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Remote Sens. 2014, 6(9), 8904-8922; doi:10.3390/rs6098904

A Comparison of Model-Assisted Estimators to Infer Land Cover/Use Class Area Using Satellite Imagery

State Key Laboratory of Earth Surface Processes and Resource Ecology, The College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
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Received: 11 April 2014 / Revised: 18 July 2014 / Accepted: 9 September 2014 / Published: 19 September 2014
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Abstract

Remote sensing provides timely, economic, and objective data over a large area and has become the main data source for land cover/use area estimation. However, the classification results cannot be directly used to calculate the area of a given land cover/use type because of classification errors. The main purpose of this study is to explore the performance and stability of several model-assisted estimators in various overall accuracies of classification and sampling fractions. In this study, the confusion matrix calibration direct estimator, confusion matrix calibration inverse estimator, ratio estimator, and simple regression estimator were implemented to infer the areas of several land cover classes using simple random sampling without replacement in two experiments: a case study using simulation data based on RapidEye images and that using actual RapidEye and Huan Jing (HJ)-1A images. In addition, the simple estimator using a simple random sample without replacement was regarded as a basic estimator. The comparison results suggested that the confusion matrix calibration estimators, ratio estimator, and simple regression estimator could provide more accurate and stable estimates than the simple random sampling estimator. In addition, high-quality classification data played a positive role in the estimation, and the confusion matrix inverse estimators were more sensitive to the classification accuracy. In the simulated experiment, the average deviation of a confusion matrix calibration inverse estimator decreased by about 0.195 with the increasing overall accuracy of classification; otherwise, the variation of the other three model-assisted estimators was 0.102. Moreover, the simple regression estimator was slightly superior to the confusion matrix calibration estimators and required fewer sample units under acceptable classification accuracy levels of 70%–90%. View Full-Text
Keywords: area estimation; classification accuracy; model-assisted inference area estimation; classification accuracy; model-assisted inference
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Li, Y.; Zhu, X.; Pan, Y.; Gu, J.; Zhao, A.; Liu, X. A Comparison of Model-Assisted Estimators to Infer Land Cover/Use Class Area Using Satellite Imagery. Remote Sens. 2014, 6, 8904-8922.

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