Assessing the accuracy of land use land cover (lulc) maps using class proportions in the reference data

Fonte C C, See L ORCID: https://orcid.org/0000-0002-2665-7065, Laso Bayas J-C, Lesiv M, & Fritz S (2020). Assessing the accuracy of land use land cover (lulc) maps using class proportions in the reference data. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. pp. 669-674 Nice, France: ISPRS Congress on Technical Commission III. DOI:10.5194/isprs-Annals-V-3-2020-669-2020.

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Abstract

Traditionally the accuracy assessment of a hard raster-based land use land cover (LULC) map uses a reference data set that contains one LULC class per pixel, which is the class that has the largest area in each pixel. However, when mixed pixels exist in the reference data, this is a simplification of reality that has implications for both the accuracy assessment and subsequent applications of LULC maps, such as area estimation. This paper demonstrates how the use of class proportions in the reference data set can be used easily within regular accuracy assessment procedures and how the use of class proportions can affect the final accuracy assessment. Using the CORINE land cover map (CLC) and the more detailed Urban Atlas (UA), two accuracy assessments of the raster version of CLC were undertaken using UA as the reference and considering for each pixel: (i) the class proportions retained from the UA; and (ii) the class with the majority area. The results show that for the study area and the classes considered here, all accuracy indices decrease when the class proportions are considered in the reference database, achieving a maximum difference of 16% between the two approaches. This demonstrates that if the UA is considered as representing reality, then the true accuracy of CLC is lower than the value obtained when using the reference data set that assigns only one class to each pixel. Arguments for and against using class proportions in reference data sets are then provided and discussed.

Item Type: Book Section
Research Programs: Ecosystems Services and Management (ESM)
Depositing User: Luke Kirwan
Date Deposited: 14 Sep 2020 07:40
Last Modified: 14 Sep 2020 07:40
URI: http://pure.iiasa.ac.at/16705

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