K.J. Ranson

Mapping biomass of a northern forest using multifrequency SAR data

The results of mapping standing biomass for a northern forest in Maine, using NASA/JPL AIRSAR polarimetric radar data, is presented. By examining the dependence of backscattering on standing biomass using backscatter modeling and aircraft data, it was determined, in agreement with other recent reports, that the cross-polarized (HV) data from longer wavelengths (L, P-band) were the best radar channels for mapping total above-ground forest biomass. The radar signal appeared to lose sensitivity to changes in biomass for dry biomass levels beyond about 15 kg/m/sup 2/ (150 Mton/Ha). The ratio of HV backscattering from two bands, a longer wavelength P (wavelength=68 cm) or L band (24 cm) to a shorter wavelength C band (6 cm), enhanced the correlation of the image signature to standing biomass (r/sup 2/=0.83 for P/C and r/sup 2/=0.79 for L/C) and showed increased sensitivity to dry biomass beyond 15 kg/m/sup 2/. >

Radar modeling of a boreal forest

The authors report on the use of microwave modeling, ground truth, and synthetic aperture radar (SAR) data to investigate the characteristics of forest stands. A mixed coniferous forest stand has been modeled at SAR frequencies (P-, L-, and C-bands). The extensive measurements of ground truth and canopy geometry parameters were performed in a 200 m-square hemlock-dominated plot inside a forest. Hemlock trees in the forest are modeled by characterizing tree trunks, branches, and needles (leaves) with randomly oriented, lossy dielectric cylinders whose area and orientation distributions are prescribed. The distorted Born approximation is used to compute the backscatter at P-, L-, and C-SAR frequencies. >

An off-nadir-pointing imaging spectroradiometer for terrestrial ecosystem studies

The Advanced Solid-state Array Spectroradiometer (ASAS), an airborne, off-nadir-pointing imaging spectroradiometer used to acquire bidirectional radiance data for terrestrial targets, is described. As its platform aircraft flies over a target, the sensor can image the target through a sequence of at least seven fore-to-aft view directions ranging up to 45 degrees on either side of nadir. ASAS acquires data for 29 spectral bands in the visible and near-infrared portions of the spectrum (465 to 871 nm) with a resolution of 15 nm. The basic ASAS data product is a sequence of digital images acquired from multiple view directions and consisting of calibrated spectral radiance values. Examples of ASAS data from field experiments are presented. The data demonstrate the combined effects of reflectance anisotropy and increased atmospheric path length on off-nadir observations. One result of these effects is a variation in vegetation indices as a function of view direction. >

A new technique to measure the spectral properties of conifer needles

Careful measurements of the spectral properties of individual leaves are required to understand interactions of radiation with vegetation and to use effectively the data from future sensors with increasingly finer spectral resolution. Instruments capable of measuring the optical properties of leaves typically have integrating spheres with sample parts at least 10 mm in diameter. However, the leaves of many grasses and conifers are too small to cover completely the sample port. We describe a technique that enables the measurements of reflectance and transmittance of narrow leaves or needles with spectroradiometers equipped with a light source and integrating sphere. Measurement procedures and formulae for optical property calculations are presented. A test of the techniques resulted in absolute reflectance differences of 3 % or less when comparing optical properties measured for whole leaves and narrow strips cut from the leaves. Thus, these techniques can accurately estimate the spectral properties of small leaves.

NASA Goddards LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (~1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT’s data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA’s Data and Information policy.

Earth Observing System AM1 mission to Earth

In 1998, NASA launches EOS-AMI, the first of a series of the Earth Observing System (EOS) satellites. EOS will monitor the evolution of the state of the earth for 18 years, starting with the morning observations of EOS-AM1 (10:30 a.m. equatorial crossing time). An integrated view of the earth, as planned by EOS, is needed to study the interchange of energy, moisture, and carbon between the lands, oceans, and atmosphere. The launch of EOS-AM1 and other international satellites marks a new phase of climate and global change research. Both natural and anthropogenic climate change have been studied for more than a century. It is now recognized that processes that vary rapidly in time and space-e.g. aerosol, clouds, land use, and exchanges of energy and moisture-must be considered to adequately explain the temperature record and predict future climate change. Frequent measurements with adequate resolution, as only possible from spacecraft, are key tools in such an effort. The versatile and highly accurate EOS-AM1 data, together with previous satellite records, as well as data from ADEOS, TRMM, SeaWiFS, ATSR, MERIS, ENVISAT, EOS-PM1, Landsat and ground-based networks is expected to revolutionize the way scientists look at climate change. This article introduces the EOS-AM1 mission and the special issue devoted to it. Following a brief historical perspective for an insight into the purpose and objectives of the mission, the authors summarize the characteristics of the five instruments onboard EOS-AM1. Specifically, they concentrate on the innovative elements of these five instruments and provide examples of the science issues that require this type of data.

Boreal forest ecosystem characterization with SIR-C/XSAR

Discusses early results obtained from Spaceborne Imaging Radar-C (SIR-C) and X-band synthetic aperture radar (XSAR) data over a boreal forest in Saskatchewan, Canada. Multifrequency and multipolarization image data were made available during the SRL-1 (Apr. 10, 1994) and SRL-2 (Oct. 1, 1994) missions. These image data sets were analyzed and maps of forest cover type and above ground woody dry biomass were generated. A portion of the Southern Study Area of the Boreal Ecosystem-Atmosphere Study (BOREAS) was mapped for forest cover type with classification accuracies on the order of 80%. Maps of estimated biomass were also produced that match observed patterns and preliminary ground data. The upper limit of sensitivity of the radar to boreal forest biomass in the study area was about 20 kg/m/sup 2/ or 200 tons/ha. The highest average observed biomass in the ground measurements was about 25 kg/m/sup 2/. The highest sensitivity of the radar to biomass was attained using April backscatter data and a ratio of L-band HV to C-band HV. Results show that radar estimates of biomass were within /spl plusmn/2 kg/m/sup 2/ at the 95% confidence level. A comparison of the April and October data sets was conducted to understand the effects of seasons on the analysis. It appears that the frozen trees and wetter background contributes to increased backscattering observed in the April data. These early results indicate that multiple polarization and multiple frequency SAR data can be used to monitor and map northern forest biomes. >

Forest biomass from combined ecosystem and radar backscatter modeling

Abstract Above-ground woody biomass is an important parameter for describing the function and productivity of forested ecosystems. Recent studies have demonstrated that synthetic aperture radar (SAR) can be used to estimate above-ground standing biomass. To date, these studies have relied on extensive ground-truth measurements to construct relationships between biomass and SAR backscatter. In this article we discuss the use of models to help develop a relationship between biomass and radar backscatter and compare the predictions with measurements. A gap-type forest succession model was used to simulate growth and development of a northern hardwood-boreal transitional forest typical of central Maine, USA. Model results of species, and bole diameter at breast height (dbh) of individual trees in a 900 m 2 stand were used to run discontinuous canopy backscatter models to determine radar backscatter coefficients for a wide range of simulated forest stands. Using model results, relationships of copolarized backscatter to forest biomass were developed and applied to airborne SAR (AIRSAR) image over a forested area in Maine. A relationship derived totally from model results was found to underestimate biomass. Calibrating the modeled backscatter with limited AIRSAR backscatter measurements improved the biomass estimation when compared to field measurements. The approach of using a combination of forest succession and remote sensing models to develop algorithms for inferring forest attributes produced comparable results with techniques using only measurements. Applying the model derived algorithm to SAR imagery produced reasonable results when mapped biomass was limited to 15 kg/m 2 or less.

Radiometric slope correction for forest biomass estimation from SAR data in the Western Sayani Mountains, Siberia

We investigated the possibility of using multiple polarization (SIR-C) L-band data to map forest biomass in a mountainous area in Siberia. The use of a digital elevation model (DEM) and a model-based method for reducing terrain effects was evaluated. We found that the available DEM data were not suitable to correct the topographic effects on the SIR-C radar images. A model-based slope correction was applied to an L-band cross-polarized (hv) backscattering image and found to reduce the topographic effect. A map of aboveground biomass was produced from the corrected image. The results indicated that multipolarization L-band synthetic aperture radar (SAR) data can be useful for estimation of total aboveground biomass of forest stands in mountainous areas.

Inversion of a lidar waveform model for forest biophysical parameter estimation

Due to its measurement principle, light detection and ranging (lidar) is particularly suited to estimate the horizontal as well as vertical distribution of forest structure. Quantification and characterization of forest structure is important for the understanding of the forest ecosystem functioning and, moreover, will help to assess carbon sequestration within forests. The relationship between the signal recorded by a lidar system and the canopy structure of a forest can be accurately characterized by physically based radiative transfer models (RTMs). A three-dimensional RTM is capable of representing the complex forest canopy structure as well as the involved physical processes of the lidar pulse interactions with the vegetation. Consequently, the inversion of such an RTM presents a novel concept to retrieve biophysical forest parameters that exploits the full lidar signal and underlying physical processes. A synthetic dataset and data acquired in the Swiss National Park (SNP) successfully demonstrated the feasibility and the potential of RTM inversion to retrieve forest structure from large-footprint lidar waveform data. The SNP lidar data consist of waveforms generated from the aggregation of small-footprint lidar returns. Derived forest biophysical parameters, such as fractional cover, leaf area index, maximum tree height, and the vertical crown extension, were able to describe the horizontal and vertical forest canopy structure.