Author ORCID Identifier
Document Type
Dissertation
Date of Award
2024
Degree Name
Doctor of Philosophy (PhD)
Department
Sustainability
First Advisor
Ranjeet John
Abstract
Kazakhstan (KZ) experienced widespread changes in ecosystem structure and function. The country is a climate and land cover/use change “hotspot” owing to political reforms, intensified grazing, and extreme climatic events. Despite their importance, there is a lack of consensus about vegetation changes, trends, and drivers in KZ. Addressing this knowledge gap is crucial for effectively managing and restoring grassland ecosystems. However, a pressing challenge is discerning anthropogenic-driven vegetation changes from climate variability and decomposing the responses to the complex human-environmental forcings. Motivated by these challenges, I employed statistical and machine learning algorithms to detect and attribute vegetation changes to social-environmental system (SES) drivers at plot, landscape, and regional scales in KZ. The first chapter quantified direct and indirect causal relationships between SES drivers and ecosystem attributes at the provincial scale. In the second chapter, I identified the optimal ranges of SES drivers influencing vegetation changes in the region. While key drivers, such as livestock density, snow cover variability, and droughts, were identified, their attribution at the regional scales was constrained by coarser input datasets. Moreover, detecting and attributing vegetation changes to SES drivers at fine spatial scales requires the development of datasets at a finer resolution. Therefore, in the third chapter, I developed gridded livestock density estimates (2000 to 2019) at 1 km resolution for KZ. Subsequently, in the fourth chapter, I developed one of the first sets of fine-resolution (10m) gridded estimates of canopy cover and aboveground biomass for KZ utilizing extensive in situ sampling and robust upscaling approaches. Finally, the fifth chapter combined the developed livestock density estimates with other gridded SES drivers to detect and attribute fine-scale vegetation changes to anthropogenic and climatic forces. The findings revealed that 45.71% of KZ experienced vegetation degradation, with land use change as the predominant contributor (22.54%; 0.54 million km2). Increasing livestock densities emerged as the dominant land use driver contributing to vegetation degradation. Overall, this dissertation advances the knowledge of vegetation dynamics and drivers in KZ and provides critical insights and datasets to guide restoration strategies, helping land managers, researchers, and the government achieve land degradation neutrality in KZ.
Subject Categories
Ecology and Evolutionary Biology | Environmental Sciences | Remote Sensing
Keywords
Anthropogenic impacts, Climate Change, Kazakhstan, land use, livestock, vegetation
Number of Pages
315
Publisher
University of South Dakota
Recommended Citation
Kolluru, Venkatesh, "REGIONAL AND LANDSCAPE SCALE EXAMINATION AND ATTRIBUTION OF VEGETATION CHANGES TO SOCIAL-ENVIRONMENTAL SYSTEM DRIVERS IN KAZAKHSTAN" (2024). Dissertations and Theses. 280.
https://red.library.usd.edu/diss-thesis/280
Included in
Ecology and Evolutionary Biology Commons, Environmental Sciences Commons, Remote Sensing Commons
