LINEAMENT IDENTIFICATION IN NEW YORK STATE USING REMOTE SENSING TECHNIQUES FOR GEOLOGICAL CO2 SEQUESTRATION

The objective of this research is to identify and map lineaments in central New York State (NYS) to assist in characterizing potential sites for carbon dioxide (CO₂) subsurface sequestration. One of the main driving forces behind global warming is CO₂ emissions from burning fossil fuels (e.g., Sadorsky, 2009). According to Holloway (2005), 22 billion tons of CO₂ are emitted into the atmosphere annually, a major source of which is the burning of fossil fuels for production of electricity at power plants. In this study we identified lineaments from satellite images and Digital Elevation Models (DEMs) with image processing techniques for variations in spectral and spatial reflectivity and topography. Lineaments can be reflections of tectonic fractures and faults in the bedrock, emphasized on the surface by topography, drainage, and vegetation, most of which can be identified by remotely sensed data. Knowledge of fault locations can prevent selection of unsuitable site selection for CO₂ sequestration, where CO₂ could migrate up the fault systems. Lineament patterns also can demonstrate the fracture fabric in the region, an important consideration for CO₂ sequestration. Various data sets, including multispectral satellite imagery (Landsat and ASTER) and DEMs, as well as geological data describing fractures, faults, bedrock, soils and hydrology, are used to map and validate the lineament distribution in the study area. First, linear features are enhanced with tonal, topographic and textural changes by digital image processing of the satellite imagery. Then lineaments are extracted manually by using Arc GIS 9.2 (ArcMap). Lineaments longer than 1 km are identified, digitized and stored in a Geo-Database together with attributes describing their length, orientation, etc. Lineament categories include vegetation, drainage, and topography. Rose diagrams and statistics of length and number of lineaments in each 10⁰ orientation bin are used to characterize the lineament distribution in each remotely sensed data set. The primary lineament orientations from both ASTER and DEM are northeast and northwest. These trends agree with EarthSat (1997) lineaments from Landsat images and also correspond to some fracture and fault systems digitized in Fisher et al (2010), but do not reflect the most abundant fracture sets.