Improving soil salinity prediction with high resolution DEM derived from LiDAR data
Paper
Paper/Presentation Title | Improving soil salinity prediction with high resolution DEM derived from LiDAR data |
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Presentation Type | Paper |
Authors | Liu, Xiaoye (Author), Peterson, Jim (Author), Zhang, Zhenyu (Author) and Chandra, Shobhit (Author) |
Journal or Proceedings Title | Proceedings of the 9th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS 2005) |
Number of Pages | 3 |
Year | 2005 |
Conference/Event | 9th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS 2005) |
Event Details | 9th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS 2005) Event Date 17 to end of 19 Oct 2005 Event Location Beijing, China |
Abstract | The aim of this study is to investigate the capability of integration of LiDAR derived terrain and hydrological features with other salinity related datasets to improve prediction of areas at risk from salinity in a catchment area in Victoria, Australia. Terrain and hydrological features including slope, drainage density and hilltop were generated from LiDAR derived DEM and a relative low quality DEM separately. These features were combined with other salinity related datasets to predict areas at risk from salinity. The results showed that using LiDAR-derived high quality DEM can improve the accuracy of salinity risk prediction. |
Keywords | salinity; Victoria; LiDAR; |
ANZSRC Field of Research 2020 | 370704. Surface water hydrology |
410402. Environmental assessment and monitoring | |
410601. Land capability and soil productivity | |
Public Notes | c. ISPMSRS. |
Byline Affiliations | Monash University |
https://research.usq.edu.au/item/9zy9x/improving-soil-salinity-prediction-with-high-resolution-dem-derived-from-lidar-data
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