Geology topics

Thomas R.H. Holmes

Publications and source records attributed to Thomas R.H. Holmes.

2 recordsLinked to original sources

USGS2025

Toward a near-lossless image compression strategy for the NASA/USGS Landsat Next mission

As orbiting Earth imaging platforms carry more complex and capable instruments, efficient methods are needed to reduce the time and cost associated with storing and downlinking greater volumes of image data. The upcoming NASA/USGS Landsat Next mission, with an increase in spatial and spectral resolution over previous Landsat missions, is no exception. Landsat Next will produce nearly six times the amount of image data per day over either of the current Landsat 8 or Landsat 9 observatories. Near-lossless compression, where the image after compression is not identical to the original image, allows for the efficient storage and transmission of all image data while meeting the mission’s global coverage, temporal revisit frequency, and science measurement and performance requirements. Although the Landsat user community is understandably cautious about lossy compression, it is possible to constrain the maximum loss, or error, introduced during compression, ensuring that any added error remains within the intrinsic noise level of the instrument. The Consultative Committee for Space Data Systems image compression standard, CCSDS 123.0-B-2, was chosen for the Landsat Next mission because it is an internationally supported standard suited for integration with space hardware, and it allows control over the magnitude and distribution of compression error. Using several proxy datasets as a surrogate for Landsat Next image data, an investigation was performed to determine a preliminary set of parameter values that would keep the added compression error within acceptable limits. The results of these studies demonstrate that near-lossless image compression can be utilized by the Landsat Next instruments to store and downlink all science data without compromising image quality or mission requirements.

Remote Sensing of Environment↗

USGS2021

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet , recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Remote Sensing of Environment↗