Engineering topics

Wood, B. L.

Publications and source records attributed to Wood, B. L..

9 records

NASA NTRS2000

A Remote Sensing/Geographic Information Systems Approach in the Selection of Mars Sites of Biological Interest

The search for extinct or extant life on Mars is the search for past or present liquid water, respectively. There are numerous signs of past liquid water on Mars in the form of dry river valleys, paleolakes, and their associated flow and sediment patterns. While some of these features are recent (Amazonian, 1.8 billion years ago to present), there is no evidence that any are currently flowing. Liquid water on the surface would only be possible at those sites with sufficiently high temperatures and pressure. The key to the selection of sites on Mars to search for evidence of life is the search for the presence of water. An approach to this problem is the use of remotely sensed data incorporated in a geographic information system (GIS). A GIS is a computer-based system capable of assembling, storing, manipulating, and displaying geographically referenced information, i.e., data identified according to their locations. In planetary studies these data are acquired from remote sensing (RS) platforms (orbiters). These data are co-registered layers and, through the use of GIS analysis functions, areas on these layers can be selected as a function of the information desired. Our work used existing data layers from the Viking and Mars Global Surveyor missions to determine where water could be possible in liquid form on the Martian surface, based on the phase diagram for water.

Lobitz, B. M.↗

NASA NTRS2000

Remote sensing and human health: new sensors and new opportunities

Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Systeme Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.

Satellite Communications↗

NASA NTRS2000

Education, outreach and the future of remote sensing in human health

The human health community has been slow to adopt remote sensing technology for research, surveillance, or control activities. This chapter presents a brief history of the National Aeronautics and Space Administration's experiences in the use of remotely sensed data for health applications, and explores some of the obstacles, both real and perceived, that have slowed the transfer of this technology to the health community. These obstacles include the lack of awareness, which must be overcome through outreach and proper training in remote sensing, and inadequate spatial, spectral and temporal data resolutions, which are being addressed as new sensor systems are launched and currently overlooked (and underutilized) sensors are newly discovered by the health community. A basic training outline is presented, along with general considerations for selecting training candidates. The chapter concludes with a brief discussion of some current and future sensors that show promise for health applications.

Review, Tutorial↗

NASA NTRS1997

Landscape characterization of peridomestic risk for Lyme disease using satellite imagery

Remotely sensed characterizations of landscape composition were evaluated for Lyme disease exposure risk on 337 residential properties in two communities of suburban Westchester County, New York. Properties were categorized as no, low, or high risk based on seasonally adjusted densities of Ixodes scapularis nymphs, determined by drag sampling during June and July 1990. Spectral indices based on Landsat Thematic Mapper data provided relative measures of vegetation structure and moisture (wetness), as well as vegetation abundance (greenness). A geographic information system (GIS) was used to spatially quantify and relate the remotely sensed landscape variables to risk category. A comparison of the two communities showed that Chappaqua, which had more high-risk properties (P < 0.001), was significantly greener and wetter than Armonk (P < 0.001). Furthermore, within Chappaqua, high-risk properties were significantly greener and wetter than lower-risk properties in this community (P < 0.01). The high-risk properties appeared to contain a greater proportion of broadleaf trees, while lower-risk properties were interpreted as having a greater proportion of nonvegetative cover and/or open lawn. The ability to distinguish these fine scale differences among communities and individual properties illustrates the efficiency of a remote sensing/GIS-based approach for identifying peridomestic risk of Lyme disease over large geographic areas.

Topography, Medical↗

NASA NTRS1997

Remote Sensing the Patterns of Vector-borne Disease in El Nino and non-El Nino Years

The relationship between El Nino and non-El Nino and the patterns of vector-borne disease can be viewed at a variety of spatial and temporal scales. At one extreme are long term predictions of changing precipitation and temperature patterns at continental and global scales. At the opposite extreme are the local or site specific ecological changes associated with the long term events. In order to understand and address the human health consequences of El Nino events, especially the patterns of vector-borne diseases, it is necessary to combine both scales of observation. At a local or regional scale the patterns of vector-borne diseases are determined by temperature, precipitation, and habitat availability. These factors, as well as disease incidence can be altered by El Nino events. Remote sensing data such as that acquired by the NOAA AVHRR and Landsat TM sensors can be used to characterize and monitor changing ecological conditions and therefore predict vector-borne disease patterns. The authors present the results of preliminary work on the analysis of historical AVHRR and TM data acquired during El Nino and nonfatal Nino years to characterize ecological conditions in Peru on a monthly basis. This information will then be combined with disease data to determine the relationship between changes in ecological conditions and disease incidence. Our goal is to produce a sequence of remotely sensed images which can be used to show the ecological and disease patterns associated with long term El Nino events and predictions.

Wood, B. L.↗

NASA NTRS1992

Estimating high mosquito-producing rice fields using spectral and spatial data

The cultivation of irrigated rice provides ideal larval habitat for a number of anopheline vectors of malaria throughout the world. Anopheles freeborni, a potential vector of human malaria, is associated with the nearly 240,000 hectares of irrigated rice grown annually in Northern and Central California; therefore, this species can serve as a model for the study of rice field anopheline population dynamics. Analysis of field data revealed that rice fields with early season canopy development, that are located near bloodmeal sources (i.e., pastures with livestock) were more likely to produce anopheline larvae than fields with less developed canopies located further from pastures. Remote sensing reflectance measurements of early-season canopy development and geographic information system (GIS) measurements of distanes between rice fields and pastures with livestock were combined to distinguish between high and low mosquito-producing rice fields. Using spectral and distance measures in either a discriminant or Bayesian analysis, the identification of high mosquito-producing fields was made with 85 percent accuracy nearly two months before anopheline larval populations peaked. Since omission errors were also minimized by these approaches, they could provide a new basis for directing abatement techniques for the control of malaria vectors.

Wood, B. L.↗

NASA NTRS1986

Trace element-induced stress in freshwater wetland vegetation: Preliminary results

Airborne Imaging Spectrometer (AIS) data were acquired over an area of freshwater wetlands in Central California on September 23, 1985. Plant samples were subsequently collected along the flight line with the goal of relating plant tissue chemistry to spectral reflectance in the near-infrared region. It was determined that a consistent relationship existed between spectral response and plant tissue chemistry. This was especially evident in the 1500 to 1700 nm region.

Wood, B. L.↗

NASA NTRS1985

AIS Investigation of Agricultural Monocultures

Airborne Imaging Spectrometer (AIS) data were acquired over an agricultural area in eastern San Joaquin County, California in July, 1984. Cover type information was subsequently collected for all fields along this flight line. The lack of detailed ground data on individual fields, however, limited AIS data analysis to a qualitative comparison of the spectral reflectance curves for a total of nine cover types. Based on this analysis, it appears that cover types with a positive slope in the 1550 to 1700 nm region have a higher spectral response in the 1200 to 1300 nm region compared to those cover types with a negative slope in the 1550 to 1700 nm region. Within cover type, spectral variability was also found to be greater than that between cover types. Given the lack of additional field data, the reason for these differences is a matter of speculation.

Wood, B. L.↗

NASA NTRS1983

Application of a U.S.-based analysis approach to Argentina crop identification

A generic, U.S.-based analysis approach was evaluated with respect to corn and soybean identification in Argentina. Using crop separability expectations derived from the analysis of Argentina ancillary data and U.S. spectral data, the approach was applied to Argentina spectral data by an expert analyst. Eight classes were detected and labeled independent of ground data. A high correspondence between the labels and limited ground data was achieved. It was concluded that an approach of this type could be applied to Argentina without major difficulty.

Odenweller, J. B.↗