Abstract
Operational satellite remote sensing data can provide the temporal repeatability necessary to capture phenological differences among species. This study develops a multitemporal stacking method coupled with spectral analysis for extracting information from Landsat imagery to provide species-level information. Temporal stacking can, in an approximate mathematical sense, effectively increase the 'spectral' resolution of the system by adding spectral bands of several multitemporal images. As a demonstration, multitemporal linear spectral unmixing is used to successfully delineate cheatgrass (Bromus tectorum) from soil and surrounding vegetation (77% overall accuracy). This invasive plant is an ideal target for exploring multitemporal methods because of its phenological differences with other vegetation in early spring and, to a lesser degree, in late summer. The techniques developed in this work are directly applicable for other targets with temporally unique spectral differences.
| Original language | English |
|---|---|
| Pages (from-to) | 3441-3462 |
| Number of pages | 22 |
| Journal | International Journal of Remote Sensing |
| Volume | 30 |
| Issue number | 13 |
| DOIs | |
| State | Published - 2009 |
Funding
This research was funded by the Pacific Northwest Regional Collaboratory as part of a Pacific Northwest National Laboratory (PNNL) project, funded by NASA. We are grateful to Janelle Downs, Jerry Tagestad and Gregg Petrie of PNNL and Shane Cherry of the Idaho National Laboratory for their valuable input and expertise. Thomas Windholz and Keith Weber of the ISU GIS Training and Research Center provided valuable input and data. We thank Jacob Mundt for assistance in editing and we also thank two anonymous reviewers.