Kirsten de Beurs


Rapid urbanization, changing croplands and increasing population health vulnerabilities in the China-Central Asia-West Asia Economic Corridor

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Grant Period: December 18, 2019 – December 17, 2020

This project is in collaboration with Dr. Katherine Hirschfeld and Dr. Daniel Hicks. Others working on this project are PhD students Anthony Mayberry, several undergraduate students, and data researcher Braden Owsley.

In this project, we investigate the growth of cities and the dryland system land use transitions in Central Asia as a result of China’s Belt and Road initiative, a large planned series of Chinese investments in the region. The New Silk road, an enhanced transportation corridor, will traverse the most populous and most fertile agricultural regions of Central Asia. Competition between urban growth and croplands, and their induced interaction, often enhances the risk of disease epidemics. This is an acute concern for Central Asia where existing health and sanitary conditions compound risks of emerging infectious diseases. Many households in the region lack safe drinking water and connected sanitary facilities, and current plans for urban development do not appear to address these infrastructure deficits.

Our research area covers and specifically focuses on the Central Asian regions subject to China’s Belt and Road Initiative, also called the China – Central Asia – West Asia Economic Corridor. This region ranges from Almaty in southeast Kazakhstan, to Tashkent and Samarkand in southeast Uzbekistan, Bishkek in north central Kyrgyzstan, and Dushanbe in western Tajikistan.

Figure 1: China – Central Asia – West Asia planned (new and upgraded) and existing railroads. The nine study cities are highlighted with stars. Data from Sasha Trubetskoy (

We are also working with several international collaborators who are living in or are from the region of analysis to facilitate in situ analysis and data assimilation.

Research Questions:

  1. How have past investment and infrastructure developments (1995-2014) led to varying patterns of urbanization, resource, and population flows, and changing land use across Central Asian cities? How will the ongoing development of the BRI, and/or proximity to BRI connected cities impact the emerging urban ecosystems, microbial and human ecologies in these areas going forward (2014-2020)?
  2. What are the economic and social effects of BRI investments on urban and peri-urban areas in Central Asia? How far improved is connectivity between cities, and what impact is this likely to have on patterns of land use, zoonotic disease risks, agriculture, and economic development?
  3. What new infectious disease risks exist and how will they impact economic growth?  To what extent is BRI-connectivity creating uneven patterns of rural-urban development and how might this impact urbanization, growth, and population health in the future?
Overview map of Bishkek (Kyrgyzstan) and its immediate 50km surroundings based on freely available OpenStreetMap vector data. Note that the general land use of the regions is identified, as well as the building footprint (inset map). Undergraduate students from the University of Oklahoma have completed the digitization of the building footprint data for Bishkek. We estimate that approximately 80% of the buildings were digitized at the beginning of this project. 
Image Classification Bishkek (Kyrgyzstan), 2019
Percentage of lights in three different classes (> 40, >20, >10) over the years for Bishkek and its immediate surroundings (50km). The percentage of the area with low and intermediate lighting is increasingly most rapidly, until the last year, when the highly lit area expands from less than 1% to just over 2% of the region. Note that the graph starts at 75%.  The total area lit (pixels in class 1, 2, or 3) 232km2 in 2020.

Published Project Papers

Sokolik, IN; Shiklomanov, AI; Xin, X; de Beurs, KM; Tatarskii, V; 2020. Quantifying the anthropogenic signature in drylands of Central Asia and its impact on water scarcity and dust emissions. Landscape Dynamics of Drylands across Greater Central Asia: People, Societies and Ecosystems

Henebry, GM; de Beurs, KM; John, R; Owsley, BC; Kariyeva, J; Chymyrov, A; Mirzoev, M; Recent Land Surface Dynamics Across Drylands in Greater Central Asia. Landscape Dynamics of Drylands across Greater Central Asia: People, Societies and Ecosystems

Other Papers about Central Asia:

KM de Beurs, GM Henebry, BC Owsley, IN Sokolik. 2018. Large scale climate oscillation impacts on temperature, precipitation and land surface phenology in Central Asia. Environmental Research Letters 13 (6)

KM de Beurs, GM Henebry, BC Owsley, I Sokolik. 2015. Using multiple remote sensing perspectives to identify and attribute land surface dynamics in Central Asia 2001–2013. Remote Sensing of Environment 170, 48-61

GM Henebry, KM de Beurs, CK Wright, R John, E. Lioubimtseva. 2013. The Drylands of East Asia in Hemispheric Context. Dryland East Asia (DEA): Land dynamics amid social and climate change, 23-44

E Lioubimtseva, KM de Beurs, GM Henebry. 2013. Grain production trends in Russia, Ukraine, and Kazakhstan in the context of the global climate variability and change. Climate change and water resources, 121-141

CK Wright, KM de Beurs, ZK Akhmadieva, PY Groisman, GM Henebry. 2009. Reanalysis data underestimate significant changes in growing season weather in Kazakhstan. Environmental Research Letters 4 (4), 045020

KM de Beurs, CK Wright, GM Henebry. 2009. Dual scale trend analysis for evaluating climatic and anthropogenic effects on the vegetated land surface in Russia and Kazakhstan. Environmental Research Letters 4 (4), 045012

KM de Beurs, GM Henebry. 2008. War, drought, and phenology: changes in the land surface phenology of Afghanistan since 1982. Journal of Land Use Science 3 (2-3), 95-111

KM de Beurs, GM Henebry. 2004. Trend analysis of the Pathfinder AVHRR Land (PAL) NDVI data for the deserts of Central Asia. IEEE Geoscience and Remote Sensing Letters 1 (4), 282-286

KM de Beurs, GM Henebry. 2004. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment 89 (4), 497-509


Land Use Patterns and Political Instability as Predictors for the Re-emergence of Malaria in the Caucasus

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This project is in collaboration with: Dr. Katherine Hirschfeld. Others working on the project are PhD student Bradley Brayfield, MS student James Worden, and data researcher Braden Owsley. We also collaborate with Dr. Ani Melkonyan.

Caucasus region during Soviet times.

Malaria was once very prevalent throughout southern Russia and the Caucasus. The World Health Organization (WHO) estimates that approximately 200,000 people were ill with malaria in Armenia in the early 1930s, with another 600,000 in Azerbaijan. 

The Caucasus is naturally hospitable to the malaria mosquito, but large-scale eradication efforts during the Soviet period resulted in an almost total disappearance. The region was first declared malaria free in 1975, but in the 1980s and 1990s, malaria reappeared in the Caucasus, the Central Asian republics, and to a lesser extent in Russia because of the war in Afghanistan, the fall of the Soviet Union, and the appearance of unstable successor states. Outmigration, agricultural development projects and collapse of existing public health prevention activities also contributed to the resurgence of malaria in the region. Past and recent outbreaks in the Trans-Caucasian countries have underlined the fact that all these countries are situated within epidemic-prone areas. In 2015, Europe, Central Asia and the Caucasus were free of malaria transmission once more, for the first time in almost 30 years (World Health Organization 2015).

This project combines spatial and temporal analysis to improve predictive modeling in disease ecology and international health. The overall objective is to apply remotely sensed data for the development of suitability maps for malaria in the Caucasus. Once these maps are created, the goal is to distinguish and isolate the effects of political instability, which include the cessation of malaria prevention, from the land use and land cover impact on malaria transmission by creating precise timelines of each country’s post-Soviet historical trajectory. Institutional failures under unstable governments during periods of conflict could undermine or interrupt public health work so that disease vectors proliferate. This research is based on a solid set of remote sensing methods, which will be expanded by the incorporation of SAR imagery. The development of error surfaces by comparing multiple data streams presents a significant new development.

We investigate trends in land use in the Caucasus between 1984 and 2019 and focus specifically on the change in agriculture from rain-fed to irrigation, forest fragmentation because of overharvesting and natural causes, and changes in open surface water. We also examine the implications of these changes in terms of their impact on the vulnerability of the population (social system) to re-emergence of vector-borne diseases such as P. vivax Malaria.

Malaria prevalence per 100,000 from 1922. Malaria was especially prevalent in the Volga river valley and the South Caucasus. The area shaded with dots are part of the Volga river valley.

Published Project Papers

Worden J, de Beurs KM. 2020. Surface water detection in the Caucasus, International Journal of Applied Earth Observation and Geoinformation 91

Hirschfeld, K. 2020. Microbial insurgency: Theorizing global health in the Anthropocene. The Anthropocene Review.


Cross-Scale Interactions Among Climate, Land-Use, and River-Water Quality – New Zealand

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Relationships between land use and water quality are complex with feedbacks, legacy effects, and cross-scale interactions (CSI). 

CSI occur when processes at one spatial or temporal scale interact with processes at finer or broader scales. In systems where CSI are connected, a change in an environmental driver such as climate or land use can result in positive feedbacks and cascading events that lead to dramatic and widespread changes in system dynamics. This project used sophisticated geospatial tools to analyze multi-resolution environmental datasets over multiple spatio-temporal scales to assess how CSI between changing climate and land use affect river water quality in New Zealand (NZ) from hours to decades, and from small catchments to regional drainage basins. Using MODIS BRDF-adjusted reflectance data (C6), we created a land disturbance dataset for NZ from the year 2000 to 2017 with 8-day temporal resolution and 463-m spatial resolution. This national-scale land disturbance dataset, the first of its kind, revealed that intense land uses such as livestock grazing and forest clear-cuts leave the land bare for considerable periods. When compared with one of the most comprehensive river water quality datasets in the world, we found that these intense land uses have led to excess sediment runoff and nutrient enrichment in almost half of NZ’s rivers. Over time, the CSI among climate, land use, and river water quality have led to nonlinear patterns where landscapes have switched from supply-limited to transport-limited in terms of sediment runoff. Nutrient runoff has been transport-limited (i.e., a function of rainfall frequency-magnitude-duration) for most impacted rivers. Due to best management practices (and possibly from the conversion of sheep farming to cattle farming), sediment concentrations have been declining in 34 of the 77 rivers we measured, while nitrogen concentrations have increased in 33 of 77 rivers. While clearer rivers are seen as an improvement in water quality; when combined with increasing nutrients, warmer water, and lower flows, the perfect recipe for toxic algae blooms is created, which has only recently been brought to the public’s attention. Our results indicate that this problem could worsen given the increasing trends we found in water temperatures, inorganic nutrients, and water clarity.    

Published Papers directly linked with the project:

JP Julian, KM de Beurs, RJ Owsley, B.C., Davies-Colley, AE Ausseil. 2017. River water quality changes in New Zealand over 26 years: response to land use intensity. Hydrology and Earth System Sciences 21, 1149-1171

I Kamarinas, JP Julian, AO Hughes, BC Oswley, KM de Beurs. 2016. Nonlinear Changes in Land Cover and Sediment Runoff in a New Zealand Catchment Dominated by Plantation Forestry and Livestock Grazing. Water 8 (436)

KM de Beurs, BC Owsley, JP Julian. 2016. Disturbance analyses of forests and grasslands with MODIS and Landsat in New Zealand. International Journal of Applied Earth Observation and Geoinformation 45, 42-54

TV Tran, KM de Beurs, JP Julian. 2016. Monitoring forest disturbances in Southeast Oklahoma using Landsat and MODIS images. International Journal of Applied Earth Observation and Geoinformation 44, 42-52

JP Julian, NA Wilgruber, KM de Beurs, PM Mayer, RN Jawarneh. 2015. Long-term impacts of land cover changes on stream channel loss. Science of the Total Environment 537, 399-410

Other Papers derived from methodology developed for this project:

KM de Beurs, NS McThompson, BC Owsley, GM Henebry. 2019. Hurricane damage detection on four major Caribbean islands. Remote Sensing of Environment

D Kingfield, K de Beurs. 2017. Landsat Identification of Tornado Damage by Land Cover and an Evaluation of Damage Recovery in Forests. Journal of Applied Meteorology and Climatology


Hurricane damage detection on four major Caribbean islands

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Hurricane damage detection on four major Caribbean islands

Tropical cyclones are natural events that transform into natural disasters as they approach and reach land. In 2017 alone, tropical cyclones caused an estimated $215 billion in damage. While MODIS data are regularly used in the analysis of hurricanes and typhoons, damage studies typically focus on just a few events without providing a comprehensive overview and comparison across events. The MODIS record is now sufficiently long to enable standardization in time, allowing us to extend previously developed disturbance methodology and to remove dependency on land cover datasets. We apply this new approach to detect the impact of both droughts and hurricanes on the four largest Caribbean islands since 2001. We find that the percentage of disturbed land on the four islands varies from approximately 0–50% between 2001 and 2017, with the highest percentages coinciding with major droughts in Cuba, and Hurricane Maria in Puerto Rico. We demonstrate that (1) Hurricane Maria resulted in significant disturbance across 50% of Puerto Rico (4549 km2), and (2) gradual recovery started about 2.5 months after the hurricane hit. While our approach focuses on the identification of damage arising from hurricanes, it is also capable of identifying the damage from droughts. This approach ultimately enables a better understanding of the combined effects of these two natural hazards across island landscapes.

Link to paper