An integrated view of data quality in Earth observation

Yang, Xiaoyu; Blower, Jon; Bastin, Lucy; Lush, Victoria; Zabala, Alaitz; Masó, Joan; Cornford, Dan; Díaz, Paula; Lumsden, Jo

doi:10.1098/rsta.2012.0072

Cited by 31 publications

(25 citation statements)

References 12 publications

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“…This overlap arose because several authors first used frequentist non-spatial approaches to identify the significant covariates [54,60,65,66,70–96] and then applied these covariates in a Bayesian geostatistical model [2,4,95,97–112]. Two papers (3%) quantified the uncertainty arising due to questionnaires data, as well as the uncertainty arising due to combining age-groups in the predictions [71,101].…”

Section: Resultsmentioning

confidence: 99%

“…Data quality refers to the evaluation in terms of fitness-for-use for a given application [11]. This evaluation addresses the completeness, logical consistency, time, attribute and positional accuracy of spatial data [57–60]. Different measurements of the same variable may even have different qualities according to the sensitivity, specificity and accuracy of the instrument or measurement technique.…”

Section: Methodsmentioning

confidence: 99%

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Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

et al. 2016

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BackgroundSpatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their interpretation, and consequences for the mapped values. Using currently published literature on the spatial epidemiology of helminth infections we identified: (1) the main uncertainty sources, their definition and quantification and (2) how uncertainty is informative for STH programme managers and scientists working in this domain.Methodology/Principal FindingsWe performed a systematic literature search using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol. We searched Web of Knowledge and PubMed using a combination of uncertainty, geographic and disease terms. A total of 73 papers fulfilled the inclusion criteria for the systematic review. Only 9% of the studies did not address any element of uncertainty, while 91% of studies quantified uncertainty in the predicted morbidity indicators and 23% of studies mapped it. In addition, 57% of the studies quantified uncertainty in the regression coefficients but only 7% incorporated it in the regression response variable (morbidity indicator). Fifty percent of the studies discussed uncertainty in the covariates but did not quantify it. Uncertainty was mostly defined as precision, and quantified using credible intervals by means of Bayesian approaches.Conclusion/SignificanceNone of the studies considered adequately all sources of uncertainties. We highlighted the need for uncertainty in the morbidity indicator and predictor variable to be incorporated into the modelling framework. Study design and spatial support require further attention and uncertainty associated with Earth observation data should be quantified. Finally, more attention should be given to mapping and interpreting uncertainty, since they are relevant to inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

et al. 2016

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“…Image metadata was set according to [30,31], including tags and fields proposed by the European Prestandard (1997,1998) and the Open Geospatial Consortium. In addition, careful attention was given to properly documenting image metadata complying with ISO standards (19115, 19115-2, 19139 and 19139-2).…”

Section: Image Metadatamentioning

confidence: 99%

A New Integrated High-Latitude Thermal Laboratory for the Characterization of Land Surface Processes in Alaska’s Arctic and Boreal Regions

Cristóbal

Graham

Buchhorn

et al. 2016

Data

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Alaska's Arctic and boreal regions, largely dominated by tundra and boreal forest, are witnessing unprecedented changes in response to climate warming. However, the intensity of feedbacks between the hydrosphere and vegetation changes are not yet well quantified in Arctic regions. This lends considerable uncertainty to the prediction of how much, how fast, and where Arctic and boreal hydrology and ecology will change. With a very sparse network of observations (meteorological, flux towers, etc.) in the Alaskan Arctic and boreal regions, remote sensing is the only technology capable of providing the necessary quantitative measurements of land-atmosphere exchanges of water and energy at regional scales in an economically feasible way. Over the last decades, the University of Alaska Fairbanks (UAF) has become the research hub for high-latitude research. UAF's newly-established Hyperspectral Imaging Laboratory (HyLab) currently provides multiplatform data acquisition, processing, and analysis capabilities spanning microscale laboratory measurements to macroscale analysis of satellite imagery. The specific emphasis is on acquiring and processing satellite and airborne thermal imagery, one of the most important sources of input data in models for the derivation of surface energy fluxes. In this work, we present a synergistic modeling framework that combines multiplatform remote sensing data and calibration/validation (CAL/VAL) activities for the retrieval of land surface temperature (LST). The LST Arctic Dataset will contribute to ecological modeling efforts to help unravel seasonal and spatio-temporal variability in land surface processes and vegetation biophysical properties in Alaska's Arctic and boreal regions. This dataset will be expanded to other Alaskan Arctic regions, and is expected to have more than 500 images spanning from 1984 to 2012.

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“…Different communities have different views and understandings of the subject, which causes confusion, a lack of harmonization of data across communities and omission of vital quality information [2,8]. In some of the literature data quality is defined as features and characteristics of data that bear on its ability to meet the needs and requirements of the user [9].…”

Section: It Is All About Quality!mentioning

confidence: 99%

Exploring the Possibility of Semi-Automated Quality Evaluation of Spatial Datasets in Spatial Data Infrastructure

Mobasheri

2013

j.ict.res.appl.

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Abstract. Over the past decades, World Wide Web technology has developed greatly. One of the most important outcomes of this technology is to share data in a worldwide domain. A considerable amount of available data have spatial components and are hence called spatial data. The level of quality that spatial datasets conform plays an important role in their reliability for use in projects. This research aims to overview spatial data quality elements and select appropriate elements suitable for means of semi-automated quality evaluation. In this paper, the ISO 19100 series of standards for geographic information is used as basis for quality evaluation. The possibilities of use of different spatial data quality elements for semi-automated quality evaluation are explored and discussed. Finally, based on argumentation and reference to other research studies, a list of spatial data quality elements and sub-elements suitable for semiautomated quality evaluation of spatial datasets are presented.

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An integrated view of data quality in Earth observation

Cited by 31 publications

References 12 publications

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

A New Integrated High-Latitude Thermal Laboratory for the Characterization of Land Surface Processes in Alaska’s Arctic and Boreal Regions

Exploring the Possibility of Semi-Automated Quality Evaluation of Spatial Datasets in Spatial Data Infrastructure

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