No abstract is provided for this article.
This paper assesses the effect of changes in solar zenith angle (SZA) and sensor changes on reflectances in channel 1, channel 2, and normalized difference vegetation index (NDVI) from the advanced very high resolution radiometer (AVHRR) Pathfinder land data set for the period July 1981 through September 1994. First, the effect of changes in SZA on channel reflectances and NDVI is derived from equations of radiative transfer in vegetation media. Starting from first principles, it is rigorously shown that the NDVI of a vegetated surface is a function of the maximum positive eigenvalue of the radiative transfer equation within the framework of the theory used and its assumptions. A sensitivity analysis of this relation indicates that NDVI is minimally sensitive to SZA changes, and this sensitivity decreases as leaf area increases. Second, statistical methods are used to analyze the relationship between SZA and channel reflectances or NDVI. It is shown that the use of ordinary least squares can generate spurious regressions because of the nonstationary property of time series. To avoid such a confusion, the authors use the notion of cointegration to analyze the relation between SZA and AVHRR data. Results are consistent with the conclusion of theoretical analysis from equations of radiative transfer. NDVI is not related to SZA in a statistically significant manner except for biomes with relatively low leaf area. From the theoretical and empirical analysis, they conclude that the NDVI data generated from the AVHRR Pathfinder land data set are not contaminated by trends introduced from changes in solar zenith angle due to orbital decay and changes in satellites (NOAA-7, 9, 11). As such, the NDVI data can be used to analyze interannual variability of global vegetation activity.
No abstract is provided for this article.
Empirical evidence from multiple sources show the Earth has been warming since the late 19th century. More recently, evidence for this warming trend is strongly supported by satellite data since the late 1970s from the cryosphere, atmosphere, oceans, and land that confirms increasing temperature trends and their consequences (e.g., reduced Arctic sea ice, rising sea level, ice sheet mass loss, etc.). At the same time, satellite observations of the Sun show remarkably stable solar cycles since the late 1970s, when direct observations of the Sun's total solar irradiance began. Numerical simulation models, driven in part by assimilated satellite data, suggest that future-warming trends will lead to not only a warmer planet, but also a wetter and drier climate depending upon location in a fashion consistent with large-scale atmospheric processes. Continued global warming poses new opportunities for the emergence and spread of fungal disease, as climate systems change at regional and global scales, and as animal and plant species move into new niches. Our contribution to this proceedings is organized thus: First, we review empirical evidence for a warming Earth. Second, we show the Sun is not responsible for the observed warming. Third, we review numerical simulation modeling results that project these trends into the future, describing the projected abiotic environment of our planet in the next 40 to 50 years. Fourth, we illustrate how Rift Valley fever outbreaks have been linked to climate, enabling a better understanding of the dynamics of these diseases, and how this has led to the development of an operational predictive outbreak model for this disease in Africa. Fifth, We project how this experience may be applicable to predicting outbreaks of fungal pathogens in a warming world. Lastly, we describe an example of changing species ranges due to climate change, resulting from recent warming in the Andes and associated glacier melt that has enabled amphibians to colonize higher elevation lakes, only to be followed shortly by the emergence of fungal disease in the new habitats.
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 system for spectrally monitoring the stages of crop development for corn and soybeans based upon red and photographic infrared spectral radiances is proposed. The red and photographic infrared spectral radiance, highly correlated with the green leaf area index or green leaf biomass, enable nondestructive monitoring of the crop canopy throughout the growing season. Five distinct periods are apparent which are related to crop development for corn and soybeans.
The Earth s climate is determined by irradiance from the Sun and properties of the atmosphere, oceans, and land that determine the reflection, absorption, and emission of energy within our atmosphere and at the Earth s surface. Since the 1970s, Earth-viewing satellites have complimented non-satellite geophysical observations with consistent, quantitative, and spatially-continuous measurements that have led to an unprecedented understanding of the Earth s climate system. I will describe the Earth s climate system as elaborated by satellite and in situ observations, review arguments against global warming, and show the convergence of evidence for human-caused warming of our planet.
The African real time environmental monitoring using imaging satellites (Artemis) system, which should monitor precipitation and vegetation conditions on a continental scale, is presented. The hardware and software characteristics of the system are illustrated and the Artemis databases are outlined. Plans for the system include the use of hourly digital Meteosat data and daily NOAA/AVHRR data to study environmental conditions. Planned mapping activities include monthly rainfall anomaly maps, normalized difference vegetation index maps for ten day and monthly periods with a spatial resolution of 7.6 km, ten day crop/rangeland moisture availability maps, and desert locust potential breeding activity factor maps for a plague prevention program.
Ebola hemorrhagic fever, named after the Ebola River in Central Africa, first appeared in June 1976, during an outbreak in Nzara and Maridi, Sudan. In September 1976, a separate outbreak was recognized in Yambuku, Democratic Republic of the Congo (DRC). One fatal case was identified in Tandala, DRC, in June 1977, followed by another outbreak in Nzara, Sudan, in July 1979. Ebola hemorrhagic fever outbreaks results in a very high mortality of patients who contract the disease: from 50 to 80% of infected people perish from this highly virulent disease. Death is gruesome, with those afflicted bleeding to death from massive hemorrhaging of organs and capillaries. The disease was not identified again until the end of 1994, when three outbreaks occurred almost simultaneously in Africa. In October, an outbreak was identified in a chimpanzee community studied by primatologists in Tal, Cote d'lvoire, with one human infection. The following month, multiple cases were reported in northeast Gabon in the gold panning camps of Mekouka, Andock, and Minkebe. Later that same month, the putative index case of the 1995 Kikwit, DRC, outbreak was exposed through an unknown mechanism while working in a charcoal pit. In Gabon, two additional outbreaks were reported in February and JuIy,1996, respectively, in Mayibout II, a village 40 km south of the original outbreak in the gold panning camps, and a logging camp between Ovan and Koumameyong, near Booue. The largest Ebola hemorrhagic fever epidemic occurred in Gulu District, Uganda from August 2000 to January 2001. In December 2001, Ebola reappeared in the Ogooue-lvindo Province, Gabon with extension into Mbomo District, The Republic of the Congo lasting until July 2002. Since 2002 there have been several outbreaks of Ebola hemorrhagic fever in Gabon and adjacent areas of Congo. Of interest is the seasonal context and occasional temporal clustering of Ebola hemorrhagic fever outbreaks. Near simultaneous appearances of Ebola epidemics in Nzara, Sudan and Yambuku, DRC in 1976 occurred within two months of each other in two geographic locations separated by hundreds of kilometers involving two separate viral strains (Sudan and Zaire EBO strains). The outbreaks of Tal, Cote d'lvoire; Mekouka, Gabon; and Kikwit, DRC in late 1994 also occurred within months of each other in three different geographic regions involving two different viral strains (Cote d'lvoire and Zaire EBO strains). Fifteen years passed between the 1976-9 and 1994-6 temporal clusters of Ebola cases without identification of additional cases.
There are two distinct categories of remotely sensed data: satellite data and aerial data or photographs. Unlike aerial photographs, satellite data have been routinely available for most of the earth’s land areas for more than two decades and therefore are preferred for reliably monitoring global vegetation conditions. Satellite data are the result of reflectance, emission, and/or back scattering of electromagnetic energy from earth objects (e.g., vegetation, soil, and water). The electromagnetic spectrum is very broad, and only a limited range of wavelengths is suitable for earth resource monitoring and applications. The gaseous composition (O2, O3, CO2, H2O, etc.) of the atmosphere, along with particulates and aerosols, cause significant absorption and scattering of electromagnetic energy over some regions of the spectrum. This restricts remote sensing of the earth’s surface to certain “atmospheric windows,” or regions in which electromagnetic energy can pass through the atmosphere with minimal interference. Some such windows include visible, infrared, shortwave, thermal, and microwave ranges of the spectrum. The shortwave-infrared (SWIR) wavelengths are sensitive to moisture content of vegetation, whereas the thermal-infrared region is useful for monitoring and detecting plant canopy stress and for modeling latent and sensible heat fluxes. Thermal remote sensing imagery is acquired both during the day and night, and it measures the emitted energy from the surface, which is related to surface temperatures and the emissivity of surface materials. This chapter focuses on the contribution of visible and infrared wavelengths to global drought monitoring, and chapter 6 discusses visible, infrared, and thermal wave contributions. Under microwave windows, the satellite data can be divided into two categories: active microwave and passive microwave. Chapters 7 and 8 describe applications of passive and active microwave remote sensing to drought monitoring, respectively. Early use of satellite data was pioneered by the Landsat series originally known as the Earth Resource Technology Satellite (ERTS; http://landsat7. usgs.gov/index.php). Landsat was the first satellite specifically designed for broad-scale observation of the earth’s land surface.
Analysis of variance methods has been applied to in situ grassland spectral reflectance data in order to determine the classes or levels of total wet biomass that can be resolved spectrally by a single narrow band measurement. Ground-truth clipping of blue grama grass plots was performed immediately following spectral reflectance measurements at 91 wavelength intervals which were 0.005 microns apart over the spectral range from 0.350 to 0.800 microns. It was found that the photographic infrared region of 0.750 to 0.800 microns could be used to distinguish three classes or levels of total wet biomass. Four or five classes, particularly at higher biomass levels, could not be distinguished by this technique.
High spatial resolution imagery from the Landsat satellites has provided us with the longest record of terrestrial observations since 1972. Recognizing the need to provide high‐resolution satellite data to the scientific community, NASA's Commercial Remote Sensing Program Office has been working to assemble the first wall‐to‐wall global coverage of ortho‐rectified Landsat Thematic Mapper and Multi Spectral Scanner data to support scientific studies. This database is expected to be of great value to researchers studying a variety of problems. The need for high‐resolution data sets arises from the low spatial autocorrelation of land surface properties [ Townshend and Justice , 1988; Townshend and Justice , 1990]. Even in areas with near‐natural land cover, spatial variability can exist at the finest scales due to local terrain variability and associated microclimatic influences on vegetation types and associations. Human‐induced changes, such as deforestation, are the source of many of the finest‐scale alterations and can significantly drive global change. Currently, we have very poor data on where land cover is being changed, what changes are occurring, and the land cover properties of the altered land [ Townshend et al ., 1991].
A ground-based, hand-held radiometer, configured to measure red and photographic infrared spectral radiances, was successfully used to collect in situ temporal spectral measurements of corn and soybean crops. Significant relationships were found between the radiance data and the biomass, plant height, percentage crop cover, percentage crop chlorosis, and percentage leaf loss. The results of this experiment show conclusively that hand-held radiometers can be used to collect spectral data that are highly correlated to several agronomic variables. These findings suggest approaches for agronomic research, and confirm the value of remote sensing of agricultural targets.
Contrary to assertions of widespread irreversible desertification in the African Sahel, a recent increase in seasonal greenness over large areas of the Sahel has been observed, which has been interpreted as a recovery from the great Sahelian droughts. This research investigates temporal and spatial patterns of vegetation greenness and rainfall variability in the African Sahel and their interrelationships based on analyses of Normalized Difference Vegetation Index (NDVI) time series for the period 1982–2003 and gridded satellite rainfall estimates. While rainfall emerges as the dominant causative factor for the increase in vegetation greenness, there is evidence of another causative factor, hypothetically a human-induced change superimposed on the climate trend.
