The forests of the eastern United States reflect complex and highly dynamic patterns of change. This thesis seeks to explore the highly variable nature of these changes and to develop techniques that will enable researchers to examine their temporal and spatial patterns. The objectives of this research are to: 1) determine whether the forest change dynamics in the eastern US differ across levels of the urban hierarchy; 2) identify and explore key micropolitan areas that deviate from anticipated trends in forest change; and 3) develop and apply techniques for Big Data exploration of Landsat satellite images for forest cover analysis over large regions. Results demonstrate that forest change at the micropolitan level of urbanization differs from rural and metropolitan forest dynamics. The work highlights the dynamic nature of forest change within the Piedmont Atlantic megaregion, largely attributed to the forestry industry. This is by far the most dominant change phenomenon in the region but is not necessarily indicative of permanent forest change. A longer temporal analysis may be required to separate the contribution of the forest industry from permanent forest conversion in the region. Techniques utilized in this work suggest that emerging tools that provide supercomputing/parallel processing capabilities for the analysis of big satellite data open the door for researchers to better address different landscape signals and to investigate large regions at a high temporal and spatial resolution. The opportunity now exists to conduct initial assessments regarding spatio-temporal land cover trends in the southeast in a manner previously not possible. / Master of Science

This thesis presents a case study on the application of deep learning methods for the dense prediction of land cover types in oblique ground-based photography. While deep learning approaches are widely used in land cover classification of remote-sensing data (i.e., aerial and satellite orthoimagery) for change detection analysis, dense classification of oblique landscape imagery used in repeat photography remains undeveloped. A performance evaluation was carried out to test two state-of the-art architectures, U-net and Deeplabv3+, as well as a fully-connected conditional random fields model used to boost segmentation accuracy. The evaluation focuses on the use of a novel threshold-based data augmentation technique, and three multi-loss functions selected to mitigate class imbalance and input noise. The dataset used for this study was sampled from the Mountain Legacy Project (MLP) collection, comprised of high-resolution historic (grayscale) survey photographs of Canada’s Western mountains captured from the 1880s through the 1950s and their corresponding modern (colour) repeat images. Land cover segmentations manually created by MLP researchers were used as ground truth labels. Experimental results showed top overall F1 scores of 0.841 for historic models, and 0.909 for repeat models. Data augmentation showed modest improvements to overall accuracy (+3.0% historic / +1.0% repeat), but much larger gains for under-represented classes. / Graduate

Remote sensing technology is increasingly being used for rapid detection and visualization of changes caused by catastrophic events. This thesis presents a semi-automated feature-based approach to the identification of building conditions especially in affected areas using GIS and remote sensing information. For image analysis, a new ‘Detected Part of Contour’ (DPC) feature is developed for the assessment of building integrity. The DPC calculates a part of the building contour that can be detected in the remotely sensed image. Additional texture features provide information about the area inside the buildings. The effectiveness of the proposed method is proved by high overall classification accuracy for different study cases. The results demonstrate that the ‘map-to-image’ strategy enables extracting valuable information from the remotely sensed image for each individual vector object, thereby being a better choice for change detection within urban areas.

Detecting changes in land cover through time using remotely sensed imagery is a powerful application that has seen increased use as imagery has become more widely available and inexpensive. Before a time series of remotely sensed imagery can be used for change detection, images must first be standardized for effects outside of real surface change. This thesis established a validation protocol to evaluate the effectiveness of an automated technique for normalizing temporally separate but spatially coincident imagery. Using the concept of pseudo-invariant features between master-slave image pairs, spatially coincident dark and bright points are identified from images and a regression equation is calculated to normalize slave images to a master. I used two sets of imagery to test the performance of the standardization process, a spatially coincident, but temporally variable time series, and spatially and temporally variable images. I tested the underlying statistical assumptions of this approach, and performed simple image subtraction to validate the reduction of master-slave differences using invariant locations. In addition I tested the possibility of reducing between-sensor differences by applying simple linear regression to comparable bands of MSS and TM sensors. Image subtraction showed decreases in master-slave differences as a result of the standardization process, and the process behaved appropriately when there should be no difference between master and slave images (adjacent, but temporally identical imagery). I also found that comparable bands between MSS and TM sensors are similar enough that linear regression may not significantly reduce between-sensor differences.

To successfully protect and manage wetlands, efficient and accurate tools are needed to identify where wetlands are located, the wetland type, what condition they are in, what are the stressors present, and the trend in their condition. Wetland mapping and monitoring are useful to accomplish these tasks. Wetland mapping and monitoring with optical remote sensing data has mainly focused on using a single image or using image acquired over two seasons within the same year. Now that Landsat data are available freely, a multi-temporal approach utilizing images that span multiple seasons and multiple years can potentially be used to characterize wetland dynamics in more detail. In addition, newer remote sensing techniques such as Light Detection and Ranging (lidar) can provide highly detailed and accurate topographic information, which can improve our ability to discriminate wetlands. Thus, the overall objective of this study was to investigate the utility of lidar and multi-temporal Landsat data for mapping and monitoring of wetlands. My research is presented as three independent studies related to wetland mapping and monitoring. In the first study, inter-annual time series of Landsat data from 1985 to 2009 was used to map changes in wetland ecosystems in northern Virginia. Z-scores calculated on tasseled cap images were used to develop temporal profile for wetlands delineated by the National Wetland Inventory. A change threshold was derived based on the Chi-square distribution of the Z-scores. The accuracy of a change/no change map produced was 89% with a kappa value of 0.79. Assessment of the change map showed that the method used was able to detect complete wetland loss together with other subtle changes resulting from development, harvesting, thinning and farming practices. The objective of the second study was to characterize differences in spectro-temporal profile of forested upland and wetland using intra and inter annual time series of Landsat data (1999-2012). The results show that the spector-temporal metrics derived from Landsat can accurately discriminate between forested upland and wetland (accuracy of 88.5%). The objective of the third study was to investigate the ability of topographic variables derived from lidar to map wetlands. Different topographic variables were derived from a high resolution lidar digital elevation model. Random forest model was used to assess the ability of these variables in mapping wetlands and uplands area. The result shows that lidar data can discriminate between wetlands and uplands with an accuracy of 72%. In summary, because of its spatial, spectral, temporal resolution, availability and cost Landsat data will be a primary data source for mapping and monitoring wetlands. The multi-temporal approach presented in this study has great potential for significantly improving our ability to detect and monitor wetlands. In addition, synergistic use of multi-temporal analysis of Landsat data combined with lidar data may be superior to using either data alone for future wetland mapping and monitoring approaches. / Ph. D.

The Mississippi Institute for Forest Inventory (MIFI) is the only cost-effective large-scale forest inventory system in the United States with sufficient precision for producing reliable volume/weight/biomass estimates for small working circle areas (procurement areas). When forest industry is recruited to Mississippi, proposed working circles may overlap existing boundaries of bordering states leaving a gap of inventory information, and a remote sensing-based system for augmenting missing ground inventory data is desirable. The feasibility of obtaining acceptable cubic foot volume estimates from a Landsat-derived volume estimation model (Wilkinson 2011) was assessed by: 1) an initial study to temporally validate Landsat-derived cubic foot volume outside bark to a pulpwood top estimates in comparison with MIFI ground truth inventory plot estimates at two separate time periods, and 2) re-developing a regression model based on remotely sensed imagery in combination with available MIFI plot data. Initial results failed to confirm the relationships shown in past research between radiance values and volume estimation. The complete lack of influence of radiance values in the model led to a re-assessment of volume estimation schemes. Data outlier trimming manipulation was discovered to lead to false relationships with radiance values reported in past research. Two revised volume estimation models using age, average stand height, and trees per-acre and age and height alone as independent variables were found sufficient to explain variation of volume across the image. These results were used to develop a procedure for other remote sensing technologies that could produce data with sufficient precision for volume estimation where inventory data are sparse or non-existent.

The Mississippi and Alabama barrier islands are sensitive landforms that are affected by hurricanes, longshore currents, and available sediment, yet these effects are difficult to quantify with traditional ground-based surveying. In this study, Landsat satellite imagery was used to evaluate changes in barrier island area and centroid position from 1990 and 2005. When hurricanes are infrequent (1999?2003), barrier islands generally increased in total area and showed only moderate repositioning of their centroid locations. However, when hurricanes were frequent (1994?1999 and 2004?2005), barrier islands showed substantial decreases in area and dramatic repositioning of their island centroid locations. This was especially true following Hurricane Katrina (2005). From 1990 to 2005, the general movement of barrier islands was westerly and most islands experienced an overall reduction in area (-18%). The results of this research are similar to findings reported in the literature and illustrate the suitability of using Landsat imagery to study geomorphic changes.

No description available.

Change detection is currently a topic of great interest to theoretic geographic researchers. The necessity to map, monitor, and model land cover change is also important to a variety of applied fields as varied as urban planning and military intelligence. This research compares five algorithms to map urban land cover change in the greater Las Vegas, Nevada metropolitan area. Landsat Thematic Mapper imagery acquired on May 1990 and May 2000 was used as the primary data. The change detection methods yielded simple maps of change vs. no change. These algorithms included image differencing, image ratioing, image regression, vegetation index differencing, and principal components analysis. Each of these techniques accurately identified areas of land cover with moderate levels of accuracy and produced overall change detection accuracy values between 60% and 76% depending on the method. The highest accuracy was obtained by the image ratioing method using the red spectral band (76%). As expected, the determination of change detection thresholds for each technique was critical to the accuracy produced by the algorithm. Moreover, the type of statistic used in optimizing that threshold was also a significant impacting the final accuracy. The approach of using a set of ground points to calibrate the change detection threshold proved to have significant merit.

No description available.