Geology topics

Peter Strobl

Publications and source records attributed to Peter Strobl.

5 recordsLinked to original sources

USGS2025

Towards seamless global 30-meter terrestrial monitoring: Evaluating 2022 cloud free coverage of harmonized Landsat and Sentinel-2 (HLS) V2.0

Global observations at 30-m ground sampling distance (GSD) are now possible at a cadence of 1-3 days by combining Landsat 8 and 9 with Sentinel-2A and -2B satellites. Previous studies characterizing pixel-level Landsat-class measurement frequency used data from different sources but offered little information on observation availability after rigorous quality screening. This study examined the coverage frequency of HLS V2.0 data for 2022, the first year all four satellites data were available. These data have had quality control filtering and harmonization, and therefore reflect the spatial-temporal distribution of usable observations. On average, HLS data provide observations every 1.6 days at the global scale, and 2.2 days in the data-scarce tropical regions, regardless of cloud cover. The global mean and median cloud-free observations were 69 and 64, respectively. The frequency of good-quality observations varies geographically and seasonally due to changes in satellite swath overlap, cloud frequency, and solar illumination. High latitudes (>~75°N) exhibit the highest number of cloud-free observations between March and September. However, data are unavailable during winter months due to low solar elevation angles and boreal regions have a lower number of clear observations in the summer months. The tropical regions have the lowest number of clear observations. More frequent HLS observations could improve terrestrial monitoring. We mapped the monthly and weekly number of clear observations globally to show where HLS data could support monthly or sub-weekly time series applications.

IEEE Geoscience and Remote Sensing Letters↗

USGS2024

Need and vision for global medium-resolution Landsat and Sentinel-2 data products

Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly ≤ "> ≤ 30-m spatial resolution affords an opportunity for the generation of global medium- resolution products every few days. Our goal is to both identify the information needs and provide direction towards the generation of a suite of global, high-level, systematically-generated, medium-resolution products designed for both management and science. Our vision builds on the success of the NASA MODIS/VIIRS product suite, while recognizing the unique strengths of medium-resolution satellite data given their higher spatial resolution and longer time series. We propose a suite of 13 essential products that enable the characterization of the current state and changes in the biosphere, cryosphere, and hydrosphere, and would fill information needs identified by the Committee on Earth Observation Satellites for the Global Climate Observing System and the Global Terrestrial Observing System, by the National Research Council of the US National Academies in the decadal survey, and by others. These products are: land cover, land cover change, burned area, forest loss, vegetation indices, phenology, dynamic habitat indices, albedo, land surface temperature, snow cover, ice extent, surface water extent, and evapotranspiration. Furthermore, we provide a list of desirable products poised for addition to the essential products (e.g., crop type, emissivity, and ice sheet velocity). Lastly, we suggest aspirational products requiring further algorithm development (e.g., forest structure and crop yield). For the identified essential products, algorithms are in place, making it feasible to begin generating products systematically. These products should be accompanied by quality and accuracy assessments undertaken following consensus protocols. Five decades after the first Landsat satellite, and two decades after the MODIS products were first produced, it is time now for readily available, standardized, and consistent high-level products built upon medium-resolution imagery, thereby fulfilling the promise and the vision that inspired the Landsat program since its inception.

Remote Sensing of Environment↗

USGS2024

Novel approach for ranking DEMs: Copernicus DEM improves one arc second open global topography

We present a practical approach to intercompare a range of candidate digital elevation models (DEMs) based on predefined criteria and a statistically sound ranking approach. The presented approach integrates the randomized complete block design (RCBD) into a novel framework for DEM comparison. The method presented provides a flexible, statistically sound, and customizable tool for evaluating the quality of any raster—in this case, a DEM—by means of a ranking approach, which takes into account a confidence level and can use both quantitative and qualitative criteria. The users can design their own criteria for the quality evaluation in relation to their specific needs. The application of the RCBD method to rank six 1′′ global DEMs, considering a wide set of study sites, covering different morphological and land cover settings, highlights the potentialities of the approach. We used a suite of criteria relating to the differences in the elevation, slope, and roughness distributions compared to reference DEMs aggregated from 1- to 5-m light detection and ranging (LiDAR)-derived DEMs. Results confirmed the significant superiority of Copernicus DEM (CopDEM) 1′′ and its derivative forests and buildings removed DEM (FABDEM) as the overall best 1′′ global DEMs. They are slightly better than Advanced Land Observing Satellite (ALOS) and clearly outperform NASADEM and SRTM, which are, in turn, much better than advanced spaceborne thermal emission and reflection radiometer (ASTER).

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2022

Fifty years of Landsat science and impacts

Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application, and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resolution Earth observation data and substantially increased usage and led to a proliferation of science and application opportunities. Here, we highlight key developments over the past 50 years of the Landsat program that have influenced and changed our scientific understanding of the Earth system. Major scientific and programmatic impacts have been realized in the areas of agricultural crop mapping and water use, climate change drivers and impacts, ecosystems and land cover monitoring, and mapping the changing human footprint. The introduction of Landsat collection processing, coupled with the free and open data policy, facilitated a transition in Landsat data usage away from single images and towards time series analyses over large areas and has fostered the widespread use of science-grade data. The launch of Landsat-9 on September 27, 2021, and the advanced planning of its successor mission, Landsat-Next, underscore the sustained institutional support for the program. Such support and commitment to continuity is recognition of both the historic impact the program, and the future potential to build upon Landsat's remarkable 50-year legacy.

Remote Sensing of Environment↗

USGS2021

Digital elevation models: Terminology and definitions

Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background.

Remote Sensing↗