THE EFFECT OF EASTERN KENTUCKY COAL FIRES ON LOCAL AIR QUALITY AND PERSONAL HEALTH

The purpose of this project was to investigate student learning in the areas of earth science and environmental responsibility using the subject of coal fires. Eastern Kentucky, where this study was performed, has several coal fires burning that affect the local air quality and may also affect the health of people living near them. This study was conducted during the regular education of 9th grade Earth Science classroom in Russell Independent Schools, located in Russell, Kentucky. Students conducted internet research, read current articles on the subject of coal fire emissions and effect on local ecology, and demonstrated what they learned through summative assessments. There were several aspects of coalmines and coal fires that students studied. Students were able to take this knowledge and information and use it as a learning tool to gain a better understanding of their own environment. Using the local history and geology of coalmines, along with the long tradition of mine production, was a very beneficial starting point, allowing students to learn about environmental impact, stewardship of their local environment, and methods of preserving and protecting the ecosystem.

Land use/cover change using Remote Sensing and Geographic Information Systems : Pic Macaya National Park, Haiti

A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park.