Carried out in collaboration with the Occoquan Water Monitoring Lab, this thesis presents the results of research that sought to ascertain the spatial distribution of chlorophyll-a concentrations in the Lake Manassas Reservoir using a combination of Landsat TM satellite imagery and ground based field measurements. Images acquired on May 14, 1998 and March 8, 2000 were analyzed with chlorophyll-a measurements taken on 13, 1998 and March 7, 2000. A ratio of Landsat TM band 3: Landsat Band 4 was used in a regression with data collected at eight water quality monitoring stations run by the Occoquan Watershed Monitoring Lab. Correlation coefficients of 0.76 for the 1998 data and 0.73 for the 2000 data were achieved. Cross validation statistical analysis was used to check the accuracy of the two models. The standard error and error of the estimate were reasonable for the models from both years. In each instance, the ground data was retrieved approximately 24 hours before the Landsat Image acquisition and was a potential source of error. Other sources of error were the small sample size of chlorophyll-a concentration measurements, and the uncertainty involved in the location of the water quality sampling stations. / Master of Science

<p> Although remote sensing has been used in certain aspects of transportation studies, it appears that little attention has been given to its application in parking assessment. For this reason, a study has been undertaken to demonstrate that oblique aerial photographs can be effectively used to investigate.parking characteristices in shopping plazas, in particular to determine the rates of occupancy and turnover that occur in the available parking stalls. From a light aircraft and a helicopter, panchromatic prints and colour slides of two shopping plazas in Dundas and Hamilton, Ontario, were taken with hand-held 35 mm cameras. Photographs were taken every 15 minutes over a period of 1 hour. A comparison of the films used indicates that the presence of colour in the slides permits easier differentiation between vehicles and hence easier identification of the changes that occur at each parking stall. Procedures for extracting and recording data from the photographs and analysing the results were described. In addition to obtaining information on occupancy and turnover, it is demonstrated that the aerial view permits on excellent assessment of the effectiveness of the vehicular system. The ease of vehicle movement, the effectiveness of signing, parking stall preferences, the occurrence of illegal parking and the separation of delivery vehicles and passenger cars can all be deduced from the photography. </p> / Thesis / Candidate in Philosophy

This study examined the usefulness of remote sensing to monitor peatlands, and more specifically Sphagnum moss ‘health’. Results from this study show that thermal imaging can be used to monitor Sphagnum productivity, as when the surface temperature of Sphagnum exceeds a threshold value (30.8 °C in the field and 18.2 °C in the laboratory), Sphagnum quickly changes from being productive to being unproductive. The Enhanced Normalized Difference Vegetation Index (ENDVI) can also be used in a similar manner, where if the ENDVI value is high (above 0.11 in the field and -0.12 in the laboratory), Sphagnum will be productive, and otherwise, it will be stressed. A classification scheme was developed to monitor peatland recovery to fire disturbance. By utilizing the ENDVI, leaf area index and aboveground biomass within a recovering peatland can be mapped, as well as the recovery trajectory of the groundcover. The findings of this study highlight the potential use of remote sensing to assess the driving factors of Sphagnum moss stress, as well as quickly and expansively aid in peatland recovery trajectory. / Thesis / Master of Science (MSc)

Vernal pools are essential breeding habitat for amphibians - the vertebrates most at-risk across the globe. Unfortunately, due to their small sizes and temporary nature, vernal pools are prone to indiscriminate destruction. This is the case in southern Ontario as most vernal pools have already been destroyed by human development. As such there is an urgent need to map remaining vernal pools in relatively undeveloped forested regions, such as the District Municipality of Muskoka in South-Central Ontario. This thesis aims to head-start the creation of a community-based vernal pool mapping project using LiDAR in South-Central Ontario. This goal has been broken down into two chapters with their own sub-objectives. In one chapter, we implemented a pilot study for integrating community involvement in potential vernal pool mapping across the Muskoka River Watershed (i.e., the major watershed of the District of Muskoka). We built a protocol and survey based on past vernal pool projects and studies which effectively integrated citizen involvement and also implemented novel online components (e.g., a portal) for vernal pool field-work. Our efforts were successful with positive feedback for the online components and a majority of the potential vernal pools located by our volunteers were probable vernal pools. In the other chapter, we developed two potential vernal pool mapping protocols using LiDAR based on regional characteristics of pools across the District of Muskoka in the Muskoka River Watershed and Coastal Georgian Bay. We demonstrated that the best mapping protocol for each of the two regions were associated with the protocol that was based on their respective pool characteristics. Moreover, we determined that while LiDAR can increase the accuracy of vernal pool mapping efforts, this is not always the case, especially when mapping vernal pools that occur in expansive bedrock laden regions. / Thesis / Master of Science (MSc) / Vernal pools, small forested temporary wetlands, provide essential breeding habitat for amphibians - the most threatened vertebrate group across earth. Unfortunately, most vernal pools in southern Ontario have been destroyed by human development, and there is an urgent need to map remaining vernal pools in relatively undeveloped forested regions, such as the District Municipality of Muskoka in South-Central Ontario. We implemented a pilot study for integrating community involvement in potential vernal pool mapping and developed two potential vernal pool mapping protocols using LiDAR based on regional pool characteristics. With successful integration of volunteers and mapping accuracies above 80%, we hope our findings will aid future vernal pool conservation, especially by head-starting the creation of a community-based vernal pool mapping project using LiDAR in South-Central Ontario.

Deep-Learning (DL) methods have been widely used for Remote Sensing (RS) applications in the last few years, and they allow improving the analysis of the temporal information in bi-temporal and multi-temporal RS images. DL methods use RS data to classify geographical areas or find changes occurring over time. DL methods exploit multi-sensor or multi-temporal data to retrieve results more accurately than single-source or single-date processing. However, the State-of-the-Art DL methods exploit the heterogeneous information provided by these data by focusing the analysis either on the spatial information of multi-sensor multi-resolution images using multi-scale approaches or on the time component of the image time series. Most of the DL RS methods are supervised, so they require a large number of labeled data that is challenging to gather. Nowadays, we have access to many unlabeled RS data, so the creation of long image time series is feasible. However, supervised methods require labeled data that are expensive to gather over image time series. Hence multi-temporal RS methods usually follow unsupervised approaches. In this thesis, we propose DL methodologies that handle these open issues. We propose unsupervised DL methods that exploit multi-resolution deep feature maps derived by a Convolutional Autoencoder (CAE). These DL models automatically learn spatial features from the input during the training phase without any labeled data. We then exploit the high temporal resolution of image time series with the high spatial information of Very-High-Resolution (VHR) images to perform a multi-temporal and multi-scale analysis of the scene. We merge the information provided by the geometrical details of VHR images with the temporal information of the image time series to improve the RS application tasks. We tested the proposed methods to detect changes over bi-temporal RS images acquired by various sensors, such as Landsat-5, Landsat-8, and Sentinel-2, representing burned and deforested areas, and kinds of pasture impurities using VHR orthophotos and Sentinel-2 image time series. The results proved the effectiveness of the proposed methods.

Modeling and simulation tools are described and implemented in a single workflow to develop a means of simulating a ship wake followed by simulated synthetic aperture radar (SAR) and infra-red (IR) images of these ship wakes. A parametric study across several different ocean environments and simulated remote sensing platforms is conducted to generate a preliminary data set that is used for training and testing neural network--based ship wake detection models. Several different model architectures are trained and tested, which are able to provide a high degree of accuracy in classifying whether input SAR images contain a persistent ship wake. Several data fusion models are explored to understand how fusing data from different SAR bands may improve ship wake detection, with some combinations of neural networks and data fusion models achieving perfect or near-perfect performance. Finally, an outline for a future study into multi-physics data fusion across multiple sensor modalities is created and discussed. / Doctor of Philosophy / This dissertation focuses on using computer simulations to first simulate the wakes of ships on the ocean surface, and then simulate airborne or satellite-based synthetic aperture radar (SAR) and infra-red (IR) images of these ship wakes. These images are used to train machine learning models that can be given a SAR or IR image of the ocean and determine whether or not the image contains a ship wake. The testing shows good preliminary results and some models are able to detect ship wakes in simulated SAR images with a high degree of accuracy. Data fusion models are then created which seeks to fuse data sources together in order to improve ship wake detection. These data fusion models are tested using the simulated SAR images, and some of these data fusion models show a positive impact on ship wake detection. Next steps for future research are documented, such as data fusion of SAR and IR data in order to study how fusion of these sensors impacts ship wake detection compared to just a single SAR sensor or multiple SAR sensors fused together.

The capabilities of a number of remote-sensing techniques for biophysical mapping in the subarctic have been examined at Pen Island in northwestern Ontario. After a two week field reconnaissance, colour infrared aerial photography was studied and a detailed biophysical map of the area was produced. Using this knowledge LANDSAT satellite data of the site were investigated. In a visual analysis of the data, the majority of the units identified in the airphoto interpretation were detected, and these were distinguished primarily by their spectral characteristics. Digital analysis of the satellite data using the Bendix MAD system allowed many of the classes of the earlier studies to be delineated and also permitted the classification to be readily extended beyond the original site. In both LANDSAT analyses specific biophysical units could be mapped from the satellite data but could not be identified without the airphoto interpretation. / Thesis / Master of Science (MSc)

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No description available.

Global Climate Models (GCMs) are indispensable tools for modeling climate change projections. Due to approximations, errors are introduced in the GCM computations of atmospheric radiation. The existing methodologies for the comparison of the GCM-computed shortwave fluxes (SWF) exiting the top of the atmosphere (TOA) against satellite observations do not separate the model errors in terms of the atmospheric and surface components. A new methodology has been developed for estimating the GCM systematic errors in the SWF at the TOA under clear-sky (CS) conditions. The new methodology is based on physical principles and utilizes in-situ measurements of SWF at the surface. This error adjustment methodology (EAM) has been validated by comparing GCM results against satellite measurements from the Clouds and the Earth’s Radiant Energy System (CERES) mission. The EAM was implemented in an error estimation model for solar radiation (EEMSR), and then applied to examine the hypothesis that the Community Climate System Model (CCSM), one of the most widely used GCMs, was deficient in representing the annual phenology of vegetation in many areas, and that satellite measurements of vegetation characteristics offered the means to rectify the problem. The CCSM computed monthly climatologies of TOA-CS-SWF were compared to the CERES climatology. The incorporation of satellite-derived land surface parameters improved the TOA SWF in many regions. However, for more meaningful interpretations of the comparisons, it was necessary to account for the uncertainties arising from the radiation calculations of CCSM. In-situ measurements from two sites were used by EMBC to relate the observations and model estimates via a predictive equation to derive the errors in TOA CS-SWF for monthly climatologies. The model climatologies were adjusted using the computed error and then compared to CERES climatology at the two sites. The new results showed that at one of the sites, CCSM consistently overestimated the atmospheric transmissivity whereas at the other site the CCSM overestimated during the spring, summer and early fall and underestimated during late fall and winter. The bias adjustment using the EMBC helped determine more clearly that at the two sites the utilization of satellite-derived land surface parameters improved the TOA CS-SWF.