The view from above

olive trees
Remote sensing means soon we may be able to detect plant diseases before visual symptoms occur. CC0: ulleo Via Pixabay

We might not all be comfortable with the idea of unmanned drones, or government satellites scanning all corners of the earth but the data produced from these constant eye-in-the-skys is proving ever more useful in today’s environmental struggles.


This type of data-collection is referred to as ‘remote sensing’ and it can be used to map variables including weather patterns and vegetation distribution across geographical areas via satellites, drones or planes. This data can then be analysed to create projections into the future and be used to predict the distribution of organisms by searching for areas which align with its ‘ecological niche’; a combination of biotic and abiotic factors present necessary for the organism to survive. The ways in which this information can be used to measure and predict impacts are constantly expanding and increasingly accurate.

Landsat is perhaps the most commonly known and widely available remote sensing data set; producing easily accessible high quality systematic, geometric, radiometris and terrain corrected data. It is maintained as part of a joint initiative between the U.S. Geological Surveys (USGS) and NASA and has been producing multispectral data using a series of satellites for over 40 years. Landsat data has long been used to monitor global deforestation, with historical data showing the alarming losses that have occurred over the last few decades. Recently, however, the speed of collection and interpretation has increased to the point where it is now, perhaps quite depressingly, possible to watch patches of forest being cleared in almost real time online. On the bright side, the impact of having such informative and accessible data means that more groups responsible for deforestation are being held to account as impacts can be quantified and loggers are easier to find.

A more recent usage of remote sensing data is for monitoring the outbreak of plant pathogens. Spectral information gathered from leaf surfaces has allowed scientists to model the distribution of different vegetation types, species and even diseases. By being able to identify infected individuals quickly and cheaply, the spread can be controlled in a faster and more targeted manner.

Stemphylium vesicarium is the fungus responsible for purple spot in asparagus and is currently prevented by the application of fungicides to the crop. However, research has shown there may be a better way. Academics in the USA have found that the visible and near-infrared field spectroscopy (UNISPEC-DC) data from Pleiades-1A and Landsat 8 can be used to determine the distribution and severity of the disease across asparagus fields. This means fungicides can be applied in a targeted manner, reducing the overall amount of pesticides needed [1]. A similar paper on soybean sought to identify wavebands capable of distinguishing between healthy and diseased plants found that whilst the model was able to identify healthy plants it was less able to discriminate between individual diseases [2].

A step beyond this work is that of a recent paper published in Nature Plants [3] which has managed to use spectral data along with thermography to identify infected plants before symptoms are even visible to the human eye. The authors studied the occurrence of Xylella fastidiosa; a bacterial plant pathogen which has recently expanded in distribution from the Americas to Asia and Europe. Whilst the bacterium is able to infect >350 plant species the focus of the paper was solely on olive trees which are being negatively impacted as the pathogen spreads around the Mediterranean. By attaching a specialist camera to an unmanned plane the researchers were able to systematically scan olive orchards for non-visible signs of disease. The researchers had a hit rate of >80%, finding that asymptomatic trees identified by remote sensing developed disease symptoms at twice the rate of those the system didn’t manage to identify.

The identification of diseased plants can have important consequences outside of agriculture too; the impact of disease on the occurrence of wildfires has also been investigated using remote sensing [4]. In this study the authors mapped the distribution of sudden oak death using KOMPSAT-2 images and burn severity using post-fire Landsat data, finding that disease affected areas were more likely to cause higher burn severity through increases in surface fuels.

Being able to remotely map the occurrence, distribution and spread of deforestation, fire or even plant diseases can greatly reduce the time and effort needed to mitigate their effects. It may also allow for more environmentally friendly management and reduce the need for large-scale pesticide spraying or lead to more effective fire prevention. As remote sensing data becomes easier to generate and of higher quality we may see even more uses come to light in the next few years, helping us monitor our global environment on an unprecedented scale.



  1. 2018. Identification of purple spot disease on asparagus crops across spatial and spectral scales. Computers and Electronics in Agriculture148, pp.322-329.
  2. Bajwa, S.G., Rupe, J.C. and Mason, J., 2017. Soybean disease monitoring with leaf reflectance. Remote Sensing9(2), p.127.
  3. Zarco-Tejada, P.J., Camino, C., Beck, P.S.A., Calderon, R., Hornero, A., Hernández-Clemente, R., Kattenborn, T., Montes-Borrego, M., Susca, L., Morelli, M. and Gonzalez-Dugo, V., 2018. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, p.1.
  4. Chen, G., He, Y., De Santis, A., Li, G., Cobb, R. and Meentemeyer, R.K., 2017. Assessing the impact of emerging forest disease on wildfire using Landsat and KOMPSAT-2 data. Remote Sensing of Environment195, pp.218-229.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s