Marine turtles may respond to projected climatic changes by shifting their nesting range to climatically suitable areas, which may
result in either increased exposure to threats or fewer threats. Therefore, there is the need to identify whether habitat predicted to
be climatically suitable for marine turtle nesting in the future will be affected by future threats and hinder marine turtles’ ability to
adapt. We modelled the geographic distribution of climatically suitable nesting habitat for marine turtles in the USA under future
climate scenarios, identified potential range shifts by 2050, determined impacts from sea-level rise, and explored changes in
exposure to coastal development as a result of range shifts. Overall nesting ranges of marine turtle species were not predicted to
change between the current and future time periods, except for the northern nesting boundaries for loggerhead turtles. However,
declines in climatically suitable nesting grounds were predicted; loggerhead turtles will experience the highest decreases (10%) in
climatically suitable habitat followed by green (7%) and leatherback (1%) turtles. However, sea-level rise is projected to inundate
78–81% of current habitat predicted to be climatically suitable in the future, depending on species and scenario. Nevertheless,
new beaches will also form, and suitable nesting habitat could be gained, with leatherback turtles potentially experiencing the
biggest percentage gain in suitable habitat.
Mountainous regions are changing rapidly across the world due to both land-use change and climate change.
Given the importance of mountainous regions for ecosystem services and endemic biodiversity, monitoring these
changes is essential. Satellite data provide a great resource to map land-cover change in mountainous regions,
however mapping is especially challenging there because topographic complexity affects reflectance. The socalled
‘topographic effect’ has been successfully corrected for in case studies of small areas, but a comparison of
large-area classifications and land-cover change analyses with and without topographic correction is missing.
Here, we performed a long-term land-cover change assessment for a large mountainous region, i.e., the Caucasus
Mountains with topographic correction. Our two goals were 1) to examine the effect of topographic correction
on land-cover classification for a large mountainous region, and 2) to assess land-cover changes since 1987
across the Caucasus based on the full Landsat archive. Both the complex topography and the history of land-use
changes, especially after the collapse of the Soviet Union in 1991, make the Caucasus Mountains an ideal study
area to understand topographic effects on large-area land-cover mapping for the last three decades. First, we
compared a non-topographically-corrected Landsat classification for 2015 with a classification that was topographically-
corrected with an enhanced C-correction for the same year and assessed the accuracy of both.
Second, we derived topographically-corrected Landsat classifications for six dates to assess changes in cropland
and forest from 1987 to 2015, based on class probabilities and post-classification comparisons. In regard to our
first goal, topographic correction improved the overall accuracy of the classification only by 2% (from 79 to
81%), but disagreement rates were as high as 100% in mountainous regions, especially among forest types. In
regard to our second goal, we found that cropland loss was the most prevalent change process since 1987.
Cropland loss was particularly widespread in Georgia and Armenia until 2000, and in Azerbaijan until 2005. The
North Caucasus (the Russian Federation) had more stable cropland over time, most likely due to different land
reforms after the collapse of the Soviet Union, and the prevalence of flat landscapes and very fertile soils, which
make cultivation easier than in the South Caucasus. Rates of forest change throughout the Caucasus Mountains
were surprisingly low, with forest loss and forest gain being roughly equal. Forest loss was most likely related to
both illegal logging and natural disturbance, whereas forest gain was most likely due to cropland abandonment.
Land degradation affects over one-third of the global land area and is projected to become even more widespread
due to climate change and land use pressures. Despite being a critical issue for climate change mitigation,
biodiversity conservation, and food security, the detection of the onset, duration, and magnitude of land de-
gradation remains challenging, as is early identification of short-term vegetation loss preceding land degrada-
tion. Here, we present a new approach for monitoring both short-term vegetation loss and decadal degradation
in grasslands using satellite data. Our approach integrates Spectral Mixture Analysis and temporal segmentation,
and analyzes dense time-series of satellite observations in three steps. First, we unmix all available satellite
observations and aggregate them into monthly composites. Second, we calculate the annual Cumulative
Endmember Fractions and examine their piecewise trends among years to determine the onset, duration, and
magnitude of short-term vegetation loss and decadal degradation. Third, we attribute a decrease in the green
vegetation fraction with a concomitant increase in either open soil, or non-photosynthetic vegetation. We tested
our method mapping short-term vegetation loss and decadal degradation in grasslands in the Caucasus Ecoregion
using the 2001–2018 time series of MODIS 8-day reflectance data. We found strong patterns of short-term
vegetation loss and decadal degradation, mostly in the eastern part of the Caucasus Ecoregion in areas of desert-
and semi-desert natural vegetation. Short-term vegetation loss episodes (3–9 years) were more common and had
greater magnitude than decadal degradation (≥10 years), especially in steppe regions. On average, 9.3% of
grassland area was subjected annually to either decadal, or short-term vegetation loss. Desiccation, i.e., the shift
from green vegetation to dry vegetation, was the most prevalent type of change pathway, with green vegetation
loss to open soil coming second. Decadal degradation and short-term vegetation loss rates were the highest in dry
areas where the potential natural vegetation is sub-shrub deserts, or halophytic, alluvial, and wet lowland
forests. Our findings support known general degradation patterns in the Caucasus Ecoregion, but provide better
understanding of ongoing processes, by detecting exact location, timing, and magnitude of changes. More
broadly, our method advances the monitoring of grasslands by detecting both decadal degradation and short-
term vegetation loss. This flexibility supports adaptive degradation monitoring, aids sustainable land manage-
ment, and provides new information for carbon stock analyses and biodiversity conservation.
Ecotourism is developing rapidly in biodiversity hotspots worldwide, but there is limited and mixed
empirical evidence that ecotourism achieves positive biodiversity outcomes. We assessed whether ecotourism
influenced forest loss rates and trajectories from 2000 to 2017 in Himalayan temperate forests. We compared forest
loss in 15 ecotourism hubs with nonecotourism areas in 4 Himalayan countries. We used matching statistics to
control for local-level determinants of forest loss, for example, population density, market access, and topography.
None of the ecotourism hubs was free of forest loss, and we found limited evidence that forest-loss trajectories in
ecotourism hubs were different from those in nonecotourism areas. In Nepal and Bhutan, differences in forest loss
rates between ecotourism hubs and matched nonecotourism areas did not differ significantly, and the magnitude
of the estimated effect was small. In India, where overall forest loss rates were the lowest of any country in
our analysis, forest loss rates were higher in ecotourism hubs than in matched nonecotourism areas. In contrast,
in China, where overall forest loss rates were highest, forest loss rates were lower in ecotourism hubs than
where there was no ecotourism. Our results suggest that the success of ecotourism as a forest conservation
strategy, as it is currently practiced in the Himalaya, is context dependent. In a region with high deforestation
pressures, ecotourism may be a relatively environmentally friendly form of economic development relative to
other development strategies. However, ecotourism may stimulate forest loss in regions where deforestation rates
are low.
Ecotourism is developing rapidly in biodiversity hotspots worldwide, but there is limited and mixed
empirical evidence that ecotourism achieves positive biodiversity outcomes. We assessed whether ecotourism
influenced forest loss rates and trajectories from 2000 to 2017 in Himalayan temperate forests. We compared forest
loss in 15 ecotourism hubs with nonecotourism areas in 4 Himalayan countries. We used matching statistics to
control for local-level determinants of forest loss, for example, population density, market access, and topography.
None of the ecotourism hubs was free of forest loss, and we found limited evidence that forest-loss trajectories in
ecotourism hubs were different from those in nonecotourism areas. In Nepal and Bhutan, differences in forest loss
rates between ecotourism hubs and matched nonecotourism areas did not differ significantly, and the magnitude
of the estimated effect was small. In India, where overall forest loss rates were the lowest of any country in
our analysis, forest loss rates were higher in ecotourism hubs than in matched nonecotourism areas. In contrast,
in China, where overall forest loss rates were highest, forest loss rates were lower in ecotourism hubs than
where there was no ecotourism. Our results suggest that the success of ecotourism as a forest conservation
strategy, as it is currently practiced in the Himalaya, is context dependent. In a region with high deforestation
pressures, ecotourism may be a relatively environmentally friendly form of economic development relative to
other development strategies. However, ecotourism may stimulate forest loss in regions where deforestation rates
are low.
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.
Sheep grazing on the eroded mountain side between Steantsminda and Gudauri townlets in Georgia. Photo by Volker Radeloff.
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.
Fig 1: High resolution true color images (captured from Google Earth) showing high-mountain summer pasture in North-West Azerbaijan in 2006 and 2017. Yellow arrow in the 2017 scene shows location of a shepherds hut and enclosure. The graph above presents soil endmember time series showing an overall increase in soil reflectance over time. Timing of acquisition of both images is marked in red on the graph.
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.
The Caucuses region (encompassing parts of Russia, Georgia, Armenia and Azerbaijan) has experienced extreme political upheavals. The collapse of the Soviet Union meant that the four countries became sovereign. Their powerful neighbors — Russia, Iran and Turkey – maintained strong geopolitical interests in the newly independent nations of Georgia, Armenia and Azerbaijan. As a result, the Caucasus has experienced four armed conflicts since 1991. In light of such extreme social and political disruption, Johanna wanted to know how cropland and forests had changed.
Figure 1: Land cover/ land use map of the Caucasus.
From previous research, some by former SILVIS lab members Drs. Mihai Nita and Catalina Munteanu, we know that some areas in Eastern Europe saw rapid cropland abandonment after the USSR collapsed. Other areas experienced forest clearing during the soviet era, and forest regrowth afterwards. Why do countries that are ostensibly similar geopolitically show such a wide range of land use outcomes?
“I want to make a clear link between land use and socio-political changes, but to do that you first have to describe where and when the land use changes have taken place.” Johanna says. To do that, she used Landsat imagery from 1987 to 2015 and mapped changes in land use and land cover.
Johanna found that there was some cropland abandonment in the Caucasus, particularly during the transition period in the 1990s and the time of armed conflicts. However, the cropland abandonment rate is far lower than the one apparent in eastern European countries that also experienced the breakdown of the Soviet Union. She has also found that forest as stayed surprisingly steady during the study period in the Caucasus.
Figure 2: Coniferous and mixed forest in Borjomi, Georgia. (Picture: V.Radeloff)
Her findings are rather surprising, since we expect political instability to interfere with cropland, and to make forests vulnerable for illegal harvesting. But in the Caucasus, Johanna explains, the steep, inaccessible terrain may have protected the forests from large clear cuts; even though the extracting of single valuable trees is widespread. Cropland, on the other hand is related to demand for food: cultivation continued wherever possible, unless we find armed conflicts in the region.
Figure 3: Preliminary results of abandoned arable land in Chechnya between 1987 and 2015
“What I can say is that land use patterns and outcomes are extremely dependent on the local context, especially in such a diverse region like the Caucasus” Johanna cautioned.
Forest fragmentation can lead to habitat reduction, edge increase, and exposure to disturbances.
A key emerging policy to protect forests is payments for ecosystem services (PES), which
offers compensation to landowners for environmental stewardship. Mexico was one of the first countries
to implement a broad-scale PES program, enrolling over 2.3 Mha by 2010. However, Mexico’s
PES did not completely eliminate deforestation in enrolled parcels and could have increased incentives
to hide deforestation in ways that increased fragmentation. We studied whether Mexican forests
enrolled in the PES program had less forest fragmentation than those not enrolled, and whether the
PES effects varied among forest types, among socioeconomic zones, or compared to the protected
areas system. We analyzed forest cover maps from 2000 to 2012 to calculate forest fragmentation. We
summarized fragmentation for different forest types and in four socioeconomic zones. We then used
matching analysis to investigate the possible causal impacts of the PES on forests across Mexico and
compared the effects of the PES program with that of protected areas. We found that the area covered
by forest in Mexico decreased by 3.4% from 2000 to 2012, but there was 9.3% less forest core area.
Change in forest cover was highest in the southern part of Mexico, and high-stature evergreen tropical
forest lost the most core areas (17%), while oak forest lost the least (2%). Our matching analysis
found that the PES program reduced both forest cover loss and forest fragmentation. Low-PES areas
increased twice as much of the number of forest patches, forest edge, forest islets, and largest area of
forest lost compared to high-PES areas. Compared to the protected areas system in Mexico, high-PES
areas performed similarly in preventing fragmentation, but not as well as biosphere reserve core zones.
We conclude that the PES was successful in slowing forest fragmentation at the regional and country
level. However, the program could be improved by targeting areas where forest changes are more frequent,
especially in southern Mexico. Fragmentation analyses should be implemented in other areas
to monitor the outcomes of protection programs such as REDD+ and PES.
Forest fragmentation can lead to habitat reduction, edge increase, and exposure to disturbances.
A key emerging policy to protect forests is payments for ecosystem services (PES), which
offers compensation to landowners for environmental stewardship. Mexico was one of the first countries
to implement a broad-scale PES program, enrolling over 2.3 Mha by 2010. However, Mexico’s
PES did not completely eliminate deforestation in enrolled parcels and could have increased incentives
to hide deforestation in ways that increased fragmentation. We studied whether Mexican forests
enrolled in the PES program had less forest fragmentation than those not enrolled, and whether the
PES effects varied among forest types, among socioeconomic zones, or compared to the protected
areas system. We analyzed forest cover maps from 2000 to 2012 to calculate forest fragmentation. We
summarized fragmentation for different forest types and in four socioeconomic zones. We then used
matching analysis to investigate the possible causal impacts of the PES on forests across Mexico and
compared the effects of the PES program with that of protected areas. We found that the area covered
by forest in Mexico decreased by 3.4% from 2000 to 2012, but there was 9.3% less forest core area.
Change in forest cover was highest in the southern part of Mexico, and high-stature evergreen tropical
forest lost the most core areas (17%), while oak forest lost the least (2%). Our matching analysis
found that the PES program reduced both forest cover loss and forest fragmentation. Low-PES areas
increased twice as much of the number of forest patches, forest edge, forest islets, and largest area of
forest lost compared to high-PES areas. Compared to the protected areas system in Mexico, high-PES
areas performed similarly in preventing fragmentation, but not as well as biosphere reserve core zones.
We conclude that the PES was successful in slowing forest fragmentation at the regional and country
level. However, the program could be improved by targeting areas where forest changes are more frequent,
especially in southern Mexico. Fragmentation analyses should be implemented in other areas
to monitor the outcomes of protection programs such as REDD+ and PES.
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.
Figure 2 A field abandoned due to soil salinization (Khorezm, Uzbekistan)
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”.
Figure 3: Example of a temporal segmentation with related Landsat imagery (RGB: NIR, SWIR 1, Red), for the pixel indicated by either a black or white crosshair (Yin et al., 2018)
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.
