Ponce-Hernandez, Raul

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

This thesis focuses on the design of a modelling framework consisting of loose-coupling of a sequence of spatial and process models and procedures necessary to predict future flood events for the years 2030 and 2050 in Tabasco Mexico. Temperature and precipitation data from the Hadley Centers Coupled Model (HadCM3), for those future years were downscaled using the Statistical Downscaling Model (SDSM4.2.9). These data were then used along with a variety of digital spatial data and models (current land use, soil characteristics, surface elevation and rivers) to parameterize the Soil Water Assessment Tool (SWAT) model and predict flows. Flow data were then input into the Hydrological Engineering Centers-River Analysis System (HEC-RAS) model. This model mapped the areas that are expected to be flooded based on the predicted flow values. Results from this modelling sequence generate images of flood extents, which are then ported to an online tool (ADAPT) for display. The results of this thesis indicate that with current prediction of climate change the city of Villahermosa, Tabasco, Mexico, and the surrounding area will experience a substantial amount of flooding. Therefore there is a need for adaptation planning to begin immediately.

Author Keywords: Adaptation Planning, Climate Change, Extreme Weather Events, Flood Planning, Simulation Modelling

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

This thesis uses three approaches to assess the potential of permaculture in Central Ontario. This was done using a vegetable field trial and modelling programs to determine the effectiveness of permaculture to decrease negative impacts of climate change based on projected climate values derived from regional circulation models. The first approach showed no statistical difference (P<0.05) of applying varied volumes and combinations of organic amendments on crop yields. The second approach indicated permaculture may be a sustainable production system with respect to soil erosion when compared to traditional agricultural practices. The third approach was inconclusive due to the lack of quantitative literature on permaculture management impacts on biomass yields, soil carbon or nutrient retention, which were missing from basic and scientific literature searches. The models used within this thesis include USLE, RUSLE2, AgriSuite, RothC and Holos.

Author Keywords: Agriculture, Climate Change, Computer Modelling, Permaculture, Soil Erosion and Assessment

This thesis presents an exploration of the requirements for and development of a software tool to calculate Certified Emission Reduction (CERs) credits for afforestation and reforestation projects conducted under the Clean Development Mechanism (CDM). We examine the relevant methodologies and tools to determine what is required to create a software package that can support a wide variety of projects involving a large variety of data and computations. During the requirements gathering, it was determined that the software package developed would need to support the ability to enter and edit equations at runtime. To create the software we used Java for the programming language, an H2 database to store our data, and an XML file to store our configuration settings. Through these choices, we can build a cross-platform software solution for the purpose outlined above. The end result is a versatile software tool through which users can create and customize projects to meet their unique needs as well as utilize the features provided to streamline the management of their CDM projects.

Author Keywords: Carbon Emissions, Climate Change, Forests, Java, UNFCCC, XML

This thesis proposes an agro-ecological zoning (AEZ) methodology of southern Ontario for the characterization and mapping of agro-ecological zones during the historical term (1981-2010), and their shifts into the long-term (2041-2070) projected climate period. Agro-ecological zones are homogenous areas with a unique combination of climate, soil, and landscape features that are important for crop growth. Future climate variables were derived from Earth System Models (EMSs) using a high emission climate forcing scenario from the Intergovernmental Panel on Climate Change 5th Assessment Report. The spatiotemporal shifts in agro-ecological zones with projected climate change are analyzed using the changes to the length of growing period (LGP) and crop heat units (CHU), and their manifestation in agro-climatic zones (ACZ). There are significant increases to the LGP and CHU into the long-term future. Two historical ACZs exist in the long-term future, and have decreased in area and shifted northward from their historical locations.

Author Keywords: Agro-climatic Zones, Agro-ecological Zones, Agro-ecological Zoning, Climate Change, Crop Heat Units, Length of Growing Period

Soil mineralizable nitrogen (N) is the main component of soil N supply in humid temperate regions and should be considered in N fertilizer recommendations. The objectives of this study were to determine the potentially mineralizable N parameters, and improve N fertilizer recommendations by evaluating a suite of soil N tests in southwestern Ontario. The study was conducted over the 2013 and 2014 growing seasons using 19 field sites across southwestern Ontario. The average potentially mineralizable N (N0) and readily mineralizable N (Pool I) were 147 mg kg-1 and 42 mg kg-1, respectively. Pool I was the only soil N test that successfully predicted RY in 2013. The PPNT and water soluble N (WSN) concentration (0-30cm depth) at planting were the best predictors of fertilizer N requirement when combing data from 2013 and 2014. When soils were categorized based on soil texture, the relationships also improved. Our findings suggest that N fertilizer recommendations for grain corn can be improved, however, further field validations are required.

Author Keywords: corn, nitrogen, nitrogen mineralization, soil nitrogen supply, soil N test, southwestern Ontario