In this thesis, 2D Local Discriminant Bases (LDB) algorithm is used to 2D search structure to classify remotely sensed data. 2D Linear Discriminant Analysis (LDA) method is converted into an M-ary classifier by combining majority voting principle and linear distance parameters. The feature extraction algorithm extracts the relevant features by removing the irrelevant ones and/or combining the ones which do not represent supplemental information on their own. The algorithm is implemented on a remotely sensed airborne data set from Tippecanoe County, Indiana to evaluate its performance. The spectral and spatial-frequency features are extracted from the multispectral data and used for classifying vegetative species like corn, soybeans, red clover, wheat and oat in the data set.

Hydrocarbon (HC) microseepages can be indicator of possible reservoirs. For that reason, mapping the microseepages has potential to be used in petroleum exploration. This study presents a methodology for mapping HC microseepages and related clay mineral alteration in Gemrik Anticline, Adiyaman. For this purpose samples were collected from the potential seepage zones and tested by geochemical analysis. All samples were found to contain some HC. Then, an ASTER image of the region was obtained and a band combination was generated to map this particular region. To map related clay mineral alteration, firstly reflectance spectra of samples were measured using field spectrometer. Secondly, spectrally-known samples were analyzed in USGS Library to determine the reflectance spectra of the constitutional clay minerals in the samples. Lastly, the reflectance characteristics of selected end v members were represented as ASTER band combinations based on their spectral absorption characteristics and literature information. Crosta Technique was used to determine required principal components to map HC microseepage and related clay mineral alteration. Then, this methodology is applied to the whole ASTER image. Ground truth study showed that more than 65% of the revisited anomalies show similar prospects to that of the referenced anticline regardless of their geochemical content. In order to certify the ASTER band combination for mapping HC microseepages, anomalous and non-anomalous pixels were selected from the resultant HC map and given as training data samples to AdaBoost loop which is an image processing algorithm. It has been found that ASTER band combination offered for mapping HC microseepages is similar to that of AdaBoost Algorithm output.

Chlorophyll-a is a parameter which can be used to understand the trophic state of water bodies. Therefore, monitoring of this parameter is required. Yet, distribution of chlorophyll-a in water bodies is not homogeneous and exhibits both spatial and temporal variations. Therefore, frequent sampling and high sample sizes are needed for the determination of chlorophyll-a quantities. This would in return increase the sampling costs and labor requirement, especially if the topography makes the location hard to reach. Remote sensing is a technology that can aid in handling of these difficulties and obtain a continuous distribution of chlorophyll-a concentrations in a water body. In this method, reflectance from water bodies in different wavelengths is used to quantify the chlorophyll-a concentrations. In previous studies in literature, empirical regression models that use the reflectance values in different bands in different combinations have been derived. Yet, prediction performances of these models decline especially in shallow lakes. In this study, the spatial distribution of chlorophyll-a in shallow Lake Eymir is determined using both regression models and artificial neural network models that use hybrid inputs. Unlike the models generated before, field measured parameters which can influence the reflectance values in remotely sensed images have been used in addition to the reflectance values. The parameters that are considered other than reflectance values are photosynthetically active radiation (PAR), secchi depth (SD), water column depth, turbidity, dissolved oxygen concentration (DO), pH, total suspended solids (TSS), total dissolved organic matter (TDOM), water and air temperatures, wind data and humidity. Reflectance values are obtained from QuickBird and World View 2 satellite images. Effect of using hybrid input in mapping the reflectance values to chlorophyll-a concentrations are studied. In the context of this study, three different high-resolution satellite images are analyzed for the spatial distribution of chlorophyll-a concentration in Lake Eymir. Field and laboratory studies are conducted for the measurement of parameters other than the reflectance values. Principle component analysis is applied on the collected data to decrease the number of model input parameters. Then, linear and non-linear regression and artificial neural network (ANN) models are derived to model the chlorophyll-a concentrations in Lake Eymir. Results indicate that ANN model shows better predictability compared to regression models. The predictability of ANN model increases with increasing variation in the dataset. Finally, it is seen that in determination of chlorophyll-a concentrations using remotely sensed data, models with hybrid inputs are superior compared to ones that use only remotely sensed reflectance values.

The assimilation process to produce daily Snow Water Equivalent (SWE) maps is modified by using Helsinki University of Technology (HUT) snow emission model and AMSR-E passive microwave data. The characteristics of HUT emission model is analyzed in-depth and discussed with respects to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed for snow over mountainous areas. Performance of the modified model is checked against original and other modified cases against ground truth data covering 2003-2007 winter periods. A new approach to calculate grain size and density is integrated inside the developed data assimilation process. An extensive validation is successfully carried out by means of snow data measured at ground stations during 2008-2010 winter periods. Validation results were less satisfactory for SWE smaller than 75.0 mm and greater than 200.0 mm. Overestimation is especially observed for stations located below 1750.0 m elevation where SWE is less than 75.0 mm. Applied methodology is fine tuned to improve its performance for shallow snow depths observed below 1750 m elevation using a relationship that integrates 10.7 GHz channel data. But an underestimation for SWE greater than 150 mm could not beresolved due to microwave signal saturation that is expected in dense snowpack.

This study investigates the actual glaciers and the major properties of glacial landscapes (valleys, cirques and lakes) located over the Eastern Black Sea mountain chain using Geographic Information Systems (GIS) and Remote Sensing (RS) technologies. A database is created for each glacial feature that includes fundamental properties of each landscape. Data layers used in the study include digital and analog topographic maps, satellite images, geological maps and drainage maps. The studies carried out yielded identification of 93 glacial valleys (30 main, 63 tributary), 1222 cirques and 685 lakes. Several properties (length, size, aspect, elevation, slope, orientation, roundness, elongation) of each glacial landscape are investigated for the northern and southern parts separately. The frequency of each landscape is found to be more in the northern part of the area. Total area of the actual glacier is found as 0.43 to 0.53 km2 by two methods of remote sensing applications.

In conventional remote sensing, visible-near infrared (VNIR) and shortwave infrared (SWIR) part of the electromagnetic spectrum (EMS) have been utilized for lithological discrimination extensively. Additionally, TIR part of the EM spectrum can also be utilized for discrimination of surface materials either through emissivity characteristics of materials or through radiance as in VNIR and SWIR. In this study, ASTER thermal multispectral infrared data is evaluated in regard to lithological discrimination and mapping through emissivity values rather than conventional methods that utilize radiance values. In order to reach this goal, Principle Component Analysis (PCA) and Decorrelation Stretch techniques are utilized for ASTER VNIR and SWIR data. Furthermore, the spectral indices which directly utilize the radiance values in VNIR, SWIR and TIR are also included in the image analysis. The emissivity values are obtained through Temperature-Emissivity Separation (TES) algorithm. The results of the image analyses, except spectral indices, are displayed in RGB color composite along with the geological map for visual interpretation. The results showed that utilizing emissivity values possesses potential for discrimination of organic matter bearing surface mixtures which has not been possible through the conventional methods. Additionally, PCA of emissivity values may increase the level of discrimination even further. Since the emissivity utilization is rather unused throughout in literature and new, further assessment of accuracy is highly recommended along with the field validations.

Pinus taeda L. dynamics, migration patterns and genetic structure were investigated over geological time scale (the past 21,000 years), historical time scale (the past 500 years) and recent time scale (the past 50 years ago) using multi-source data and an interdisciplinary approach. Population genetics, microsatellite markers, DNA fingerprinting, fossil records, geological history, historical records, aerial photographs, soil maps, weather data, remote sensing and geographic information systems (GIS) were used to assess the dynamics of P. taeda populations especially for the Lost Pines (LP), a disjunct population at the westernmost edge of the species range. Pinus taeda populations east and west of the Mississippi River Valley are genetically differentiated. Eastern populations had higher allelic diversity and diagnostic alleles than western populations. Gene flow estimates are high. Allelic diversity and diagnostic alleles patterns are attributed to the prevailing wind direction. Differentiation east and west of the MRV was attributed to separation to two refugia during the Pleistocene. The Lost Pines population is believed to have undergone one or more bottleneck events with loss of rare alleles. Despite the bottleneck, allelic richness was similar for the LP and the control population from the Western Gulf (WG) population. Population size contraction of the LP was attributed to climate change in central Texas over geological time scale. The natural origin of the Lost Pines was investigated. Multivariate and clustering techniques and assignment and exclusion methods using DNA markers show that the LP population shared ancestry with the WG populations with no evidence for admixture from other sources. Historical records parallel this conclusion. With the absence of logging within Bastrop and Buescher State Parks, P. taeda area and patch size increased from 1949 to 1995. Thirty six percent of the pine patches observed in 1949 had disappeared by 1995 by merging. Landscape pattern analysis shows significant dynamics. The distribution of P. taeda in Bastrop County was associated with sandy light topsoils, clayey heavy sub-soils and high permeable soils. Pinus taeda grow on various soil types as well. Growing on these soils under current climatic conditions may compensate for the precipitation regime in this area.

We derive tropospheric column BrO during the ARCTAS and ARCPAC field campaigns in spring 2008 using retrievals of total column BrO from the satellite UV nadir sensors OMI and GOME-2 using a radiative transfer model and stratospheric column BrO from a photochemical simulation. We conduct a comprehensive comparison of satellite-derived tropospheric BrO column to aircraft in-situ observations of BrO and related species. The aircraft profiles reveal that tropospheric BrO, when present during April 2008, was distributed over a broad range of altitudes rather than being confined to the planetary boundary layer (PBL). Perturbations to the total column resulting from tropospheric BrO are the same magnitude as perturbations due to longitudinal variations in the stratospheric component, so proper accounting of the stratospheric signal is essential for accurate determination of satellite-derived tropospheric BrO. We find reasonably good agreement between satellite-derived tropospheric BrO and columns found using aircraft in-situ BrO profiles, particularly when satellite radiances were obtained over bright surfaces (albedo >0.7), for solar zenith angle <80 degree and clear sky conditions. The rapid activation of BrO due to surface processes (the bromine explosion) is apparent in both the OMI and GOME-2 based tropospheric columns. The wide orbital swath of OMI allows examination of the evolution of tropospheric BrO on about hourly time intervals near the pole. Low surface pressure, strong wind, and high PBL height are associated with an observed BrO activation event, supporting the notion of bromine activation by high winds over snow. We also provide monthly climatological maps of free tropospheric BrO volume mixing ratio (VMR) derived using the so-called cloud slicing technique. In this approach, the derived slope of the total column BrO versus cloud pressure is proportional to free tropospheric BrO VMR. Estimated BrO VMR shows a minimum in the tropics and greater values at higher latitudes in both hemispheres. High tropospheric BrO VMR at high latitudes in spring could be influenced by near-surface bromine activation.

<p>During several years research has been performed at the Department of Laser Systems, the Swedish Defense Research Agency (FOI), to develop methods to produce high resolution 3D environment models based on data acquired with airborne laser systems. The 3D models are used for several purposes, both military and civilian applications, for example mission planning, crisis management analysis and planning of infrastructure.</p><p>We have implemented a new format to store laser point data. Instead of storing rasterized images of the data this new format stores the original location of each point. We have also implemented a new method to detect outliers, methods to estimate the ground surface and also to divide the remaining data into two classes: buildings and vegetation.</p><p>It is also shown that it is possible to get more accurate results by analyzing the points directly instead of only using rasterized images and image processing algorithms. We show that these methods can be implemented without increasing the computational complexity.</p>

<p>The remote sensing technology is increasingly being used to study land use and vegetation cover changes and identify changes that has occur through different land use activities which may have negative impact on the sustainability of the environment, biodiversity protection and conservation. With increase in population growth rate in Sudan, there has been an increase for food crop production with agriculture playing a prominent role in livelihood security for the increasing population.</p><p> </p><p>The increase use of irrigation and mechanisation has brought about an increase in demand for agricultural land use in Sudan with the conversion of other land use types and vegetation for agricultural land use. This does have effect and impact on the vegetation and environment with the country highly exposed to the incidence of environmental and social hazards and disasters including drought and desertification, deforestations, floods, loss of biodiversity, ethnic conflicts and poverty.</p><p> </p><p>The research study work focused on agricultural land use changes in the country with the aim of investigating the agricultural land use changes that has occurred in the country from 1986 to 2002 using the remote sensing technique. This is important for agricultural land use planning and sustainability monitoring to reduce the negative impact of agricultural land use for crop production and increase long term resource use and environmental sustainability. Two remote sensing methods were used for the classification analysis to identify the land use changes namely the NDVI and the parallelepiped classification techniques. The NDVI method was used to identify the changes in the agricultural land use vegetation cover classes and determine the magnitude of changes in land area use that has occurred from 1986 to 2002 when the former and latter remote sensing images were acquired. The parallelepiped classification technique was however used to identify the aggregate agricultural land use changes in the area of study and conversion to and from other categories of land use. A qualitative analytic technique was also used to identify the possible causes of the changes that have occurred in Sudan in the study period using empirical materials.</p><p> </p><p>The research study result gives information on the role the remote sensing technology can play in analyzing land use cover changes for agricultural land use sustainability monitoring.</p>