Graduation Year

2021

Document Type

Thesis

Degree

M.A.

Degree Name

Master of Arts (M.A.)

Degree Granting Department

Geography

Major Professor

Ruiliang Pu, Ph.D.

Committee Member

Joni Downs Firat, Ph.D.

Committee Member

He, Jin, Ph.D.

Keywords

geographically weighted regression, spatial-temporal pattern, wildfire occurrence

Abstract

Known as the “Lung of the world”, the Amazon rainforest produces more than 20% of the oxygen of the world, which was originally a carbon pool for mitigating climate change, but in recent years it has become a significant carbon emitter due to wildfires. In 2019, a large-scale fire occurred in the Amazon rainforest, causing serious damage to the ecosystem and to humans as well. Therefore, managing wildfires effectively has become an urgent task for fire authorities. This thesis tried to incorporate spatial analysis and spatial statistics approaches to study wildfire from two aspects, namely the temporal and spatial distribution and spatial relationships in the Brazilian Amazônia Biome in 2019.

First, this thesis uses different spatial analysis approaches to provide information on broad-scale wildfire occurrence spatiotemporal patterns in the Amazônia Biome. Spatial analysis methods of kernel density, average nearest neighbor (ANN), spatial autocorrelation (Global Moran’s I), and hot spot analysis were adopted to assess wildfire occurrence spatial patterns such as spatial distribution characteristics, spatial clustering, and spatial hotspots in the Amazônia Biome. Using the ANN tool, the existence of statistically significant clusters of wildfire occurrences was identified at the state level. Also, Global Moran's I was employed to determine spatial autocorrelation based on the locations of wildfires, and the results revealed that the wildfire incidents were aggregated. The Getis-Ord Gi* and the Optimized Hot Spot Analysis (OHSA) tool were used to identify statistically significant locations of high values clusters (hotspots) for wildfire occurrences across the state, and at the municipality level. Further, several outliers at the state level were detected using the Optimized Cluster and Outlier Analysis. Second, spatial statistics models were used to demonstrate differences in the contributions of these factors and variation in their effects across different locations. We mainly attempted to investigate the relationship between the number of wildfires and topographical, vegetation cover, land use, anthropogenic, and meteorological factors by using the ordinary least square (OLS), global (quasi) Poisson, geographically weighted Gaussian regression (GWGR), and geographically weighted Poisson regression (GWPR), and to compare and evaluate the fitting and performance of different models.

Results indicate that wildfire spatial patterns exhibit strong regional differences, and seasonal patterns are different as well. The results of wildfire temporal pattern analysis indicate that the spatial variation and temporal contribution of wildfire across the biome of each state are different. Therefore, the fire department should formulate different fire plans and schedules for fire combat planning. In the vast and heterogeneous territory of the Amazônia Biome, the task of fire prevention and suppression can be challenging, requiring strategic planning to prioritize resources to the most critical areas. In terms of spatial distribution, wildfire occurrences were concentrated in some regions, including the southern Amazonas, the central part of Roraima, eastern Acre, the northernmost part of Rondônia, and southwest Pará, the southern and the northeastern of Pará, and the southeastern part of Mato Grosso.

The spatial relationship analysis reveals that the distribution and frequency of wildfires in the Amazônia Biome are strongly influenced by anthropogenic factors (e.g., deforestation and agricultural activities). For both global models, the forest loss, pastureland area, edge proportion, and the number of forest patches were strongly positively associated with an increased number of wildfire occurrences. As a result, the areas with extensive deforestation and agriculture, along with the wildland-farmland interface, require more attention from the fire brigades.

In comparison with the global models, GWR models provided a more detailed understanding of spatial relationships between contributing drivers and wildfires. The number of forest patches was positively related to the wildfire counts in eastern Acre, eastern Amazona, western Pará, and central Mato Grosso but negatively related to the wildfire counts in western Maranhão. As a result of the assessment of model fitting and predictions, it was found that the GWR models had better model fitting than the global models, yielded more accurate and consistent parameter estimations, and generated more realistic spatial distributions for the model predictions.

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