This dissertation employed a random forest algorithm for Land Use Land Cover (LULC) classification and proposed and tested a modified forest transition framework in the Greater Shawnee National Forest (GSNF), Illinois. Subsequently, a machine learning-based multilayer artificial neural network was used to assess the LULC of the GSNF between 2019 and 2050 utilizing IPCC-based projected climate data. The accuracy of LULC classification was evaluated using Kappa statistics and Producer and User accuracies. The Stepwise Regression, Support Vector Machine, Random Forest, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models were compared to quantify terrestrial carbon stock. Similarly, InVEST, FRAGSTAT, and Maxent models were used for habitat quality analysis and to estimate the probability of bobcat distribution. The terrestrial carbon stock, habitat quality, and bobcat distribution were quantified across three spatial resolutions, 30, 60, and 90 meters, to assess if there were substantial differences in the represented trends of these measures of Ecosystem Services (ES). The LULC analysis showed varying levels of temporal and spatial variabilities with increased deciduous forest (1.35%), mixed forest (26.40%), agricultural land (2.15%), and urbanized areas (6.70%) between 1990 and 2019. Notably, the LULC intensity analysis exhibited stability from 2001 to 2019, consistent with the forest transition framework proposed in the study. However, when integrating temperature and precipitation projections derived from the IPCC, notable changes in forest cover were observed from the western to eastern sectors within the central region of the GSNF. In all IPCC based scenarios, overall forest cover (deciduous, evergreen, and mixed) declined. The classification accuracy of the LULC assessment ranged from 92.9% to 95.9%, accompanied by kappa statistics ranging from 0.89 to 0.94. The prediction accuracy of LULC change was validated for the year 2019, ranging from 77.99% to 84.67%, with kappa statistics between 0.79 and 0.81, depending on the scenario, and predictions were extended to the year 2050. The terrestrial carbon stock in GSNF varied from 15 to 212 MgC per hectare across different models. The RF model performed best at 90 meters resolution with FIA-based data, with RMSE values of 17.45, 18.73, and 20.05, and R-squared values of 0.53, 0.48, and 0.43 for 2001, 2010, and 2019, respectively. The findings indicated that while the InVEST model provides a broad and generalized approach to quantifying carbon storage, the random forest (RF) model is essential for obtaining more accurate and precise estimations. LULC has gradually become more fragmented over time, leading to a decline in average habitat quality from 1990 (0.724±0.215) to 2019 (0.689±0.192). Regardless of increased forest density, the proportion of high-quality habitats (habitat quality score above 0.83) decreased by 5% during the study period. Interestingly, there was a notable increase in the probability score of bobcat distribution from 1990 (0.327±0.123) to 2019 (0.347±0.084). The study revealed a strong correlation between habitat quality and the probability of bobcat distributions, indicating a mutual influence between the two factors. This dissertation suggests that the LULC change of the GSNF follows the forest transition framework and has a significant implication on ecosystem services, such as carbon storage and habitat quality. These results are instrumental for sustainable land management to optimize terrestrial carbon stock and habitat quality, thereby mitigating the impacts of climate change.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:dissertations-3263 |
| Date | 01 August 2024 |
| Creators | Thapa, Saroj |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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