Browsing by Author "Hassan, Quazi K."

Now showing 1 - 18 of 18
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    Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review
    (Multidisciplinary Digital Publishing Institute, 2015-01-05) Mosleh, Mostafa K.; Hassan, Quazi K.; Chowdhury, Ehsan H.
    Rice is one of the staple foods for more than three billion people worldwide. Rice paddies accounted for approximately 11.5% of the World’s arable land area during 2012. Rice provided ~19% of the global dietary energy in recent times and its annual average consumption per capita was ~65 kg during 2010–2011. Therefore, rice area mapping and forecasting its production is important for food security, where demands often exceed production due to an ever increasing population. Timely and accurate estimation of rice areas and forecasting its production can provide invaluable information for governments, planners, and decision makers in formulating policies in regard to import/export in the event of shortfall and/or surplus. The aim of this paper was to review the applicability of the remote sensing-based imagery for rice area mapping and forecasting its production. Recent advances on the resolutions (i.e., spectral, spatial, radiometric, and temporal) and availability of remote sensing imagery have allowed us timely collection of information on the growth and development stages of the rice crop. For elaborative understanding of the application of remote sensing sensors, following issues were described: the rice area mapping and forecasting its production using optical and microwave imagery, synergy between remote sensing-based methods and other developments, and their implications as an operational one. The overview of the studies to date indicated that remote sensing-based methods using optical and microwave imagery found to be encouraging. However, there were having some limitations, such as: (i) optical remote sensing imagery had relatively low spatial resolution led to inaccurate estimation of rice areas; and (ii) radar imagery would suffer from speckles, which potentially would degrade the quality of the images; and also the brightness of the backscatters were sensitive to the interacting surface. In addition, most of the methods used in forecasting rice yield were empirical in nature, so thus it would require further calibration and validation prior to implement over other geographical locations.
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    Development and Application of Water Quality Classification Models
    (2013-09-09) Akbar, Tahir Ali; Achari, Gopal; Hassan, Quazi K.
    Though surface water quality is a dynamic quantity; factors, such as increase in population, changes in climate, and anthropogenic activities impose more variability in recent times. The main objectives of this thesis were to: (i) develop models for classification of raw surface water quality, (ii) analyze the spatial patterns and temporal trends of surface water quality, (iii) obtain exceedances of parameters in each class; and (iv) develop remote sensing based models for Canadian Water Quality Index (CWQI) and turbidity. A methodology was developed using principal component analysis (PCA) and clustering techniques on the basis of 19 water quality parameters for 18 lakes of Alberta. Three principal components (PCs) were indicators of hardness, salinity and biological activities for lakes. The surface water quality showed deterioration as the cluster number increased from 1 to 5. The most deteriorated quality of water was found in Cardinal Lake, Moonshine Lake, Winagami Lake, Miquelon Lake and Saskatoon Lake. A total exceedance model was developed for clusterization of surface water quality for 12 major rivers of Alberta. The PCs were the indicators of watershed geology, mineralization and anthropogenic activities related to land use/cover for rivers. The clusters showed a strong relationship with CWQI classes. Snow melting deteriorated the surface water quality of rivers due to anthropogenic activities from different land uses/covers. There was increasing trend for the mean exceedance of the parameters as the cluster number increased from low to high. Empirical models were developed for Canadian Water Quality Index and turbidity using 31 scenes of Landsat-5 TM satellite data for the Bow River. The significant models were 14 for CWQI and 12 for turbidity. 100% matching was found for 72% and 83% of data in best-fit models for CWQI and turbidity respectively. The variation in the Bow River water quality was due to climatic changes and irrigation.
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    Development of a Remote Sensing-Based “Boro” Rice Mapping System
    (Multidisciplinary Digital Publishing Institute, 2014-03-03) Mosleh, Mostafa K.; Hassan, Quazi K.
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    Development of Flow Forecasting Models in the Bow River at Calgary, Alberta, Canada
    (Multidisciplinary Digital Publishing Institute, 2014-12-24) Veiga, Victor B.; Hassan, Quazi K.; He, Jianxun
    River flow forecasting is critical for flood forecasting, reservoir operations, and water resources management. However, flow forecasting can be difficult, challenging and time consuming due to the spatial and temporal variability of climatic conditions and watershed characteristics. From a practical point of view, a simple and intuitive approach might be more preferable than a complex modeling approach. In this study, our objective was to develop short-term (i.e., daily) flow forecasting models in the Bow River at the city of Calgary, Alberta, Canada. Here, we evaluated the performance of several regression models, along with a newly proposed “base difference” model, by using antecedent daily river flow values from three gauge stations (i.e., Banff, Seebe, and Calgary). Our analyses revealed that using a multivariable linear regression formulated as a function of upstream gauge stations (i.e., Banff or Seebe) and the station of interest (i.e., Calgary) using antecedent flows demonstrated strong relationships (i.e., having r2 (coefficient of determination) and RMSE (root-mean-square deviation) of approximately 0.93 and 14 m3/s, respectively). As such, we opted to suggest that the use of Banff and Calgary stations in forecasting the flows at Calgary could be considered as it would require a relatively lower number of gauge stations.
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    Environmental Modelling
    (2018) Hassan, Quazi K.
    This is a lecture note series for the course ENGO 583/ENEN 635 Environmental Modelling. These cover the following topics: nature and purpose of environmental modelling; the top-down and the bottom-up approaches; typology of environmental models; definition of fundamental concepts; steps involved in designing and building a model; calibration, verification and validation of models; scale dependency; sensitivity analysis; characteristics, architecture and functioning of selected environmental models.
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    Evaluating Potential of MODIS-based Indices in Determining “Snow Gone” Stage over Forest-dominant Regions
    (MDPI Publishing, 2010-05) Sekhon, Navdeep S.; Hassan, Quazi K.; Sleep, Robert W.
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    Forest Fire Danger/Risk Forecasting: A Remote Sensing Approach
    (2020-03) Ahmed, M. Razu; Hassan, Quazi K.; Gupta, Anil; Kibria, Md Golam
    Forest/wildland fires are natural disasters that create a significant threat to the communities living in the vicinity of the forested landscape. To minimize the risk concerning resiliency of those urban communities to forest fires, my overall objective was to develop primarily remote sensing (RS)-based models assessing potential risks at the wildland-urban interface (WUI) and making predictions of danger conditions in the environs forest/vegetation. I investigated the risks associated with WUI for the Fort McMurray community and danger conditions in the northern part of Alberta, Canada. For developing the risk modelling framework at WUI, I employed primarily a WorldView-2 satellite image acquired on June 06, 2016. I estimated structural damages due to the devastating 2016 Horse River wildland fire (HRF) that entered the community on May 03, 2016. Besides, I analyzed the presence of vegetation at the WUI to identify the associated risks according to the FireSmart Canada guidelines. My remote sensing-based estimates of the number of structural damages identified a strong linear relationship (i.e., r2 value of 0.97) with the ground-based estimates. Besides, all damaged structures were found associated with the existence of vegetation within the 30m buffer/priority zone of the WUI. It was revealed that approximately 30% of the areas of the WUI were vulnerable due to the presence of vegetation, in which approximately 7% were burned during the 2016 HRF event that led the structural damages. In addition, I developed a new medium-term (i.e., four days) model to forecast forest fire danger conditions using RS-derived biophysical variables of vegetation. I primarily employed Terra MODIS (moderate resolution imaging spectroradiometer)-derived four-day composites of daily surface temperature, normalized difference vegetation index and normalized difference water index. The model was able to detect about 75% of the fire events in the top two danger classes (i.e., very high and high) when evaluated with the historical ground-based forest fire occurrences during the fire seasons of 2015–2017. Besides, the model was able to predict the 2016 HRF event with about 67% agreement. Finally, I developed an operational near real-time (NRT) model to forecast forest fire danger conditions for a day to the next 8 days. Here, I employed Terra MODIS-acquired NRT data from NASA's LANCE (land, atmosphere near real-time capability for earth observing system), where data are made available to the public domain within 2.5 hours of satellite observation. The NRT model was successful in producing forecasted forest fire danger maps at any given time. These developed risk/forecast models would be very useful for the stakeholders in the forest fires management strategies of saving life, property, and community.
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    Photochemical, UV and Ozone Based Advanced Oxidation Processes for Treatment of Aqueous Contaminants
    (2018-04-27) Somathilake, Madduma Kankanamalage Purnima Thejani; Achari, Gopal; Tay, Joo Hwa (Andrew); Kimura-Hara, Susana; Ponnurangam, Sathish; Vargas, Carlos; Hassan, Quazi K.; Mulligan, Catherine N.
    In this research, advanced oxidation processes for the degradation of certain aqueous contaminants in municipal and industrial wastewater were studied. Carbamazepine (CBZ) was selected as it is an emerging contaminant in municipal wastewater. Sulfolane and acid extractable organics (AEOs) of oil sands process water (OSPW) were selected as candidates for industrial wastewater. The studies were conducted using a variety of advanced oxidation processes including a sunlight mediated photochemical process using ferric ions. Degradation kinetics of different oxidation processes in spiked water and post-secondary treated wastewater/sulfolane contaminated groundwater/OSPW were investigated. Batch experiments as well as flow through photo-reactor experiments were conducted. Degradation of CBZ in spiked water and post-secondary treated wastewater was studied using UVC, UVA, UVC/H2O2, UVA/H2O2, UVC/TiO2, UVA/TiO2, O3, UVC/O3 and UVA/O3 in a batch photo-reactor. The optimum parameters of each process were identified and their impacts on degradation rates were investigated. Relationship between UV intensity and CBZ degradation rate was extrapolated to the performance of the UV disinfection unit of a local wastewater treatment plant in Calgary, Canada. Addition of 100 mg/L of H2O2 to the secondary treated wastewater effluent entering UV disinfection unit could achieve over 60% degradation of CBZ. The effective parameters of UVC/H2O2 process in the batch experiments were further advanced to a flow through photo-reactor for degradation of CBZ. The kinetics of degradation in the flow-through reactor were in good agreement with the relationships developed using batch photo-reactor in spiked water and post-secondary treated wastewater. Experiments on ozone and photo-assisted ozone for the treatment of CBZ have shown that photo-assisted ozonation leads to greater mineralization of CBZ in water/wastewater matrices. This study recommends that UV and ozone doses for photo-assisted ozonation applications should also consider organic constituents of water matrices along with target contaminants of interest. Finally, the feasibility of a sunlight mediated photochemical process using ferric ions for the treatment of a wide range of organic contaminants was studied. Batch experiments were conducted under natural sunlight to study the degradation of CBZ, sulfolane and AEOs of OSPW. Complete CBZ degradation and more than 60% reduction of AEOs were obtained using this process. The presence of organic matter decreased the kinetics of sulfolane degradation where 30% degradation in spiked contaminated groundwater was noted. The unique ability to degrade a wide range of contaminants from this process using only natural solar irradiation, suggest a higher application potential in wastewater treatment.
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    Remote Sensing For An Improved Geospatial Flash Flood Susceptibility Modeling Over An Arid Environment
    (2020-03-31) Khadra, Mohamed Shawky Mohamed Ali; El-Sheimy, Naser; Hassan, Quazi K.; Zhang, Yun; Kattan, Lina; Wang, Xin; Charif, Omar
    Flash floods are the foremost cause of irretrievable environmental damage in the arid Arabian Peninsula. The better understanding of the geomorphologic, topographic, climatic, and hydrologic characteristics of a selected watershed, and determining their geospatial relationships with respect to the flood extent are the core steps for mitigating and minimizing negative impacts of flooding. Therefore, the overall aim of the current study was to employ different remote sensing datasets in predicting prone areas to flash floods in the 'wilayats' (i.e., cities) of El Hamra, Bahla, and Nizwa, Ad Dakhiliyah Governate, the Sultanate of Oman as an example of the arid areas. Precipitation is a crucial variable for studying various climate-related research such as flash flood monitoring and prediction. The performance of five global satellite precipitation estimates (GSPEs) was evaluated using the available sub-daily and daily ground rainfall records. Generally, the five sub-daily and daily GSPEs showed good performance compared to the in-situ measurements. Moreover, statistical error models were employed to quantify the uncertainties in the daily GSPEs. Accurate digital terrain model (DTM) and channel network/orders with fine spatial details are mandatory for flood extent modeling. The DTM and its derived channel network have been employed successfully in many studies to extract various geomorphometric, topographic and hydrologic attributes. Therefore, a new pixel-based method was developed to quantify the horizontal accuracy of channel networks/orders-based three global DEMs using those extracted from LiDAR datasets as references. The vertical accuracy of global DEMs were also evaluated utilizing reference LiDAR elevation data. PALSAR DTM (12.5 m) and its derived channel network/orders were found to be the optimal candidates to derive geospatial layers required for flood susceptibility modeling. Flood susceptibility models were developed to define the likelihood of future flash flooding in the study area. The spatial relationships between flood triggering factors and flood inventory map were quantified. The integrated bivariate and multivariate statistical methods-based flood susceptibility models provided precise maps to predict future flood-prone areas under a rainfall intensity close to that which prevailed during the past flood event, at both high- and low- lands. The developed flood susceptibility models can contribute to mitigating the negative impacts of flash floods.
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    Remote Sensing of Local Warming and Its Application Over Alberta
    (2018-07-25) Rahaman, Khan Rubayet; Hassan, Quazi K.; He, Jianxun; Rangelova, Elena V.; Sumon, Kazi Z.; Levy, Jason
    The effects of a changing climate (i.e., temperature in particular) vary and will continue to vary significantly from global to local level. Changes of temperature at global and regional scale are somehow defined by using several models and simulations. However, temperature change at local scales (i.e., community or local government level) are not well defined and needs particular attention to address future adaptation policies in the face of climate change. For instance, this thesis is built on a hypothesis that temperature does vary locally in comparison to the predicted models at the regional and global scales. To address this critical issue, this study sets up the goal to delineate local warming maps using satellite-borne remote sensing data at 15 m spatial resolution. In doing so, firstly, 1 km spatial resolution warming map is prepared in the whole province of Alberta for 1961-2010 time period using MODIS-derived 8-days composite images at 1 km spatial resolution. Secondly, to enhance the spatial resolution of derived 1 km warming map, data fusion technique is used from Landsat-8 OLI-derived data to generate high spatial resolution EVI, NDVI, and NDWI. Finally, long-term warming trend map is produced at 15m spatial resolution to represent the changes of temperature normals (i.e., 1961-2010 time period) as a final outcome. Results have demonstrated that the proposed methods of delineating high spatial resolution local warming map has strong accuracy while comparing with the climate station-derived temperature data (r2 = 0.80 in case of 1961-1990; and r2 = 0.78 in case of 1981-2010). Similarly, while comparing the results of warming trend maps derived at 1 km and 15 m spatial resolution, outcomes have proved strong relationship too (r2 = 0.96 in case of 1961-1990; r2 = 0.95 in case of 1981-2010). Finally, this study explicitly brings the notion that of local level warming map can be produced at high spatial resolution and can be critical for local governments to initiate future policies depending on evidence in future climate change adaptation planning.
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    Remote Sensing of Local Warming and Its Application Over Alberta
    (2018-07-25) Rahaman, Khan Rubayet; Hassan, Quazi K.; He, Jianxun; Levy, Jason; Rangelova, Elena V.; Sumon, Kazi Z.
    The effects of a changing climate (i.e., temperature in particular) vary and will continue to vary significantly from global to local level. Changes of temperature at global and regional scale are somehow defined by using several models and simulations. However, temperature change at local scales (i.e., community or local government level) are not well defined and needs particular attention to address future adaptation policies in the face of climate change. For instance, this thesis is built on a hypothesis that temperature does vary locally in comparison to the predicted models at the regional and global scales. To address this critical issue, this study sets up the goal to delineate local warming maps using satellite-borne remote sensing data at 15 m spatial resolution. In doing so, firstly, 1 km spatial resolution warming map is prepared in the whole province of Alberta for 1961-2010 time period using MODIS-derived 8-days composite images at 1 km spatial resolution. Secondly, to enhance the spatial resolution of derived 1 km warming map, data fusion technique is used from Landsat-8 OLI-derived data to generate high spatial resolution EVI, NDVI, and NDWI. Finally, long-term warming trend map is produced at 15m spatial resolution to represent the changes of temperature normals (i.e., 1961-2010 time period) as a final outcome. Results have demonstrated that the proposed methods of delineating high spatial resolution local warming map has strong accuracy while comparing with the climate station-derived temperature data (r2 = 0.80 in case of 1961-1990; and r2 = 0.78 in case of 1981-2010). Similarly, while comparing the results of warming trend maps derived at 1 km and 15 m spatial resolution, outcomes have proved strong relationship too (r2 = 0.96 in case of 1961-1990; r2 = 0.95 in case of 1981-2010). Finally, this study explicitly brings the notion that of local level warming map can be produced at high spatial resolution and can be critical for local governments to initiate future policies depending on evidence in future climate change adaptation planning.
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    Remote Sensing of Wildland Fire-induced Risk Assessment Framework
    (National Fire Information Database (NFID) Project, 2017-12) Hassan, Quazi K.; Ahmed, M. Razu; Rahaman, Khan Rubayet
    Wildland fire is one of the critical natural hazards that pose a significant threat to the communities located in the vicinity of forested/vegetated areas. In this report, our overall goal was to use very high spatial resolution (0.5-2.4m) satellite images to develop wildland fire-induced risk framework. We considered two extreme fire events, such as the 2016 HRF over Fort McMurray, and 2011 Lesser Slave Lake fire in Alberta. Thus, our activities included the: (i) estimation of the structural damages; and (ii) delineation of the wildland-urban interface (WUI) and its associated buffers at certain intervals, and their utilization in assessing potential risks. Our proposed method of remote sensing-based estimates was compared with the ground-based information available from the Planning and Development Recovery Committee Task Force of Regional Municipality of Wood Buffalo (RMWB) and National Fire Information Database (NFID); and found strong linear relationships (i.e., r2-value of 0.97 with a slope of 0.97 for the 2016 HRF over Fort McMurray; and 378 from satellite image vs. 407 from 378 from satellite image vs. 407 from NFID system for the 2011 Lesser Slave Lake fire). Upon delineating the WUI and its associated buffer zones at 10m, 30m, 50m, 70m and 100m distances; we found existence of vegetation within the 30m buffers from the WUI for all of the damaged structures. In addition, we noticed that the relevant authorities had removed vegetation in some areas between 30m and 70m buffers from the WUI in case of Fort McMurray area, which was proven to be effective in order to protect the structures in the adjacent communities. Furthermore, we mapped the wildland fire-induced vulnerable areas upon considering the WUI and its associated buffers. We found that there were still some communities that had the existence of vegetation within the buffer zones; thus such vegetation should be removed and monitored regularly in order to reduce the wildland fire-induced risks.
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    Remote sensing-based determination of boreal spring phenology in Alberta
    (2011) Sekhon, Navdeep S.; Hassan, Quazi K.
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    Remote sensing-based determination of deciduous and understory phenology over boreal forest
    (2011) Rahman, Kazi Mahmudur; Hassan, Quazi K.; Haque, Anis S.
    Phenology of the deciduous trees and understory grasses are the vegetation developmental stages influenced by the climatic variables. The study of the deciduous and understory phenology is important in understanding plant growth, net ecosystem CO2 exchange, forest flammability, forest hydrology, risk of insect infestation, etc. The objective of the study was to determine the phenological stages of deciduous [i.e., deciduous leaf out (DLO), deciduous leaf fall (DLF)] and understory grass green-up [i.e., green grass stage (GGS)] over the boreal forested region in the Canadian province of Alberta. In this study, the MODIS-based 8-day: (i) surface temperature (Ts)-images to derive the equivalent air temperature (T O ; used to determine DLF), (ii) surface reflectances for calculation of normalized difference water index (NDWI: used to determine DLO and GGS) and (iii) accumulated growing degree days (AGDD: a favourable temperature regime for plant growth: used to determine DLO and GGS). The temporal dynamics of AGDD, T O and NDWI was analysed, in conjunction with in-situ DLO, DLF and GGS observations in determining the optimal thresholds for DLO in 2006 (i.e., 80 degree-days and NDWI 0.325), DLF in 2006-2007 (i.e., 4 °C) and GGS in 2006 (i.e., 90 degree-days and NDWI 0.45). The implementation of these thresholds revealed reasonable agreements [i.e. , on an average (91.9% of the DLO and 94.2% of the GGS for AGDD) and (65% of both DLO and GGS for NDWI) within ±2 periods or ±16 days of deviations during 2007-2008; and 77.4% of the DLF for Ta within same deviations during 2008)] with compare to the in-situ observed data.
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    Seasonal and Temporal Variations of Chloride Level in Riverine Environment
    (2018-03-23) Situ, Qianru; He, Jianxun; Hassan, Quazi K.; Rangelova, Elena V.
    Chloride is one of essential elements for the health of all organisms, humans included; however at the elevated level, chloride has detrimental impacts on ecosystem. Chloride originates from both natural sources and anthropogenic sources (e.g., municipal treated wastewater and emission from industries), which suggests that chloride level in natural water bodies (e.g., rivers) is affected by both natural factors (e.g., hydro-meteorological variables) and anthropogenic activities. Among various anthropogenic activities, the application of road salts, which is a common practice to improve the driving condition of roads in winters across Canada, has been shown to attribute to the elevation of chloride level. The impact of the use of road salts on riverine chloride level can be seasonal (during snow-melt season) and over a prolonged period. Furthermore, river flow primarily drives the seasonal variation of riverine chloride. Therefore this thesis aimed to investigate the intra-annual/seasonal variation and the inter-annual/temporal change of chloride in rivers/streams and to assess the impact of the application of roads salts on chloride across Canada through statistically analyzing available data including chloride concentration, the use of road salts, and flow datasets. The seasonal variation of chloride was detected and chloride was negatively correlated with river flow in general. However, the seasonal variation pattern of chloride and its dependency on flow appeared to be different between large and small rivers in a certain degree. The temporal change (especially increasing trend) in chloride was found at many stations. It was identified that either flow, anthropogenic activities, or their combination is ascribed to cause the temporal change of chloride. The increasing riverine chloride concentration at many stations was qualitatively linked to the increase of the application of road salts, while more elaborated research is recommended for quantitative investigation. In addition, the Code of Practice for the Environmental Management of Road Salts, which was published in 2004 by Environment and Climate Change Canada (former name, Environment Canada) to better manage the use of road salts in order to mitigate its impact on chloride, appeared to help in reducing the increasing trend of chloride level.
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    Solar Energy Modelling over a Residential Community in the City of Calgary, Alberta, Canada
    (2011-05-26) Hassan, Quazi K.; Rahman, K. Mahmud; Haque, Anis S.; Ali, Ahad
    Solar energy is an abundant source of renewable/sustainable energy, which has an enormous potential in reducing the foot print of the greenhouse gases. In this paper, we presented a modelling framework of estimating solar energy over a portion of a residential community of Sandstone in the northwest of Calgary, Canada. We calculated the actual daily incident solar radiation as a function of latitude, day of year, and possible day light hours; and also employed high-resolution remote sensing images to calculate the effective roof area for installing photovoltaic cells. Strong relationships (2∶0.91-0.98) were observed between the ground-based measurements and the modelled actual incident solar radiation at three test locations in Alberta. Over the portion of Sandstone, ~1706.49 m2 roof surface area was suitable for potential installation of the photovoltaic cells. With 15% efficient photovoltaic cells, our analysis revealed that we might be able to produce significant amount (i.e., in the range of ~67–100%) of electrical energy needs of the residents of Sandstone community during the period between April and September.
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    Vegetation and Forest Fire Dynamics in Alberta: A Ground and Remote Sensing Data Analysis
    (2024-07-02) Dastour, Hatef; Hassan, Quazi K.; Achari, Gopal; Ahmed, M. Razu
    This thesis presents a comprehensive analysis of the interplay between forest fires and vegetation dynamics in Alberta, Canada, under the lens of climate change. By synthesizing data from remote sensing, climate records, and fire databases, the study reveals the intricate relationships between vegetation cover changes and climatic factors throughout 2001–2022. It highlights the significant lead and lag times between the Normalized Difference Vegetation Index (NDVI) and climate variables such as Land Surface Temperature (LST), relative humidity, and precipitation, offering insights into the temporal dynamics of vegetation response to climatic influences. The research further explores the patterns and trends of forest fires, correlating them with interpolated climate data across various subregions. Using trend analysis and anomaly detection methods, the study identifies significant warming and drying trends, alongside variable precipitation changes, which have influenced both human-caused and lightning-induced forest fires. The findings underscore the differential impact of climate variables on fire occurrence and source, with notable patterns emerging in subregions like Athabasca Plain and Central Mixedwood. Building on these insights, the thesis develops a robust forest fire spread model, validated through high-precision simulations of the 2011 Slave Lake and 2016 Fort McMurray wildfires. The model leverages regional physical features, climatic data, and MODIS datasets to offer accurate fire behavior predictions. The phased simulation approach adapts to dynamic factors such as weather conditions and firefighting strategies, enhancing the model's applicability for effective fire management. Ultimately, this thesis aims to bridge the gap between theoretical understanding and practical application, providing valuable contributions to Alberta's forest fire management and community protection strategies. The research paves the way for more informed decision-making in the face of climate change by offering a nuanced understanding of fire-vegetation-climate interactions and developing an advanced predictive model.