Foliar dust information extraction and dust source identification in mining area based on Sentinel-2 data

SHUAI Shuang; ZHANG Zhi; LYU Xinbiao; CHEN Si; MA Zicheng; XIE Cuirong

doi:10.11834/jrs.20221051

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Foliar dust information extraction and dust source identification in mining area based on Sentinel-2 data
Vol. 27, Issue 9, Pages: 2165-2178(2023)
Received：24 February 2021，

Published：07 September 2023
DOI： 10.11834/jrs.20221051
稿件说明：

移动端阅览

帅爽，张志，吕新彪，陈思，马梓程，谢翠容.2023.基于哨兵二号数据的矿区叶面降尘信息提取及尘源识别.遥感学报，27（9）： 2165-2178 DOI： 10.11834/jrs.20221051.

Shuai S，Zhang Z，Lyu X B，Chen S， Ma Z C and Xie C R. 2023. Foliar dust information extraction and dust source identification in mining area based on Sentinel-2 data. National Remote Sensing Bulletin， 27（9）：2165-2178 DOI： 10.11834/jrs.20221051.

摘要

叶面降尘遥感监测是评估矿山粉尘污染状况的重要手段之一，与自然粉尘相比，重金属富集的矿山粉尘对人类健康和植被生长造成更严重的威胁。以往叶面降尘遥感监测大多针对降尘量进行反演和监测，而没有研究矿区粉尘与自然粉尘的差异。本文使用哨兵二号数据，以内蒙古自治区甲乌拉—查干铅锌银矿区为例，在分析叶面降尘的光谱响应特征基础上，基于特征向量主成分选择FPCS（Feature-oriented Principal Components Selection）方法，提取研究区叶面降尘范围和强度，在分析矿山尘源与自然尘源光谱特征差异基础上，建立了尘源光谱指数DSI（Dust-source Spectrum Index），用于区分矿山粉尘与自然粉尘，并分析了叶面降尘类型、强度与矿山地物分布的相关性，以及主要矿山尘源的扬尘扩散特征。结果表明叶面降尘导致植被可见光波段反射率升高、近红外波段反射率降低、植被红边“蓝移”，远离粉尘源方向，可见光波段反射率逐渐降低，红边位置逐渐向长波方向移动；矿山尘源与自然尘源光谱特征存在差异，矿山尘源叶面降尘像元在864.7 nm附近显示反射率吸收特征；FPCS成功提取了研究区叶面降尘的范围和强度，DSI能有效区分矿山降尘与自然降尘，提取的矿山叶面降尘像元与矿山地物空间相关性强；研究区主要矿山尘源为废石堆和矿山道路，其中废石堆扬尘扩散强度和距离大于矿山道路。该研究可为矿山开发粉尘污染状况快速评估提供一种技术思路。

Abstract

Remote sensing monitoring of foliar dust is one of the important methods to assess mine dust pollution. Compared with natural dust

mine dust

enriched with heavy metals

poses a more serious threat to human health and vegetation growth. Recently

most of foliar dust monitoring carried out inversion and monitoring for the amount of foliar dust

without distinguishing between mine dust and natural dust in mining areas. In this paper

methods were proposed to extract foliar dust information and to identify dust source

taking the Jiawula–Chagan Pb-Zn-Ag mining area in Inner Mongolia as an example.

The Feature-oriented Principal Component Selection (FPCS) method was applied to extract the distribution and intensity of foliar dust based on the analysis of the spectral response characteristics of foliar dust fall for Sentinel-2 data. Moreover

a Dust-source Spectrum Index (DSI) was proposed based on the reflectance differences between mine foliar dust and natural foliar dust

and DSI was used to distinguish the extracted foliar dust information between mine dust and natural dust. Then

the correlation between the type of dust source

the intensity of foliar dust

and the distribution of mine objects was analyzed

and the main mine dust sources and their dust diffusion characteristics were assessed. Finally

minimum noise fraction and pixel purity index methods were applied to analyze spectral characteristics difference and mineral composition difference between natural dust and mine dust.

Results showed foliar dust increased the vegetation reflectance in visible regions (B1—B4)

decreased the vegetation reflectance in near infrared regions (B7—B8A)

and shifted the red edge of the vegetation to shorter wavelength direction. The spectral reflectance gradually decreased with the increase of distances away from the mine dust sources

while the spectral reflectance gradually decreased in visible regions and increased in near infrared regions with the increase of distances away from dry lake dust sources. Mine dust sources and the affecting vegetation showed reflectance absorption characteristics near 864.7 nm (B8A)

attributed to pyrite oxidation. The distribution and intensity of foliar dust were successfully extracted by FPCS and compared with the reported experience model. DSI could distinguish mine dust fall from natural dust fall. The extracted mine foliar dust pixels had a strong spatial correlation with the mine objects. The main mine dust sources in the study area were mine dumps and mine roads

the dust diffusion intensity of mine dumps was greater

and the dust diffusion of mine dumps spread farther.

The sensitivity of Sentinel-2 reflectance to identify the intensity of foliar dust fall and to distinguish the type of dust source was verified. FPCS could be applied to extract the distribution and intensity of foliar dust

without ground sampling. The extracted mine foliar dust pixels were classified into mine dust fall and natural dust fall using the proposed DSI and achieved excellent results. This paper provided a method for the rapid assessment of dust pollution in mine areas.

关键词

Keywords

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