Assessing the Uses of Contemporary Aerial and Space Borne Sources of Remote Sensing Data
Published in Environment. Tags: Environment.
The history of remote sensing goes back as far as 1936 when the combination of photography and aviation provided a new method of studying urban environments (Furgate, Tarnavsky & Stow, 2010). Aerial remote sensing in the form of photography from aircraft was of great use during World War Two (St. Joseph, 1945) but the first major post-war application was in archaeology (St. Joseph, 1945).
Up to the 1970s, remote sensing primarily involved low resolution film-based systems and the limited availability of imagery constrained applications mainly to urban planning (Furgate, Tarnavsky & Stow, 2010). It was not until the launch of the Landsat-1 satellite in 1972 (NASA, 2019a) that remote sensing began to develop more rapidly.
Remote sensing was brought into mainstream awareness by the recommendations in Our Common Future (Brundtland et al., 1987). Since then, remote sensing has become a crucial tool to allow environmental assessment and provide data essential to achieving sustainability goals. Perhaps the most widely publicly discussed example of this is imagery showing damage to the Amazon rainforest (Renó et al., 2011) which would not have been possible without remote sensing.
Focusing on the era since Landsat-1, this essay will consider the development and application of space borne remote sensing techniques alongside aerial applications. The more recent development of Unmanned Aerial Vehicles (UAVs) will also be examined.
A Contemporary History of Applications
Since 1972, space borne sources of remote sensing data have been used for a wide range of applications beginning with the formal adoption of satellite data by the Commission for Agricultural Meteorology in 1974 (Sivakumar et al., 2004).
Landsat has the longest history but there are other major imaging satellites from organisations around the world such as the French SPOT satellites, Indian Remote Sensing System and QuickBird, operated by the private firm Digital Globe (Furgate, Tarnavsky & Stow, 2010).
Although satellite data can be used alone, it is often combined with ground or aerial observations either to provide additional data that cannot be collected from space (e.g. due to resolution limitations) or to calibrate results (Marx, McFarlane & Alzahrani, 2017).
Data collected from aircraft has long complemented satellite data but in the last decade UAV technology has become available (Singh & Frazier, 2018). The last 5 years has seen a boom in the use of UAVs for terrestrial applications, particularly in agriculture, geomorphology and forestry (Singh & Frazier, 2018). This suggests that decreasing technology costs are associated with an increase in adoption of remote sensing data. Starting with the release of Landsat data for free in 2008 (USGS, 2008), technology improvements bring associated cost decreases thereby allowing studies to be conducted which would have been expensive or impossible in the past.
Whether it is generating public awareness of the extent of deforestation in the Amazon rainforest (Renó et al., 2011), examining urban deforestation and regrowth (Gong et al., 2013) or monitoring growth of desert cities (Yagoub, 2004), we can expect the application of remote sensing to continue to increase as technology becomes more accessible.
The remainder of this essay will examine the advantages and disadvantages of these technologies, and how they are being used together.
Space borne sources: advantages and disadvantages
As the first generally available source of remote sensing data, Landsat has the unique advantage of historical records going back to 1972 for a range of sensor types and bands (NASA, 2019d). However, this does not necessarily mean that a continuous dataset is available – there are gaps in records caused by a variety of issues including data loss and cloud cover (Goward et al., 2006). A good example of where this has caused problems is Marx, McFarlane & Alzahrani (2017) where they were unable to obtain any data for 1991, 1994, 1995 and 2000 due to cloud cover, and only partial images for 1987, 1993 and 1996. Given they were attempting to study a 30-year period, these limitations have real effects on long term studies.
In 2003, Landsat-7 suffered a sensor failure which resulted in a 6-week record gap and up to 25% of data for all future captures being unavailable (NASA, 2019b). Landsat-8 was launched in 2013 (NASA, 2019c) which restored complete imaging, but the 10-year gap shows the long lead time required to deploy new systems and technologies or recover from sensor failure.
That considered, when operating normally a major advantage of satellite imagery is regular revisit frequency. No additional effort is required to get a continuous timeline of sensor data from regions around the globe. This has proven crucial in disaster relief efforts to compare damage to the pre-disaster state (Kakooei & Baleghi, 2017).
However, revisit frequency is different for each satellite platform and there is no guarantee that weather conditions will allow successful data capture (Jay et al., 2019). This limits the usefulness of satellite data in applications such as analysing rice cultivation where timeliness is crucial (Inoue et al., 2012; Launay & Guerif, 2005).
This means that whilst satellites have advantages in terms of their theoretical ability to continuously collect data from variety of sensors across a large geographical area, their operation cannot be relied upon. Depending on the data required, such problems can be mitigated by combining data from aerial sources.
Aerial sources: advantages and disadvantages
One of the major advantages of aerial data is the speed of access to data – images can often be made available in less than an hour when UAVs are involved (Kakooei & Baleghi, 2017). However, due to the size of the data and depending on how the data is analysed, processing can take longer than satellite data (Ruwaimana et al., 2018), especially with time needed to stitch the imagery together in a mosaic (Moran, Inoue & Barnes, 1997).
Unlike satellites which tend to have fewer variables and are calibrated on deployment, aerial conditions such as altitude and atmospheric conditions require careful ongoing calibration (Moran, Inoue & Barnes, 1997). Indeed, Landsat-7 acts as an in-orbit standard for calibration of other systems (NASA, 2019) which shows the stability that satellite data offers.
A major challenge with aerial data has been the low resolution, but UAVs are now able to offer sub-centimetre pixel resolutions (Jay et al., 2019). This is important because features of interest can be distributed across multiple pixels or within a single pixel, both of which cause challenges in analysis (Furgate, Tarnavsky & Stow, 2010). Certain field applications, such as evaluating crop breeds, require resolutions higher than satellites can offer (Sankaran et al., 2015) and so UAVs have opened a new area of remote sensing which was previously impossible.
Unlike satellites which have a large range of sensors, researchers must choose from different types of UAVs each with different characteristics. Power source and maximum payload size determine which sensors are available (Sankaran et al., 2015) but costs also vary, ranging from $1,000 to $100,000 (Singh & Frazier, 2018).
Having more choice allows researchers to prioritise what is important to their study. Contrasted to free data available from Landsat, budgetary decisions are now part of the equation, rather than assuming access to the best data available. This means factors such as cost, access to the study area and control over the data can be deciding factors at the expense of overall accuracy (Kavoosi et al., 2018).
Although it introduces more complexity, choice is having a positive effect, as shown by the increase in the numbers of studies published using data from UAVs (Singh & Frazier, 2018).
Combining aerial and space borne sources
Logistical challenges are providing opportunities to combine multiple sources of remote sensing imagery. For example, GPS signals being blocked by dense forest canopy or lack of road infrastructure make ground observations difficult (Marx, McFarlane & Alzahrani, 2017), but this can now be solved with aerial imaging. Just as comparisons of urban growth used to be limited by cost and processing challenges (Masek, Lindsay & Goward, 2000), technology improvements are allowing new applications to emerge at lower cost.
Novel combinations of both sources of sensor data are being found. These range from increasing the level of detail in existing forest monitoring (Dash, Pearse & Watt, 2018) or entirely new applications in areas such as disaster relief, where overhead imagery is being combined with oblique angles to create 3D representations of damaged buildings (Kakooei & Baleghi, 2017).
UAVs are being suggested as alternatives to what would previously have been achieved using fieldwork (Kattenborn et al., 2019). In Renó et al. (2011), satellite imagery was augmented using field observations – human interviews and photography, including using aircraft to survey the edges of the target site. The paper does not explain the decision to use aerial photography outside the central area but assuming this was because of the logistical challenges, the fieldwork could now be achieved at lower cost using UAVs instead.
That said, it is not always the case that more accurate data is automatically better. Existing methods are unable to deal with newer, very high-resolution data (X. Zhang et al., 2017) but techniques are starting to be developed (Jay et al., 2017; Jay et al., 2019) to take advantage of higher levels of accuracy.
UAVs have been the subject of much hype which has not been matched by deployment (Freeman & Freeland, 2015). This is perhaps due to how sales and marketing efforts have focused on commercial agriculture rather than academic research. UAV manufacturers want to maximise revenue and agriculture is a business they can sell to for a profit. Academic research is not. Although they may not be highly profitable, academics often have time and motivation to develop new analysis techniques not possible within the constraints of commercial agriculture. Manufacturers could therefore see academia as a collaboration partner rather than as a commercial customer. More research is needed here to understand how technologies are adopted in each sector. To expand sales, it has been suggested that more practical analysis methods are needed for commercial demand to increase (Hunt & Daughtry, 2018).
Another challenge is the remaining uncertainties around the legality of UAVs. 33 countries currently have some form of regulations (The Drone Info, 2019) but this varies by location. Some countries allow hobbyist usage, some ban drones altogether and all place restrictions on commercial usage (Cracknell, 2017).
The variety of options and multiple considerations of payload capacity, battery size and sensor types when selecting systems contrasts with the freely available repository of satellite imaging. Users now need to think about the options and what that means for cost. This means that whilst studies such as Sankaran et al. (2015) are useful in comparing technical parameters, they miss crucial cost calculations. Studies such as Singh & Frazier (2018) are more useful in that regard.
Over 80 years since the first use of remote sensing data, the field has experienced several distinct stages. Starting with aerial photographic data, the launch of Landsat-1 in 1972 was a turning point for global earth observation. This was further catalysed by making the data free in 2008. Satellites have been a major source of remote sensing data for some time but the last decade has seen an increase in aerial observations with the introduction of accurate and affordable UAV systems.
Very high-resolution data can be collected in short timeframes with UAVs (Kakooei & Baleghi, 2017) and new methods of processing that data are being developed (Jay et al., 2017; Jay et al., 2019). From freely available to high-resolution commercial options (Marx, McFarlane & Alzahrani, 2017), a variety of satellite data exist to complement aerial and UAV data (Singh & Frazier, 2018).From a single satellite and aircraft photography in 1972 to a range of space borne and aerial options in 2019, such variety allows users to determine their own trade-offs, allowing them to choose the sources of data that make sense for their requirements. That this has resulted in an increase in studies published (Singh & Frazier, 2018) should not be a surprise. We should expect more to follow in future.
Brundtland, G. H., Khalid, M., Agnelli, S., Al-Athel, S. & Chidzero, B. (1987) Our common future.
Cracknell, A. P. (2017) UAVs: regulations and law enforcement. International Journal of Remote Sensing. 38 (8-10), 3054-3067. Available from: doi: 10.1080/01431161.2017.1302115.
Dash, P. J., Pearse, D. G. & Watt, S. M. (2018) UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health. Remote Sensing. 10 (8), Available from: doi: 10.3390/rs10081216.
Freeman, P. K. & Freeland, R. S. (2015) Agricultural UAVs in the U.S.: potential, policy, and hype. Remote Sensing Applications: Society and Environment. 2 35-43. Available from: doi: https://doi.org/10.1016/j.rsase.2015.10.002.
Furgate, D., Tarnavsky, E. & Stow, D. (2010) A Survey of the Evolution of Remote Sensing Imaging Systems and Urban Remote Sensing Applications. In: Rashed, T. & Jurgens, C. (eds.). Remote sensing of urban and suburban areas. Dordrecht; London, Springer. pp. 119-139.
Gong, C., Yu, S., Joesting, H. & Chen, J. (2013) Determining socioeconomic drivers of urban forest fragmentation with historical remote sensing images. Landscape and Urban Planning. 117 57-65. Available from: doi: https://doi.org/10.1016/j.landurbplan.2013.04.009.
Goward, S., Arvidson, T., Williams, D., Faundeen, J., Irons, J. & Franks, S. (2006) Historical Record of Landsat Global Coverage. Photogrammetric Engineering & Remote Sensing. 72 (10), 1155-1169. Available from: doi: 10.14358/PERS.72.10.1155.
Hunt, E. R. & Daughtry, C. S. T. (2018) What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing. 39 (15-16), 5345-5376. Available from: doi: 10.1080/01431161.2017.1410300.
Inoue, Y., Sakaiya, E., Zhu, Y. & Takahashi, W. (2012) Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sensing of Environment. 126 210-221. Available from: doi: https://doi.org/10.1016/j.rse.2012.08.026.
Jay, S., Baret, F., Dutartre, D., Malatesta, G., Héno, S., Comar, A., Weiss, M. & Maupas, F. (2019) Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops. Remote Sensing of Environment. 231 110898. Available from: doi: https://doi.org/10.1016/j.rse.2018.09.011.
Jay, S., Gorretta, N., Morel, J., Maupas, F., Bendoula, R., Rabatel, G., Dutartre, D., Comar, A. & Baret, F. (2017) Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery. Remote Sensing of Environment. 198 173-186. Available from: doi: https://doi.org/10.1016/j.rse.2017.06.008.
Kakooei, M. & Baleghi, Y. (2017) Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment. International Journal of Remote Sensing. 38 (8-10), 2511-2534. Available from: doi: 10.1080/01431161.2017.1294780.
Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C. & Fassnacht, F. E. (2019) UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote Sensing of Environment. 227 61-73. Available from: doi: https://doi.org/10.1016/j.rse.2019.03.025.
Kavoosi, Z., Raoufat, M. H., Dehghani, M., Abdolabbas, J., Kazemeini, S. A. & Nazemossadat, M. J. (2018) Feasibility of satellite and drone images for monitoring soil residue cover. Journal of the Saudi Society of Agricultural Sciences. Available from: doi: https://doi.org/10.1016/j.jssas.2018.06.001.
Launay, M. & Guerif, M. (2005) Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agriculture, Ecosystems & Environment. 111 (1), 321-339. Available from: doi: https://doi.org/10.1016/j.agee.2005.06.005.
Marx, A., McFarlane, D. & Alzahrani, A. (2017) UAV data for multi-temporal Landsat analysis of historic reforestation: a case study in Costa Rica. International Journal of Remote Sensing. 38 (8-10), 2331-2348.
Masek, J. G., Lindsay, F. E. & Goward, S. N. (2000) Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations. International Journal of Remote Sensing. 21 (18), 3473-3486.
Moran, M. S., Inoue, Y. & Barnes, E. M. (1997) Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment. 61 (3), 319-346. Available from: doi: https://doi.org/10.1016/S0034-4257(97)00045-X.
NASA. (2019a) Landsat 1. Available from: https://landsat.gsfc.nasa.gov/landsat-1/ [Accessed 2019-10-14].
NASA. (2019b) Landsat 7. Available from: https://landsat.gsfc.nasa.gov/landsat-7/ [Accessed 2019-10-19].
NASA. (2019c) Landsat 8. Available from: https://landsat.gsfc.nasa.gov/landsat-8/ [Accessed 2019-10-19].
NASA. (2019d) Landsat 8 Bands. Available from: https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands/ [Accessed 2019-10-19].
Renó, V. F., Novo, Evlyn M. L. M., Suemitsu, C., Rennó, C. D. & Silva, T. S. F. (2011) Assessment of deforestation in the Lower Amazon floodplain using historical Landsat MSS/TM imagery. Remote Sensing of Environment. 115 (12), 3446-3456. Available from: doi: https://doi.org/10.1016/j.rse.2011.08.008.
Ruwaimana, M., Satyanarayana, B., Otero, V., Muslim, A.,M., Syafiq A., M., Ibrahim, S., Raymaekers, D., Koedam, N. & Dahdouh-Guebas, F. (2018) The advantages of using drones over space-borne imagery in the mapping of mangrove forests. Plos One. 13 (7), e0200288. Available from: https://doi.org/10.1371/journal.pone.0200288.
Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J., Miklas, P. N., Carter, A. H., Pumphrey, M. O., Knowles, N. R. & Pavek, M. J. (2015) Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy. 70 112-123. Available from: doi: https://doi.org/10.1016/j.eja.2015.07.004.
Singh, K. K. & Frazier, A. E. (2018) A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications. International Journal of Remote Sensing. 39 (15-16), 5078-5098. Available from: doi: 10.1080/01431161.2017.1420941.
Sivakumar, M., Roy, P. S., Harmsen, K. & Saha, S. K. (2004) Satellite remote sensing and GIS applications in agricultural meteorology. Proceedings of the Training Workshop. 7-11 July 2003, Dehradun, India, WMO/TD No. 1182.Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.119.1636&rep=rep1&type=pdf .
St. Joseph, J. K. (1945) Air Photography and Archaeology. The Geographical Journal. 105 (1), 47-59. Available from: doi: 10.2307/1789545.
The Drone Info. (2019) The Current State of Global Drone Regulations. Available from: https://www.thedroneinfo.com/the-current-state-of-global-drone-regulations/ [Accessed 2019-10-15].
USGS. (2008) Imagery for Everyone. Available from: https://www.usgs.gov/land-resources/nli/landsat/april-21-2008-imagery-everyone [Accessed 2019-10-14].
X. Zhang, G. Chen, W. Wang, Q. Wang & F. Dai. (2017) Object-Based Land-Cover Supervised Classification for Very-High-Resolution UAV Images Using Stacked Denoising Autoencoders. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10 (7), 3373-3385. Available from: https://ieeexplore.ieee.org/document/7879207. Available from: doi: 10.1109/JSTARS.2017.2672736.
Yagoub, M. M. (2004) Monitoring of urban growth of a desert city through remote sensing: Al-Ain, UAE, between 1976 and 2000. International Journal of Remote Sensing. 25 (6), 1063-1076. Available from: doi: 10.1080/0143116031000156792.