Monitoring the responses of leaf functional traits and vegetation structure to environmental change is key to understanding the responses of ecological systems to anthropogenic global change. Advancement in earth observation technologies provide new opportunities to answer complex ecological questions at scales ranging from the community to the landscape level. This thesis investigates the impacts of environmental change on foliar traits and forest structure. In particular, it investigates the impacts of El Niño on leaf traits using hyperspectroscopy and on the structure of forests modified by oil palm plantations using airborne LiDAR. The first chapter explores the potential and limitations of using hyperspectral data to estimate leaf traits remotely. Working with six temperate tree species in contrasting soil types, I explore the relationship between leaf structure and chemistry and spectral characteristics. We show that interspecific differences in leaf traits were generally much stronger than intraspecific differences related to soil type. We highlight the difficulties that can arise in detecting within-species variation owing to “constellation effects” but demonstrate the power of spectroscopy to predict traits. The second chapter investigates the effects of drought on leaf traits and spectra, by taking measurements during and after an El Niño event. Pigments were particularly lower after the drought, when rain was more frequent, sunshine duration was shorter and radiation was lower, indicating increased greenness and forest resilience to climatic variation. Spectral information was also shown to be effective at detecting the impacts of droughts on leaf traits. Upscaling to the landscape level, the third chapter focuses on methods for estimating aboveground carbon stocks in oil palm plantations using airborne LiDAR. We show that an area-based approach is more accurate than tree-centric methods, although the latter may be useful to detect, extract and count individual oil palm trees from images. The fourth chapter explores the effects of El Niño drought on the canopy structure of tropical forests in Borneo using repeat airborne LiDAR. Our results reveal extensive leaf shedding caused by extreme high temperatures and Vapour-Pressure Deficit. Regenerating short forests on ridges were particularly more vulnerable to climatic variation owing to greater heat and atmospheric dryness. In conclusion, this thesis integrates field measurements and remote sensing to narrow down the uncertainties of vegetation responses to environmental changes.