Classification Accuracy for Stratification with Remotely Sensed Data
Tools are developed that help specify the classification accuracy required from remotely sensed data. These tools are applied during the planning stage of a sample survey that will use poststratification, prestratification with proportional allocation, or double sampling for stratification. Accuracy...
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| Veröffentlicht in: | Forest science 2003-06, Vol.49 (3), p.402-408 |
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| container_title | Forest science |
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| creator | Czaplewski, Raymond L. Patterson, Paul L. |
| description | Tools are developed that help specify the classification accuracy required from remotely sensed data. These tools are applied during the planning stage of a sample survey that will use poststratification, prestratification with proportional allocation, or double sampling for stratification. Accuracy standards are developed in terms of an “error matrix,” which is familiar to remote sensing specialists. In addition, guidance is provided to determine when new remotely sensed classifications are needed to maintain acceptable levels of statistical precision with stratification. FOR. SCI. 49(3):402–408. |
| doi_str_mv | 10.1093/forestscience/49.3.402 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 0015-749X |
| ispartof | Forest science, 2003-06, Vol.49 (3), p.402-408 |
| issn | 0015-749X 1938-3738 |
| language | eng |
| recordid | cdi_proquest_journals_197725164 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford University Press Journals All Titles (1996-Current) |
| subjects | Accuracy Classification Forestry Inventory Sensors Statistical analysis |
| title | Classification Accuracy for Stratification with Remotely Sensed Data |
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