Analysis Ready Data: Enabling Analysis of the Landsat Archive

Dwyer, John L.; Roy, David P.; Sauer, Brian; Jenkerson, Calli B.; Zhang, Hankui K.; Lymburner, Leo

doi:10.20944/preprints201808.0029.v1

Cited by 128 publications

(123 citation statements)

References 54 publications

(36 reference statements)

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“…As an example, Level 3 products are the first that are mapped on a regular grid, whereas the lower-level products are still in georectified swath geometry (e.g., the Landsat Worldwide Reference System 2 (WRS-2) path/row system). In contrast, the key element of ARD is to provide gridded data [13,16,17]-regardless of product level. This is for e.g., reflected in ESA's production and distribution strategy of Sentinel-2 data as they already include gridding on Level 1 [6]-although still using local Universal Transverse Mercator (UTM) zones with a substantial amount of redundant data between overlapping and neighboring tiles.…”

Section: Product Level and Data Cube Definitionmentioning

confidence: 99%

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

Frantz

2019

Remote Sensing

196

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Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.

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Section: Product Level and Data Cube Definitionmentioning

confidence: 99%

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

Frantz

2019

Remote Sensing

196

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“…Recently, the provision of Landsat data in analysis‐ready formats (Dwyer et al. ; Wulder et al. ) and rapidly improving computational capacities (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the provision of Landsat data in analysisready formats (Dwyer et al 2018;Wulder et al 2019) and rapidly improving computational capacities (e.g. through cloud computing platforms; Gorelick et al 2017), have dramatically improved the possibilities for analyzing large sets of Landsat images.…”

Section: Introductionmentioning

confidence: 99%

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Oeser

Heurich

Senf

et al. 2019

Remote Sens Ecol Conserv

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Up‐to‐date and fine‐scale habitat information is essential for managing and conserving wildlife. Studies assessing wildlife habitat commonly rely on categorical land‐cover maps as predictors in habitat models. However, broad land‐cover categories often do not adequately capture key habitat features and generating robust land‐cover maps is challenging and laborious. Continuous variables derived directly from satellite imagery provide an alternative for capturing land‐cover characteristics in habitat models. Improved data availability and processing capacities now allow integrating all available images from medium‐resolution sensors in compositing approaches that derive spectral‐temporal metrics at the pixel level, summarizing spectral responses over time. In this study, we assessed the usefulness of such metrics derived from Landsat imagery for mapping wildlife habitat. We categorize spectral‐temporal metrics into habitat metrics characterizing different aspects of wildlife habitat. Comparing the performance of these metrics against categorical land‐cover maps in habitat models for lynx, red deer and roe deer, we found that models using habitat metrics consistently outperformed models based on categorical land‐cover maps, with average improvements of 13.7% in model AUC and 9.7% in the Continuous Boyce Index. Performance increases were larger for seasonal habitat models, indicating that the habitat metrics capture intra‐annual variability in habitat conditions better than land‐cover maps. Comparing suitability maps to ancillary data further revealed that our habitat metrics were sensitive to fine‐scale heterogeneity in habitat associated with forest structure. Overall, our study highlights the considerable potential of Landsat‐based spectral temporal metrics for assessing wildlife habitat. Given these metrics can be derived directly and in an automatized fashion from globally and freely available Landsat imagery, they open up new possibilities for monitoring habitat dynamics in space and time.

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“…One such development is the provision of analysis-ready data (ARD) suitable for analysis in large purpose-developed data cubes (e.g., Gorelick et al, 2017;Lewis et al, 2017). ARD are "data that have been processed to allow analysis with a minimum of additional user effort" (Dwyer et al, 2018(Dwyer et al, , p. 1365. They contain long, wide, and deep archives of time-series data (e.g., Landsat; Wulder et al, 2008Wulder et al, , 2016 collected under repeated spatial and temporal sampling frameworks.…”

Section: Lclu Data and Conte X Tmentioning

confidence: 99%

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use

Comber

Wulder

2019

Transactions in GIS

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Data are increasingly spatio‐temporal—they are collected some‐where and at some‐time. The role of proximity in spatial process is well understood, but its value is much more uncertain for many temporal processes. Using the domain of land cover/land use (LCLU), this article asserts that analyses of big data should be grounded in understandings of underlying process. Processes exhibit behaviors over both space and time. Observations and measurements may or may not coincide with the process of interest. Identifying the presence or absence of a given process, for instance disentangling vegetation phenology from stress, requires data analysis to be informed by knowledge of the process characteristics and, critically, how these manifest themselves over the spatio‐temporal unit of analysis. Drawing from LCLU, we emphasize the need to identify process and consider process phase to quantify important signals associated with that process. The aim should be to link the seriality of the spatio‐temporal data to the phase of the process being considered. We elucidate on these points and opportunities for insights and leadership from the geographic community.

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Analysis Ready Data: Enabling Analysis of the Landsat Archive

Cited by 128 publications

References 54 publications

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use

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