Spatial Analysis For Conservation and Sustainability
Land Use
Land use change is currently the largest threat to biodiversity, and exacerbates the detrimental effects of climate change. We are interested in novel types of land use change, such as housing growth in the WUI, and widespread land abandonment after socioeconomic shocks, and how such changes affect biodiversity.
Every year, wildfires destroy thousands of buildings in the United States, especially in the rapidly growing wildland-urban interface, where homes and wildland vegetation meet or intermingle. After a wildfire there is a window of opportunity for residents and public agencies to re-shape patterns of development, and avoid development in locations that are inherently at higher risk of wildfire destruction. We examined 28 of the most destructive wildfires in California, the state where most buildings are destroyed by wildfires, to evaluate whether locations of rebuilt and newly constructed buildings were adaptive (i.e., if building occurred in lower risk areas). In total, these fires burned 7,075 buildings from 1970 to 2009. We found minimal evidence for adaptation both in the number and placement of buildings post-fire. Rebuilding was common: 58% of the destroyed buildings were rebuilt within three to six years, and 94% within thirteen to twenty-five years after the fire. Similarly, we found minimal trends toward lower risk areas in the placement of 2,793 rebuilt and 23,404 newly constructed buildings over the course of 13–25 yr. In fact, long-term data revealed that relative risk of new construction either did not change significantly over time or increased. A destructive wildfire could provide an opportunity to assess and change building practices, yet our results show that such change is largely not occurring. As wildfires increasingly threaten communities, this lack of change could result in growing rates of destruction and loss of life.
Maps are a key instrument and important data source for a wide range of research from global modeling to detailed ecological studies of a specific species. However different scales of tasks require proper instruments including a suitable maps detalization. For instance, a scientist who is interested in the general trends of agriculture abandonment may not have to pay too much attention to which specific fields are not in use anymore. However, for a conservation biologist studying a rare species, detailed maps of habitats, such as abandoned crops, is critical. However, it is difficult to make such detailed maps for large areas. Global maps are many, but they lack necessary details, while fine-scale maps only cover small areas if they exist at all. Unfortunately, using inappropriate scale of the input information either makes the results too general to be sensible or leads to incorrect conclusions.
In practical terms, precise mapping is a matter of balance of time and efforts versus the desired quality of results. The more accurate is a map the more resources are required to make it. But the amount of the resources necessary for creating a good map for a large area may be beyond what project managers can afford.
Coming back to the abandonment and land cover mapping, the maps are important for a variety of tasks including economic (re)development, nature conservation, and agriculture improvements. Thus, the absence of proper maps could make ecological and economic problems even worse.
Part of my research is about the level of accuracy we could (or should) achieve when mapping large areas. I have chosen the Eurasian Steppe as a test site because it is vast, large areas of abandonment, as well as permanently used field,) and rich diversity of natural vegetation. At the same time, it is one of the most transformed landscapes in Eurasia where biodiversity conservation and preserving intact steppes as the source of both rare and dominant native species to re-habit the man-made vacuum is a top priority. What makes the mapping of these areas challenging though is that the natural vegetation, mainly grasses and herbs, is spectrally very similarly to agriculture in satellite images.
I am planning to test several mapping techniques taking into account the advantages of each and adjust them to specific conditions of the steppe. The random forest algorithm is easy and fast enough to make initial maps. These maps show general land cover of an area and allow to reveal sources of mismapping. The segmentation algorithm is helpful in drawing more clear borders but fails to distinguish objects that have similar reflection while belonging to different classes. The understanding of general structure gained from the initial maps gives better reasons to divide a large heterogeneous area into smaller and more solid parts where differences between the mapping classes are higher than in-class variability. Ultimately, I hope to achieve two results. The first is understanding of how to combine existing methods to improve the whole map quality. The second is to create maps suitable for ecological research, preserving biodiversity and the establishment of new protected areas.
Over the last century, US agriculture greatly intensified and became industrialized, increasing in inputs and yields while decreasing in total cropland area. In the industrial sector, spatial agglomeration effects are typical, but such changes in the patterns of crop types and diversity would have major implications for the resilience of food systems to global change. Here, we investigate the extent to which agricultural industrialization in the United States was accompanied by agglomeration of crop types, not just overall cropland area, as well as declines in crop diversity. Based on countylevel analyses of individual crop land cover area in the conterminous United States from 1840 to 2017, we found a strong and abrupt spatial concentration of most crop types in very recent years. For 13 of the 18 major crops, the widespread belts that characterized early 20th century US agriculture have collapsed, with spatial concentration increasing 15-fold after 2002. The number of counties producing each crop declined from 1940 to 2017 by up to 97%, and their total area declined by up to 98%, despite increasing total production. Concomitantly, the diversity of crop types within counties plummeted: in 1940, 88% of counties grew >10 crops, but only 2% did so in 2017, and combinations of crop types that once characterized entire agricultural regions are lost. Importantly, declining crop diversity with increasing cropland area is a recent phenomenon, suggesting that corresponding environmental effects in agriculturally dominated counties have fundamentally changed. For example, the spatial concentration of agriculture has important consequences for the spread of crop pests, agrochemical use, and climate change. Ultimately, the recent collapse of most agricultural belts and the loss of crop diversity suggest greater vulnerability of US food systems to environmental and economic change, but the spatial concentration of agriculture may also offer environmental benefits in areas that are no longer farmed.
After 1991, major events, such as the collapse of socialism and the transition to market economies, caused land use change across the former USSR and affected forests in particular. However, major land use changes may have occurred already during Soviet rule, but those are largely unknown and difficult to map for large areas because 30-m Landsat data is not available prior to the 1980s. Our goal was to analyze the rates and determinants of forest cover change from 1967 to 2015 along the Latvian-Russian border, and to develop an object-based image analysis approach to compare forest cover based on declassified Corona spy satellite images from 1967 with that derived from Landsat 5 TM and Landsat 8 OLI images from 1989/1990 and 2014/2015. We applied Structurefrom- Motion photogrammetry to orthorectify and mosaic the scanned Corona images, and extracted forest cover from Corona and Landsat mosaics using object-based image analysis in eCognition and expert classification. In a sensitivity analysis, we tested how the scale parameters for the segmentation affected the accuracy of the change maps. We analyzed forest cover and forest patterns for our full study area of 22,209 km2, and applied propensity score matching approach to identify three Latvian-Russian pairs of 15 × 15 km cells, which we compared. We attained overall classification accuracies of 92% (Latvia) and 93% (Russia) for the forest/non-forest change maps of 1967–1989, and 91% (Latvia) and 93% (Russia) for 1989–2015, and our results were robust in regards to the segmentation scale parameter. Sample-based forest cover gain from 1967 to 1989 differed notably between the two countries (18.5% in Latvia and 23.6% in Russia), but was generally much higher prior to 1989 than from 1989 to 2015 (8.7% in Latvia and 9.7% in Russia). Furthermore, we found rapid de-fragmentation of forest cover, where forest core area increased, and proportions of isolated patches and forest corridors decreased, and this was particularly pronounced in Russia. Our findings highlight the need to study Soviet-time land cover and land use change, because rural population declines and major policy decisions such as the collectivization of agricultural production, merging of farmlands and agricultural mechanization led already during Soviet rule to widespread abandonment and afforestation of remote farmlands. After 1991, government subsidies for farming declined rapidly in both countries, but in Latvia, new financial aid from the EU became available after 2001. In contrast, remoteness, lower population density, and less of a legacy of intensive cultivation resulted in higher rates of forest gain in Russia. Including Corona imagery in our object-based image analysis workflow allowed us to examine half a century of forest cover changes, and that resulted in surprising findings, most notably that forest area gains on abandoned farm fields were already widespread during the Soviet era and not just a postsocialist land use change trend as had been previously reported.
European Russia rapidly transitioned after the collapse of the Soviet Union from state socialism to a market economy. How did this political and economic transformation interact with ecological conditions to determine forest loss and gain? We explore this question with a study of European Russia in the two decades following the collapse of the Soviet Union. We identify three sets of potential determinants of forest-cover change—supply-side (environmental), demand-side (economic), and political/administrative factors. Using new satellite data for three distinct types of forest-cover change—logging, forest fires, and forest gain—we quantify the relative importance of these variables in province-level regression models during periods of a) state collapse (1990s), and b) state growth (2000s). The three sets of covariates jointly explain considerable variation in the outcomes we examine, with size of forest bureaucracy, autonomous status of the region, and prevalence of evergreen forests emerging as robust predictors of forest-cover change. Overall, economic and administrative variables are significantly associated with rates of logging and reforestation, while environmental variables have high explanatory power for patterns of forest fire loss.
Cropland abandonment is a widespread land-use change, but it is difficult to monitor with remote sensing because it is often spatially dispersed, easily confused with spectrally similar land-use classes such as grasslands and fallow fields, and because post-agricultural succession can take different forms in different biomes. Due to these difficulties, prior assessments of cropland abandonment have largely been limited in resolution, extent, or both. However, cropland abandonment has wide-reaching consequences for the environment, food production, and rural livelihoods, which is why new approaches to monitor long-term cropland abandonment in different biomes accurately are needed. Our goals were to 1) develop a new approach to map the extent and the timing of abandoned cropland using the entire Landsat time series, and 2) test this approach in 14 study regions across the globe that capture a wide range of environmental conditions as well as the three major causes of abandonment, i.e., social, economic, and environmental factors. Our approach was based on annual maps of active cropland and non-cropland areas using Landsat summary metrics for each year from 1987 to 2017. We streamlined perpixel classifications by generating multi-year training data that can be used for annual classification. Based on the annual classifications, we analyzed land-use trajectories of each pixel in order to distinguish abandoned cropland, stable cropland, non-cropland, and fallow fields. In most study regions, our new approach separated abandoned cropland accurately from stable cropland and other classes. The classification accuracy for abandonment was highest in regions with industrialized agriculture (area-adjusted F1 score for Mato Grosso in Brazil: 0.8; Volgograd in Russia: 0.6), and drylands (e.g., Shaanxi in China, Nebraska in the U.S.: 0.5) where fields were large or spectrally distinct from non-cropland. Abandonment of subsistence agriculture with small field sizes (e.g., Nepal: 0.1) or highly variable climate (e.g., Sardinia in Italy: 0.2) was not accurately mapped. Cropland abandonment occurred in all study regions but was especially prominent in developing countries and formerly socialist states. In summary, we present here an approach for monitoring cropland abandonment with Landsat imagery, which can be applied across diverse biomes and may thereby improve the understanding of the drivers and consequences of this important land-use change process.
The United States (U.S.) federal government provides imagery obtained by federally funded Earth Observation satellites typically at no cost. For many years Landsat was an exception to this trend, until 2008 when the United States Geological Survey (USGS) made Landsat data accessible via the internet for free. Substantial increases in downloads of Landsat imagery ensued and led to a rapid expansion of science and operational applications, serving government, private sector, and civil society. The Landsat program hence provides an example to space agencies worldwide on the value of open access for Earth Observation data and has spurred the adaption of similar policies globally, including the European Copernicus Program. Here, we describe important aspects of the Landsat free and open data policy and highlight the importance and continued relevance of this policy.
This article examines the impact of the 1850 Austro-Hungarian customs union on production land-use outcomes. Using newly digitized data from the Second Military Survey of the Habsburg Monarchy, we apply a spatial discontinuity design to estimate the impact of trade liberalization on land use. We find that the customs union increased cropland area by 8 percent per year in Hungary between 1850 and 1855, while forestland area decreased by 6 percent. We provide suggestive evidence that this result is not confounded by the emancipation of the serfs, population growth, or technological change in agriculture.
Sarybash village in Azerbaijan and its surroundings with fragments of woody vegetation. Left image: Corona; Right image: recent Google imagery
Humans have altered natural landscapes for millennia, but especially so in recent decades. Recognizing how landscapes have changed over time is critical to understanding the relationship between humans and their environment. In studying this relationship, detailed land cover maps are a key resource. However, to date, there are few detailed land cover maps for the mid-20th century due to of the minimal amount of spatial data available during this time. Afag is developing a novel approach to detailed land cover mapping that will provide more context to mid-20th century landscape changes. Her final maps will incorporate land cover classes such as forest, grassland, agricultural land, water, and urban area.
Gold mining in Sakdrisi mountain slopes, Kazreti, Georgia. Left image: Corona; Right image: recent Google imagery
Afag is working on a methodology that will automatically map land cover maps anywhere on the globe from high resolution black and white imagery captured by Corona satellites, i.e., former US spy satellites. Afag is testing her methodology in the Caucasus eco-region encompassing the countries of Azerbaijan, Russia, Georgia, and Armenia, which has diverse ecosystem. This region has areas of extreme elevation and unique biomes. That means that if her methods work in this very diverse region, they should be applicable anywhere. Afag is using object-based image analysis to extract important land use information from satellite data. The classifications are conducted in Google Earth Engine and results in detailed land cover maps.
Growth of Aghdash city in Azerbaijan. Left image: Corona; Right image: recent Google imagery
Land cover maps are a valuable for both scientists and land managers. They provide means to evaluate how past land use decisions have affected the current land cover. The maps may also be helpful for projecting future land use changes. Additionally, land use maps can assist in identifying the scale at which politics and wars affect the landscape over time. Ultimately, Afag’s methodology will have the capability to automatically map mid-20th century land cover in large areas across the globe. Her research will help solve tough questions relating to landscape changes over the last half-century.
Altınçayır creek dam in Turkey constructed in 1995-2001. Left image: Corona; Right image: recent Google imagery
Habitat connectivity is essential to facilitate species movement across fragmented landscapes, but hard to achieve at broad scales. The enforcement of existing land use policies could improve habitat connectivity, while providing legal support for implementation. Our goal was to evaluate how forest connectivity is affected if forests are restored according to existing riparian buffer regulations in Chile. We simulated forest restoration within 30 and 200 m of rivers in 99 large watersheds, following two sections of the forest regulation. We mapped habitat for two model forest species that have different minimum habitat sizes (15 and 30 ha), and for each we identified forest habitats and corridors using image morphology analysis. To quantify change in connectivity, we used a network graph index, the Relative Equivalent Connected Area. We found that both 30- and 200-m riparian buffers could have a positive effect on habitat connectivity. The 200-m buffers increased connectivity the most where forest cover was 20–40% (40% mean increase in connectivity index), while the 30-m buffers increased connectivity the most where forest cover was 40–60% (30% mean increase in connectivity index). The effect of riparian restoration scenarios was similar for both model species, suggesting that effective implementation of existing forest regulation could improve connectivity for fauna with a range of minimum habitat size requirements. Our findings also suggest that there is some flexibility in the buffer sizes that, if restored, would increase habitat connectivity. This flexibility could help ease the social and economic cost of implementing habitat restoration in productive lands.
