Hu, BaoxinItuen, Ima-Obong2024-11-072024-11-072024-09-162024-11-07https://hdl.handle.net/10315/42510There is an interesting effect of the changing climate: it has been observed in Northern Ontario that the warming weather has lengthened the growing season. Consequently, there has been increased land use conversion from natural forests to agriculture in Northern Ontario to capitalise on the new economic opportunities resulting from longer growing seasons. However, the long-term effects of land conversion on soil carbon, nutrients, and greenhouse gas emissions (GHGs) are unknown. This research study leveraged advanced remote sensing technology to detect changes in land cover and land use. A major problem addressed in this work was the lack of landcover classification data which is current, extends over the entire study area, and is of high spatial and temporal resolution with which to analyse the study region properly. This study proposed a new change detection framework which uses the best available data for landcover classification and disturbance information, even in the event of scarce input or training data. Thus, an analyst can progressively adapt the available data sources to present a more accurate assessment of the disturbances. More accurate input data for the carbon models was shown in this study to decrease carbon and GHG estimates by between 15% and 32%. The study concludes with tools that an everyday user can utilize to examine their region. The applications can run for free on their devices, permitting users to discover the landcover changes in their region, and estimate forest loss or gain. The applications could serve as a bridge to discussions concerning the areas that are best suited to concentrate carbon sequestration efforts.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Remote sensingElectronic Thesis or Dissertation2024-11-07Landcover classificationChange detectionGoogle Earth EngineCarbon modellingGreat Clay Belt