Remote sensing

Understanding snowpack variability is important as it plays an imperative role in environmental, hydrologic, and atmospheric systems. Research questions related to three linked areas were investigated in this thesis: 1) scaling issues in snow hydrology, 2) forest-snowpack relationships, and 3) methods of integrating snow water equivalent (SWE) into a hydrologic model for a large, forested drainage basin in central Ontario. The first study evaluated differences in SWE across process, measurement, and model scales. Point scale snowpack measurements could be bias corrected using scaling factors derived from a limited number of transect measurements and appropriately stratified point scale measurements may be suitable for replacing transect scale mean SWE when transect data are not possible to collect. Comparison of modelled products to measurements highlighted the importance of understanding the spatial representativeness of in-situ measurements and the processes those measurements represent when validating snow products or assimilating data into models.The second study investigated the efficacy of field-based, and remotely sensed datasets to describe forest structure and resolve forest-snowpack relationships. Canopy cover was highly correlated with melt rate and timing at the site scale however, significant correlations were present in 2016 but not 2017, which was attributed to interannual differences in climate. Peak SWE metrics did not correlate well with forest metrics. This was likely due to mid-winter melt events throughout both study years, where a mix of accumulation and melt processes confounded forest-snowpack relationships. The third study evaluated the accuracy of the Copernicus SWE product and assessed the impact of calibrating and assimilating SWE data on model performance. The bias corrected Copernicus product agreed with measured data and provided a good estimate of mean basin SWE. Calibration of a hydrologic model to subbasin SWE substantially improved modelled SWE performance. Modelled SWE skill was not improved by assimilating SWE into the calibrated model. All models evaluated had similar streamflow performance, indicating streamflow in the study basin can be accurately estimated using a model with a poor representation of SWE. The findings from this work improved knowledge and understanding of snow processes in the hydrologically significant Great Lakes-St Lawrence Forest region of central Ontario.

Author Keywords: data assimilation, hydrologic model, multi-objective calibration, remote sensing, scale, snow

ABSTRACT

Development of Forest Degradation Indicators from Long-term Trajectories of Multispectral Satellite Images, and their Projections into the Future under Climate Change, in Ontario, Canada

Md. Mozammel Hoque

Ontario forests are affected by natural and anthropogenic disturbances leading to forest degradation, which significantly impact local ecosystems, health, safety, and economy. This thesis develops a methodology for the continuous assessment, mapping, and monitoring of present and historic (1972–2020) forest disturbances, and future forest degradation trends and projections, using remote sensing data, ground measurements, and predictive models in an Ontario forested area. After testing four supervised classification algorithms, support vector machine was found to be the most robust, consistent, and effective for land cover classification. Seven vegetation indices derived from Landsat and MODIS platforms were used to derive forest degradation indicators (FDIs), which were combined into one composite forest degradation indicator (CFDI) for each year, using the principal component analysis image fusion approach. The CFDI was the most informative indicator. The computed FDIs from available large multispectral image stacks were statistically related to historical climate variables. These relationships were used to project future FDIs related to climate variables derived from General Circulation Models through multiple linear regression models. Spatially-explicit maps of relevant climatic variables and of long-term historical forest degradation were developed from the LandTrendr trajectory analysis. Climate variables P, MA1, MA2, and CFDI were strongly correlated, allowing for the development of a model with a high coefficient of determination, R2 (0.93), and low RMSE (0.28) to predict future values. Forest disturbances (as CFDI) were also monitored from 1972–2020. Overall, these relationships allowed for to the creation of spatially-explicit, long-term historical forest degradation maps derived from the Landtrendr trajectory analysis. Historical and future forest degradation maps identified the areas with projected high vulnerability to climate change, as well as the actual and potential changes in forest cover under climate change. The results indicated 2050 will experience an average temperature increase of 3.0°C, projected yearly decrease in precipitation of 109.5 mm, evapotranspiration increase of 73.0 mm, and moisture deficits of 28.47 mm (MA1) and 37.60 mm (MA2), leading to increased forest degradation.

Author Keywords: Climate change impacts, Forest degradation indicators, Forest disturbance and degradation, Land cover classification, Projections of 2050 forest degradation under climate change, Remote sensing technology

Built upon remote sensing and GIS littoral zone characterization methodologies of the past decade, a series of loosely coupled models aimed to test, compare and synthesize multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite based optical data sets in the Gulf of St. Lawrence, Canada, eco-region. Bathymetry and relative intensity metrics for the MBES and ALB data sets were run through a quantitative and qualitative comparison, which included outputs from the Benthic Terrain Modeller (BTM) tool. Substrate classification based on relative intensities of respective data sets and textural indices generated using grey level co-occurrence matrices (GLCM) were investigated. A spatial modelling framework built in ArcGISTM for the derivation of bathymetric data sets from optical satellite imagery was also tested for proof of concept and validation. Where possible, efficiencies and semi-automation for repeatable testing was achieved using ArcGISTM ModelBuilder. The findings from this study could assist future decision makers in the field of coastal management and hydrographic studies.

Keywords: Seafloor terrain characterization, Benthic Terrain Modeller (BTM), Multi-beam SONAR, Airborne LiDAR Bathymetry, Satellite Derived Bathymetry, ArcGISTM ModelBuilder, Textural analysis, Substrate classification

A wind tunnel study was conducted to investigate aeolian impact ripples in sand beds of varied texture from coarsely skewed to bimodal. Experimental data is lacking for aeolian megaripples, particularly in considering the influence of wind speed on ripple morphometrics. Additionally, the modelling community requires experimental data for model validation and calibration.

Eighteen combinations of wind speed and proportion of coarse mode particles by mass were analysed for both morphometrics and optical indices of spatial segregation. Wind tunnel conditions emulated those found at aeolian megaripple field sites, specifically a unimodal wind regime and particle transport mode segregation. Remote sensing style image classification was applied to investigate the spatial segregation of the two differently coloured size populations.

Ripple morphometrics show strong dependency on wind speed. Conversely, morphometric indices are inversely correlated to the proportion of the distribution that was comprised of coarse mode particles. Spatial segregation is highly correlated to wind speed in a positive manner and negatively correlated to the proportion of the distribution that was comprised of coarse mode particles. Results reveal that the degree of spatial segregation within an impact ripple bedform can be higher than previously reported in the literature.

Author Keywords: Aeolian, Impact Ripples, Megaripple, Self-organization, Wind Tunnel

Currently, UAVs are deployed to measure crop health in a timely manner by mapping vegetation indices. A study using two different fields was conducted in order to search for a relationship that may exist between crop health and soil fertility. A UAV equipped with sensor technology was used for mapping of vegetation indices which were then statistically compared to soil nutrient data collected via soil sampling. Elevation data was also collected which was then statistically compared to soil nutrients as well as crop health. Results of this study were unfortunately impacted by variables outside of the researcher's control. Moisture became the greatest limiting factor in 2016 followed by an excess of rain in 2017. Results did not show any promising correlations as moisture uncontrollably became the defining variable. Further research in a more controlled setting will need to be conducted in order to explore this potential relationship.

Author Keywords: Agriculture, Multispectral Imagery, Precision Agriculture, Proximal Soil Sensing, Remote Sensing, Unmanned Aerial Vehicle

The forestry sector has a well-developed history of using remote sensing to identify structural characteristics of forests and to detect and attribute changes that occur in forested landscapes. Monitoring the recovery of disturbed forests is an important factor in long term forest management. However, forest that is recovered spectrally may not be recovered when considered in terms of a Free to Grow assessment. A Free to Grow assessment is used in Ontario to determine whether a disturbed site will likely achieve a desired future state, i.e., is recovered according to a forestry perspective. The objective of this research was to determine the relationship between a pixel-based Landsat Time Series of spectral recovery and the results of Free to Grow assessments. Spectral trajectories were generated from representative pixels within known harvested forest areas. Results indicate that while Free to Grow sites often achieve spectral recovery (>90%), many non-Free to Grow sites were classified as spectrally recovered, suggesting that improved methods of spectral recovery monitoring are needed.

Author Keywords: forest recovery, Free to Grow, Landsat Time Series, LandTrendr, Pixel-based, spectral recovery

This thesis presents a palaeotopographic reconstruction of the Burleigh Bay region of Stony Lake (Kawartha Lakes Region, Ontario) from 12,600 cal BP to present. The paleotopographic reconstructions are used to model paleoshoreline locations and archaeological site potential for the Late Paleoindian and early Archaic periods. Isostatic rebound following the end of the last glacial period has altered the topography in the region and water levels are now artificially managed by dams constructed in the 1830s. I completed a high-resolution bathymetric survey using a kayak equiped with a GPS coupled single-beam sonar. Utilizing GIS technology and isostatic rebound response surface models, I created paleotopographic reconstructions for 12,600 cal BP, 11,500 cal BP, 7,000 cal BP, 5,700 cal BP, and present. Results show that water levels in Burleigh Bay have been regressing over time until dam construction. Early site potential is centered in northern inland areas. Site potential following 7,000 cal BP is concentrated in northern areas flooded by the dam. Based on the reconstructions, surveys in lacustrine granite shield regions that follow the Ontario Standards and Guidelines for Consultant Archaeologists risk missing areas of high archaeological potential for early sites in these environments. Paleolandscape reconstructions would alleviate this issue by modeling paleoshoreline changes over time.

Author Keywords: Canadian Shield, Early Archaic, Isostatic Rebound, Kawartha Lakes, Late Paleoindian, Paleolandscape

In this thesis, a methodological framework for the assessment and monitoring of forest degradation based on remote sensing techniques and field data, as part of the REDD+ programme, is presented. The framework intends to support the implementation of a national Monitoring, Verification and Report (MRV) system in developing countries. The framework proposed an operational definition of forest degradation and a set of indicators, namely Canopy Cover (CC), Aboveground Biomass (AGB) and Net Primary Productivity (NPP), derived from remote sensing data. The applicability of the framework is tested in a sub-deciduous tropical forest in the Southeast of Mexico. The results from the application of the methodological framework showed that the higher rates of forest degradation, 1596-2865 ha·year-1, occur in areas with high population density. Estimations of aboveground biomass in these degraded areas span from 1 to 24 Mg·ha-1, with a rate of carbon fixation ranging from 130 to 246 gC·m2·year. The results also showed that 43 % of the forests of the study area remain with no evident signs of degradation, as detected by the indicators selected, during the period evaluated. The integration of the different elements conforming the methodological framework for the assessment and monitoring of forest degradation enabled the identification of areas that maintain a stable condition and areas that change over the period evaluated. The methodology outlined in this thesis also allows for the identification of the temporal and spatial distributions of forest degradation based on the indicators selected, and it is expected to serve as the basis for operations of the REDD+ programme with the appropriate adaptations to the area in turn.

Author Keywords: Forest degradation, Monitoring, REDD+, Remote Sensing, Tropical forest