Mathias Disney

First operational BRDF, albedo nadir reflectance products from MODIS

With the launch of NASA’s Terra satellite and the MODerate Resolution Imaging Spectroradiometer (MODIS), operational Bidirectional Reflectance Distribution Function (BRDF) and albedo products are now being made available to the scientific community. The MODIS BRDF/Albedo algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model and multidate, multispectral data to provide global 1-km gridded and tiled products of the land surface every 16 days. These products include directional hemispherical albedo (black-sky albedo), bihemispherical albedo (white-sky albedo), Nadir BRDF-Adjusted surface Reflectances (NBAR), model parameters describing the BRDF, and extensive quality assurance information. The algorithm has been consistently producing albedo and NBAR for the public since July 2000. Initial evaluations indicate a stable BRDF/Albedo Product, where, for example, the spatial and temporal progression of phenological characteristics is easily detected in the NBAR and albedo results. These early beta and provisional products auger well for the routine production of stable MODIS-derived BRDF parameters, nadir reflectances, and albedos for use by the global observation and modeling communities.

Hyperspectral remote sensing of foliar nitrogen content

A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact—it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423–855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710–790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.

Nondestructive estimates of above‐ground biomass using terrestrial laser scanning

Summary: Allometric equations are currently used to estimate above-ground biomass (AGB) based on the indirect relationship with tree parameters. Terrestrial laser scanning (TLS) can measure the canopy structure in 3D with high detail. In this study, we develop an approach to estimate AGB from TLS data, which does not need any prior information about allometry. We compare these estimates against destructively harvested AGB estimates and AGB derived from allometric equations. We also evaluate tree parameters, diameter at breast height (DBH) and tree height, estimated from traditional field inventory and TLS data. Tree height, DBH and AGB data are collected through traditional forest inventory, TLS and destructive sampling of 65 trees in a native Eucalypt Open Forest in Victoria, Australia. Single trees are extracted from the TLS data and quantitative structure models are used to estimate the tree volume directly from the point cloud data. AGB is inferred from these volumes and basic density information and is then compared with the estimates derived from allometric equations and destructive sampling. AGB estimates derived from TLS show a high agreement with the reference values from destructive sampling, with a concordance correlation coefficient (CCC) of 0·98. The agreement between AGB estimates from allometric equations and the reference is lower (CCC = 0·68-0·78). Our TLS approach shows a total AGB overestimation of 9·68% compared to an underestimation of 36·57-29·85% for the allometric equations. The error for AGB estimates using allometric equations increases exponentially with increasing DBH, whereas the error for AGB estimates from TLS is not dependent on DBH. The TLS method does not rely on indirect relationships with tree parameters or calibration data and shows better agreement with the reference data compared to estimates from allometric equations. Using 3D data also enables us to look at the height distributions of AGB, and we demonstrate that 80% of the AGB at plot level is located in the lower 60% of the trees for a Eucalypt Open Forest. This method can be applied in many forest types and can assist in the calibration and validation of broad-scale biomass maps.s

Monte Carlo ray tracing in optical canopy reflectance modelling

This paper reviews the use of Monte Carlo (MC) methods in optical canopy reflectance modelling. Their utility, and, more specifically, MC ray tracing for the numerical simulation of the radiation field within a vegetation canopy, are outlined. General issues pertinent to implementation and exploitation of such methods are discussed, such as the descriptions of canopy structure and radiometric properties required for their use. Strategies for the reduction of variance, which form the core of the application of MC methods to canopy reflectance modelling are presented, and examples given of the type of information which may be obtained from canopy reflectance modelling using MC ray tracing. The use of MC methods in the development of models of canopy development, driven by fundamental properties such as radiation interception are discussed.

Impact of land cover uncertainties on estimates of biospheric carbon fluxes

Large-scale bottom-up estimates of terrestrial carbon fluxes, whether based on models or inventory, are highly dependent on the assumed land cover. Most current land cover and land cover change maps are based on satellite data and are likely to be so for the foreseeable future. However, these maps show large differences, both at the class level and when transformed into Plant Functional Types (PFTs), and these can lead to large differences in terrestrial CO2 fluxes estimated by Dynamic Vegetation Models. In this study the Sheffield Dynamic Global Vegetation Model is used. We compare PFT maps and the resulting fluxes arising from the use of widely available moderate (1 km) resolution satellite-derived land cover maps (the Global Land Cover 2000 and several MODIS classification schemes), with fluxes calculated using a reference high (25 m) resolution land cover map specific to Great Britain (the Land Cover Map 2000). We demonstrate that uncertainty is introduced into carbon flux calculations by (1) incorrect or uncertain assignment of land cover classes to PFTs; (2) information loss at coarser resolutions; (3) difficulty in discriminating some vegetation types from satellite data. When averaged over Great Britain, modeled CO2 fluxes derived using the different 1 km resolution maps differ from estimates made using the reference map. The ranges of these differences are 254 gC m−2 a−1 in Gross Primary Production (GPP); 133 gC m−2 a−1 in Net Primary Production (NPP); and 43 gC m−2 a−1 in Net Ecosystem Production (NEP). In GPP this accounts for differences of −15.8% to 8.8%. Results for living biomass exhibit a range of 1109 gC m−2. The types of uncertainties due to land cover confusion are likely to be representative of many parts of the world, especially heterogeneous landscapes such as those found in western Europe.

On the potential of CHRIS/PROBA for estimating vegetation canopy properties from space

The Compact High Resolution Imaging Spectrometer (CHRIS), to be launched on board the PROBA (Project for On‐Board Autonomy) satellite in 2001/2002, will provide remotely‐sensed data for terrestrial and atmospheric applications. The mission is intended to demonstrate the potential of a compact, low‐cost, imaging spectrometer when combined with a small, agile satellite platform. CHRIS will provide data in 18–62 user‐selectable spectral channels in the range 400 nm to 1050 nm (1.25 nm ‐ 11 nm intervals) at a nominal spatial resolution of either 25 m or 50 m. Since PROBA can be pointed off‐nadir in both the along‐track and across‐track directions, it will be possible to use CHRIS to sample the Bidirectional Reflectance Distribution Function (BRDF) of the land surface. This combination of an agile satellite and a highly configurable sensor offers the unique potential to acquire high spatial resolution, spectral BRDF data sets and, from these, to study the biophysical and biochemical properties of vegetation canopies. It will also provide an important means of validating similar data sets from other, coarser spatial resolution sensors, such as VEGETATION, POLDER2, MODIS and MISR. This paper presents key features of the instrument, and explores the potential of CHRIS for estimating canopy biophysical parameters from space by means of a LUT‐based BRDF model inversion scheme.

Quantifying Surface Reflectivity for Spaceborne Lidar via Two Independent Methods

Spaceborne differential absorption lidar has been proposed for accurate measurements of atmospheric CO2 (and surface properties). Lidar instruments typically observe the highest possible surface reflectance due to observing in the retroreflection direction (the so-called ldquohotspotrdquo) where viewed shadow is minimized. The range of observed reflectance will determine instrument dimensions and signal-to-noise ratio, but it is difficult to predict this range globally a priori. Two complementary methods are presented for estimating lidar reflectivity over a range of vegetated surface types. The first method simulates the expected response of a lidar instrument from multiangle multispectral reflectance data. The second method uses detailed 3-D vegetation structural models and Monte Carlo ray tracing to simulate the lidar signal. The simulations are used to validate the first method and assess the impact of possible instrument configurations. Both methods agree well and are robust to error in observations, with predicted lidar reflectivity (at 1570 and 2050 nm here) typically between 10% and 33% higher relative to off-nadir reflectance and ranging from 0.02 to ~ 0.7. We use the 3-D simulations to show that the impact of shifted on-off lidar pulses is not likely to be significant for accuracy of retrieved CO2, and we demonstrate that the 3-D simulation method is a flexible and powerful way of prototyping future spaceborne lidar missions.

Upscaling as ecological information transfer: a simple framework with application to Arctic ecosystem carbon exchange

Transferring ecological information across scale often involves spatial aggregation, which alters information content and may bias estimates if the scaling process is nonlinear. Here, a potential solution, the preservation of the information content of fine-scale measurements, is highlighted using modeled net ecosystem exchange (NEE) of an Arctic tundra landscape as an example. The variance of aggregated normalized difference vegetation index (NDVI), measured from an airborne platform, decreased linearly with log(scale), resulting in a linear relationship between log(scale) and the scale-wise modeled NEE estimate. Preserving three units of information, the mean, variance and skewness of fine-scale NDVI observations, resulted in upscaled NEE estimates that deviated less than 4% from the fine-scale estimate. Preserving only the mean and variance resulted in nearly 23% NEE bias, and preserving only the mean resulted in larger error and a change in sign from CO2 sink to source. Compressing NDVI maps by 70–75% using wavelet thresholding with the Haar and Coiflet basis functions resulted in 13% NEE bias across the study domain. Applying unique scale-dependent transfer functions between NDVI and leaf area index (LAI) decreased, but did not remove, bias in modeled flux in a smaller expanse using handheld NDVI observations. Quantifying the parameters of statistical distributions to preserve ecological information reduces bias when upscaling and makes possible spatial data assimilation to further reduce errors in estimates of ecological processes across scale.

Rapid characterisation of forest structure from TLS and 3D modelling

Raumonen et al.[1] have developed a new method for reconstructing topologically consistent tree architecture from TLS point clouds. This method generates a cylinder model of tree structure using a stepwise approach. Disney et al.[2] validated this method with a detailed 3D tree model where structure is known a priori, establishing a reconstruction relative error of less than 2%. Here we apply the same method to data acquired from Eucalyptus racemosa woodland, Banksia ameula low open woodland and Eucalyptus spp. open forest using a RIEGL VZ-400 instrument. Individual 3D tree models reconstructed from TLS point clouds are used to drive Monte Carlo ray tracing simulations of TLS with the same characteristics as those collected in the field. 3D reconstruction was carried out on the simulated point clouds so that errors and uncertainty arising from instrument sampling and reconstruction could be assessed directly. We find that total volume could be recreated to within a 10.8% underestimate. The greatest constraint to this approach is the accuracy to which individual scans can be globally registered. Inducing a 1cm registration error lead to a 8.8% total volumetric overestimation across the data set.

A New Global fAPAR and LAI Dataset Derived from Optimal Albedo Estimates: Comparison with MODIS Products

We present the first comparison between new fAPAR and LAI products derived from the GlobAlbedo dataset and the widely-used MODIS fAPAR and LAI products. The GlobAlbedo-derived products are produced using a 1D two-stream radiative transfer (RT) scheme designed explicitly for global parameter retrieval from albedo, with consistency between RT model assumptions and observations, as well as with typical large-scale land surface model RT schemes. The approach does not require biome-specific structural assumptions (e.g., cover, clumping, understory), unlike more detailed 3D RT model approaches. GlobAlbedo-derived values of fAPAR and LAI are compared with MODIS values over 2002–2011 at multiple flux tower sites within selected biomes, over 1200 × 1200 km regions and globally. GlobAlbedo-derived fAPAR and LAI values are temporally more stable than the MODIS values due to the smoothness of the underlying albedo, derived via optimal estimation (assimilation) using an a priori estimate of albedo derived from an albedo “climatology” (composited multi-year albedo observations). Parameters agree closely in timing but with GlobAlbedo values consistently lower than MODIS, particularly for LAI. Larger differences occur in winter (when values are lower) and in the Southern hemisphere. Globally, we find that: GlobAlbedo-derived fAPAR is ~0.9–1.01 × MODIS fAPAR with an intercept of ~0.03; GlobAlbedo-derived LAI is ~0.6 × MODIS LAI with an intercept of ~0.2. Differences arise due to the RT model assumptions underlying the products, meaning care is required in interpreting either set of values, particularly when comparing to fine-scale ground-based estimates. We present global transformations between GlobAlbedo-derived and MODIS products.