1  An Introduction to Remote Sensing

Week 1 focused on introducing remote sensing as a technique for research.

1.1 Summary

Remote sensing refers to techniques used to gather data for an object or area of interest without directly measuring it. This often makes use of sensors aboard satellites or aircraft (Li et al. 2020; Watts, Ambrosia, and Hinkley 2012), but can also employ handheld measurements such as LIDAR (Dong and Chen 2017).

Figure 1.1: Remote Sensing Meme. Source: Link

This has become an important tool for environmental research due to the large amounts of data becoming available, with relatively high temporal resolution.

1.1.1 How do they work?

Remote sensors measure electromagnetic radiation emitted or reflected from a study area. This energy originates from the sun, the sensor itself, or even man-made lights.

The workflow of the sensor can be seen below:

1.1.2 Broad Classifications

Sensors can be broken down into two broad classes, being passive and active.

Figure 1.2: Broad classifications of remote sensors.

Image Source: Lopez Ornelas (2016)

=================== Energy Source	Active | Passive | ============================================================================+===================================================================================+ Emits electromagnetic waves from the sensor. | Uses reflected energy from the sun
Benefit	Can penetrate through clouds and vegetation. | - Cheaper and requires less specialised hardware compared to Active sensors. | | | Control of the sensor leads to highly accurate data. | - A wider range of wavelengths provides a more comprehensive view. |
Limitation	More expensive than passive sensors due to the required energy source. | - Less effective during poor weather conditions. | | | The emitted radiation can impact wildlife. | - Suffers from shadow effects from vegetation (E.g. trees), soil and buildings. |

1.1.3 Electromagnetic (EM) waves

The EM energy measured by the sensor is not equal to that which is emitted by the source. This is due to interferences such as energy absorption on a surface, or scattering from particles within the atmosphere.

The different wavelength of EM radiation interacts with surfaces in different ways, making some ideal for remotely sensing planetary surfaces (not limited to Earth observation).

Figure 1.3: Strength of reflection from surfaces

Image Source: Young and Onoda (2017)

1.1.4 Resolution

The level of detail is referred to as resolution. Remotely sensed data features four forms of resolution (similar to the resolution of a camera).

	Spatial	Temporal	Spectral	Radiometric
Description	The unit area that a single pixel spans.	The time between usable successive images (revisit time).	The number of electromagnetic wave bands in an image.	The number of bits representing the digitised waves. |
Importance	Providing more detailed data on the study area.	Ability to identify change through time.	Allows more detailed information on the properties of the study area.	Identifying finer variations in reflected energy.
Units	Feet or meters squared.	Days	Wavelength or band	Bits

1.2 Applications

The ability of satellites to revisit the same point on earth every 16 days, allows for an extensive list of applications.

(Szpakowski and Jensen 2019)

Many academic studies use remotely sensed data to quantify changes temporally.

1. Flood and drought assessment

SAR data can be used to identify water surface anomalies, allowing the identification of flooding or drought over large spatial extents. Lopez et al. (2020) used data to calculate the surface water areal fraction (SWAF) of the Amazon river basin to quantify changes in water content after the drought in 2010.

Figure 1.4: Changes in SWAF in the Amazon basin during a drought event in 2010.

CASA’s own Ollie Ballinger is at the forefront of remote sensing application innovation. Specifically within the intelligence gathering stream.

2. Radar Interference Tracker (Ballinger 2022)

Accidentally maximizing (instead of minimising) the background noise of the Sentinel-1 SAR (Synthetic Aperture Radar) imagery revealed interference caused by active missile defence systems spread across the middle east. Allowing those with the technical skills to manipulate remotely sensed data to identify locations of the armed forces activity. This phenomenon has been found to occur due to the overlap of wavelength frequencies between military radar systems and those used by SAR satellites.

Figure 1.5: Converging lines of interference over an Airbase in Bahrain.

Image Source: Dan (2020)

3. Open Source Damage Detection in Ukraine

Topography data (likely from the SAR satellite sensor) was used by Ollie to identify variation before and after an event, allowing the quantification of building height change. This can be overlaid with OSM (Open Street Map) data to allow the prediction of the number of residents displaced by the distraction during the conflict, in addition to starting to identify where resources should be allocated best to rebuild Ukraine’s infrastructure.

1.3 Reflection

The use of passive remotely sensed data appears to have the significant flaw of being obstructed by poor weather conditions with cloud cover, in addition to the limited spatial resolution. Both of these factors would mean that the analysis of small-scale temporal and spatial change could prove inaccurate or unfeasible. However, if the limitations are considered before conducting scientific research the methodology can provide valuable insights for academics, policymakers and the wider group of stakeholders.

Surely this limitation with atmospheric interference leads to seasonal changes in the number of images available for analysis, as during the winter months data will be unavailable or inaccurate.

Future Uses: I would be interested in making use of remote sensing data to further develop research I have done in the past. For my undergraduate dissertation, I examined the spatial and temporal effects the implementation of the 2019 Ultra Low Emission Zone (ULEZ) had on air pollution concentration. This made use of London’s extensive network of research-grade pollution monitors. This methodology was limited by the need to ‘interpolate’ between the values to carry out a spatial analysis. This limitation would be resolved through the use of Sentinel 5P, which can monitor NO₂. This area of research is currently primarily using this outdated / traditional approach to pollution monitoring, which don’t give the suitable spatial resolution needed to answer the important questions related to policy evaluation.

Ballinger, Ollie. 2022. “Radar Interference Tracker: A New Open Source Tool to Locate Active Military Radar Systems.” Bellingcat. https://www.bellingcat.com/resources/2022/02/11/radar-interference-tracker-a-new-open-source-tool-to-locate-active-military-radar-systems/.

Dan, Harel. 2020. “X Marks The Spot: Identifying MIM-104 Patriot Batteries From Sentinel-1 SAR Multi-temporal Imagery.” Medium.

Dong, Pinliang, and Qi Chen. 2017. LiDAR Remote Sensing and Applications. Boca Raton: CRC Press. https://doi.org/10.4324/9781351233354.

Li, Jun, Yanqiu Pei, Shaohua Zhao, Rulin Xiao, Xiao Sang, and Chengye Zhang. 2020. “A Review of Remote Sensing for Environmental Monitoring in China.” Remote Sensing 12 (7): 1130. https://doi.org/10.3390/rs12071130.

Lopez Ornelas, Fernanda. 2016. “The Mexican Water Forest: Benefits of Using Remote Sensing Techniques to Assess Changes in Land Use and Land Cover.” PhD thesis. https://doi.org/10.13140/RG.2.2.11855.43685.

Lopez, T., A. Al Bitar, S. Biancamaria, A. Güntner, and A. Jäggi. 2020. “On the Use of Satellite Remote Sensing to Detect Floods and Droughts at Large Scales.” Surveys in Geophysics 41 (6): 1461–87. https://doi.org/10.1007/s10712-020-09618-0.

Szpakowski, David M., and Jennifer L. R. Jensen. 2019. “A Review of the Applications of Remote Sensing in Fire Ecology.” Remote Sensing 11 (22): 2638. https://doi.org/10.3390/rs11222638.

Watts, Adam C., Vincent G. Ambrosia, and Everett A. Hinkley. 2012. “Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use.” Remote Sensing 4 (6): 1671–92. https://doi.org/10.3390/rs4061671.

Young, Oran, and Masami Onoda. 2017. “Satellite Earth Observations in Environmental Problem-Solving.” In Satellite Earth Observations and Their Impact on Society and Policy, 3–27. https://doi.org/10.1007/978-981-10-3713-9_1.