Crops ›› 2020, Vol. 36 ›› Issue (3): 177-183.doi: 10.16035/j.issn.1001-7283.2020.03.027

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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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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

Fig.1

Vegetation coverage extraction results of each vegetation index"

Table 1

Precision analysis of vegetation coverage %"

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

Fig.2

Flow chart of vegetation coverage extraction"

Fig.3

Statistical histogram of VDVI in cotton, peanut and maize"

Fig.4

Extraction of vegetation coverage of cotton, peanut and maize test areas"

Table 2

Precision evaluation of maize supervised classification"

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

Table 3

Accuracy analysis of vegetation coverage in the experimental areas %"

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

Table 4

Accuracy analysis of vegetation coverage in validation areas %"

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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