Many change detection methods are being utilized in this project to identify areas of change over time and characterize landscape dynamics. These methods include:
- Channel/Scene Integration: Near-infrared channels (e.g., channel 4 of Landsat TM) from scenes of different periods composited as a qualitative method of assessing regional change.
- Multidate Composite: Multiscene data stack representing different time periods used as the input or feature set for an unsupervised classification for defining change and no-change spectral clusters.
- Principal Components Analysis: Change is assessed through the derivation of eigenvectors that relate spectral channels from scenes collected at different time periods to generate components, and eigenvalues that indicate the percent variance associated with each of the defined components.
- Image Algebra: Two channels of the same spectral region and wavelengths for two different time periods are ratioed, and image differencing is achieved by subtracting the spectral responses of one date from that of the other.
- Binary Mask: Uses a multidate image composite recoded into a binary mask consisting of areas, which have changed and not changed between two dates.
- Post-Classification: Classification of two scenes from different dates assessed on a pixel-by-pixel basis and reported through a change matrix.
Change trajectories are also being implemented. Change trajectories are analogous to panel data analysis in which survey responses are mapped over time through multiple observations. However, here the unit of observation is the pixel, and its “life history” is constituted by the values derived from the component images of a satellite time-series.