application of UAV remote sensing to monitoring of farmlands[J]. Acta Pedologica SinicaActa Pedologica Sinica, 2019, 56(4): 773-784.
7. [7]
  徐朋, 徐伟诚, 罗阳帆, 赵祚喜. 基于无人机可见光遥感影像的耕地精准分类方法研究[J]. 中国农业科技导报中国农业科技导报, 2019, 21(6): 79-86.
  XU P, XU W C, LUO Y F, ZHAO Z X. Precise classification of cultivated land based on visible remote sensing image of UAV[J]. Journal of Agricultural Science and Technology, 2019, 21(6): 79-86.
8. [8]
  丁雷龙, 李强子, 杜鑫, 田亦陈, 袁超. 基于无人机图像颜色指数的植被识别[J]. 国土资源遥感国土资源遥感, 2016, 28(1): 78-86.
  DING L L, LI Q Z, DU X, TIAN Y C, YUAN C. Vegetation extraction method based on color indices from UAV images[J]. Remote Sensing for Land and ResourcesRemote Sensing for Land and Resources, 2016, 28(1): 78-86.
9. [9]
  刘艳慧, 蔡宗磊, 包妮沙, 刘善军. 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]. 生态环境学报生态环境学报, 2018, 27(11): 2023-2032.
  LIU Y H, CAI Z L, BAO N S, LIU S J. Research of grassland vegetation coverage and biomass estimation method based on major quadrat from UAV photogrammetry[J]. Ecology and Environmental SciencesEcology and Environmental Sciences, 2018, 27(11): 2023-2032.
10. [10]
  汪小钦, 王苗苗, 王绍强, 吴云东. 基于可见光波段无人机遥感的植被信息提取[J]. 农业工程学报农业工程学报, 2015, 31(5): 152-159.
  WANG X Q, WANG M M, WANG S Q, WU Y D. Extraction of vegetation information from visible unmanned aerial vehicle images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(5): 152-159.
11. [11]
  FRANCISCO G R, SINDHUJA S, JOE M M, WON S L, JESPER R, REZA E. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees[J]. Computers and Electronics in Agriculture, 2013, 91(): 106-115. doi:
12. [12]
  郭健. 基于植被指数和叶绿素荧光的总初级生产力估算[J]. 北京测绘北京测绘, 2019, 33(4): 427-432.
  GUO J. Estimation of gross primary productivity based on vegetation index and chlirophyll fluorescence[J]. Beijing Surveying and MappingBeijing Surveying and Mapping, 2019, 33(4): 427-432.
13. [13]
  武红旗, 范燕敏, 靳瑰丽, 夏小伟. 伊犁绢蒿荒漠草地植物光谱特征[J]. 草业科学草业科学, 2019, 36(7): 1765-1773.
  WU H Q, FAN Y M, JIN G L, XIA X W. Spectral characteristics of main plant in Seriphidium transiliense desert grassland[J]. Pratacultural SciencePratacultural Science, 2019, 36(7): 1765-1773.
14. [14]
  冷若琳, 张瑶瑶, 谢健全, 李芙凝, 胥刚, 崔霞. 基于多光谱数据与小型无人机的甘南草地非生长季植被覆盖度[J]. 草业科学草业科学, 2019, 36(11): 2742-2751.
  LENG R L, ZHANG Y Y, XIE J Q, LI F N, XU G, CUI X. An analysis of fractional vegetation cover of the Gannan glasslan in the nongrowing season based on multispectral data and small UVAs[J]. Pratacultural SciencePratacultural Science, 2019, 36(11): 2742-2751.
15. [15]
  鲁军景, 孙雷刚, 黄文江. 作物病虫害遥感监测和预测预警研究进展[J]. 遥感技术与应用遥感技术与应用, 2019, 34(1): 21-32.
  LU J J, SUN L G, HUANG W J. Research progress in monitoring and forecasting of crop diseases and pests by remote sensing[J]. Remote Sensing Technology and Application, 2019, 34(1): 21-32.
16. [16]
  李鹏飞, 郭小平, 顾清敏, 张昕, 冯昶栋, 郭光. 基于可见光植被指数的乌海市矿山排土场坡面植被覆盖信息提取研究[J]. 北京林业大学学报北京林业大学学报, 2020, 42(6): 102-112.
  LI P F, GUO X P, GU Q M, ZHANG X, FENG C D, GUO G. Vegetation coverage information extraction of mine dump slope in Wuhai City of Inner Mongolia based on visible vegetation index[J]. Journal of Beijing Forestry UniversityJournal of Beijing Forestry University, 2020, 42(6): 102-112.
17. [17]
  徐大伟. 呼伦贝尔草原区不同草地类型分布变化及分析. 北京:中国农业科学院博士学位论文, 2019.
  XU D W. Dynamic change and analysis of different grassland types distribution in the Hulunber grassland. PhD Thesis. Beijing: Chinese Academy of Agricultural Sciences, 2019.
18. [18]
  HAGUE T, TILLETT N D, WHEELER H. Automated crop and weed monitoring in widely spaced cereals[J]. Precision AgriculturePrecision Agriculture, 2006, 7(1): 21-32. doi:
19. [19]
  WOEBBECKE D M, MEYER G E, VON BARGEN K, MORTENSEN D A. Color indexes for weed identification under various soil, residue, and lighting condition[J]. American Society of Agricultural Engineers Meeting, 1994, 38(1): 259-269.
20. [20]
  CAMARGO N, JOAO. A combined Statistical-soft Computing Approach for Classification and Mapping Weed Species in Minimum-Tillage Systems. Lincoln: The University of Nebraska, 2004.
21. [21]
  LOUHAICHI M, BORMAN M M, JOHNSON D E. Spatially located platform and aerial photography for documentation of grazing impacts on wheat[J]. Geocarto InternationalGeocarto International, 2001, 16(1): 65-70. doi:
22. [22]
  KATAOKA T, KANEKO T, OKAMOTO H, HATA S. Crop growth estimation system using machine vision. Kobe: Proceedings 2003 IEEE / ASME International Conference on Advanced Intelligent Merchatronics, 2003.
23. [23]
  GUIJARRO M, PAJARES G, RIOMOROS I, HERRERA P J, BURGOSARTIZZU X P, RIBEIRO A. Automatic segmentation of relevant textures in agricultural images[J]. Computers and Electronics in Agriculture, 2011, 75(1): 75-83. doi:
24. [24]
  WOEBBECKE D M, MEYER G E, VONVARGEN K. Color indexes for weed identification under various soil, residue, and lighting conditions[J]. Transactions of the American Society of Agricultural Engineers, 1995, 38(): 259-269. doi:
25. [25]
  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[J]. 农业机械学报农业机械学报, 2018, 49(10): 240-249.
  NIU Y X, ZHANG L Y, HAN W T. Extraction methods of cotton coverage based on Lab color space[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(10): 240-249.
26. [26]
  许高建, 沈杰, 徐浩宇. 基于Lab颜色空间下的小麦赤霉病图像分割[J]. 中国农业大学学报中国农业大学学报, 2021, 26(10): 149-156.
  XU G J, SHEN J, XU H J. Image segmentation of wheat scab based on Lab color space[J]. Journal of China Agricultural University, 2021, 26(10): 149-156.
27. [27]
  李苗苗. 植被覆盖度的遥感估算方法研究. 北京: 中国科学院研究生院硕士学位论文, 2003.
  LI M M. The method of vegetation fraction estimation by remote sensing. Master Thesis. Beijing: Graduate School of Chinese Academy of Sciences, 2003.
28. [28]
  周涛, 胡振琪, 韩佳政, 张浩. 基于无人机可见光影像的绿色植被提取[J]. 中国环境科学中国环境科学, 2021, 41(5): 2380-2390.
  ZHOU T, HU Z Q, HAN J Z, ZHANG H. Green vegetation extraction based on visible light image of UAV[J]. China Environmental ScienceChina Environmental Science, 2021, 41(5): 2380-2390.
29. [29]
  万炜, 肖生春, 陈小红, 滕泽宇, 丁爱军, 韩超, 颜长珍. 无人机遥感在野外植被盖度调查中的应用: 以阿拉善荒漠区灌木为例[J]. 干旱区资源与环境干旱区资源与环境, 2018, 32(9): 150-156.
  WAN W, XIAO S C, CHEN X H, TENG Z Y, DING A J, HAN C, YAN C Z. Application of unmanned aerial vehicles to field vegetation coverage survey: A study of shrubs on Alxa desert[J]. Journal of Arid Land Resources and Environment, 2018, 32(9): 150-156.
30. [30]
  CHEN J J, ZHAO X N, ZHANG H Z, QIN Y, YI S H. Evaluation of the accuracy of the field quadrat survey of alpine grassland fractional vegetation cover based on the satellite remote sensing pixel scale[J]. ISPRS International Journal of Geo-Information, 2019, 8(11): 497-. doi:
31. [31]
  CHEN J J, YI S H, QIN Y, WANG X Y. Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai-Tibetan Plateau[J]. Journal of Remote SensingJournal of Remote Sensing, 2016, 37(8): 1922-1936. doi:
1. [1]
  伏帅 , 张勇辉 , 李佳吕 , 王萌榛 , 彭璐 , 冯琦胜 , 梁天刚 . 不同植被指数和无人机航高对草地盖度估测精度的影响. 草业科学, 2021, 38(1): 11-19. doi:
2. [2]
  于惠 , 吴玉锋 , 牛莉婷 . 基于无人机可见光图像的荒漠草地覆盖度估算. 草业科学, 2021, 38(8): 1432-1438. doi:
3. [3]
  陈建军 , 宜树华 , 任世龙 , 秦彧 , 王晓云 . 疏勒河上游高寒草地植被盖度反演及精度评价. 草业科学, 2014, 8(1): 56-65. doi:
4. [4]
  任世龙 , 宜树华 , 陈建军 , 秦彧 , 王晓云 . 基于不同数码相机和图像处理方法的高寒草地植被盖度估算的比较 . 草业科学, 2014, 8(6): 1007-1013. doi:
5. [5]
  冷若琳 , 张瑶瑶 , 谢建全 , 李芙凝 , 胥刚 , 崔霞 . 基于多光谱数据与小型无人机的甘南草地非生长季植被覆盖度. 草业科学, 2019, 36(11): 2742-2751. doi:
6. [6]
  夏颖 , 范建容 , 张茜彧 , 毕永清 . 复合植被指数在稀疏高寒草原植被盖度遥感反演中的应用. 草业科学, 2017, 11(9): 1767-1777. doi:
7. [7]
  王杰 , 李卫朋 . 基于灵活的时空融合模型的植被覆盖度与植被指数关系. 草业科学, 2017, 11(2): 264-272. doi:
8. [8]
  林茜 , 郭飞 , 黄昌春 , 杨雪峰 . 基于MODIS的植被指数对估测塔里木河中下游植被盖度的适用性. 草业科学, 2016, 10(12): 2434-2441. doi:
9. [9]
  张灏 , 杨超 . 草地蘑菇圈对植被及土壤真菌的影响. 草业科学, 2019, 36(7): 1774-1780. doi:
10. [10]
  关欣 , 安沙舟 , 荀其蕾 , 董乙强 , 孔晓晶 , 杨娇 . 巴音布鲁克草原高寒草原植被覆盖度反演模型构建与评价. 草业科学, 2018, 12(5): 949-955. doi:
11. [11]
  何美悦 , 王迎新 , 彭泽晨 , 常生华 , Saman Bowatte , 刘永杰 , 侯扶江 . 祁连山草原地上生物量和物种丰富度的空间格局. 草业科学, 2020, 37(10): 2012-2021. doi:
12. [12]
  孙珊珊 , 满都呼 , 伟军 , 朝克图 , 阿焱 , 张恒 , 付和平 , 武晓东 , 袁帅 . 基于无人机的东北鼢鼠种群数量调查最佳尺度选择. 草业科学, 2021, 38(9): 1841-1849. doi:
13. [13]
  武红旗 , 范燕敏 , 靳瑰丽 , 夏小伟 . 伊犁绢蒿荒漠草地植物光谱特征. 草业科学, 2019, 36(7): 1765-1773. doi:
14. [14]
  边慧芹 , 王雪梅 . 基于综合分段优势的植被覆盖度模型比较. 草业科学, 2021, 38(3): 432-442. doi:
15. [15]
  王玮 , 冯琦胜 , 郭铌 , 沙莎 , 胡蝶 , 王丽娟 , 李耀辉 . 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. 草业科学, 2015, 9(12): 1969-1979. doi:
16. [16]
  王志伟 , 王茜 , 李世歌 , 王普昶 , 刘秀峰 , 谢彩云 , 史健宗 , 吴佳海 , 王小利 , 陆瑞霞 , 莫本田 . 贵州喀斯特近30年植被生长特征分析. 草业科学, 2016, 10(11): 2180-2188. doi:
17. [17]
  闫俊杰 , 刘海军 , 崔东 , 陈晨 . 近15年新疆伊犁河谷草地退化时空变化特征. 草业科学, 2018, 12(3): 508-520. doi:
18. [18]
  陆荫 , 杨淑霞 , 李晓红 . 甘南州高寒天然草地生长状况遥感监测. 草业科学, 2021, 38(1): 32-43. doi:
19. [19]
  乌云嘎 , 查木哈 , 张晓东 , 袁帅 , 付和平 , 武晓东 , 甘红军 . 荒漠区啮齿动物群落与植物因子的冗余分析. 草业科学, 2014, 8(12): 2323-2332. doi:
20. [20]
  杨东 , 杨秀春 , 金云翔 , 徐斌 . 基于文献计量的草地生物量遥感监测研究进展. 草业科学, 2021, 38(9): 1782-1792. doi:

凯时66

图 1 无人机影像处理流程

Figure 1. Processing flow of UAV images

下载: 全尺寸图片幻灯片

图 2 基于植被指数的像元二分模型的精度评价结果

Figure 2. The accuracy of the dimidiate pixel model based on vegetation index

下载: 全尺寸图片幻灯片

图 3 荒漠草地的植被盖度估测精度比较图

Figure 3. Comparison of accuracy of vegetation cover estimation in desert grasslands

下载: 全尺寸图片幻灯片

表 1 采样点分布及其草地类型

Table 1. Distribution of sampling points and grassland types

草原类型 Grassland type	地区 Region	地理位置 Geographic site	样点数 Number of sampling
荒漠草地 Desert grassland	内蒙古鄂尔多斯市 Ordos, Inner Mongolia	108.75° E, 38.46° N	5
	内蒙古阿拉善盟 Alax, Inner Mongolia	101.78° E, 39.11° N	1
	甘肃武威 Wuwei, Gansu	102.83° E, 38.56° N	3
	新疆昌吉州 Changji, Xinjiang	88.94° E, 44.35° N	1
草甸草地 Meadow grassland	新疆哈密市 Hami, Xinjiang	88.94° E, 44.35° N	6
	新疆阿勒泰地区 Aletai, Xinjiang	93.75° E, 43.32° N	3
	新疆昌吉州 Changji, Xinjiang	92.60° E, 43.60° N	1
典型草地 Typical grassland	甘肃平凉市 Pingliang, Gansu	107.16° E, 35.18° N	1
	甘肃庆阳市 Qingyang, Gansu	107.32° E, 35.49° N	7
	内蒙古包头市 Baotou, Inner Mongolia	110.22° E, 40.94° N	2

下载: 导出CSV

表 2 植被指数公式表

Table 2. Formula table of vegetation index

植被指数 Vegetation index	全称 Full name	公式 Formulas	参考文献 Reference
VEG	植被因子指数 Vegetative index	$VEG={g} \div ({ {r}^{0.667}\times {b}^{0.333} })$	[18]
ExG	超绿指数 Excess green index	$ ExG= 2g-r-b $	[19]
ExGR	超绿超红差分指数 Excess green minus excess red index	${E}{x}{G}{R}=\mathrm{ }{E}{x}{G}-(1.4r-g)$	[20]
GLI	绿叶指数 Green leaf index	$GLI=({2g-r-b}) \div ({2g + r + b})$	[21]
CIVE	植被颜色指数 Color index of vegetation extraction	${C}{I}{V}{E}=\mathrm{ }0.441r-0.811g + 0.385b + 18.78745$	[22]
COM	组合指数 Combination index	COM = 0.25ExG + 0.3ExGR + 0.33CIVE + 0.12VEG	[23]
WI	Woebbecke指数 Woebbecke index	$WI=({g-b}) \div ({r-g})$	[24]
Lab	Lab指数 Lab index	$ Lab=a $	[25]
公式中：r、g、b分别为照片RGB颜色空间中的红、绿、蓝三通道灰度值的归一化值；a为Lab颜色空间中的a通道灰度值。　The r, g, and b in the formula represent the normalized values of the gray values of the red, green, and blue channels in the RGB color space, and a represents the gray values of a channel in the Lab color space.

下载: 导出CSV

表 3 草地植被盖度估测误差表

Table 3. Estimation error table of grassland coverage

植被指数 Vegetation	草地类型 Grassland types	平均绝对误差 MAE/%	均方根误差 RMSE/%
ExG	草甸 Meadow	1.65	1.53
	典型 Typical	2.00	1.19
	荒漠 Desert	5.49	4.71
ExGR	草甸 Meadow	1.74	1.88
	典型 Typical	1.85	1.44
	荒漠 Desert	1.61	2.08
VEG	草甸 Meadow	2.81	4.60
	典型 Typical	4.57	3.88
	荒漠 Desert	1.52	1.13
CIVE	草甸 Meadow	2.53	3.48
	典型 Typical	2.83	3.42
	荒漠 Desert	1.52	1.68
COM	草甸 Meadow	1.65	1.97
	典型 Typical	2.60	4.55
	荒漠 Desert	1.51	5.81
LAB	草甸 Meadow	2.02	1.97
	典型 Typical	3.92	4.55
	荒漠 Desert	3.63	5.81
GLI	草甸 Meadow	1.64	1.29
	典型 Typical	5.64	6.46
	荒漠 Desert	2.97	4.28
WI	草甸 Meadow	3.85	7.62
	典型 Typical	4.54	3.76
	荒漠 Desert	2.36	2.77
植被指数全称如表1所列；下同。　Full names of vegetation are the same as those in Table 1; this is applicable for the following figures and tables as well.

下载: 导出CSV

表 4 不同草地类型阈值变化情况

Table 4. Changes of threshold under different types of grasslands

植被指数 Vegetation index	阈值 Threshold
植被指数 Vegetation index	草甸草地 Meadow	典型草地 Typical	荒漠草地 Desert
ExG	0.03	0.04	−0.01
ExGR	−0.04	−0.05	−0.26
VEG	1.11	1.10	0.97
CIVE	18.74	18.74	18.77
COM	6.37	6.31	6.23
Lab	−4.31	−2.95	8.17
GLI	0.02	0.02	−0.01
WI	−0.05	−0.02	0.18
DVI	—	—	−0.16

下载: 导出CSV

凯时66

点击查看大图

图(3)表(4)

计量

PDF下载量: 12
文章访问数: 670
HTML全文浏览量: 253

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

凯时66

声明！

基于无人机图像的草地植被盖度估算方法比较

作者简介: 伏帅(1999-)，男，陕西渭南人，在读硕士生，研究方向为草地遥感。E-mail: fush21@onlabwinterschool.com

通讯作者: 冯琦胜(1983-)，男，甘肃镇原人，高级实验师，博士，研究方向为草地遥感。E-mail: fengqsh@onlabwinterschool.com

English

Comparison of grassland vegetation coverage extraction algorithms from UAV technology

Corresponding author: FENG Qisheng E-mail: fengqsh@onlabwinterschool.com

凯时66

计量

文章相关

通讯作者: 陈斌, bchen63@163.com

Corresponding author: FENG Qisheng　E-mail: fengqsh@onlabwinterschool.com