A New Site Index Model for Intensively Managed Loblolly Pine (Pinus taeda) Plantations in the West Gulf Coastal Plain

Site index (SI) estimation for loblolly pine (Pinus taeda L.) plantations is important for the successful management of this important commercial tree species in the West Gulf Coastal Plain of the United States. This study evaluated various SI models for intensively managed loblolly plantations in the West Gulf Coastal Plain using data collected from permanent plots installed in intensively managed loblolly pine plantations across east Texas and western Louisiana. Six commonly used SI models (Cieszewski GADA model, both Chapman-Richards ADA and GADA models, both Schumacher ADA and GADA models, and McDill-Amateis GADA model) were fit to the data and compared. The Chapman-Richards GADA model and the McDill-Amateis GADA model were similar and best in their fit statistics. These two models were further compared to the existing models (Diéguez-Aranda et al. 2006 (DA2006), Coble and Lee 2010 (CL2010)) commonly used in the region. Both the Chapman-Richards GADA and the McDill-Amateis GADA models consistently predicted greater heights up to age 25 than the models of DA2006 and CL2010, with larger height differences for the higher quality sites, but predicted shorter heights thereafter. Ultimately, the McDill-Amateis GADA model was chosen as the best model for its consistency in predicting reasonable heights extrapolated beyond the range of the data. Foresters can use this model to make more informed silvicultural prescriptions for intensively managed loblolly pine plantations in the West Gulf Coastal Plain

Using Remotely Sensed Data to Quantify Contaminated Brine Sites in Southwest Texas

Although field checking of contaminated brine sites is relatively straight forward, the ability to field check a large and expansive area like southwest Texas can be time consuming and expensive. A more robust method is needed to accurately quantify brine contaminated sites in a more timely, efficient and cost effective manner. The overall goal of the project was to test a remote sensing methodology to accurately quantify the spatial extent and total acreage of contaminated brine sites in southwest Texas as a result of oil exploration. Landsat ETM+ data of southwest Texas were obtained and classified using supervised classification methodology with a maximum likelihood classification algorithm. Supervised classified was chosen since brine contaminated soil areas have distinct spectral signatures, especially in the dry season, which are easily distinguishable as training sites. Results indicate that Landsat ETM+ data can be an effective tool to use in quantifying previously unknown brine contaminated areas larger than 2 acres in southwest Texas to ascertain the spatial extent of contaminated brine sites as an aid in land reclamation/restoration

A momentum-space representation of Feynman propagator in Riemann-Cartan spacetime

We first construct generalized Riemann-normal coordinates by using autoparallels, instead of geodesics, in an arbitrary Riemann-Cartan spacetime. With the aid of generalized Riemann-normal coordinates and their associated orthonormal frames, we obtain a momentum-space representation of the Feynman propagator for scalar fields, which is a direct generalization of Bunch and Parker's works to curved spacetime with torsion. We further derive the proper-time representation in $n$ dimensional Riemann-Cartan spacetime from the momentum-space representation. It leads us to obtain the renormalization of one-loop effective Lagrangians of free scalar fields by using dimensional regularization. When torsion tensor vanishes, our resulting momentum-space representation returns to the standard Riemannian results.Comment: 12 page

Quantifying Land Cover Change Due to Petroleum Exploration and Production in the Haynesville Shale Region Using Remote Sensing

The Haynesville Shale lies under areas of Louisiana and Texas and is one of the largest gas plays in the U.S. Encompassing approximately 2.9 million ha, this area has been subject to intensive exploration for oil and gas, while over 90% of it has traditionally been used for forestry and agriculture. In order to detect the landscape change in the past few decades, Landsat Thematic Mapper (TM) imagery for six years (1984, 1989, 1994, 2000, 2006, and 2011) was acquired. Unsupervised classifications were performed to classify each image into four cover types: agriculture, forest, well pad, and other. Change detection was then conducted between two classified maps of different years for a time series analysis. Finally, landscape metrics were calculated to assess landscape fragmentation. The overall classification accuracy ranged from 84.7% to 88.3%. The total amount of land cover change from 1984 to 2011 was 24%, with 0.9% of agricultural land and 0.4% of forest land changed to well pads. The results of Patch-Per-Unit area (PPU) index indicated that the well pad class was highly fragmented, while agriculture (4.4-8.6 per sq km) consistently showed a higher magnitude of fragmentation than forest (0.8-1.4 per sq km)

Identifying Well Pads in the Haynesville Shale Region, Louisiana and Texas, with Digital Imagery

The Haynesville Shale is an underlying rock formation in northwest Louisiana and northeast Texas that contains vast quantities of natural gas. With new technology has come the ability to extract more natural gas from one of the largest gas deposits in the United States. With increased production, increased change in the local ecosystem will occur. It is necessary to examine oil and gas exploration effects on the local ecosystem due to changes in land cover, such as habitat loss and increased soil erosion. Remotely sensed imagery were utilized to ascertain the use of various digital image processing techniques to determine which digital transformation would more accurately identify current well pads within the Haynesville Shale region. Techniques evaluated included digital ratios, digital vegetation indices and digital principal component analysis. Results indicate that all vegetation indices and principal component analysis were extremely useful in visually identifying well pad locations while the effectiveness of digital ratios depended on the ratio utilized

Using GIS for Selecting Trees for Thinning

Thinning removes trees within a stand to regulate the level of site occupancy and subsequent stand development. Before thinning is applied, foresters determine the amount of residual growing stock, the spatial distribution of the residual trees, and the criteria used to select trees to cut. In this study, a portion of a loblolly pine (Pinus taeda) plantation was surveyed through a complete tree tally with the coordinates of each individual tree recorded. The dataset was then processed in a GIS program composed in Arc Marco Language (AML) applying a moving circular quadrat system superimposed over the study area. In each quadrant, tree attributes including DBH (nearest 0.1 inch), basal area (sq ft per ac), and density (trees per unit area) were utilized as determining factors for tree selection. A 3D visualization before and after thinning was created with a goal of equal distribution of trees across the stand

Toll-like receptor 3 activation is required for normal skin barrier repair following UV damage.

UV damage to the skin leads to the release of noncoding RNA (ncRNA) from necrotic keratinocytes that activates Toll-like receptor 3 (TLR3). This release of ncRNA triggers inflammation in the skin following UV damage. Recently, TLR3 activation was also shown to aid wound repair and increase the expression of genes associated with permeability barrier repair. Here, we sought to test whether skin barrier repair after UVB damage is dependent on the activation of TLR3. We observed that multiple ncRNAs induced expression of skin barrier repair genes, that the TLR3 ligand Poly (I:C) also induced expression and function of tight junctions, and that the ncRNA U1 acts in a TLR3-dependent manner to induce expression of skin barrier repair genes. These observations were shown to have functional relevance as Tlr3-/- mice displayed a delay in skin barrier repair following UVB damage. Combined, these data further validate the conclusion that recognition of endogenous RNA by TLR3 is an important step in the program of skin barrier repair