Name
Abstract | ||
|---|---|---|
Lidar sensing has been found to be a useful method of monitoring the dynamics and mass balance of glaciers, ice caps, and ice sheets. However, it is also known that ice surfaces can have complex 3-dimensional structure, which can challenge their accurate retrieval with lidar sensing. In support of future lidar sensing satellite missions, such as the upcoming ICESat-2, a joint research project was recently initiated between the Rochester Institute of Technology (RIT) and the University at Buffalo to study lidar sensing of complex ice surfaces. This effort is supported by NASA's Remote Sensing Theory program and is aimed at advancing the science of lidar sensing. The general approach is to 1) define realistic complex ice surfaces, 2) render lidar image simulations, and 3) compare the resulting data to the known surfaces to gain insight into the phenomenology of lidar sensing of snow and ice. The project will build on existing scientific understanding of light scattering from snow and ice as well as lidar sensor system modeling with a systems engineering end-to-end perspective. Initial results show the simulations capturing realistic scattering of photons in snow volumes and the resulting point clouds measured by a model spaceborne lidar system. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6350733 | IGARSS |
Keywords | Field | DocType |
optical radar,optical sensors,remote sensing by radar,spaceborne radar,ICESat-2,LIDAR image simulation,LIDAR sensing satellite mission,NASA remote sensing theory program,RIT,complex 3-dimensional structure,complex ice surfaces,first principle modeling,glaciers,ice caps,ice sheets,joint research project,mass balance,spaceborne lidar system,glaciers,lidar,modeling,phenomenology,simulation | Meteorology,Glacier,Satellite,Computer science,Remote sensing,Ice sheet,Sensor system,Lidar,Point cloud,Snow,Light scattering | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.46 |
References | Authors | |
2 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John P. Kerekes | 1 | 194 | 35.38 |
| Adam Goodenough | 2 | 3 | 1.58 |
| Scott Brown | 3 | 12 | 3.25 |
| Jiashu Zhang | 4 | 1122 | 75.03 |
| Beáta Csathó | 5 | 13 | 2.77 |
| Anton Schenk | 6 | 1 | 0.46 |
| Sudhagar Nagarajan | 7 | 10 | 1.29 |
| Robert Wheelwright | 8 | 1 | 0.80 |