Land Use

Viewed from satellite images, the Caucasus Mountains are a mosaic of forests, rangelands and agriculture stretched across rugged topography between the Caspian and Black Seas. The political and social history in this region is long and turbulent and most recently has resulted in land abandonment. Globally, agricultural land returning to a wilder state is not a trend we are used to seeing. However, land abandonment does not always equate to thriving ecosystems.

Land degradation is another important force shaping this part of the world, and often it can be traced to livestock overgrazing. In many cases, cattle or sheep are grazed near villages, rather than dispersed across the landscape. This creates a distinctive pattern that turns up in satellite imagery.

The Caucasus Mountains have been farmed intensively for thousands of years. The grasslands and forests that we see today are not in an unaltered state, but rather have changed through time in response to human pressures. Some changes happen on the ground quickly, while others are subtle and have happened over long periods of time, but in general, both can be detected with remote sensing. In the broadest sense, the goal of this research is to find those changes and determine why they are happening.

Katarzyna Ewa Lewinska (Kasia) joined the SILVIS Lab in the summer of 2018 to work with the team studying land use in the Caucasus Mountains. With a strong remote sensing background, an eye for detail, and an intense curiosity about complex problems like this one, she has already begun to make progress.

And why is it important to understand how land cover is changing and what is driving these changes? Land cover influences the ranges of many species, the health of watersheds, nutrient cycling, and the global carbon balance. Our understanding of carbon balance is limited, and carbon sequestration is not uniform across land cover types, but rather influenced by many local-scale factors including land degradation. In models, changing the way degradation is accounted for can yield results that vary by 40-200%, and so it is important to be intentional about the way degradation is defined. Besides being a complex problem, it is also a far reaching one with important implications for understanding the carbon balance and natural resources management of our planet, as 75% of land area is currently degraded and this proportion is projected to increase to 90% in the next 20 years.

Because the Caucasus Mountains are so diverse, this region is an ideal natural laboratory for understanding land use and degradation. Kasia explains the incredible variation in her study system: besides the normal degradation, the complex political and social layer, and the projected climate change, the region at its most basic level remains huge and diverse. In the northwest the climate is mild; in the southeast it’s dry and tropical. Only two of the world’s biomes are not represented here. And then there are mountains.

It is no wonder that this region has already been the focus of SILVIS Lab research. Among other projects, current postdoc He Yin and PhD student Johanna Buchner have created land use/land cover maps that track the changes occurring on this landscape every 5 years since 1987.

Kasia hopes to begin disentangling the roles of grazing and climate change in land degradation, and to become more familiar with the region’s ecology through fieldwork in the coming years. Although it’s still in the early stages, this project has a lot of momentum, and it will be interesting to see what is uncovered.

Agricultural land abandonment is a prominent land use change across the globe. From Eastern Europe to the core of the Amazonian forest, land dedicated to agriculture has been abandoned. These new unused land areas may provide opportunities for conservation and carbon storage. They can also be seen as potential threats to social security and to the spread of fire. Agriculture abandonment is also an important indicator of economic growth and stability. However, abandoned agriculture, and closely related land cover classes such as fallow fields, and grasslands, are not yet routinely mapped with remote sensing. He Yin wants to contribute to the science of remote sensing by creating agriculture abandonment maps that have high precision in time and location. Better maps (i.e. with greater resolution) would help improve our understanding of the drivers that lead to agricultural land abandonment and would be useful for planning sustainable landscapes.

Satellite images have precious information on plant phenology, which is the “key” to distinguishing active agriculture from other land use types. For instance, early in the season a plowed field has open soil ready to be planted. Then, as plants grow taller the amount of green leaf area increases. Later in the season, when crops are ready for the market, comes the harvest, and the amount of leaf area decreases. “These abrupt changes in the amount of plants in a field can be detected in the satellite images, and allows us to confirm that the field is under production” – says, He Yin.

Agricultural land abandonment mapping is challenging because of the heterogeneity and complexity of agricultural land use. Some years a field may be left fallow, to give time for the soil to recover. However, fallow land is not abandoned land. A fallow field may return to production after a year or two. An abandoned field contrasts with a production field in that abandoned field presents a natural decay of the vegetation late in the growing season. He Yin used a technique called ‘temporal segmentation’ to map land abandonment from 30-m Landsat time series (Yin et al. 2018). “When a field has no agriculture for more than 5 years, then we classify it as abandoned land”.

However, Landsat images are not immune from problems. From a Landsat time series, there is a set of images that are not useful because they contain clouds or part of the picture is shaded by topographic features. This increases inaccuracy and limits the areas that can be included in analysis. To solve this challenge, He Yin is planning to use a new NASA product, the Harmonized Landsat Sentinel-2. This product merges imagery from Landsat with imagery from Sentinel-2 and the merged product provides increased temporal resolution of the data. “Repeated 5-day observations will likely improve our mapping precision,” says He Yin.

He Yin and his colleagues mapped land abandonment in the Caucasus recently (Yin et al. 2018), and are ready now for a much larger challenge. They are starting to map agriculture abandonment globally. They want to test their time series approach in many regions of the world, using the improved set of satellite imagery provided by Harmonized Landsat Sentinel-2. He Yin hopes that these explorations will help to have better maps of land abandonment from places with varying topographies and biomes, and, most importantly, where agricultural systems are complex.

Niwaeli’s interest in smallholder woodlots started when she was conducting her Master’s research in Tanzania, at the southern end of the East African Rift. She saw rural farmers planting pine and eucalyptus in farms that are located near a forest edge on land that had been fallow for 15 years. Her collaborators, working at another forest edge site in the Uganda portion of the Rift, were noticing a similar conversion from cropland to tree plantations. She wanted to know how much smallholder tree planting was occurring near Rift forests, and why.

Niwaeli used Google Earth to get a first impression of the distribution of woodlots. She randomly selected 60 locations in a small portion of the Rift, and digitized all the woodlots in the area. So far the team has digitized more than 4000 individual woodlots. “The really striking thing is how small these woodlots are: 93% are less than 1 hectare, and the average area is 0.45 hectares”, she said. Niwaeli extrapolated the area of woodlots she found in the digitized subset to the entire Tanzanian Southern Highlands area and estimated 80.000 ha of smallholder woodlots. That does not sound like a big number, however, it is only ~ 10.000 ha less than the amount held by the biggest tree plantation owner, the Tanzanian government. A big question remains though: Why would a subsistence farmer plant trees with a slow turnaround rate, instead of growing food crops?

In contrast to the US where farmers tend to own large and contiguous fields, in Niwaeli’s study site, farmers tend to own multiple small parcels of land that are spread throughout the landscape. Niwaeli thinks that the farmers make some allocation choices about which pieces of land will have crops, and which ones will have trees. She assumes that those parcels that are closer to the edge of the forests are not that great for growing food crops and are used for planting trees if the farmer can grow food somewhere else. “This could explain our initial field observations where we saw trees near forest edges” she added.

From the digitization, however, Niwaeli has seen that the woodlots are not limited to just forest edges, so the question of how farmers allocate land to trees is yet to be fully answered. She plans to further investigate the spatial distribution of tree plantations by mapping woodlots using Sentinel-2 satellite imagery. This will also provide a more accurate quantification of woodlot extent; and allow her to explore why some areas end up with woodlots while others do not.