Publications

With the proliferation of personal computers and CD-ROM devices in secondary school classrooms there is the opportunity to make available scientific study packages of current environmental issues using CD-ROM media. The Aspen Global Change Institute and scientists from NASA's Goddard Space Flight Center are developing an integrated package of selected Landsat satellite data sets emphasizing tropical forests, publicly available image analysis software for PCs, ancillary information such as photographs, and specific lesson plans, all on a CD-ROM titled "The Changing Forest", for use in secondary school science classes. Emphasis will be placed on simplicity and ease of use, coupled to specific lesson plans developed around environmental issues and concepts for which satellite data provide a unique information source. Themes are introduced that include the extent, rate, fragmentation and edge effect associated with deforestation and remote sensing technologies in land cover assessment. Selected CD-ROM content and interactive strategies to engage the student user are discussed.

The spectral methods of vegetation analysis not only measure herbage biomass on a nondestructive basis but also can be adapted to aircraft and satellite devices to map the spatial distribution over an area in an efficient and economical fashion. This study reviews the ground-based in situ field spectrometry in the 0.350-0.800 micron region of the spectrum. A statistical analysis of in situ spectroreflectance data from sample plots of the shortgrass prairie shows that green biomass, chlorophyll concentration, and leaf water content are directly interrelated to that composite property of the plot which is called functioning green biomass. Spectrocorrelation data indicate the spectral regions of optimum sensitivity for a remote estimation of the green biomass, chlorophyll, and leaf water content. The near-infrared region of the spectrum shows a high positive spectrocorrelation to these three sample parameters, regardless of the amount of standing dead vegetation.

All known Rift Valley fever(RVF) outbreaks in Kenya from 1950 to 1998 followed periods of abnormally high rainfall. On an interannual scale, periods of above normal rainfall in East Africa are associated with the warm phase of the El Niño/Southern Oscillation (ENSO) phenomenon. Anomalous rainfall floods mosquito-breeding habitats called dambos, which contain transovarially infected mosquito eggs. The eggs hatch Aedes mosquitoes that transmit the RVF virus preferentially to livestock and to humans as well. Analysis of historical data on RVF outbreaks and indicators of ENSO (including Pacific and Indian Ocean sea surface temperatures and the Southern Oscillation Index) indicates that more than three quarters of the RVF outbreaks have occurred during warm ENSO event periods. Mapping of ecological conditions using satellite normalized difference vegetation index (NDVI) data show that areas where outbreaks have occurred during the satellite recording period (1981-1998) show anomalous positive departures in vegetation greenness, an indicator of above-normal precipitation. This is particularly observed in arid areas of East Africa, which are predominantly impacted by this disease. These results indicate a close association between interannual climate variability and RVF outbreaks in Kenya.

Red and photographic infrared spectral radiances were correlated with soybean total leaf area index, green leaf area index, chlorotic leaf area index, green leaf biomass, chlorotic leaf biomass, and total biomass. The most significant correlations were found to exist between the IR/red radiance ratio data and green leaf area index and/or green leaf biomass (r squared equals 0.85 and 0.86, respectively). These findings demonstrate that remote sensing data can supply information basic to soybean canopy growth, development, and status by nondestructive determination of the green leaf area or green leaf biomass.

A stochastic leaf radiation model based upon physical and physiological properties of dicot leaves has been developed. The model accurately predicts the absorbed, reflected, and transmitted radiation of normal incidence as a function of wavelength resulting from the leaf-irradiance interaction over the spectral interval of 0.40-2.50 microm. The leaf optical system has been represented as Markov process with a unique transition matrix at each 0.01-microm increment between 0.40 microm and 2.50 microm. Probabilities are calculated at every wavelength interval from leaf thickness, structure, pigment composition, and water content. Simulation results indicate that this approach gives accurate estimations of actual measured values for dicot leaf absorption, reflection, and transmission as a function of wavelength.

The 'normalized difference vegetation indices' (NVI) derived from AVHRR radiances are combined with field data of soil respiration and a global map of net primary productivity to prescribe, for the globe, the seasonal exchange of CO2 between the atmosphere and the terrestrial biosphere. The monthly fluxes of CO2 thus obtained are used as inputs to a 3-D tracer transport model which uses winds generated by a 3-D atmospheric general circulation model to advect CO2 as an inert constituent. Analysis of the 3-D model results shows reasonable agreement between the simulated and observed annual cycles of atmospheric CO2 at the locations of the remote monitoring stations. The application is shown of atmospheric CO2 distributions to calibrate the NVI in terms of carbon fluxes. The approach suggests that the NVI may be used to provide quantitative information about long term and global scale variations of photosynthetic activity and of atmospheric CO2 concentrations provided that variations in the atmospheric circulation and in atmospheric composition are known.

[1] Ahlbeck [2002] raises an important issue: Is the increase in the atmospheric concentration of CO2 [Keeling and Whorf, 2001] partially responsible for the increase in the normalized difference vegetation index (NDVI), which we report in the work of Zhou et al. [2001]? To demonstrate its effect, Ahlbeck [2002] adds the atmospheric concentration of CO2 (hereinafter referred to as CO2) to equation (11) in the Zhou et al. [2001] article. Ahlbeck [2002] finds that CO2 is correlated with NDVI and concludes that "fertilizing due to increased atmospheric carbon dioxide concentration also may increase greenness." As described below, this conclusion, and the results on which it is based, is a statistical artifact. When the regression equation is specified correctly, we find that there is no relation between the NDVI and the atmospheric concentration of CO2. [4] Why does the presence of a time trend eliminate the statistical significance of CO2? The answer can be seen in the work of Ahlbeck [2002, Figure 1]. The atmospheric concentration of CO2 rises steadily over the sample period, 1982–1999. As such, CO2 "looks like" a time trend. As such, the two variables in equation (2) are highly correlated. The resulting colinearity causes ordinary least squares to overstate the size of the standard errors associated with β1 and β3. Under these conditions, the regression coefficients appear to be statistically insignificant. [6] If CO2 affects NDVI (and not some other variable that "looks like" a time trend), movements in CO2 which are faster or slower than the linear time trend will have explanatory power about NDVI beyond the explanatory power of the linear time trend; that is, if CO2 does affect NDVI, NDVI should rise faster than predicted by the linear time trend in years when CO2 increases faster than the time trend. Similarly, NDVI should increase slower than predicted by the linear time trend in years when CO2 increases slower than the time trend. [8] When equation (4) is estimated from data for North America and Eurasia, the estimate for β3 is not statistically different from zero. This result indicates that movements in CO2, faster or slower than a time trend have no explanatory power about NDVI beyond a time trend. This implies that the result found by Ahlbeck [2002] is caused by the similarity between CO2 and a time trend during the sample period, and not the effect of CO2 on NDVI. [10] We recognize that the interpretation of the statistical results described above is limited by the small sample size of the North American and Eurasian data sets. These limits can be alleviated by combining the two data sets and using F tests to evaluate whether the coefficients estimated from the North American data set are equal to those estimated from the Eurasian data set [Hsiao, 1986]. These F tests indicate that we cannot reject restrictions that equalize the coefficients other than the intercepts for equations (1), (2), (4), and (5). Under these conditions, the equations can be estimated from the combined data set using a fixed effects estimator. This allows us to estimate results that have more than twice the degrees of freedom than those estimated from the individual data sets. These results confirm those described above (Table 1). [11] Together, these results indicate that there is no evidence that increases in the atmospheric concentration of CO2 are responsible for the increases in NDVI described by Zhou et al. [2001]. Although CO2 correlates with NDVI, the relation described by Ahlbeck [2002] is a coincidence based on the similarity between CO2 and a linear time trend. The mechanism that lays behind the linear increase in NDVI is uncertain but could include forest regrowth following the effects of human disturbance and/or the decay of the increase in aerosol optical depth associated with the volcanic eruption of El Chichon at the start of the sample period (L. Zhou et al., manuscript in preparation, 2001).

The NOAA-7 polar orbiting sun-synchronous operational satellite carries the 5-channel advanced very high-resolution radiometer which acquires data globally at a spatial resolution of 4 km on a daily basis. These data provide a means for frequently monitoring global vegetation at continental scales. Techniques for compositing and cloud screening a green leaf density vegetation index product for Africa are presented for 9 sequential days beginning August 16, 1982 and are compared with a semi-operational vegetation index product produced by NOAA.

Two NOAA AVHRR images are presented which provide a comparison of the geographic distribution of an integration of the normalized difference vegetation index (NDVI) for the Sahel zone in Niger for the growing seasons of 1986 and 1987. The production of the images and the application of the images for resource management are discussed. Daily large area coverage with a spatial resolution of 1.1 km at nadir were transformed to the NDVI and geographically registered to produce the images.

A satellite-based 1° by 1° normalized difference vegetation index (NDVI) data set has been processed to derive land surface parameters for general circulation models of the atmosphere (GCMs). Prior to calculation of the land surface parameters, corrections were applied to the source NDVI data set to account for (i) obvious anomalies in the data time-series, (ii) the effect of variations in solar zenith angle, (iii) data dropouts in cold regions where a temperature threshold procedure designed to screen for clouds also eliminates cold land surface points, and (iv) persistent cloud cover in the tropics. An outline of the procedures for calculating land surface parameters from the corrected NDVI data set is given, and a brief description is provided of source material that was used in addition to the NDVI data. The data sets summarized in this paper should represent improvements over prescriptions currently used in land surface parameterizations in that the spatial and temporal dynamics of key land surface parameters, in particular of those related to vegetation, are obtained from direct measurements rather than indirectly inferred from survey-based land cover classifications.

No abstract is provided for this article.

Landsat satellite images from the mid-1980s and early 1990s were used to map tropical forest extent and deforestation in approximately 800,000 km<SUP>2</SUP> of Amazonian Bolivia. Forest cover extent, including tropical deciduous forest, totaled 472,000 km<SUP>2</SUP> while the area of natural non-forest formation totaled 298,000 km<SUP>2</SUP>. The area deforested totaled 15,000 km<SUP>2</SUP> in the middle 1980s and 28,800 km<SUP>2</SUP> by the early 1900s. The rate of tropical deforestation in the &gt; 1,000 mm y<SUP>-1</SUP> precipitation forest zone of Bolivia was 2,200 km<SUP>2</SUP> y<SUP>-1</SUP> from 1985-1986 to 1992-1994. We document a spatially-concentrated 'deforestation zone' in Santa Cruz Department where &gt; 60 percent of the Bolivian deforestation is occurring at an accelerating rate in areas of tropical deciduous dry forest.

The spatial and temporal distribution of atmospheric CO2 and of its 13C/12C ratio have been simulated with a three-dimensional atmospheric transport model based on the observed wind fields of the Global Weather Experiment in 1978/1979. Modeled sources and sinks of carbon at the surface of the Earth include: (1) CO2 released from fossil fuel combustion, (2) exchange of CO2 with the terrestrial biosphere by specification of net primary productivity from satellite data and heterotrophic respiration from surface temperature, (3) air-sea exchange of CO2 driven by prescribed temporal and spatial variations of CO2 in the surface waters. The relative contributions of individual source components are calculated by running the model with each component separately. This paper describes the results of the model simulations in terms of the seasonal cycle and synoptic time scale variations. Acceptable agreement with observations is achieved for northern hemisphere and tropical stations. In the southern hemisphere the model overestimates the observed seasonal cycle. The relationship between the 13C/12C isotopic ratio and CO2 concentration is discussed as a potential means to distinguish biological and oceanic contributions to the seasonal cycle.