基于无人机可见光影像的绿色植被提取

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Abstract Using visible light images taken by unmanned aerial vehicle (UAV) as data source, a new green vegetation index named as Difference Enhanced Vegetation Index (DEVI) was proposed based on the analysis of healthy green vegetation spectral characteristics and the differences of pixel values among different bands of typical ground objects in visible light images of UAVs. DEVI utilized the information of red, green and blue visible bands, which can not only eliminate the interference caused by the difference of pixel values in a single band of green wave of different ground objects, but also enhance the characteristic that the reflectivity of green wave of green vegetation is greater than that of red and blue bands. This new index and 8 other common visible light vegetation indexes were used to extract the green vegetation by the threshold method in the study area, and then the support-vector machine (SVM)-based supervised classification method and the ground truth area of interest (ROIs) were used to evaluate the extraction accuracy. The results showed that the extraction accuracy of DEVI was significantly better than the other eight vegetation indexes. When the threshold method of image histogram visual detection was adopted, the overall accuracy was 98.98%, the Kappa coefficient was 0.9791, and the relative error was 1/83. Meanwhile, the gray image histogram of vegetation index calculated by DEVI had a good bimodal shape, which could quickly determine the threshold value, and the threshold value was generally located between 0.9 and 1. To verify whether DEVI has good applicability and reliability, this study chose three typical areas to conduct the feasibility verification analysis:area with high vegetation coverage, area with dense regions of buildings, and area with discretely distributed vegetation. The results showed that the green vegetation information in regions with dense buildings and discretely distributed vegetation could be extracted with high precisions by DEVI. The overall accuracy was 98.42% and 98.56%, the Kappa coefficient was 0.9610 and 0.9635, and the relative error was 1/125 and 1/91, respectively. However, the extraction accuracy in areas with high vegetation coverages was slightly less accurate with the overall accuracy of 97.40%, a Kappa coefficient of 0.9371, and a relative error of 1/53. Therefore, the new DEVI could extract the green vegetation information from UAV visible light images in typical vegetation covered areas in an effective, high-precision and low-cost way. Therefore, DEVI is a feasible method for the green vegetation monitoring research in terrestrial ecosystems.

Key words： UAV remote sensing visible light image difference enhanced vegetation index green vegetation extraction

Received: 29 September 2020

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X171.1

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	ZHOU Tao
	HU Zhen-qi
	HAN Jia-zheng
	ZHANG Hao

Cite this article:

ZHOU Tao,HU Zhen-qi,HAN Jia-zheng等. Green vegetation extraction based on visible light image of UAV[J]. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(5): 2380-2390.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2021/V41/I5/2380

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