Browsing by Author "Feng, Leyang"

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    Accuracy Assessment of the NLCD 2006 Impervious Surface for Roanoke and Blacksburg
    Zhao, Suwen; Feng, Leyang; Shao, Yang; Dymond, Randel L. (2014)
    Impervious surface map products are important for the study of urbanization, urban heat island effects, watershed hydrology, water pollution, and ecosystem services in general. At the conterminous US scale, impervious surfaces are mapped for 2001 and 2006. The accuracy of the 2006 NLCD impervious surface, however, has not been thoroughly examined, especially for small and intermediate size cities (e.g., regional city). In this study, we selected two transects in two cities and visually interpreted aerial photo to develop impervious surface reference maps. We then compared percent impervious surface of the NLCD and aerial photo-interpreted reference maps. The comparison was conducted at 90m resolution to minimize the errors in image registration. Overall, we found that the 2006 NLCD impervious surface matched well with our reference data, although slight skewness at two extremes is present. The R² and RMSE statistics improved when the two datasets are compared at coarse aggregation levels (e.g. 180m).
  • Application of SEBAL Model for Mapping Evapotranspiration in Iowa Using MODIS Time-Series Data
    Feng, Leyang (2014)
    Evapotranspiration (ET), including evaporation from soil surface and vegetation transpiration, is an important variable for water and energy balances on the Earth's surface. Quantifying evapotranspiration (ET) from agriculture fields is important for field water planning and management. Also, knowledge of spatio-temporal distribution of evapotranspiration (ET) on large scales, can provide important information on a variety of water resources issues such as water distribution evaluation, water use by different land surfaces and better management of ground and surface water resources. The main method used traditionally to measure ET are subject to individual, field or landscape scales, but regional ET cannot be measure directly or interpolated due to the inherent spatial heterogeneity of the land surface. Due to the development of remote sensing technology, critical land surface variables with spatial distribution can be acquired easily, such as surface albedo, fractional vegetation cover, and land surface temperature. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) is applied to time series of MODerate Resolution Imaging Spectroradiometer (MODIS) Level 3 data of reflectance and surface temperature measurements to estimate monthly evapotranspiration in Iowa. Spatial distribution and seasonal variation of ET were also analyzed on a large scale.
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    Sensitivity Analysis of Hot/Cold Pixel Selection in SEBAL Model for ET Estimation
    Feng, Leyang (Virginia Tech, 2015-06-15)
    The objective of this study was to evaluate the sensitivity of instantaneous latent heat flux (LE) estimation from Surface Energy Balance Algorithm for Land (SEBAL) by changing hot/cold pixel selections. The SEBAL model was programed in a Matlab environment and applied to Lower Fox Watershed in northeast Illinois using two Landsat 5 Thematic Mapper images acquired in summer 2006. Unlike most previous studies where hot/cold pixel were manually selected by image analysts, we emphasized an automated hot/cold pixel selection based on land cover map, normalized difference vegetation index (NDVI) map, and land surface temperature (LST) map. Various combinations of hot/cold pixels were automatically selected along the LST gradient. The LE estimations were then validated against ground-based eddy covariance observation. Results show that the LE estimations from SEBAL were sensitive to both hot and cold pixel selections and tend to be more sensitive to cold pixel selection. The absolute percentage difference (APD) of LE estimation compared with field observation data can range from 0.67% to 67.2% by varying hot and cold pixel combinations. The location of hot/cold pixels appears to have minor impact on SEBAL LE estimation. The LE estimation become acceptable (APD < 10%) when using the hot/cold pixels with a slightly higher/lower LST than LST extremes from the study area. This study provides insights into hot/cold pixel selection and the sensitivity of SEBAL-based LE estimation. Future research on SEBAL ET estimation should focus on enhancing automated hot/cold pixel selection algorithm to improve the model's operational use.