Abstract
Abstract. Estimating area of impervious land cover is the most useful and one of the ecological assessment indexes of urban and regional environment. Global land cover maps are inevitably misclassified, which affects the quality and application of the data. Statistical approach for assessing the accuracy is critical to understand the global change information and area estimation is usually based on sample data with a probability-based estimator. However, research on evaluation of multi-temporal global impervious land cover maps has not been implemented. In this study, spatial characteristics of the data are considered to assess the thematic map accuracy with a two-stage stratified random sampling plan. The first stage of stratification is determined by the global urban ecoregion and the second one is determined by land cover classes. Additionally, sample size of both map stage and pixel stage are calculated using a probability sampling model. A response design is constructed for a per-pixel accuracy assessment and blind interpretation is implemented using sample pixels and its surrounding area. Our method is applied to the multi-temporal global impervious land cover maps between 2000 and 2010 with a time interval of 5 years and the estimated area in different epoch are listed in detail. The main contribution of our research is illustrating the details for calculating the proportion area of impervious land cover and corresponding confidence intervals based on the reference classification. The experimental results show that the increasing area of the impervious surface according to the sample unit shows good agreement with the urbanization and descriptive accuracy assessments by user’s, producer’s and overall accuracy are shown respectively.
Highlights
Area estimation is one of the most critical indicators to assess the accuracy of the land cover data from the remotely sensed imagery, which plays an important role in the development of productive economy and establishing scientifically accounting application to policy approaches for global ecological sustainability.Attempts to estimate the area of the single land cover map or land cover change has a far-reaching impact on societies and environments, reflecting the range of human activities on natural environment and ecosystem (Schneider et al, 2010)
We present a two-stage stratified sampling for estimating the area of the global impervious land cover maps from 2000 to 2010 based on the classification error matrix of stratified sampling and validated the effectiveness of this approach
If independent samples are obtained by the use of the simple random sampling, the great majority of the sample units would fall in the area where no impervious surface exists, leading to a higher variance in the estimated accuracy of the population
Summary
Attempts to estimate the area of the single land cover map or land cover change has a far-reaching impact on societies and environments, reflecting the range of human activities on natural environment and ecosystem (Schneider et al, 2010). The aim of area estimation is estimating the reference area of land cover class or that of a land cover change (Stehman, 2009). Area estimation based on a sample of reference observations, and design-based inference for the entire population (Stehman, 1997) can be a good choice when the estimation refers to a large scale region or some ground surveys are difficult to reach. Stratified sampling design with the consideration of data features and will provide a good result of area estimation. The stratified random sampling is defined as selecting a random sample from each stratum, and implemented with the map classes defined as a stratum
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