废弃矿山植被覆盖度无人机遥感快速提取技术

doi:10.16843/j.sswc.2020.02.016

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Abstract [Background] The excessive exploitation of mineral resources has caused the ecological environment of the mining area and its surrounding areas deteriorated severely, which has adversely affected human production and life. Vegetation restoration and reconstruction is an integral part of the ecological restoration work in mining area. Vegetation coverage monitoring is critical for mine greening restoration.[Methods] This study relied on field surveys and low-altitude aerial surveys of drones conducted in July 2018 to monitor the ecological rehabilitation benefits of an abandoned coal mine, Xiyuan Fourth Team Coal Mine Treatment Area in Fangshan district around Beijing, which underwent ecological restoration and control in 2016. In this study, a set of aerial image system for unmanned aerial vehicles (UAV) in the abandoned mining area with integrated stable gimbal and image acquisition was built based on a small multi-rotor drone and a ground control station. The drone flying height was 120 m, and the ground resolution of aerial images was 0.012 m. An accurate and fast method for extracting the vegetation coverage of the abandoned mine was proposed by converting drone images from RGB color space to HSV color space and limiting the range of H and S values. At the same time, the vegetation coverage of the same drone aerial image was extracted using the ENVI software via the supervised classification method to evaluate the extraction accuracy.[Results] The drone aerial photography was used to achieve the acquisition of high-resolution drone aerial images of centimeter-level vegetation in the abandoned mining areas, low system cost, simple maintenance and high efficiency. By converting the visible spectrum drone aerial image from the RGB color space mode to the HSV color space, the segmenting threshold value by limiting the S ≥ 0.2 and the H ≥ 47.1 allowed to quickly extract the vegetation part and further calculate the vegetation coverage to achieve accurate extraction with an error ≤ 6.857 6%, and under the same aerial photography conditions, the threshold settings of the S and H values were stable. Compared with the vegetation coverage extracted by supervised classification, the extraction result based on the HSV color space threshold segmentation method was lower, and the extraction error was getting smaller and smaller with the increase of vegetation coverage.[Conculsions] This study proposes a new method of quickly extracting vegetation coverage using drone aerial images, which provides a new idea for evaluating the ecological restoration effect of abandoned mines with high accuracy and efficiency.

Key words： vegetation coverage UAV HSV color space supervised classification threshold segmentation

Received: 08 July 2019

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TP79

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	Articles by authors
	WANG Meiqi
	YANG Jianying
	SUN Yongkang
	WANG Gaoping
	XIE Yuhong

Cite this article:

WANG Meiqi,YANG Jianying,SUN Yongkang, et al. Remote sensing rapid extraction technology for abandoned mine vegetation coverage via UAV[J]. SSWC, 2020, 18(2): 130-139.

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http://journal12.magtechjournal.com/Jweb_stbc/EN/10.16843/j.sswc.2020.02.016 OR http://journal12.magtechjournal.com/Jweb_stbc/EN/Y2020/V18/I2/130

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