Although field-based measurements at individual mine sites are critical methods for assessing site conditions and recovery needs, these methods cannot be applied retrospectively to provide insight into previous site conditions and can be time- and cost-prohibitive when applied across large geographic extents.
Remote sensing, however, can provide measurements of past and present surface characteristics and can be applied across large geographic extents, often for substantially smaller costs.
During the past two decades, the use and availability of remote sensing data have increased considerably, providing numerous methods for evaluating degradation and recovery of mine lands.
These data and methods offer a broad spatial and temporal scope (some dating back half a century) that land managers can leverage for site prioritization, recovery design, and long-term assessments of recovery.
However, mine lands occur in many ecosystem types characterized by different minerals, geology, hydrology, and vegetation; therefore, the use of remote sensing to address region-wide recovery tasks requires technical expertise and some amount of field data to inform analytical methods and provide evaluation and validation of remote sensing data products.
Remote sensing is the activity of assessing surface characteristics with data acquired by a sensor that is not in direct contact with the surface (Lillesand and others, 2015). The concept behind remote sensing is that different surface materials reflect and absorb different regions of the electromagnetic spectrum, enabling their surface properties to be assessed from a sensor at a distance.
Many remote sensing instruments (sensors) are passive and measure electromagnetic radiation (for example, visible light, radio waves, gamma rays, and X-rays) emitted by the sun and reflected from an object’s surface.
Conversely, active sensors send pulses of electromagnetic radiation that reflect off a surface and return back to the sensor. Sensors can be ground based, such as instruments mounted on tripods or towers used to measure surface properties of a single plant or a small patch of grass, on aircraft (for example, balloons, remotely piloted aircraft systems, helicopters, or airplanes), or on satellites orbiting the Earth, such as Landsat Operational Land Imager and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors.
Tradeoffs among spectral, temporal, and spatial resolutions generally depend on the type of sensor and platform; for example, sensors that are ground- or aircraft-mounted often have a higher spatial resolution, whereas satellite-based sensors have a lower spatial resolution. Understanding sensor types and wavelength properties is crucial for selecting appropriate sensors for specified applications.
Each remote-sensing platform (ground, airborne, or satellite) may include one or more sensors that capture spectral data as point or gridded surfaces. Biotic and abiotic features, such as dense forests, grasslands, or exposed rocks absorb and reflect differently across the electromagnetic spectrum and exhibit different spectral curves that can be distinguished separately.
Passive sensors, such as photographic cameras, spectroradiometers, and passive microwaveradiometers, measure emitted and reflected radiation across differing wavelengths and are commonly used to map land cover types and vegetation. Active sensors can often function at night and are less affected by clouds and weather—although some may work in conjunction with passive sensors that require daylight.
Sensors detecting microwaves (wavelengths between 1 millimeter [mm] and 1 meter [m]) may be affected less by atmospheric conditions because microwaves more effectively penetrate water droplets than visible wavelengths (380–780 nanometers [nm]) and infrared wavelengths (780 nm–1 mm). Microwave sensors are often used for meteorology (for example, measuring surface winds), oceanography (for example, mapping sea ice and currents), or measuring soil moisture.
In mining applications, remote sensing provides a method for assessing surface characteristics (such as vegetation type and condition) or soil properties (such as texture, the presence of minerals, or moisture content).
Remote sensing is commonly used to evaluate vegetation recovery on mine lands (refer to McKenna and others, 2020) and to map the geographic extent of mining operations and related disturbances (for example, Hao and others, 2019; Gong and others, 2021).
For example, remote sensing can monitor changes in the spatial footprint of mining activities and vegetation recovery through time to document relations between mining activities and vegetation or soil conditions. Residual effects of mining, such as the presence of toxic metals (for example, arsenic, chromium, copper, lead, nickel, and zinc), can also be assessed using a combination of field data and remote sensing imagery (Peng and others, 2016).
Remote sensing methods, such as lidar, can also be used to measure changes in topographic relief caused by excavation, deposition, and infilling that was done during mining activities (for example, Banerjee and Raval, 2022). Recent advances in computing capacity and the increased availability of remote sensing data have led to a sharp increase in the use of remote sensing to assess mine lands in the past decade (McKenna and others, 2020).
Remote sensing alone may not provide enough information to guide recovery efforts, or to thoroughly assess vegetation recovery through time. For example, at sites where the onset of mining activities occurred prior to the availability of remote sensing data, premining conditions are nearly impossible to determine.
In nearly all reviewed studies, in situ field surveys were conducted to measure vegetation or soil properties to use in conjunction with remote sensing. Field surveys are important for monitoring vegetation change; for example, one study evaluated 15 mines in southwestern Virginia solely using field surveys and historical vegetation records to define vegetation recovery success (Holl, 2002).
However, field studies are usually labor intensive, limiting the scope of their temporal and spatial extents and precluding analyses of long-term or regional trends. Remote sensing offers a method to complement field surveys by providing surface characteristics for unsurveyed periods at low costs and for back-in-time assessments. Thus, researchers frequently pair field data with remote sensing data to train models to characterize mine land conditions in areas other than those areas where field data are collected.
For example, field data from a single mine or collected during a single time period can be used to estimate surface conditions on other mine lands or at other time periods. Doing so allows more efficient use of limited, and often expensive, field sampling.
Applications of remote sensing also often incorporate ancillary data, such as topographic characteristics or soil type, to provide additional information to characterize mine lands. Combining remote sensing with ancillary data can significantly increase the precision and accuracy of monitoring mine land recovery.
Reference
United States Geological Survey, Land Management Research Program, « Remote Sensing for Monitoring Mine Lands and Recovery Efforts. », Circular 1525.
