Mississippi State University

About: Mississippi State University is a education organization based out in Starkville, Mississippi, United States. It is known for research contribution in the topics: Population & Catfish. The organization has 14115 authors who have published 28594 publications receiving 700030 citations. The organization is also known as: The Mississippi State University of Agriculture and Applied Science & Mississippi State University of Agriculture and Applied Science.

Role of Silanol Group in Sn-Beta Zeolite for Glucose Isomerization and Epimerization Reactions

Abstract: Density functional calculations are used to elucidate the role of the silanol group adjacent to the active site Sn metal center of the Sn-BEA zeolite in the isomerization and epimerization of glucose. We find that the silanol group plays an important role in the isomerization reaction, wherein hydride transfer and subsequent proton transfer occur in a single step with a lower energy of activation. Epimerization, on the other hand, proceeds via a mechanism similar to the Bilik mechanism and has lower activation barrier when the silanol group does not participate directly in the transition state. Our calculations indicate that cooperative effects, often encountered in enzymatic catalysis, promote hydride transfer in the isomerization reaction but not for the Bilik mechanism for epimerization.

Influence of fusing lidar and multispectral imagery on remotely sensed estimates of stand density and mean tree height in a managed loblolly pine plantation

Abstract: Stereo aerial photography has long been used to measure tree density and height photogrammetrically. Recent attempts have been made to locate and measure trees automatically in high-resolution digital imagery. This study used small-footprint lidar (1.057 μm, 1 mrad divergence, 0.67 m footprint) and high-resolution (0.61 m) multispectral (550, 675, 700, and 800 nm) data sets to estimate stem counts and tree heights in 15-yr-old loblolly pine stands. A data fusion process was used to combine the datasets. Tree identification accuracy and mean height estimation derived from the separate and fused data sets were compared against field data. Tree identification was more accurate using spectral data (78.6% and 92.4%) than lidar data (64.7% and 87.3%) within the two planting densities, respectively. The fused dataset improved accuracy oftree identification over the single-datasetapproaches (83.5% and 94.8%). Plot-level mean height of lidar-located trees provided the best estimates of mean field height (average difference = 0.15 m). Missed trees for all methods were shorter than mean field height by up to 3.03 m (fused data). These results indicate fusion of spectral and lidar data will likely improve estimates of mean tree height and stem density. Increased lidar posting density is identified as a key factor to improve tree recognition and measurement.