Forrest G. Hall

The interpretation of spectral vegetation indexes

Empirical studies report several plausible correlations between transforms of spectral reflectance, called vegetation indexes, and parameters descriptive of vegetation leaf area, biomass and physiological functioning. However, most indexes can be generalized to show a derivative of surface reflectance with respect to wavelength. This derivative is a function of the optical properties of leaves and soil particles. In the case of optically dense vegetation, the spectral derivative, and thus the indexes, can be rigorously shown to be indicative of the abundance and activity of the absorbers in the leaves. Therefore, the widely used broad-band &near-infrared vegetation indexes are a measure of chlorophyll abundance and energy absorption.

A Landsat surface reflectance dataset for North America, 1990-2000

The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center has processed and released 2100 Landsat Thematic Mapper and Enhanced Thematic Mapper Plus surface reflectance scenes, providing 30-m resolution wall-to-wall reflectance coverage for North America for epochs centered on 1990 and 2000. This dataset can support decadal assessments of environmental and land-cover change, production of reflectance-based biophysical products, and applications that merge reflectance data from multiple sensors [e.g., the Advanced Spaceborne Thermal Emission and Reflection Radiometer, Multiangle Imaging Spectroradiometer, Moderate Resolution Imaging Spectroradiometer (MODIS)]. The raw imagery was obtained from the orthorectified Landsat GeoCover dataset, purchased by NASA from the Earth Satellite Corporation. Through the LEDAPS project, these data were calibrated, converted to top-of-atmosphere reflectance, and then atmospherically corrected using the MODIS/6S methodology. Initial comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat surface reflectance compared to the standard MODIS reflectance product (the greater of 0.5% absolute reflectance or 5% of the recorded reflectance value). The rapid automated nature of the processing stream also paves the way for routine high-level products from future Landsat sensors.

On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance

The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m surface reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m surface reflectance are used to produce a synthetic daily surface reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts surface reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes

The Boreal Ecosystem–Atmosphere Study (BOREAS): An Overview and Early Results from the 1994 Field Year

Abstract The Boreal Ecosystem Atmosphere Study (BOREAS) is large-scale international field experiment that has the goal of improving our understanding of the exchanges of radiative energy, heat water, CO2, and trace gases between the boreal forest and the lower atmosphere. An important objective of BORES is collect the data needed to improve computer simulation models of the processes controlling these exchanges so that scientists can anticipate the effects of global change. From August 1993 through September 1994, a continuous set of monitoring measurements—meteorology, hydrology, and satellite remote sensing—were gathered over the 1000 × 1000 km BOREAS study region that covers most of Saskatchewan and Manitoba, Canada. This monitoring program was punctuated by six campaigns that saw the deployment of some 300 scientists and aircrew into the field, supported by 11 research aircraft. The participants were drawn primarily from U.S. and Canadian agencies and universities, although there were also important ...

BOREAS in 1997: Experiment overview, scientific results, and future directions

The goal of the Boreal Ecosystem-Atmosphere Study (BOREAS) is to improve our understanding of the interactions between the boreal forest biome and the atmosphere in order to clarify their roles in global change. This overview paper describes the science background and motivations for BOREAS and the experimental design and operations of the BOREAS 1994 and BOREAS 1996 field years. The findings of the 83 papers in this journal special issue are reviewed. In section 7, important scientific results of the project to date are summarized and future research directions are identified.

Satellite remote sensing of surface energy balance: Success, failures, and unresolved issues in FIFE

The FIFE staff science group, consisting of the authors, developed and evaluated process models relating surface energy and mass flux, that is, surface rates, to boundary layer and surface biophysical characteristics, that is, surface states. In addition, we developed and evaluated remote sensing algorithms for inferring surface state characteristics. In this paper we report the results of our efforts. We also look in detail at the sensor and satellite platform requirements (spatial resolution and orbital requirements) as driven by surface energy balance dynamics and spatial variability. We examine also the scale invariance of the process models and remote sensing algorithms, that is, to what degree do the remotely sensed parameters and energy balance relations translate from the patch level where they were developed to the mesoscale level where they are required? Finally, we examine the atmospheric correction and calibration issues involved in extending the remotely sensed observations within a season and between years. From these investigations we conclude that (1) existing formulations for the radiation balance and latent heat components of the surface energy balance equation are valid at the patch level. (2) Many of the surface physiological characteristics that parameterize these formulations can be estimated using satellite remote sensing at both local and regional scales; a few important ones cannot. (3) The mathematical structures relating radiation and surface energy flux to remote sensing parameters are, for the most part, scale invariant over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) study area. The conditions for scale invariance are derived. (4) The precision of satellite remote sensing estimates of surface reflectance, calibrated and corrected for atmospheric effects, is no worse than about 1% absolute. The errors may actually be smaller, but an upper bound of 1% results from sampling variance caused by differences among the satellite and ground sensors in spatial resolution, atmospheric effects, and calibration. (5) Afternoon cumulus in the study area required both the Landsat and the SPOT satellites for monitoring of the vegetation dynamics. This result implies the need for multiple polar orbiters, or geosynchronous satellites in an operational implementation. We found that canopy Fpar, the fraction of incident photosynthetically active radiation absorbed by a canopy, can be estimated with an error of about 10% using remote sensing, provided that regional variability in the reflectance of the canopy substrate is dealt with properly. We also found that spectral vegetation indices (VIs) respond primarily to the photosynthetically active radiation absorbed by the live or green component of the canopy as opposed to its necrotic or dead vegetation. This is of critical importance since radiation absorption by the live part of the canopy is the rate-limiting process for photosynthesis and other key process rates such as evaporation. We found for the FIFE study area the surface moisture content at O to 10 cm to be another key rate-limiting variable in photosynthesis and evaporation. At gravimetric soil moisture levels below 20%, photosynthesis and evaporation were strongly attenuated. Only microwave sensors have shown potential for satellite remote sensing of soil moisture and only in the top few centimeters. Hydrological models may also play a critical role in monitoring root zone soil moisture levels, but additional research is needed. From our review of the research of others in FIFE we conclude that downwelling shortwave radiation and surface albedo are also amenable to remote sensing. Unfortunately, from our research we also found that the remote estimation of surface temperature to useful accuracies is problematical; consequently, the use of thermal infrared measurements to infer sensible heat flux is probably not feasible to acceptable accuracies.

Remote sensing of forest biophysical structure using mixture decomposition and geometric reflectance models

Using geometric shadow and linear mixture models we develop and evaluate an algorithm to infer several important structural parameters of stands of black spruce (Picea mariana), the most common boreal forest dominant. We show, first, that stand reflectances for this species can be represented as linear combinations of the reflectances of more elemental radiometric components: sunlit crowns, sunlit background, and shadow. Secondly, using a geometric model, we calculate how the fractions of these radiometric elements covary with each other. Then, using hand-held measurements of the reflectances of the sunlit background, sphagnum moss (Sphagnum spp.), and assuming shadow reflectance to be that of deep, clear lakes, we infer the reflectance of sunlit crowns from the geometric shadow model and low- altitude reflectance measurements acquired by a helicopter-mounted radiometer. Next, we as- sume that the reflectance for all black spruce stands is simply a linear combination of shadow, sunlit crown, and sunlit background reflectance, weighted in proportion to the relative areal fractions of these pixel elements. We then solve a set of linear equations for the areal fractions of these elements using as input helicopter observations of total stand reflectance. Using this algorithm, we infer the values for the areal proportions of sunlit canopy, sunlit background, and shadow for 31 black spruce stands of varying biomass density, net primary productivity, etc. We show empirically and theoretically that the areal proportions of these radiometric elements are related to a number of stand biophysical characteristics. Specifically, the shadow fraction is increasing with increasing biomass density, average diameter at breast height, leaf area index (LAI), and aboveground net primary productivity (NPP), while sunlit background fraction is decreasing. We show that the end member fractions can be used to estimate biomass with a standard error of -2 kg/M2, LAI with a standard error of 0.58, dbh with a standard error of -2 cm, and aboveground NPP with a standard error of 0.07 kg . m-2. yr- I. We, also show that the fraction of sunlit canopy is only weakly correlated with the biophysical variables and are thus able to show why a popular vegetation index, Normalized Difference Vegetation Index (NDVI), does not provide a useful measure of these biophysical characteristics. We do show, however, that NDVI should be related to the fraction of photosynthetically active radiation incident upon and absorbed by the canopy. This work has convinced us that a paradigm shift in the remote sensing of biophysical characteristics is in order-a shift away from direct inference of biophysical characteristics from vegetation indices and toward a two-step process, in which (1) stand-level reflectance is approximated in terrns of linear combinations of reflectance-invariant, spectrally distinct com- ponents (spectral end members) and mixture decomposition used to infer the areal fractions of these components, e.g., shadow, sunlit crown, and sunlit background, followed by (2) the use of radiative transfer models to compute biophysical characteristic values as a function of the end member fractions.

Vegetation Dynamics and Rainfall Sensitivity of the Amazon

Significance Understanding the sensitivity of tropical vegetation to changes in precipitation is of key importance for assessing the fate of the Amazon rainforest and predicting atmospheric CO2 levels. Using improved satellite observations, we reconcile observational and modeling studies by showing that tropical vegetation is highly sensitive to changes in precipitation and El Niño events. Our results show that, since the year 2000, the Amazon forest has declined across an area of 5.4 million km2 as a result of well-described reductions in rainfall. We conclude that, if drying continues across Amazonia, which is predicted by several global climate models, this drying may accelerate global climate change through associated feedbacks in carbon and hydrological cycles. We show that the vegetation canopy of the Amazon rainforest is highly sensitive to changes in precipitation patterns and that reduction in rainfall since 2000 has diminished vegetation greenness across large parts of Amazonia. Large-scale directional declines in vegetation greenness may indicate decreases in carbon uptake and substantial changes in the energy balance of the Amazon. We use improved estimates of surface reflectance from satellite data to show a close link between reductions in annual precipitation, El Niño southern oscillation events, and photosynthetic activity across tropical and subtropical Amazonia. We report that, since the year 2000, precipitation has declined across 69% of the tropical evergreen forest (5.4 million km2) and across 80% of the subtropical grasslands (3.3 million km2). These reductions, which coincided with a decline in terrestrial water storage, account for about 55% of a satellite-observed widespread decline in the normalized difference vegetation index (NDVI). During El Niño events, NDVI was reduced about 16.6% across an area of up to 1.6 million km2 compared with average conditions. Several global circulation models suggest that a rise in equatorial sea surface temperature and related displacement of the intertropical convergence zone could lead to considerable drying of tropical forests in the 21st century. Our results provide evidence that persistent drying could degrade Amazonian forest canopies, which would have cascading effects on global carbon and climate dynamics.

Physically based classification and satellite mapping of biophysical characteristics in the southern boreal forest

Fundamental problems inherent to the existing land cover and biophysical characteristic algorithms are fourfold: (1) their failure to deal physically with global and interannual variations in surface reflectance arising from Sun and view angle variations, (2) decoupling of the land cover classification algorithm from the biophysical characteristic inference algorithm with no ability to control biophysical parameter estimation error arising from misclassification, (3) invalid statistical assumptions within classification algorithms used to model reflectance distribution functions, and (4) sole reliance on vegetation indices that can limit performance for several major land cover classes. To address these problems, we develop an integrated, physically based classification and biophysical characteristics estimation algorithm that utilizes canopy reflectance models to account directly for signature variations from Sun angle, topographic, and other variations. Our approach fuses into a single algorithm both land cover classification and biophysical characteristics estimation, permitting one set of physically based canopy reflectance models to be used for both. The use of canopy reflectance models eliminates the need for unrealistic assumptions, such as multivariate-normal distributions, underlying many classification algorithms. Using the algorithm, we have classified a 10,000 km2 area of the BOREAS southern study area. Our classification shows that low-productivity wetland conifer is the dominant land cover and that nearly 7% of the area is occupied by boreal fens, a major source of methane. In addition, nearly 23% of the area has been disturbed by either fire or logging in the last 20 years, suggesting an important role for disturbance to the regional carbon budget. A thorough evaluation of the physically based classifier within the southern study area shows accuracies superior to those obtained with conventional statistically based algorithms, implying even better performance when extended over multiple Landsat frames since the physically based approach can account directly for regional variations in reflectance resulting from varying illumination and viewing conditions (topography, Sun angle). The conifer biomass density estimation algorithm is based on our discovery of a convenient natural relationship between crown height and volumetric density that renders the biomass density for black spruce stands independent of tree height, and a function only of sunlit canopy fraction. This permits us to calculate directly the relationship between reflectance and biomass density. An evaluation of the algorithm using ground sites shows our algorithm can estimate black spruce biomass density with a root-mean-square error of 2.73 kg/ym2 for correctly classified sites. Our evaluation also demonstrates the importance of correct classification. Rootmean-square errors for misclassified sites were 3.96 kg/m2. Using this approach we have estimated the biomass density in the BOREAS southern study area for the dominant land cover type in the circumpolar boreal ecosystem, wetland black spruce. These results show a bimodality to the biomass density regional distribution, controlled perhaps by underlying topographic and edaphic factors.

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.