Author Archives: Julia Ramsauer

Radar vs. Optical: Optimising Satellite Use in Land Cover Classification

An optical image of Kliuchevskoi volcano on the left, with a radar image on the left (Image credit: Michigan Tech Volcanology, Image Cropped)

Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series (2020), Lopes et al., Methods in Ecology and Evolution, https://doi.org/10.1111/2041-210X.13359

The Crux

Most ecologist has at some point run across or used a land cover map in their career. Whether it’s used for figuring out the canopy diversity of a forest, or figuring out which habitat a species is using, land cover maps are incredibly useful tools for everyone from conservationists to architects. But have you ever wondered how they are produced?

Until recently, land cover maps were created using either images from optical satellites or images from radar satellites with a coarse to medium spatial resolution (check out the Did You Know Section for more details, or the image above for an example). Combined with classification algorithms, land cover maps can be created automatically. That makes it sound simple, but the final output depends greatly on the quality and amount of images you use for the classification. Since 2014, the Copernicus Programme has made satellite imaginary freely available at high spatial and temporal spatial resolution. Due to this, optical and radar images can be combined more efficiently to produce land cover classification maps with enhanced accuracy. This is especially useful in tropical and boreal areas, as optical images often don’t show the entire landscape due to persistent cloud over.

Another important factor to consider when producing land cover maps is seasonality. Does the area you intend to classify change over the months? In tropical areas, the answer would probably be no, not too much. To test this hypothesis, the authors of this study investigated whether the use of a time series of radar and optical images improves the classification accuracy of a tropical peatland in the Jambi Province, Indonesia.

What They Did

To classify an area, you need to decide on your land cover categories. In this study, the authors used prior knowledge of the region acquired during field visits and high spatial resolution Google Earth images to select the categories and to indicate where in the study area these categories are found. Marked as polygons on the map, they were later used by the classification algorithm to classify the remaining pixels of the image. They chose to map the following categories: water, peat swamp forest, urban, palm trees, Acacia trees, fern/scrublands, bare ground, and mangrove.

As satellite data is freely available online, they downloaded all optical images (Sentinel-2) and radar images (Sentinel-1) acquired by satellites in 2017. After performing the necessary editing procedures, they ran the classification algorithm with six different combinations of input images: the annual temporal average (the mean pixel value of all images) or the complete time series of either optical or radar images and a combination of both. This greatly eases the computational power of the process but at the cost of possibly important information, such as the importance of seasonality. Comparing the accuracy of the resulting maps, the authors assessed the influence of seasonality on the classification results and whether using a combination of optical and radar images produces better maps than using just one of the two.

Did You Know: Optical vs. Radar Images

The two main types of satellite data used in remote sensing are optical and radar images. Optical satellite imagery is a great way to view the world as the human eye does. However, optical sensors measure reflected solar light, thus only function in the daytime and can’t penetrate through clouds. Radar sensors on the other hand, can image at both day and night and in almost all weather conditions. Moreover, radar images can reveal “secrets” of the land cover that are not visible to the human eye, such as soil moisture or inundated vegetation, marine pollution, or forest biomass.

What They Found

Surprisingly, the most accurate land cover classification maps were produced by using the time series of all images instead of a temporal average, indicating that seasonality does play a role. Certain categories benefited more from this approach, with palm trees, fern/scrublands, and mangrove mapped more accurately when multiple images were used. Overall, the combination of optical and radar images (and the use of time series) created even better maps, minimising classification errors. For example, radar data often confused palm trees and forest, while in optical data undetected clouds were classified as urban areas.

Comparison of land cover classification results using a temporal average (left) and time series (right) of a combination of optical and radar images (Image Credit: Lopes et al., 2020)

Problems

The authors measured the classification accuracy with the so called Kappa-coefficient. Their results showed that classification accuracy based on radar time series was higher than classifications based on optical time series. However, more accurate results don’t necessarily mean better maps. Thus, when analyzing your maps, potential errors due to the similarity of categories in optical or radar data need to be considered.

So What?

In the past, land cover maps often were based on classifying single optical images. However, accurately monitoring land cover is critical to ensure effective conservation and restoration action, and to inform ongoing policies and strategies. Freely available satellite data, combined with recent computational advances, significantly improve our ability to implement advanced and more correct classifications. This study clearly demonstrated that efforts to go beyond classical approaches (using several images of optical and radar satellites) do pay off by significantly improving produced land cover maps.

Julia Ramsauer is a landscape ecologist currently working on the integration of ecosystem services in the Mediterranean region. You can see here recent work on Ecology for the Masses on her profile at this link, or to listen to the latest episode of her podcast, Environmental Science Careers, you can follow her on Twitter here.

Adaptation of Forests to Climate Change: Is It Possible?

Urbión Model Forest in Castilla y León, Spain (Image Credit: Julia Ramsauer)

In a world in which it’s still tough to convince many people that climate change is a very real phenomena, figuring out ways to tackle climate change is an even more difficult problem to wrap our heads around. In general, there are two strategies we can use: (1) mitigation (reducing the accumulation of greenhouse gases in the atmosphere) and, (2) adaptation (reducing the vulnerability of societies and ecosystems facing the impacts of climate change).

In my last piece (linked here), I wrote about the effects of climate change on forests. But what about the reverse, and their potential to mitigate climate change? Forests are crucial for climate change mitigation – they literally suck carbon out of the atmosphere. At the same time, forest adaptation will be necessary to avoid degradation of forest ecosystems due to a changing climate: an extremely complex task.

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Introduced Species Might Restore Ecological Functions Lost During The Ice Age

Image Credit: hbieser, Pixabay Licence, Image Cropped

Introduced herbivores restore Late Pleistocene ecological functions (2020) Lundgren et al., PNAS, https://doi.org/10.1073/pnas.1915769117

The Crux

The fauna of the Pleistocene (also known as the Ice Age) was not that dissimilar to the communities of animals which inhabit our planet now. However, many more large land mammals inhabited all kinds of ecosystems. By the end of the Pleistocene, many of them were extinct, mainly due to climate change impacts (glaciers got larger and restricted their ragne) and prehistoric human impacts like over-hunting, habitat alteration, and introduction of new diseases. The decline of large-bodied herbivores in the Late Pleistocene (LP from here on) led to many ecological changes including reduced nutrient cycling and dispersal, reduced primary productivity, increased wildfire frequency and intensity, and altered vegetation structure. These changes have become our norm.

Scientists usually study species introduction under the premise that they are ecologically novel. However, the introduction of large herbivores has been found to drive changes in the environment, potentially restoring or introducing novel ecological functions similar to pre-extinction Late Pleistocene conditions. This week’s researchers wanted to investigate what sort of role introduced mammals played in restoring ecological interactions by investigating their functional similarity with LP species.

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The Impact of Climate Change on European Forests

Province of Lleida, Catalonia, Spain (Image Credit: Julia Ramsauer, CC BY 2.0, Image Cropped)

As carbon emissions rise globally, finding ways to reduce emissions and store carbon are coming to the forefront of modern science. Forests are huge carbon stores thanks to the copious amount of photosynthesis they conduct. As climate change increases temperatures, trees become a very important tool in the fight against rising emissions. One study even described forest restoration overwhelmingly more powerful than all other proposed climate change solutions. You might think: “So let’s go and plant trees!” Unfortunately, it’s not so easy.

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COP25: A Short Review and On-Site Experiences

Image Credit: Julia Ramsauer, CC BY 2.0

Last Sunday, the UN Climate Change Conference (COP25) in Madrid came to an end. It was a summit that marked the end of a year in which climate change has transformed into a climate emergency and in which society has woken up to the urgency of the situation. Taking into account the pressure from global demonstrations of groups like Fridays for Future and Extinction Rebellion, and the fact that even scientists have further challenged world leaders on the urgent need to act by striking in September, hopes for the outcomes of this year’s climate summit were big. For a couple of days, I was in Madrid to participate in the part of the COP25 that was open to the public and to march with thousands of others in the biggest demonstration ever held in Spain.

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