Available at: https://digitalcommons.calpoly.edu/theses/2853
Date of Award
6-2024
Degree Name
MS in Statistics
Department/Program
Statistics
College
College of Science and Mathematics
Advisor
Hunter Glanz
Advisor Department
Statistics
Advisor College
College of Science and Mathematics
Abstract
The Amazon rainforest, a vital ecosystem of immense biodiversity and global climate significance, faces the ongoing threat of deforestation driven by agricultural expansion. This thesis employs remote sensing techniques, focusing on the Enhanced Vegetation Index (EVI) derived from Landsat satellite imagery, to track land cover dynamics within the Amazon. The study examines historical land cover changes in current plantations in Peru and Brazil, regions where the exact timing of deforestation is uncertain. By analyzing EVI measurements dating back to 1984, inflection points indicative of deforestation events preceding plantation establishment are identified. Statistical modeling techniques, including spline fitting to analyze time series data and Random Forest algorithms for calibration, are employed to enhance the accuracy of EVI measurements. Additionally, predictions for deforestation years derived from ALOS satellite data are compared with those from Landsat imagery, revealing discrepancies and underscoring the need for methodological refinement.