NDLI: Spatial sampling to assess classification accuracy of remotely sensed data

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Spatial sampling to assess classification accuracy of remotely sensed data

Content Provider	Scilit
Author	Michener, W. K. Brunt, J. W. Stafford, S. G.
Copyright Year	1994
Description	Selecting a proper sample design is important in assessing the classification accuracy of remotely sensed data. However, design guidelines often do not account for spatial autocorrelation of map error or are based on simulations from a few select landscapes and cannot easily be generalized beyond those data sets. This study reviews the effect of spatial autocorrelation and cost on the relative efficiency of three unbiased sample designs within a stratified framework. These designs are simple random sampling, systematic sampling and cluster sampling. To illustrate the results, sampling simulations are performed on possible error patterns in a vegetation cover map of the Great Basin ecoregion. This map was generated from Landsat Thematic Mapper data by the US Fish and Wildlife Service's Gap Analysis Program. Book Name: Environmental Information Management And Analysis
Related Links	https://content.taylorfrancis.com/books/download?dac=C2004-0-26727-X&isbn=9780429076688&doi=10.1201/9781482272505-21&format=pdf
Ending Page	202
Page Count	18
Starting Page	185
DOI	10.1201/9781482272505-21
Language	English
Publisher	Informa UK Limited
Publisher Date	1994-08-08
Access Restriction	Open
Subject Keyword	Book Name: Environmental Information Management And Analysis Remote Sensing Ecoregion Classification Accuracy Spatial Autocorrelation Cluster Sampling Sample Design Sensed Data
Content Type	Text
Resource Type	Chapter

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