An evaluation of spatial data and analysis for identifying potentially favorable areas for manual well drilling: Zinder region of Niger
by Thomas, Sean A., M.S., UNIVERSITY OF NEVADA, RENO, 2010, 117 pages; 1480802

Abstract:

This thesis evaluated a variety of straightforward spatial data and analysis techniques for identifying potentially favorable areas for manual well drilling in the Zinder region of Niger. A key question was whether environmental variables derived from publicly available spatial data had the capacity to augment groundwater depth data for mapping these potentially favorable areas. Some variables considered were: a new calculation of vegetation persistence derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data, MODIS night land surface temperature, and lineament properties, topographic convergence index, and landforms derived from the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). Regression tree analysis showed that geology and soils were the strongest variables for predicting groundwater depth in the study area. The results indicated and parsimony dictates that a geology map and adequate groundwater data are sufficient to map favorable areas for manual well drilling. However, the regression tree analysis also revealed that the combination of relatively high vegetation persistence and low night land surface temperature were related to shallow groundwater depth and can improve favorability mapping for manual well drilling. Additional research is needed to describe these relationships further. Among the output was a procedural outline for favorability mapping, which uses common hydrogeologic and terrain criteria to differentiate between topography and recharge controlled water tables, to direct the choice of variables used in future mapping efforts. Ultimately, several maps of favorable areas for manual well drilling for the Zinder region were created using geology, groundwater depth, and threshold values of environmental variables from the regression tree analysis.

 
AdviserJames M. Thomas
SchoolUNIVERSITY OF NEVADA, RENO
SourceMAI/ 49-01, p. , Oct 2010
Source TypeThesis
SubjectsGeology; Hydrologic sciences; Remote sensing
Publication Number1480802
Adobe PDF Access the complete dissertation:
 

» This is an open access dissertation.
  Use the link below to access the full text PDF of this graduate work:
  http://gradworks.umi.com/1480802.pdf
  Use the link below to search and retrieve all open access dissertations:
  http://pqdtopen.proquest.com

About ProQuest Dissertations & Theses
With over 2.3 million records, the ProQuest Dissertations & Theses (PQDT) database is the most comprehensive collection of dissertations and theses in the world. It is the database of record for graduate research.

The database includes citations of graduate works ranging from the first U.S. dissertation, accepted in 1861, to those accepted as recently as last semester. Of the 2.3 million graduate works included in the database, ProQuest offers more than 1.9 million in full text formats. Of those, over 860,000 are available in PDF format. More than 60,000 dissertations and theses are added to the database each year.

If you have questions, please feel free to visit the ProQuest Web site - http://www.proquest.com - or call ProQuest Hotline Customer Support at 1-800-521-3042.