In line with the NRC?s Decadal Survey, NASA is planning the HyspIRI mission, where a hyperspectral imager will be launched in approximately 2013. The purpose of HyspIRI is to characterize land surface composition and vegetation types for ecosystem health. In order to better prepare end-users for the utilization of HyspIRI observations, it is critical that we conduct feasibility studies beforehand. Precision mapping of vegetative species can be a key input to ecological forecasting models. In particular, highly accurate mapping of invasive species can provide valuable insight to climate change, since new invasive species may disperse into novel regions as a response climate change. This project will exploit hyperspectral multitemporal Earth observations for high precision vegetation mapping (PVM). This project will create proxy HyspIRI data via archived datasets of existing hyperspectral sensors, vegetation datasets collected with ground-based hyperspectral sensors by the PIs in recent NASA, USGS, and DHS funded projects. Since these ground-based sensors have a much higher spatial and spectral resolution, the data?s resolution can be downscaled and atmospheric effects can be integrated to create the HyspIRI proxy data. These datasets will be subjected to new, advanced hyperspectral analysis techniques developed by the PIs for PVM, resulting in quantitative assessments of target vegetation (e.g. invasive species) detection accuracies and false alarm rates. The outcomes of this project will be (i) HyspIRI proxy data for invasive species applications, (ii) PVM algorithms that exploit hyperspectral imagery, (iii) feasibility studies demonstrating the capacity for improving vegetation mapping via HyspIRI observations, (iv) quantitative comparison of existing invasive species decision support tools (i.e. the Invasive Species Forecasting System - ISFS Framework) with and without the inclusion of new PVM models, (v) integration plan for PVM models and ISFS for a variety of ecological forecasting and agriculture applications. Application Areas: Ecological Forecasting, Agriculture