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作物杂志,2020, 第3期: 177–183 doi: 10.16035/j.issn.1001-7283.2020.03.027

• 生理生化·植物营养·栽培耕作 • 上一篇    下一篇

利用无人机遥感技术提取农作物植被覆盖度方法研究

王猛1,2, 隋学艳1,2, 梁守真1,2, 侯学会1,2, 梁永全3   

  1. 1山东省农业可持续发展研究所,250100,山东济南
    2农业农村部华东都市农业重点实验室,250100,山东济南
    3山东省智慧矿山信息技术重点实验室,266590,山东青岛
  • 收稿日期:2019-11-04 修回日期:2019-11-26 出版日期:2020-06-15 发布日期:2020-06-10
  • 作者简介:王猛,主要从事农业遥感方面的研究,E-mail: wangm389@163.com
  • 基金资助:
    山东省智慧矿山信息技术重点实验室开放课题;山东省农业科学院农业科技创新工程青年基金

Research on the Method of Extracting Crop Vegetation Coverage Using UAV Remote Sensing Technology

Wang Meng1,2, Sui Xueyan1,2, Liang Shouzhen1,2, Hou Xuehui1,2, Liang Yongquan3   

  1. 1Shandong Institute of Agriculture Sustainable Development, Jinan 250100, Shandong, China
    2Key Laboratory of East China Urban Agriculture, Ministry of Agriculture and Rural Affairs, Jinan 250100, Shandong, China
    3Shandong Key Laboratory of Wisdom Mine Information Technology, Qingdao 266590, Shandong, China
  • Received:2019-11-04 Revised:2019-11-26 Online:2020-06-15 Published:2020-06-10

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摘要：

基于无人机的遥感信息获取技术已广泛应用在农业领域。无人机遥感平台获取农作物信息技术具有高时效、高分辨率、低成本、快速、准确等特点,是目前精准农业中农田信息获取的重要手段之一。利用无人机遥感技术获取可见光影像,以棉花、花生和玉米为研究对象,选取不同的植被指数进行可见光图像阈值分割,结合研究区域可见光影像监督分类结果,确定3种作物提取植被覆盖度方法。试验结果表明,利用无人机可见光图像植被指数阈值分类方法,可以有效提取作物植被覆盖度。该方法对于棉花、花生和玉米3种作物植被覆盖信息的提取精度较高。

关键词: 无人机, 农作物, 覆盖度, 提取, 植被指数

Abstract:

Remote sensing information acquisition technology based on unmanned aerial vehicle (UAV) has been widely used in agriculture. The use of drone platform to obtain crop remote sensing information is one of the most important methods of obtaining farmland information in precision agriculture. It is characterized by high aging, high resolution, low cost, fast and accurate. UAV remote sensing technology is used to obtain visible light image, cotton, peanut and maize were selected as research materials, different vegetation indices were selected for visible image threshold segmentation. Combined with the supervised classification results of visible light images in the study area, the method of extracting vegetation coverage from vegetation indices of three crops was determined. The experimental results showed that the vegetation index coverage could be effectively extracted using vegetation index threshold classification method. The verification results showed that the method has high precision for extracting vegetation cover information from cotton, peanut and maize.

Key words: UAV, Crop, Coverage, Extraction, Vegetation index

图1

各植被指数的植被覆盖度提取结果"

表1

试验区域植被覆盖度精度分析"

植被指数
Vegetation index		 EXG VDVI RGRI BGRI RGBVI
地面照片
Ground photo		 93.67 98.12 97.89 89.71 98.01

图2

植被覆盖度提取流程图"

图3

棉花、花生和玉米VDVI统计直方图"

图4

棉花、花生和玉米试验区域植被覆盖度提取"

表2

玉米监督分类精度分析"

地物Ground object 玉米（像元）Maize (pixel) 土壤（像元）Soil (pixel) 样本总数Total sample size 用户精度User accuracy
玉米（像元）Maize (pixel) 29 611 54 29 665 0.9981
土壤（像元）Soil (pixel) 257 22 405 22 662 0.9886
样本总数Total sample size 29 868 22 459 52 327
生产者精度Producer accuracy 0.9914 0.9976

表3

试验区域植被覆盖度精度分析"

植被覆盖度
Vegetation coverage		 棉花
Cotton		 花生
Peanut		 玉米
Maize
监督分类Supervised classification 54.80890 35.00390 69.71960
VDVI阈值分类
VDVI threshold classification		 55.59880 36.18230 71.09840
绝对误差Absolute error 0.78993 1.17843 1.37882
误差Error 1.44124 3.36658 1.97767

表4

验证区域植被覆盖度精度分析"

植被覆盖度
Vegetation coverage		 棉花
Cotton		 花生
Peanut		 玉米
Maize
监督分类Supervised classification 57.00270 35.43940 69.03740
VDVI阈值分类
VDVI threshold classification		 57.99880 36.98030 71.16510
绝对误差Absolute error 0.99610 1.54090 2.12770
误差Error 1.74746 4.34798 3.08195
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