Design and Analysis for Thematic Map Accuracy Assessment

Stehman, Stephen V.; Czaplewski, Raymond L.

doi:10.1016/s0034-4257(98)00010-8

Cited by 596 publications

(334 citation statements)

References 24 publications

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“…Rather, the following pixel-based TQ 2 Is were estimated from the crisp OAMTRX = FrequencyCount(A × B) estimated wall-to-wall with spatial unit x equal to pixel. An OA(OAMTRX = FrequencyCount(A × B)) ± 0% was computed in line with (Baraldi et al, 2014; Pontius & Millones, 2011; Stehman & Czaplewski, 1998). This OA estimate is guided by the binary relationship R: A = VocabularyOfColorNames ⇒ B = LegendOfObjectClassNames identified and community-agreed upon in advance; refer to this text above.…”

Section: Methodsmentioning

confidence: 99%

“…This OA estimate is guided by the binary relationship R: A = VocabularyOfColorNames ⇒ B = LegendOfObjectClassNames identified and community-agreed upon in advance; refer to this text above. In an OAMTRX = FrequencyCount(A × B) estimated from a wall-to-wall inter-map comparison, where no sample data is investigated, any adopted TQ 2 I features a degree of uncertainty in measurement equal to ± 0%; for example, see Equation (1).User’s and producer’s accuracies, computed in (Baraldi et al, 2014; Pontius & Millones, 2011; Stehman & Czaplewski, 1998), were replaced by class-conditional probabilities, p ( r | t ) of reference class r given test class t and, vice versa, p ( t | r ) of test class t given reference class r , with r = 1 , …, RC , and t = 1, …, TC , where RC = |B| = b = ObjectClassLegendCardinality and TC = |A| = a = ColorVocabularyCardinality are the total numbers of reference and test classes, respectively. …”

Section: Methodsmentioning

confidence: 99%

“…User’s and producer’s accuracies, computed in (Baraldi et al, 2014; Pontius & Millones, 2011; Stehman & Czaplewski, 1998), were replaced by class-conditional probabilities, p ( r | t ) of reference class r given test class t and, vice versa, p ( t | r ) of test class t given reference class r , with r = 1 , …, RC , and t = 1, …, TC , where RC = |B| = b = ObjectClassLegendCardinality and TC = |A| = a = ColorVocabularyCardinality are the total numbers of reference and test classes, respectively.…”

Section: Methodsmentioning

confidence: 99%

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GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

et al. 2018

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ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2—Validation—accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

et al. 2018

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“…When a BIVRFTAB is estimated from a geospatial population with or without sampling, it is called overlapping area matrix (OAMTRX) (Baraldi et al, 2014; Baraldi, Bruzzone, & Blonda, 2005; Baraldi et al, 2006; Beauchemin & Thomson, 1997; Lunetta & Elvidge, 1999; Ortiz & Oliver, 2006; Pontius & Connors, 2006). When the binary relationship R: A ⇒ B is a bijective function (both 1–1 and onto), i.e., when the two categorical variables A and B estimated from a single population coincide, then the BIVRFTAB instantiation is square and sorted; it is typically called confusion matrix (CMTRX) or error matrix (Congalton & Green, 1999; Lunetta & Elvidge, 1999; Pontius & Millones, 2011; Stehman & Czaplewski, 1998). In a CMTRX, the main diagonal guides the interpretation process.…”

Section: Problem Background Of Color Naming In Cognitive Sciencementioning

confidence: 99%

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

et al. 2018

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ESA defines as Earth Observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006–2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static color names. Typically, a vocabulary of MS color names in a MS data (hyper)cube and a dictionary of land cover (LC) class names in the scene-domain do not coincide and must be harmonized (reconciled). The present Part 1—Theory provides the multidisciplinary background of a priori color naming. The subsequent Part 2—Validation accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data 2006 map, based on an original protocol for wall-to-wall thematic map quality assessment without sampling, where the test and reference maps feature the same spatial resolution and spatial extent, but whose legends differ and must be harmonized.

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“…Accuracy is determined by comparing the map land-cover label to the "true" or "reference" land cover at a sample of sites. An accuracy assessment protocol consists of three major components (Stehman and Czaplewski 1998). The response design is the methodology for obtaining the reference land-cover label.…”

Section: Introductionmentioning

confidence: 99%

Combining Accuracy Assessment of Land-Cover Maps With Environmental Monitoring Programs

Stehman

Czaplewski

Nusser

et al. 2000

Monitoring Ecological Condition in the Western United States

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A scientifically valid accuracy assessment of a large-area, land-cover map is expensive. Environmental monitoring programs offer a potential source of data to partially defray the cost of accuracy assessment while still maintaining the statistical validity. In this article, three general strategies for combining accuracy assessment and environmental monitoring protocols are described. These strategies range from a fully integrated accuracy assessment and environmental monitoring protocol, to one in which the protocols operate nearly independently. For all three strategies, features critical to using monitoring data for accuracy assessment include compatibility of the land-cover classification schemes, precisely co-registered sample data, and spatial and temporal compatibility of the map and reference data. Two monitoring programs, the National Resources Inventory (NRI) and the Forest Inventory and Monitoring (FIM), are used to illustrate important features for implementing a combined protocol. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. Abstract.A scientifically valid accuracy assessment of a large-area, land-cover map is expensive. Environmental monitoring programs offer a potential source of data to partially defray the cost of accuracy assessment while still maintaining the statistical validity. In this article, three general strategies for combining accuracy assessment and environmental monitoring protocols are described. These strategies range from a fully integrated accuracy assessment and environmental monitoring protocol, to one in which the protocols operate nearly independently. For all three strategies, features critical to using monitoring data for accuracy assessment include compatibility of the land-cover classification schemes, precisely co-registered sample data, and spatial and temporal compatibility of the map and reference data. Two monitoring programs, the National Resources Inventory (NRI) and the Forest Inventory and Monitoring (FIM), are used to illustrate important features for implementing a combined protocol.

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Design and Analysis for Thematic Map Accuracy Assessment

Cited by 596 publications

References 24 publications

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

Combining Accuracy Assessment of Land-Cover Maps With Environmental Monitoring Programs

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