Fuel loading prediction models developed from aerial photographs of the Sangre de Cristo and Jemez mountains of New Mexico, USA

Fuel load prediction equations that made use of aerial photographs were developed for Mixed Conifer, Ponderosa Pine (Pinus ponderosa Dougl. ex Laws.) and Pinyon-Juniper (Pinus edulis Engelm.)-(Juniperusmonosperma Engelm.) cover types from one-time measurements made in the Santa Fe watershed (SFWS) located in the Sangre de Cristo Mountains of northern New Mexico, and at Los Alamos National Laboratory (LANL) located in the Jemez Mountains of northern New Mexico. The results of the watershed data set were favorable and exhibited a high degree of relative accuracy. The results from the LANL data set did not share the same degree of accuracy, but rather exhibited a high degree of error. Use of these or similar prediction equations may be limited to certain regions and community types that exhibit similar regional characteristics such as terrain, soil, and weather conditions. Applied use of the prediction equations required less time than traditional fuel sampling performed onsite, but suffered from a loss of accuracy. It is strongly suggested that additional study of this method be undertaken to generate more accurate and reliable equations. Hopefully, more accurate equations may augment existing fuel sampling techniques and be put to practical use for fire planning purposes

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

Effectiveness of spiritual care training for rehabilitation professionals: An exploratory controlled trial

Background: Spirituality may play an important role in neurorehabilitation, however research findings indicate that rehabilitation professionals do not feel well equipped to deliver spiritual care. Objective: To evaluate a spiritual care training program for rehabilitation professionals. Methods: An exploratory controlled trial was conducted. Participants enrolled in a two-module spiritual care training program. Spiritual care competency was measured with the Spiritual Care Competency Scale. Confidence and comfort levels were measured using the Spiritual Care Competency Scale domains. The Spirituality and Spiritual Care Rating Scale assessed participant attitudes and knowledge. Measures were administered three times: pre-program, post-program and six weeks follow-up. Results: The training (n = 41) and control (n = 32) groups comprised rehabilitation professionals working in spinal cord or traumatic brain injury units. No between-group differences were observed on the study variables at the pre-program time point. Multilevel models found that levels of spiritual care competency, confidence, comfort, and ratings on existential spirituality increased significantly for the training group (versus control) post-program (p \u3c 0.05) and these significant differences were maintained at follow-up. Conclusions: A brief spiritual care training program can be effective in increasing levels of self-reported competency, confidence and comfort in delivery of spiritual care for rehabilitation professionals

Quantitation of Gait and Stance Alterations Due to Monosodium Iodoacetate–induced Knee Osteoarthritis in Yucatan Swine

Knee osteoarthritis is one of the most common causes of chronic pain worldwide, and several animal models have been developed to investigate disease mechanisms and treatments to combat associated morbidities. Here we describe a novel method for assessment of locomotor pain behavior in Yucatan swine. We used monosodium iodoacetate (MIA) to induce osteoarthritis in the hindlimb knee, and then conducted live observation, quantitative gait analysis, and quantitative weight-bearing stance analysis. We used these methods to test the hypothesis that locomotor pain behaviors after osteoarthritis induction would be detected by multiparameter quantitation for at least 12 wk in a novel large animal model of osteoarthritis. MIA-induced knee osteoarthritis produced lameness quantifiable by all measurement techniques, with onset at 2 to 4 wk and persistence until the conclusion of the study at 12 wk. Both live observation and gait analysis of kinetic parameters identified mild and moderate osteoarthritis phenotypes corresponding to a binary dose relationship. Quantitative stance analysis demonstrated the greatest sensitivity, discriminating between mild osteoarthritis states induced by 1.2 and 4.0 mg MIA, with stability of expression for as long as 12 wk. The multiparameter quantitation used in our study allowed rejection of the null hypothesis. This large animal model of quantitative locomotor pain resulting from MIA-induced osteoarthritis may support the assessment of new analgesic strategies for human knee osteoarthritis

The Use of Aerial Photography for Development of Fuel Loading Prediction Models Within Three Cover Types in the Jemez and Sangre de Cristo Mountains of New Mexico

Fuel load prediction equations that make use of aerial photographs were developed for Mixed Conifer, Ponderosa pine (Pinus ponderosa Dougl. Ex Laws.) and Pinyon-Juniper (Pinus edulis Engelm.) (Juniperus monosperma Engelm.) cover types from one time measurements made in the .Santa Fe watershed located in the Sangre de Cristo Mountains of northern New Mexico. Additional fuel sampling occurred at Los Alamos National Laboratory (LANL) located in the Jemez Mountains of Northern New Mexico. Use of these or similar prediction equations may be limited to certain regions and community types that exhibit similar regional characteristics such as terrain, soils, and weather conditions. This was demonstrated when the prediction equations developed from the Santa Fe watershed data set was applied to both the watershed and LANL data sets for comparison. The results of the watershed data set were favorable and exhibited a high degree of relative accuracy. The results from tl1e LANL data set did not share tl1e same degree of accuracy but rather exhibited a high degree of error. This may strongly indicate possible limitations for applied use of prediction equations of this nature to regions tl1at exhibit similar characteristics such as terrain, soils and weather. Small difference in site characteristics, such as the amount of precipitation or evapotranspiration that occurs, may have an effect on tl1e amount of bio-mass or fuels generated on one site that is not reflected on another site even though they may be within a few miles of each other. Applied use of the prediction equations required less time than traditional fuel sampling performed on-site, but suffered from a loss of accuracy. It is strongly suggested that additional study of this method be undertaken to generate more accurate and reliable equations. Hopefully, more accurate equations may augment existing fuel sampling techniques and be put to practical use in the future for fire planning purposes

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

Soil and Plant Characteristics Compared Between Abandoned Natural Gas Drill Pads and Adjacent Areas, Barksdale Air Force Base, Louisiana

Natural gas demand is projected to continue to rise. To meet this demand an increase in exploration and drilling will occur. This paper identifies some long term ecological impacts that remain following the plug and abandonment of the drill pads