多背景条件下铁矿区降尘量的光谱反演实验研究

doi:10.12068/j.issn.1005-3026.2022.11.012

东北大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (11): 1607-1612.DOI: 10.12068/j.issn.1005-3026.2022.11.012

多背景条件下铁矿区降尘量的光谱反演实验研究

马保东，杨湘儒，刘全，车德福

(东北大学资源与土木工程学院，辽宁沈阳110819)

发布日期:2022-12-06
通讯作者: 马保东
作者简介:马保东(1983-)，男，河北宽城人，东北大学副教授.
基金资助:
国家自然科学基金资助项目(41871310)；中央高校基本科研业务费专项资金资助项目(N2124005，N2001020).

Experimental Study on Spectral Retrieval of Dustfall in Iron Ore Areas Under Multi-background Conditions

MA Bao-dong， YANG Xiang-ru， LIU Quan， CHE De-fu

School of Resources & Civil Engineering， Northeastern University， Shenyang 110819， China.

Published:2022-12-06
Contact: MA Bao-dong
About author:-
Supported by:
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摘要/Abstract

摘要： 我国一些矿区的粉尘污染比较严重，利用遥感技术可实现全面、快速的降尘量监测.在铁矿区开展四类典型背景下的级差降尘光谱测量实验，研究不同背景下降尘量的反演精度差异.结果显示，对于高光谱模型(350~2500nm)，基于900nm光谱吸收指数(SAI)建模时植物背景反演精度最高(误差为4.92g/m²)；基于统计方法获得优势波段进行反演，发现植被和油毡楼顶的反演精度较高(误差分别为6.02和7.35g/m²).对于多光谱模型(按Landsat-8 OLI波段设置)，亦是植物和油毡楼顶背景在第7波段反演精度较高(误差分别为6.19和7.93g/m²).综合来看，植物背景下的降尘量反演精度最高，可作为降尘遥感监测的首选；若受限于生长季，可优先选择油毡楼顶背景.在无高光谱数据时，则以多光谱的第7波段为首选反演波段.

关键词: 铁矿区；降尘量；反演精度；多背景；光谱

Abstract: Dust pollution is serious in some mining areas. Remote sensing could monitor dustfall comprehensively and rapidly. Four kinds of typical backgrounds were selected to carry out iron dustfall spectroscopy measurement to study the accuracy difference of dustfall retrieval. The results show that the retrieval accuracy of plant background was the highest(error is 4.92g/m²) based on 900nm spectral absorption index(SAI)in the hyperspectral model(350~2500nm). By retrieving from the dominant band obtained by the statistical method， the retrieval accuracy on the plant and linoleum roof was relatively high(error is 6.02 and 7.35g/m²， respectively). For the multi-spectral model(according to Landsat-8 OLI bands)， the retrieving accuracy on the plant and linoleum roof was also high in the 7th band(error is 6.19 and 7.93g/m²， respectively). In summary， the dustfall retrieval accuracy under the plants background is the highest， which can be used as the first choice for dust fall remote sensing monitoring; if it is limited by the growing season， the background of linoleum roof can be the first choice. If there is no hyperspectral data， the 7th band of multispectral should be the first choice.

Key words: iron ore area; dustfall; inversion accuracy; multi-background; spectra

中图分类号:

P237

马保东，杨湘儒，刘全，车德福. 多背景条件下铁矿区降尘量的光谱反演实验研究[J]. 东北大学学报（自然科学版）, 2022, 43(11): 1607-1612.

MA Bao-dong， YANG Xiang-ru， LIU Quan， CHE De-fu. Experimental Study on Spectral Retrieval of Dustfall in Iron Ore Areas Under Multi-background Conditions[J]. Journal of Northeastern University(Natural Science), 2022, 43(11): 1607-1612.

参考文献

[1]Ma B D，Li X，Liang A，et al.Experimental and numerical investigation of dustfall effect on remote sensing retrieval accuracy of chlorophyll content［J］.Sensors，2019，19(24):5530.
[2]Arrivabene H P，da Costa Souza I，Có W L O，et al.Effect of pollution by particulate iron on the morphoanatomy，histochemistry，and bioaccumulation of three mangrove plant species in Brazil［J］.Chemosphere，2015，127:27-34.
[3]Neff J C，Ballantyne A P，Farmer G L，et al.Increasing eolian dust deposition in the western United States linked to human activity［J］.Nature Geoscience，2008，1(3):189-195.
[4]韩跃新，孙永升，李艳军，等.我国铁矿选矿技术最新进展［J］.金属矿山，2015(2):1-11.(Han Yue-xin，Sun Yong-sheng，Li Yan-jun，et al.New development on mineral processing technology of iron ore resources in China［J］.Metal Mine，2015(2):1-11.)
[5]张正偲，董治宝.降尘收集方法对降尘效率的影响［J］.环境科学，2013，34(2):499-508.(Zhang Zheng-si，Dong Zhi-bao.Effect of dust deposition collection methods on collection efficiency［J］.Environmental Science，2013，34(2):499-508.)
[6]Vermote E，Justice C，Claverie M，et al.Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product［J］.Remote Sensing of Environment，2016，185:46-56.
[7]Hmida S B，Kallel A，Gastellu-Etchegorry J P，et al.Crop biophysical properties estimation based on LiDAR full-waveform inversion using the DART RTM［J］.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2017，10(11):4853-4868.
[8]Jacquemoud S，Verhoef W，Baret F，et al.PROSPECT+SAIL models:a review of use for vegetation characterization［J］.Remote Sensing of Environment，2009，113:S56-S66.
[9]莱昂，项月琴，张良培.风化及其它类荒漠漆表面层对高光谱分辨率遥感的影响(一)［J］.环境遥感，1996，11(2):138-150.(Lyon R J P，Xiang Yue-qin，Zhang Liang-pei.The effects of weathering and other varnish-like coatings on high spectral resolution airborne imagery(part one)［J］.Remote Sensing of Environment China，1996，11(2):138-150.)
[10]Pascucci S，Belviso C，Cavalli R M，et al.Using imaging spectroscopy to map red mud dust waste:the Podgorica Aluminum Complex case study［J］.Remote Sensing of Environment，2012，123:139-154.
[11]Kayet N，Pathak K，Chakrabarty A，et al.Assessment of foliar dust using Hyperion and Landsat satellite imagery for mine environmental monitoring in an open cast iron ore mining areas［J］.Journal of Cleaner Production，2019，218:993-1006.
[12]Yan X，Shi W，Zhao W，et al.Mapping dustfall distribution in urban areas using remote sensing and ground spectral data［J］.Science of the Total Environment，2015，506:604-612.
[13]孙腾腾，林文鹏，李莹，等.不同滞尘量对城市植物冠层光谱的影响［J］.光谱学与光谱分析，2017，37(8):2539-2545.(Sun Teng-teng，Lin Wen-peng，Li Ying，et al.Effect of different dust weight levels on unban canopy reflectance spectroscopy［J］.Spectroscopy and Spectral Analysis，2017，37(8):2539-2545.)
[14]Ma B D，Pu R，Wu L，et al.Vegetation index differencing for estimating foliar dust in an ultra-low-grade magnetite mining area using Landsat imagery［J］.IEEE Access，2017，5:8825-8834.
[15]徐奥，马保东，李兴春，等.植物叶面铁尾矿粉尘光谱测试与定量反演实验［J］.国土资源遥感，2017，29(1):164-169.(Xu Ao，Ma Bao-dong，Li Xing-chun，et al.Spectral testing and quantitative inversion for dust of iron tailings on leaf［J］.Remote Sensing for Land & Resources，2017，29(1):164-169.)

多背景条件下铁矿区降尘量的光谱反演实验研究

Experimental Study on Spectral Retrieval of Dustfall in Iron Ore Areas Under Multi-background Conditions

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