Monitoring the Prairie: Applications of Geospatial Research Techniques to the Study of Grassland and Prairie Environments

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 1791

Special Issue Editors


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Guest Editor
Department of Geography and Spatial Science, Kansas State University, Manhattan, KS, USA
Interests: climatology; remote sensing; landscape ecology; spatial analysis

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Guest Editor
Office of Faculty Development, and Department of Geography and Atmospheric Science, University of Kansas, Lawrence, KS, USA
Interests: vegetation geography; environmental history; soils; water resources; spatial analysis

Special Issue Information

Dear Colleagues,

Prairies and other forms of grassland environments are among the most widespread of Earth’s biomes, and represent the native assemblage of plants and animals for roughly a third of the land area on the planet. Dynamic and responsive systems, they also present an opportunity to identify early warning signs of ecosystem change, in response to global environmental shifts, such as climate change, rising atmospheric CO2, increased nitrogen deposition, and alterations in land use or management practices. This Special Issue of Applied Sciences, “Monitoring the Prairie”, seeks to compile recent scholarly research that applies remote sensing, GIS, data analytics, or image processing to the study of grasslands and prairies, with the goal of furthering our understanding of how prairies, as representative ecosystems, respond to multiple factors of global change and interact with human systems.
Possible topics include but are not limited to the following:

  • Species distribution mapping and modeling;
  • Techniques for monitoring grassland variation and changes;
  • Empirical and process modeling of prairie biophysical properties;
  • Integration of multiple data sources for grassland analysis;
  • Nutrient and water cycling in grasslands;
  • Land use and land cover change in grasslands;
  • Ecological effects of grassland management—fire and grazing;
  • Vegetative effects of soil and groundwater contamination;
  • Human economic and health factors in prairie land use and management;
  • Stochastic factors in grassland restoration;
  • Adaptive conservation and avoidance of ecosystem state shift.

Prof. Dr. Douglas G. Goodin
Prof. Dr. Laura M. Moley
Guest Editors

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Keywords

  • Geospatial analysis
  • Prairie
  • Grasslands
  • GIS
  • Remote sensing
  • Big data
  • Environmental change
  • Environmental monitoring
  • Geographic imaging
  • Vegetation geography
  • Landscape ecology.

Published Papers (1 paper)

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Research

17 pages, 2550 KiB  
Article
Assessment of Human-Related Driving Forces for Reduced Carbon Uptake Using Neighborhood Analysis and Geographically Weighted Regression: A Case Study in the Grassland of Inner Mongolia, China
by Zongyao Sha and Ruren Li
Appl. Sci. 2020, 10(21), 7787; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217787 - 03 Nov 2020
Cited by 2 | Viewed by 1376
Abstract
The ever-rising concentration of atmospheric carbon is viewed as the primary cause for global warming. To discontinue this trend, it is of urgent importance to either cut down human carbon emissions or remove more carbon from the atmosphere. Grassland ecosystems occupy the largest [...] Read more.
The ever-rising concentration of atmospheric carbon is viewed as the primary cause for global warming. To discontinue this trend, it is of urgent importance to either cut down human carbon emissions or remove more carbon from the atmosphere. Grassland ecosystems occupy the largest part of the global land area but maintain a relatively low carbon sequestration flux. While numerous studies have confirmed the impacts on grassland vegetation growth from climate changes and human activities, little work has been done to understand the driving forces for a reduced carbon uptake (RCU)—a loss in vegetation carbon sequestration because of inappropriate grassland management. This work focused on assessing RCU in the grassland of Inner Mongolia and understanding the influential patterns of the selected variables (including grazing intensity, road network, population, and vegetation productivity) related to RCU. Neighborhood analysis was proposed to locate optimized grassland management practices from historical data and to map RCU. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were applied to explore the driving forces for RCU. The results indicated that the human-related factors, including stock grazing intensity, population density, and road network were likely to present a spatially varied impact on RCU, which accounted for more than 1/4 of the total carbon sequestration. Full article
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