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|a The use of remote sensing data for drought assessment and monitoring in southwest Asia |h [electronic resource] |y English. |
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|a Colombo, Sri Lanka : |b International Water Management Institute, |c 2004. |
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|a Research report (International Water Management Institute) |n 85 |y English. |
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|a Reports may be copied freely and cited with due acknowledgment. |
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|a Thenkabail, P.S., Gamage, M.S.D.N., Smakhtin, V.U. (2004). The use of remote sensing data for drought assessment and monitoring in Southwest Asia. International Water Management Institute (IWMI) |
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|a This paper provides a description of the near-real-time drought monitoring and reporting system developed for Afghanistan, Pakistan, and parts of western India. It describes its data processing methodology, analytical techniques, and maintenance requirements. Historical data for the monitoring system is from the AVHRR satellite sensor. The MODIS satellite sensor provides the contemporary data. Data from this sensor is available from NASA and USGS at no cost. The project’s most significant contribution is its development of regressions relationships for AVHRR and MODIS satellite data. These sensors have different resolution levels, pre-processing methods, and optical characteristics, so an inter-sensor relationship must be devised in order to conduct any analysis that incorporates both types of data sets. The resulting analyses can be conducted for 8-day intervals at a 500-meter resolution. The system uses NDVI (normalized difference vegetation index) and VCI (vegetation condition index), which are widely used vegetation indices familiar to GIS-RS specialists. VCI is a comparative moisture index that was used in this project to measure the current NDVI against the minimum NDVI level obtained over the entire time period. As this system depends on comparing historical data with contemporary data, continued maintenance tasks are required, such as regular downloads of MODIS data at 8- or 16- day intervals. Data processing can be done on a variety of commercially available software packages, but ERDAS was found to be the most user-friendly for this project. Space requirements are substantial, but are not expected to be as large as those used in this research phase, for which project data from each day consumed approximately 1 Gb of storage space. The methodology for generating analyses using non-matching datasets is a critical tool for drought monitoring in regions where ground truth data is difficult to obtain. NDVI and VCI measurements can be applied to these combined data types to provide valuable information on drought conditions. The prompt detection of drought conditions over the study region has practical implications for those whose DRR infrastructure is limited, and who are thus ill-equipped to prepare for water shortages. |
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|a Abbreviations p. iv; Summary p. v; Introduction p. 1; Data and Methods p. 3; Results and Discussion p. 8; Conclusion p. 22; Literature Cited p. 23 |
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|a Electronic reproduction. |c Florida International University, |d 2013. |f (dpSobek) |n Mode of access: World Wide Web. |n System requirements: Internet connectivity; Web browser software. |
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|a Natural hazards and disasters |z Middle East |x Droughts. |
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|a Remote sensing |z Middle East. |
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|a Thenkabail, Prasad S. |g Senior Researcher in Remote Sensing |u International Water Management Institute, Colombo, Sri Lanka.. |
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|a Gamage, M. S. D. Nilantha |g Research Officer in Remote Sensing and GIS |u International Water Management Institute, Colombo, Sri Lanka.. |
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|a Smakhtin, Vladimir U. |g Principal Scientist in Hydrology and Water Resources |u International Water Management Institute, Colombo, Sri Lanka.. |
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|a Disaster Risk Reduction Program, Florida International University (DRR/FIU), |e summary contributor. |
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|u http://dpanther.fiu.edu/dpService/dpPurlService/purl/FI13042521/00001 |y Click here for full text |
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|a http://dpanther.fiu.edu/sobek/content/FI/13/04/25/21/00001/FI13042521_coverthm.jpg |
