FLOOD HAZARD STUDIES IN THE MISSISSIPPI RIVER BASIN USING REMOTE SENSING1

ABSTRACT. The Spring 1973 Mississippi River flood was investigated using remotely sensed data from ERTS-1. Both manual and automatic analyses of the data indicate that ERTS-I is extremely useful as a regional tool for flood management. Quantitative estimates of area flooded were made in St. Charles County, Missouri and Arkansas. Flood hazard mapping was conducted in three study areas along the Mississippi River using pre-flood ERTS-1 imagery enlarged to 1:250,000 and 1:100,000 scale. The flood prone areas delineated on these maps correspond to areas that would be inundated by significant flooding (approximately the 100 year flood). The flood prone area boundaries were generally in agreement with flood hazard maps produced by the U. S. Army Corps of Engineers and U. S. Geological Survey although the latter are somewhat more detailed because of their larger scale. Initial results indicate that ERTS-1 digital mapping of flood prone areas can be performed at 1:62,500 which is comparable to some conventional flood hazard map scales.     

Anatomic correlates of ultrasonographic prenatal diagnosis

In utero sonographic diagnoses from forty-five malformed infants were correlated with their autopsy findings. Fifty-two malformations were diagnosed prenatally in 42 of the patients but 90 additional malformations were not. Nine sonographically diagnosed abnormalities were not confirmed at autopsy. Factors compromising sonographic diagnosis included: limited examinations, small fetal size, timing of examination, oligohydramnios, fetal position, nature of the malformation and unfamiliarity of the ultrasonographer with specific malformation syndromes. In vitro ultrasonography is an invaluable tool of diagnosing congenital malformations but has limitations.     

Correlates of bushmeat hunting among remote rural households in Gabon, Central Africa

Hunted wild animals (i.e., bushmeat) are a main source of protein for many rural populations in the tropics, and the unsustainable harvest of these animals puts both human food security and ecosystem functioning at risk. To understand the correlates of bushmeat consumption, we surveyed 1219 households in 121 rural villages near three newly established national parks in Gabon. Through the surveys we gathered information on bushmeat consumption, income, and material assests. In addition, we quantified land cover in a 5-km radius around the village center and distance of the village center to the nearest park boundary. Bushmeat was not a source of income for most households, but it was the primary animal protein consumed. Ninety-seven percent of households consumed bushmeat at least once during a survey period of 12 days. Income or wealth, land cover, distance of village to the nearest park boundary, and level of education of the head of the household were among the factors that significantly related to the likelihood of consuming any of the 10 most commonly consumed species of bushmeat. Household size was the predictor most strongly associated with quantities of bushmeat consumed and was negatively related to consumption. Total bushmeat consumption per adult male equivalent increased as household wealth increased and decreased as distance of villages to park boundaries increased. Bushmeat consumption at the household level was not related to unit values (i.e., price estimates for a good that typically does not have a market value; estimates derived from willingness to sell or trade the good for items of known price) of bushmeat or the price of chicken and fish as potential substitutes. The median consumption of bushmeat at the village level, however, was negatively related to village mean unit values of bushmeat across all species. Our results suggest that a lack of alternative protein sources motivated even the wealthiest among surveyed households to consume bushmeat. Providing affordable, alternative protein sources to all households would likely reduce unsustainable levels of bushmeat consumption in rural Gabon.     

Enhancing generic ecological model for short-term prediction of Southern North Sea algal dynamics with remote sensing images

Physically based numerical modelling follows from the basic understanding of the underlying mechanisms and is often represented by a set of (partial differential) equations. It is one of the main approaches in population dynamics modelling. The emphasis of the model introduced in this paper is on the simulation of short-term spatial and temporal dynamics of harmful algal bloom (HAB) events. Total suspended matter (TSM) concentration is one of the dominant factors for harmful algal bloom (HAB) prediction in North Sea. However, the modelling of suspended matter contains a high degree of uncertainty in this area. Therefore, this research aims to achieve a better estimation for the short-term prediction of harmful algal bloom development in both space and time by using spatially distributed TSM retrieved from remotely sensed images as physically based model inputs. In order to supply complete spatially covered datasets for the physically based model instrument: generic ecological model (GEM), this research retrieves TSM information from MERIS images by means of proper estimation techniques including biharmonic splines and self-learning cellular automata. A better estimation of HAB spatial pattern development is achieved by adding spatially distributed TSM data as inputs to original GEM model, and it proved that chlorophyll-a concentration in this area is very sensitive to TSM concentration.