Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) are important ground cover types for desertification monitoring and land management. Hyperspectral remote sensing has been proven effective for separating NPV from bare soil, but few studies determined fractional cover of PV (f pv) and NPV (f npv) using multispectral information. The purpose of this study is to evaluate seve...

Google Earth now hosts high-resolution imagery that spans twenty percent of the Earth's landmass and more than a third of the human population. This contemporary highresolution archive represents a significant, rapidly expanding, cost-free and largely unexploited resource for scientific inquiry. To increase the scientific utility of this archive, we address horizontal positional accuracy (geore...

This study aims to estimate forest above-ground biomass and biomass components in a stand of Picea crassifolia (a coniferous tree) located on Qilian Mountain, western China via low density small-footprint airborne LiDAR data. LiDAR points were first classified into ground points and vegetation points. After, vegetation statistics, including height quantiles, mean height, and fractional cover we...

Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observat...

Cardiac glycosides are group of natural products found to be effective against various cancers. The aim of this study is to propose a binding mode for different conformation of cardiac glycoside analogues to Na, K ATPase pump which is an important target for various cancer cell lines. Quantitative structure activity relationships model were developed with the cytotoxic activity (Expt. IC50) of ...

Autonomous vehicles use a variety of sensors and machine-learned models to predict the behavior surrounding road users. Most in literature focus on quantitative error metrics like root mean square (RMSE) learn report their models' capabilities. This tends ignore more important behavioral aspect models, raising question whether these really human-like behavior. Thus, we propose analyze output mu...

The automatic compression strategy proposed by Gergel et al. is a near-optimal lossy compression scheme for a given collection of images whose interimage relationships are unknown. Their algorithm uses the root mean square error (RMSE) as a measure of the similarity between two images, in order to predict the compressibility of the difference image. Gergel et al. found that RMSE performed well ...

This paper addresses a path loss prediction and optimization by means of Lee’s model for Jiza town, south of Amman city, Jordan. Data were collected from mobile companies in Jordan in the frequency band of 900, and 1800 MHz. The calibrated Lee’s, Hata, and Egli’s path loss models were compared with respect to the original collected data. It is found that for the 900 MHz Lee’s prediction model h...