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Estimation of forest stand disturbance through implementation of Vegetation Change Tracker algorithm using Landsat time series Stacked imagery in coastal Georgia (Poster Research Summary)
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| 1. | Title | ##plugins.generic.mcfnsReadingTools.titleLabel## | Estimation of forest stand disturbance through implementation of Vegetation Change Tracker algorithm using Landsat time series Stacked imagery in coastal Georgia (Poster Research Summary) |
| 2. | Creator | ##plugins.generic.mcfnsReadingTools.creators## | Shingo Obata; Warnell School of Forestry and Natural Resources University of Georgia Athens, GA 30602 USA; Giappone |
| 3. | Subject | ##plugins.generic.mcfnsReadingTools.keywords## | Forest disturbance; Landsat Imagery; Time series; Image Stacks |
| 3. | Subject | ##plugins.generic.mcfnsReadingTools.disciplines## | GIS; Remote Sensing; Forest Management |
| 4. | Description | ##plugins.generic.mcfnsReadingTools.abstract## | Knowledge of forest disturbances is the key information relevant to forest resource management and distribution of forest ecosystem structures. The objective of this research was to identify and date forest disturbances between 1984 and 2016 on 30 meter spatial resolution Landsat images of coastal Georgia through implementation of the modified Vegetation Change Tracker algorithm. First, we created Landsat Time Series Stack (LTSS) by stacking annual Landsat 5 TM and 8 OLI imagery, which cover WRS2- Path 17/Row 38. We calculated the inter-band Forest Z score (IFZ) for each imagery contained in LTSS. IFZ is a vegetation index calculated for each pixel in each year to measure the likelihood that the land use of a pixel is a forest. We have tested an algorithm automatically detecting the specific year of forest disturbance at the pixel level. To detect the disturbance the algorithm considers the time series changes in IFZ value for each pixel. The result of such analysis was summarized in the form of a disturbance year map, in which each pixel is assigned specific year of disturbance as its value. We used this analysis to estimate the areas of forest disturbances in each year. We made an accuracy assessment for the disturbance map using test points determined by a stratified sampling method. The results of this research provide a better age class description of our study area making possible to assess its location-specific information about forest disturbances and age structure. |
| 5. | Publisher | ##plugins.generic.mcfnsReadingTools.publisher## | Contemporary Journal Concept Press |
| 6. | Contributor | ##plugins.generic.mcfnsReadingTools.contributors## | |
| 7. | Date | ##plugins.generic.mcfnsReadingTools.date## | 2018-03-30 |
| 8. | Type | ##plugins.generic.mcfnsReadingTools.type## | Text.Serial.Journal; Vegetation Change Tracker algorithm; Google Earth Pro. Visual Inspection; |
| 9. | Format | ##plugins.generic.mcfnsReadingTools.format## | PDF (inglese); application/pdf |
| 10. | Identifier | ##plugins.generic.mcfnsReadingTools.identifier## | https://mcfns.com/index.php/Journal/it/article/view/10.9 |
| 11. | Source | ##plugins.generic.mcfnsReadingTools.source## | Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); V. 10 N. 1: MCFNS March 30, 2018; 32-32 (1) |
| 11. | Source | ##plugins.generic.mcfnsReadingTools.onlineIssn## | 1946-7664 |
| 12. | Language | ##plugins.generic.mcfnsReadingTools.language## | en |
| 13. | Relation | ##plugins.generic.mcfnsReadingTools.suppFiles## | |
| 14. | Coverage | ##plugins.generic.mcfnsReadingTools.coverageGeo## | |
| 15. | Rights | ##plugins.generic.mcfnsReadingTools.rights## | Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS) 2018 |