Natural fluctuations in the availability of critical stopover sites coupled with anthropogenic destruction of wetlands,
land-use change, and anticipated losses due to climate change present migratory birds with a formidable challenge.
Space based technology in concert with bird migration modeling and geographical information analysis yields new
opportunities to shed light on the distribution and movement of organisms on the planet and their sensitivity to human
disturbances and environmental changes. At the NASA Goddard Space Flight Center, we are creating ecological
forecasting tools for science and application users to address the consequences of loss of wetlands, flooding, drought or
other natural disasters such as hurricanes on avian biodiversity and bird migration. We use an individual-based bird
biophysical migration model, driven by remotely sensed land surface data, climate and hydrologic data, and biological
field observations to study migratory bird responses to environmental change in North America. Simulation allows us to
study bird migration across multiple scales and can be linked to mechanistic processes describing the time and energy
budget states of migrating birds. We illustrate our approach by simulating the spring migration of pectoral sandpipers
from the Gulf of Mexico to Alaska. Mean stopover length and trajectory patterns are consistent with field observations.
In this paper, we present our first results towards understanding high temporal frequency thermal infrared response from a dense grass canopy. The model is driven by slowly varying, time-averaged meteorological conditions and by high frequency measurements of local and within canopy profiles of relative humidity and wind speed, and compared to high frequency thermal infrared observations. Previously, we have employed three-dimensional ray tracing to compute the intercepted and scattered solar and IR radiation fluxes and for final scene rendering. For the turbulent fluxes, simple resistance models for latent and sensible heat with one-dimensional profiles of relative humidity and wind speed are used. Our modeling approach has proven successful in capturing the directional and diurnal variation in background thermal infrared signatures. We hypothesize that at these scales, where the model is typically driven by time-averaged, local meteorological conditions, the primary source of thermal variance arises from the spatial distribution of sunlit and shaded foliage elements within the canopy and the associated radiative interactions.
In recent experiments, we have begun to focus on the high temporal frequency response of plant canopies in the thermal infrared at 1 sec to 5 min intervals. At these scales, we hypothesize turbulent mixing plays a more dominant role. Our results indicate that in the high frequency domain, the vertical profile of temperature change is tightly coupled to the within canopy wind speed. In the results reported here, the canopy cools from the top down with increased wind velocities and heats from the bottom up at low wind velocities.
We present a comparison of images from the ETM+ sensor on Landsat-7 and the ALI instrument on EO-1 over a test site in Rochester, NY. The site contains a variety of features, ranging from water of varying depths, deciduous/coniferous forest, grass fields, to urban areas. The nearly coincident cloud-free images were collected just one minute apart on 25 August, 2001. We atmospherically corrected each image with the 6S atmosphere model, using aerosol optical thickness and water vapor column density measured by a Cimel sun photometer within the Aerosol Robotic Network (Aeronet), along with ozone density derived from NCEP data. We present three-color composites from each instrument that show excellent qualitative agreement. We present ETM+ and ALI reflectance spectra for water, grass, and urban targets. We make a more detailed comparison for our forest site, where we use measured geometric and optical properties as input to the SAIL canopy reflectance model, which we compare to the ETM+, ALI, and EO-1 Hyperion reflectance spectra.
This paper describes our hyperspectral reflectance modeling of a forest canopy based on measured input parameters and comparison with Earth Observing - 1 (EO-1) Advanced Land Imager (ALI) and Hyperion data. The model uses a high resolution, three-dimensional (3D) ray-tracing approach to estimate the intercepted and scattered solar radiation at multiple narrow wavelength bands. We present the comparisons of the effects of woody biomass, leaf litter, and clumping on reflectance signatures. The experimental data used for the model were collected in a hardwood forest canopy in Rochester, New York. Model calculations also are compared to a more simplified, low-resolution 3D model and a simple, multi-layer differential equation model.
Data assimilation methods applied to hydrologic models can incorporate spatially distributed maps of near surface temperature, especially if such measurements can be reliably inferred from satellite observations. Uncalibrated thermal IR imagery sometimes is scaled to temperature units to obtain such observations using the assumption that dense forest canopies are close to air temperature. For fully leafed deciduous forest canopies in the summer, this approximation is usually valid within 2C. In a leafless canopy, however, the materials views are thick boles and branches and the forest floor, which can store heat and yield significantly higher variations. Winter coniferous forests are intermediate with needles and branches being the predominant viewed materials. The US Dept of Energy's Multispectral Thermal Imager (MTI) is an experimental satellite with the capability to perform quantitative scene measurements in the reflective and thermal infrared region respectively. Its multispectral thermal IR capability enables quantitative surface temperature retrieval if pixel emissivity is known. MTI is pointable and targets multiple times in the winter and spring of 2001 at the Howland, Maine AmeriFlux research site operated by the University of Maine. Supporting meteorological and optical depth measurements also were made from three towers at the site. Directional thermal models of forest woody materials and needles are driver by the surface measurements and compared to satellite data to help evaluate the relationship between air temperature and satellite thermal measurements as a function of look angles, day and night.
Satellite observations of agricultural and other plant canopies in the thermal IR regime have generally been at spatial scales of tens to hundreds of meters. Use of the thermal IR at higher resolutions is confounded by the mixture problem and other associated scaling issues. Advances in sensor technology will extend our capabilities for IR measurements to shorter wavelengths and yield improved spatial resolutions. However, experience with aircraft remote sensing observations has indicated that care must be exercised in understanding the interaction effects of viewing geometry at these higher resolutions. The utilization and scaling of observables with multi-resolution remote sensing data sets remain a difficult problem. At high spatial resolution the three-dimensional character of scene components contained within a pixel must be considered. In this paper, we explore the variability in brightness temperature and the co-variation of NDVI with brightness temperature as a function of viewing geometry and changing spatial resolution. Using three- dimensional models for both canopy reflectance and thermal IR exitance, we employ a theoretical analysis for an agricultural scene where previous comparisons and measurements were available.
We present a simple, three-dimensional vegetation canopy thermal infrared exitance model for agricultural scenes. Computer graphics and ray-tracing techniques are used to estimate three-dimensional canopy view factors and scene shadows. The view factors are used to weight the individual contributions of soil and vegetation emission computed by steady-state energy budget formulations. We compare the three- dimensional model results to a one-dimensional formulation for an agricultural test site from the Hydrologic Atmospheric Pilot Experiment and Modelisation du Bilan Hydrique. The root mean square error is daylight brightness temperature for the one dimensional model was 2.5 degrees Celsius and 2.0 degrees Celsius for the three dimensional model.
We present a new approach for estimating land surface fluxes using remote sensing optical and thermal IR observations. We employ an artificial neural network and train it with a radiosity reflectance model. We then apply the network without retraining to extract geometrical view factors from AVIRIS imagery. We use the retrieved view factors and in- situ meteorological data to drive a surface energy balance model. Theoretical directional view factors were retrieved with an average absolute error of 15 percent. Hemispherical view factors were retrieved with a root mean square error of 6 percent. Surface net radiation estimated using the AVIRIS imagery and the surface energy balance model varied form 520 W m-2 to 650 W m-2 and are consistent with tower measurements. The retrieved view factors may also be used to model mixed pixel response for directional thermal IR data.
Models of surface temperatures of two land surface types based on their energy budgets were developed to simulate the effects of environmental factors on thermal radiant exitance. The performance of these models is examined in detail. One model solves the non-linear differential equation for heat diffusion in solids using a set of submodels for surface energy budget components. The model performance is examined under three desert conditions thought to be a strong test of the submodels. The accuracy of the temperature predictions and submodels is described. The accuracy of the model is generally good but some discrepancies between some of the submodels and measurements are noted. The sensitivity of the submodels is examined and is seen to be strongly controlled by interaction and feedback among energy components that are a function of surface temperature. The second model simulates vegetation canopies with detailed effects of surface geometry on radiant transfer in the canopy. Foliage solar absorption coefficients are calculated using a radiosity approach for a three layer canopy and long wave fluxes are modeled using a view factor matrix. Sensible and latent heat transfer through the canopy are also simulated using nearby meteorological data but heat storage in the canopy is not included. Simulations for a coniferous forest canopy are presented and the sensitivity of the model to environmental inputs is discussed.
A Monte Carlo ray tracing model for canopy birdirectional reflectance is applied to forest canopy architectures consisting of inhomogeneous mixtures of coniferous and both shade- tolerant and intolerant hardwood species. The model assumes a multi-layered canopy of known optical properties, statistical composition and geometric arrangement and, for the results reported here, further assumed Lambertian scattering of canopy elements. Upward and downward direct, diffuse and multiply scattered solar irradiance is traced through the canopy and from a reflecting background. The stand-level architecture for the model was derived from a forest growth succession model which predicted canopy structure height profiles as a function of site condition and environmental driving variables. Fifteen theoretical successional stages consisting of five age class distributions for three different site indices were simulated using the forest growth model. Fifteen forest growth replications for each of the forest plots were generated. Monte Carlo reflectance predictions were then made for each of these theoretical cases with ten canopy reflectance replications per plot. The model was applied to field and aircraft data collected September 8, 1990 from a hemlock-spruce-fir forest canopy at the Northern Experimental Forest study site near Howland, Maine, the location of a NASA sponsored Forest Ecosystem Dynamics Multi-sensor Aircraft Campaign. Model predicted bidirectional reflectance distribution functions are compared to representative samples of AVIRIS (airborne visible/infrared imaging spectrometer) observations corresponding to successional age class distributions of 50, 75, 100 and 200 years for dry mesic and wet sites.
An extensive multisensor airborne and field campaign was conducted in the Northern Experimental
Forest near Bangor, Maine, during September 1989 to acquire a data set to allow us to test various
modeling hypotheses concerning the interaction of optical, thermal, and microwave electromagnetic
radiation within northern coniferous forest canopies and their underlying backgrounds. This experiment
represents a significant component of a Forest Ecosystem Dynamics project, which is concerned
with the response of northern forests to climatic and other environmental changes. Extensive
ground control information, basic remote-sensing measurements, and comprehensive calibration data
were obtained to support the interpretation of numerous airborne sensor measurements and to provide
both static and dynamic biophysical input parameters to electromagnetic scattering and absorption
models. Aircraft instruments deployed included a multifrequency, quadpolarized synthetic
aperture radar, a solid-state array bidirectional imager, and the Thematic Mapper Simulator. In
addition, a suite of helicopter-borne nonimaging systems was utilized, consisting of a multifrequency
laser polarimeter and both narrow- and broad-band spectrometers. This paper presents background
on this first Forest Ecosystems Dynamics field campaign, provides a progress report on the
analysis of the collected data and related modeling activities, and outlines plans for future experiments
at different points in the phenological cycle.