Download - WE1.L09.5 - ESTIMATION OF FOREST BIOMASS CHANGE FROM FUSION OF RADAR AND LIDAR MEASUREMENTS
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DESDynI TIME
Es+ma+on of Forest Biomass Change from Fusion of Radar and Lidar Measurements
Sassan Saatchi (Jet Propulsion Laboratory/UCLA) Ralph Dubayah (University of Maryland) David Clark (University of Missouri )
Robin Chazdon (University of ConnecCcut) David Hollinger (USDA Forest Service)
Other contributors: Hank Shugart (University of Virginia)
Michael Lefsky (Colorado State University) ScoJ Hensley (JPL)
Maxim Neumann (JPL)
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DESDynI
ECOSYSTEM STRUCTURE Baseline Requirements
The DESDynI Mission shall map aboveground woody biomass within the greater of 20 Mg/ha or 20% (errors not to exceed 50 Mg/ha), at a spaMal resoluMon of 250 m globally and 100 m for areas of low biomass annually ( < 100 Mg/ha).
The DESDynI Mission shall map global areas of disturbance at 100 resoluMon annually and measure subsequent regrowth to an accuracy of 4 Mg/ha/yr* at 100 (1-‐ha) resoluMon.
Measurement requirements for SAR and Lidar Fusion are:
Lidar: 5 beams on sun-‐synchronous orbit with at least 50 shots within a 600 m grid at the equator at the end of 5 years.
Radar: Polarimetric (linear polarizaMons) L-‐band SAR 25-‐35 degrees incidence angle with 100m resoluMon (>100 looks) Two seasons of polarimetric coverage for annual biomass maps
Monthly global imaging capability at dual-‐pol (linear polarizaMon) for mapping disturbance and biomass change
Mission Life+me: 5 years
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DESDynI
DESDynl Mission ObjecMves
Inventory
Disturbance
DeforestaMon
Recovery
Logging
Aboveground Biomass from Fusion Of Lidar and Radar
Mapping Deforesta+on and Disturbance
Mapping Degrada+on (logging, infesta+on)
Forest Recovery
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DESDynI
Depending on antecedent history, a forest with the biomass level associated with a mature forest, could be storing carbon, losing carbon or staying the same.
This means that a single biomass “snapshot” does not completely reveal forest carbon dynamics.
Changes of Forest Biomass
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DESDynI
Large and Small Scale Dynamics are Different
and Influenced by Structure
Small-Scale Dynamics
Large-Scale Dynamics
≠
Scale of Forest Biomass
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DESDynI
2005 storm killed between 300,000 and 500,000 trees in the area of Manaus which is equivalent to 30 percent of the annual deforesta+on in that same year for the Manaus region, which experiences rela+vely low rates of deforesta+on.
+mber losses from Hurricane Katrina alone amount to roughly 4.2 billion cubic feet of +mber (15-‐19 billion board feet), spread over 5 million acres of light to heavily damaged forest land in Mississippi, Alabama, and Louisiana.
2005 Storm in Amazon Killed ½ Million Trees (Negron-‐Juarez et al., 2010)
2005 Katrina Hurricane Forest impact was equivalent to 25% of annual forest Sequestra+on (chambers et al., 2007)
Forest Disturbance
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DESDynI
Lucas et al. 2002
Forest Recovery Process
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DESDynI
Statement of Problem
1. DESDynl Es+ma+on of Annual Deforesta+on (Radar)
2. DESDynl Es+ma+on of disturbance (Fire, Storms, etc.) (Radar)
3. DESDynl Es+ma+on of Forest Degrada+on (Radar)
4. DESDynl Es+ma+on of Forest biomass loss (Radar/Lidar)
5. DESDynl Es+ma+on of Forest biomass recovery (Radar/Lidar)
( accuracy/precision, resolu1on, temporal coverage)
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DESDynI
Old Growth Height 1997
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DESDynI
Old Growth Height 2006
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DESDynI
Changes in Forest Height
Height difference: h(2006)-‐h(1997)
Mean: 1.18 m Stdev: 8.1 m
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DESDynI
Secondary Forest Height 1997
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DESDynI
Secondary Forest Height 2006
Mean:4.84 m Stdev: 6.2 m
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DESDynI
Growth Dynamics From Lidar
• Sampling lidar can be used to observe dynamics
– Not efficient for forest loss mapping (compared to radar or TM)
– Can directly measure growth/loss in canopy at footprint or grid scale • Orbital cross-‐overs could provide millions of direct observaMons
Amplitude
ElevaM
on
1998
2005
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DESDynI
La Selva Forest Dynamics (2005-‐1998)
Biomass Change [Mg/ha] 0.5 ha Old Growth Plots
Field Es+mate
r2 = 0.79
1:1 line
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DESDynI
16
SAR Measurement of Disturbance
• Annual forest disturbance, deforestation, degradation, fragmentation are mapped at 100 m resolution
a) Disturbance: -‐ 12.5 dB b) Disturbance: -‐ 5 dB
d) Disturbance: -‐ 1.0 dB c) Disturbance: -‐ 2.5 dB
Maximum Likelihood ClassificaMon
90% classificaMon accuracy
€
σ dist0 ≅ 0.78σ ref
0
At 100 m resolu+on (~100 looks) forest degrada+on of 1.0 dB change can be classified at 90% accuracy by LHV channel only.
€
class[N,µ] = 0.9 =1−Gamma[N −
N log(µ)−1+ µ
]
Gamma(N)µ = 0.78 : -1.04 dB
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DESDynI
Requirement for PolarizaMon (Disturbance)
17
HH, HV, VV HH
HV VV
1. Single pol (HH) data will map disturbance with ~50% accuracy 2. Dual-‐pol data will be the Minimum requirement to map Disturbance with ~80% accuracy) 3. Quad-‐pol data will provide map Disturbance with > 90% accuracy
ResoluMon: 100 m Radar BW: 40 MHz
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DESDynI
Global Biomass Change Requirements
Brown & Schroeder 1999
Average Production: ~5 Mg/ha/yr
2.5-3% of counties had wood production > 10 Mg/ha/yr
Hardwoods
Softwoods
Temperate & Boreal Forests
Average Biomass Production of forests after disturbance: ~4 Mg/ha/yr
Tropical Forests
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DESDynI
SAR Measurement of Bioamss Recovery
Recovery Phase
Disturbance Even
Assump+ons for mapping forest recovery:
• Rate of RegeneraMon: 4 Mg/ha/yr Biomass EsMmaMon Accuracy: 20 Mg/ha ResoluMon: 100 m (> 100 looks) Aker 5 year SAR will measure 4Mg/ha/yr biomass change at 100 m ResoluMon • in US temperate forests about 50% of forests produce > 4 Mg/ha/yr.
• AssumpMon: radar looks achieved from azimuth and range averaging
• Aker 3 years, SAR will not meet the requirement of biomass change
• 3 year mission will only cover forests with > 7 Mg/ha/yr recovery. Over US forests, this is about 15% of forests.
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DESDynI
Radar Forest DegradaMon Index
€
RFDI =HH −HVHH + HV
HV HH
HH: Dominated by volume & volume-‐surface Scajering HV: Dominated by volume scajering RFDI Sensi+vity to calibra+on is small RFDI Sensi+vity to topography and slope is small
ALOS La Selva Costa Rica
RFDI
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DESDynI
RFDI to map disturbance, DeforestaMon, Intensive Logging
LHH LHV LHV Texture
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DESDynI
RFDI over Slopes ALOS PALSAR Peru
ALOS PALSAR Peru
Forest Savanna
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DESDynI
RFDI & Changes in Biomass
ALOS PALSAR Mosaic (Borneo) UAVSAR Howland Forest 100 m ResoluMon 80 MHz Bandwidth
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DESDynI
Radar Forest DegradaMon Index For Forest Recovery EsMmates
€
p(I0
< I0 >) =
NNI0N −1
< I0 >N (N −1)!exp −
NI0< I0 >
⎧
⎨ ⎪
⎩ ⎪
⎫
⎬ ⎪
⎭ ⎪
where < I0 > is the mean intensity of a homogeneous region at time t0N is equivalent number of looksFor two independent measurements I0 = HH and I1 = HV , the difference and ratios will followthe integration of the joint probability over I0d = I0 − I1
p( d< I0 >
,< I1>) =
NN exp −NI0
< I0 >
⎧
⎨ ⎪
⎩ ⎪
⎫
⎬ ⎪
⎭ ⎪
(< I0 > + < I1>)N (N −1)!× (N −1+ j)
j!(N −1− j)!j = 0
j = N −1∑
r = I1/I0
p( r< I0 >
,< I1>) = (2N −1)!r NrN −1(r + r )2N (N −1)!N
where r =< I1> / < I0 >
RFDI =I0− I
1I0
+ I1
Change Detection will be performed between the ratio of RFDI for two dates.
RFDI =I0− I
1I0
+ I1 Delta (RFDI)*20
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DESDynI
RFDI Base Forest Recovery ALOS June 2007
ALOS June 2010
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DESDynI
RFDI Base Forest Recovery ALOS June 2007
ALOS June 2010
RFDI10-‐ RFDI07
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DESDynI
For N lidar samples We have (N-‐1)! Δσ samples
25 m 100 m
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DESDynI 28
L-‐band Measurement of Recovery
Radar & Lidar Fusion of Recovery
Baysian MLE Method
20% error in biomass change is detectable at 100-‐250 m resolu+on
€
N : Number of looksProb. of Error :PE =1/2 − f (x) + f (1/ x)
Lombardo and Oliver, 2001 Rignot & vanZyl 1995
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DESDynI
SUMMARY
• Quad-‐Pol data is required to measure disturbance and recovery from L-‐band SAR data
• Increasing cross-‐points in Lidar will provide es+mates of biomass changes at the stand and ecosystem levels
• RFDI based on dual-‐pol data will provide the most consistent index to classify deforesta+on, degrada+on and recovery. However, more research is needed to assess its quan+ta+ve capability for measuring biomass loss and gain.
• Fusion of L-‐band polarimetry and Lidar has the poten+al of quan+fying stand scale patch scale changes in biomass.
• Use of repeat pass interferometry along with RFDI has the poten+al of mapping forest regrowth.