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Landsat-based detection of trends in disturbance and recovery algorithm
LandTrendr is set of spectral-temporal segmentation algorithms that are useful for change detection in a time series of moderate resolution satellite imagery (primarily Landsat) and for generating trajectory-based spectral time series data largely absent of inter-annual signal noise. The LandTrendr algorithm has been used for analysis of change in Landsat spectral time series data. Here, we review LandTrendr for the Google Earth Engine (GEE) platform.
Google Earth Engine Scripts:
Google Earth Engine Scripts:
SERVIR Science Coordination Office Example Outputs:
SERVIR Science Coordination Office Example Outputs:
Collection Parameters:
Start Year: 2000 | End Year: 2020 | Start Day: 01-01 | End Day: 05-31 | Index: NDVI | Mask: cloud, shadow, snow, water
Parameters
Run Parameters
MaxSegments: 6
Spike Threshold: 0.75
VertexCountOvershoot: 3
PreventOneYearRecovery: False
Recovery Threshold: 1
pvalThreshold: 0.2
bestModelProportion: 0.75
MinObservationsNeeded: 6
Change Parameters
Magnitude Value: 50
Durration Value: 50
Preval Value: 300
mmu Value: 6
Parameters
Run Parameters
MaxSegments: 5
Spike Threshold: 0.9
VertexCountOvershoot: 3
PreventOneYearRecovery: False
Recovery Threshold: 0.5
pvalThreshold: 0.1
bestModelProportion: 0.75
MinObservationsNeeded: 6
Change Parameters
Magnitude Value: 100
Durration Value: 50
Preval Value: 400
mmu Value: 6
Parameters
Run Parameters
MaxSegments: 4
Spike Threshold: 1
VertexCountOvershoot: 3
PreventOneYearRecovery: False
Recovery Threshold: 0.25
pvalThreshold: 0.05
bestModelProportion: 0.75
MinObservationsNeeded: 6
Change Parameters
Magnitude Value: 200
Durration Value: 50
Preval Value: 500
mmu Value: 6
GEE-visualization
var pal1 = ['#9400D3', '#4B0082', '#0000FF', '#00FF00', '#FFFF00', '#FF7F00', '#FF0000'];
var yodVizParms = {min: 2000, max: 2014, palette: pal1};
Contacts
Contacts
Robert Kennedy
robert.kennedy@oregonstate.edu
Emil Cherrington
emil.cherrington@nasa.gov
Christine Evans
cae0004@uah.edu
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