diff --git a/04-collecting-and-editing-data.Rmd b/04-collecting-and-editing-data.Rmd
index 9d7f15f..14d114c 100644
--- a/04-collecting-and-editing-data.Rmd
+++ b/04-collecting-and-editing-data.Rmd
@@ -22,7 +22,7 @@ The ability of a geomatician to answer research questions or produce a map or ot
 1. Become familiar with a wide range of spatial datasets and strategies to access them
 2. Identify several sources of historical spatial information, including historical maps and aerial photos, and the steps required to analyze them as spatial information
 2. Recognize good practices and strategies for writing and reading metadata
-4. Understand the components of Global Navigation Satellite Systems (GNSS) and how location on Earth is triangulated from these systems
+4. Understand how to collect data using Global Navigation Satellite Systems (GNSS)
 
 ::::
 
@@ -69,7 +69,7 @@ Try using a web search to find the government open data pages for your city, pro
 
 This section introduces the census at a cursory level before launching into the applied question of how to find census data for your spatial analysis, using the Census of Canada as an example.
 
-A **census** generally refers to a complete count by government of a specific region's population by age, gender, language, income, housing and other demographic characteristics. Census data inform public policy, such as allocation of public funds, transportation network planning, and electoral area delineation. Census data also provide researchers with an opportunity to gain insight into the social and, to a lesser extent, environmental fabric of a country and are increasingly used in environmental and social-ecological research that aims to address social elements of environmental challenges [@tomscha_guide_2016] [@biggs_routledge_2021]. Censuses are typically conducted once every five years (e.g., Canada) or every 10 years (e.g., Unites States).
+A **census** generally refers to a complete count by government of a specific region's population by age, gender, language, income, housing and other demographic characteristics. Census data inform public policy, such as allocation of public funds, transportation network planning, and electoral area delineation. Census data also provide researchers with an opportunity to gain insight into the social and, to a lesser extent, environmental fabric of a country and are increasingly used in environmental and social-ecological research that aims to address social elements of environmental challenges [@tomscha_guide_2016] [@biggs_routledge_2021]. Censuses are typically conducted once every five years (e.g., Canada) or every 10 years (e.g., United States).
 
 In addition to demographics, many nations survey information related to economics or specific industries, such as agriculture. For example, Canada's Census of Agriculture captures information on fertilizers, irrigation, livestock, farm types, and crop production across Canada. The Longform Census in Canada surveys additional questions but is only sent to a subset of the population, and the data from it are then estimated for the entire population.
 
@@ -270,7 +270,58 @@ Once the control points are set, a transformation is applied to mold the histori
 
 ### Documenting Georeferencing 
 
-During the process of georeferencing you must document the number of control points and the root mean square error (RMSE). Although there are multiple sources of uncertainty in the spatial precision of a historical map, uncertainty should be characterized where possible to demonstrate rigour in your methods and for communicating uncertainty. 
+During the process of georeferencing you must document the number of control points and the root mean square error (RMSE). Although there are multiple sources of uncertainty in the spatial precision of a historical map, uncertainty should be characterized where possible to demonstrate rigour in your methods and for communicating uncertainty.
+
+## Collecting Data from Global Navigation Satellite Systems
+
+Most likely, the type of data that you will need to collect in the field will be location. You might need to locate some plots in the forest or walk the perimeter of a rare ecosystem in order to map these features. To do this, we rely on Global Navigation Satellite Systems (GNSS). In this section, we will explore how GNSS works, what are the sources of error, identify the main segments, and discuss how you can collect your own positional data from a GNSS.
+
+### How GNSS Work
+
+A GNSS is, fundamentally, a ranging system. Range is simply the distance between two points that are connected by a line-of-sight. One of those points is you, holding your GNSS receiver, and the other point is a satellite that is orbiting somewhere between 20,000 and 23,000 km above the surface of the Earth. The satellite is broadcasting a radio wave signal that is traveling at the speed of light $c = 299,792,458 m·s^{-1}$, which at a distance of 20,000 km would take about 67 _milliseconds_ to reach you on the ground if the satellite was directly above you. But the range to a single satellite cannot tell you much. For example, how would you know where you are if you knew that you were 20,000 km away from Vancouver, Canada? You could literally be 20,000 km in any direction, and you might visualise that as a sphere with a radius $r = 20,000 km$. The radius of the Earth's semi-major axis measures only $a=6,378.137km$ for the World Geodetic System 1984 (WGS 1984), which means a range of 20,000 km could place you anywhere on Earth, but also many other locations off in space. So how do we narrow our possible location and solve our position on Earth? We need more satellites!
+
+With a range to a single satellite, you know you are somewhere on the surface of a sphere with a radius equal to that range. With known ranges to two satellites, you can reduce your possible positions to the intersection of the two spheres that are formed from the two ranges (Figure \@ref(fig:04-two-gnss-satellites). It is important to recognize here that when you intersect two spheres, the arc (line) that forms the intersection represents the possible locations where both ranges are true. In other words, we have now reduced our possible positions from a 3D surface of a sphere to a 2D arc.
+
+```{r 04-two-gnss-satellites, fig.cap = fig_cap, out.width= "75%", echo = FALSE}
+    fig_cap <- paste0("Range to two satellites gives an intersection, which spatially is represented as a 2D arc of possible positions. Pickell, CC-BY-SA-4.0.")
+    knitr::include_graphics("images/04-two-gnss-satellites.PNG")
+```
+<br/>
+
+Adding the range to a third satellite gives us two more intersecting arcs, one with the first satellite and another with the second satellite. The intersection of these three arcs reduces our possible positions to just two locations (Figure \@ref(fig:04-three-gnss-satellites) and usually only one of these is actually logical relative to Earth (i.e., one position is in space and one is on Earth).
+
+```{r 04-three-gnss-satellites, fig.cap = fig_cap, out.width= "75%", echo = FALSE}
+    fig_cap <- paste0("Range to three satellites gives us three intersections. These three arcs intersect only at two positions. Pickell, CC-BY-SA-4.0.")
+    knitr::include_graphics("images/04-three-gnss-satellites.PNG")
+```
+<br/>
+
+
+
+But the satellite itself is of course in motion, orbiting the Earth at a speed of about 14,000 km·s^{-1}, which complicates things in two ways. First, the range to the satellite is always changing because the satellite is moving. Second, the satellite will eventually disappear over the horizon and then you do not have a line-of-sight anymore. 
+
+Ranging
+Clocks
+Trilateration
+Carrier phases
+Pseudorandom code
+
+### Sources of Error
+
+Dilution of precision: geometric, horizontal, vertical, time
+Ionosphere delay
+total electron count
+multipath errors
+line of sight
+obstructions
+
+### Segments of GNSS
+
+Users
+Space
+Ground control
+
+### Planning Your GNSS Data Collection
 
 ## Summary 
 
diff --git a/15-LiDAR-acquisition-and-analysis.Rmd b/15-LiDAR-acquisition-and-analysis.Rmd
index 271cefb..ccd9e98 100644
--- a/15-LiDAR-acquisition-and-analysis.Rmd
+++ b/15-LiDAR-acquisition-and-analysis.Rmd
@@ -1,5 +1,4 @@
 ```{r, include=FALSE}
-baseurl <- "https://ubc-geomatics-textbook.github.io/geomatics-textbook/"
 covlicence <- "https://opendata.vancouver.ca/pages/licence/"
 library("dplyr")
 library("rgdal")
@@ -85,7 +84,7 @@ Unlike passive remote sensing technologies, LiDAR has the advantage of being abl
 
 ## Components of a LiDAR System
 
-## Lasers
+### Lasers
 
 **Lasers** are a very important component of LiDAR systems. Here we will discuss some basic concepts, as well as the technical parameters that are important when interpreting LiDAR data. LiDAR lasers are typically beams of **near-infrared** light (800 -- 1,550 nm, typically 1,064 nm), and are used because these wavelengths are considered eye safe [@white_best_2013]. Green wavelengths are used for bathymetric LiDAR (i.e. water penetrating LiDAR), but are less common, and not typically used on land [@uf_geomatics_-_fort_lauderdale_lidar_2016-2]. Laser scanners use rotating or oscillating mirrors in order to 'scan' a scene in multiple dimensions [@uf_geomatics_-_fort_lauderdale_lidar_2016-1]. When these scanners are placed on a moving platform (e.g. a plane), we can cover large areas [@uf_geomatics_-_fort_lauderdale_lidar_2016-1]. LiDAR sensors scan in a variety of ways; zig-zag, rotation mirror line, and push broom scanners are the most common, and use the same principles as those described for sensors in **Chapter 12 (double check, dynamically link)** [@uf_geomatics_-_fort_lauderdale_lidar_2016-1].
 
@@ -123,24 +122,20 @@ fig_cap = "(ref:15-LiDAR-Unit-caption)"
 knitr::include_graphics("images/15-LiDAR-Unit.png")
 ```
 
-## Position and Orientation
+### Global Navigation Satellite Systems
 
-## Global Navigation Satellite Systems
-
-In order for us to use the calculation from Figure \@ref(fig:15-Concept-of-LiDAR), we need to know the exact position of the scanner in space. For airborne and mobile platforms, we can can do this by using the **global navigation satellite system (GNSS)**, as well as post processing using a local GNSS reference station. This helps us to improve accuracy by not comparing our position to a known local position in order to ensure our location is known as accurately as possible. GNSS concepts are covered further in [Chapter 4](./collecting-and-editing-data.html).
-
-(ref:15-XYZ-coordinates-caption) An on board GNSS is required toprovide an accurate location of the LiDAR scanner in space. ('A key component of a lidar system is a GPS', @neon_education_key_2014, CC-BY-NC-SA-2.0).
+In order for us to use the calculation from Figure \@ref(fig:15-Concept-of-LiDAR), we need to know the exact position of the scanner in space. For airborne and mobile platforms, we can can do this by using the **global navigation satellite system (GNSS)**, as well as post processing using a local GNSS reference station. This helps us to improve accuracy by not comparing our position to a known local position in order to ensure our location is known as accurately as possible. GNSS concepts are covered further in [Chapter 4](https://www.opengeomatics.ca/collecting-and-editing-data.html).
 
 ```{r 15-XYZ-coordinates, fig.cap = fig_cap, out.width="70%", echo = FALSE}
 fig_cap = "(ref:15-XYZ-coordinates-caption)"
 knitr::include_graphics("images/15-XYZ-coordinates.png")
 ```
 
-## Inertial Measurement Unit (IMU)
+### Inertial Measurement Unit (IMU)
 
 An **inertial measurement unit (IMU)** consists of gyroscopes and accelerometers and measures the attitude and acceleration of the aircraft along the X, Y, Z axis. This data is combined with the GNSS data to provide a precise location of the scanner in space. This information becomes very important for airborne platforms where wind conditions can cause the orientation of the scanner to change subtly. We refer to the orientation of the platform in space by using the terms pitch, roll, and yaw, to describe which was the scanner is facing (see \@ref(fig:15-LiDAR-System)).
 
-## Clocks
+### Clocks
 
 LiDAR point data needs to be synced with positioning data in order to know exactly where the point is in space. To do this, a very accurate GNSS clock is used to time stamp the laser scanning data [@uf_geomatics_-_fort_lauderdale_lidar_2016-1]. Accurate clocks are imperative for producing accurate point clouds; one nanosecond (i.e., one billionth of a second) corresponds to a 30 cm travel distance, as seen in Figure \@ref(fig:15-LiDAR-Discrete-Full-Waveform) [@uf_geomatics_-_fort_lauderdale_lidar_2016-2]!
 
@@ -148,11 +143,11 @@ LiDAR point data needs to be synced with positioning data in order to know exact
 
 LiDAR units can be attached to a variety of platforms. Traditionally, LiDAR units for forestry research were mounted on airplanes and helicopters, as units were large and cumbersome, however ground based units such as terrestrial laser scanning (TLS), and mobile laser scanning (MLS) have also been developed. These units tend to have a very high point density, and TLS is often used in modeling tree architecture.
 
-## Airplanes and Helicopters
+### Airplanes and Helicopters
 
 Airplanes and helicopters are still the most common platforms for LiDAR data collection. This is due to their ability to collect large amounts of data in one acquisition. LiDAR units that are designed for airplanes are more powerful than those designed for drones, leading to increased penetration in forests, even though the flight height is significantly increased. Data storage issues can also be mitigated as the platform is not as sensitive to weight restrictions when compared to drones. With increased pulse rates, data acquisitions can also be dense enough to do individual tree crown work. Since helicopters are capable of flying at lower altitudes, slower speeds, and following terrain, they are capable of collecting more dense data, but higher operating costs [@white_best_2013]. Typical point densities range from 5 - 200 points·m^-2^.
 
-## Drones
+### Drones
 
 As laser units have decreased in size, we have been able to mount LiDAR units to smaller platforms. Drones (also known as unmanned aerial vehicles (UAV) or remotely piloted aerial systems (RPAS)) are extremely convenient to collect high density point clouds over small areas. They typically fly relatively close to the forest (e.g. 50 m above the trees) and fly a pre-defined flight route so ensure evenly spaced data collection. Typical point densities range from hundreds to thousands of points per meter squared. A major limitation of drones is currently battery life, as acquisition campaigns are limited by how long the drone can fly for (approximately 20 - 30 minutes). This means that collecting large amounts of data is difficult in remote locations.
 
@@ -163,15 +158,15 @@ fig_cap = "(ref:15-LiDAR-on-Drone-caption)"
 knitr::include_graphics("images/15-LiDAR-on-Drone.png")
 ```
 
-## Mobile Laser Scanning
+### Mobile Laser Scanning
 
 Mobile Laser Scanning (MLS) includes people (sometimes called 'backpack LiDAR') and moving vehicles. This version of LiDAR has been used by transportation and engineering companies to precisely map things like road or building conditions, and as the units have reduced in size there has been and increase in interest for use in forestry. Current MLS units such as panel 2 in Figure \@ref(fig:15-LiDAR-Platforms) can be carried around by the user in a forest easily. The point density for that specific unit is approximately 6,000 points·m^-2^ while the battery can last for over an hour. Typical point densities are highly dependent on what the MLS is mounted to; and can range from hundreds to thousands of points per square meter.
 
-**Terrestrial Laser Scanning**
+### Terrestrial Laser Scanning
 
 Terrestrial Laser Scanning (TLS) is a static system, meaning that is is placed in one location, takes measurements of a scene, and can then be moved (see panel 1 of Figure \@ref(fig:15-LiDAR-Platforms). These units are used by engineers for surveying, and are used by foresters to precisely measure trees. This information can be used to build quantitative structure models (QSM), to precisely estimate tree parameters. Unlike the other platforms mentioned here, TLS is not necessarily a fast data collection technique. Due to occlusion, the TLS instrument needs to be placed in multiple positions around a plot to be able to fully visualize the trees/plot, which means that a one hectare plot can take three to six days to survey [@wilkes_understanding_2015]! Typical point densities are in the thousands of points per meter squared.
 
-## Satellite
+### Satellites
 
 Satellites are a relatively new platform for LiDAR sensors. Two current satellite sensors are highlighted here; ICESat-2 (Ice, Cloud and land Elevation Satellite) and GEDI (Global Ecosystem Dynamics Investigation). Both are examples of large-footprint, profiling LiDAR systems. ICESat-2 was launched in 2018, with the objectives to determine sea-ice thickness and measure vegetation canopy height for large scale biomass estimation [@nasa_icesat-2_2021]. The ICESat-2 LiDAR sensor is composed of 6 beams in 3 pairs, each with a footprint size of 13 m [@nasa_icesat-2_2021]. GEDI is a LiDAR sensor that was installed on the International Space Station in 2018, with 3 lasers producing 10 parallel tracks of observations, and a footprint size of 25 m [@nasa_gedi_2021]. The GEDI sensor can be seen in panel 4 of Figure \@ref(fig:15-LiDAR-Platforms).
 
@@ -185,11 +180,11 @@ knitr::include_graphics("images/15-LiDAR-Platforms.png")
 ## Types of LiDAR
 -->
 
-## Discrete Return
+### Discrete Return
 
 **Discrete return** LiDAR is the most common type of LiDAR. Here, the reflected energy from a pulse is converted into return targets referenced in time and space [@white_best_2013]. Discrete return systems can often record multiple returns (often 5 or more), as long as the energy from each return meets the energy threshold to be classified as a return. Returns are classified per pulse, by the arrival sequence in which the return signals are detected by the sensor [@uf_geomatics_-_fort_lauderdale_lidar_2016]. In the case where a laser pulse intercepts a solid object (such as a building), only a single return would occur; in contrast if the laser pulse can penetrate through the object (e.g. the forest), some of the energy will be returned to the instrument where the pulse intercepts stems/branches/leaves [@white_best_2013]. Figure \@ref(fig:15-LiDAR-Discrete-Full-Waveform) shows an example of this; a discrete return system would interpret the returned echo as two returns, as there are two peaks in the returned echo corresponding to a canopy and a ground hit. In a forested landscape, first returns often represent the upper canopy, while last returns correspond to the ground or objects near the ground [@white_best_2013].
 
-## Full Waveform
+### Full Waveform
 
 **Full waveform** LiDAR systems record reflected energy from each laser pulse as a continuous signal [@white_best_2013].Traditionally, discrete return systems would only digitally store the discrete returns, without recording the complete analog return [@uf_geomatics_-_fort_lauderdale_lidar_2016]. In contrast, full waveform systems digitize the entire analog signal at high sampling rates [@uf_geomatics_-_fort_lauderdale_lidar_2016]. This can be seen on the right hand side of Figure \@ref(fig:15-LiDAR-Discrete-Full-Waveform). This type of LiDAR creates much larger file sizes which were an issue in the past, but with modern storage capabilities it is becoming a viable alternative to discrete return LiDAR.
 
@@ -200,6 +195,16 @@ fig_cap = "(ref:15-LiDAR-Discrete-Full-Waveform-caption)"
 knitr::include_graphics("images/15-LiDAR-Discrete-Full-Waveform.png")
 ```
 
+### Your Turn!
+
+Use our online tool to visualize and explore a discrete return LiDAR point cloud. You can visualize different attributes of the point cloud including elevation, classification, number of returns, return number, and intensity. Why do you think the intensity of returns varies across the vegetation? What do you notice about the pattern of return number relative to the point elevation? What information do you think could be used to classify the returns?
+
+```{r 15-lidar-visualization-static, fig.cap = fig_cap, out.width= "75%", echo = FALSE}
+    fig_cap <- paste0("Online tool for visualizing and exploring the properties of a small LiDAR point cloud. Data from City of Vancouver and licensed under the <a href='",covlicence,"'>Open Government License - Vancouver</a>. <a href='https://www.opengeomatics.ca/viz/lidar-viz/'>Click here to access the interactive tool.</a> Juno Yoon, CC-BY-SA-4.0.")
+    knitr::include_graphics(here::here("images", "15-lidar-visualization-static.PNG"))
+```
+<br/>
+
 ## Emerging Technology
 
 **Single Photon LiDAR** is an emerging technology that allows wall-to-wall mapping detecting photons reflected off of surfaces more efficiently than traditional LiDAR sensors [@swatantran_rapid_2016]. While traditional LiDAR systems emit high energy laser beams, single photon systems transmit shorter, lower energy pulses [@swatantran_rapid_2016]. These systems use a green laser (532 nm), and due to the high efficiency nature of the technology can acquire high density point clouds (12 - 30 points·m^-2^) up to 30 times faster than traditional systems, and operate at higher altitudes [@swatantran_rapid_2016]. The system is also capable of penetration semi-porous obscurations such as vegetation, ground fog and thin clouds during daytime and night-time operations [@swatantran_rapid_2016].
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