Leaf area index (LAI) is an important indicator of ecosystem condition and an important input to many ecosystem models. Remote sensing offers the only feasible method of estimating LAI at regional scales, and land managers can efficiently monitor changes in vegetation condition by using satellite data products such as estimates of LAI from the MODIS instrument onboard Terra and Aqua. However, many ecosystem processes occur at spatial scales finer than those available from MODIS. Students investigated different techniques for mapping LAI at multiple temporal and spatial scales, and created high-resolution (30m) LAI maps for Yosemite National Park using Landsat data in combination with ground-based measurements collected using three optical in-situ instruments: LAI-2000, DHP, and the TRAC instrument. In-situ data with three spectral vegetation indices derived from Landsat Thematic Mapper was compared imagery: Reduced Simple Ratio, Simple Ratio, and Normalized Difference Vegetation Index to identify statistical relationships that could be applied to map LAI for the park at higher spatial resolutions to supplement observations available from MODIS. Pixel values from the Landsat-derived LAI map were resampled to 1km and compared to LAI estimates from MODIS to assess agreement between LAI estimates derived from the two sensors. The MODIS LAI product was particularly useful because of its high temporal resolution and when supplemented with periodic, higher resolution mapping using Landsat data, could be used to efficiently monitor current and future vegetation changes in Yosemite.