Although global development and economic growth has reduced poverty by the millions, the impacts of changing climates still pose as a global challenge. Natural disasters caused by extreme weather events such as droughts, fires, storms and floods are becoming more intense, more frequent, and less predictable as our planet grows warmer (USAID, 2014). Shifting rainfall patterns, rising sea levels, rising temperatures, and the acidification of our oceans continue to threaten and further worsen the existing challenges (2014). USAID remains optimistic regardless of the risks presented. These risks offer opportunities for individuals to come together to develop technology capable of efficiently conducting high resolution earth observations to create predictive models, and harnessing renewable clean energy to reduce carbon emissions, etc. (2014).
It starts by incorporating climate change knowledge into development programs that allow people to practice and develop their preparedness for all possible climate scenarios that could occur (USAID, 2014). At USAID, this is referred to the “climate-smart development”. The ultimate goal is to equip individuals and communities with tools and strategies to develop resilience to the gradual changing climates (2014). One way of doing so is by teaching and training people how to effectively use satellite technologies such as LIDAR, SAR, INSAR, etc. This is an emphasis because there are too few people around the world that specializes in using such a powerful resource because the capability to access and apply climate information remains much at large out of reach for many communities and nation states (2014). Accurate climate information can be used for making better decisions about infrastructure, energy planning, transportation, agriculture, etc. that can increase resilience (2014).
USAID supports a multitude of programs that work with remote sensing technology such as SERVIR Global, Project NOAH (Nationwide Operational Assessment of Hazards) in the Philippines, and GOAL in Honduras (USAID, 2014; USAID, 2018). Both NOAH and GOAL are used to create high-resolution images using LIDAR technology to produce three-dimensional maps of neighbourhoods that are at risk of storm surges and flooding events (USAID, 2014; USAID, 2018). These programs help to provide valuable information that are then used to build accurate predictive hydrological models that can predict the movement of water, and its effects on landslides, flooding hazards, and etc. (USAID, 2014; USAID, 2018). These predictions aid in the design of strategic flood mitigation infrastructure, such as forest restoration, surface water drainage systems, and affordable safe housing to further improve the flood resilience of these communities (USAID, 2014; USAID 2018).
Moving forward with newer and more advanced Remote Sensing technologies, LIDAR is by far the most accurate way of modeling and mapping a given terrain or community, and its technology is unbounded. As mentioned, that there are too few people around the world that specialize in RS, especially with LIDAR. We need more people to become involved with such discipline. In light of the existing Remote Sensing programs supported by USAID, this specialty can also be further implemented to the youth in these communities. There needs to be more academic programs and internships that aids to school the youth who are interested in this discipline. So, here is a small introduction to LIDAR.
I. What is LIDAR?
LIDAR stands for Light Detection and Ranging; it is a remote sensing system that uses laser beams (pulses) to measure distance via the travel time it takes for the pulses to return (NOAA Coastal Services Center, 2012). The surface information is recorded as x, y, z data, and intensity. These generate precise and accurate georeferenced spatial three-dimensional information about the characteristics of the target surface (2012).
II. Brief History of LIDAR
LIDAR technology first originated in the early 1960s after the invention of the laser (Goyer and Watson, 1963). It was initially used for meteorological applications such as measuring clouds and mapping particulates in the atmosphere (Goyer and Watson, 1963; NOAA Coastal Services Center, 2012). The first space-based LIDAR instrument was a laser altimeter aboard the APOLLO 15 mission to the moon in 1971; it was used to measure the Moon’s topography and planetary surface height to map the Moon’s surface (Abshire, 2010; Sun, 2012). The efficiency, lifetime and resolution of most laser technology and space LIDAR improved dramatically by the late 1980s (Sun, 2012). This was when NASA launched their first laser altimetry systems, the NASA Atmospheric and Oceanic LIDAR and Airborne Topographic Mapper.
In 1994, NASA launched the LIDAR in Space Technology Experiment (LITE) into space to help researchers and scientists better understand Earth’s global climate (NASA, 1994). It was used to collect data on Earth’s cloud cover, and to track various atmospheric particulates such as aerosols (NASA, 1994). This mission was meant to validate LIDAR technologies for spaceborne applications, and to gain operational experience that would benefit the development of future systems on free-flying satellite platforms. The mission only operated for 53 hours covering 1.4 million km of ground track, resulting in a collection of 40 GB of data. Commercial spaceborne, airborne, and waterborne LIDAR systems were then subsequently developed after the LITE such as NASA’s Ice, Cloud and Land Elevation Satellite (ICESat) and NASA’s Cloud-Aerosol and Infrared Pathfinder Satellite observation (CALIPSO) (NASA, 1994). The technology has since been improved and further innovated by other companies like Velodyne, Leddartech, Limunar, Geoscience, Sweep and more. Today these companies are developing smaller, lighter, more cost efficient and user friendly LIDAR systems that can be mounted onto UAVs, drones, vehicles and more. The development of automated vehicles that can communicate with one another poses as the newest challenge to date (Cameron, 2017).
III. Brief explanation on how LIDAR works
A LIDAR instrument emits rapid pulses of laser light at a target surface. The spectrum ranges from near-infrared (NIR) to ultraviolet (UV) depending on the given model used, and the purposes for which they are used for, e.g. spaceborne LIDAR systems used for terrestrial mapping generally emit NIR light at the Earth because they have to be eye-safe, whereas LIDAR systems used for meteorology use UV light.
The photons bounce off of a surface and is sent back to the LIDAR instrument, where the time of flight is recorded. The sensor also records the backscatter signal in x, y, z data for georeferencing, and intensity (depending on the instrument) for accuracy assessments, and feature detection and extraction (ESRI, 2016).
Distance is measured by the product of half the photon’s time of flight and the speed of light.
The system then combines all the information and computes the precise echo position of each return signal.
To make use of the raw point cloud data, it must be digitized onto a software. There are many freely available, open-source software like Quick Terrain Reader (QTR) or game engines like Unity that can be used.
The final step is to analyze, filter, and classify the types of terrain surfaces the user is interested in.
IV. Advantages of using LIDAR
High accuracy data can be collected very quickly, which reduces human dependence because many processes are automated.
It can be used regardless of time of the day because they illuminate their own energy, which means that they are not affected by any variations in light.
It is unaffected by extreme weather which means that it can be relied on during extreme weather events to produce live predictive models for people on the ground.
It can be used in dense forests, and even under water.
It is at large unaffected by any geometrical distortions such as slant-range, layovers, foreshortening, and compressions unlike other forms of remote sensing methods.
V. Disadvantages of using LIDAR
The quality of data can be affected by weather conditions such as heavy rain, or low hanging clouds due to the refraction of the laser pulses.
It can be expensive, depending on the types of application and the type of models used. For instance, it can cost more to use LIDAR as a way of collecting small samples of data.
There is a large volume of data that is generally collected by LIDAR, which means that it requires a lot of storage, and computation power to project the data into usable information. It requires high levels of analysis and interpretation to make sense of the data.
It may affect human vision depending on the power of the laser beams used.
Accuracy of the data collected can be skewed by high sun angle and reflections.
Restrictions in flying UAVs or drones in particular districts.
Restrictions in bringing valuable technology to developing countries that could potentially seize the equipment and sell it back to you for a high price.
VI. How to build your own LIDAR
Individuals interested in learning more about LIDAR can always seek out the internet’s archive of open source tutorials and lectures. There are also many step by step lessons on how to build your own LIDAR sensors for experimental uses. The links can be found in the links section.
The cost of building a LIDAR depends on the application. It can range from $200 to $85,000 and upwards. For experimental and learning purposes, some people may only want to build one for under $1000 such as the Sweep V1 360° Scanner (Robotshop, 2018). For research and practical applications, the instruments must be more powerful and thus be more expensive.
Helen Wang is currently a senior BSc student majoring in Geography with a concentration in Geomatics, and minoring in Psychology at the University of Victoria in Victoria, British Columbia, Canada. She has taken an interest in Remote Sensing (RS) of the environment because it is a powerful tool that can be used by a multitude of applications such as monitoring vegetation health in agriculture, identifying the movement of pollutants, mapping landslides, navigation, aiding the development of flood resilient communities, and more. New innovations and the improvement of RS technology each day work in consort to reduce its cost and increase its usability down to the civilian level. She aspires to work in the field of RMS that handles tracking the movement of man-made debris in the ocean and space, as well as monitoring Earth’s cryosphere regarding its effects on climate change. This blog is a small introduction on LIDAR technology, and how it is used by USAID, as well as the possible potentials in the future.
DIY 360 Degree Realtime Outdoor LIDAR with ROS Support:
Make Your Own LIDAR Sensor by Erich Styger (Dec 08, 2016):
Making a Lidar scanner in the home shop with a lathe, a pair of STM32s, and a Garmin Lidar Lite:
Abshire, J.B. (2010). NASA’s Space LiDAR Measurements of Earth and Planetary Surfaces. Retrieved from https://ntrs.nasa.gov/search.jsp?R=20100031189
Cameron, O. (2017). An Introduction to LIDAR: The Key Self-Driving Car Sensor. Retrieved from https://news.voyage.auto/an-introduction-to-lidar-the-key-self-driving-car-sensor- a7e405590cff
ESRI. (2016). What is lidar intensity data? Retrieved from http://desktop.arcgis.com/en/arcmap/10.3/manage-data/las-dataset/what-is-intensity-data- .htm
Goyer, G.G. and Watson, R. (1963). The Laser and its Application to Meteorology. Bulletin of the American Meteorological Society, 44(9), 564-570. Retrieved from s https://www.jstor.org/stable/26247220
NASA (1994). LITE: Measuring the Atmosphere with Laser Precision. Retrieved from https://www.nasa.gov/centers/langley/news/factsheets/LITE.html
NOAA Coastal Services Center. (2012). Lidar 101: An Introduction to LiDAR Technology, Data, and Applications. Retrieved from https://coast.noaa.gov/data/digitalcoast/pdf/lidar- 101.pdf
Robotshop (2018). Sweep V1 360° Laser Scanner. Retrieved from https://www.robotshop.com/ca/en/sweep-v1-360-laser-scanner.html
Sun, X. (2012). Space-Based lIdar Systems. Retrieved from https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120012916.pdf
USAID (2014). Global Climate Change Initiative: Program Profiles. Retrieved from https://pdf.usaid.gov/pdf_docs/PA00KJGW.pdf
USAID (2018). USAID/OFDA Supports Remote-Sensing Technology to Mitigate Disaster Risk in Honduras. Retrieved from https://www.usaid.gov/sites/default/files/documents/1866/ofdalac_newsletter_march_201 8.pdf