Airborne Lidar Data Processing and Information Extraction

Abstract

Lidar is changing the paradigm of terrain mapping and gain- ing popularity in many applications such as floodplain mapping, hydrology, geomorphology, forest inventory, urban planning, and landscape ecology. One of the major barriers for a wider applica- tion of lidar used to be the high cost of data acquisition. However, this problem has been greatly alleviated with the thrilling devel- opments in hardware. The first commercial airborne lidar system was introduced just ten years ago (Flood, 2001). Now, the latest system is capable of transmitting 100,000 pulses per second from an altitude of up to 2km. The pulse repetition rate has reached a maximum of more than 150 kHz and has increased by about 10-fold within the last 5 years; correspondingly, the cost of data collection has decreased by about 10 times within the same time period. Nowadays users can obtain data with a density of >1 pulses per m2 for several hundred dollars per square mile. The dramati- cally decreasing cost of data collection encourages more and more users to embrace this innovative technology in their application and research. For example, North Carolina has collected statewide lidar to help the Federal Emergency Management Agency (FEMA) update their digital flood insurance rate maps (Stoker et al., 2006). A wealth of free lidar data are also accessible to the public from the websites maintained by governmental agencies such as the U.S. Geological Survey (the Center for Lidar Information, Coordina- tion and Knowledge: CLICK), National Oceanic and Atmospheric Administration (Coastal Service Center), and U.S. Army Corps of Engineers (the Joint Airborne Lidar Bathymetry Technical Center of Expertise: JALBTCX). Although lidar data has become more affordable for average users, how to effectively process the raw data and extract useful information remains a big challenge. Compared to image process- ing, lidar is appealing in many aspects. For example, the users do not have to worry about geometric, atmospheric, and radiometric corrections. However, lidar data have some characteristics that post new challenges. First of all, lidar is essentially a kind of vector data. Different from raster data, the spatial locations of laser points have to be explicitly stored, making the file size much larger than imag- ery given the same “nominal” spatial resolution. Second, how to extract useful information from these seemingly random points is a relatively new research topic. The generation of digital elevation models (bare earth) is the largest and fastest growing application of lidar data (Stoker et al., 2006). However, the research on automat- ing the production of bare earth is still in its infancy. To make this situation worse, until recently, researchers tended not to publish their methods (Zhang et al., 2003, Chen et al., 2007). Besides terrain mapping, there is an endless list of areas where lidar has a potential application but they have not been adequately explored. I have developed a software (dubbed Tiffs: Toolbox for Lidar Data Filtering and Forest Studies) for processing lidar data and extract- ing bare earth and forest structure information. I will discuss the challenges and needs for lidar data distribution, management, and processing. I hope this article can shed light on the topic, not only for other software developers, but also for data providers and end users of lidar.