People following the Landsat Data Continuity Mission (LDCM) know that NASA handed the controls over to the USGS on May 30, 2013 and Landsat 8 was born. Landsat 8 builds on a 40+ year heritage of earth resources remote sensing by providing free access to multispectral imagery on a global scale.
Landsat imagery has long been used in Defense and Intelligence circles as a valuable source of GEOINT to monitor land cover change, assess agricultural yields, and as a visualization backdrop for training and battlefield simulations.
New sensors on Landsat 8, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS), provide significant improvements over the Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) instruments on previous Landsat missions. This post will explore a few ways these improvements could lead to greater adoption of Landsat data in GEOINT operations.
The Landsat 8 OLI carries two new spectral bands. The first is a deep blue channel in the visible portion of the spectrum. Information collected in this band is useful for characterizing coastal water and atmospheric aerosols. From a Defense and Intelligence perspective, this band could help to produce more accurate near shore water depth assessments; a key component to maritime mission planning.
The second new band on the OLI covers a known water absorption feature in the shortwave infrared region of the spectrum. This band is strategically positioned to detect the presence of cirrus clouds. This band is used as an input to a new Quality Assurance overlay that is included with each Landsat 8 product. Together they indicate the presence of clouds, water and snow. These data could enable more accurate change detection results as clouds are often responsible for false alarms when conducting reflectance based analysis between dates. Intelligence organizations depend on accurate, global scale, change detection to assess whether their foundation data (i.e., base maps) are current.
The last improvement I’ll mention is the increased signal-to-noise ratio achieved by moving from a whisk-broom to a push-broom sensor design. The push-broom design essentially allows Landsat 8 to get a longer look at the ground and increases the sensitivity of the radiance data collected. The improved signal-to-noise may slightly increase what is visually interpretable in the imagery but has larger implications when it comes to quantitative methods such as vegetation analysis, land cover classification, and sub-pixel material classification. The increased radiometric sensitivity may move Defense and Intelligence analysts to select Landsat 8 over higher spatial resolution assets to: delineate cover and concealment areas (e.g., dense vegetation), map the extent of water inundation or to perform a broad area search for manmade objects that are out of place.
What do you think? Will these improvements lead to new or more accurate applications in the Defense and Intelligence sector?