This document summarizes a study analyzing local climate change in Rio Branco, Brazil due to deforestation. The researchers used Landsat thermal band data and a land cover classification map to model temperature anomalies as distance from forest increases. They found some relationship between distance and temperature anomalies, but the direction and amount were not uniform, warranting further inquiry. The study aimed to test whether deforested areas have higher temperatures than forested areas when correcting for elevation differences.

1. y = a +bx a = mean temperature at sea level b = lapse rate (rate at which temperature changes) y = predicted temperature image x = DEM y = 19.069720 + (- 0.004833) x Local Climate Change Analysis in Rio Branco, Brazil By Rebecca Alper and Rahul Rakshit Reclass Boolean Anomalies Greybody Reclass Predicted Temperature SRTM Distance Landcover Emissivity Introduction Conclusion Methodology Acknowledgements Climate change at a global scale is well documented in the scientific community. Regional or local climate change is a subject that has not gained as much attention by scientists but has important ramifications for regions such as rainforests in Brazil. With the advent of the ‘thermal band’ in satellite imagery, such investigations at a finer resolution are now possible. Aim: Our study focuses on the effects of deforestation on climate in and around Rio Branco, Brazil, as it is representative of large-scale deforestation. Hypothesis: We hypothesized that deforested areas, correcting for differences in elevation, should have higher temperatures than their forested counterparts. We used the Landsat thermal band 6.1 coupled with a classified landcover map of the area to model temperature anomalies as distance from forest increases. Our research also tested the new ‘Thermal module’ in IDRISI Andes, inputting an emissivity image created from Landsat Bands 3 and 4 using NDVI. We conclude from the study that although there exists some relationship between distance and temperature anomalies but their direction and amount are non uniform. Therefore, this study warrants further inquiry. We would like to thank Professor Ron Eastman for the project suggestion and subsequent advising, Jacqueline Vadjunec and her team for providing the classified landcover map, University of Maryland for the satellite imagery, Google Earth, and IDRISI for all the help on this project. Landcover Landsat Thermal (6.1) SRTM Difference 7X7 Filter Reclass Boolean Landsat Bands 3:4 NDVI Emissivity Thermal Greybody Resample 30 m Regress Distance Reclass Temperature Anomalies Regress ANOMALIES DISTANCE