Utilization of satellite-derived high resolution land use/land cover data for the meteorological, emissions, and air quality modeling

Houston-Galveston area (HGA) is one of the most severe ozone non-attainment regions in the US. A primary goal of this research is to study how the changes in the land use (LU) and land cover (LC) impacts on the air quality in the HGA. Recently, the Texas Forest Service (TFS), with the support of Texas Commission on Environmental Quality (TCEQ), have compiled a new high-resolution LU/LC dataset for the eight counties surrounding the HGA to characterize regional changes in the vegetation and tree species. The updated map of LU/LC was produced using satellite imagery and ancillary datasets. A supervised classification process using image processing software was employed to define the 8 land cover classes and 15 land use classes.

Changes in the LU/LC not only affect physical characteristics involved in surface flux and radiative energy exchanges, but also influence the amount of biogenic emissions produced by the vegetation. Both the meteorological and emissions input data are key in determining the air quality in a region. The MM5 codes were modified to accommodate the new LU/LC categories in addition to the original USGS 25 LU/LC categories. Utilization of the new LU/LC in the MM5 simulation with the NOAH land-surface model resulted in less scatters in the temperature predictions compared with the surface observations than the simulation with the original USGS LU/LC data. For the biogenic emissions estimates, TCEQ have been utilizing a biogenic LU/LC dataset, which is partially based on the USGS data. The higher the spatial resolution of the data and the larger numbers of plant species available, the better the estimations that can be expected. Combined with the field plot information, satellite estimates of tree cover (stratified by the land use type specified in the new dataset), and detailed leaf mass density (LMD) data for HGA developed by the US Forest Service, we estimated changes in the biogenic emissions. The new biogenic emissions showed significant differences over the areas with large changes in the vegetation cover. Finally, we simulated the CMAQ air quality model with the new meteorological and emissions inputs to study the overall impacts of using the updated LU/LC dataset in the photochemical modeling of high ozone events in the Houston-Galveston nonattainment area.