3D modelling of canopy structure for optical remote sensing

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3D modelling of canopy structure for optical
remote sensing
Dr. Mathias Disney
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• Aims
• Benchmarking of simplified optical scattering
  models
• Explore/understand relationship between canopy
  variables and EO
• e.g. LAI, biomass, structure, radiometric
  properties (chlorophyll, dry matter, water etc.),
  understory
• Biophysical parameter retrieval
• Interface EO to ecological process models
• Commonality of structure radiation
  interception??
• ALL REQUIRE
• scattering model driven by structural description

 
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• Benchmarking
• Test assumptions made in various simplified RT
  models
• www.enamors.org/RAMI/rami.htm
• The RAdiation transfer Model Intercomparison
  (RAMI) Exercise Results from the second phase
  forthcoming in JGR - Atmospheres.

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• Trees
• E.g. Empirically-based Treegrow model
• Modify light extinction, height, dbh to match
  observed
• Measurements made at Harwood San Rossore
• Adapt for Sitka using parameterisations developed
  by Cochrane Ford (Herriott-Watt, 1978)

 
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• Needles
• Understand impact of needle shape and
  distribution
• impact on PAR
• Use Fibonacci phyllotaxy
• Published data for many species
• Big impact potentially on multiple scattering

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50m
 
Scots pine canopy at 3m spacing, 10 and 20 years
old
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• Modelling
• Shape and distribution have BIG impact on
  multiple scattering...at shoot scale
• BUT gets smeared out canopy scale
• Cylinders NOT appropriate.....

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• Other sources of structural info?
• LIDAR, in situ laser scanning

• Canopy height
• Canopy gap fraction and vertical profile of
  foliage
• Parameterise? Relate to other measures

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• Parameter retrieval
• Use 3D models to generate LUTs of scattering for
  range of structural realisations
• Structure gives expectation of canopy state i.e.
  constraint on parameter space
• Retrieve height, dbh, LAI, chlorophyll,
  understory and angular info.

AM 8 views max.
PM 9 views max.
Composite of airborne multiple flightlines over
Harwood Forest (13/7/03)
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• Crops
• Aim estimate crop inventory, status, etc. from
  EO
• e.g. Wheat growth model driven by thermal time
  (INRA Grignon)
• Based on detailed (plant leaf-level)
  observations
• Simulate EO signal
• Explicit 3D description allows any type of canopy

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• Coupling structural ecosystem models
• Build 3D forest structure model test ability to
  simulate EO data
• Convert representation to that used by ecosystem
  model(s)
• Tricky part generalisation of structure? Common
  representation?
• Test ability to model CO2 fluxes with this
  representation
• Data assimilation
• Grow canopy
• assimilate CO2 data alone assimilate EO data
  alone assimilate both datasets.....