Engineering topics

Beck, Louisa R.

Publications and source records attributed to Beck, Louisa R..

5 records

NASA NTRS2001

Remote Sensing, GIS, and Vector-Borne Disease

The concept of global climate change encompasses more than merely an alteration in temperature; it also includes spatial and temporal covariations in precipitation and humidity, and more frequent occurrence of extreme weather events. The impact of these variations, which can occur at a variety of temporal and spatial scales, could have a direct impact on disease transmission through their environmental consequences for pathogen, vector, and host survival, as well as indirectly through human demographic and behavioral responses. New and future sensor systems will allow scientists to investigate the relationships between climate change and environmental risk factors at multiple spatial, temporal and spectral scales. Higher spatial resolution will provide better opportunities for mapping urban features previously only possible with high resolution aerial photography. These opportunities include housing quality (e.g., Chagas'disease, leishmaniasis) and urban mosquito habitats (e.g., dengue fever, filariasis, LaCrosse encephalitis). There are or will be many new sensors that have higher spectral resolution, enabling scientists to acquire more information about parameters such as soil moisture, soil type, better vegetation discrimination, and ocean color, to name a few. Although soil moisture content is now detectable using Landsat, the new thermal, shortwave infrared, and radar sensors will be able to provide this information at a variety of scales not achievable using Landsat. Soil moisture could become a key component in transmission risk models for Lyme disease (tick survival), helminthiases (worm habitat), malaria (vector-breeding habitat), and schistosomiasis (snail habitat).

Beck, Louisa R.↗

NASA NTRS1997

Remotely Sensing Larval Population Dynamics of Rice Field Anophelines

The primary objective of both studies was to determine if RS and GIS techniques could be used to distinguish between high and low larval-producing rice fields in California. Results of the first study suggested that early-season green-up and proximity to livestock pastures were positively correlated with high larval abundance. Based on the early-season spectral differences between high and low larval-producing fields, it appeared that canopy development and tillering influenced mosquito habitat quality. At that time, rice fields consisted of a mixture of plants and water, a combination that allowed An. freeborni females to lay eggs in partial sunlight, protected from both predators and wind. This established a population earlier in the season than in other, 'less-green' fields where tillering and plant emergence was too minimal for ovipositioning. The study also indicated the importance of the distance that a mosquito would have to fly in order to take a bloodmeal prior to ovipositing. These associations were fully explored in an expanded study two years later. The second study confirmed the positive relationship between early season canopy development and larval abundance, and also demonstrated the relationship between abundance and distance-to-pasture. The association between greenness (as measured using NDVI), distance-to-pasture, and abundance is illustrated. The second study also indicated the siginificance of the landscape context of rice fields for larval production. Fields that included opportunities for feeding and resting within the flight range of the mosquito had higher abundances than did fields that were in a homogeneous rice area.

Beck, Louisa R.↗

NASA NTRS1994

Kriging in the Shadows: Geostatistical Interpolation for Remote Sensing

It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.

Rossi, Richard E.↗

NASA NTRS1994

Remote Sensing as a Landscape Epidemiologic Tool to Identify Villages at High Risk for Malaria Transmission

A landscape approach using remote sensing and Geographic Information System (GIS) technologies was developed to discriminate between villages at high and low risk for malaria transmission, as defined by adult Anopheles albimanus abundance. Satellite data for an area in southern Chiapas, Mexico were digitally processed to generate a map of landscape elements. The GIS processes were used to determine the proportion of mapped landscape elements surrounding 40 villages where An. albimanus data had been collected. The relationships between vector abundance and landscape element proportions were investigated using stepwise discriminant analysis and stepwise linear regression. Both analyses indicated that the most important landscape elements in terms of explaining vector abundance were transitional swamp and unmanaged pasture. Discriminant functions generated for these two elements were able to correctly distinguish between villages with high ind low vector abundance, with an overall accuracy of 90%. Regression results found both transitional swamp and unmanaged pasture proportions to be predictive of vector abundance during the mid-to-late wet season. This approach, which integrates remotely sensed data and GIS capabilities to identify villages with high vector-human contact risk, provides a promising tool for malaria surveillance programs that depend on labor-intensive field techniques. This is particularly relevant in areas where the lack of accurate surveillance capabilities may result in no malaria control action when, in fact, directed action is necessary. In general, this landscape approach could be applied to other vector-borne diseases in areas where: 1. the landscape elements critical to vector survival are known and 2. these elements can be detected at remote sensing scales.

Beck, Louisa R.↗

NASA NTRS1991

Spectral and spatial characterization of rice field mosquito habitat

Irrigated rice provides an ideal breeding habitat for Anopheles free-borni, the western malaria mosquito, throughout California. In a 1985 study, it was determined that early-season rice canopy development, as monitored using remotely sensed data, could be used to distinguish between high and low mosquito producing rice fields. This distinction could be made over two months prior to peak mosquito production. It was found that high-producing fields were located in an area characterized by a diversity of land use, including livestock pastures, whereas the low-producing fields were in an area devoted almost exclusively to the cultivation of rice. The ability to distinguish between high and low mosquito producing fields prior to peak mosquito production is important in terms of mosquito habitat surveillance and control.

Wood, Byron L.↗