000			05215nas|a2200445 i 4500
005			20260817162500.0
008			040806s2003 th uu m rtt 00| a1eng d
035			_a.b11935984
099		9	_aAIT Diss. no.SR-03-02
100	1		_aWaktola, Daniel Kassahun
245	1	0	_aAnalysis of the spectral signature of soils and prediction of nutrients through remote sensing and GIS based models
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2003
300			_a101 p.
490	1		_aDissertation ; _vno. SR-03-02
500			_aSubmitted in partial fulfillment of the requirement for the degree of Doctor of Philosophy, School of Advanced Technologies
520			_aEmploying remote sensing tools for monitoring and mapping of soil fertility parameters could offer enhanced possibilities. This is due to the objectivity of data, wider area coverage, continuous monitoring, and faster acquisition capabilities than the conventional methods. However, apart from the challenges induced from plant cover, other obstacles like indiscriminability of soil nutrients from satellite platform and the incompatibility of the sensors' resolutions considerably hinder the direct application of the technology. This research was undertaken with a prime objective of developing predictive models for Soil Organic Matter (SOM), phosphorus (P), potassium (K), and iron (Fe), from quantified spectral-chemical patterns. Data were computed from multi sources of detecting platforms, viz., laboratory-based spectrometer, field-based photometer, and optical satellite-based sensors. Samples were collected from plough layers of Lop Buri tropical soils, Thailand, during a satellite-synchronized ground survey. Both radiometric and chemical analysis were carried out following the standard field and laboratory procedures. The first part of the research addressed the modeling of nutrients from the field and laboratory generated reflectance spectra. The data were synthesized across a 10, 20, 50, and 100 nm bandwidth categories. Accordingly, a single, double, and 100 nm-based bandwidth categories were synthesized from the field spectral data. Stepwise Multiple Regression, Polynomial models, and Artificial Neural Network (ANN) analysis were employed for the identification of nutrient-sensitive bands and subsequent model development. The results revealed higher degree of predictions obtainable from the laboratory than the field conditions. ANN appeared to offer better prediction than the Multiple Regression and Polynomial models. Furthermore, the narrower bandwidth categories have outperformed the broader bandwidth category, where the trend, in this research, is termed as "bandwidth decay effect''. In the second pa1t, the radiometrically, geometrically, and atmospherically corrected IRS-ID satellite data were processed through several indices to yield nutrient-specific prediction models. These models were built through the synthetically cloned channels, called "Spech·al Band Cloning" (SBC). The SBC had interwoven the fine-scaled spectrometer signatures with the course-scaled satellite data. This enabled the development of an original modeling approach. The conclusion that emerges from this model is that, although SBC has performed lower than the spectrometer-based models, it has produced adequate results, which were impossible to realize from the satellite alone. An R2 of 0.72, 0.62, 0.70, and 0.68 were attained for SOM, P, K, and Fe, respectively. Measured and predicted maps of each soil nutrient type, along with the overall weighted fertility surface, were generated from the interpolated nutrient surfaces, performed on a GIS environment. Models were validated on unused (independent) dataset, vis-a-vis the conventionally measured soil nutrients, and had attained an R2 of0.69, 0.49, 0.64, and 0.60 for SOM, P, Kand Fe, respectively.
502			_aThesis (Ph.D.) - Asian Institute of Technology, 2003
650		0	_aSoils _xAnalysis
650		0	_aSoil fertility
700	1		_aTripathi, Nitin Kumar, _eChairperson
700	1		_aHonda, Kiyoshi, _eExamination Committee
700	0		_aApisit Eiumnoh, _eExamination Committee
710	2		_aAlemaya University, _eScholarship donor
710	2		_aWorld Bank, _eScholarship donor
810	2		_aAsian Institute of Technology. _tDissertation ; _vno. SR-03-02
856			_3Full-Text _u http://203.159.5.9/ait-thesis/detail.php?q=B07850
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