466 publications from this institution
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.
No abstract is provided for this article.
Coincident Scanning Microwave Multi-channel Radiometer 37 GHz and Advanced Very High Resolution Radiometer normalized difference vegetation index satellite data have been compared from drought-affected regions of sub-Saharan Africa and northeastern Brazil for the time period of 1980–1985. Although the two satellite data types can be highly correlated, differences between them were found for the Sahel zone in 1985 and for northeastern Brazil from 1984–1985. These findings suggest that scattering or surface roughness contributions may be greater than previously assumed for the 37 GHz microwave data. A programme of field measurements should be undertaken to increase our understanding of natural vegetation at 37 GHz and higher microwave frequencies.
Satellite data and ground rainfall measurements have been used to study variations in the size of the Sahara Desert from 1980 to 1997. Through a combination of the satellite and ground data, the 200 mm/yr precipitation boundary was mapped for the Saharan-Sahelian region by year. Although highly significant year-to-year variation in the size of the Sahara Desert has occurred, no systematically increasing or decreasing trend from 1980 to 1997 was evident. The area of the Sahara Desert varied from 9,980,000 sq km in 1984 to 8,600,000 sq km in 1994 and had an average area of 9,150,000 sq km from 1980 to 1997.
A new method has been developed for the global assessment of trace gases and particulates emission from tropical biomass burning. The method is based on remote sensing of one emitted product: particulates. It uses daily meteorological satellite data with resolution of 1 km 2 . The visible (0.63 μm) and near‐infrared (0.84 μm) bands are used to determine the mass of particulates in the emitted smoke and to estimate the relative contribution of flaming and smoldering fires to the resulting smoke. The mid‐IR (3.5–3.9 μm) and the thermal infrared (10.5–11.4 μm) bands are used to detect and count fires in order to integrate the smoke result with the whole season and for the whole area of interest. The thermal channels are sensitive enough to detect flaming fires as small as 10 m×10 m and smoldering fires as small as 30 m×30 m. The detected mass of emitted particulates is converted into a mass of emitted trace gases using published relations between the emitted particulates and trace gases for the flaming and smoldering phases. The technique can be applied to regions where intensive biomass burning takes place. It is capable of monitoring the extent of current biomass burning, discovering new deforestation frontiers (unknown otherwise), and estimating quantitative contribution of biomass burning to changes in atmospheric composition. The method has been applied to a limited area where substantial deforestation has taken place. Analysis of the 1987 burning season shows that in Brazil (in a limited area between 6.5°–15.5°S and 55°–67°W) during the 3 months of the dry season (July 1 to September 30) there are up to 8000 fires a day (observed from space) each contributing 4500 t of CO 2 , 750 t of CO, and 26 t of CH 4 to the atmosphere. During the dry season of 1987, it is estimated that 240,000 fires were burning in this area resulting in the emission of 1×10 13 g of particulates, 7×10 12 g of CH 4 , 2×10 14 g of CO, and 1×10 15 g of CO 2 . A comparison to estimates of global emissions is also given.
Data from two different satellites, a digital land cover map, and digital census data were analyzed and combined in a geographic information system to study the effect of urbanization on photosynthetic productivity in the United States. Results show that urbanization can have a measurable but variable impact on the primary productivity of the land surface. Annual productivity can be reduced by as much as 20 days in some areas, but in resource limited regions, photosynthetic production can be enhanced by human activity. Overall, urban development reduces the productivity of the land surface, and those areas with the highest productivity are directly in the path of urban sprawl.
Outbreaks of Rift Valley fever (RVF) virus in Africa are characterized by distinct spatial and temporal patterns that are directly related to specific environmental parameters associated with mosquito vectors that function in the maintenance (endemic) and transmission (epizootic) cycles of the virus. National Oceanic and Atmospheric Administration (NOAA) satellites with limited resolution (1 km) can indirectly measure rainfall inexpensively over subcontinental or continental areas at a high temporal frequency, and identify regions with a high potential for viral activity. Within regions of likely viral activity, mosquito vector-breeding habitats (dambos) are identified and mapped with archived, high-quality, cloud-free, higher resolution data from LANDSAT (thematic mapper resolution 30 m) and SPOT (multispectral resolution 20 m) satellites. Active sensors, like airborne synthetic aperture radar systems (range resolution 1.6 m), are capable of detecting flooded habitats, even through cloud cover. By identifying flooded habitats within regions with a high likelihood of RVF activity, the potential source foci of an outbreak may be detected close to real-time and control efforts implemented prior to the start of a RVF epidemic/epizootic.
Imagery from the National Oceanic and Atmospheric Administration satellite's Advanced Very High Resolution Radiometer sensor has been used to identify an area about 100 × 400 km in Rondonia (Brazil) where massive forest clearing or deforestation is occurring. A field study verified the area of the clearing, which is associated with a large colonization program.
Multitemporal satellite data have application in the detection and quantification of drought through the ability of these data to estimate the photosynthetic capacity of the terrestrial surface and record microwave surface brightness at the 37 GHz frequency. With proper calibration and registration, comparisons can be made between and among years for specific months using the photosynthetic capacity and the 37 GHz microwave surface brightness for selected time periods or growing seasons. This technology has application in identifying and quantifying areas experiencing drought.
The difference of the vertically and horizontally polarized brightness temperatures observed by the 37 GHz channel of the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite are correlated temporally with three indicators of vegetation density, namely the temporal variation of the atmospheric CO2 concentration at Mauna Loa (Hawaii), rainfall over the Sahel and the normalized difference vegetation index derived from the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA-7 satellite. We find the SMMR 37GHz and AVHRR provide complementary data sets for monitoring global vegetation, the 37 GHz data being more suitable for arid and semi-arid regions as these data are more sensitive to changes in sparse vegetation. The 37 GHz data might be useful for understanding desertification and indexing CO2 exchange between the biosphere and the atmosphere.
This paper gives an overview of how certain meteorological data used in studies of the population dynamics of arthropod vectors of disease may be predicted using remotely sensed, satellite data. Details are given of the stages of processing necessary to convert digital data arising from satellite sensors into ecologically meaningful information. Potential sources of error in these processing steps are also highlighted. Relationships between ground-measured meteorological variables (saturation deficit, ground temperature and rainfall) and data from both the National Oceanic and Atmospheric Administration's, polar-orbiting, meteorological satellites and the geostationary, Meteosat satellite are defined and examples detailed for Africa. Finally, the current status of existing satellite platforms and future satellite missions are reviewed and potential data availability discussed. How such satellite-based predictions have proved valuable in understanding the distribution of tsetse fly species in Cote d'Ivoire and Burkina Faso will be the subject of a future review.
No abstract is provided for this article.