|
Designing a Geospatial Database for Forest Operations in Lat Krathing Plantation, Chachoengsao Province |
|---|---|
| รหัสดีโอไอ | |
| Creator | Panas Srinualkaew |
| Title | Designing a Geospatial Database for Forest Operations in Lat Krathing Plantation, Chachoengsao Province |
| Contributor | Payattipol Narangajavana, Nopparat Kaakkurivaara |
| Publisher | Kasetsart University |
| Publication Year | 2564 |
| Journal Title | Thai Journal of Forestry |
| Journal Vol. | 40 |
| Journal No. | 2 |
| Page no. | 111-127 |
| Keyword | Geospatial, Database, Lat Krathing plantation |
| URL Website | https://li01.tci-thaijo.org/index.php/tjf/article/view/251277 |
| Website title | Thai Journal of Forestry |
| ISSN | 2730-2180 |
| Abstract | The important thing of the harvesting in a plantation management is the utilization of wood timber from forest operations. Forest inventory, timber procurement, harvesting, and the planned planting area must all be assessed annually to get the greatest benefits from a forest plantation. Previously, forestry operations relied on the experience of park supervisors familiar with a plantation's geography. However, geospatial technology has made great advances. Getting spatial information has become more convenient but extracting descriptive information can still have difficulties while relating with the geospatial data. The purpose of this research is to design and build a spatial database using the annual data collected from the Lat Krathing plantation in Chachoengsao province. We used spatial and descriptive data to create a database to examine and analyze the operations of the forest plantation. The results show that the estimated timber volume of Eucalyptus spp. and Acacia mangium during the logging period (2006-2018) had a root mean square error (RMSE) value of 2,082.03 and 1,997.78 ton, respectively. From the comparison, it was found that there was no statistically significant difference (at a 95% confidence level), with p-values of 0.181 and 0.071. As for the forest area under plantation during the same period, the RMSE value was 396.05 rai. We found no statistically significant difference (at 95% confidence level) with a p-value of 0.126. The designed geospatial database can be put into practice and can be further developed with additional studies to analyze the system requirements, database design, program design, development test and their evaluation. |
