Donald E. Strebel

Large-Scale Patterns of Forest Succession as Determined by Remote Sensing

The spatial pattern of and the transition rates between forest ecological states were inferred for °260 000 pixel—sized (3600 m2) landscape units using stallite remote sensing. Transition rates were estimated from 1973 to 1983 Landsat images of the study area, classified into ecological states associated with forest succession. The effects of classification error on transition rate estimates were modeled and error adjustments made. Classification of the 1973 and 1983 Landsat images of the 900 km2 study region required a relatively small set of ground—observed and photo—interpreted plots in 1983, with a total area of just 1.62 km2. An innovative technique for correcting multiyear Landsat images for between—image differences in atmospheric effects and sensor calibration, permitted classification of the 1973 Landsat image using 1983 ground observations. Given current Landsat data, and ground observations in one year, this technique would permit monitoring of forest succession and dynamics for nearly a 20—yr period. Results of applying these techniques to a forest ecosystem showed that during the 10—yr observation period it was patchy and dynamic. For both a wilderness and a nonwilderness area in the study region, sizeable values of transition rates were observed and over half of the landscape units were observed to change state: however, a Markov analysis, using the observed transition probabilities, suggests that at the regional level neither the wilderness nor the nonwilderness areal proportions of ecological states are undergoing rapid change.

Effects of spatial variability in topography, vegetation cover and soil moisture on area-averaged surface fluxes: A case study using the FIFE 1989 data

A modified version of the simple biosphere model (SiB) of Sellers et al. (1986) was used to investigate the impact of spatial variability in the fields of topography, vegetation cover, and soil moisture on the area-averaged fluxes of sensible and latent heat for an area of 2×15 km (the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) testbed area) located within the FIFE area. This work builds on a previous study of Sellers et al. (1992a) but makes use of a superior data set (FIFE 1989 rather than FIFE 1987) and has a sharper focus on the nonlinear effects of soil wetness on evapotranspiration. The 2×15 km testbed area was divided into 68×501 pixels of 30×30 m spatial resolution, each of which could be assigned topographic, vegetation condition, and soil moisture parameters from satellite and in situ observations gathered in FIFE-89. One or more of these surface fields was area averaged in a series of simulation runs to determine the impact of using large-area means of these initial/boundary conditions on the area-integrated (aggregated) surface fluxes. Prior to these simulations some validation work was done with the model. The results of the study can be summarized as follows: (1) SiB was initialized with satellite and airborne remotely sensed data for vegetation condition and soil wetness, respectively. The surface fluxes calculated by SiB compared well with surface-based and airborne flux observations. (2) Analyses and some of the simulations indicated that the relationships describing the effects of moderate topography on the surface radiation budget are near linear and thus largely scale invariant. The relationships linking the simple ratio (SR) vegetation index, the canopy conductance parameter ∇F, and the canopy transpiration flux are also near linear and similarly scale invariant to first order (see also Sellers et al., 1992a). Because of this it appears that simple area-averaging operations can be applied to these fields with relatively little impact on the calculated surface heat fluxes. (3) The relationships linking surface and root-zone soil wetness to the soil surface and canopy transpiration rates are nonlinear. However, simulation results and observations indicate that soil moisture variability decreases significantly as the study area dries out, which partially cancels out the effects of these nonlinear functions. (4) The near-infrared surface reflectance ρN estimated from atmospherically corrected satellite data may be a better predictor of vegetation condition than a two-band vegetation index, such as the SR, at least for the grasslands represented in the FIFE area. These results support the use of simple averages of topographic and vegetation parameters to calculate surface energy and heat fluxes over a wide range of spatial scales, from a few meters up to many kilometers. Although the relationships between soil moisture and evapotranspiration are nonlinear for intermediate soil wetnesses, the dynamics of soil drying act to progressively reduce soil moisture variability and thus the impacts of these nonlinearities on the area-averaged surface fluxes. These findings indicate that we can use mean values of topography, vegetation condition, and soil moisture to calculate the surface-atmosphere fluxes of energy, heat, and moisture at larger length scales to within an acceptable accuracy for climate-modeling work.

Linking knowledge among spatial and temporal scales: Vegetation, atmosphere, climate and remote sensing

Global scale modeling is reviewed with respect to global circulation models, biosphere-atmosphere models, and climate-biosphere models. These different models focus on short to long time scale interactions between atmospheric and surface systems. Remote sensing is shown to play a central role in acquisition of data for these models, and an experiment, termed FIFE, is described, which is the first attempt to take simultaneous land surface observations of meteorological and biophysical parameters at sufficient resolution to test hypotheses linking the vegetated surface and circulation within the lower atmosphere.

FIFE: Analysis and results — A review

Abstract The initial year of field experimentation of the First ISLSCP Field Experiment (FIFE), conducted over a 15×15km grasslands study area in the central US is complete and analyses are in progress. FIFE was conceived to better understand the interaction of vegetated land surfaces with the atmosphere and how to observe and quantify this interaction using satellite remote sensing. Preliminary data analysis from an April 1988 workshop show that energy and mass flux data collected at several diurnal cycles during different parts of the growing season, are of high quality. From these data we clearly observe the surface vegetation stomatal control on latent heat flux. In addition, the magnitude of this control appears to be measureable from remote sensing observations of reflected radiation. Furthermore, canopy radiometric brightness temperature as measured in the 10.4 to 12.3 μm is linearly related to seasonal variations in canopy aerodynamic temperature and thus may provide a useful measure of sensible heat flux from the surface. Airborne monitoring of the mass and heat flux in the atmospheric boundary layer (ABL) appear adequate to study the energy and mass budgets above the test site and to relate the ABL flux to surface measurements.

Two-Dimensional Leaf Orientation Distributions

Combined inclination/azimuth leaf angle distributions are important for accurate models of vegetation canopy reflectance. It is shown that appropriate mathematical representations can be constructed from beta distributions under most circumstances. This is illustrated by analyzing observational data on soybean leaves and balsam fir needles. There are some problems when the data is imprecise and when correlations between inclination and azimuth angle are induced by heliotropism. Otherwise, the two-dimensional beta-type distribution appears to be a versatile tool for describing complete inclination/azimuth leaf angle distributions.

The FIFE Data Publication Experiment

Abstract The First ISLSCP Field Experiment (FIFE) provided an opportunity to test the concept of data publication for long-term access to valuable scientific data. In analogy with the procedures used in research publication, the FIFE Information System and NASA’s Pilot Land Data System adapted the functions performed by authors, editors, and publishers to an information management environment. Procedures and standards were developed to organize, quality check, document, and review data and associated supporting information for publication on a series of five CD-ROM volumes. The CD-ROM series has been successfully published and is in widespread use in the scientific community. The preliminary indications are that this publication will pass the “20-year test” recommended by a committee of the National Research Council for preserving global change data. It is concluded that the data publication approach, using near-permanent distributable publication units like CD-ROMs, is an important addition to the tools ...

Image Processing Software For Providing Radiometric Inputs to land surface climatology models

During the First International Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), 80 gigabytes of image data were generated from a variety of satellite and airborne sensors in a multidisciplinary attempt to study energy and mass exchange between the land surface and the atmosphere. To make these data readily available to researchers with a range of image data handling experience and capabilities, unique image-processing software was designed to perform a variety of nonstandard image-processing manipulations and to derive a set of standard-format image products. The nonconventional features of the software include: (1) adding new layers of geographic coordinates, and solar and viewing conditions to existing data; (2) providing image polygon extraction and calibration of data to at-sensor radiances; and, (3) generating standard-format derived image products that can be easily incorporated into radiometric or climatology models. The derived image products consist of easily handled ASCII descriptor files, byte image data files, and additional per-pixel integer data files (e.g., geographic coordinates, and sun and viewing conditions). Details of the solutions to the image-processing problems, the conventions adopted for handling a variety of satellite and aircraft image data, and the applicability of the output products to quantitative modeling are presented. They should be of general interest to future experiment and data-handling design considerations.

Quantifying desert-fringe vegetation changes from satellite observations by inverting plant-element models

In previous studies, desert-fringe vegetation densities were assessed in an ungrazed semi-arid rangeland in Utah and in an animal exclosure in the arid Sinai. Applying satellite measurements of reflectances, the dark vertical cylinders (DVC) model for desert-scrub (characterized by predominantly vertical architecture) was inverted. In the present study a new plane-parallel model is presented, which treats the canopy as a layer of small Lambertian spheres (SLS), or small facets (leaves) with a specific distribution of the leaf area, where the Schonberg function of the angle between the solar beam and the view direction specifies the reflectance from the canopy. The SLS model is inverted with the satellite-measured reflectances of the Sinai exclosure and the surrounding overgrazed, practically bare-soil terrain. The SLS-model inversion results are compared with DVC results. Both models provide plausible canopy characterizations, but the SLS model is more realistic when viewing from the zenith with sun at a high elevation. The reflectance ratio of the dark plant-elements to the bright soil is key to assessing the density of the plants. In the inversion of the AVHRR visible and near-infrared data, the plant element reflectances in the infrared are adjusted so that the plant optical density in the infrared matches that determined in the visible spectral region. Early in the dry summer season (after the winter rains), high infrared reflectances are inferred, but sharply lower infrared reflectances, appropriate for the dried out plants, are found in the later stages of the dry season. This result, that the physical changes in the plant conditions can be assessed, is highly encouraging for our SLS modeling effort.

The FIFE Data

The FIFE [First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment] collected data on and around the Konza Prairie Research Natural Area near Manhattan, Kansas during 1987–1989. FIFE is one of the most complex interdisciplinary research efforts yet undertaken in earth science. The objective is to combine the skills of remote sensing scientists, atmospheric physicists, meteorologists, biologists, and modelers to provide not only more detailed biophysical understanding of the land-surface-atmosphere interactions, but also a means of monitoring these interactions using satellite remote sensing. The principal focus of the project is the exchange of energy, heat, mass (CO2 and H2O), and momentum between the (vegetated) land surface and the atmosphere.