Estimating accuracy of land-cover composition from two-stage cluster sampling
Remote Sensing of Environment
By: S.V. Stehman, J.D. Wickham, L. Fattorini, T.D. Wade, F. Baffetta, and J.H. Smith
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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.
| 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 |
| Google Analytic Metrics | Metrics page |