Sparse coding, a method of explaining sensory data with as few dictionary bases as possible, has attracted much attention in computer vision. For visual object category recognition, `1 regularized sparse coding is combined with the spatial pyramid representation to obtain state-of-the-art performance. However, because of its iterative optimization, applying sparse coding onto every local featur...

Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height and the aboveground biomass at a 30 m spatial resolution in Maryland by combining Geoscience Laser Altimeter System (GLAS) data and Landsat spectr...

Forest canopy height is an important biophysical variable for quantifying carbon storage in terrestrial ecosystems. Active light detection and ranging (lidar) sensors with discrete-return or waveform lidar have produced reliable measures of forest canopy height. However, rigorous procedures are required for an accurate estimation, especially when using waveform lidar, since backscattered signal...

A decade-long pronounced increase in temperatures the Arctic, especially Barents Sea region, resulted a global warming hotspot over Svalbard. Associated changes cryosphere are consequence and lead to demand for monitoring of glacier changes. This study uses spaceborne laser altimetry data from ICESat ICESat-2 missions obtain ice elevation mass change rates between 2003–2008 2019. Elevation deri...

Low-cost adsorbents were tested to remove anionic surfactants from laundry rinsing water to allow re-use of water. Adsorbents were selected corresponding to the different surfactant adsorption mechanisms. Equilibrium adsorption studies of linear alkyl benzene sulfonate (LAS) show that ionic interaction results in a high maximum adsorption capacity on positively charged adsorbents of 0.6-1.7 gLA...

Missing data usually present special problems for statistical analyses, especially when the data are not missing at random, that is, when the ignorability principle defined by Rubin (1976) does not hold. Recently, a substantial number of articles have been published on model-based procedures to handle nonignorable missing data due to item nonresponse (Holman & Glas, 2005; Glas & Pimentel, 2008;...