Iain H. Woodhouse

Using satellite radar backscatter to predict above-ground woody biomass: A consistent relationship across four different African landscapes

[1] Regional-scale above-ground biomass (AGB) estimates of tropical savannas and woodlands are highly uncertain, despite their global importance for ecosystems services and as carbon stores. In response, we collated field inventory data from 253 plots at four study sites in Cameroon, Uganda and Mozambique, and examined the relationships between field-measured AGB and cross-polarized radar backscatter values derived from ALOS PALSAR, an L-band satellite sensor. The relationships were highly significant, similar among sites, and displayed high prediction accuracies up to 150 Mg ha � 1 (±� 20%). AGB predictions for any given site obtained using equations derived from data from only the other three sites generated only small increases in error. The results suggest that a widely applicable general relationship exists between AGB and L-band backscatter for lower-biomass tropical woody vegetation. This relationship allows regional-scale AGB estimation, required for example by planned REDD (Reducing Emissions from Deforestation and Degradation) schemes. Citation: Mitchard, E. T. A., S. S. Saatchi, I. H. Woodhouse, G. Nangendo, N. S. Ribeiro, M. Williams, C. M. Ryan, S. L. Lewis, T. R. Feldpausch, and P. Meir (2009), Using satellite radar backscatter to predict above-ground woody biomass: A consistent relationship across four different African landscapes, Geophys. Res. Lett., 36, L23401, doi:10.1029/2009GL040692.

Speckle filtering and coherence estimation of polarimetric SAR interferometry data for forest applications

Recently, polarimetric synthetic aperture radar (SAR) interferometry has generated much interest for forest applications. Forest heights and ground topography can be extracted based on interferometric coherence using a random volume over ground coherent mixture model. The coherence estimation is of paramount importance for the accuracy of forest height estimation. The coherence (or correlation coefficient) is a statistical average of neighboring pixels of similar scattering characteristics. The commonly used algorithm is the boxcar filter, which has the deficiency of indiscriminate averaging of neighboring pixels. The result is that coherence values are lower than they should be. In this paper, we propose a new algorithm to improve the accuracy in the coherence estimation based on speckle filtering of the 6/spl times/6 polarimetric interferometry matrix. Simulated images are used to verify the effectiveness of this adaptive algorithm. German Aerospace Center (DLR) L-Band E-SAR data are applied to demonstrate the improved accuracy in coherence and in forest height estimation.

Determining land-surface parameters from the ERS wind scatterometer

The ERS-1 wind scatterometer (WSC) has a resolution cell of about 50 km but provides a high repetition rate (less than four days) and makes measurements at multiple incidence angles. In order to retrieve quantitative geophysical parameters over land surfaces using this instrument, a method is presented that applies a mixed-target modeling approach to estimate subpixel fractional vegetation cover at a regional scale. The model represents the footprint area as a combination of part dense, homogeneous vegetation and part bare soil (with homogeneous roughness and dielectric properties). Inversion of this model is then carried out using a retrieval procedure that incorporates a priori information in a quantitative manner The method is applied to the estimation of fractional cover over an area in Africa using WSC data from 1992 to 1995. Retrieved parameters are also compared to ground measurements made in the area during the 1992 HAPEX-Sahel campaign. The procedure illustrates the applicability of WSC data for measuring geophysical parameters over land and offers the potential of deriving a physically-based alternative to empirical indices for estimating regionally-variable parameters.

A Multispectral Canopy LiDAR Demonstrator Project

The first demonstration of a multispectral light detection and ranging (LiDAR) optimized for detailed structure and physiology measurements in forest ecosystems is described. The basic principle is to utilize, in a single instrument, both the capacity of multispectral sensing to measure plant physiology [through normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI)] with the ability of LiDAR to measure vertical structure information and generate “hot spot” (specular) reflectance data independent of solar illumination. A tunable laser operated at four wavelengths (531, 550, 660, and 780 nm) was used to measure profiles of the NDVI and the PRI. Laboratory-based measurements were conducted for live trees, demonstrating that realistic values of the indexes can be measured. A model-based analysis demonstrates that the LiDAR waveforms cannot only capture the tree height information but also picks up the seasonal and vertical variation of NDVI inside the tree canopy.

A Polarimetric Target Detector Using the Huynen Fork

The contribution of synthetic aperture radar polarimetry in target detection is described and found to add valuable information. A new target detection methodology that makes novel use of the polarization fork of the target is described. The detector is based on a correlation procedure in the target space, and other target representations (e.g., Huynen parameters or ¿ angle) can be employed. The mathematical formulation is general and can be applied to any kind of single target; however, in this paper, the detection is optimized for the odd and even bounces (the first two elements of the Pauli scattering vector) and for the oriented dipoles. Validation against real data shows significant agreement with the expected results based on the theoretical description.

Predicting backscatter-biomass and height-biomass trends using a macroecology model

This study describes the use of a plant structure model from the field of macroecology to make generalized predictions about backscatter-biomass and height-biomass trends from synthetic aperture radar data over forests. A theoretical relationship between canopy height and biomass density is derived. The predicted values of the height exponent are comparable with those from the remote sensing literature. A radiative transfer model parameterized by the macroecology model is also used to predict trends in P-band backscatter. The results imply that backscatter can saturate even for low-opacity canopies and decreasing basal area results in backscatter saturating at lower biomass levels. The theoretical analysis is supported by reference to a range of published results.

Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data

We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to represent a forest canopy or tree, and discuss the recovery of structure and depth profiles that relate to photochemical properties. We first demonstrate how simple vegetation indices such as the Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and light absorption, and Photochemical Reflectance Index (PRI) which is a measure of vegetation light use efficiency, can be measured from multispectral data. We further describe and demonstrate our layered approach on single wavelength real data, and on simulated multispectral data derived from real, rather than simulated, data sets. This evaluation shows successful recovery of a subset of parameters, as the complete recovery problem is ill-posed with the available data. We conclude that the approach has promise, and suggest future developments to address the current difficulties in parameter inversion.

Forest height retrieval from commercial X-band SAR products

This paper assesses the accuracy and reliability of tree height retrieval over coniferous plantations using X-band interferometry. Factors such as crown shape, density, tree height, incidence angle, and slope have been assessed and quantified using a simple polarimetric radar interferometry simulator to determine their impact on height retrieval. Results from model simulation show that the most important factors are: crown shape, plantation density, and tree height. Variation in viewing angle and small slopes (<30/spl deg/) appear to have only small effects. These results appear to be in reasonably good agreement with the retrieved tree height from airborne X-band Intermap data over coniferous plantations in the U.K.

Design and Evaluation of Multispectral LiDAR for the Recovery of Arboreal Parameters

Multispectral light detection and ranging (LiDAR) has the potential to recover structural and physiological data from arboreal samples and, by extension, from forest canopies when deployed on aerial or space platforms. In this paper, we describe the design and evaluation of a prototype multispectral LiDAR system and demonstrate the measurement of leaf and bark area and abundance profiles using a series of experiments on tree samples “viewed from above” by tilting living conifers such that the apex is directed on the viewing axis. As the complete recovery of all structural and physiological parameters is ill posed with a restricted set of four wavelengths, we used leaf and bark spectra measured in the laboratory to constrain parameter inversion by an extended reversible jump Markov chain Monte Carlo algorithm. However, we also show in a separate experiment how the multispectral LiDAR can recover directly a profile of Normalized Difference Vegetation Index (NDVI), which is verified against the laboratory spectral measurements. Our work shows the potential of multispectral LiDAR to recover both structural and physiological data and also highlights the fine spatial resolution that can be achieved with time-correlated single-photon counting.

Detecting Depolarized Targets Using a New Geometrical Perturbation Filter

Target detectors using polarimetry are often focused on single targets, since these can be characterized in a simpler and deterministic way. The algorithm proposed in this paper is aimed at the more difficult problem of partial-target detection (i.e., targets with arbitrary degree of polarization). The authors have already proposed a single-target detector employing filters based on a geometrical perturbation. In order to enhance the algorithm to the detection of partial targets, a new vector formalism is introduced. The latter is similar to the one exploited for single targets but suitable for complete characterization of partial targets. A new feature vector is generated starting from the covariance matrix and exploited for the perturbation method. Validation against L-band fully polarimetric airborne E-SAR and ALOS PALSAR data and X-band dual-polarimetric TerraSAR-X data is provided with significant agreement with the expected results. Additionally, a comparison with the supervised Wishart classifier is presented revealing improvements.