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

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

Assessing Ecological Functions of Bottomland Hardwood Wetlands Using Remote Sensing and Geographic Information Systems

Bottomland hardwoods are one of the most rapidly diminishing wetland ecosystems due to agricultural clearing, development, and reservoir construction. As society has become more aware of the functions of wetlands, so has the importance in conservation of these valuable resources. The objective of this study was to compare the accuracy of Remote Sensing and GIS based functional assessment to the field based Hydrogeomorphic (HGM) approach. Remote sensing models were developed using a combination of soil maps, soil information, QuickBird ® multispectral satellite imagery, LiDAR derived Digital Elevation Model (DEM), and LiDAR derived Canopy Height Model. Results, although mixed, indicated that Remote Sensing and GIS show promise to be an alternative to the traditional field based wetland assessment method

Common neural basis of motor sequence learning and word recognition and its relation with individual differences in reading skill

To investigate the neural basis of a common statistical learning mechanism involved in motor sequence learning and decoding, we recorded brain activation from participants during a serial reaction time (SRT) task and a word reading task using functional magnetic resonance imaging. In the SRT task, a manual response was made depending on the location of a visual cue, and the order of the locations was either fixed or random. In the word reading task, visual words were passively presented. In the inferior frontal gyrus pars triangularis (IFGpTr) and the insula, differences in activation between the ordered and random condition in the SRT task and activation to printed words in the word reading task were correlated with the participants' decoding ability. We speculate that extraction of statistically predictable patterns in the IFGpTr and insula contributes to both motor sequence learning and orthographic learning, and therefore predicts individual differences in decoding skill

A GIS tool for plant spatial pattern analysis

A GIS program, ArcPlantPattern, was developed with Visual Basic .NET and ArcObjects as an ArcGIS extension to assist the investigation of plant distribution patterns (species composition as occurrence probability and spacing as distances among species) and to design planting plan maps for patch planting. ArcPlantPattern is the first software of its kind. It can be used for arid and semiarid lands reclamation, burned area rehabilitation, or designing landscapes with a required plant community distribution. ArcPlantPattern may also be applicable to other spatial point pattern analysis, such as geology, geography and wildlife habitat