Abstract
This study demonstrated novel remote monitoring techniques for mining-impacted surface waters using spectral data from two different platforms (multispectral sUAS and handheld hyperspectral sensors) and the feasibility of using sUAS-derived multispectral imagery to estimate in-situ metal concentrations in two passive mine drainage treatment systems. Strong linear relationships (e.g. R2adj. > 0.74) were found between multispectral reflectance and various in-situ constituent concentrations (e.g. Fe, Li, Mn, Pb, and Zn). Developed ordinary least squares (OLS) models estimated mean metal concentrations within 1% of the observed value and a 70% confidence interval. Validation at a separate site treating waters of a different geologic origin allowed us to assess the models’ site-specificity. Validation of some models was not possible within this study’s statistical constraints (e.g. ± 25% of the observed in-situ value). However, two models were validated and when the linear relationships were examined with site-specific spectra (i.e. sUAS-derived multispectral imagery), significant improvements to the models were observed. Employing hyperspectral remote sensing techniques yielded a novel identification procedure for optically shallow waters. This exponential relationship (e.g. R2 = 0.73) evaluates the feasibility of using remote sensing technologies to assess water quality before any model development efforts. A tool capable of identifying remote sensing interferences will be crucial for the future of environmental remote sensing. Using sUAS to estimate in-situ water quality provides a new way to monitor passive mine water treatment systems, potentially advancing the efficiency and cost-effectiveness of monitoring and altering traditional environmental remote sensing strategies.
Zusammenfassung
Diese Studie wurde durchgeführt, um neuartige Fernüberwachungsmethoden für bergbaubedingte Oberflächengewässer unter Verwendung von Spektraldaten zweier verschiedener Plattformen (multispektrale sUAS und tragbare hyperspektrale Sensoren) zu demonstrieren und die Durchführbarkeit der Verwendung von sUAS-abgeleiteten multispektralen Bildern zur Schätzung von in-situ-Metallkonzentrationen in zwei passiven Grubenentwässerungssystemen aufzuzeigen. Die Ergebnisse beschreiben starke lineare Beziehungen (z. B. R2 adj. > 0,74) zwischen dem multispektralen Reflexionsgrad und verschiedenen in-situ-Konzentrationen von Bestandteilen (z. B. Fe, Li, Mn, Pb und Zn). Die entwickelten Modelle der gewöhnlichen kleinsten Quadrate (OLS) schätzten die mittleren Metallkonzentrationen innerhalb von 1 % des beobachteten Wertes und eines Konfidenzintervalls von 70 %. Die Validierung an einem separaten Standort, an dem Wasser eines anderen geologischen Ursprungs behandelt wurde, ermöglichte es uns, die Standortspezifität der Modelle zu bewerten. Die Validierung einiger Modelle war im Rahmen der statistischen Einschränkungen dieser Studie nicht möglich (z. B. ± 25 % des beobachteten in-situ-Wertes). Allerdings wurden zwei Modelle doch validiert, und als die linearen Beziehungen mit ortsspezifischen Spektren (d. h. mit von sUAS abgeleiteten Multispektralbildern) untersucht wurden, konnten erhebliche Verbesserungen der Modelle festgestellt werden. Durch den Einsatz hyperspektraler Fernerkundungstechniken konnte ein neuartiges Identifizierungsverfahren für optisch flache Gewässer entwickelt werden. Diese exponentielle Beziehung (z. B. R2 = 0,73) liefert eine Bewertung der Durchführbarkeit des Einsatzes von Fernerkundungstechnologien zur Messung der Wasserqualität vor der Entwicklung von Modellen. Ein Instrument, das in der Lage ist, Interferenzen der Fernerkundung zu erkennen, wird für die Zukunft der Umwelt-Fernerkundung von entscheidender Bedeutung sein. Der Einsatz von sUAS zur Abschätzung der Wasserqualität vor Ort bietet eine neue Möglichkeit zur Überwachung passiver Grubenwasseraufbereitungssysteme, die die Effizienz und Kosteneffizienz der Überwachung steigern und die traditionellen Strategien der Umweltfernerkundung verändern könnte.
Resumen
Este estudio se llevó a cabo para demostrar nuevas técnicas de monitorización remota de aguas superficiales afectadas por la minería utilizando datos espectrales de dos plataformas diferentes (sUAS multiespectral y sensores hiperespectrales de mano) y para destacar la viabilidad de utilizar imágenes multiespectrales derivadas de sUAS para estimar las concentraciones de metales in situ en dos sistemas de tratamiento de drenaje minero pasivo. Los resultados describen fuertes relaciones lineales (por ejemplo, R2 adj. > 0.74) entre la reflectancia multiespectral y varias concentraciones de componentes in situ (por ejemplo, Fe, Li, Mn, Pb y Zn). Los modelos de mínimos cuadrados ordinarios (OLS) desarrollados estimaron las concentraciones medias de metales dentro del 1% del valor observado y un intervalo de confianza del 70%. La validación en un lugar distinto en el que se trataron aguas de origen geológico diferente nos permitió evaluar la especificidad del lugar de los modelos. La validación de algunos modelos no fue posible dentro de las limitaciones estadísticas de este estudio (por ejemplo, ± 25% del valor in situ observado). Sin embargo, se validaron dos modelos y cuando se examinaron las relaciones lineales con espectros específicos del lugar (es decir, imágenes multiespectrales derivadas de sUAS), se observaron mejoras significativas en los modelos. El empleo de técnicas de teledetección hiperespectral dio lugar a un novedoso procedimiento de identificación de aguas ópticamente poco profundas. Esta relación exponencial (por ejemplo, R2 = 0.73) permite evaluar la viabilidad del uso de las tecnologías de teledetección para evaluar la calidad del agua antes de cualquier esfuerzo de desarrollo de modelos. Una herramienta capaz de identificar las interferencias de la teledetección será crucial para el futuro de la teledetección medioambiental. El uso de sUAS para estimar la calidad del agua in situ proporciona una nueva forma de supervisar los sistemas pasivos de tratamiento del agua de las minas, avanzando potencialmente la eficiencia y la rentabilidad de la supervisión y alterando las estrategias tradicionales de teledetección Ambiental.
研究提出的新型远程监测技术利用了两种不同平台光谱数据 (多光谱无人机系统和手持式高光谱传感器), 应用于受采矿影响地表水体, 突出了应用无人机多光谱图像评估两个矿井水被动处理场水体的金属离子原位测试浓度的可行性。研究结果描述了多光谱反射率与各原位成分浓度 (如铁、锂、锰、铅和锌) 之间的强线性关系 (例如R2 > 0.74)。所建普通最小二乘法 (OLS) 模型估算的平均金属浓度在观测值1%以内, 置信区间为70%。通过一处单独的不同地质来源的现场处理水体验证, 评估模型的场地特定性。在研究的统计约束以内 (例如, 原位观测值的 ± 25%之间), 一些模型无法验证。然而, 两个模型得到了验证; 当用特定地点的光谱 (例如, 无人机多光谱图像) 检验线性关系之后, 模型明显得到改善。采用高光谱遥感技术产生了一种识别光学浅水体的新程序。这种指数函数关系 (如R 2 = 0.73) 为模型建立之前利用遥感技术进行水质评估提供了可行性评价。一种能够识别遥感干扰的工具对环境遥感的未来至关重要。基于无人机系统的原位水质评价技术为监测矿井水被动处理提供了一种新方法, 有望提升传统环境遥感方法监测与预警的效率和效益。
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This work was supported by the Grand River Dam Authority (Agreements GRDA 060910 and GRDA 08272015). The authors appreciate the support from members of the Center for Restoration of Ecosystems and Watersheds (CREW), the additional spectral instrumentation provided by the Earth Observation and Modeling Facility (EOMF), and access to the property of private landowners.
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Holzbauer-Schweitzer, B.K., Nairn, R.W. Using sUAS for the Development and Validation of Surface Water Quality Models in Optically Deep Mine Waters. Mine Water Environ 41, 237–251 (2022). https://doi.org/10.1007/s10230-022-00847-w
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DOI: https://doi.org/10.1007/s10230-022-00847-w