M. Craig Dobson

Michigan microwave canopy scattering model

Abstract The Michigan Microwave Canopy Scattering model (MIMICS) is based on a first-order solution of the radiative-transfer equation for a tree canopy comprising a crown layer, a trunk layer and a rough-surface ground boundary. The crown layer is modelled in terms of distributions of dielectric cylinders (representing needles and/or branches) and discs (representing leaves), and the trunks are treated as dielectric cylinders of uniform diameter. This report describes MIMICS I, which pertains to tree canopies with horizontally continuous (closed) crowns. The model, which is intended for use in the 0·5-10GHz region at angles greater than 10° from normal incidence, is formulated in terms of a 4 × 4 Stokes-like transformation matrix from which the backscattering coefficient can be computed for any transmit/receive polarization configuration.

The use of Imaging radars for ecological applications : A review

At the behest of NASAs Mission to Planet Earth, the National Research Council recently conducted a review on the current status and future directions for earth science information provided by spaceborne synthetic aperture radars. As part of this process, a panel of 16 scientists met to review the utility of SAR for monitoring ecosystem processes. The consensus of this ecology panel was that the demonstrated capabilities of imaging radars for investigating terrestrial ecosystems could best be organized into four broad categories: 1) classification and detection of change in land cover; 2) estimation of woody plant biomass; 3) monitoring the extent and timing of inundation; and 4) monitoring other temporally-dynamic processes. The major conclusions from this panel were: 1) Multichannel radar data provide a means to classify land-cover patterns because of its sensitivity to variations in vegetation structure and vegetation and ground-layer moisture. The relative utility of data from imaging radars versus multispectral scanner data has yet to be determined in a rigorous fashion over a wide range of biomes for this application. 2) Imaging radars having the capability to monitor variations in biomass in forested ecosystems. This capability is not consistent among different forest types. The upper levels of sensitivity for L-band and C-band systems such as SIR-C range between <100 t ha−1 for complex tropical forest canopies to ∼250 t ha−1 for simpler forests dominated by a single tree species. Best performance for biomass estimation is achieved using lower frequency (P- and L-band) radar systems with a cross-polarized (HV or VH) channel. 3) Like-polarized imaging radars (HH or VV) are well suited for detection of flooding under vegetation canopies. Lower frequency radars (P- and L-band) are most optimal for detecting flooding under forests, whereas higher frequency radars (C-band) work best for wetlands dominated by herbaceous vegetation. 4) It has been shown that spaceborne radars that have been in continuous operation for several years [such as the C-band (VV) ERS-1 SAR] provide information on temporally dynamic processes, such as monitoring a) variations in flooding in nonwooded wetlands, b) changes in the frozen/thawed status of vegetation, and c) relative variations in soil moisture in areas with low amounts of vegetation cover. These observations have been shown to be particularly important in studying ecosystems in high northern latitudes.

Active Microwave Soil Moisture Research

This paper summarizes the progress achieved in the active microwave remote sensing of soil moisture during the four years of the AgRISTARS program. Within that time period, from about 1980 to 1984, significant progress was made toward understanding 1) the fundamental dielectric properties of moist soils, 2) the influence of surface boundary conditions, and 3) the effects of intervening vegetation canopies. In addition, several simulation and image-analysis studies have identified potentially powerful approaches to implementing empirical results over large areas on a repetitive basis. This paper briefly describes the results of laboratory, truck-based, airborne, and orbital experimentation and observations.

Land-cover classification and estimation of terrain attributes using synthetic aperture radar

Abstract This paper presents progress toward a geophysical and biophysical information processor for synthetic aperture radar (SAR). This processor operates in a sequential fashion to first classify terrain according to structural attributes and then apply class-specific retrievals for geophysical and biophysical properties. Structural and electrical attributes control the radar backscattering from terrain. Experimental data and theoretical results illustrate the sensitivity of synthetic aperture radar to structural properties, such as surface roughness and canopy architecture, to soil moisture and to the aboveground biomass of vegetation and its moisture status. Accurate land-cover classification is of great value in many types of regional- to global-scale modeling, and is also an essential precursor to many techniques for extracting geophysical and biophysical information from SAR data. The sensitivity of SAR to the structural features of terrain leads to landcover classification into simple and easily interpreted structural classes. Knowledge-based, hierarchical classifiers require no a priori information or statistical understanding of a local scene, and are found to yield overall accuracy in excess of 90%. Classification using existing data from the orbital ERS-1 and JERS-1 SARs yield unambiguous land-cover categorizations at greater accuracy and resolution than that afforded by an unsupervised classification of Normalized Difference Vegetation Index as derived from multitemporal AVHRR data. Level I of the SAR terrain classifier differentiates three structural classes; surfaces, short vegetation, and tall vegetation. These classes can be quantized, averaged over the appropriate grid scale and used directly as roughness inputs to general circulation models. Level II of the classifier differentiates vegetation classes on the basis of growth form and leaf type. This level of structural classification is essential in order to improve the performance of semi-empirical approaches for retrieving near-surface soil moisture and aboveground biomass.

Multitemporal Land-Cover Classification Using SIR-C/X-SAR Imagery

Abstract The dual-flight program (April and October) for the SIR-C/X-SAR instrument aboard the shuttle Endeavor was designed expressly to acquire Synthetic Aperture Radar (SAR) imagery at two significantly different seasons. At the Michigan Forests Test Site (MFTS), the April mission occurred at the beginning of the spring thaw and the October mission occurred just prior to and during the fall color change. Four scenes are evaluated at a constant incidence angle. Seven features are extracted from the SAR data for potential use in classification using powers at different frequencies and polarizations. Given multiseason SIR-C/X-SAR imagery, there are three possible approaches in the classifier development: 1) Under the assumption that the scene does not change significantly as a function of time, develop one classification for a set of x scenes using n features, with x times the number of samples per feature; 2) ignore the multiseason availability and develop independent classifications for each scene using n features; 3) develop a true multitemporal classification where N of features equals n (number of features) times x (number of scenes). Each of these is applied using a combination knowledge-based and Bayesian classifier. Level II (roughly forest community) results show that the true multitemporal April/October classification works very well (97%), as do those for the individual scenes (>90%). A pooled classifier works poorly (April=90%, October=77%) and shows that temporal changes in phenology and moisture conditions contribute significant noise in terrain classification.

Integration of remotely sensed radar imagery in modeling and mapping of forest biomass and net primary production

Abstract New remote sensing programs provide the opportunity to optimize the connection of remotely sensed data with key parameters in measuring and modeling net primary production (NPP). Synthetic aperture radars (SARs) are discussed in terms of their ability to measure more directly certain parameters related to NPP. The purpose of this paper is to introduce SAR-based methodologies and results for (1) deriving parameters which may be considered input datasets for NPP models and (2) the subsequent application of an aboveground annual NPP (ANNP) model for these datasets. Derivations are land cover and biophysical parameters including forest height, aboveground forest tree biomass (and carbon fraction), and belowground coarse root biomass (and carbon fraction). An allometric ANPP model is applied to demonstrate the applicability of these SAR-derived datasets to NPP modeling. Results are regional quantifications and mapped distributions of forest height, above and belowground tree biomass (and carbon fraction), aboveground ANPP, and the relationship of forest stage to production.

Characterizing Carbon in a Northern Forest by Using SIR-C/X-SAR Imagery

Abstract A significant large-scale question in ecology and earth systems science pertains to the amount of carbon (C) stored in terrestrial vegetation. In this paper, a synthetic aperture radar (SAR)–based methodology is developed and evaluated for quantification of several key vegetative C components—both natural and human induced—of the northern forest. Specifically, the methodology provides estimates of C stored in living forest vegetation, above-ground C gain from annual growth (aboveground net primary productivity, or ANPP), and C removal due to managed forest disturbance in the form of clear-cutting. The inputs are shuttle imaging radar (SIR)-C/X-SAR- derived terrain classifications, SIR-C/X-SAR-derived biomass estimation, and allometric relations and equations developed for the northern hardwood and conifer forest in general and from local test stand data. Results are mapped quantitatively in the image domain for above-ground C storage, below-ground C storage, above- to below-ground ratios, total C in living woody vegetation, and forest absolute and relative ANPP rates. Numeric estimates also are extracted from each of these in tabular form; for example, results show that the forested parts of the sampled area contain 2.73×10 9 kg of aboveground C and 4.86×10 8 kg of below-ground C in 51,448 ha. When combined with a SAR-derived classification, similar quantifications can be extracted for each of the several forest communities present in the region. Estimates of forest ANPP show that it ranges from 0.5 kg/m 2 /yr of biomass to 2.7 kg/m 2 /yr in the test site, with an average of 1.09 kg/m 2 /yr. Estimation of C removal due to clear-cutting is done by using multidate classifications of SAR imagery and a procedure including image differencing and decision rules. Clear-cuts that were made between SIR-C/X-SAR Shuttle Radar Laboratory (SRL)-1 (April) and SRL-2 (October) are identified by differencing the classified imagery. When combined with the SAR-derived biomass image, results show that an estimated 300 ha of forest with 6.02×10 6 kg of C were removed in this 6-month period.