Users, uses, and value of Landsat satellite imagery: results from the 2012 survey of users

Miller, Holly M.; Richardson, Leslie; Koontz, Stephen R.; Loomis, John B.; Koontz, Lynne

doi:10.3133/ofr20131269

Cited by 19 publications

(13 citation statements)

References 14 publications

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“…Landsat data are in constant use for agricultural management, yield forecasting and insurance, for land use and cover change, for forestry, water resource management, study of ecosystem services and functioning, for climate science and climate change studies, for studying snow and ice, coastal areas, deserts, geology, soils, urban change and transport among many other applications. The data are used in science programs, they are used by educators in schools and universities worldwide, they help decision makers develop, implement, evaluate and refine policies on scales from local to global; they form a basis for international environmental agreements, for legal use, for agribusiness, humanitarian aid and homeland security (Miller et al 2013).…”

Section: Background and Current Archive Statusmentioning

confidence: 99%

“…For continental land masses the Landsat mission is achieving effectively an 8 day revisit. have an estimated annual value of nearly $1.8 billion (Miller et al, 2013).…”

Section: Joint Acquisition Strategymentioning

confidence: 99%

“…The clear societal value of the Landsat archive has led to a new programmatic commitment to data continuity (see NSTC, 2014;Miller et al 2013). Since 2014, the U.S. has pursued a Sustainable Land Imaging (SLI) program as an interagency partnership between NASA and USGS in order to secure Landsat continuity over the next decades.…”

Section: Looking Forward: Sustainable Land Imagingmentioning

confidence: 99%

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The global Landsat archive: Status, consolidation, and direction

Wulder

White

Loveland

et al. 2016

Remote Sensing of Environment

597

363

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New and previously unimaginable Landsat applications have been fostered by a policy change in 2008 that made analysis-ready Landsat data free and open access. Since 1972, Landsat has been collecting images of the Earth, with the early years of the program constrained by onboard satellite and ground systems, as well as limitations across the range of required computing, networking, and storage capabilities. Rather than robust on-satellite storage for transmission via high bandwidth downlink to a centralized storage and distribution facility as with Landsat-8, a network of receiving stations, one operated by the U.S. government, the other operated by a community of International Cooperators (ICs), were utilized. ICs paid a fee for the right to receive and distribute Landsat data and over time, more Landsat data was held outside the archive of the United State Geological Survey (USGS) than was held inside, much of it unique. Recognizing the critical value of these data, the USGS began a Landsat Global Archive Consolidation (LGAC) initiative in 2010 to bring these data into a single, universally accessible, centralized global archive, housed at the Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. The primary LGAC goals are to inventory the data held by ICs, acquire the data, and ingest and apply standard ground station processing to generate an L1T analysis-ready product. As of January 1, 2015 there were 5,532,454 images in the USGS archive. LGAC has contributed approximately 3.2 million of those images, more than doubling the original USGS archive holdings. Moreover, an additional 2.3 million images have been identified to date through the LGAC initiative and are in the process of being added to the archive.. The impact of LGAC is significant and, in terms of images in the collection, analogous to that of having had two additional Landsat-5 missions. As a result of LGAC, there are regions of the globe that now have markedly improved Landsat data coverage, resulting in an enhanced capacity for mapping, monitoring change, and capturing historic conditions. Although future missions can be planned and implemented, the past cannot be revisited, underscoring the value and enhanced significance of historical Landsat data and the LGAC initiative. The aim of this paper is to report the current status of the global USGS Landsat archive, document the existing and anticipated contributions of LGAC to the archive, and characterize the current acquisitions of Landsat-7 and Landsat-3 8. Landsat-8 is adding data to the archive at an unprecedented rate as nearly all terrestrial images are now collected. We also offer key lessons learned so far from the LGAC initiative, plus insights regarding other critical elements of the Landsat program looking forward, such as acquisition, continuity, temporal revisit, and the importance of continuing to operationalize the Landsat program.

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Section: Background and Current Archive Statusmentioning

confidence: 99%

“…For continental land masses the Landsat mission is achieving effectively an 8 day revisit. have an estimated annual value of nearly $1.8 billion (Miller et al, 2013).…”

Section: Joint Acquisition Strategymentioning

confidence: 99%

Section: Looking Forward: Sustainable Land Imagingmentioning

confidence: 99%

See 1 more Smart Citation

The global Landsat archive: Status, consolidation, and direction

Wulder

White

Loveland

et al. 2016

Remote Sensing of Environment

597

363

View full text Add to dashboard Cite

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“…The value of EO data and their technical characteristics have been the focus of numerous studies using quantitative analytical methods (Bouma et al, 2011;Miller et al, 2013;Loomis et al, 2015;Straub et al, 2019). There is also a rich and growing body of literature on open data ecosystems in a variety of contexts (Lindman et al, 2005;Zuiderwijk et al, 2014;Styrin et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

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

Molder

Schenkein

McConnell

et al. 2022

Front. Environ. Sci.

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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.

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“…Today, Landsat-8, launched in 2013, collects imagery in the coastal, blue, green, red, near infrared, and two infrared bands at a resolution of 30 m, in addition, two thermal bands at a resolution of 100 m (Roy et al, 2014). Its character of free data for users has facilitated its adoption for realizing diverse agricultural studies (Miller et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Analysis of Agricultural Landscape Images: A Superpixel-based Approach

Pedrero¹,

Adolfo²

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In the last hundred years, the world population has tripled and is still growing dramatically but resources have remained the same, causing changes in food supply outlook. According to the Food and Agriculture Organization (FAO), global food production will need to grow by 70% in order to satisfy the food and feed demand of a population of 9 billion people by 2050. In the current scenario, with limited arable lands and water scarcity, this means a greater pressure than ever before on productive land; thus today's main challenge is to put agriculture on a more sustainable and productive long-term path. In this context, the study of the variability within plots represents an opportunity for improving agricultural management. For most farmers, agricultural plots are variable which implies that not all areas within them require the same management; for example: some areas have specific needs of water, fertilizers, or pesticides in order to be more profitable. Therefore, adjusting the management of the plots based on the variability within them opens a route to a variable farm management able to improve cost/yield relationship.New generation of optical remote sensors placed on aircraft, satellite platforms and drones, offers accessible and useful data of very-high resolution for monitoring and determining spatial variability of agricultural fields at plot level. However, the landscape complexity makes the manual analysis of the variability within an agricultural plot a highly time-consuming and expensive task. At the same time, it hinders considerably the monitoring of a high number of agricultural plots simultaneously. Moreover this very-high spatial resolution represents a challenge for traditional approaches of analysis based on pixels, unable to handle the withinclass spectral variability, intrinsic to this type of images. Therefore, it exists a critical need to develop methodologies for efficiently and automatically extracting and analyzing information from very-high resolution images, that would allow stakeholders to enable variable farm management. In this context, it is of particular interest to automatically detect region dynamics within the agricultural plots.The aim of this work is the development of a methodology for automatic generation of spatial and temporal information on the dynamics of agricultural land, particularly at plot level. In this sense, the study of agricultural scenes has been addressed through an object-based approach, exploring superpixel methods, which are seen as a link between the pixels of the image and objects of interest. To this end a superpixel method for multi-spectral images has been proposed. This method has been exploited in different applications at three different scales of interest:vii (i) to analyze fragmented agricultural scenes, (ii) to delineate agricultural plots, and (iii) to capture the internal variability of agricultural plots.The analysis of fragmented agricultural scenes has been approached by two methodologies. The first one focuses on providing a fram...

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