Name
Abstract | ||
|---|---|---|
Climate change has become a hot topic in recent years. Flood is one of the most common natural hazards caused from extreme climate change. Scientists have spent a lot of money and time on monitoring flood in past decades. The development of Remote Sensing and Geographic Information System (GIS) brings new ways for scientists to analyze, monitor, and predict floods. Remote Sensing provides an alternative method to traditional flood survey with very fine temporal resolution data with much lower cost. Scientists have been utilizing data from MODIS satellite to detect flood in a lot of research. In this paper, flood duration layers are generated with utilizing Remote Sensing based flood data from Dartmouth Flood Observatory. The flood event layers provide detail view of flood events at pixel level. Flood data is currently processed and managed by RFCLASS website which developed by Center for Spatial Information Science and Systems. Few experiments have been designed to explore the possibility of minimizing cloud impact. Result indicated that there is a huge decrease in total events. Flood data generated in this research is ready to serve further research such as crop loss from flood. However, flood data is not fully accurate due to the similarity of spectral pattern between shadow and water surface. Further study is needed in order to remove error caused by shadow. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/Agro-Geoinformatics.2017.8047064 | 2017 6th International Conference on Agro-Geoinformatics |
Keywords | Field | DocType |
Remote Sensing,MODIS,Remote sensing based Flood Crop Loss Assessment Service System,flood duration | Meteorology,Spatial analysis,Observatory,Geographic information system,Satellite,Climate change,Environmental science,Natural hazard,Flood myth,Cloud computing | Conference |
ISSN | ISBN | Citations |
2334-3168 | 978-1-5386-3885-9 | 1 |
PageRank | References | Authors |
0.37 | 1 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Lin | 1 | 44 | 23.07 |
| Liping Di | 2 | 811 | 98.92 |
| Eugene Genong Yu | 3 | 1 | 0.70 |
| Junmei Tang | 4 | 6 | 3.57 |
| ranjay shrestha | 5 | 9 | 3.84 |
| Md. Shahinoor Rahman | 6 | 7 | 3.99 |
| Lingjun Kang | 7 | 7 | 1.86 |
| Ziheng Sun | 8 | 82 | 15.31 |
| Chen Zhang | 9 | 8 | 3.31 |
| Lei Hu | 10 | 10 | 3.35 |
| Guangyuan Yang | 11 | 1 | 0.70 |
| Zhengwei Yang | 12 | 203 | 20.16 |