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Multisensor fusion remote sensing te...

University of Central Florida.

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Multisensor fusion remote sensing technology for assessing multitemporal responses in ecohydrological systems.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multisensor fusion remote sensing technology for assessing multitemporal responses in ecohydrological systems./
Author:	Makkeasorn, Ammarin.
Description:	225 p.
Notes:	Adviser: Ni-Bin Chang.
Contained By:	Dissertation Abstracts International68-08B.
Subject:	Engineering, Environmental. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3276374
ISBN:	9780549182030

Multisensor fusion remote sensing technology for assessing multitemporal responses in ecohydrological systems.
Makkeasorn, Ammarin.

Multisensor fusion remote sensing technology for assessing multitemporal responses in ecohydrological systems. - 225 p.

Adviser: Ni-Bin Chang.

Thesis (Ph.D.)--University of Central Florida, 2007.

This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere.

ISBN: 9780549182030Subjects--Topical Terms:

783782
Engineering, Environmental.

Multisensor fusion remote sensing technology for assessing multitemporal responses in ecohydrological systems.
LDR:06041nam 2200325 a 45 001 856132
005 20100708
008 100708s2007 eng d
020 $a 9780549182030
035 $a (UMI)AAI3276374
035 $a AAI3276374
040 $a UMI $c UMI
100 1 $a Makkeasorn, Ammarin. $3 1022893
245 1 0 $a Multisensor fusion remote sensing technology for assessing multitemporal responses in ecohydrological systems.
300 $a 225 p.
500 $a Adviser: Ni-Bin Chang.
500 $a Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 5097.
502 $a Thesis (Ph.D.)--University of Central Florida, 2007.
520 $a This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere.
520 $a A new evolutionary computational, supervised classification scheme ( Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future.
520 $a Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity.
520 $a To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction. (Abstract shortened by UMI.)
590 $a School code: 0705.
650 4 $a Engineering, Environmental. $3 783782
650 4 $a Hydrology. $3 545716
650 4 $a Remote Sensing. $3 1018559
690 $a 0388
690 $a 0775
690 $a 0799
710 2 0 $a University of Central Florida. $3 1018467
773 0 $t Dissertation Abstracts International $g 68-08B.
790 $a 0705
790 1 0 $a Chang, Ni-Bin, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3276374