Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation

By Pontus Olofsson, Giles M. Foody, Stephen V. Stehman, Curtis E. Woodcock. 2013

975 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/3d9d222211fd0a4b69bb3dc96f9908bd9bc85be2

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In Geography, Planning and Development

In Global and Planetary Change

Citations in the corpus

Global Mangrove Extent Change 1996-2020: Global Mangrove Watch Version 3.0 (2022, methodology): “Using the omission and commission statistics from the accuracy assessment, the mapped area estimates for the mangrove extent and gains and losses were adjusted to reflect the impact of known sources of map error [44,45].”
Guidelines for the application of IUCN Red List of Ecosystems categories and criteria (2015, methodology): “Good practices in data processing and analysis (Olofsson et al., 2014; Olofsson et al., 2013; Fuller et al., 2003) should be employed to minimise bias in estimates of areal change over a time-series of spatial data.”
Good practices for estimating area and assessing accuracy of land change (2014, background): “Olofsson et al. / Remote Sensing of Environment 148 (2014) 42–57”
The distribution and amount of carbon in the largest peatland complex in Amazonia (2014, methodology): “Confidence intervals for peatland area were generated separately using the method described by [55], whereby the confusion matrix of the”
GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery (2020, background): “Assessing the accuracy of land-cover products is an essential step in describing the quality of the products before they are used in related applications (Olofsson et al., 2013).”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, background): “…sensing-derived forest biomass/carbon estimates has been recognized and much research has been conducted in the past decade (Gahegan and Ehlers 2000; Crosetto et al. 2001; Wang et al. 2009; Gonzalez et al. 2010; Olofsson et al. 2013; Rocchini et al. 2013; Montesano et al. 2014; Zhang et al. 2014).”
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data (2019, methodology): “Therefore, in order to provide unbiased area estimates of the output classes, error-adjusted strati ﬁ ed estimation of the error matrix was conducted following Olofsson et al. (2013).”
Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis (2018, methodology): “We used the approach described by Olofsson et al. (2013) to compute the error-adjusted area estimates (at 95% con ﬁ dence interval).”
A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring (2016, background): “More generally, assessing accuracy is challenging in the context of LUCC studies, where information on sampling designs, error matrices and accuracy measures, such as user’s, producer’s and overall accuracy, are not consistently and clearly reported [101,102], and there is still disagreement on reli”
Agricultural land change in the Carpathian ecoregion after the breakdown of socialism and expansion of the European Union (2013, methodology): “Validation results were finally adjusted for potential sampling bias and then used to derive error adjusted area estimates and the 95% confidence intervals around these estimates (Card 1982, Olofsson et al 2013).”
Global land use / land cover with Sentinel 2 and deep learning (2021, methodology): “From the error matrix over each region, we also compute the area of each class with 95% confidence error bounds [13].”
Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes (2014, background): “…simple random sampling, a stratified estimator using the map classes as ‘poststrata’ (the technique is commonly referred to as post-stratified estimation) usually results in a smaller SE relative to the conventional simple random sampling estimator (Card 1982; Olofsson et al. 2013; Stehman 2013).”