Vegetation Regeneration and Disturbance Estimates through Time, forest change detection algorithm. This algorithm generates a yearly clear-sky composite from satellite imagery, calculates a spectral vegetation index for each pixel in that composite, spatially segments the vegetation index image into patches, temporally divides the time series into differently sloped segments, and then labels those segments as disturbed, stable, or regenerating. Segmentation at both the spatial and temporal steps are performed using total variation regularization.

The output consists of a 1D array per pixel containing the slope of fitted trend lines. Negative values indicate disturbance and positive values regeneration.

See: Hughes, M.J., Kaylor, S.D. and Hayes, D.J., 2017. Patch-based forest change detection from Landsat time series. Forests, 8(5), p.166.

ee.Algorithms.TemporalSegmentation.Verdet(timeSeries, tolerance, alpha, nRuns)Image

Collection from which to extract VeRDET scores. This collection is expected to contain 1 image for each year, sorted temporally.

toleranceFloat, default: 0.0001

Convergence tolerance.

alphaFloat, default: 0.03333333333333333

Regularization parameter for segmentation.

nRunsInteger, default: 100

Maximum number of runs for convergence.