Thesis (Ph.D.) -- Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

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A number of tests were conducted to determine the realizable accuracies of the Global Positioning System for natural resource conditions. The effects of terrain, forest canopy, number of consecutive position fixes, and PDOP on accuracy were evaluated. Position accuracies were determined for a total of 27 sites: three replicate sites selected for each of nine distinct conditions: three canopy (deciduous, coniferous, open) and three terrain (ridge, slope, valley) in all possible combinations. Each site was visited ten times over a span of eight months to collect position data, for ten replicates of 60, 100, 200, 300, and 500 position fixes. The mean differentially corrected positional accuracy for all sites was 4.35 meters with 95 percent of the positions estimated within 10.2 meters of the true value. The least accurate differential position data were observed at coniferous sites. Positional accuracy was higher for deciduous sites and the most accurate differential position data was collected at open sites. Accuracy increased with increasing number of position fixes. When the number of position fixes increased from 60 to 500, mean accuracy increased 46.7% at deciduous sites, 32.8% at coniferous sites, and 44.5% at open sites. The average time required by the GPS receiver to lock onto four satellites and begin collecting positions varied from one to two minutes. The most time was spent collecting position fixes at coniferous sites. No correlation was found between accuracy and the receiver's distance from the base-station. Nine replicates of 300 position fixes were averaged for six sites, which ranged from 43 kilometers to 247 kilometers from a Virginia Tech base-station. Mean accuracy ranged from 1.48 meters to 2.43 meters. GPS position data were evaluated for ease of conversion to GIS formats. Conversion was accomplished without problems. / Master of Science

The relationships between the spectral properties of Arizona soils and rangelands and their characteristics were studied. The per cent reflectance of soils was determined using a multispectral hand-held radiometer, and the spectral response of Arizona rangeland sites was measured by scanners aboard an orbiting satellite. These spectral properties were related, by means of stepwise multiple regressions, to various soil and site characteristics. This research is presented in three chapters. The first chapter describes the relationships between soil properties and their spectral reflectance as determined in a laboratory environment. The second chapter attempts to correlate spectral properties of soils measured with a radiometer and that measured by scanners aboard an orbiting satellite for a small area near Winkelman, Arizona. The third chapter describes the relationships between the properties of 243 rangeland sites in central and southeastern Arizona and Landsat spectral data values. Determinations of Munsell soil colors and the radiometrically measured reflectance of 163 soils led to the development of charts for converting Munsell color to reflectance. Little difference was found between Munsell color measured in the sun and that measured indoors, and on the average, soil scientists were in agreement 80 per cent of the time. Munsell value, organic carbon, carbonates, and Munsell chroma explained 80 per cent of the variability within the reflectance measurements of these soils. The spectral response of the less than 2 mm soil fraction collected from rangeland surfaces was significantly different from the spectral response of coarser fragments collected from the same surface. In the Winkelman area the radiometrically measured reflectance of the less than 2 mm fraction alone accounted for 46 per cent of the variability and the reflectance of the 13 to 76 mm fraction accounted for 17 per cent of the variability within the satellite measured response. This area had a low vegetative cover and soil-geologic features, particularly soil color, correlated best with the Landsat digital data. Seventy-six per cent of the satellite data were explained by the interaction of the per cent coarse fragments times its reflectance, the average slope of the sites and the per cent soil less than 2 mm fraction times its reflectance. The relationship between the properties of 110 rangeland sites in central Arizona and the sum of the four Landsat spectral bands was determined. The sum of brush and forest crown densities, elevation, soil color,Geology of the site, and the per cent of surface covered with cobbles explained 82 per cent of this variation. An evaluation of field measurements only to explain the variability among mapping units showed the sum of brush and forest crown densities, elevation, clay content, and fragments greater than 2 mm explained 67 per cent of this variation. When satellite data were added to the field measurement site characteristics, the ratio of satellite scanner bands 4+5 to 6+7 becomes the most significant factor in explaining the variation among mapping unit symbols and a greater per cent of the variability could be explained. A similar study conducted on 133 sites in southeastern Arizona gave different results as only 41 per cent of the variability could be explained. It was shown that for central and southern Arizona rangelands, it is possible to define specific relationships between site characteristics and satellite measured spectral response. Less than ten site characteristics and their interactions explain considerable portions of the variability between mapping units for a given survey. These relationships are unique for specific locations, but they could easily be developed for a survey area and effectively used in the mapping process.

This study investigated the possibility of using ground-based remotely sensed hyperspectral observations with a special emphasis on detection of water, weed and nitrogen stresses contributing towards in-season decision support for precision crop management (PCM). / A three factor split-split-plot experiment, with four randomized blocks as replicates, was established during the growing seasons of 2003 and 2004. Corn (Zea mays L.) hybrid DKC42-22 was grown because this hybrid is a good performer on light soils in Quebec. There were twelve 12 x 12m plots in a block (one replication per treatment per block) and the total number of plots was 48. Water stress was the main factor in the experiment. A drip irrigation system was laid out and each block was split into irrigated and non-irrigated halves. The second main factor of the experiment was weeds with two levels i.e. full weed control and no weed control. Weed treatments were assigned randomly by further splitting the irrigated and non-irrigated sub-blocks into two halves. Each of the weed treatments was furthermore split into three equal sub-sub-plots for nitrogen treatments (third factor of the experiment). Nitrogen was applied at three levels i.e. 50, 150 and 250 kg N ha-1 (Quebec norm is between 120-160 kg N ha-1). / The hyperspectral data were recorded (spectral resolution = 1 nm) mid-day (between 1000 and 1400 hours) with a FieldSpec FR spectroradiometer over a spectral range of 400-2500 run at three growth stages namely: early growth, tasseling and full maturity, in each of the growing season. / There are two major original contributions in this thesis: First is the development of a hyperspectral data analysis procedure for separating visible (400-700 nm), near-infrared (700-1300 nm) and mid-infrared (1300-2500 nm) regions of the spectrum for use in discriminant analysis procedure. In addition, of all the spectral band-widths analyzed, seven waveband-aggregates were identified using STEPDISC procedure, which were the most effective for classifying combined water, weed, and nitrogen stress. The second contribution is the successful classification of hyperspectral observations acquired over an agricultural field, using three innovative artificial intelligence approaches; support vector machines (SVM), genetic algorithms (GA) and decision tree (DT) algorithms. These AI approaches were used to evaluate a combined effect of water, weed and nitrogen stresses in corn and of all the three AI approaches used, SVM produced the best results (overall accuracy ranging from 88% to 100%). / The general conclusion is that the conventional statistical and artificial intelligence techniques used in this study are all useful for quickly mapping combined affects of irrigation, weed and nitrogen stresses (with overall accuracies ranging from 76% to 100%). These approaches have strong potential and are of great benefit to those investigating the in-season impact of irrigation, weed and nitrogen management for corn crop production and other environment related challenges.

Papyrus (Cyperus papyrus .L) swamp is the most species rich habitat that play vital hydrological, ecological, and economic roles in central tropical and western African wetlands. However, the existence of papyrus vegetation is endangered due to intensification of agricultural use and human encroachment. Techniques for modelling the distribution of papyrus swamps, quantity and quality are therefore critical for the rapid assessment and proactive management of papyrus vegetation. In this regard, remote sensing techniques provide rapid, potentially cheap, and relatively accurate strategies to accomplish this task. This study advocates the development of techniques based on hyperspectral remote sensing technology to accurately map and predict biomass of papyrus vegetation in a high mixed species environment of St Lucia- South Africa which has been overlooked in scientific research. Our approach was to investigate the potential of hyperspectral remote sensing at two levels of investigation: field level and airborne platform level. First, the study provides an overview of the current use of both multispectral and hyperspectral remote sensing techniques in mapping the quantity and the quality of wetland vegetation as well as the challenges and the need for further research. Second, the study explores whether papyrus can be discriminated from each one of its coexistence species (binary class). Our results showed that, at full canopy cover, papyrus vegetation can be accurately discriminated from its entire co-existing species using a new hierarchical method based on three integrated analysis levels and field spectrometry under natural field conditions. These positive results prompted the need to test the use of canopy hyperspectral data resampled to HYMAP resolution and two machine learning algorithms in identifying key spectral bands that allowed for better discrimination among papyrus and other co-existing species (n = 3) (multi-class classification). Results showed that the random forest algorithm (RF) simplified the process by identifying the minimum number of spectral bands that provided the best overall accuracies. Narrow band NDVI and SR-based vegetation indices calculated from hyperspectral data as well as some vegetation indices published in literature were investigated to test their potential in improving the classification accuracy of wetland plant species. The study also evaluated the robustness and reliability of RF as a variables selection method and as a classification algorithm in identifying key spectral bands that allowed for the successful classification of wetland species. Third, the focus was to upscale the results of field spectroscopy analysis to airborne hyperspectral sensor (AISA eagle) to discriminate papyrus and it co-existing species. The results indicated that specific wavelengths located in the visible, red-edge, and near-infrared region of the electromagnetic spectrum have the highest potential of discriminating papyrus from the other species. Finally, the study explored the ability of narrow NDVI-based vegetation indices calculated from hyperspectral data in predicting the green above ground biomass of papyrus. The results demonstrated that papyrus biomass can be modelled with relatively low error of estimates using a non-linear RF regression algorithm. This provided a basis for the algorithm to be used in mapping wetland biomass in highly complex environments. Overall, the study has demonstrated the potential of remote sensing techniques in discriminating papyrus swamps and its co-existing species as well as in predicting biomass. Compared to previous studies, the RF model applied in this study has proved to be a robust, accurate, and simple new method for variables selection, classification, and modelling of hyperspectral data. The results are important for establishing a baseline of the species distributions in South African swamp wetlands for future monitoring and control efforts. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.

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Approved for public release, distribution is unlimited / ine discrimination of similar soil classes was produced by the BRDF variations in the high-spatial resolution panchromatic image. Texture analysis results depended on the directionality of the gray level co-occurrence matrix (GLCM) calculation. Combining the different modalities of analysis did not improve the overall classification, perhaps illustrating the consequences of the Hughes paradox (Hughes, 1968) / Flight Lieutenant, Royal Thai Air Force

Wetland vegetation is of fundamental ecological importance and is used as one of the vital bio-indicators for early signs of physical or chemical degradation in wetland systems. Wetland vegetation is being threatened by expansion of extensive lowland areas of agriculture, natural resource exploitation, etc. These threats are increasing the demand for detailed information on vegetation status, up-to-date maps as well as accurate information for mitigation and adaptive management to preserve wetland vegetation. All these requirements are difficult to produce at species or community level, due to the fact that some parts of the wetlands are inaccessible. Remote sensing offers nondestructive and real time information for sustainable and effective management of wetland vegetation. The application of remote sensing in wetland mapping has been done extensively, but unfortunately the uses of narrowband hyperspectral data remain unexplored at an advanced level. The aim of this study is to explore the potential of hyperspectral remote sensing for wetland vegetation discrimination at species level. In particular, the study concentrates on enhancing or improving class separability among wetland vegetation species. Therefore, the study relies on the following two factors; a) the use of narrowband hyperspectral remote sensing, and b) the integration of vegetation properties and vegetation indices to improve accuracy. The potential of vegetation indices and red edge position were evaluated for vegetation species discrimination. Oneway ANOVA and Canonical variate analysis were used to statistically test if the species were significantly different and to discriminate among them. The canonical structure matrix revealed that hyperspectral data transforms can discriminate vegetation species with an overall accuracy around 87%. The addition of biomass and water content variables improved the accuracy to 95.5%. Overall, the study demonstrated that hyperspectral data and vegetation properties improve wetland vegetation separability at species level. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.