1  Introduction

Welcome to Remote Sensing! This week we will cover the basic grounds – what is light? What is an electromagnetic waves, how do satellites work?

1.1 Summary

1.1.1 Lecture Overview: What is Remote Sensing?

1.1.2 Electromagnetic Spectrum

The electromagnetic spectrum is made up of thousands of bands, including visible light, UV, infrared, radar, FM, TV and short wave.

Source: GISGeography (2017)

• The earth’s surface either absorbs energy or transmits energy.
• The colour we see is the visible light waves that is reflected off of the object. Apple is red because it reflects red and absorbs all other visible light.

1.1.3 Resolutions of remote sensing images

Spatial: The size of a raster grid per pixel (cm/m). The smaller the measure, the more detailed the image.

Spectral: The number of bands from the electromagnetic spectrum being surveyed. Often earth surfaces require multispectral data to form a true colour image.

• i.e. green vegetation mostly requires red and near-infrared bands to detect, whereas soil requires mid-infrared bands (6 and 7) to be detected; bodies of water can be detected mostly within visible light (RBGs).
• Landsat data surveys visible light, near infrared and short wave infrared.

An example from the practical: The below scatter image was produced from sentinel data of Dhakar, Bangladesh. Band 4 = Red Band 8 = NIR * High NIR and Low Red - high vegetation since veg peaks in NIR (see above) * Low red Low NIR - Wet soil.

Temporal: How frequently the data is collected. Often there is a direct trade-off between pixel resolution and update frequency – the higher the resolution, the lower the update frequency. Good rm data is spenny!!

Radiometric: Able to identify difference in light or reflectance of Earth surface.

• The higher the bit, the higher the depth, the higher the ability to detect texture.

Data format: Generally raster, but depending on sensor.

• LiDAR is point data in x, y and z (height) → good for elevation models.

Source: Jensen (1986)

A bit of thinking: Applications of varying temporal and spatial resolution, what occasion it’s best for and what satellite provides that spatial resolution. An interesting point is RM data for emergency responses require fairly granular spatial resolution and frequent temporal resolution – so as to track minute changes in landscapes. However as mentioned above, there are direct trade offs between temporal and pixel resolution. For satellites to provide both high temporal and spatial resolution requires huge sums of cost and investment. With growing threats from the climate crisis on the urban landscape – do we currently have the capacity to effectively monitor damages and respond on time?

1.1.4 Factors influencing Electromagnetic waves

A lot of things happen from when electromagnetic wave is emitted from sun/satellites and when it hits the earth surface. And these things could interfere with the images and data we get.

Rayleigh Scattering: Scattering of waves off of molecules in the air (the atmosphere).

• Blue waves are smaller, making it easier to scatter → sky = blue.
• Higher the scatter/absorption, deeper the colour. Deep ocean is dark because there are more water molecules to scatter and absorb waves → no reflection.

Bidirectional Reflectance Distribution Function (BRDF):

• changing angles of sensors and levels of illumination
• earth surface that is smooth/diffuse that causes reflectance to go in different directions.
• Shadows: Backscattering - sun behind observer | forward scattering - sun opposite observer.

Polarization (SAR data):

• EMR waves with 2 waves oscillating perpendicularly. How they reflect depends on texture of the earth surface, moisture, salinity, density, orientation.
• Single: same polarization transmitted and received
• Dual: transmit one, get another
• Quad: transmit and receive up to 4 types.

1.1.5 Practical: Landsat, Sentinel, QGIS, SNAP

Data Source: Sentinel Data: Copernicus Open Access Hub Landsat Data: Earth Explorer USGS Boundary Data (for masking): https://gadm.org/

Colour compositions: True Colour: colours we see with our eyes B2, B3, B4. False Colour: composite of waves human eyes cannot see. * Infrared: B8, B4, B3. Plants reflect NIR and green light and absorbing red. * Agriculture: B11, B8, B2. Detecting healthy vegetation in dark green * Moisture:(B8A-B11)/(B8A+B11). Detecting water stressed.

1.2 Application

Since the emergence of accessible Landsat Satellite images in 1967, earth data has been used for a wide range of purposes including tracking land use, urbanisation, drought, wildfires, biomass changes and other natural and human caused changes (USGS, nd).

1.2.0.1 Environment

The Intergovernmental Panel on Climate Change stated earth-observing satellites are a critical and valuable tool to track changes and improving climate predictions (“ESA and Climate” (n.d.)). Coupled with substantial environmental changes in recent decades, remote sensing data has allowed changes to be tracked and analysed over the last century. Sultana and Satyanarayana (2020) have used satellite imagery to assess the rate of urbanisation and urban heat island intensities in urban India. Matricardi et al. (2010) analysed the effects of tropical forest degradation as a result of logging and fire, and implemented policy recommendation accordingly, taking advantage of the extensive spectral bands available. Extensive reports by international climate driven bodies have used earth data to measure glacier and sea ice decline, sea level rise and climate modelling.

1.2.0.2 Urban Development

Development of urban areas can be measured with medium to high spatial resolution satellite images including SPOT, Landsat, and Aster, providing a large mass of data on urban growth (Patino and Duque (2013)). Sutton (2003) measured the sprawl of cities using nighttime satellite imagery, using lights as a proxy for urban activities. Elkhrachy (2022) has specifically used SAR data to detect depth of flash flood water in risk zones. “Site Suitability Evaluation for Urban Development Using Remote Sensing, GIS and Analytic Hierarchy Process (AHP) | SpringerLink” (n.d.) used earth data and Analytic Hierarchy Process technique to evaluate site suitability for urban development. They specifically looked for geomorphology, transport network, land use/cover and access to ground water. 1 meter spatial resolution images from from IKONOS data was used.

Source: “Site Suitability Evaluation for Urban Development Using Remote Sensing, GIS and Analytic Hierarchy Process (AHP) | SpringerLink” (n.d.)

1.2.0.3 Assisting social analysis

Satellite imagery has allowed for a spatial perspective when it comes to studying social processes such as urban poverty, quality of life (WEBER and HIRSCH (1992)), residential desirability (Green (1957)). Generating estimation about the urban population was reported to be one of the top 5 recurrent research theme within remote sensing urban environments Phinn et al. (2002). For example, Li and Weng (2007) derived environmental variables such as greenness, temperature from Landsat EMT+ as proxies to material and environmental welfare and crowdedness. This is integrated with US census data to generate a Quality of Life Index. Duque et al. (2015) used Quickbird images of 0.6m spatial resolution (extremely high res!!) to generate a intra-urban Slum index. This was executed through per-pixel classification of urban texture and structures.

1.3 Reflection

• Remote sensing data has in a way revolutionised how people approach spatial analysis, providing an unprecedented amount of data about the earth’s surface with lrelatively ow barriers to access compared to previous available methods, such as costal surveying. This has been especially true since remote sensing data became publicly available, democratising access.
• Utilising the varying spectral properties of earth surfaces and objects, our understanding and ability to analyse urban areas has significantly improved, with scientists more easily able to map high density buildings, transport networks and urban vegetation. RM data essentially removes the need to physically surface overviews of urban characteristics (although detailed, high resolution analysis still relies on manual surveying).
• Although powerful, earth data is often used in conjunction with other data, especially demographic, social and economic data for urban analysis to translate into urban policies. For example, satellite imaging can be used to map certain visible characteristics of urban poverty, alongside census data to give a more nuanced picture.
• There is a wide range of analysis that could be done with RM data, including regression analysis, neural network deep learning, principal component analysis and classification. The options are endless!!!!

Duque, Juan C., Jorge E. Patino, Luis A. Ruiz, and Josep E. Pardo-Pascual. 2015. “Measuring Intra-Urban Poverty Using Land Cover and Texture Metrics Derived from Remote Sensing Data.” Landscape and Urban Planning 135 (March): 11–21. https://doi.org/10.1016/j.landurbplan.2014.11.009.

Elkhrachy, Ismail. 2022. “Flash Flood Water Depth Estimation Using SAR Images, Digital Elevation Models, and Machine Learning Algorithms.” Remote Sensing 14 (3): 440. https://doi.org/10.3390/rs14030440.

“ESA and Climate.” n.d. https://www.esa.int/Applications/Observing_the_Earth/Space_for_our_climate/ESA_and_climate.

GISGeography. 2017. “Why the Atmospheric Window Matters in Earth Science.” https://gisgeography.com/atmospheric-window/.

Green, Norman. 1957. “Aerial Photographic Interpretation and the Social Structure of the City.” PHOTOGRAMMETRIC ENGINEERING.

Jensen, J. R. 1986. “Introductory Digital Image Processing: A Remote Sensing Perspective.” https://www.osti.gov/biblio/5166368.

Li, G., and Q. Weng. 2007. “Measuring the Quality of Life in City of Indianapolis by Integration of Remote Sensing and Census Data.” International Journal of Remote Sensing 28 (2): 249–67. https://doi.org/10.1080/01431160600735624.

Matricardi, Eraldo A. T., David L. Skole, Marcos A. Pedlowski, Walter Chomentowski, and Luis Claudio Fernandes. 2010. “Assessment of Tropical Forest Degradation by Selective Logging and Fire Using Landsat Imagery.” Remote Sensing of Environment 114 (5): 1117–29. https://doi.org/10.1016/j.rse.2010.01.001.

Patino, Jorge E., and Juan C. Duque. 2013. “A Review of Regional Science Applications of Satellite Remote Sensing in Urban Settings.” Computers, Environment and Urban Systems 37 (January): 1–17. https://doi.org/10.1016/j.compenvurbsys.2012.06.003.

Phinn, S., M. Stanford, P. Scarth, A. T. Murray, and P. T. Shyy. 2002. “Monitoring the Composition of Urban Environments Based on the Vegetation-Impervious Surface-Soil (VIS) Model by Subpixel Analysis Techniques.” International Journal of Remote Sensing 23 (20): 4131–53. https://doi.org/10.1080/01431160110114998.

“Site Suitability Evaluation for Urban Development Using Remote Sensing, GIS and Analytic Hierarchy Process (AHP) | SpringerLink.” n.d. https://link.springer.com/chapter/10.1007/978-981-10-2107-7_34.

“Site Suitability Evaluation for Urban Development Using Remote Sensing, GIS and Analytic Hierarchy Process (AHP) | SpringerLink.” n.d. https://link.springer.com/chapter/10.1007/978-981-10-2107-7_34.

Sultana, Sabiha, and A. N. V. Satyanarayana. 2020. “Assessment of Urbanisation and Urban Heat Island Intensities Using Landsat Imageries During 2000 2018 over a Sub-Tropical Indian City.” Sustainable Cities and Society 52 (January): 101846. https://doi.org/10.1016/j.scs.2019.101846.

Sutton, Paul C. 2003. “A Scale-Adjusted Measure of ‘Urban Sprawl’ Using Nighttime Satellite Imagery.” Remote Sensing of Environment, Urban Remote Sensing, 86 (3): 353–69. https://doi.org/10.1016/S0034-4257(03)00078-6.

WEBER, C., and J. HIRSCH. 1992. “Some Urban Measurements from SPOT Data: Urban Life Quality Indices.” International Journal of Remote Sensing 13 (17): 3251–61. https://doi.org/10.1080/01431169208904116.