Presentation
Time:
Mahesh Pun Dissertation Defense
Date:
1:00 pm
Scott Engineering Center Link
Room: N105
Additional Info: SLNK
DISSERTATION DEFENSE OF MAHESH PUN
Advisors: Dr. David Admiraal, Dr. Trenton Franz
APPLICATION OF REMOTE SENSING TECHNOLOGY IN WATER RESOURCES MANAGEMENT
The primary goal of this dissertation was to leverage the capabilities of remote sensing technology for capturing detailed spatial information at different spatial resolutions to monitor agricultural crops and generate more accurate datasets for water resources models. This dissertation is divided into three different research studies. In the first study, a remote sensing classification method was developed for classifying irrigated and non-irrigated fields that integrates Vegetation Indices (VIs) sensitive to phenological development of crops with SEB fluxes which account for soil moisture stress and energy and mass exchange between the vegetation surface and the atmosphere. The method was applied to a region with wide climate variation and to multiple growing seasons with results that were 92.1% accurate and explained 97% variation in National Agricultural Statistics Service (NASS) county irrigation statistics. In the second study, a new method (referred to as “footprint method”) of re-projecting Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that preserves the geometric orientation and size of satellite sensor pixels was developed. The advantages of using the footprint method are to be able to 1) properly represent satellite sensor pixel orientations in fields and 2) eliminate artifacts introduced by conventional processing methods. Statistical results of field comparison in experimental fields CSP1 and CSP2 showed improvement in Leaf Area Index (LAI) estimation when footprint was applied with reduced RMSE by 10.1% and 22%, the ubRMSE by 16.5% and 36%, and the nRMSE by 10.2% and 22% respectively. A third study explored the potential opportunities and benefits of utilizing remotely sensed precipitation data with more detail spatial variability in water resources models. The differences in spatial patterns between precipitation and recharge maps generated by interpolating data from weather stations and maps generated by combining radar measurements and weather station data were found to be substantially different. The percentage difference in annual average precipitation volume over 16 million acres of the Republican River basin area was around 14%. In a sensitivity analysis of precipitation in the watershed model, the effects of different rates of precipitation were found to be different for different types of soils, crops, and irrigation settings.
Advisors: Dr. David Admiraal, Dr. Trenton Franz
APPLICATION OF REMOTE SENSING TECHNOLOGY IN WATER RESOURCES MANAGEMENT
The primary goal of this dissertation was to leverage the capabilities of remote sensing technology for capturing detailed spatial information at different spatial resolutions to monitor agricultural crops and generate more accurate datasets for water resources models. This dissertation is divided into three different research studies. In the first study, a remote sensing classification method was developed for classifying irrigated and non-irrigated fields that integrates Vegetation Indices (VIs) sensitive to phenological development of crops with SEB fluxes which account for soil moisture stress and energy and mass exchange between the vegetation surface and the atmosphere. The method was applied to a region with wide climate variation and to multiple growing seasons with results that were 92.1% accurate and explained 97% variation in National Agricultural Statistics Service (NASS) county irrigation statistics. In the second study, a new method (referred to as “footprint method”) of re-projecting Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that preserves the geometric orientation and size of satellite sensor pixels was developed. The advantages of using the footprint method are to be able to 1) properly represent satellite sensor pixel orientations in fields and 2) eliminate artifacts introduced by conventional processing methods. Statistical results of field comparison in experimental fields CSP1 and CSP2 showed improvement in Leaf Area Index (LAI) estimation when footprint was applied with reduced RMSE by 10.1% and 22%, the ubRMSE by 16.5% and 36%, and the nRMSE by 10.2% and 22% respectively. A third study explored the potential opportunities and benefits of utilizing remotely sensed precipitation data with more detail spatial variability in water resources models. The differences in spatial patterns between precipitation and recharge maps generated by interpolating data from weather stations and maps generated by combining radar measurements and weather station data were found to be substantially different. The percentage difference in annual average precipitation volume over 16 million acres of the Republican River basin area was around 14%. In a sensitivity analysis of precipitation in the watershed model, the effects of different rates of precipitation were found to be different for different types of soils, crops, and irrigation settings.