Engineering topics
Publications and source records attributed to Dobson, M. Craig.
This paper is largely tutorial in nature and provides an overview of the microwave dielectric properties of certain natural terrestrial media (soils and vegetation) and recent results in estimating these properties remotely from airborne and orbital synthetic aperture radar (SAR).
The objectives of this research are to test the hypotheses that ecologically significant forest state parameters may be estimated from SAR data. These include estimation of above ground biomass, plant water status, and near surface soil moisture under certain forest conditions. Test hypotheses in the northern hardwoods forest community, refine them if necessary, and establish techniques for retrieving this information from orbital SARs such as SIR-C/X-SAR. This report summarizes (1) recent progress, (2) significant results and (3) research plans concerning SIR-C/X-SAR research.
In preparation for the launch of SIR-C/X-SAR and design studies for future orbital SAR, a program has made considerable progress in the development of an SAR terrain classifier and algorithms for quantification of biophysical attributes. The goal of this program is to produce a generalized software package for terrain classification and estimation of biophysical attributes and to make this package available to the larger scientific community. The basic elements of the SAR (Synthetic Aperture Radar) terrain classifier are outlined. An SAR image is calibrated with respect to known system and processor gains and external targets (if available). A Level 1 classifier operates on the data to differentiate: urban features, surfaces and tall and short vegetation. Level 2 classifiers further subdivide these classes on the basis of structure. Finally, biophysical and geophysical inversions are applied to each class to estimate attributes of interest. The process used to develop the classifiers and inversions is shown. Radar scattering models developed from theory and from empirical data obtained by truck-mounted polarimeters and the JPL AirSAR are validated. The validated models are used in sensitivity studies to understand the roles of various scattering sources (i.e., surface trunk, branches, etc.) in determining net backscatter. Model simulations of sigma (sup o) as functions of the wave parameters (lambda, polarization and angle of incidence) and the geophysical and biophysical attributes are used to develop robust classifiers. The classifiers are validated using available AirSAR data sets. Specific estimators are developed for each class on the basis of the scattering models and empirical data sets. The candidate algorithms are tested with the AirSAR data sets. The attributes of interest include: total above ground biomass, woody biomass, soil moisture and soil roughness.
The primary objective is to define the advantages of synergistically combining optical and microwave remote sensing measurements for the determination of biophysical properties important in ecosystem modeling. This objective was approached in a stepwise fashion starting with ground-based observations of controlled agricultural and orchard canopies and progressing to airborne observations of more natural forest ecosystems. This observational program is complemented by a parallel effort to model the visible reflectance and microwave scattering properties of composite vegetation canopies. The goals of the modeling studies are to verify our basic understanding of the sensor-scene interaction physics and to provide the basis for development of inverse models optimized for retrieval of key biophysical properties. These retrieval algorithms can then be used to simulate the expected performance of various aspects of Eos including the need for simultaneous SAR and HIRIS observations or justification for other (non-synchronous) relative timing constraints and the frequency, polarization, and angle of incidence requirements for accurate biophysical parameter extractions. This program completed a very successful series of truck-mounted experiments, made remarkable progress in development and validation of optical reflectance and microwave scattering models for vegetation, extended the scattering models to accommodate discontinuous and periodic canopies, developed inversion approaches for surface and canopy properties, and disseminated these results widely through symposia and journal publications. In addition, the third generation of the computer code for the microwave scattering models was provided to a number of other US, Canadian, Australian, and European investigators who are currently presenting and publishing results using the MIMICS research code.
This document contains a number of viewgraphs on surface and vegetation backscattering. A classification of vegetation based on general scattering properties is presented. Radar scattering mechanisms are discussed, and backscattering and reflection coefficients for soil back scattering models are given. Radar response to vegetation is presented, with the objectives to discriminate and classify vegetation; to estimate biomass, leaf area index (LAI), and soil moisture; and to monitor changes, including deforestation and growth. Both theory and observation (laboratory, field, air SAR, and European Remote Sensing Satellite (ERS-1) observations) are used to present backscatter coefficients and other data for various vegetation types. ERS-1 results include class statistics, comparison with theory, and biomass response and seasonal variation (LAI) for deciduous and coniferous forests.
The initial results of an experiment to examine the dependence of radar image intensity on total above-ground biomass in a southern US pine forest ecosystem are presented. Two sets of data are discussed. First, we examine two L-band (VV-polarization) data sets which were collected 5 years apart. These data sets clearly illustrate the change in backscatter resulting from the growth of a young pine stand. Second, we examine the dependence between radar backscatter and biomass as a function of radar frequency using data from the JPL Airborne Synthetic Aperture Radar (AIRSAR) and ERIM/NADC P-3 SAR systems. These results show that there is a positive correlation between above-ground biomass and radar backscatter and at C-, L-, and P-bands, but very little correlation at C-band. The biomass level for which this positive correlation holds decreases as radar frequency increases. This positive correlation is stronger at HH and HV polarizations that VV polarization at L- and P-bands, but strongest at VV polarization for C-band.
The mixed hardwood and conifer forests of northern Michigan were overflown by a 3-frequency airborne imaging radar in Apr. and Jul. 1990. A set of 10 x 10 km test sites near the University of Michigan Biological Station at Douglas Lake and within the Hiawatha National Forest in the upper peninsula of Michigan contained training stands representing the various forest species typical of forest communities across the ecotone between the coniferous boreal forest and mid-latitude hardwood and coniferous forests. The polarimetric radar data were externally calibrated to allow interdate comparisons. The Apr. flight was prior to bud-break of deciduous species and patchy snowcover was present. The Jul. flights occurred during and 2 days after heavy rain showers, and provide a unique opportunity to examine the differences in radar backscatter attributable to intercepted precipitation. Analyses show that there are significant changes in backscattering between biophysically dissimilar forest stands on any given date and also between dates for a given forest stand. These differences in backscattering can be related to moisture properties of the forest floor and the overlying canopy and also to the quantity and organizational structure of the above-ground biomass.
The utility of airborne and orbital SAR in classification, assessment, and monitoring of forest biomes is investigated through analysis of orbital synthetic aperature radar (SAR) and multifrequency and multipolarized airborne SAR imagery relying on image tone and texture. Preliminary airborne SAR experiments and truck-mounted scatterometer observations demonstrated that the three dimensional structural complexity of a forest, and the various scales of temporal dynamics in the microwave dielectric properties of both trees and the underlying substrate would severely limit empirical or semi-empirical approaches. As a consequence, it became necessary to develop a more profound understanding of the electromagnetic properties of a forest scene and their temporal dynamics through controlled experimentation coupled with theoretical development and verification. The concatenation of various models into a physically-based composite model treating the entire forest scene became the major objective of the study as this is the key to development of a series of robust retrieval algorithms for forest biophysical properties. In order to verify the performance of the component elements of the composite model, a series of controlled laboratory and field experiments were undertaken to: (1) develop techniques to measure the microwave dielectric properties of vegetation; (2) relate the microwave dielectric properties of vegetation to more readily measured characteristics such as density and moisture content; (3) calculate the radar cross-section of leaves, and cylinders; (4) improve backscatter models for rough surfaces; and (5) relate attenuation and phase delays during propagation through canopies to canopy properties. These modeling efforts, as validated by the measurements, were incorporated within a larger model known as the Michigan Microwave Canopy Scattering (MIMICS) Model.
The fundamental objectives are to test the feasibility of delineating the lateral boundary between frozen and thawed condition in the surface layer of soil from orbital microwave radiometry and secondly to examine the sensitivity of general circulation models to an explicit parameterization of the boundary condition. Physical models were developed to relate emissivity to scene properties and a simulation package was developed to predict brightness temperature as a function of emissivity and physical temperature in order to address issues of heterogeneity, scaling, and scene dynamics. Radiative transfer models were develped for both bare soil surfaces and those obscured by an intervening layer of vegetation or snow. These models relate the emissivity to the physical properties of the soil and to those of the snow or vegetation cover. A SMMR simulation package was developed to evaluate the adequacy of the emission models and the limiting effects of scaling for realistic scenarios incorporating spatially heterogeneous scenes with dynamic moisture and temperature gradients at the pixel scale.
A summary is presented of the results derived from analysis of six SIR-B data takes over an agricultural test site in west central Illinois. The first part describes the procedure used to calibrate the SIR-B imagery, the second part pertains to the observed radar response to soil moisture content, and the last part examines the information derivable from multiangle observations.