# [Southern Africa Validation of EOS (SAVE)]{.underline}

**Implementation Plan**

### Jeffrey L. Privette, PI

# This version of the SAVE Implementation Plan is for distribution to Team members and collaborators only. Please observe this limitation.

### 

**1.0 Goals**

The goals of this project, in priority, are:

1)  Validate EOS *LAI, FPAR, VI, LC, LCC, % woody cover, ozone* *and
    aerosols* over key biomes (*primary products* for SAVE)

2)  Validate EOS *1b, surface reflectance, fires, burn scar, BRDF,
    albedo, NPP* (*secondary products*)

3)  Develop methods to measure and scale field measurements over
    discontinuous canopies

4)  Support development, parameterization and validation of ecosystem &
    SVAT models

5)  Develop new methods to quantify fire fuel potential (emphasis on
    litter accumulation and understory LAI assessment)

6)  Determine primary spatial/temporal aerosol sources and deposition
    specifications

7)  Validate related products of AVHRR, Landsat7, EO-1, and DAO as
    time/necessity permit

*Progress towards these goals must be published in peer-reviewed
journals to be of use to the EOS community and us.*

**2.0 Background**

SAVE is a 3-year project (FY98 through FY00) funded by the EOS
Validation Program Office (D. Starr, Project Scientist) in response to
NASA NRA97-MTPE-03 (Type II). The primary aim of SAVE is to validate EOS
Terra atmospheric and land products in strong association with the Terra
instrument teams, particularly MODLAND, by augmenting and leveraging off
existing scientific capacity and programs in southern Africa (e.g.,
SAFARI 2000). Atmospheric measurements are largely limited to aerosol
optical depth and source attribution, and ozone profiles. Land
measurements include nearly all products from MODLAND and MISR. The
geographical distribution of SAVE atmospheric measurement sites is
circular, corresponding to the periphery of the pervasive atmospheric
high-pressure cell in the region. The distribution of land measurements
is more linear, roughly covering the north-south precipitation gradient
along the IGBP Kalahari Transect, with auxiliary sites among important
ecosystems outside the Kalahari sands.

The proposing SAVE Team included:

PRINCIPAL INVESTIGATOR: Jeffrey L. Privette NASA's GSFC (land lead)

CO-PRINCIPAL INVESTIGATORS: Anne Thompson NASA's GSFC (ozone lead)

Steve Prince Univ. of Maryland

Robert Scholes Forestek/CSIR (in-country lead)

CO- INVESTIGATORS: Robert Swap Univ. of Virginia (aerosol lead)

Niall Hanan Colorado State Univ.

Peter Dowty Univ. of Virginia

COLLABORATORS: Marc Leroy CNES/CESBIO

Paul V. Desanker Univ. of Virginia

Peter G. H. Frost Univ. of Zimbabwe

A significant number of additional scientists have been or will be
collaborating with SAVE.

**3.0 Relationship to SAFARI-2000**

As a funded, regional project, SAVE was a catalyst for moving
SAFARI-2000 from concept to implementation stage. SAFARI-2000 is an
organizational umbrella under which scientists can coordinate
independently funded efforts in southern Africa. It is led by Bob Swap,
a Co-I of SAVE, Harold Annegarn (WITS), and Bob Scholes, a SAVE Co-PI.
SAFARI-2000 is a three-year activity, with Intensive Field Campaigns to
occur in Aug/Sept '99 (dry season), Feb/Mar '00 (wet season), and
Aug/Sept '00 (dry season). The most significant of these is the Aug/Sept
'00 campaign, in which the NASA ER-2 will be deployed, with most Terra
instrument simulators. The primary focus during campaigns will be the
atmosphere, in particular aerosol and trace gas measurements. However,
SAVE will conduct more comprehensive land sampling during these periods
as well.

In the initial SAFARI-2000 planning meeting (7/98), investigators
identified a set of "Test Sites" at which more extensive measurements
and modeling activities will occur. This strategy aims to minimize
redundant and fractionated efforts, and instead encourage cost sharing
and activity synergy. For example, high-resolution satellite imagery
(Landsat, ASTER) will be acquired most frequently over these sites. Two
Test Sites, Skukuza and Mongu, are also EOS Land Validation Core Sites.
The latter are comprised of 24 sites around the world designated for
sustained use in multi-satellite product validation. Figure 1 (below)
shows the Test Sites, Kalahari sand distribution, and precipitation
gradient of southern Africa.

SAVE will work exclusively within the network of Test Sites. It
presently is one of only two SAFARI-2000 projects currently based on EOS
Terra data. It thus may also serve as the primary coordinator of other
EOS validation efforts in the region expected from various Terra
instrument teams.

![](media/image1.pct){width="4.055555555555555in"
height="4.583333333333333in"}

**Figure 1**. SAFARI-2000 Test Sites (large dots) and the distribution
of Kalahari sandy soils with isohyets of mean annual precipitation
(millimeters). The EOS Land Validation Core Sites in Mongu, Zambia and
Skukuza, S.Africa, are the solid black dots.

**4.0 General Approach**

SAVE will allocate resources according to the expected success and
biogeochemical importance of SAFARI-2000 Test Sites. The priority
landcovers, in order, are Kalahari woodland, *acacia*/*combretum*
woodland savanna, dry miombo, wet miombo, mopane woodland, desert
grassland/shrubland, and wetland. SAVE will locate aerosol
instrumentation around the atmospheric gyre at sites chosen to maximize
differences in the aerosol regimes (e.g., industrial, dust, biomass
burning, etc.).

The specific site emphases are as follows. SAVE will conduct intensive
measurements at the two EOS Core Sites, Skukuza (*acacia*/*combretum*
woodland savanna, S. Africa) and Mongu (Kalahari woodland, Zambia), and
less intensive measurements at the following SAFARI Test Sites: Etosha
National Park (desert sands/grassland, Namibia), Tshane (desert
shrubland/savanna, Botswana), Ndola (wet miombo, Zambia), Kasungu (dry
miombo, Malawi), and a site to be named in northern Mozambique (miombo).
Few, if any, ground activities will be conducted at other sites.
Ozonesonde releases will occur in Zambia, South Africa or Botswana.

SAVE will construct an above-canopy tower at both Skukuza and Mongu, and
will deploy upward and downward looking radiation sensors on the towers.
SAVE will not fund deployment of an eddy covariance system on the
towers. However, a regular system of ground transect measurements will
be conducted around each tower. Moreover, SAVE will fly a light aircraft
over the towers, the transects, and the surrounding area (to roughly 20
km x 20 km). The aircraft will be equipped with a 3-band digital imager,
whose data will be used to extrapolate tower and transect measurements.
ASTER, Landsat7 and EO-1 data will be acquired for this purpose as well.
The ground and aircraft measurements will occur continuously or
periodically, as appropriate, throughout the project lifetime.
Measurements of non-EOS products, including auxiliary parameters
required for validation of the LAI/FPAR algorithms (see Appendix IV),
will be measured during the Intensive Field Campaigns (IFCs) only.

At the remaining sites, SAVE will focus on atmosphere characterization
by deploying sunphotometers and aerosol streaker samplers. SAVE will
conduct transect and light aircraft measurements of ground data at these
sites (excepting Ben MacDhui). Otherwise, these sites will receive
minimal priority for ground data collection. Satellite data collection
will nevertheless remain a priority.

During the IFCs, extensive aerosol and trace gas data will be collected
both remotely and in situ by several aircraft flown via SAFARI-2000.
These data will be combined with SAVE atmospheric data for more rigorous
validation of the MODIS, MISR and MOPITT aerosol and trace gas products.
The most extensive atmospheric data collection will occur during the
third IFC (Aug/Sept, 2000, dry season). This will include ozonesonde
releases from selected sites.

SAVE was developed to validate EOS products in a science context, and
activities supporting direct validation must precede all others.
Nevertheless, as time and resources permit, SAVE will acquire the data
required for modeling activities such that EOS Level 3 and 4 products
(e.g., NPP) can be validated. SAVE will seek to collaborate primarily
with two process modeling studies: 1) an integrated 1-D trace gas and
energy flux ecosystem model (Gu, UVA), developed for a boreal forest, is
being modified to simulate the Mongu woodland site; 2) a
phenological/structural dynamics model of NPP is being developed and
tested at SAVE sites for fire fuel and NPP simulation (Dowty, UVA). In
addition, a gap model for savannas is planned and will be related to
land cover/use and climate trends (Caylor, UVA). SAVE is working with
these groups to ensure proper measurements (equipment, temporal and
spatial resolutions) are obtained for model inputs, constraints and
validation.

**5.0 Schedule of Activities**

  ----------------------- ---------- --- --- --- --- --- --- --- --- --- --- --- ---------- --- --- --- --- --- --- --- --- --- --- ---
  **ACTIVITIES**          **1999**                                               **2000**                                           

                          J          F   M   A   M   J   J   A   S   O   N   D   J          F   M   A   M   J   J   A   S   O   N   D

  Skukuza tower                                                                                                                     
  installation                                                                                                                      

  Mongu tower                                                                                                                       
  installation                                                                                                                      

  Pilot field campaign                                                                                                              

  Intensive field                                                                                                                   
  campaign                                                                                                                          

  **Ground/Aircraft                                                                                                                 
  Data**                                                                                                                            

  Core Site tower                                                                                                                   
  operations                                                                                                                        

  Core Site ground                                                                                                                  
  transects                                                                                                                         

  Core Site aircraft                                                                                                                
  transects                                                                                                                         

  Core Site auxiliary                                                                                                               
  properties                                                                                                                        

  Auxiliary site                                                                                                                    
  atmosphere data                                                                                                                   

  Auxiliary site ground                                                                                                             
  transects                                                                                                                         

  Ozonesonde releases                                                                                                               

  **Remote Sensing Data**                                                                                                           

  AVHRR 1 km products                                                                                                               

  Landsat 7 images                                                                                                                  

  ASTER images                                                                                                                      

  MODIS/MISR/MOPITT/\                                                                                                               
  CERES products                                                                                                                    

  **Analysis/Modeling**                                                                                                             

  Primary product                                                                                                                   
  analysis                                                                                                                          

  Secondary product                                                                                                                 
  analysis                                                                                                                          

  Gu modeling                                                                                                                       
  collaboration                                                                                                                     

  Dowty modeling                                                                                                                    
  collaboration                                                                                                                     
  ----------------------- ---------- --- --- --- --- --- --- --- --- --- --- --- ---------- --- --- --- --- --- --- --- --- --- --- ---

**6.0 Ground Rules for Use of SAVE-funded Resources/Results**

1)  All publications, presentations, and other reports describing SAVE
    activities/results must acknowledge SAVE.

2)  Significant use of SAVE resources, including data, equipment and/or
    personnel, should result in an offer of coauthorship to one or more
    of the SAVE investigators.

All SAVE data will be publicly available within 6 months of acquisition
as required under EOS program rules. However, users of SAVE data and
resources should notify J. Privette of the intended use.

**Appendix I. Investigator Roles and Responsibilities**

[Proposing Investigators]{.underline}

• Privette assumes overall responsibility for the project. He will
deploy the surface measurement equipment and guide sampling design. He
will conduct measurements of auxiliary parameters (non-EOS products)
during the IFCs. He will lead development of the aircraft sampling
package (same as the MODIS MQUALs design, without an Exotech). He will
oversee comparisons of field and EOS products, and collaborate on
modeling activities. He will assist Swap in interfacing with
SAFARI-2000.

• Swap is responsible for the aerosol components, specifically the
deployment and maintenance of the streaker and sunphotometer equipment.
He is responsible for most new equipment procurement, MOUs,
SAVE/SAFARI-2000 integration, subcontracts (including those associated
with the light aircraft program) and student worker oversight (via UVA).
He will conduct comparisons of field vs. EOS aerosol products and ensure
inputs for atmospheric models are developed. He will assist in the
atmospheric chemistry and aerosol measurements obtained by aircraft
during the IFCs to the extent that this serves SAVE (re. MOPITT val).

• Scholes will oversee construction of the Skukuza tower, and assumes
overall responsibility for the Skukuza site and relations with the S.
Africa Parks Board. He will supply historical data, and provide
technical backstopping and overall guidance. He will also conduct eddy
covariance+CO2 measurements at Skukuza, and temporarily at Mongu, should
his pending proposal to NASA TEP be approved (with Hanan). As PI, he
will integrate SAVE with the LEAD project (regional study of nitrogen
and sulphur deposition).

• Prince will provide guidance on the light aircraft sampling approach,
and may provide additional assistance and site coverage through his
funded Miombo LCLUC work, particularly around Chunga/Kafue National Park
(dry miombo, Zambia).

• Thompson will provide overall guidance on atmospheric chemistry and
aerosol sampling. She will release ozondesondes (per integration with
her SHADOZ), with more intensive releases during the '00 IFCs. For SAVE,
she will emphasize Mongu, Skukuza and one site TBD. She will compare
retrieved tropospheric ozone from the sondes and TOVS data. She is
primary liaison to the MOPITT team.

• Desanker may assist Swap with LCLUC work in SAVE. He may also help
guide Landsat7 purchase decisions, and identify subcontracted personnel
for the Kasungu and Ndola sites.

• Frost will primarily act as a technical advisor to SAVE, particularly
on fires and LCLUC.

• Hanan may oversee field data measurements (temporal, spatial
resolutions, equipment, parameters) as required for SiB, and may conduct
SiB model runs in '00 and '01. Hanan will conduct eddy covariance+CO2
measurements at Skukuza, and temporarily at Mongu, should his pending
proposal to NASA TEP be approved (with Scholes).

• Leroy and Roujean may deploy a dense PAR sampling array at the Mongu
site. They will lead acquisition of fine resolution canopy structure
measurements and modeling (extinction-based) at Mongu. Will acquire
POLDER, MERIS and SEVERI data and merge validation efforts of these
sensors and EOS. Participation may depend on funding.

[Subcontracted Investigators]{.underline}

*UVA*

• Gu will help guide the collection of surface data required for the BGC
model. He will modify a 1-D boreal system BGC model to simulate savanna
woodland systems, then run the model to produce C (primary variable),
H20 vapor, sensible heat and net radiation exchange estimates. His
emphasis will be on understanding effects of aerosol-influenced
diffuse/direct radiation on NPP for improved dynamic estimates of
epsilon via remote sensing. He plans to focus on the Mongu and Skukuza
sites only, and can validate MODIS NPP there.

• Dowty will provide guidance on the vegetation structure sampling, and
will assist in data collection (basal area, grass biomass and % woody
cover, possibly leaf and soil fluxes \[short term\]), analysis and
phenological model runs. He can assist in deriving LAI from light
attenuation. In contrast to Gu's work, Dowty will emphasize spatial (3D)
modeling, and developing maps of fire fuel potential and NPP (including
comparisons with MODIS NPP). He is particularly concerned with effects
of soil moisture on NPP. He will use field LAI to validate predicted
leaf phenology curves (LAI and leaf reflectance maps).

• Caylor (UVA Grad Student) will develop methods for estimating
historical (past 30 years) and current % woody cover, LAI, inter-tree
distance and crown size from various data sets (Dig. Photography,
DISP/CORONA, TM, AVHRR). This information will be used to develop and
validate a savanna structural dynamics model, and will be related to
trends in land cover use and disturbance. He will attempt to validate
MODIS continuous field products and VCL/SLICER data. He will assist in
field data collection during IFCs.

• Regional PhD Student (TBD). Student will oversee ground data
collection operations, including collection of data, guidance to others
collecting data, data reduction, QA, formatting, transmission to U.S.
archive, and field vs. EOS product comparisons.

[Collaborating or Associated Investigators]{.underline}

*European*

• Lewis/Roberts (Univ. Coll., London) will develop methods for relating
BRDF parameter temporal trajectories with LC types and subpixel
mixtures. They will conduct gridded structural (LAI, % cover, perhaps
albedo and spectra) sampling at Mongu. They validate MODIS BRDF/albedo
through empirical relations to aircraft digital photography, and then
scaling. May use AVHRR and SPOT VEGETATION. Expect field visits in IFCs
1 and 2.

*GSFC/UMD*

• Holben/AERONET (GSFC) will oversee calibration, swapping, and major
maintenance of CIMEL sunphotometers, and is responsible for data ingest,
processing, QA, WWW posting and archiving. Holben will also supply the
downwelling PAR sensors for the tower sites. Mukulabai (Muke) is in
charge of the Mongu sunphotometer, Bengis (Bibi) is in charge of the
Skukuza sunphotometer, and Piketh is in charge of the Ben MacDhui
sunphotometer.

• Morisette (UMD) is responsible for acquisition and dissemination of
SAFARI Test Site satellite data, including ASTER, L7, MISR, MODIS and
MOPITT. He may assist Desanker on LCLUC analysis.

• Hansen (UMD) will be the liaison from MODLAND/UMD LC and LCCC effort,
particularly the continuous fields (e.g., % cover fraction). He will
help develop appropriate field sampling methods.

• Roy, Giglio (GSFC) and Justice will work with SAVE to conduct MODIS
fire and burn scar validation around SAVE sites.

• Tucker and Kendall will provide rapid browse-quality AVHRR images
during the IFCs. Further, they will provide the appended list of AVHRR 1
km calibrated products (as funded by EOS Validation Program/D. Starr \--
see Appendix V).

• Shu (NASA's GSFC Code 553) is developing a portable NDVI sensor for
vegetation assessment on ground transects. The instrument will look both
up and down, and sample at 80Hz or faster. Campbell 21X data loggers
from Code 923 will be used with these. Expected delivery: Spring '99.
Shu is also developing an advanced sunphotometer in association with
AERONET and Privette.

• Hom (NASA's GSFC Code 923) is integrating the Dycam 3-band digital
camera system for the SAVE MQUALS light aircraft package. He is
responsible for interfacing the camera with a laptop PC for data ingest,
and later for input from a GPS receiver and albedometer. This work is in
collaboration with Huete/Univ. of Arizona.

*Regional*

• M. Scholes (WITS) will oversee the WITS subcontract and employees.

• Annegarn (WITS) will provide overall SAFARI oversight, assist M.
Scholes in WITS subcontract execution, and provide aerosol streaker
sample PIXE analysis.

• Piketh (WITS) will conduct atmospheric modeling and incorporate SAVE
data. He is in charge of the Ben MacDhui site and equipment, and is lead
investigator for ARREX.

• Eckhardt (PhD student in Skukuza) will analyze historic Landsat and
EOS satellite data, and light aircraft imagery, in an effort to quantify
fractional woody landcover, and landcover change, in Kruger Nat'l Park.
Other KNP staff supporting SAVE include Harry Biggs and Nick Zantatio.

• Mukulabai (Muke), Nyambe (Silume), Lucio (Filipe), and Versfeld
(Wilferd) are on-site personnel for Mongu, Mozambique and Etosha NP,
respectively.

**Appendix II. Measurement and analysis methods**

[LAI, FPAR, % woody cover (Privette, Dowty, Caylor, Lewis, Roujean)
*Primary.*]{.underline}

1.  For each site, develop relationships (regressions) between overstory
    crown LAI/FPAR and crown transmission.

    a.  LAI/FPAR: destructive measurements, models, leaf fall and
        ceptometer/LAI 2000

    b.  transmission: calibrated ASD FR spectrometer and portable NDVI
        meter

    c.  previous equations may be available from Scholes

2.  Develop relationships (regressions) between understory (grass)
    LAI/FPAR and NDVI (both between crown and under crown).

    a.  LAI/FPAR: destructive and ceptometer/LAI-2000 measurements

    b.  NDVI: calibrated ASD FR spectrometer and portable NDVI meter, at
        Terra overpass time.

    c.  previous equations may be available from Scholes

3.  Using portable NDVI meter (up and down looking), carry along long
    (\~2km, previously mapped and fixed) transects with bicycle to
    sample overstory gap fraction and light transmission, and understory
    NDVI variation. With relationships from Tasks 1 and 2, estimate
    total LAI/FPAR and % cover relationships.

    a.  sensor being developed by Shu (GSFC), integrated with 21X

    b.  design for transects needed (Scholes, Frost)

    c.  bike procurement by WITS

    d.  biweekly, possibly daily during leaf out and leaf fall (for
        Dowty)

4.  Conduct light aircraft transects and extrapolation region flights
    with 3-band aerial photography. Calibrated with ground % cover data
    on transects, then use relationships from 3 to estimate total
    LAI/FPAR. Develop NDVI vs. total LAI/FPAR relationship for imagery.

5.  Using ASTER and Landsat7 data, correlate total LAI/FPAR with NDVI
    and extrapolate over full scene.

6.  Compare ASTER and L7 estimates of total LAI/FPAR with MODLAND
    LAI/FPAR.

7.  Compare with Roujean/Leroy estimates at tower

[Fine Resolution LAI/FPAR (Roujean, Leroy, Privette, Dowty)
*Primary.*]{.underline}

1.  Install a dense network of continuously operating small optical
    sensors (30\*\$150) and acquire a full time series of LAI/fAPAR

2.  Develop scaling and clumping indices for woodland (Mongu)

    a.  May require TRAC measurements

3.  Compare measured FPAR to those from ADEOS-2/POLDER, MSG/SEVIRI ('00
    launches), and ENVISAT/MERIS

[VI (Privette) *Primary.*]{.underline}

1.  On light aircraft transects, continuously sample canopy with ASD FR
    spectrometer. Calibrate with panel on ground. Conduct sampling at
    Terra overpass time

    a.  ASD FR from Code 923--in house

    b.  Need Spectralon panel and second ASD

    c.  Probably an IFC activity only

2.  Convolve MODIS spectral sensitivity functions for bands 1-5 with ASD
    data, and estimate MODIS spectral response.

3.  Derive relationship between light aircraft 3-band image data and
    estimated MODIS responses as a function of % cover and LC type (%
    cover from above).

4.  Using results from 3, develop a 20 x 20 km\^2 estimate of the MODIS
    VI response.

5.  Repeat 2-4 for ASTER and L7.

6.  Compare MODLAND VI product with estimated (nadir) values from fine
    resolution data.

[BRDF/Albedo (Lewis, Privette, Dowty). *Secondary.*]{.underline}

*Note: most meaningful BRDF evaluation will occur during IFC 3 when CAR
and AirMISR data are available for the sites. Poorer quality BRDF
evaluation is possible before that time. Shortwave albedo evaluations
should be reasonable quality throughout. Below outlines a possible
method.*

1.  Co-locate the light aircraft albedometer on the tower with the
    continuous albedometers. Cross-calibrate.

2.  Overfly the tower and transects with the light aircraft carrying the
    albedometer (dual CM21 pyranometers, full range).

    a.  Conduct flight at Terra overpass time.

3.  Assess relationship between transect albedo and % woody cover and
    NDVI.

4.  If relationships exist for 3, develop map based on aircraft, ASTER
    and L7 NDVI. If relationships do not exist, grid aircraft data
    (optimal interpolation?) to MODLAND albedo grid.

5.  Compare field albedo from 3 or 4 above with MODLAND albedo product.

6.  Lewis/Roberts compare temporal trajectories from kernel-based models
    with LC structure.Parameterize BRDF model (shadow-based, such as
    Myneni's or DART) using ancillary data (see Appendix).

7.  Compare model-predicted albedo and BRDF at tower with measurements.
    If reasonable relationship is found in 6, develop gridded BRDF and
    albedo maps from %cover, NDVI etc. data with BRDF model.

8.  Compare model-generated BRDF and albedo with MODLAND BRDF and albedo
    products.

9.  Compare "SZA-extrapolated" albedo. Fit BRDF model with AMBRALS, and
    compare retrieved parameters in MODLAND product.

[Aerosols (Swap, Piketh, Holben, Muke) *Primary*]{.underline}

1.  Sunphotometers to Ben McDhui, Mongu and Skukuza

2.  Streaker samplers modified for field use and deployed alongside
    existing sunphotometers

3.  Additional sunphotometers at Etosha NP, Kasungu, Nampulu(?)

4.  Compare retrieved optical depths with MODIS and MISR values

5.  Compare aerosol size and source attributions with standard
    assumptions in MODIS and MISR algorithm

6.  Develop spatial variability and trend information for
    interpolation/extrapolation

[Land Cover Land Use Change (Caylor, Lewis, Desanker?, Prince?, Hansen?)
*Primary*]{.underline}

1.  Build a fine resolution database using aerial photos, with % woody
    cover and other parameters, at sample grids across TM scenes

    a.  emphasizes Mongu, Skukuza; possible: Ndola, Kasungu, Tshane

    b.  may include deployment of a weather station and soil moisture
        probes at Kasungu

    c.  possibly include overstory fractional cover and understory NDVI
        transect measurements

2.  Comparisons to MODIS LC and LCCC and continuous parameters
    (DeFries/Hansen) products.

[Fire and burn scar (Justice, Roy, Giglio, Scholes, Frost)
*Secondary*]{.underline}

1.  Validate MODIS active fire product:

    a.  opportunistic comparison of MODIS data with airborne remotely
        sensed data captured as part of other SAFARI worker\'s
        activities

    b.  examination of MODIS data at prescribed burns around Mongu and
        Skukza (TBD)

    c.  comparison of MODIS data with L7, ASTER and the prototype MODIS
        burned area product

2.  Validate prototype MODIS burned area product:

    a.  L7 and ASTER photographic products used to define
        burned/unburned ground reference maps every 16 days over each
        campaign

    b.  select field measurements to identify the burn status of
        ambiguous regions identified on the photos: mapping the location
        (GPS) and also state (Kodak gray scale, hand held spherical
        densiometer, hand held basal area prism)

    c.  opportunistic assessment of the timing and location of active
        fires where possible

3.  Validation locations

    a.  field work in areas no bigger than a L7 scene (\~185km by 185km)
        preferably concentrating on regions defined by an overlapping
        ASTER scene (\~60km\*60km)

        i.  Mongu, Zambia (175,71) - coordinated with SAVE + Daryl Ward
            prescribed burns (tbd)

        ii. Skukza, South Africa (168,77) - coordinated with SAVE +
            prescribed burns (tbd)

        iii. Mutoko, Zimbabwe (169,72) - coordinated with Peter Frost
             (LULC)

        iv. Maun, Botswana (174.74) - coordinated with Botswana Range
            Inventory and Monitoring project (tbd), SAVE (?)

        v.  possibility of other sites

    b.  satellite data comparisons performed at other Southern African
        locations

4.  Timing

    a.  1999 (probably just AVHRR & L5): 1 month dry season (shakeout
        and training of validation workers)

    b.  2000 (MODIS, L7, ASTER): 2 month dry season

    c.  2001 (MODIS, L7, ASTER): 1 month wet season (?), 1 month dry
        season.

[Ecosystem (BGC) modeling (Gu, Dowty, Scholes, Hanan, Privette) *Process
modeling*]{.underline}

1.  Gu's lead: *Parameterize* a boreal forest model for savanna
    woodlands, which requires

    a.  Radiation decomposition in an aerosol-rich atmosphere (diffuse
        PAR, direct PAR, diffuse NIR and direct NIR);

    b.  Soil respiration responses to soil temperature and moisture;

    c.  Sapwood volume and respiration rate;

    d.  responses of leaf stomatal conductances to environmental
        factors;

    e.  leaf photosynthetic capacity,

    f.  aerodynamic parameters (zero-plane displacement and roughness
        length) of savanna vegetation

    g.  For model *validation* , the following tower flux measurements
        are needed:

        i.  sensible heat,

        ii. latent heat,

        iii. CO2 flux

        iv. net radiation.

2.  Hanan's lead: Adapt SWAPS biophysical model (savanna SVAT) for Core
    Sites

    a.  extend for SAVE/SAFARI-measured inputs (particularly useful:
        fluxes)

    b.  validate hydrological and soil physical aspects with
        measurements (T, H2O)

*The model will be driven by commonly available meteorological variables
(air temperature, vapor pressure deficit, precipitation, wind speed and
global solar radiation). Long wave radiation can be either modeled or
measured.*

[Woodland BGC and reflectance modeling (Dowty, Gu, et al.) *Process
modeling*]{.underline}

1.  Measure

    a.  vegetation structural attributes

    b.  leaf and soil fluxes (short term, \~2 weeks growing season)

2.  Model Mongu site

    a.  integrated model includes biophysical, biogeochemical, leaf
        phenology and canopy spectral components

    b.  development, integration and evaluation of the phenological
        model

    c.  required input: continuous canopy light attenuation data

    d.  output of LAI and reflectance at plot and daily-to-annual time
        scales

[Historical Analysis (Caylor, Dowty, Privette, Eckhardt, Desanker)
*Remote sensing*]{.underline}

1.  Acquire historical CORONA data at near consistent time-of-year.

2.  Acquire historical AVHRR (incl. Pathfinder), MSS, TM and in-country
    aerial photography data, some of which will overlap with CORONA
    imagery

3.  Determine relationship between %woody cover and satellite-derived VI

4.  Develop historical maps and temporal profiles of burning and %woody
    cover

    a.  structural dynamics of tree/grass ratio, woody cover, and LAI

5.  develop a dynamic vegetation model that can predict vegetation
    structure over the Kalahari

6.  Develop statistics on fire frequency with location, LC-type, etc.

7.  Estimate historical emissions for biomass

8.  Quantify land cover conversion with societal trends (e.g., tarred
    roads, governments)

[HyMap Spectral Analysis (Privette, Eckhardt, Caylor) *Remote
sensing*]{.underline}

1.  Acquire HyMap data from Michael King

2.  Georectify

3.  Linearly unmix spectral information and map fractional cover

4.  Convolve data with ASTER, L7, MISR and MODIS spectral sensitivity
    functions

5.  Attempt to discriminate *combretum* and *acacia* woodland savannas,
    etc.

[Test Site Stratification (?) *Remote sensing*]{.underline}

1.  Develop a hierarchical tree of unique land covers subtypes over
    which ground parameters must be measured for accurate extrapolation.

    a.  Determine major factors affecting stratification, such as fire
        frequency, soil, elevation, slope, grazing intensity and
        proximity to population.

2.  Per Schimel/Davis/Friedl method used in FIFE\
    Map the subtypes and establish ground sampling transects for
    appropriate sampling of these subtypes.

**Appendix III: Existing Equipment (procured via SAVE)**

3 Streaker Samplers from PIXE

2 CM 21 fast response pyranometers (shortwave) for aircraft

1 3-band aerial digital camera (for aircraft)

3 GPS receivers (tower sites, aircraft)

3 IBM Thinkpads (tower sites)

2 Kipp and Zonen CM14 albedometers (2 each of red, shortwave) for tower
sites

2 tool sets (tower sites)

2 tape measures (100m; tower sites)

2 bundles ground flags (tower sites)

3 Campbell 21X data loggers (for bikes)

1 GPS camera (for roving fire)

1 set fire validation tools

1 power converter/inverter/transformer set

Also available via MODLAND or GSFC (i.e., non-SAVE)

3 sunphotometers (Mongu, Skukuza, BenMcDhuii) + tower mounts

2 ASD spectrometers

2 PAR sensors (AERONET)

1 LAI-2000

1 ceptometer

1 line quantum sensor (PAR)

1 digital NDVI camera

1 StowAway simple light data logger

1 leaf pressure chamber

**Appendix IV: Auxiliary parameters required for LAI/FPAR Algorithm
validation (from Myneni)**

1\. LAI and FPAR

2\. nadir or bi-directional canopy (plot) spectra (or in MODIS spectral
bands, particularly red, NIR, blue and green bands)

3\. leaf optical properties (raw spectra preferred, although MODIS bands
acceptable;

only the mean value of the species in the vertical direction needed)

4\. nadir soil/background spectra (or MODIS bands)

5\. Fractional vegetation cover (a high IDS need was noted here)

6\. Canopy allometric data (height, gap size, crown width)

7\. Phenological stage (green-up, mature, senescent) and species
composition (either

by MODLAND structural classification or traditional taxonomy).

8\. Additional parameters useful: wet/dry status (is canopy wet?), and
the amount

of non-photosynthesizing vegetation (litter, etc.) which could be
measured at one \"minimum photosynthesis\" stage of the year.

**Appendix V: Tucker's 1 km AVHRR product for SAFARI-2000**

TO: David Starr

FROM: Jackie Kendall and Jim Tucker, code 923

RE: Safari-2000 AVHRR 1-km Proposal

Date: December 15, 1998

We propose the following activities in 1999-2000:

**1**. Provide near-real time (within 6 hours of acquisition) \"quick
look\" data products for flight planning for three periods:

August/September 1999

January/February 2000

August/September 2000

At least one day time and one night time 1-km AVHRR acquisition will be
acquired and distributed within six hours of the overpass.

A. Develop simple procedures for deriving flight planning materials for
fire occurrence in the Safari-2000 mission region from NOAA AVHRR 1-km
data;

B. Assist local group (Satellite Applications Center of CSIR/Mikomtek)
in implementing \"quick look\" fire product capability including
distribution to Safari-2000 designated mission control center.

**2**. Provide post-Safari-2000 analysis products, consisting of day and
night single day full-resolution AVHRR 1-km products below, from 5^o^ to
35^o^ S and as far west as the 1-km AVHRR data allow, for three periods:

July 15 - October 15, 1999

December 15, 1999 - March 15, 2000

July 15 - October 15, 2000

Products: channel 1 and 2 reflectances (day only); channels 3, 4, and 5
brightness temperatures; latitude, longitude, solar and viewing
geometry; and NDVI, cloud, and fire.

All data will be calibrated, geometrically corrected, and mapped to
common equal area projection (TDB).

**3**. We will maintain a digital archive of all NOAA AVHRR 1-km data
collected during the time periods in #2 above. These data will be made
available to any interested requester for the marginal cost of
reproduction.

##### Appendix VI: Miscellaneous

**Outstanding measurement issues**

1\. Ground transect package

a\. develop *mounts, packaging, power supply* (Hom? '99 funds)

b\. design *sampling transect* (Dowty, Scholes, Privette?)

-must flag and GPS

2\. Light Aircraft package

a\. GPS data \-- what source?

b\. integration of albedometer on aircraft (Hom)

\--end