Imagery and GIS. Kass Green
features. The data provides a detailed view of the vertical structure of our environment. Because lidar penetrates the canopy, it provides the ability to “see under trees,” revealing roads, trails, manmade alteration of the landscape, and other landscape features that on an aerial photograph may be obscured by trees or vegetation. The lidar point cloud and its derivatives have myriad applications for land management, planning, archaeology, engineering design, hydrology, and other applications. High-resolution elevation data and forest structure metrics, such as tree height and canopy density, significantly enhance our ability to assess and monitor carbon stocks, document sea level rise, and map impervious surfaces and vegetation.
As reviewed in chapter 3, lidar data is collected as a “point cloud”; each point typically contains data representing the point’s geographic location, elevation, and return intensity. Since most modern lidar missions produce high-density point clouds (multiple points per square meter), point cloud data is among the most space and resource consuming of remotely sensed datasets. The standard data format for the point cloud is the LASer (LAS) format, and lidar data is typically distributed as LAS files. The LAS format is a transfer standard but is not optimized for direct use because of the large size of its files. However, a number of lossless compression formats exist as alternatives to the LAS format, including zLAS and LAZ, which typically result in compression factors of 5×.
Lidar data is publicly available for a growing number of states, counties, and municipalities across the United States. Existing lidar data and derivatives can often be downloaded from many sources including Open Topography (http://www.opentopography.org/), NOAA’s Digital Coast (https://coast.noaa.gov/digitalcoast/) and the USGS’s Earth Explorer (http://earthexplorer.usgs.gov/).
Lidar data is typically provided in one of the formats below:
Classified point cloud—this format is typically the least processed form made available. Access to the point cloud is needed to derive custom elevation models, to edit or change the point classification, or to visualize the point cloud directly. Working with the point cloud at a regional scale can be cumbersome and slow because the number of points in the point cloud is massive.
Lidar-derived elevation models—derived elevation models are often the most useful products for the end user. Elevation models derived from the point cloud can depict the elevation of the ground (digital terrain models) or the elevation of the highest surface (digital surface models). Digital elevation models are provided in raster or three-dimensional model format and can be used for myriad types visualization, analysis, and modeling. Hillshades derived from elevation models are an excellent way to visualize lidar-derived topography. (For more information on elevation models, see chapter 8.)
Elevation contours—contours are often derived from the point cloud and provided for download at the portals listed above.
If lidar data doesn’t exist for your area of interest, or you require lidar data with different specifications or characteristics from what does exist, here are some very useful resources to help you with your planning:
Lidar 101: An Introduction to Lidar Technology, Data, and Applications—https://coast.noaa.gov/digitalcoast/training/lidar-101.html
USGS Lidar Base Specification—http://pubs.usgs.gov/tm/11b4/
Manual of Airborne Topographic Lidar—http://www.asprs.org/Press-Releases/ASPRS-Launches-First-eBook-Manual-of-Airborne-Topographic-Lidar.html
ASPRS Positional Accuracy Standards for Digital Geospatial Data—https://www.asprs.org/pad-division/asprs-positional-accuracy-standards-for-digital-geospatial-data.html.
Radar
Radar imagery has many uses. Because of its long wavelengths and use of active sensors, radar imagery can be acquired under almost any conditions. It is not impacted by clouds or fog and can even be acquired at night. Areas that experience constant cloud cover such as the Amazon jungle and coastal Alaska, which have very little optical imagery, can be regularly imaged with radar. In addition, depending on the wavelength, radar imagery has the ability to penetrate through the leaves/canopy of the forest and image more of the forest structure or to penetrate into the soil and reveal more about the soil characteristics and wetness. Two radar images of the same area can produce an image with parallax much like a stereo pair of aerial photographs. This parallax can be used in radargrammetry (photogrammetry for radar) to produce topographic/elevation data. Another topographic mapping method called interferometry uses two radar antennas that are spaced apart and can receive a single radar signal that is out of phase. Topographic information is extracted by analyzing the phase shift.
While radar systems have been and will continue to fly on aircraft, companies that provide these services change rapidly. Space-based systems have provided greater stability and sources of radar imagery over time even though many of these missions have ended as well. The imagery and products generated from these space-based missions are more readily accessible and available for use. The very first space-based radar mission was called Seasat, launched in 1978 by the United States with the goal of monitoring ocean conditions including the polar sea ice. The radar sensor was L band using HH polarization. Unfortunately, there were technical issues with the Seasat power system and the mission lasted less than 100 days. The next radar missions were part of the US shuttle program and called the Shuttle Imaging Radar (SIR) experiments. SIR- A (1981) and SIR-B (1984) used L-band radar with HH polarization, the same as Seasat. SIR-C (1994) was designed to experiment with multiband radar and used L, C, and X bands with multiple polarizations. After this, the United States has mostly disregarded radar imagery except for the Shuttle Radar Topography Mission in 2000. The goal of that mission was to use interferometric radar imagery to create topographic data for the majority of the inhabited earth’s surface. The project was a joint one between NASA and the agency then called the National Imagery and Mapping Agency and now called the National Geospatial-Intelligence Agency (NGA). The mission was highly successful in collecting 1-arcsecond elevation data for most of the earth.
Since the early days of Seasat and the Shuttle Radar missions, many other countries have launched platforms with radar sensors into space. Of particular note are Canada, Japan, and the European Space Agency (ESA). Table 4.5 presents a summary of some of the more important radar sensors, both historically and operating today (Lillesand et al., 2015). Because of the varying look angles used to collect radar imagery, the spatial resolution of these images also varies greatly and is not recorded in this table. Almaz-1 is listed in the table because it is the first commercial radar system, however it did not last long and is not as significant a data source as the other sensors in the list.
Table 4.5. Summary of important space-based radar sensors
Soon, two new countries look to enter the radar data collection group. They are Spain, with Paz, a dual-polarization X band radar, and the United Kingdom with NovaSAR-S, a tripolarization S-band radar. In addition, Sentinel-1B was launched in April 2016. Launches are regularly postponed, and therefore it is important to check mission websites for the current status of any of these radar sensors.
Summary — Practical Considerations