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This paper examines the pattern and extent of energy development in steppe landscapes of northeast Colorado, United States. We compare the landscape disturbance created by oil and gas production to that of wind energy inside the Pawnee National Grasslands eastern side. This high-steppe landscape consists of a mosaic of federal, state, and private lands where dominant economic activities include ranching, agriculture, tourism, oil and gas extraction, and wind energy generation. Utilizing field surveys, remote sensing data and geographic information systems techniques, we quantify and map the footprint of energy development at the landscape level. Findings suggest that while oil and gas and wind energy development have resulted in a relatively small amount of habitat loss within the study area, the footprint stretches across the entire zone, fragmenting this mostly grassland habitat. Futhermore, a third feature of this landscape, the non-energy transportation network, was also found to have a significant impact. Combined, these three features fragment the entire Pawnee National Grasslands eastern side, leaving very few large intact core, or roadless areas. The primary objective of this ongoing work is to create a series of quantifiable and replicable surface disturbance indicators linked to energy production in semi-arid grassland environments. Based on these, and future results, we aim to work with industry and regulators to shape energy policy as it relates to environmental performance, with the aim of reducing the footprint and thus increasing the sustainability of these extractive activities.

Remotely sensed (RS) imagery is increasingly being adopted in investigations and applications outside of traditional land-use land-cover change (LUCC) studies. This is due to the increased awareness by governments, NGOs and Industry that earth observation data provide important and useful spatial and temporal information that can be used to make better decisions, design policies and address problems that range in scale from local to global. Additionally, citizens are increasingly adopting spatial analysis into their work as they utilize a suite of readily available geospatial tools. This paper examines some of the ways remotely sensed images and derived maps are being extended beyond LUCC to areas such as fire modeling, coastal and marine applications, infrastructure and urbanization, archeology, and to ecological, or infrastructure footprint analysis. Given the interdisciplinary approach of such work, this paper organizes selected studies into broad categories identified above. Findings demonstrate that RS data and technologies are being widely used in many fields, ranging from fishing to war fighting. As technology improves, costs go down, quality increases and data become increasingly available, greater numbers of organizations and local citizens will be using RS in important everyday applications.1 In December 2010, Google Labs released Google Earth Engine, a project that uses 25 years of Landsat TM and ETM+ data to enable global change monitoring. A primary aim is for developing nations to monitor their forests and to serve projects such as REDD (Landsat News, 2010).