Publications

Gonsamo et al. (1) use 8-km satellite data from advanced very high-resolution imaging spectroradiometer (AVHRR) global inventory modeling and mapping studies (GIMMS) to demonstrate the role of climatic oscillations, specifically the East Atlantic-West Russia (EA-WR) pattern, on interannual dynamics of Amazon greenness. Hilker et al. (2) do not investigate EA-WR but focus on the El Nino southern oscillation (ENSO), a pattern that is well known to affect climate throughout South America and the Pacific region (3). Gonsamo et al. (1) do not challenge these results but claim that EA-WR, more than ENSO, may “explain the entire ensuing year Amazon vegetation greenness dynamics.” We are unable to judge this claim based on our findings (2), but argue that the authors do not present a convincing case. EA-WR is a teleconnection pattern whose anomalies result in above-average temperatures over eastern Asia and below-average temperatures over large portions of western Russia and northeastern Africa. A connection between North Atlantic sea surface temperature (SST) and the likelihood of an El Nino onset has been demonstrated (4). A direct approach to prove the superior explanatory power of EA-WR compared with ENSO would have been to use the same normalized difference vegetation index (NDVI) dataset shown in figure 1 of Hilker et al. (2) and demonstrate a better correlation between NDVI and EA-WR. Figure 1 A–D in Gonsamo et al. actually confirms a stronger connection between annual precipitation and ENSO than with annual precipitation and EA-WR (1). The lack of correlation in figure 1 E–G of Gonsamo et al. is not surprising given the high noise level in AVHRR GIMMS that largely prevents detection of trends over tropical vegetation (5). Proof of statistical significance of changes in GIMMS NDVI is missing. The spatial patterns in figure 1H seem unconnected to those in figure 1 C and D, which begs the question of what climate factor, if not precipitation, drives those changes in NDVI. The connections between EA-WR and ensuing year precipitation and EA-WR and ensuing year NDVI (figure 1 I and J) seem to contradict the findings in figure 1 D and H; at the very least, the distinction between those figures is not clear. The Pearson R values presented in figure 1 I and J are extremely low. Parts J and K in figure 1 are not comparable because figure 1K shows monthly mean values (2), whereas figure 1J shows interannual variation. The intent of the analysis shown in figure 1K was to demonstrate that Amazon forests initially respond positively to seasonal reductions in rainfall, whereas grasslands respond negatively. Why the authors included this figure in the presented context is unclear. On a side note, Gonsamo et al. wrongly claim that Hilker et al. (2) demonstrate that a lack of correlation between moderate resolution imaging spectroradiometer (MODIS) NDVI and ENSO can be attributed to normalizing MODIS reflectance to a common view and sensor geometry (1). Hilker et al. demonstrate that directionally normalized NDVI observations show seasonal variation, contrary to previous findings (6).

Optimization of sensor wavelengths and bandwidths has been investigated based upon the analysis of in situ spectral reflectance data collected from experimental plots of blue grama grass. Sensor characteristics have been simulated by integration of spectral data over the region from 0.350 to 1.000 millimicrons. Subsequently, the integrated reflectance values were regressed against the canopy or plot variables (total wet biomass, total dry biomass, leaf water content, dry green biomass, dry brown biomass, and total chlorophyll content) to determine the relative significance between integrated reflectance and the canopy variables for the various wavelengths and bandwidths simulated. Three spectral regions of strang statistical significance (0.35-0.50, 0.63-0.69, and 0.74-1.00 millimicrons) were identified and found to be persistent both early and late in the growing season. In addition to quantifying the significance of various sensor wavelengths and bandwidths, the additive effects of adjacent spectral regions were also quantified. The results of this work enable quantitative judgments to be made regarding existing and hypothetical sensors and their effectiveness in monitoring green functioning vegetation.

Red and photographic infrared spectral radiance have been correlated with soybean total leaf area index, green leaf area index, chlorotic area index, green leaf biomass, chlorotic leaf biomass, and total biomass. The most significant correlations were found to exist between the spectral data and green leaf area index and/or green leaf biomass. These findings demonstrate that ground based remote sensing data can supply information basic to soybean canopy growth, development, and status by non-destructive determination of the green leaf area or green leaf biomass.

Nine predominantly cloud-free NOAA-7 advanced very high resolution radiometer images were obtained during a three-month period during the 1981 rainy season in the Sahel of Senegal. The 0.55-0.68 and 0.725-1.10-micron channels were used to form the normalized difference green leaf density vegetation index and the 11.5-12.5-micron channel was used as a cloud mask for each of the nine images. Changes in the normalized difference values among the various dates were closely associated with precipitation events. Six of the images spanning an eight-week period were used to generate a cumulative integrated index. Ground biomass samplings in the 30,000 sq km study area were used to assign total dry biomass classes to the cumulative index.

A self-contained, hand-held radiometer designed for field use has been constructed and tested. The 4.5-kilogram device, consisting of a strap-supported electronics module and a hand-held probe containing three sensors, is powered by flashlight and transistor radio batteries, uses two silicon and one lead sulfide detector, has three liquid-crystal displays, features sample-and-hold radiometric sampling, and is spectrally configured to Landsat-D's thematic mapper bands TM3 (0.63 to 0.69 micrometer), TM4 (0.76 to 0.90 micrometer), and TM5 (1.55 to 1.75 micrometers). The device was designed to collect ground-truth data for the thematic mapper and to facilitate ground-based, remote-sensing studies of natural materials in situ. Prototype instruments were extensively tested under laboratory and field conditions, with satisfactory results.

Over the last 10 years the Armed Forces Health Surveillance Center's Global Emerging Infections Surveillance and Response System (GEIS) partnering with NASA'S Goddard Space Flight Center and USDA's USDA-Center for Medical, Agricultural & Veterinary Entomology established and have operated the Rift Valley fever Monitoring and Prediction System to monitor, predict and assess the risk of Rift Valley fever outbreaks and other vector-borne diseases over Africa and the Middle East. This system is built on legacy DoD basic research conducted by Walter Reed Army Institute of Research overseas laboratory (US Army Medical Research Unit-Kenya) and the operational satellite environmental monitoring by NASA GSFC. Over the last 10 years of operation the system has predicted outbreaks of Rift Valley fever in the Horn of Africa, Sudan, South Africa and Mauritania. The ability to predict an outbreak several months before it occurs provides early warning to protect deployed forces, enhance public health in concerned countries and is a valuable tool use.d by the State Department in US Diplomacy. At the international level the system has been used by the Food and Agricultural Organization (FAD) and the World Health Organization (WHO) to support their monitoring, surveillance and response programs in the livestock sector and human health. This project is a successful testament of leveraging resources of different federal agencies to achieve objectives of force health protection, health and diplomacy.

The orbital ability to remotely sense vegetated targets can be improved by increased understanding of atmospheric effects, and by the use of appropriate spatial resolution, narrow spectral bandwidths, additional spectral bands, and a temporal frequency of 2 to 3 days. The effects of atmosphere on radiative transfer, remote sensing over a nonuniform surface, the choice of the red and photographic IR portions of the spectra for delinating botanical features, and areas where additional research is needed are covered.

No abstract is provided for this article.

We use spatially continuous satellite data as a correlate of precipitation within tropical Africa and show that the majority of documented Ebola hemorrhagic fever outbreaks were closely associated with sharply drier conditions at the end of the rainy season. We propose that these trigger events may enhance transmission of Ebola virus from its cryptic reservoir to humans. These findings suggest specific directions to help understand the sylvatic cycle of the virus and may provide early warning tools to detect possible future outbreaks of this enigmatic disease.

Multispectral scanner data have been processed to yield biomass maps of imagery from shortgrass prairie vegetation. The results of the image processing of these data were compared to actual biomass values measured at the time the aircraft data were acquired. The comparison demonstrated that image processing predicted 1.15 times the actual biomass present with a correlation coefficient of 0.98 for biomass ground-truth areas sampled from a flight line containing a large range of biomass values. A simple, hand-held device has been constructed which utilizes a spectral ratio between two specific wavelengths, 0.68 and 0.80 micrometers, to accurately estimate grass biomass. Several field experiments have demonstrated correlation coefficients between 0.95 to 0.98 for the hand-held device in estimating undisturbed grass canopy biomass. The hand-held device has been shown to be an accurate and expedient method for estimating grass canopy biomass. This type of device could be used to gather greater amounts of ground-truth information from overflight areas and thus would add greater statistical significance to image processing results.

The first documented Rift Valley hemorrhagic fever outbreak in the Arabian Peninsula occurred in northwestern Yemen and southwestern Saudi Arabia from August 2000 to September 2001. This Rift Valley fever outbreak is unique because the virus was introduced into Arabia during or after the 1997-1998 East African outbreak and before August 2000, either by wind-blown infected mosquitos or by infected animals, both from East Africa. A wet period from August 2000 into 2001 resulted in a large number of amplification vector mosquitoes, these mosquitos fed on infected animals, and the outbreak occurred. More than 1,500 people were diagnosed with the disease, at least 215 died, and widespread losses of domestic animals were reported. Using a combination of satellite data products, including 2 x 2 m digital elevation images derived from commercial satellite data, we show rainfall and potential areas of inundation or water impoundment were favorable for the 2000 outbreak. However, favorable conditions for subsequent outbreaks were present in 2007 and 2013, and very favorable conditions were also present in 2016-2018. The lack of subsequent Rift Valley fever outbreaks in this area suggests that Rift Valley fever has not been established in mosquito species in Southwest Arabia, or that strict animal import inspection and quarantine procedures, medical and veterinary surveillance, and mosquito control efforts put in place in Saudi Arabia following the 2000 outbreak have been successful. Any area with Rift Valley fever amplification vector mosquitos present is a potential outbreak area unless strict animal import inspection and quarantine procedures are in place.