Abstract
Ecosystem-related observations from remote sensors on satellites offer huge potential for understanding the location and extent of global land cover change. This paper presents a comparative study of three time series based algorithms for detecting changes in land cover. The techniques are evaluated quantitatively using forest fire ground truth from the state of California for 2000-2009. On relatively high quality data sets, all three schemes perform reasonably well, but their ability to handle noise and natural variability in the vegetation data differs dramatically. In particular, one of the algorithms significantly outperforms the other two since it accounts for variability in the time series.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2010 Conference on Intelligent Data Understanding, CIDU 2010 |
| Pages | 175-188 |
| Number of pages | 14 |
| State | Published - Dec 1 2010 |
| Event | NASA Conference on Intelligent Data Understanding, CIDU 2010 - Mountain View, CA, United States Duration: Oct 5 2010 → Oct 6 2010 |
Other
| Other | NASA Conference on Intelligent Data Understanding, CIDU 2010 |
|---|---|
| Country/Territory | United States |
| City | Mountain View, CA |
| Period | 10/5/10 → 10/6/10 |