Over the past three decades, the National Aeronautics and Space Administration (NASA) free Landsat data have become a valuable resource for decision-makers in various fields including environmental monitoring, emergency response, agriculture, forestry, land use, water resources, and natural resource exploration (Table 1). The European Space Agency’s (ESA) recently launched Sentinel-2 platform data can be used for the same purposes, as it has similar spectral bands as Landsat-8 (Figure 1).

Table 1. Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)

Band Wavelength
Useful for mapping
Band 1 – coastal aerosol (ultra-blue) 0.43–0.45 30 Coastal and aerosol studies
Band 2 – blue 0.45–0.51 30 Bathymetric mapping, distinguishing soil from vegetation and deciduous from coniferous vegetation
Band 3 – green 0.53–0.59 30 Emphasizes peak vegetation, which is useful for assessing plant vigor
Band 4 – red 0.64–0.67 30 Discriminates vegetation slopes
Band 5 – Near Infrared (NIR) 0.85–0.88 30 Emphasizes biomass content and shorelines
Band 6 – Short-wave Infrared (SWIR) 1 1.57–1.65 30 Discriminates moisture content of soil and vegetation; penetrates thin clouds
Band 7 – Short-wave Infrared (SWIR) 2 2.11–2.29 30 Improved moisture content of soil and vegetation and thin cloud penetration
Band 8 – Panchromatic 0.50–0.68 15 Sharper image definition
Band 9 – Cirrus 1.36–1.38 30 Improved detection of cirrus cloud contamination
Band 10 – TIRS 1 10.60–11.19 100 Thermal mapping and estimated soil moisture
Band 11 – TIRS 2 11.5–12.51 100 Improved thermal mapping and estimated soil moisture


Source: https://www.usgs.gov/faqs/what-are-best-landsat-spectral-bands-use-my-research?qt-news_science_products=0#qt-news_science_products



Landsat Transmissions
Figure 1: Placement of Landsat-7, Landsat-8, and Sentinel-2 spectral bands
source: http://landsat.gsfc.nasa.gov/?p=10643)

The AGS has been investigating the use of Landsat imagery to understand cumulative environmental effects, to assist reclamation performance tracking, and to assist with the development of effective, sustainable management practices. We used Landsat data covering both the AER’s play-based regulation pilot project area in west-central Alberta and the Cold Lake oil sands area to characterize year-to-year land disturbance and vegetation recovery by creating annual land use/cover classification maps. This analysis can be used to monitor and understand the cumulative impact of various land based activities in these areas over time.

For reclamation sites, long-term (e.g., 20 years) annual vegetative health trends can be monitored to indicate whether the site is recovering towards its original state and blending with the surrounding land cover types. Figure 2 illustrates the vegetative health trend of a reclamation site that matches with the trends of surrounding reference sites, indicating the site is ready for reclamation, while Figure 3 shows the opposite scenario. Other EO analysis techniques, including supervised classification and spectral mixture analysis, can provide a general vegetation species composition that will improve the accuracy of the analysis, since using the vegetative health trend alone does not provide information about the types of vegetation growing in the area. High-resolution commercial multispectral and hyperspectral EO data can be purchased to provide additional analysis for selected areas.

Quantification of landscape changes using EO technologies can be used as an adjunct to the AER’s current compliance monitoring activities, which include site visits that are both time consuming and costly.

Reclamation Site Match

Figure 2. Reclamation site vegetative health trend matches with the reference sites.

Reclamtion Site No Match

Figure 3. Reclamation site vegetative health trend does not match with the reference sites.


Zhang, Y., Guindon, B., Lantz, N., Shipman, T., Chao, D. and Raymond, D. (2014): Quantification of anthropogenic and natural changes in oil sands mining infrastructure land based on RapidEye and SPOT5; International Journal of Applied Earth Observation and Geoinformation, v. 29, p. 31–43, doi:10.1016/j.jag.2013.11.013.