Introduction

Real-time sensor networks, space and airborne-based remote sensing, real-time geodesy and seismology, massive geospatial databases, and large computational models are all enabling new and exciting research on the forefront of the earth sciences. However, with these technologies comes a prodigious increase in the volume and complexity of scientific data that must be efficiently managed, archived, distributed, processed, and integrated in order for it to be of use to the scientific community. Data volume, processing expertise, or computing resource requirements may be a barrier to the scientific community's access to and effective use of these datasets. An emerging solution is a shared cyberinfrastructure that provides access to data, tools, and computing resources. A key objective of geoinformatics initiatives (e.g., Sinha, 2000) is to build such cyberinfrastructure for the geosciences through collaboration between earth scientists and computer scientists.

Airborne LiDAR (Light Distance And Ranging) data have emerged as one of the most powerful tools available for documenting the Earth's topography and its masking vegetation at high resolution (defined here as pixel dimensions less than 2 meters). LiDAR-derived digital elevation models (DEMs) are typically of a resolution more than an order of magnitude better than the best-available 10-meter DEMs. The ability to use these data to construct 2.5-D and 3-D models of the Earth's topography and vegetation is rapidly making them an indispensable tool for earth science research (e.g., Carter et al., 2001).

Scientific method and the influence of technology

Due to the increasing number and sophistication of data acquisition technologies, the amount of raw data acquired has vastly increased over the last couple of decades (Berman, 2008). This explosion of scientific data, growth in scientific knowledge, and the increase in the number of studies that require access to knowledge from multiple scientific disciplines amplify the complexity of scientific problems. In order to answer these “grand challenge” scientific questions, scientists use computational methods that are evolving almost daily. The basic scientific method, however, remains the same for the individual scientist. Scientists still start with a set of questions, then observe phenomena, gather data, develop hypotheses, perform tests, negate or modify hypotheses, reiterate the process with various data, and finally come up with a new set of questions, theories, or laws (http://en.wikipedia.org/wiki/Scientific_method). A recent change in this scientific method is that it is continuously being transformed with the advances in computer science and technology. The simplest examples of this transformation are use of personal computers to record scientific activity and the way scientists publish and search for publications online. More advanced technologies within the scientific process include sensor-based observatories to collect data in real time, supercomputers to run simulations, domain-specific data archives that give access to heterogeneous data, and online interfaces to distribute computational experiments and monitor resources.