Remote sensing rapid extraction technology for abandoned mine vegetation coverage via UAV
WANG Meiqi1, YANG Jianying1, SUN Yongkang1, WANG Gaoping2, XIE Yuhong2
1. School of Soil and Water Conservation, Beijing Forestry University, 100083, Beijing, China; 2. Beijing Municipal Commission of Planning and Natural Resources, 101160, Beijing, China
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.
秦伟, 朱清科, 张学霞, 等. 植被覆盖度及其测算方法研究进展[J]. 西北农林科技大学学报(自然科学版), 2006, 34(9):163. QIN Wei, ZHU Qingke, ZHANG Xuexia, et al. Research progress on vegetation coverage and its calculation methods[J]. Journal of Northwest A&F University (Natural Science Edition), 2006, 34(9):163.
[2]
GITELSON A A. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment, 2002, 80(1):76.
[3]
QI J, MARSETT R C, MORAN M S, et al. Spatial and temporal dynamics of vegetation in the San Pedro River basin area[J]. Agricultural and Forest Meteorology, 2000, 105(1/3):1.
[4]
李苗苗, 吴炳方, 颜长珍, 等. 密云水库上游植被覆盖度的遥感估算[J]. 资源科学, 2004,26(4):153. LI Miaomiao, WU Bingfang, YAN Changzhen, et al. Remote sensing estimation of vegetation coverage in the upper Miyun reservoir[J]. Resources Science, 2004,26(4):153.
[5]
刘陈林. 基于GIS和RS技术的罗甸县喀斯特石漠化程度变化图谱研究[D]. 贵阳:贵州大学,2016:45. LIU Chenlin. Research on the variation of karst rocky desertification degree in Luodian County based on GIS and RS technology[D]. Guiyang:Guizhou Univeristy, 2016:45.
[6]
马志勇, 沈涛, 张军海, 等. 基于植被覆盖度的植被变化分析[J]. 测绘通报, 2007(3):45. MA Zhiyong, SHEN Tao, ZHANG Junhai, et al. Analysis of vegetation change based on vegetation coverage[J]. Bulletin of Surveying and Mapping, 2007(3):45.
[7]
KASISCHKE, ERIC S. Improving global estimates of atmospheric emissions from biomass burning[J]. Journal of Geophysical Research, 2004, 109(D14):D14S01.
李晓兵, 陈云浩, 张云霞. 草地植被盖度的多尺度遥感与实地测量方法综述[J]. 地球科学进展, 2003, 18(1):85. LI Xiaobing, CHEN Yunhao, ZHANG Yunxia. Areview of multi-scale remote sensing and field measurement methods for grassland vegetation coverage[J]. Advances in Earth Science, 2003, 18(1):85.
[10]
汪慧兰, 周伟华, 罗斌. 基于边缘方向自相关图与局部颜色的图像检索[C]//第12届全国图象图形学学术会议:1. WANG Huilan, ZHOU Weihua, LUO Bin. Image retrieval based on edge direction autocorrelation map and local color[C]//The 12th National Conference on Image Graphics:1.
[11]
雷添杰, 李长春, 何孝莹.无人机航空遥感系统在灾害应急救援中的应用[J]. 自然灾害学报, 2011, 20(1):178. LEI Tianjie, LI Changchun, HE Xiaoying. Application of UAV aerial remote sensing system in disaster emergency rescue[J]. Journal of Natural Disasters, 2011, 20(1):178.
[12]
马得利, 孙永康, 杨建英, 等. 基于无人机遥感技术的废弃采石场立地条件类型划分[J]. 北京林业大学学报, 2018, 40(9):94. MA Deli, SUN Yongkang, YANG Jianying, et al. Classification of site conditions for abandoned quarry based on UAV remote sensing technology[J]. Journal of Beijing Forestry University, 2018, 40(9):94.
[13]
冯海英, 冯仲科, 冯海霞. 一种基于无人机高光谱数据的植被盖度估算新方法[J]. 光谱学与光谱分析, 2017(11):3573. FENG Haiying, FENG Zhongke, FENG Haixia. Anew method for estimating vegetation coverage based on hyperspectral data of UAV[J]. Spectroscopy and Spectral Analysis, 2017(11):3573.
[14]
谢兵, 杨武年, 王芳. 无人机可见光光谱的植被覆盖度估算新方法[J/OL].测绘科学,2020(9):1 XIE Bing, YANG Wunian, WANG Fang. A new method for estimating vegetation coverage of visible light spectrum of UAV[J/OL].Science of Surveying and Mapping,2020(9):1.
[15]
汪小钦, 王苗苗, 王绍强, 等. 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, 2015, 31(5):152. WANG Xiaoqin, WANG Miaomiao, WANG Shaoqiang, et al. Vegetation information extraction based on remote sensing of visible light band UAV[J]. Transactions of the CSAE, 2015, 31(5):152.
[16]
GARCIA-RUIZ F, SANKARAN S, MAJA J M, et al. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees[J]. Computers and Electronics in Agriculture, 2013, 91:106.
[17]
秦绪佳, 程燕飞, 范颖琳, 等. 基于三边滤波的HSV颜色空间Retinex图像增强算法[J]. 小型微型计算机系统, 2016, 37(1):168. QIN Xujia, CHENG Yanfei, FAN Yinglin, et al. Reconex image enhancement algorithm based on three-edge filtering for HSV color space[J]. Journal of Chinese Computer Systems, 2016, 37(1):168.
[18]
牛亚晓, 张立元, 韩文霆, 等. 基于无人机遥感与植被指数的冬小麦覆盖度提取方法[J]. 农业机械学报, 2018, 49(4):212. NIU Yaxiao, ZHANG Liyuan, HAN Wenting, et al. Winter wheat coverage extraction method based on UAV remote sensing and vegetation index[J]. Transactions of the CSAM, 2018, 49(4):212.
[19]
李冰, 刘镕源, 刘素红, et al. 基于低空无人机遥感的冬小麦覆盖度变化监测[J]. 农业工程学报, 2012, 28(13):160. LI Bing, LIU Yuyuan, LIU Suhong, et al. Monitoring ofwinter wheat coverage change based on remote sensing of low altitude UAV[J]. Transactions of the CSAE, 2012, 28(13):160.
[20]
邓继忠, 任高生, 兰玉彬, et al. 基于可见光波段的无人机超低空遥感图像处理[J]. 华南农业大学学报, 2016, 37(6):16. DENG Jizhong, REN Gaosheng, LAN Yubin, et al. Ultra-low altitude remote sensing image processing of UAV based on visible light band[J]. Journal of South China Agricultural University, 2016, 37(6):16.
[21]
BELLVERT J, ZARCO-TEJADA P J, GIRONA J, et al. Mapping crop water stress index in a ‘Pinot-noir’ vineyard:Comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle[J]. Precision Agriculture, 2014, 15(4):361.
[22]
郭英华. 基于HSV色彩空间的图像分割[J]. 黑龙江冶金, 2011, 31(2):35. GUO Yinghua. Image segmentation based on HSV color space[J]. Heilongjiang Metallurgy, 2011, 31(2):35.
[23]
赵宇杰, 姚娟文, 赵彦敏. 基于Matlab的数字图像颜色空间转换与应用[J]. 电子技术与软件工程, 2015(5):113. ZHAO Yujie, YAO Juanwen, ZHAO Yanmin. Digitalimage color space conversion and application based on matlab[J]. Electronic Technology and Software Engineering, 2015(5):113.
[24]
闫大鹏,周兴东,刘伟,等.一种基于高斯拟合的水生植被遥感分类阈值确定方法[J].西安科技大学学报, 2018,38(5):776. YAN Dapeng, ZHOU Xingdong, LIU Wei, et al. A method for determining threshold of aquatic vegetation remote sensing classification based on Gaussian fitting[J].Journal of Xi'an University of Science and Technology, 2018,38(5):776.
[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研究[J]. 农业机械学报, 2018, 49(10):247. NIU Yaxiao, ZHANG Liyuan, HAN Wenting. Research oncotton coverage extraction method based on Lab color space[J]. Transactions of the CSAM, 2018, 49(10):247.
[26]
林开颜, 吴军辉, 徐立鸿. 彩色图像分割方法综述[J].中国图象图形学报,2005(1):1. LIN Kaiyan, WU Junhui, XU Lihong. Overview of color image segmentation methods[J].Journal of Image and Graphics,2005(1):1.
[27]
甘胜军. 利用HSV颜色空间进行车牌识别的研究[D]. 重庆:重庆邮电大学, 2016:27. GAN Shengjun. Research on license plate recognition using HSV color space[D]. Chongqing:Chongqing University of Posts and Telecommunications, 2016:27.
[28]
刘峰, 刘素红, 向阳.园地植被覆盖度的无人机遥感监测研究[J].农业机械学报,2014,45(11):250. LIU Feng, LIU Suhong, XIANG Yang. Study on remote sensing monitoring of unmanned aerial vehicle coverage in vegetation coverage[J].Transactions of the CSAM,2014,45(11):250.
[29]
韩文霆, 张立元, 张海鑫, 等. 基于无人机遥感与面向对象法的田间渠系分布信息提取[J]. 农业机械学报, 2017,48(3):210. HAN Wenting, ZHANG Liyuan, ZHANG Haixin, et al. Extraction of field canal distribution information based on UAV remote sensing and object-oriented method[J]. Transactions of the CSAM, 2017,48(3):210.