Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data

Latifovic, Rasim; Olthof, Ian

doi:10.1016/j.rse.2003.11.016

Cited by 163 publications

(112 citation statements)

References 25 publications

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“…Previous comparisons of land cover products have been made only at global [19,20], continental [18], national [16], or provincial scales [21], since they focused on general patterns of inconsistencies or indirect validation accuracy of the products, which is meaningful to large scale studies. To the knowledge of the authors, this is the first study to compare the inconsistency of three recent land cover products (the MODISLC, GlobCover 2009, and FROM-GLC) from a terrain perspective in a complex mountain-oasis-desert area.…”

Section: Discussion and Importance Of The Studymentioning

confidence: 99%

“…For example, the FROM-GLC is the only product in which no distinction is made between evergreen and deciduous forest classes, while the MODISLC is the only product in which no distinction is made between rained and irrigated crops, and GlobCover 2009 is the only product having a type definition of more than four vegetation type mosaics. To overcome the problem of conflicting classification systems, the thematic classification system of the FROM-GLC, the GlobCover 2009 and the MODISLC were converted into four common classification systems based on the original definition of classes in each land cover product, which defaults to selecting the dominant category [6,12,14,16,[18][19][20]. When some categories from the original classification systems could not completely be attributed into any category in the common classification systems due to conflicting definitions, we classified them into corresponding types based on knowledge or by referring to data like the DEM, and labeled them as ambiguous types in this study resulting in uncertainties during classification system conversion (see Section 3.2).…”

Section: Methods For Classification System Conversionmentioning

confidence: 99%

“…A specific way to compare datasets is to perform a relative comparison of various land cover maps, first reconciling their thematic classification systems into more aggregated categories after resampling the datasets to be into the same spatial resolution [15]. Using common classification systems based on the definition of each class in the original land cover products [16,17] or standards in reference to FAO [18], IGBP [12], or other dataset, some previous studies have highlighted general patterns of agreement, inconsistencies and accuracy among different land cover products at global [19,20], continental [18], national [16], and provincial scales [21]. Other studies not only demonstrated the compatibility and discrepancies between different datasets, but also qualitatively discussed the impacts of landscape inhomogeneity, thematic resolution, spatial resolution and mis-registration errors on product accuracy [20,22].…”

Section: Introductionmentioning

confidence: 99%

“…Using common classification systems based on the definition of each class in the original land cover products [16,17] or standards in reference to FAO [18], IGBP [12], or other dataset, some previous studies have highlighted general patterns of agreement, inconsistencies and accuracy among different land cover products at global [19,20], continental [18], national [16], and provincial scales [21]. Other studies not only demonstrated the compatibility and discrepancies between different datasets, but also qualitatively discussed the impacts of landscape inhomogeneity, thematic resolution, spatial resolution and mis-registration errors on product accuracy [20,22]. However, few studies have focused on quantitatively examining the uncertainties of classification system conversion, and examined the inconsistencies in the complex land surface areas or approached the subject from the perspective of the complex landscape features, including mountains, oases and deserts, where the landscape is influenced mainly by natural or artificial factors.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties in Classification System Conversion and an Analysis of Inconsistencies in Global Land Cover Products

Zhang

Maeyer

et al. 2017

IJGI

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Abstract:In this study, using the common classification systems of IGBP-17, IGBP-9, IPCC-5 and TC (vegetation, wetlands and others only), we studied spatial and areal inconsistencies in the three most recent multi-resource land cover products in a complex mountain-oasis-desert system and quantitatively discussed the uncertainties in classification system conversion. This is the first study to compare these products based on terrain and to quantitatively study the uncertainties in classification system conversion. The inconsistencies and uncertainties decreased from high to low levels of aggregation (IGBP-17 to TC) and from mountain to desert areas, indicating that the inconsistencies are not only influenced by the level of thematic detail and landscape complexity but also related to the conversion uncertainties. The overall areal inconsistency in the comparison of the FROM-GLC and GlobCover 2009 datasets is the smallest among the three pairs, but the smallest overall spatial inconsistency was observed between the FROM-GLC and MODISLC. The GlobCover 2009 had the largest conversion uncertainties due to mosaic land cover definition, with values up to 23.9%, 9.68% and 0.11% in mountainous, oasis and desert areas, respectively. The FROM-GLC had the smallest inconsistency, with values less than 4.58%, 1.89% and 1.2% in corresponding areas. Because the FROM-GLC dataset uses a hierarchical classification scheme with explicit attribution from the second level to the first, this system is suggested for producers of map land cover products in the future.

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Section: Discussion and Importance Of The Studymentioning

confidence: 99%

Section: Methods For Classification System Conversionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Uncertainties in Classification System Conversion and an Analysis of Inconsistencies in Global Land Cover Products

Zhang

Maeyer

et al. 2017

IJGI

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“…The key is to construct the validation to assess product features relevant to the final product user. The present study followed the cross tabulation approach due to its common use [1,10] and familiarity to a broad scientific community.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Temporal Stability of the Accuracy of a Time Series of Burned Area Products

et al. 2014

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Temporal stability, defined as the change of accuracy through time, is one of the validation aspects required by the Committee on Earth Observation Satellites' Land Product Validation Subgroup. Temporal stability was evaluated for three burned area products: MCD64, Globcarbon, and fire_cci. Traditional accuracy measures, such as overall accuracy and omission and commission error ratios, were computed from reference data for seven years (2001)(2002)(2003)(2004)(2005)(2006)(2007) in seven study sites, located in Angola, Australia, Brazil, Canada, Colombia, Portugal, and South Africa. These accuracy measures served as the basis for the evaluation of temporal stability of each product. Nonparametric tests were constructed to assess different departures from temporal stability, specifically a monotonic trend in accuracy over time (Wilcoxon test for trend), and differences in median accuracy among years (Friedman test). When applied to the three burned area products, these tests did not detect a statistically significant temporal trend or significant differences among years, thus, based on the small sample size of seven sites, there was insufficient evidence to claim these products had temporal instability. Pairwise Wilcoxon tests comparing yearly accuracies provided a measure of the proportion of year-pairs with significant differences and these proportions of significant pairwise differences were in turn used to compare temporal stability between BA products. The proportion of year-pairs with different accuracy (at the 0.05 significance level) ranged from 0% (MCD64) to 14% (fire_cci), OPEN ACCESSRemote Sens. 2014, 6 2051 computed from the 21 year-pairs available. In addition to the analysis of the three real burned area products, the analyses were applied to the accuracy measures computed for four hypothetical burned area products to illustrate the properties of the temporal stability analysis for different hypothetical scenarios of change in accuracy over time. The nonparametric tests were generally successful at detecting the different types of temporal instability designed into the hypothetical scenarios. The current work presents for the first time methods to quantify the temporal stability of BA product accuracies and to alert product end-users that statistically significant temporal instabilities exist. These methods represent diagnostic tools that allow product users to recognize the potential confounding effect of temporal instability on analysis of fire trends and allow map producers to identify anomalies in accuracy over time that may lead to insights for improving fire products. Additionally, we suggest temporal instabilities that could hypothetically appear, caused by for example by failures or changes in sensor data or classification algorithms.

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Investigating the spread in surface albedo for snow‐covered forests in CMIP5 models

et al. 2016

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This study investigates the role of leaf/plant area index (LAI/PAI) specification on the large spread of winter albedo simulated by climate models. To examine the sensitivity of winter albedo to LAI, we perform a sensitivity analysis using two methods commonly used to compute albedo in snow-covered forests as well as diagnostic calculations within version 4.2 of the Canadian Atmospheric Model for which PAI is systematically varied. The results show that the simulated albedo is very sensitive to negative PAI biases, especially for smaller PAI values. The LAI and surface albedo of boreal forests in the presence of snow simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) models are evaluated using satellite observations. The evaluation of CMIP5 models suggest that inaccurate tree cover fraction due to improper plant functional type specification or erroneous LAI parameterization in some models explains, in part, an observed positive bias in winter albedo over boreal forest regions of the Northern Hemisphere. This contributes to a large intermodel spread in simulated surface albedo in the presence of snow over these regions and is largely responsible for uncertainties in simulated snow-albedo feedback strength. Errors are largest (+20-40%) in models with large underestimation of LAI but are typically within ±15% when simulated LAI is within the observed range. This study underscores the importance of accurate representation of vegetation distribution and parameters in realistic simulation of surface albedo.

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Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data

Cited by 163 publications

References 25 publications

Uncertainties in Classification System Conversion and an Analysis of Inconsistencies in Global Land Cover Products

Uncertainties in Classification System Conversion and an Analysis of Inconsistencies in Global Land Cover Products

Assessing the Temporal Stability of the Accuracy of a Time Series of Burned Area Products

Investigating the spread in surface albedo for snow‐covered forests in CMIP5 models

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