Thematic Accuracy Assessment of Regional Scale Land-Cover Data

Satellite-based land cover products play a crucial role in sustainability. There are several types of land cover products, such as qualitative products with discrete classes, semiquantitative products with several classes at a predetermined ratio, and quantitative products with land cover fractions. The proportions of land cover types in the grids with coarse resolution should be considered when used at the regional scale (e.g., modeling and remote sensing inversion). However, uncertainty, which varies with spatial distribution and resolution, needs to be studied further. This study used MCD12, ESA CCI, and MEaSURES VCF land cover data as indicators of qualitative, semiquantitative, and quantitative products, respectively, to explore the uncertainty of multisource land cover data. The methods of maximum area aggregation, deviation analysis, and least squares regression were used to investigate spatiotemporal changes in forests and nontree vegetation at diverse pixel resolutions across China. The results showed that the average difference in forest coverage for the three products was 8%, and the average deviation was 11.2%. For forest cover, the VCF and ESA CCI exhibited high consistency. For nontree vegetation, the ESA CCI and MODIS exhibited the lowest differences. The overall uncertainty in the temporal and spatial changes of the three products was relatively small, but there were significant differences in local areas (e.g., southeastern hills). Notably, as the spatial resolution decreased, the three products’ uncertainty decreased, and the resolution of 0.1° was the inflection point of consistency.

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Thematic accuracy of the 1992 National Land-Cover Data for the western United States

Remote Sensing of Environment ◽

10.1016/j.rse.2004.04.002 ◽

2004 ◽

Vol 91 (3-4) ◽

pp. 452-468 ◽

Cited By ~ 80

Author(s):

J.D Wickham ◽

S.V Stehman ◽

J.H Smith ◽

L Yang

Keyword(s):

United States ◽

Land Cover ◽

Western United States ◽

Land Cover Data ◽

Thematic Accuracy

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Designing a Multi-Objective, Multi-Support Accuracy Assessment of the 2001 National Land Cover Data (NLCD 2001) of the Conterminous United States

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.74.12.1561 ◽

2008 ◽

Vol 74 (12) ◽

pp. 1561-1571 ◽

Cited By ~ 23

Author(s):

Stephen V. Stehman ◽

James D. Wickham ◽

Timothy G. Wade ◽

Jonathan H. Smith

Keyword(s):

United States ◽

Land Cover ◽

Accuracy Assessment ◽

Multi Objective ◽

Land Cover Data

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Accuracy assessment of Globeland30: a case study of Ontario,Canada

10.32920/ryerson.14647026 ◽

2021 ◽

Author(s):

Rishita Rangarh

Keyword(s):

Land Cover ◽

Accuracy Assessment ◽

Landsat Imagery ◽

Global Scale ◽

Data Set ◽

Error Matrix ◽

Successful Model ◽

Land Cover Data ◽

Made In

GlobeLand30 is the world’s first 30m high resolution land cover data set (Chen et al. 2014) and has been a successful model of Big-Data mining from a host of Landsat imagery, thereby contributing to and enhancing the existing global geospatial knowledge base (GlobeLand30 2014). As there is a lot of uncertainty and errors in the global land cover data, therefore it becomes very difficult to validate land cover on a global scale. Efforts on validating Globeland30 data have been made in various parts of the world in the past and will continue to be done. The objective of this project is to validate GlobeLand30 data set by carrying out a case study in Ontario, Canada. The adopted methodology for doing validation is by using cell-to-cell benchmarking (Maria et al. 2015), thereby deriving Error Matrix, and its derivatives, which includes overall accuracy, user accuracy, producer accuracy and kappa coefficient. The results show that an overall accuracy of 84.14% is obtained for GlobeLand30 data with consideration of shadows, which is relatively a high percentage number indicating that the GlobeLand30 data classification is highly accurate for Ontario, Canada. Keywords: land cover; GlobeLand30; accuracy assessment; Ontario

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Analysis of the Impact of Positional Accuracy When Using a Single Pixel for Thematic Accuracy Assessment

Remote Sensing ◽

10.3390/rs12244093 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4093

Author(s):

Jianyu Gu ◽

Russell G. Congalton

Keyword(s):

Land Cover ◽

Spatial Scale ◽

Classification Scheme ◽

Accuracy Assessment ◽

Land Cover Mapping ◽

Sensing Applications ◽

Assessment Unit ◽

Thematic Accuracy ◽

The Impact ◽

Single Pixel

The primary goal of thematic accuracy assessment is to measure the quality of land cover products and it has become an essential component in global or regional land cover mapping. However, there are many uncertainties introduced in the validation process which could propagate into the derived accuracy measures and therefore impact the decisions made with these maps. Choosing the appropriate reference data sample unit is one of the most important decisions in this process. The majority of researchers have used a single pixel as the assessment unit for thematic accuracy assessment, while others have claimed that a single pixel is not appropriate. The research reported here shows the results of a simulation analysis from the perspective of positional errors. Factors including landscape characteristics, the classification scheme, the spatial scale, and the labeling threshold were also examined. The thematic errors caused by positional errors were analyzed using the current level of geo-registration accuracy achieved by several global land cover mapping projects. The primary results demonstrate that using a single-pixel as an assessment unit introduces a significant amount of thematic error. In addition, the coarser the spatial scale, the greater the impact on positional errors as most pixels in the image become mixed. A classification scheme with more classes and a more heterogeneous landscape increased the positional effect. Using a higher labeling threshold decreased the positional impact but greatly increased the number of abandoned units in the sample. This research showed that remote sensing applications should not employ a single-pixel as an assessment unit in the thematic accuracy assessment.

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