Abstract
It is well-known that Earth observation (EO) data plays a critical role in scientific understanding about the global environment. There is also growing support for the use of EO data to provide context-specific insights, with significant implications for their use in decision support systems. Technological development over recent years, including cloud computing infrastructure, machine learning techniques, and rapid expansion of the velocity, volume, and variety of space-borne data sources, offer huge potential to provide solutions to the myriad environmental problems facing society and the planet. The USGS/NASA Landsat Program, the longest continuously gathered source of land surface data, has played a central role in our understanding of environmental change, particularly for its contribution of longitudinal products that offer greater context for present research and decision support activities. The challenge facing the Landsat and EO data community, however, now lies in moving beyond context-specific knowledge generation to translating such knowledge into tangible value for society. Drawing from an open data ecosystem framework and qualitative social science methods, we map the Landsat data ecosystem (LDE) and the relationships linking multiple actors responsible for processing, indexing, analyzing, synthesizing, and translating raw Landsat data into information that is useful, useable, and used by end users in particular social-environmental contexts. Both the role of Big Data and associated technologies are discussed as they relate to the ultimate use of Landsat-derived information products to guide decision-making, and key data ecosystem characteristics that shape the likelihood of these products’ use are highlighted.
Highlights
The Landsat Program is a series of Earth-observing satellite missions jointly managed by the United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA)
The Landsat data ecosystem (LDE) is composed of value chains that begin with strategic planning to develop and launch an Earth observing satellite, development of the infrastructure needed to process the data provided by the satellite, and the generation of initial products based on those data
In this paper we examine the LDE downstream of Earth Resources Observation and Science Center (EROS) by following the processing of raw reflectance data from the Landsat sensors into standardized Landsat data collections, Analysis Ready Data (ARD) products, and information products which are used in decision-making, with a focus on the organizational actors that participate in the addition of value to those products along the value chain, all from the actors’ perspective
Summary
The Landsat Program is a series of Earth-observing satellite missions jointly managed by the United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). USGS Earth Resources Observation and Science Center (EROS) is home to the Landsat Data Ecosystem Value Perceptions world’s largest collection of remotely sensed images of the Earth’s land surface and the primary source of Landsat satellite images and data products. The Landsat Program’s continuous archive (1972-present) provides a landscape-level view of Earth that enables users to better understand the scope, nature, and speed of change to the natural and built environment. Landsat represents the world’s longest continuously acquired collection of space-based moderate-resolution land remote sensing data. Landsat data contribute to decisions about land, water, and resource use that protect life, property, and the environment; advance science, technology, and education; and grow economies. Understanding the value of Landsat data takes many forms
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