The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping
The Total Operating Characteristic (TOC) measures how the ranks of an index variable distinguish between presence and absence in a binary reference variable. Previous methods to generate the TOC required the reference data to derive from a census or a simple random sample. However, many researchers...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/19/3922 |
| _version_ | 1797515785583722496 |
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| author | Zhen Liu Robert Gilmore Pontius Jr |
| author_facet | Zhen Liu Robert Gilmore Pontius Jr |
| author_sort | Zhen Liu |
| collection | DOAJ |
| description | The Total Operating Characteristic (TOC) measures how the ranks of an index variable distinguish between presence and absence in a binary reference variable. Previous methods to generate the TOC required the reference data to derive from a census or a simple random sample. However, many researchers apply stratified random sampling to collect reference data because stratified random sampling is more efficient than simple random sampling for many applications. Our manuscript derives a new methodology that uses stratified random sampling to generate the TOC. An application to flood mapping illustrates how the TOC compares the abilities of three indices to diagnose water. The TOC shows visually and quantitatively each index’s diagnostic ability relative to baselines. Results show that the Modified Normalized Difference Water Index has the greatest diagnostic ability, while the Normalized Difference Vegetation Index has diagnostic ability greater than the Normalized Difference Water Index at the threshold where the Diagnosed Presence equals the Abundance of water. Some researchers consider only one accuracy metric at only one threshold, whereas the TOC allows visualization of several metrics at all thresholds. The TOC gives more information and clearer interpretation compared to the popular Relative Operating Characteristic. Our software generates the TOC from a census, simple random sample, or stratified random sample. The TOC Curve Generator is free as an executable file at a website that our manuscript gives. |
| first_indexed | 2024-03-10T06:53:07Z |
| format | Article |
| id | doaj.art-810e832679b24fca85d817aabb0a4945 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:53:07Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-810e832679b24fca85d817aabb0a49452023-11-22T16:42:48ZengMDPI AGRemote Sensing2072-42922021-09-011319392210.3390/rs13193922The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood MappingZhen Liu0Robert Gilmore Pontius Jr1Clark Labs, Clark University, Worcester, MA 01602, USASchool of Geography, Clark University, Worcester, MA 01602, USAThe Total Operating Characteristic (TOC) measures how the ranks of an index variable distinguish between presence and absence in a binary reference variable. Previous methods to generate the TOC required the reference data to derive from a census or a simple random sample. However, many researchers apply stratified random sampling to collect reference data because stratified random sampling is more efficient than simple random sampling for many applications. Our manuscript derives a new methodology that uses stratified random sampling to generate the TOC. An application to flood mapping illustrates how the TOC compares the abilities of three indices to diagnose water. The TOC shows visually and quantitatively each index’s diagnostic ability relative to baselines. Results show that the Modified Normalized Difference Water Index has the greatest diagnostic ability, while the Normalized Difference Vegetation Index has diagnostic ability greater than the Normalized Difference Water Index at the threshold where the Diagnosed Presence equals the Abundance of water. Some researchers consider only one accuracy metric at only one threshold, whereas the TOC allows visualization of several metrics at all thresholds. The TOC gives more information and clearer interpretation compared to the popular Relative Operating Characteristic. Our software generates the TOC from a census, simple random sample, or stratified random sample. The TOC Curve Generator is free as an executable file at a website that our manuscript gives.https://www.mdpi.com/2072-4292/13/19/3922floodremote sensingstratified random samplingTotal Operating Characteristic (TOC)water index |
| spellingShingle | Zhen Liu Robert Gilmore Pontius Jr The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping Remote Sensing flood remote sensing stratified random sampling Total Operating Characteristic (TOC) water index |
| title | The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping |
| title_full | The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping |
| title_fullStr | The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping |
| title_full_unstemmed | The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping |
| title_short | The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping |
| title_sort | total operating characteristic from stratified random sampling with an application to flood mapping |
| topic | flood remote sensing stratified random sampling Total Operating Characteristic (TOC) water index |
| url | https://www.mdpi.com/2072-4292/13/19/3922 |
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