Documentation Revision Date: 20180720
Data Set Version: 1
Summary
All products were generated with the prototype NISAR interferometric processor ISCE (Interferometric Software Computing Environment) and the PLAnT toolbox (PolarimetricInterferometric Lab and Analysis Tool) starting from polarimetric SAR (PolSAR) singlelookcomplex (SLC) images. PolInSAR canopy height and uncertainty products were derived using an algorithm based on the randomvolumeoverground (RVoG) and structuredvolumeoverground (SVoG) models. Uncertainty layers were generated using a parallelized MetropolisHastings algorithm with adaptive tuning samples from the modeled probability distribution. Polarimetric Coherence TomoSAR (PCT) forest structure products were derived employing a Legendre polynomial expansion to solve for forest vertical profile.
There are 51 data files in GeoTIFF (.tif ) format with this dataset
Citation
Lavalle, M., B.V. Riel, G. Shiroma, and B.P. Hawkins. 2018. AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1601
Table of Contents
 Data Set Overview
 Data Characteristics
 Application and Derivation
 Quality Assessment
 Data Acquisition, Materials, and Methods
 Data Access
 References
Data Set Overview
The study sites for this product generation effort included Mondah, Rabi and Lope National Parks, Gabon (data from Pongara will be available in the near future). The areas contain extensive mangrove and inland forest coverage, and a wide range of forest canopy heights up to 50 m. This dataset contains forest vertical structure and associated uncertainty products derived by applying multibaseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PCTomoSAR) techniques. The data were collected from multiple repeatpass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in FebruaryMarch 2016. In addition, supplementary data products based on various intermediate parameters of the UAVSAR data are provided and include radar backscatter, coherence, and viewing and terrain geometry.
All products were generated with the prototype NISAR interferometric processor ISCE (Interferometric Software Computing Environment) and the PLAnT toolbox (PolarimetricInterferometric Lab and Analysis Tool) starting from polarimetric SAR (PolSAR) singlelookcomplex (SLC) images that can be requested through the UAVSAR website. PolInSAR canopy height and uncertainty products were derived using an algorithm based on the randomvolumeoverground (RVoG) and structuredvolumeoverground (SVoG) models. Uncertainty layers were generated using a parallelized MetropolisHastings algorithm with adaptive tuning samples from the modeled probability distribution. Polarimetric Coherence TomoSAR (PCT) forest structure products were derived employing a Legendre function to solve for forest vertical profile.
Project: AfriSAR
AfriSAR was an airborne campaign that collected radar, lidar and field measurements of forests in Gabon, West Africa. The mission was a collaboration between NASA, the European Space Agency (ESA) and the Gabonese Space Agency. During the 2016 AfriSAR campaign, NASA UAVSAR and LVIS instruments collected data that are being used to derive forest canopy height, structure and topography. The AfriSAR data will help prepare for and calibrate current and upcoming spaceborne missions such as NISAR and BIOMASS that aim to gauge the role of forests in Earth’s carbon cycle.
Related Dataset:
Denbina, M., M. Simard, B.V. Riel, B.P. Hawkins, and N. Pinto. 2018. AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1589
Related Publications:
M. Lavalle, G. H. X. Shiroma, P. Agram, E. Gurrola, G. F. Sacco and P. Rosen, "Plant: Polarimetricinterferometric Lab and Analysis Tools for ecosystem and landcover science and applications," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016, pp. 53545357. doi: 10.1109/IGARSS.2016.7730395
Pourshamsi, M., M. Garcia, M. Lavalle, and H. Balzter, “A machine learning approach to PolInSAR and lidar fusion for improved forest canopy height estimation using NASA AfriSAR campaign data,” accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Special Issue, 2017.
Riel, B., M. Denbina, and M. Lavalle, “Assessing uncertainties of forest height estimation from polarimetric interferometric SAR data,” accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Special Issue, 2017.
Acknowledgements:
This work was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
Data Characteristics
Spatial Coverage: Three sites: Mondah, Rabi and Lope National Parks, Gabon (data from Pongara will be available in the near future)
Spatial Resolution: 1 arc second (approx. 30 m) pixel spacing matching the SRTM DEM
Temporal Coverage: 20160225 to 20160308
Temporal Resolution: Once
Table 1. Study Area (coordinates in decimal degrees)
Site  Westernmost Longitude  Easternmost Longitude  Northernmost Latitude  Southernmost Latitude 

Lope, Mondah, and Rabi National Parks, Gabon  9.169444444  11.86416667  0.61  2.067777778 
Table 2. Coordinates of the three individual sites.
Site  Western Extent  Eastern Extent  Northern Extent  Southern Extent 

Lope National Park, Gabon  11.40666667  11.86417  0.084722222  0.372777778 
Mondah National Park, Gabon  9.169444444  9.881111111  0.6011060  0.406666667 
Rabi National Park, Gabon  9.6777778  10.13555556  1.626944444  2.0677778 
Data File Information
The dataset is composed of 51 data files in GeoTIFF (.tif) format. Each of the three sites has 17 data files (17 x 3 = 51). This includes canopy height, uncertainty and structure as well as supplemental data files used to derive canopy height and support biomass estimation. The supplementary data products are based on various intermediate parameters of the ISCE/PLAnT data processing and include radar backscatter, coherence, and terrain geometry.
Table 3. File names and descriptions
Note that SITE refers to Monda, Rabi, and Lope.
File names  Units  Descriptions 

Products below are derived from multibaseline polarimetric interferometry technique 


SITE_polinsar_canopy_height.tif  meters  The canopy height hv is the average tree height in meters estimated via PolInSAR technique within the multilooked radar resolution cell estimated by an algorithm based on the randomvolumeoverground (RVoG) model. It includes the effects of temporal decorrelation. It is recommended to use this product in conjunction with its uncertainty and mask products described below. 
SITE_polinsar_canopy_height _uncertainty.tif  meters  The canopy height uncertainty product represents the standard deviation in meters of the canopy height product hv described above. 
SITE_polinsar_canopy_coherence.tif  none  The canopy coherence γ_{v} is the interferometric complex coherence of the canopydominated scattering mechanism. Coherence estimates from multiple baselines were merged into a single product after minimizing a cost function based on the mask products described below. Note that the ground phase has been already removed from this product. 
SITE_polinsar_ground_coherence.tif  none  The ground coherence γ_{g} is the interferometric complex coherence of the grounddominated scattering mechanism. Ground here means both soil direct and soiltrunk scattering. Coherence estimates from multiple baselines were merged into a single product after minimizing a cost function based on the mask products described below. The ground phase has been already removed from this product. 
SITE_polinsar_mask_separation.tif  none  The mask coherence separation is the distance between γ_{v} and γ_{g} in the complex plane. 
SITE_polinsar_mask_error.tif  meters  The mask coherence error is the CramerRao bound of the standard deviation of the interferometric canopy coherence phase arg(γ_{v}) scaled by the merged vertical wavenumber kz. This product represents the precision of the canopy phase height estimates, which is related to the uncertainty of the tree height estimates. 
SITE_polinsar_mask_location.tif  none  The mask coherence location is the Euclidean distance between the line passing through γ_{g} and γ_{v} in the complex plane and the origin of the axis. It represents the minimum coherence magnitude over the space of polarimetric scattering mechanisms. 
SITE_polinsar_vertical_wavenumber.tif  rad/meters  The merged vertical interferometric wavenumber kz represents the vertical sensitivity of the interferometer. Its units are inverse of meter. It changes with incidence angle, local terrain slope, range distance and perpendicular baseline. 
The products below were derived using the coherence tomography technique 

SITE_pct_coefficient_a10.tif  none  The coherence tomography firstorder coefficient is the coefficient a_{10} of the Legendre polynomial expansion of the canopy coherence γ_{v}. It can be used to estimate the first order vertical profile at a given elevation within the canopy. The coefficient tells how vegetation structure approximated by a linear function is vertically distributed. This coefficient was calculated using the products canopy height and Legendre functions f_{0}, f_{1}. 
SITE_pct_coefficient_a20.tif  none  The coherence tomography secondorder coefficient is the coefficient a_{20} of the Legendre polynomial expansion of the canopy coherence v. It tells how vegetation structure approximated by secondorder function is vertically distributed. This coefficient has been calculated using the products canopy height and Legendre functions f_{0}, f_{1} and f_{2} . 
SITE_pctf0.tif  none  The zeroorder Legendre function is the function f_{0} of the Legendre polynomial expansion of the canopy coherence. 
SITE_pct_legendre_function_f1.tif  none  The firstorder Legendre function is the function f_{1} of the Legendre expansion of the canopy coherence. 
SITE_pct_legendre_function_f2.tif  none  The secondorder Legendre function is the function f_{2} of the Legendre expansion of the canopy coherence. 
Supplementary data products  
SITE_cross_pol_backscatter.tif  dB  The average crosspolarimetric backscatter is the HV backscatter averaged (incoherently) over time for all available tracks for each site. No radiometric terrain correction has been applied to this product. This product is distributed either as gamma crosssection (Lope site) or as sigma crosssection with area projected on the flat ellipsoid (Rabi and Mondah sites). 
SITE_local_incidence_angle.tif  degrees  The local incidence angle is the angle in degrees between the look vector from the target to radar and the normal to the local terrain. 
SITE_terrain_slope_angle.tif  degrees  The terrain slope angle is the angle in degrees between the normal to the local ellipsoid and the normal to the local terrain along range. The 1arcsec SRTMv3 was used to calculate the normal to the local terrain. 
SITE_projection_angle.tif  degrees  The projection angle is the angle in degrees between the radar image plane and the normal to the local terrain. The 1arcsec SRTMv3 was used to calculate the normal to the local terrain. 
Table 4. Properties of the .tif files
For all files: Map units are in degrees; No data value=9999; # bands=1; scaling=1.
File name 
Min value 
Max value 
Mean 

lope_cross_pol_backscatter.tif  160.4  1.6  14.8 
lope_local_incidence_angle.tif  6  122.3  50.4 
lope_pct_coefficient_a10.tif  8  7.7  0 
lope_pct_coefficient_a20.tif  0.9  370.7  15 
lope_pct_legendre_function_f0.tif  0.1  0.9  0.6 
lope_pct_legendre_function_f1.tif  0.4  0.2  0.3 
lope_pct_legendre_function_f2.tif  0.2  0  0.1 
lope_polinsar_canopy_coherence.tif  0.5  0.9  0.2 
lope_polinsar_canopy_height.tif  0  91.6  26 
lope_polinsar_canopy_height_uncertainty.tif  3.1  12  9.2 
lope_polinsar_ground_coherence.tif  0.2  0.9  0.5 
lope_polinsar_mask_error.tif  0  3.92E+08  521.9 
lope_polinsar_mask_location.tif  0  0.9  0.3 
lope_polinsar_mask_separation.tif  0  0.8  0.3 
lope_polinsar_vertical_wavenumber.tif  3.3  4.5  0.1 
lope_projection_angle.tif  1.2  86.2  40.6 
lope_terrain_slope_angle.tif  48.8  82.9  1.3 
mondah_cross_pol_backscatter.tif  58  0  21.5 
mondah_local_incidence_angle.tif  8.8  91.3  49.9 
mondah_pct_coefficient_a10.tif  9.3  8.2  0.1 
mondah_pct_coefficient_a20.tif  0.7  564.3  20.6 
mondah_pct_legendre_function_f0.tif  0.1  0.9  0.7 
mondah_pct_legendre_function_f1.tif  0.4  0.4  0.1 
mondah_pct_legendre_function_f2.tif  0.2  0  0 
mondah_polinsar_canopy_coherence.tif  0.4  0.9  0.1 
mondah_polinsar_canopy_height.tif  0  70.4  17.6 
mondah_polinsar_canopy_height_uncertainty.tif  0  12  7.1 
mondah_polinsar_ground_coherence.tif  0  0.9  0.4 
mondah_polinsar_mask_error.tif  0  237773.2  1.4 
mondah_polinsar_mask_location.tif  0  0.9  0.4 
mondah_polinsar_mask_separation.tif  0  1  0.2 
mondah_polinsar_vertical_wavenumber.tif  1000.2  580.8  0 
mondah_projection_angle.tif  2.5  81.5  40.3 
mondah_terrain_slope_angle.tif  31.2  70.7  0.2 
rabi_cross_pol_backscatter.tif  58  5.7  14.5 
rabi_local_incidence_angle.tif  9.4  93.1  50 
rabi_pct_coefficient_a10.tif  4.5  3.6  0 
rabi_pct_coefficient_a20.tif  0.6  139.3  5.4 
rabi_pct_legendre_function_f0.tif  0.1  0.9  0.5 
rabi_pct_legendre_function_f1.tif  0.4  0  0.3 
rabi_pct_legendre_function_f2.tif  0.2  0  0.1 
rabi_polinsar_canopy_coherence.tif  0.4  0.8  0 
rabi_polinsar_canopy_height.tif  0  79.6  25.5 
rabi_polinsar_canopy_height_uncertainty.tif  2.2  12  5.8 
rabi_polinsar_ground_coherence.tif  0  0.9  0.4 
rabi_polinsar_mask_error.tif  0  3.05E+09  7999.8 
rabi_polinsar_mask_location.tif  0  0.9  0.1 
rabi_polinsar_mask_separation.tif  0  0.8  0.4 
rabi_polinsar_vertical_wavenumber.tif  1.4  0  0.1 
rabi_projection_angle.tif  2.5  81.3  40.5 
rabi_terrain_slope_angle.tif  31.1  69.2  0.6 
Spatial information common to all files:
GEOGCS['WGS 84', DATUM['WGS_1984'
SPHEROID ['WGS 84',6378137,298.257223563
AUTHORITY['EPSG','7030']]
AUTHORITY['EPSG','6326']]
PRIMEM['Greenwich',0]
UNIT['degree',0.0174532925199433]
AUTHORITY['EPSG','4326']])
Companion files
There are two companion files with this dataset:
AfriSAR_PolInSARWorkshop_Lavalle.pdf: A workshop presentation on the UAVSAR AfriSAR Campaign. This provides additional details on instrumentation, flights, and UAVSAR.
Polarimetric_CT.pdf: This guide document in .pdf format.
Application and Derivation
Canopy height and vertical structure products from African tropical rain forests will help parameterize models of carbon storage in a previouslyunderstudied region. This information can also inform policymakers working on climate mitigation and forest conservation policies.
Quality Assessment
Canopy height uncertainty products are included with this dataset and represent the standard deviation in meters of the canopy height. A probabilistic approach was used to represent the distribution of possible canopy parameter values using the Bayesian Theorem. A parallelized MetropolisHastings algorithm with adaptive tuning samples from the modeled probability distribution was used to produce the uncertainty estimates.
Data Acquisition, Materials, and Methods
The study sites included Mondah, Rabi and Lope National Parks, Gabon. The study areas contain extensive mangrove and inland forest coverage, and a wide range of forest canopy heights up to 50 m.
The table below shows the number of flights for each site used to generate the PolInSAR and Polarization Coherence TomoSAR products. The site of Mondah was imaged by several UAVSAR flights resulting in a larger number of zero and long interferometric baselines. The sites of Lope and Rabi have different soil and vegetation characteristics compared to Mondah and feature stronger topographic variations as well as a wider range of biomass values. The UAVSAR flights over Rabi and Mondah were designed to allow for denser multibaseline stacks with short time intervals between each track in order to apply specifically multibaseline PolInSAR and TomoSAR techniques.
Table 5. Flight numbers per site.
Site  Number of flight tracks  Number of processed tracks  Number of longbaseline pairs 

Lope National Park, Gabon  7  21  21 
Mondah National Park, Gabon  9  36  33 
Rabi National Park, Gabon  10  45  45 
The specific algorithms used to generate these products are detailed the publications listed below in the References section. Figure 2 summarizes the main steps of the processing chain based on the ISCE/PLAnT packages used to generate the PolInSAR and Polarization Coherence TomoSAR products.
Figure 2. Diagram of the PolInSAR and Polarization Coherence TomoSAR processing (Lavalle et al., 2017).
Generation of the Data Products
Multibaseline Polarimetric Interferometry Technique
Canopy height: Canopy height estimated by an algorithm based on the randomvolumeoverground (RVoG) model. Thus, it includes the effects
of temporal decorrelation. No lidar or field data were used to calibrate or constrain the inversion algorithm. The objective of this product was to assess a radaronly estimate of canopy height in order to understand the extent to which PolInSAR algorithms work on tropical African forests. AfriSAR sites Lope and Rabi have been imaged with short repeat intervals. Some flights over Mondah have longer repeat interval. The effect of water beneath mangroves was deliberately ignored in order to have a common basis for comparing all sites with the same algorithm. Note that the geometric effects of the slanted radar resolution cell were not corrected, which is important when comparing PolInSARderived heights with lidarderived heights. It is recommended to use this product in conjunction with its uncertainty and mask products.
Figure 3. Canopy height product for the Mondah site generated using the ISCE/PLAnT processing chain.
Canopy height uncertainty: The standard deviation in meters of the canopy height product hv described above. A probabilistic approach was used to represent the distribution of possible canopy parameter values by using Bayes’ Theorem:
p (mγ) ∝ p (γR(m), σ_{γ}) p (m) p (σ_{γ})
where:
m is the vector of our canopy parameters,
γ is the vector of the observed coherences,
R(m) is the output of the RVoG model for a given m, and
σ_{γ} is the standard deviation of the observed coherences.
Figure 4. Canopy height uncertainty product for the Mondah site generated using the ISCE/PLAnT processing chain.
Canopy coherence: The canopy coherence γv was obtained by maximizing the distance between canopy and grounddominated coherences over the space of polarimetric scattering mechanisms. Coherence estimates from multiple baselines were merged in a single product after minimizing a cost function based on the mask products. The canopy coherence can be used to estimate canopy height with basic PolInSAR algorithms.
Ground coherence: The ground coherence was obtained by maximizing the distance between canopy and grounddominated coherences over the space of polarimetric scattering mechanisms. Coherence estimates from multiple baselines are merged in a single product after minimizing a cost function based on the mask products. The ground coherence is linked to the properties of both soil and canopy.
Mask coherence separation: The distance d =  γ_{v}  γ_{g}  between γ_{v} and γ_{g} in the complex plane. Large values of d are preferred in order for the ground phase estimation (achieved via RVoG linecircle intersection) to be robust against the effects of the noise. Bare ground and short vegetation tend to have a small d, although noise and temporal decorrelation can result in large values of d even for short vegetation. It is recommended to use this mask in conjunction with the canopy height product. This mask can be also used to find correlation between PolInSAR observables and ecological parameters.
Mask coherence error: The CramerRao bound of the standard deviation of the interferometric canopy coherence phase arg(γ_{v}) scaled by the merged vertical wavenumber kz. This product represents the precision of the canopy phase height estimates, which is related to the uncertainty of the tree height estimates. It is recommended to use this product in conjunction with the canopy height product and external validation datasets. The mask is also useful to find correlation between the canopy height uncertainty product and the canopy phase standard deviation.
Mask coherence location: The Euclidean distance between the line passing through γg and γv in the complex plane and the origin of the axis. It represents the minimum coherence magnitude over the space of polarimetric scattering mechanisms. A very small value of coherence location (RVoG line near the origin) or very large value of coherence location (canopy and ground coherences close to each other) lead typically to larger canopy height estimation errors. It is recommended to use this mask in conjunction with the canopy height product. This mask can be also used to find correlation between PolInSAR observables and ecological parameters.
Merged vertical interferometric wavenumber: The vertical sensitivity of the interferometer, and changes with incidence angle, local terrain, slope, range distance and perpendicular baseline. For AfriSAR, the vertical wavenumber ranges typically from 0 m1 to 0.6 m1 across the set of interferometric baselines. The merged vertical wavenumber product is the vertical wavenumber of the baseline that has been selected in order to achieve good separation between γ_{v} and γ_{g} while keeping high the canopy coherence. Note that a wrong baseline selection may lead to large canopy height estimation error. As a rule of thumb, if a sample is expected to have short vegetation and the selected merged kz happens to be small, then it is possible that the optimum baseline selection failed (due for instance to speckle noise or mismatch between selection metric and forest structure). It is recommended to look at this product when comparing radarderived canopy height with other data sources (e.g., lidar).
Coherence Tomography Products
Coherence tomography firstorder coefficient: The coefficient a_{10} of the Legendre polynomial expansion of the canopy coherence γ_{v}. It can be used to estimate the first order vertical profile f(z) at a given elevation z within the canopy, 0 < z < hv. The coefficient tells how vegetation structure approximated by a linear function is vertically distributed. This coefficient has been calculated using the products canopy height hv and Legendre functions f_{0}, f_{1} . Values of a_{10} smaller than zero indicate scattering coming predominantly from the ground. It is recommended to use this product in conjunction with the PolInSAR mask products. Note that failure in the optimum multibaseline selection will affect this product.
Coherence tomography secondorder coefficient: The coefficient a_{20} of the Legendre polynomial expansion of the canopy coherence v. It tells how vegetation structure approximated by secondorder function is vertically distributed. This coefficient has been calculated using the products canopy height hv and Legendre functions f_{0}, f_{1} and f_{2}. It can be used to estimate the secondorder vertical profile f(z) for a given elevation z within the canopy, 0 < z < hv. Note that temporal decorrelation might affect this product significantly. It is recommended to use this product in conjunction with the PolInSAR coherence mask products. In case of large errors, using a firstorder approximation for the vertical profile might lead to better results.
Figure 5. Secondorder PCTomoSAR Legendre coefficient for representing the vertical structure for the Mondah site generated using the ISCE/PLAnT processing chain.
Zeroorder Legendre function: The function f_{0} of the Legendre polynomial expansion of the canopy coherence. In the zeroorder expansion, the canopy coherence (after ground phase removal) is approximated by
γ_{v} ~ e^{jkz hv/2} f_{0}
The function f_{0} is the Sinc coherence model of a uniform random layer of height hv.
Firstorder Legendre function: The function f_{1} of the Legendre expansion of the canopy coherence. In the first order expansion, the canopy coherence (after ground phase removal) is given by
γ_{v} ~ e^{jkz hv/2} (f_{0} + a_{10}f_{1})
Secondorder Legendre function: The function f_{2} of the Legendre expansion of the canopy coherence. In the secondorder expansion, the canopy coherence (after ground phase removal) is given by
γ_{v} ~ e^{jkz hv/2} (f_{0} + a_{10}f_{1} + a_{20}f_{2})
where a_{10} and a_{20} are the first and second order coherence tomography coefficients, respectively.
Supplementary Products
Average crosspolarimetric backscatter: The HV backscatter averaged (incoherently) over time for all available tracks for each site. While time averaging does not reduce speckle, it reduces the effects of thermal noise and dielectric changes. No radiometric terrain correction has been applied to this product. This product is distributed either as gamma crosssection (Lope site) or as sigma crosssection with area projected on the flat ellipsoid (Rabi and Mondah sites). The gamma crosssection can be conveniently used to correct for terrain slope using the local incidence angle and the projection angle.
Figure 6. Average crosspolarimetric backscatter for the Mondah site generated using the ISCE/PLAnT processing chain.
Local incidence angle: The angle in degrees between the look vector from the target to radar and the normal to the local terrain. The 1arcsec SRTMv3 was used to calculate the normal to the local terrain. The vertical interferometric sensitivity depends on the local incident angle. Therefore, this product can be used to investigate the dependence of the PolInSAR and TomoSARderived products on look direction and incidence angle.
Terrain slope angle: The angle in degrees between the normal to the local ellipsoid and the normal to the local terrain along range. The 1arcsec SRTMv3 has was to calculate the normal to the local terrain. For each image sample, terrain slope was approximated by taking one sample before and after in the slantrange projected DEM and dividing by two times the multilooked pixel spacing. PolSAR and PolInSAR measurements as well as forest ecological characteristics depend on terrain slope. Thus, this product can be used in conjunction with other products to quantify the effect of terrain slope on PolSAR, PolInSAR and TomoSARderived products.
Projection angle: The angle in degrees between the radar image plane and the normal to the local terrain. The 1arcsec SRTMv3 was used to calculate the normal to the local terrain. The projection angle is typically used to correct the radiometric distortions induced by terrain slope by applying the normalization factor cos(proj_angle)/cos(inc_angle). This product can also be used to investigate the effects of the imaging geometry (look direction and local topography) on the interferometric coherence magnitude and phase.
A Note about Lidar Data
Lidar data collected by the LVIS instrument onboard a B200 aircraft were used to assist data analysis and product assessment. Refer to the companion file included with this dataset for additional details. The LiDAR data are not included with this dataset.
For additional information, refer to Lavelle et al., 2017, and the companion file AfriSAR_PolInSARWorkshop_lavallel.pdf.
Data Access
These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools
Contact for Data Center Access Information:
 Email: uso@daac.ornl.gov
 Telephone: +1 (865) 2413952
References
Cloude, S.R., Polarization coherence tomography. Radio Sci., 41(4), Aug. 2006.
Lavalle, M., B. Hawkins, and S. Hensley, “Tomographic Imaging with UAVSAR: Current status and new results from the 2016 AfriSAR campaign,” in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2017. 10.1109/IGARSS.2017.8127498
Papathanassiou K.P. and S.R. Cloude. Singlebaseline polarimetric SAR interferometry. 39(11):2352–2363, Nov. 2001.
Pourshamsi, M., M. Garcia, M. Lavalle, and H. Balzter, “A machine learning approach to PolInSAR and lidar fusion for improved forest canopy height estimation using NASA AfriSAR campaign data,” accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Special Issue, 2017.
Riel, B., M. Denbina, and M. Lavalle, “Assessing uncertainties of forest height estimation from polarimetric interferometric SAR data,” accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Special Issue, 2017.