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Full-Text Articles in Physical Sciences and Mathematics
Southeastern U.S. Vegetation Response To Enso Events (1989–1999), Albert J. Peters, Lei Ji, Elizabeth A. Walter-Shea
Southeastern U.S. Vegetation Response To Enso Events (1989–1999), Albert J. Peters, Lei Ji, Elizabeth A. Walter-Shea
Center for Advanced Land Management Information Technologies: Publications
El Niño/Southern Oscillation (ENSO) is considered one of the most powerful forces driving anomalous global weather patterns. Large-scale seasonal precipitation and temperature changes influenced by ENSO have been examined in many areas of the world. The southeastern United States is one of the regions affected by ENSO events. In this study, remote sensing detection of vegetation response to ENSO phases is demonstrated with one-kilometer biweekly Normalized Difference Vegetation Index (NDVI) data (1989–1999) derived from the Advanced Very High Resolution Radiometer (AVHRR). The impacts of three ENSO phases, cold, warm and neutral, on vegetation were analyzed with a focus on two …
The Use Of Noaa Avhrr Data For Assessment Of The Urban Heat Island Effect, K. P. Gallo, A. L. Mcnab, T. R. Karl, J. F. Brown, J. J. Hood, J.D. Tarpley
The Use Of Noaa Avhrr Data For Assessment Of The Urban Heat Island Effect, K. P. Gallo, A. L. Mcnab, T. R. Karl, J. F. Brown, J. J. Hood, J.D. Tarpley
Center for Advanced Land Management Information Technologies: Publications
A vegetation index and a radiative surface temperature were derived from satellite data acquired at approximately 1330 LST for each of 37 cities and for their respective nearby rural regions from 28 June through 8 August 1991. Urban-rural differences for the vegetation index and the surface temperatures were computed and then compared to observed urban-rural differences in minimum air temperatures. The purpose of these comparisons was to evaluate the use of satellite data to assess the influence of the urban environment on observed minimum air temperatures (the urban heat island effect). The temporal consistency of the data, from daily data …