Landsat Data Ecosystem Case Study: Actor Perceptions of the Use and Value of Landsat

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.

Value of Information and Decision Pathways: Concepts and Case Studies

“…We now provide some DDP examples that help illustrate some of the challenges and opportunities identified in our "Value Explorations" (Table 1, and Glynn et al, 2022, this issue). Many of the examples (DDPs 2-5) are constructs based on the experiences and knowledge of the co-authors, with respect to the biophysical sciences and VOI and economic studies (e.g., Watson et al, 1984;Bernknopf and Shapiro, 2015;Chiavacci et al, 2020;Molder et al, 2022).…”

“…In some cases, however, it may be appropriate to express value using different types of metrics. For example, Molder et al (2022) describe use of a qualitative approach to provide a better understanding of the value and value chains for Landsat information (Wu et al, 2019). The approach mapped the Landsat data ecosystem to include the diversity of actors and uses of the information, and thereby provided a useful system perspective on Landsat VOI.…”

“…Given limited resources, understanding the potential or realized benefits resulting from the use of certain information can illuminate its utility, including in the digital economy (Wilson, 2015;Goldfarb and Tucker, 2019;Laybats and Tredinnick, 2020). Economists refer to such benefits as the value of information (VOI), which can be quantitative or qualitative in nature (Keisler et al, 2014;Molder et al, 2022). Although VOI studies (Howard, 1966;Raiffa, 1970) can be extremely useful for elucidating how information changed or could change decisions and resulting actions, they may fail to incorporate a full understanding of the behavioral and societal factors affecting the value and use of particular information (Glynn et al, 2022, this issue).…”

Assessing long-term coral reef degradation in Indonesia’s Tiworo strait marine conservation area using remote sensing and rapid appraisal for fisheries approaches

“…However, the combination of remote-sensing technology, in-situ data, and social surveys can provide alternative approaches for monitoring changes in reef composition at high spatial and temporal resolution (Andréfouët et al 2003;Foo and Asner 2019;Chen et al 2022). These approaches can facilitate monitoring in remote areas that are not directly accessible including coral reef ecosystems (Purkis 2018;De et al 2021;Molder et al 2022).…”

“…Remote-sensing technology has been used in a variety of coral reef studies, including the classification of benthic habitats (Zhang 2015;Roelfsema et al 2018;Zhang et al 2018;Molder et al 2022), detection of changes in coral composition (Andréfouët et al 2007;Hedley et al, 2016;Parsons et al 2018;Purkis 2018;Nimalan et al 2021) and benthic reflectance at the bottom (Lee et al 2013;Barnes et al 2014;Thompson et al 2017;Roelfsema et al 2021) and estimation of coastal water bathymetry (Lyzenga et al 2006;Mishra et al 2006;Pacheco et al 2015;Minghelli et al 2021). Frequently, these studies only analyze changes in benthic habitat cover without identifying habitat status and types of threats.…”