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Estimating accuracy of land-cover composition from two-stage cluster sampling

Remote Sensing of Environment

By:
, , , , , and
DOI: 10.1016/j.rse.2009.02.011

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Abstract

Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), root mean square error (RMSE), and correlation (CORR) to quantify accuracy of land-cover composition for a general two-stage cluster sampling design, and for the special case of simple random sampling without replacement (SRSWOR) at each stage. The bias of the estimators for the two-stage SRSWOR design is evaluated via a simulation study. The estimators of RMSE and CORR have small bias except when sample size is small and the land-cover class is rare. The estimator of MAD is biased for both rare and common land-cover classes except when sample size is large. A general recommendation is that rare land-cover classes require large sample sizes to ensure that the accuracy estimators have small bias. ?? 2009 Elsevier Inc.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Estimating accuracy of land-cover composition from two-stage cluster sampling
Series title:
Remote Sensing of Environment
DOI:
10.1016/j.rse.2009.02.011
Volume
113
Issue:
6
Year Published:
2009
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Remote Sensing of Environment
First page:
1236
Last page:
1249