Landsat as the wall-to-wall phase of a hierarchical global ... · Landsat as the wall-to-wall phase...
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Landsat as the wall-to-wall phase of a hierarchical global
forest monitoring system
Sean Healey, Svetlana Saarela, Zhiqiang Yang, Warren Cohen, Göran Ståhl, Paul Patterson, GEDI Science Team
Landsat Science Team meeting August 8, 2018 Boulder, CO
G E D I L I D A R
G l o b a l E c o s y s t e m s D y n a m i c s I n v e s t i g a t i o n
GEDI (Global Ecosystem Dynamics Investigation)
S P A C E - X
D R A G O N
C A P S U L EExposed Facility
Investigator-led Venture-Class mission, 3-laser waveform lidar(PI: Dubayah)
Launch Readiness Date: 29 November
Range: 51 ̊S to 51°N
Primary science deliverable is a 1-km grid of forest biomass prediction, but, approximately 10% of cells will have no clear returns
OBI-WAN (Online Biomass Inference using Waveforms And iNventory)
• The user identifies an area of interest in a Google Earth Engine app, and gets back a forest carbon storage report
• Potential users: forest reserves, individual companies, municipalities
• User-defined areas may also have zero or few clear GEDI returns
Forest Reserve
Unprotected Forest
Traditional Inventory
No modeling
Model-Based Inference
Uses a field-to-Landsat model
Estimate variance is a function of the sample
design
Estimate variance is a function of model
parameter uncertainty
Hybrid Inference
Field-to-lidar model
Hierarchical Model-Based Inference
Uses a field-to-lidar and a lidar-to-Landsat model
Estimate variance is a function of BOTH sample
design and model parameter uncertainty
Estimate variance is a function of parameter
uncertainty of two models
(McRoberts et al., 2014, RSE)
(Ståhl et al., 2016) (Saarela et al., 2016)
Field plots
Lidar tracks
Landsat grid
With disturbance history as a predictor (with reflectance) Overall r2 = -.7
Without disturbance history as a predictor (with reflectance) Overall r2 = -.63
MSS Tangent
Splicing MSS and TM/ETM+/OLI ensemble-based disturbance maps
MSS Tangent
MSS ensemble
1985 1995 20181972
TM ensemble
Harmonize during overlap
MSS Cloud Mask:A random scene: LANDSAT/LM01/C01/T2/LM01_048030_19730809
GEE code available, based on Braaten et al., 2015 RSE
DN DN with BQA mask TOA with MSSmask
MSS Tangent
MSS data status on August 2• From USGS
• Globally there are 1,318,867 MSS images in collection 1
• From GEE
Global
Sensor Tier 1 Tier 2
Landsat 1 2029 136825
Landsat 2 1765 245393
Landsat 3 156 123527
Landsat 4 416 171230
Landsat 5 348 531961
CONUS
Sensor Tier 1 Tier 2
Landsat 1 122 32295
Landsat 2 143 53717
Landsat 3 15 24608
Landsat 4 21 21757
Landsat 5 22 70056
MSS Tangent
Take-home: The free access and high level of processing of the MSS archive enable meaningful improvements in prediction of current biomass
Generalized Hierarchical Model-Based Estimation (Saarela et al., in review)
• Now accounts for clustered lidar data and heteroskedastic models
Saarela et al., In review (Case A) (Case B)
Using Landsat and GEDI together will:
1. Help us fill in areas with no GEDI shots
2. In some cases give us the most precise estimates of biomass
3. Produce a really good Landsat-resolution biomass map