Tropical forests play a vital role in climate change because they may act as sinks or sources of CO2 depending on their management and responses to climate. Satellite imagery indicates that 3.8% of Myanmar's forests were lost between 2001 and 2020, but the extent to which commercial forestry is degrading the remaining forests is not well established. Although Myanmar’s forests provide a range of economic, environmental, social, and cultural benefits, detailed knowledge of the country’s forest resources is still lacking. My thesis focuses on evaluating how carbon storage and tree species composition in Myanmar's tropical forests are affected by its management history and environmental factors. Chapter 1 introduces the ecology of Myanmar’s forests. It describes historical and current forest management systems and the nation’s commitment to international climate change mitigation interventions. Chapter 2 evaluates the drivers of aboveground carbon density (ACD) variation across the nation’s forests using a national ACD map derived from data collected by NASA’s space-borne LiDAR sampler (GEDI). Using matching analyses, I find that terrain (elevation, slope, and aspect) and climatic attributes (including precipitation) are positively related to ACD, plateauing at higher values. ACD is negatively affected by anthropogenic factors, declining close to cities and roads. Strictly protected forests store twice as much carbon as less regulated forests, indicating that establishing protected areas in Myanmar is an effective strategy for protecting high-carbon forests. Chapter 3 compares remotely sensed biomass estimates of ACD with traditional forest inventory datasets, using a government-funded national forest inventory (NFI) covering 320,000 km2 of dry tropical forests. I use the same statistical approach as employed in Chapter 2 to predict the influences of biophysical factors on ACD. Biophysical factors influence field estimates of ACD in qualitatively the same way as found using LiDAR data in Chapter 2. However, field estimates of ACD are about 1/3 the value of estimates based on remote sensing data iii and estimates from inventory plots and GEDI are found to be only weakly correlated even when data are averaged within municipalities to reduce the effects of local noise. This difference has probably resulted from human error associated with forest inventory, such as misidentification of tree species, using different sampling methods, and omitting trees from the inventory, but more work is required to understand why there are strong discrepancies. Chapter 4 analyses tree species distribution patterns, as this information is required for effective forest management yet is lacking at the national scale. Annual mean temperature, temperature in the wettest season, temperature in the summer, and precipitation in the driest season are found to be important climatic determinants of species composition. I conclude that the deciduous forests of Myanmar, which have been extensively selectively harvested for valuable timber, have the potential to sequester large quantities of carbon and protect tree diversity if adequately protected. Chapter 5 explores the effectiveness of a protected area at maintaining different forest types and conserving biodiversity. Focusing on forest inventory data I collected at the Popa Mountain Park, I evaluate biophysical influences on ACD on tree species composition and carbon. Elevation positively correlates with ACD, but species richness does not. Non-Metric Multidimensional Scaling analysis reveals that elevation is the most important determinant of species diversity in the park. Additionally, comparisons of ACD within the park and matched production forests across the entire country show that the park stores ten times the amount of ACD compared to the exploited production forests.