Vegetation coverage information extraction of mine dump slope in Wuhai City of Inner Mongolia based on visible vegetation index

Li Pengfei; Guo Xiaoping; Gu Qingmin; Zhang Xin; Feng Changdong; Guo Guang

doi:10.12171/j.1000-1522.20190252

Journal of Beijing Forestry University > 2020 > 42(6): 102-112. > DOI: 10.12171/j.1000-1522.20190252

Li Pengfei, Guo Xiaoping, Gu Qingmin, Zhang Xin, Feng Changdong, Guo Guang. Vegetation coverage information extraction of mine dump slope in Wuhai City of Inner Mongolia based on visible vegetation index[J]. Journal of Beijing Forestry University, 2020, 42(6): 102-112. DOI: 10.12171/j.1000-1522.20190252

Citation:

PDF (1619 KB)

Vegetation coverage information extraction of mine dump slope in Wuhai City of Inner Mongolia based on visible vegetation index

1.
School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
2.
Yangchangwan Coal Mine, Ningxia Coal Industry Co., Ltd., China Energy Group, Yinchuan 751409, Ningxia, China

More Information

Received Date: June 09, 2019
Revised Date: October 05, 2019
Available Online: May 29, 2020
Published Date: June 30, 2020

Graphical Abstract

Abstract

Abstract

ObjectiveThis paper aims to use visible vegetation index to get the slope vegetation coverage of mine dump quickly and accurately, and to provide an effective method for the vegetation investigation of slope in Wuhai mining area, Inner Mongolia of northern China.
MethodThis study selected one of the typical mine dumps in the Wuhai City to choose a suitable visible vegetation index for local research, estimate the dump vegetation coverage, and furthermore try to provide a new method for extracting the dump slope vegetation coverage by means of Quadrat survey, UAV remote sensing and visible vegetation index.
Result(1) Different visible vegetation indexes had varied vegetation extraction effects. Among them, the darker part of red-green ratio index (RGRI) and blue-green ratio index (BGRI) represented the larger vegetation index, while the brighter part of other common visible vegetation indexes represented the larger vegetation index. (2) Gray image feature values of different visible vegetation indexes mainly distributed in range of [− 1, 1] in the research area. There was a big overlap of pixel value between vegetation and land in the gray image of normalized green-blue difference index (NGBDI) and BGRI constructed from blue and green bands, which means some partial confusion between them. (3) Visible-band difference vegetation index (VDVI) can extract slope vegetation quickly and accurately among common visible vegetation indices. The average recognition accuracy of VDVI vegetation index was 93.4% by manual visual interpretation and error matrix, showing that VDVI vegetation index could be more suitable for the vegetation extraction on the mine dump in Wuhai City, and was better than the vegetation index of other common visible light. The vegetation coverage of the study area was approximately 20.4% by this method.
ConclusionVisible vegetation index, an unsupervised classified method, could be a new method for investigating vegetation coverage of mine dump slope without selecting reference features by people but extract slope vegetation coverage directly and has a broad application prospect. The study also indicates that VDVI shows higher extraction accuracy of extracting vegetation coverage of mine dump slope in the Wuhai City, which means a practical significance to guide the vegetation restoration in local mine dump.
- unmanned aerial vehicles,
- visible-bands,
- vegetation index,
- dump,
- Wuhai City

FullText(HTML)

References (34)

References

[1]	李登科, 范建忠, 王娟. 陕西省植被覆盖度变化特征及其成因[J]. 应用生态学报, 2010, 21(11):2896−2903. Li D K, Fan J Z, Wang J. Change characteristics and their causes of fractional vegetation coverage (FVC) in Shaanxi Province[J]. Chinese Journal of Applied Ecology, 2010, 21(11): 2896−2903.
[2]	Chen B, Xu G, Coops N C, et al. Changes in vegetation photosynthetic activity trends across the Asia-Pacific region over the last three decades[J]. Remote Sensing of Environment, 2014, 144: 28−41. doi: 10.1016/j.rse.2013.12.018
[3]	韩文霆, 邵国敏, 马代健, 等. 大田玉米作物系数无人机多光谱遥感估算方法[J]. 农业机械学报, 2018, 49(7):134−143. doi: 10.6041/j.issn.1000-1298.2018.07.017 Han W T, Shao G M, Ma D J, et al. Estimating method of crop coefficient of maize based on UAV multispectral remote sensing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(7): 134−143. doi: 10.6041/j.issn.1000-1298.2018.07.017
[4]	虞连玉, 蔡焕杰, 姚付启, 等. 植被指数反演冬小麦植被覆盖度的适用性研究[J]. 农业机械学报, 2015, 46(1):231−239. doi: 10.6041/j.issn.1000-1298.2015.01.033 Yu L Y, Cai H J, Yao F Q, et al. Applicability of vegetation indices to estimate fractional vegetation coverage[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(1): 231−239. doi: 10.6041/j.issn.1000-1298.2015.01.033
[5]	孙刚, 黄文江, 陈鹏飞, 等. 轻小型无人机多光谱遥感技术应用进展[J]. 农业机械学报, 2018, 49(3):1−17. doi: 10.6041/j.issn.1000-1298.2018.03.001 Sun G, Huang W J, Chen P F, et al. Advances in UAV based multispectral remote sensing applications[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(3): 1−17. doi: 10.6041/j.issn.1000-1298.2018.03.001
[6]	郭铌. 植被指数及其研究进展[J]. 干旱气象, 2003, 21(4):71−75. Guo N. Vegetation index and its advances[J]. Arid Meteorology, 2003, 21(4): 71−75.
[7]	罗亚, 徐建华, 岳文泽, 等. 植被指数在城市绿地信息提取中的比较研究[J]. 遥感技术与应用, 2006, 21(3):212−219. doi: 10.3969/j.issn.1004-0323.2006.03.008 Luo Y, Xu J H, Yue W Z, et al. A comparative study of extracting urban vegetation information by vegetation indices from thematic mapper images[J]. Remote Sensing Technology and Application, 2006, 21(3): 212−219. doi: 10.3969/j.issn.1004-0323.2006.03.008
[8]	Groten S M E. NDVI:crop monitoring and early yield assessment of Burkina Faso[J]. Title Remote Sensing, 1993, 14(8): 1495−1515. doi: 10.1080/01431169308953983
[9]	Neigh C S R, Tucker C J, Townshend J R G. North American vegetation dynamics observed with multi-resolution satellite data[J]. Remote Sensing of Environment, 2008, 112(4): 1749−1772.
[10]	Piao S, Nan H, Hunting F C, et al. Evidence for a weakening relationship between inter annual temperature variability and northern vegetation activity[J/OL]. Nature Communications, 2014, 5: 5018 [2019−09−13]. https://www.nature.com/articles/ncomms6018.
[11]	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):152−159. doi: 10.3969/j.issn.1002-6819.2015.05.022 Wang X Q, Wang M M, Wang S Q, et al. Extraction of vegetation information from visible unmanned aerial vehicle images[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(5): 152−159. doi: 10.3969/j.issn.1002-6819.2015.05.022
[12]	毛智慧, 邓磊, 贺英, 等. 利用色调—亮度彩色分量的可见光植被指数[J]. 中国图象图形学报, 2017, 22(11):1602−1610. Mao Z H, Deng L, He Y, et al. Vegetation index for visible-light true-color image using hue and lightness color channels[J]. Journal of Image and Graphics, 2017, 22(11): 1602−1610.
[13]	周勇兵, 朱永清. 基于可见光波段的城市航空影像植被提取[J]. 绿色科技, 2016(16):247−250. doi: 10.3969/j.issn.1674-9944.2016.16.097 Zhou Y B, Zhu Y Q. Vegetation extraction in urban aerial image on the basis of visible light wave[J]. Journal of Green Science and Technology, 2016(16): 247−250. doi: 10.3969/j.issn.1674-9944.2016.16.097
[14]	邓继忠, 任高生, 兰玉彬, 等. 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):16−22. doi: 10.7671/j.issn.1001-411X.2016.06.003 Deng J Z, Ren G S, Lan Y B, et al. Low altitude unmanned aerial vehicle remote sensing image processing based on visible band[J]. Journal of South China Agricultural University, 2016, 37(6): 16−22. doi: 10.7671/j.issn.1001-411X.2016.06.003
[15]	丁雷龙, 李强子, 杜鑫, 等. 基于无人机图像颜色指数的植被识别[J]. 国土资源遥感, 2016, 28(1):78−86. doi: 10.6046/gtzyyg.2017.01.12 Ding L L, Li Q Z, Du X, et al. Vegetation extraction method based on color indices from UAV images[J]. Remote Sensing for Land and Resources, 2016, 28(1): 78−86. doi: 10.6046/gtzyyg.2017.01.12
[16]	张正健, 李爱农, 边金虎, 等. 基于无人机影像可见光植被指数的若尔盖草地地上生物量估算研究[J]. 遥感技术与应用, 2016, 31(1):51−62. Zhang Z J, Li A N, Bian J H, et al. Estimating aboveground biomass of grassland in zoige by visible vegetation index derived from unmanned aerial vehicle image[J]. Remote Sensing Technology and Application, 2016, 31(1): 51−62.
[17]	赵静, 杨焕波, 兰玉彬, 等. 基于无人机可见光图像的夏季玉米植被覆盖度提取方法[J]. 农业机械学报, 2019, 50(5):232−240. doi: 10.6041/j.issn.1000-1298.2019.05.027 Zhao J, Yang H B, Lan Y B, et al. Extraction method of summer corn vegetation coverage based on visible light image of unmanned aerial vehicle[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(5): 232−240. doi: 10.6041/j.issn.1000-1298.2019.05.027
[18]	张泽民, 吕昌河, 谢苗苗, 等. 基于WorldView 2影像的矿区植被重建效果评估[J]. 生态学报, 2018, 38(4):1301−1310. Zhang Z M, Lü C H, Xie M M, et al. Evaluation of vegetation restoration effects in mining areas based on WorldView 2 images[J]. Acta Ecologica Sinica, 2018, 38(4): 1301−1310.
[19]	赵廷宁, 张玉秀, 曹兵, 等. 西北干旱荒漠区煤炭基地生态安全保障技术[J]. 水土保持学报, 2018, 32(1):1−5. Zhao T N, Zhang Y X, Cao B, et al. Eco-security technology for coal mining bases in the northwestern arid desert regions in China[J]. Journal of Soil and Water Conservation, 2018, 32(1): 1−5.
[20]	Gitelson A A, Kaufman Y J, Stark R, et al. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment, 2002, 80(1): 76−87. doi: 10.1016/S0034-4257(01)00289-9
[21]	Hunt E R, Cavigelli M, Daughtry C S T, et al. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status[J]. Precision Agriculture, 2005, 6(4): 359−378. doi: 10.1007/s11119-005-2324-5
[22]	Woebbecke D M, Meyer G E, Von Bargen K, et al. Color indices for weed identification under various soil, residue, and lighting conditions[J]. Transactions of the ASAE, 1995, 38(1): 259−269. doi: 10.13031/2013.27838
[23]	孙国祥, 汪小旵, 闫婷婷, 等. 基于机器视觉的植物群体生长参数反演方法[J]. 农业工程学报, 2014, 30(20):187−195. doi: 10.3969/j.issn.1002-6819.2014.20.023 Sun G X, Wang X C, Yan T T, et al. Inversion method of flora growth parameters based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2014, 30(20): 187−195. doi: 10.3969/j.issn.1002-6819.2014.20.023
[24]	Hague T, Tillett N D, Wheeler H. Automated crop and weed monitoring in widely spaced cereals[J]. Precision Agriculture, 2006, 7(1): 21−32. doi: 10.1007/s11119-005-6787-1
[25]	Verrelst J, Schaepman M E, Koetz B, et al. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data[J]. Remote Sensing of Environment, 2008, 112(5): 2341−2353. doi: 10.1016/j.rse.2007.11.001
[26]	Sellaro R, Crepy M, Trupkin S A, et al. Cryptochrome as a sensor of the blue/green ratio of natural radiation in Arabidopsis[J]. Plant Physiology, 2010, 154(1): 401−409. doi: 10.1104/pp.110.160820
[27]	Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62−66. doi: 10.1109/TSMC.1979.4310076
[28]	李了了, 邓善熙, 丁兴号. 基于大津法的图像分块二值化算法[J]. 微计算机信息, 2005, 21(24):76−77. doi: 10.3969/j.issn.1008-0570.2005.24.027 Li L L, Deng S X, Ding X H. Binarization algorithm based on image partition derived from Da-jin Method[J]. Control and Automation, 2005, 21(24): 76−77. doi: 10.3969/j.issn.1008-0570.2005.24.027
[29]	闫利, 莫楠, 费亮, 等. 倾斜影像整体变分模型阴影检测算法改进[J]. 遥感信息, 2017, 32(2):54−59. doi: 10.3969/j.issn.1000-3177.2017.02.009 Yan L, Mo N, Fei L, et al. Improved shadow detection algorithm based on total variation model using oblique images[J]. Remote Sensing Information, 2017, 32(2): 54−59. doi: 10.3969/j.issn.1000-3177.2017.02.009
[30]	卞雪, 马群宇, 刘楚烨, 等. 基于低空可见光谱的植被覆盖率计算[J]. 水土保持通报, 2017, 37(5):270−275. Bian X, Ma Q Y, Liu C Y, et al. Vegetation coverage calculation based on low altitude visible spectrum[J]. Bulletin of Soil and Water Conservation, 2017, 37(5): 270−275.
[31]	朱孟, 周忠发, 赵馨, 等. 基于无人机遥感的喀斯特高原峡谷区火龙果单株识别提取方法[J]. 热带地理, 2019, 39(4):502−511. Zhu M, Zhou Z F, Zhao X, et al. Recognition and extraction method of single dragon fruit plant in plateau-canyon areas based on UAV remote sensing[J]. Tropical Geography, 2019, 39(4): 502−511.
[32]	郭鹏, 武法东, 戴建国, 等. 基于无人机可见光影像的农田作物分类方法比较[J]. 农业工程学报, 2017, 33(13):112−119. doi: 10.11975/j.issn.1002-6819.2017.13.015 Guo P, Wu F D, Dai J G, et al. Comparison of farmland crop classification methods based on visible light images of unmanned aerial vehicles[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(13): 112−119. doi: 10.11975/j.issn.1002-6819.2017.13.015
[33]	李莹, 于海洋, 王燕, 等. 基于无人机重建点云与影像的城市植被分类[J]. 国土资源遥感, 2019, 31(1):149−155. doi: 10.6046/gtzyyg.2019.01.20 Li Y, Yu H Y, Wang Y, et al. Classification of urban vegetation based on unmanned aerial vehicle reconstruction point cloud and image[J]. Remote Sensing for Land and Resources, 2019, 31(1): 149−155. doi: 10.6046/gtzyyg.2019.01.20
[34]	郭震冬, 顾正东, 许盛, 等. 利用无人机技术进行社区植被覆盖率调查[J]. 北京测绘, 2017(5):88−91. Guo Z D, Gu Z D, Xu S, et al. The design and function development of the rural land ownership data processing software[J]. Beijing Surveying and Mapping, 2017(5): 88−91.

[1]	Zhou Kerou, Chen Zhuo, Yu Zhucheng, Zhong Yang, Shang Ce. Population structure and genetic diversity of Bretschneidera sinensis in Xianxialing Nature Reserve, Zhejiang Province of eastern China[J]. Journal of Beijing Forestry University, 2024, 46(11): 76-82. DOI: 10.12171/j.1000-1522.20230211
[2]	Qu Kai, Guo Haoping, Wang Baorui, Zhou Wenling, Hou Lili, Li Qin, Li Jihong, Cheng Tiantian. Genetic diversity analysis of Chionanthus retusus natural population based on SRAP molecular markers[J]. Journal of Beijing Forestry University, 2020, 42(12): 40-50. DOI: 10.12171/j.1000-1522.20200212
[3]	Yao Junxiu, Mao Xiuhong, Li Shanwen, Liu Xueliang, Wu Dejun. Genetic diversity of germplasm resources of Leuce based on SSR fluorescent marker[J]. Journal of Beijing Forestry University, 2018, 40(6): 92-100. DOI: 10.13332/j.1000-1522.20170429
[4]	ZHOU Peng, LIN Wei, ZHU Qin, ZHOU Xiang-bin, WU Lin-ying, CHEN Xiao-yang. Genetic diversity of Machilus pauhoi assessed by SRAP markers.[J]. Journal of Beijing Forestry University, 2016, 38(9): 16-24. DOI: 10.13332/j.1000-1522.20150423
[5]	CHEN Ling-na, MA Qing-guo, ZHANG Jun-pei, ZHOU Bei-bei, PEI Dong. Development of BAC-SSR markers in walnut and its application in genetic diversity analysis[J]. Journal of Beijing Forestry University, 2014, 36(6): 24-29. DOI: 10.13332/j.cnki.jbfu.2014.06.008
[6]	LI Tian, GUO Jun-e, ZHENG Cheng-shu, SUN Xia, SUN Xian-zhi. Genetic diversity and construction of fingerprinting of chrysanthemum cultivars by CDDP markers[J]. Journal of Beijing Forestry University, 2014, 36(4): 94-101. DOI: 10.13332/j.cnki.jbfu.2014.04.018
[7]	YU Xiao-nan, JI Li-jing, WANG Qi. Research advances in molecular genetic diversity of Paeonia L.[J]. Journal of Beijing Forestry University, 2012, 34(3): 130-136.
[8]	LIAO Hui-rong, GU Wan-chun, MING Jun. Determining genetic diversity of natural population of Syringa oblatausing allozyme markers.[J]. Journal of Beijing Forestry University, 2009, 31(5): 84-89.
[9]	LI Lun-guang, HE Ping, HE Wei. Genetic diversity of fiveneedle pine blister rusts detected by random amplified microsatellite (RAMS) in China.[J]. Journal of Beijing Forestry University, 2008, 30(6): 112-118.
[10]	ZHANG Yu-rong, LUO Ju-chun, YU Jin-xiu. Genetic diversity of the endangered plant Abies ziyuanensis detected by ISSR markers[J]. Journal of Beijing Forestry University, 2007, 29(6): 41-46. DOI: 10.13332/j.1000-1522.2007.06.012

Cited By

Get Citation

PDF

XML

Article views (2069) PDF downloads (93)

Turn off MathJax

Article Contents

Abstract

References

Vegetation coverage information extraction of mine dump slope in Wuhai City of Inner Mongolia based on visible vegetation index

Abstract

References

Related Articles

Catalog

Vegetation coverage information extraction of mine dump slope in Wuhai City of Inner Mongolia based on visible vegetation index

Abstract

References

Related Articles

Catalog

Export File

Citation

Format

Content