Showing papers by "Scott J. Goetz published in 2003"

Climatic suitability for malaria transmission in Africa, 1911-1995.

Abstract: Time series analysis of a climate-driven model of malaria transmission shows limited evidence for an increase in suitability during the last century across Africa. Outside areas where climate was always or never suitable, <17% of the continent showed significant trends in malaria transmission. Of these areas, 5.7% showed positive deterministic trends, 6.1% had negative deterministic trends, and 5.1% exhibited stochastic trends. In areas with positive trends, precipitation, rather than temperature, was the primary forcing variable. This analysis highlights the need to examine the relationship between climate and malaria more closely and to fully consider nonclimatic factors as drivers of increased malaria transmission across the continent.

Monitoring and predicting urban land use change applications of multi-resolution multi-temporal satellite data

Abstract: The ability to map and monitor the spatial extent of the built environment, and associated temporal changes, has important societal and economic relevance. Multitemporal satellite data now provide the potential for mapping and monitoring urban land use change, but require the development of accurate and repeatable techniques that can be extended to a broad range of conditions and environments. We have developed an approach using Landsat imagery, trained with the high resolution data sets, that identifies impervious surface areas (buildings, roads, etc) at subpixel resolution. We report on application of the approach over a range of scales, from the local to the entire Chesapeake Bay Watershed (168,000 km 2 ). We also developed maps of past changes in the built environment, used them to calibrate a spatial predictive model, and generated maps of expected future change under various policy scenarios out to year 2030. We believe these techniques have applicability to a wide range of applications.

Combining MISR, ETM+ and SAR data to improve land cover and land use classification for carbon cycle research

Abstract: Accurate and reliable information about land cover and land use is essential to carbon cycle and climate change modeling. While historical regional-to-global scale land cover and land use data products had been produced by AVHRR and MSS/TM, this task has been advanced by sensors such as MODIS and ETM since the latter 1990s. While the accuracies and reliabilities of these data products have been improved, there have been reports from the modeling community that additional work is needed to reduce errors so that the uncertainties associated with the global carbon cycle and climate change modeling can be addressed. Remotely sensed data collected in different wavelength regions, at different viewing geometries, usually provide complementary information. Their combination has the potential to enhance remote sensing capabilities in discriminating important land cover components. In this paper, we studied multi-angle data fusion, and optical-SAR data fusion for land cover classification at regional spatial scale in the temperate forests of the eastern United States. Data from EOS-MISR, Landsat-ETM+ and RadarSat-SAR were used. The results showed significantly improved land cover classification accuracy when using the data fusion approach. These results may benefit future land cover products for global change research.