Bottomland hardwood forests are highly productive ecosystems which perform many important ecological services. Unfortunately, many bottomland hardwood forests have been degraded or lost. Accurate land cover mapping is crucial for management decisions affecting these disappearing systems. SPOT-5 imagery from 2005 was combined with Light Detection and Ranging (LiDAR) data from 2006 and several ancillary datasets to map a portion of the bottomland hardwood system found in the Sulphur River Basin of Northeast Texas. Pixel-based classification techniques, rulebased classification techniques, and object-based classification techniques were used to distinguish nine land cover types in the area. The rule-based classification (84.41% overall accuracy) outperformed the other classification methods because it more effectively incorporated the LiDAR and ancillary datasets when needed. This output was compared to previous classifications from 1974, 1984, 1991, and 1997 to determine abundance trends in the area’s bottomland hardwood forests. The classifications from 1974-1991 were conducted using identical class definitions and input imagery (Landsat MSS 60m), and the direct comparison demonstrates an overall declining trend in bottomland hardwood abundance. The trend levels off in 1997 when medium resolution imagery was first utilized (Landsat TM 30m) and the 2005 classification also shows an increase in bottomland hardwood from 1997 to 2005, when SPOT-5 10m imagery was used. However, when the classifications are re-sampled to the same resolution (60m), the percent area of bottomland hardwood consistently decreases from 1974-2005. Additional investigation of object-oriented classification proved useful. A major shortcoming of object-based classification is limited justification regarding the selection of segmentation parameters. Often, segmentation parameters are arbitrarily defined using general guidelines or are determined through a large number of parameter combinations. This research justifies the selection of segmentation parameters through a process that utilizes landscape metrics and statistical techniques to determine ideal segmentation parameters. The classification resulting from these parameters outperforms the classification resulting from arbitrary parameters by approximately three to six percent in terms of overall accuracy, demonstrating that landscape metrics can be successfully linked to segmentation parameters in order to create image objects that more closely resemble real-world objects and result in a more accurate final classification.

Master of Arts / Department of Geography / J. M. Shawn Hutchinson / Windbreaks are a valuable resource in conserving soils and providing crop protection in western Kansas and other Great Plains states. Currently, Kansas has neither an up-to-date inventory of windbreak locations nor an assessment of their condition. The objective of this study is to develop remote sensing and geographic information system methods that rapidly identify and assess the condition of windbreaks in Ford County, Kansas. Ford County serves as a pilot study area for method development with the intent of transferring those methods to other counties/regions in Kansas and the Great Plains. A remote sensing technique known as object-based classification was used to classify windbreaks using color aerial photography acquired through the 2008 National Agricultural Imagery Program. Object-based classification works by segmenting imagery where areas with similar spectral, shape, and textural properties are grouped into vectors (i.e., objects) that are later used as the basis for image classification. Using this technique, 355 windbreaks, totaling nearly 1,012 acres (410 hectares), were identified in Ford County. When compared to a spatial data set of confirmed windbreak locations generated via a heads-up digitizing process, the location of windbreaks identified using object-based classification results agreed approximately 81% of the time. Mean textural and spectral values were then combined and used to place identified windbreaks into three condition categories (good, fair, and poor) using a manual classification approach. Analysis showed the area of windbreaks in good condition to be 170 hectares, with the remaining 171 hectares of windbreaks falling in the fair or poor classes. Methods detailed in this study proved successful at rapidly identifying windbreak location and for providing useful condition class results for windbreak renovation and restoration planning.

In west-central Alberta increased landscape fragmentation has lead to increased human use, having negative effects on wildlife such as the grizzly bear (<i>Ursus arctos</i> L.). Recently, grizzly bears in the Foothills Model Forest were found to select clear cuts of different age ranges as habitat and selected or avoided certain clear cuts depending on the site preparation process employed. Satellite remote sensing offers a practical and cost-effective method by which cut areas, their age, and site preparation activities can be quantified. This thesis examines the utility of spectral reflectance of SPOT-5 pansharpened imagery (2.5m spatial resolution) to identify and map 44 regenerating stands sampled in August 2005. Using object based classification with the Normalized Difference Moisture Index (NDMI), green, and short wave infrared (SWIR) bands, 90% accuracy can be achieved in the detection of forest disturbance. Forest structural parameters were used to calculate the structural complexity index (SCI), the first loading of a principal components analysis. The NDMI, first-order standard deviation and second-order correlation texture measures were better able to explain differences in SCI among the 44 forest stands (R2=0.74). The best window size for the texture measures was 5x5, indicating that this is a measure only detectable at a very high spatial resolution. Age classes of these cut blocks were analysed using linear discriminant analysis and best separated (82.5%) with the SWIR and green spectral bands, second order correlation under a 25x25 window, and the predicted SCI. Site preparation was best classified (90.9%) using the NDMI and homogeneity texture under a 5x5 window. Future applications from this research include the selection of high probability grizzly habitat for high spatial resolution imagery acquisition for detailed mapping initiatives.

In west-central Alberta increased landscape fragmentation has lead to increased human use, having negative effects on wildlife such as the grizzly bear (<i>Ursus arctos</i> L.). Recently, grizzly bears in the Foothills Model Forest were found to select clear cuts of different age ranges as habitat and selected or avoided certain clear cuts depending on the site preparation process employed. Satellite remote sensing offers a practical and cost-effective method by which cut areas, their age, and site preparation activities can be quantified. This thesis examines the utility of spectral reflectance of SPOT-5 pansharpened imagery (2.5m spatial resolution) to identify and map 44 regenerating stands sampled in August 2005. Using object based classification with the Normalized Difference Moisture Index (NDMI), green, and short wave infrared (SWIR) bands, 90% accuracy can be achieved in the detection of forest disturbance. Forest structural parameters were used to calculate the structural complexity index (SCI), the first loading of a principal components analysis. The NDMI, first-order standard deviation and second-order correlation texture measures were better able to explain differences in SCI among the 44 forest stands (R2=0.74). The best window size for the texture measures was 5x5, indicating that this is a measure only detectable at a very high spatial resolution. Age classes of these cut blocks were analysed using linear discriminant analysis and best separated (82.5%) with the SWIR and green spectral bands, second order correlation under a 25x25 window, and the predicted SCI. Site preparation was best classified (90.9%) using the NDMI and homogeneity texture under a 5x5 window. Future applications from this research include the selection of high probability grizzly habitat for high spatial resolution imagery acquisition for detailed mapping initiatives.

The main goal of this study was to map and measure forest change in the southwestern part of Madagascar near the city of Toliara in the period 1990-2010. Recent studies show that forest change in Madagascar on a regional scale does not only deal with forest loss, but also with forest growth However, it is unclear how the study area is dealing with these patterns. In order to select the right classification method, pixel-based classification was compared with object-based classification. The results of this study shows that the object-based classification method was the most suitable method for this landscape. However, the pixel-based approaches also resulted in accurate results. Furthermore, the study shows that in the period 1990–2010, 42% of the forest cover disappeared and was converted into bare soil and savannahs. Next to the change in forest, stable forest regions were fragmented. This has negative effects on the amount of suitable habitats for Malagasy fauna. Finally, the scaling structure in landscape patches was investigated. The study shows that the patch size distribution has long-tail properties and that these properties do not change in periods of deforestation.

Possibilities of object based image analysis for monitoring of meadow vegetation and management in the Krkonoše Mountains National Park Abstract The main aim of the thesis was to evaluate possibilities of Object Based Image Analysis (OBIA) of WorldView-2 satellite image data and aerial optical scanner for meadow vegetation and managment types classification in Krkonoše Mountains National Park. The classification was based on legend prepared by botanist of the national park. The second goal was to compare classification accuracy of Object Based Image Analysis and neural net classification method that was used by Pomahačová (2012) for the same area and the same WorldView-2 data. OBIA for meadow vegetation was conducted using SVM algorithm and "Decision Tree" algorithm. The classification accuracy was estimated using reference points from the field. The thesis puts the requirements (optimal parameters and conditions) for successfull object based classification of mountain meadow vegetation into a new perspective. Key words: Object based classification, meadows, WorldView-2, aerial optical scanner, SVM, KRNAP

Useful information extraction from satellite images for the use of other higher level applications such as road network extraction and update, city planning etc. is a very important and active research area. It is seen that pixel-based techniques becomes insufficient for this task with increasing spatial resolution of satellite imaging sensors day by day. Therefore, the use of object-based techniques becomes indispensable and the segmentation method selection is very crucial for object-based techniques. In this thesis, various segmentation algorithms applied in remote sensing literature are presented and a segmentation process that is based on watersheds and multi-scale segmentation is proposed to use as the segmentation step of an object-based classifier. For every step of the proposed segmentation process, qualitative and quantitative comparisons with alternative approaches are done. The ones which provide best performance are incorporated into the proposed algorithm. Also, an unsupervised segmentation accuracy metric to determine all parameters of the algorithm is proposed. By this way, the proposed segmentation algorithm has become a fully automatic approach. Experiments that are done on a database formed with images taken from Google Earth&reg / software provide promising results.

The role of mangroves as essential fish habitat has been a focus of extensive research. However, recent evidence has shown that this role should not be evaluated in isolation from surrounding habitats such as seagrass beds and hard-bottom communities. For example, submerged aquatic vegetation (SAV) communities provide potential sources of food and shelter for fish species that may reside in the mangroves, but may also undergo ontogenetic migrations and daily home-range movements into neighboring habitats. The connectivity between the mangrove fish community and the surrounding seascape may be influenced by the level of patchiness, fragmentation, and spatial heterogeneity of adjacent SAV habitats (i.e., SAV seascape structure). The spatial patterns and heterogeneity of SAV seascape structures are driven by internal and external regulatory mechanisms operating at different spatial and temporal scales. In addition, it is likely that many fish species inhabiting the mangrove zones have different home ranges, and foraging and migratory patterns; therefore, different mangrove fish species may respond to seascape heterogeneity at different scales. There are few studies that have assessed the influence and connectivity of benthic habitats adjacent to mangroves for estuarine fish populations at multiple scales. The present research used an exploratory seascape approach in Biscayne Bay (Florida, USA) to evaluate patterns in the patch composition and configuration of SAV communities, and to examine relationships between seascape structural metrics and the abundance, diversity, and distribution of fishes that utilize the adjacent mangrove shoreline as nursery and/or adult habitat. This seascape approach consisted of: a) the multi-scale characterization of the SAV distribution across the seascape with metrics developed in Landscape Ecology, Geographic Information Systems and Remote Sensing; b) multivariate analyses to identify groups with significantly distinct SAV seascape structures within the most heterogeneous scale, and identify possible mechanisms driving the observed SAV seascape structures; and c) an assessment of the mangrove fish community responses to SAV seascape structures. By applying a set of multivariate analyses (e.g., ANOSIM, MDS plots, hierarchical clustering), the buffer within 200 m from shore was identified as the scale with the highest structural heterogeneity. At this scale, two major SAV seascape structures (i.e., areas with similar SAV spatial arrangement and composition) were identified: a fragmented SAV seascape (FSS) structure and a continuous SAV seascape (CSS) structure. Areas with CSS were characterized by large, uniform SAV patches. In contrast, areas with FSS were characterized by a higher density of smaller, more complex SAV patches. Furthermore, the areas with CSS and FSS structures clustered in zones of the bay with distinct salinity properties. The areas with CSS structures were mostly located in zones characterized by high and stable salinity. However, the areas with FSS concentrated in zones that are influenced by freshwater discharges from canals and with low and variable salinity. The responses of fish diversity metrics were not constrained to the scale at which the greatest spatial heterogeneity of SAV seascape structures was observed (i.e., the seascape composition and configuration within 200 m from shore), but was related to SAV seascape characteristics across different scales. The majority of the variability of the fish diversity metrics in the mangrove shoreline was explained by SAV seascape structures within the smaller scales (i.e., 100-400 m from shore), and SAV seascape structures that represented the level of fragmentation and/or the percent of suitable habitat. Different conceptual models were proposed to illustrate and understand the ecological dynamics behind the relationship between the diversity of the mangrove fish community and the structure of the adjacent SAV seascape. In general, the diversity and abundance of fishes is influenced by the type and level of fragmentation of the SAV seascape, which, in turn, influence the proportion of the seascape used for foraging and refuge by fish. In conclusion, this research quantified how the release of large pulses of freshwater into near-shore habitats of coastal lagoons can influence the seascape structure of SAV communities. Namely, freshwater inputs produce fragmentation in otherwise fairly homogeneous SAV meadows. The outcome of this research highlights the importance of seascape characteristics as indicators of ecosystem-level modifications and alterations affecting the spatial distribution, assemblage, and diversity of marine nearshore habitats in coastal regions heavily influenced by human activities. In addition, the results illustrated the cascading effects and synergistic influences of near-shore habitat spatial assemblages on the composition and diversity of estuarine fish communities. Lastly, and very importantly, the relationships established in this project provide quantitative and qualitative information on patterns of species-habitat associations needed for the improved synergistic management and protection of coastal habitats and fisheries resources.

Cette étude a été effectuée pour améliorer l'efficacité des mesures de stockage de carbone par des techniques de télédétection dans les plantations de Para rubber (Hevea brasiliensis) en Thaïlande. Les estimations des méthodes actuelles de stockage de carbone s’opèrent à l’aide de la classification classique basée sur le système des pixels basée sur des images de moyenne résolution et produit donc des résultats d’une grande incertitude. En revanche, dans cette étude, la méthode utilisée est basée sur des images de très haute résolution provenant du satellite THAICHOTE, associés à des mesures sur le terrain, dans le bassin de Mae num Prasae. L’utilisation de l'objet en fonction des classifications, les plantations cartographiées, leur âge et leur circonférence ont été estimées à partir d'un modèle paramétrique dérivé de données spectrales, de texture et 3D. L'étude propose une information de texture plus utile que l'information spectrale pour capturer l’architecture des arbres du couvert et donc l'âge de la canopée. Un spectrale de Global Environment Monitoring (GEMI) et quatre texturales de Homogeneity, Dissimilarity, Contrast et Variance ont été utilisées dans l'ajustement du modèle (régression R2 = 0,87) pour estimer la circonférence et l'âge des arbres tandis que le Canopy Height Model (CHM) de 3D n’était pas autorisée pour construire l'information de classement d'images. Environ 154 km2 des 232 km2 de la zone étudiée sont couverts par des plantations. La quantité totale de la biomasse et des stocks de carbone s’élève à 2,23 mégatonnes et 0,99 mégatonnes C, respectivement avec une incertitude de 11%. En 2011, la superficie totale séquestrée était de 121 tCO2 par des plantations. / This study explored to the improve efficiency of measurements of carbon stock by remote sensing techniques on Para rubber (Hevea brasiliensis) plantations in East Thailand. Current methods of carbon stock estimations use classical pixel based classification on middle-resolution images and thus produce results with a large uncertainty. In this study, the method use very high resolution images from the THAICHOTE satellite, associated to field measurements to estimates the carbon stock and its evolution in the Mae num Prasae watershed. Using object based classifications, the plantations have been mapped and their age and girth have been estimated from a parametric model derived from spectral, textural, 3D information and field data. The results of this study show that these data can be used to map Para rubber plantation and distinguish age classes of trees in the plantations. The study propose that textural information is more useful than spectral information to capture tree canopy architecture and thus the age of the canopy. One spectral of Global Environment Monitoring (GEMI) and four textural information of Homogeneity, Dissimilarity, Contrast and Variance were used in the fit model (multiple linear regression R2=0.87) for estimating the Para rubber tree girth and age while the 3D information (canopy height model: CHM) was not appropriated to build the image classification information. Around 154 km2 of the 232 km2 of the studied area are covered by Para rubber plantations. The total amount of biomass and carbon stocks are 2.23 Megatons and 0.99 Megatons C respectively with uncertainty of 11%. In 2011, the total area sequestered 121 tCO2 by Para rubber plantations.

Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census block level. The maximum total estimation accuracy that can be attained by the models is 94.21%. Accuracy assessments suggest that the GWR models outperformed linear regression models due to their better handling of spatial heterogeneity. Models generated from building volume and area gave better results. The models have lower accuracy in both densely populated census blocks and sparsely populated census blocks, which could be partly attributed to the lower accuracy of the LiDAR data used.