Geology topics

Jason Laura

Publications and source records attributed to Jason Laura.

9 recordsLinked to original sources

USGS2025

Medium resolution lunar topography: An updated view of the lunar surface from stereophotogrammetrically derived DTMs

Digital Terrain Models (DTMs) are essential for lunar exploration, supporting critical applications such as landing site selection,mission planning, and in situ resource utilization. This study presents a comprehensive reprocessing of the Kaguya Terrain Camera (TC) dataset using the NASA Ames Steropipeline to generate over 130,000 medium-resolution DTMs with nearly global lunar coverage to ±70°. We observed significant bowing in the DTMs derived using the publicly available interior orientation and have used a novel technique, in the planetary sciences, to re-estimate the boresight and eight-parameter distortion coefficients. Inter-DTM vertical alignment errors are also present due to spacecraft jitter and we present the methodology used to jitter correct the DTMs. DTMs are aligned to the Lunar Orbiter Laser Altimeter (LOLA) point cloud and absolute errors are mean centered to zero with a mean standard deviation of 24 meters. Leveraging advanced stereophotogrammetry techniques, updated sensor models, and rigorous alignment to the Lunar Orbiter Laser Altimeter (LOLA) dataset, we achieved horizontal accuracies inside the orthoimage resolution (mean of 30 meters per pixel) and 2-4 meters vertically. These high-quality DTMs serve as critical inputs for planetary science, engineering, and future lunar mission planning. The methods and insights gained from this large-scale processing effort establish a framework for automating DTM derivation on other planetary bodies.

Conference Paper↗

USGS2024

Landed planetary sensor model support with the community sensor model

This note describes recent work to develop landed planetary sensor model support conforming to the widely adopted Community Sensor Model (CSM) standard. We describe the CSM specification and its use in the planetary sciences, introduce the test sensors carried aboard the Mars Science Laboratory (MSL) Curiosity Rover, and describe the engineering research requirements fulfilled. Ultimately, we demonstrate use of a CSM framing sensor model for the NavCam and MastCam instruments including stereo terrain extraction and map projection using the NASA Ames Stereopipeline over a multi-day traverse and multi-image correspondence identification needed as an input to stereophotogrammetric block bundle adjustment using the USGS maintained ISIS library.

Conference Paper↗

USGS2024

Current status of the community sensor model standard for the generation of planetary digital terrain models

The creation of accurate elevation models (topography) from stereo images are critical for a large variety of geospatial activities, including the production of digital orthomosaics, change detection, landing site analysis, geologic mapping, rover traverse planning, and spectral analysis. The United Stated Geological Survey, Astrogeology Science Center, continues to transition the supported planetary sensor models to the Community Sensor Model (CSM) standard. This paper describes the current state of use for this photogrammetric standard, supported sensor model types, and qualitatively compares derived topography between SOCET SET and SOCET GXP ( ® BAE Systems) using HiRISE stereo images of Mars. Our transition to the CSM standard will ensure an uninterrupted capability to make these valuable products for Mars and many other extraterrestrial planets and moons.

Remote Sensing↗

USGS2022

Introduction to the Python Hyperspectral Analysis Tool (PyHAT)

Spectroscopic data are rich in information and are commonly used in planetary research. Many mission teams, research labs, and individual research scientists derive thematic products from multi- and hyperspectral data sets and apply spectroscopic analysis techniques to derive new understanding. The PyHAT is a powerful and versatile, free, and open-source Python library designed to support exploratory spectral data analysis, the derivation of mission generated thematic products, and the application of statistical learning methods to spectral data. We present a general overview of the software architecture and identify the classes of users we seek to support. Four case studies demonstrate the use for both orbital and in-situ (landed) hyperspectral data.

Book chapter↗

USGS2021

Knowledge inventory of foundational data products in planetary science

Some of the key components of any Planetary Spatial Data Infrastructure (PDSI) are the data products that end-users wish to discover, access, and interrogate. One precursor to the implementation of a PSDI is a knowledge inventory that catalogs what products are available, from which data producers, and at what initially understood data qualities. We present a knowledge inventory of foundational PSDI data products: geodetic coordinate reference frames, elevation or topography, and orthoimages or orthomosaics. Additionally, we catalog the available gravity models that serve as critical data for the assessment of spatial location, spatial accuracy, and ultimately spatial efficacy. We strengthen our previously published definitions of foundational data products to assist in solidifying a common vocabulary that will improve communication about these essential data products.

The Planetary Science Journal↗

USGS2020

Planetary sensor models interoperability using the community sensor model specification

This paper presents the photogrammetric foundations upon which the Community Sensor Model specification depends, describes common coordinate system and reference frame transformations that support conversion between image sensor (charge‐coupled device) coordinates to some arbitrary body coordinate, and describes the U.S. Geological Survey Astrogeology Community Sensor Model implementation ( https://github.com/USGS-Astrogeology/usgscsm ). We present a new image support data specification that provides the position, pointing, timing, and metadata information necessary to properly locate a pixel or observations location on a body and describe a system architecture designed to explicitly identify the responsibilities of software components within a larger pipeline or analytical environment. This paper concludes with a set of experiments that illustrate positional and pointing error in the sensor location and the impact on the computed surface location.

Earth and Space Science↗

USGS2017

Towards a planetary spatial data infrastructure

Planetary science is the study of planets, moons, irregular bodies such as asteroids and the processes that create and modify them. Like terrestrial sciences, planetary science research is heavily dependent on collecting, processing and archiving large quantities of spatial data to support a range of activities. To address the complexity of storing, discovering, accessing, and utilizing spatial data, the terrestrial research community has developed conceptual Spatial Data Infrastructure (SDI) models and cyberinfrastructures. The needs that these systems seek to address for terrestrial spatial data users are similar to the needs of the planetary science community: spatial data should just work for the non-spatial expert. Here we discuss a path towards a Planetary Spatial Data Infrastructure (PSDI) solution that fulfills this primary need. We first explore the linkage between SDI models and cyberinfrastructures, then describe the gaps in current PSDI concepts, and discuss the overlap between terrestrial SDIs and a new, conceptual PSDI that best serves the needs of the planetary science community.

ISPRS International Journal of Geo-Information↗

USGS2017

Large crater clustering tool

In this paper we present the Large Crater Clustering (LCC) tool set, an ArcGIS plugin that supports the quantitative approximation of a primary impact location from user-identified locations of possible secondary impact craters or the long-axes of clustered secondary craters. The identification of primary impact craters directly supports planetary geologic mapping and topical science studies where the chronostratigraphic age of some geologic units may be known, but more distant features have questionable geologic ages. Previous works (e.g., McEwen et al., 2005; Dundas and McEwen, 2007) have shown that the source of secondary impact craters can be estimated from secondary impact craters. This work adapts those methods into a statistically robust tool set. We describe the four individual tools within the LCC tool set to support: (1) processing individually digitized point observations (craters), (2) estimating the directional distribution of a clustered set of craters, back projecting the potential flight paths (crater clusters or linearly approximated catenae or lineaments), (3) intersecting projected paths, and (4) intersecting back-projected trajectories to approximate the local of potential source primary craters. We present two case studies using secondary impact features mapped in two regions of Mars. We demonstrate that the tool is able to quantitatively identify primary impacts and supports the improved qualitative interpretation of potential secondary crater flight trajectories.

Computers & Geosciences↗

USGS2016

Modeling martian thermal inertia in a distributed memory high performance computing environment

Modeling martian surface properties fusing high resolution, spatially enabled, remotely sensed data and derived thermophysical modeling is an essential tool for surface property characterization studies. In this work, we describe the development of a thermal inertia modeling tool that integrates the KRC thermal model and a nine-dimensional parameter interpolation with inputs draw from remotely sensed data. Our model is classifiable as operating in both the Big Data and Big Process domains. We utilize the KRC thermal model to generate a dense lookup table. We show that the overall size of the lookup table necessary to derive thermal inertia can be reduced, through sampling, by approximately 82% while maintaining a high level of accuracy at those regions of the parameter space where thermal inertia is most frequently derived. This level of data reduction supports the distributed, in-memory application of our model and we illustrate the computational performance through a classic scaling experiment. This work extends previous modeling efforts by allowing for pixel scale thermal inertia modeling at the highest spatial scales allowed, and enabling surface properties investigations at spatial scales relevant to addressing high-priority science and engineering questions.

Conference Paper↗