About

Desheng Liu is a Professor in the Department of Geography at The Ohio State University. His educational background includes a Ph.D. in Environmental Science from the University of California, Berkeley, obtained in 2006, along with a Master's degree in Statistics and a Bachelor's degree in GIS from Wuhan University, China. His research focuses on developing geo-spatial data analysis methodologies for monitoring and modeling environmental and ecological processes, drawing upon remote sensing and spatial statistics approaches. A central theme in his work is the statistical modeling of the spatial or spatial-temporal dimensions of environmental processes. He teaches courses related to quantitative geographical methods, remote sensing, and spatial statistics, and has contributed to the field through research on land cover change, thermal infrared radiance downscaling, object-based classification, and statistical analysis of spatial data.

Research topics

• Political Science
• Computer Science
• Industrial organization
• Market economy
• Economic system
• Economics
• Management
• Business
• Economic growth
• Cartography
• Remote sensing
• Meteorology
• Engineering
• Civil engineering
• Geography
• Environmental science

Selected publications

• Mapping field-scale daily evapotranspiration using unbiased spatio-temporal fusion approach over heterogeneous surface

  GIScience & Remote Sensing · 2026-03-05

  articleOpen accessSenior author

  Accurate estimation of daily evapotranspiration (ET) at the field scale is essential for agricultural water management, particularly in arid and semi-arid regions, yet existing satellite products often suffer from spatiotemporal trade-offs. To overcome this limitation, we generated high-resolution daily ET data using two data fusion approaches based on the unbiased variant of Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ubESTARFM). In the first approach (LST-fused ET), ubESTARFM was used to generate high spatio-temporal resolution land surface temperature (LST) data, which served as the key input for the soil moisture-coupled Two-Source Energy Balance (TSEB-SM) model to estimate high-resolution daily ET. In the second approach (Fused ET), high-resolution daily ET data were directly generated by applying ubESTARFM to fuse ET products derived from MODIS and Landsat observations using the TSEB-SM model. Results showed that LST-fused ET agreed better with eddy covariance (EC) observations, yielding a lower RMSE of 0.469 mm/day (compared to 0.567 mm/day for Fused ET) and a significantly smaller systematic bias (−0.149 mm/day vs. −0.430 mm/day) at the relatively heterogeneous Boyagin site. Furthermore, LST-fused ET demonstrated superior spatial consistency with ECOSTRESS results as benchmarks over heterogeneous surfaces, achieving a significantly lower MAPE of 0.49% compared to 9.53% for Fused ET. This limitation of Fused ET, primarily attributed to pixel-matching biases, could be mitigated by incorporating dynamic, high-resolution LAI into the fusion process as a structural constraint, thereby improving accuracy while maintaining efficiency. Moving forward, improving the efficiency and accuracy of the Fused ET could provide a pragmatic and scalable pathway for large-area, field-scale daily ET mapping, supporting agricultural water-use monitoring and water resource management in arid and semi-arid regions.

  PublisherDOI

• Forecasting the silent spread: Assessing the environmental risk of beech leaf disease in the face of climate change

  Forest Ecology and Management · 2026-04-19

  article
  PublisherDOI

• RoLaSTIM: a novel method for lakeshore type identification and utilization rate assessment

  Figshare · 2026-01-01

  articleOpen access

  Lakeshores are both vital components of lake ecosystems and non-renewable resources that support human development. Understanding their utilization is essential for assessing human–lake interactions, yet accurate lakeshore type identification remains challenging because of the complex water–land interfaces and diverse natural and anthropogenic contexts of lakes. To address this, we developed RoLaSTIM (Robust Lake Shore Type Identification Method), an operational approach for classifying lakeshores as natural or artificial based on spatial analysis and land cover conditions. RoLaSTIM was applied to 12 typical global lakes, and their annual Shore Utilization Rates (SURs) were analysed from 2000 to 2022. RoLaSTIM achieved high classification accuracy (area-adjusted overall accuracy: 98.93 ± 0.02%, weighted F1 score: 98.94%), benefiting from a two-level buffer strategy and corresponding physical rules that minimize misclassification. Key factors influencing accuracy included the lake SUR, artificial lakeshore length, land cover complexity, and satellite image resolution. Moreover, we identified four distinct SUR evolution trends: increasing, stable, inverted (‘∩’-shaped), and fluctuating (‘M’-shaped). RoLaSTIM is suitable for robust and consistent lakeshore classification at large scales, supporting efficient SUR assessment and long-term monitoring. This method fills a critical gap in global lakeshore research and offers valuable insights for lakeshore management, conservation, planning, and sustainable development.

  PublisherDOI

• Corrigendum to ''Forecasting the silent spread: Assessing the environmental risk of beech leaf disease in the face of climate change'' [For. Ecol. Manag. Vol 614, August (2026), 123797]

  Forest Ecology and Management · 2026-05-22

  article
  PublisherDOI

• Nexus between urban green space and adult frequent mental distress: differentiated non-linear environmental pathways and racial heterogeneity

  Landscape and Urban Planning · 2026-01-31

  articleOpen access

  • Tree canopy and grass show distinct, non-linear association with mental distress. • Urban heat, air pollution, and noise mediate the UGS-mental health relationship. • The associations between UGS and mental distress vary across racial groups. • The mental health benefits of trees are greater in communities of color. • Planners should prioritize trees over grass in UGS planning. Amid escalating adult mental distress in urban areas, urban green space (UGS) is increasingly recognized as an environmental feature for mitigating distress burden and associated environmental stressors. Effective and equitable UGS planning requires a nuanced understanding of the associations between UGS types and frequent mental distress (FMD), as well as their heterogeneity across racial and ethnic neighborhoods. Using spatial regressions within a Piecewise Structural Equation Modeling (PSEM) framework, this study investigates both direct associations between two UGS types (e.g., trees and grass) and FMD, as well as indirect pathways through land surface temperature (LST), air pollution (PM 2.5 ), and anthropogenic noise. Findings reveal that UGS types have distinct, and often opposing, non-linear associations with FMD and its environmental mediators. Tree canopy exhibits a direct negative association with FMD, with diminishing marginal effects as canopy cover increases, and a U-shaped association with PM 2.5 , while grass shows positive associations with FMD and PM 2.5 concentrations. Although both UGS types are negatively associated with LST and noise levels, trees show a significantly stronger association with temperatures. We also identify significant racial and ethnic heterogeneity in these associations. The overall negative marginal effect of tree canopy on FMD is significant in communities of color but statistically insignificant in predominantly White tracts. This disparity is driven by both direct association with FMD and indirect pathways through LST mitigation, which are significant only in communities of color. These findings challenge one-size-fits-all greening narratives and provide evidence for context-specific, equity-oriented UGS planning aiming at mitigating urban mental distress and advancing restorative environmental justice.

  PublisherDOI

• RoLaSTIM: a novel method for lakeshore type identification and utilization rate assessment

  Open MIND · 2026-01-01

  article

  Lakeshores are both vital components of lake ecosystems and non-renewable resources that support human development. Understanding their utilization is essential for assessing human–lake interactions, yet accurate lakeshore type identification remains challenging because of the complex water–land interfaces and diverse natural and anthropogenic contexts of lakes. To address this, we developed RoLaSTIM (Robust Lake Shore Type Identification Method), an operational approach for classifying lakeshores as natural or artificial based on spatial analysis and land cover conditions. RoLaSTIM was applied to 12 typical global lakes, and their annual Shore Utilization Rates (SURs) were analysed from 2000 to 2022. RoLaSTIM achieved high classification accuracy (area-adjusted overall accuracy: 98.93 ± 0.02%, weighted F1 score: 98.94%), benefiting from a two-level buffer strategy and corresponding physical rules that minimize misclassification. Key factors influencing accuracy included the lake SUR, artificial lakeshore length, land cover complexity, and satellite image resolution. Moreover, we identified four distinct SUR evolution trends: increasing, stable, inverted (‘∩’-shaped), and fluctuating (‘M’-shaped). RoLaSTIM is suitable for robust and consistent lakeshore classification at large scales, supporting efficient SUR assessment and long-term monitoring. This method fills a critical gap in global lakeshore research and offers valuable insights for lakeshore management, conservation, planning, and sustainable development.

  DOI

• RoLaSTIM: a novel method for lakeshore type identification and utilization rate assessment

  International Journal of Digital Earth · 2026-02-24

  articleOpen access

  Lakeshores are both vital components of lake ecosystems and non-renewable resources that support human development. Understanding their utilization is essential for assessing human–lake interactions, yet accurate lakeshore type identification remains challenging because of the complex water–land interfaces and diverse natural and anthropogenic contexts of lakes. To address this, we developed RoLaSTIM (Robust Lake Shore Type Identification Method), an operational approach for classifying lakeshores as natural or artificial based on spatial analysis and land cover conditions. RoLaSTIM was applied to 12 typical global lakes, and their annual Shore Utilization Rates (SURs) were analysed from 2000 to 2022. RoLaSTIM achieved high classification accuracy (area-adjusted overall accuracy: 98.93 ± 0.02%, weighted F1 score: 98.94%), benefiting from a two-level buffer strategy and corresponding physical rules that minimize misclassification. Key factors influencing accuracy included the lake SUR, artificial lakeshore length, land cover complexity, and satellite image resolution. Moreover, we identified four distinct SUR evolution trends: increasing, stable, inverted (‘∩’-shaped), and fluctuating (‘M’-shaped). RoLaSTIM is suitable for robust and consistent lakeshore classification at large scales, supporting efficient SUR assessment and long-term monitoring. This method fills a critical gap in global lakeshore research and offers valuable insights for lakeshore management, conservation, planning, and sustainable development.

  PublisherDOI

• Wildfire risk in Alaska: Spatial association between social vulnerability, wildfire hazard, and wildfire mitigation programs

  Landscape and Urban Planning · 2025-02-17 · 3 citations

  articleOpen accessSenior authorCorresponding

  • Key factors in social vulnerability and wildfire risk vary across social contexts. • Federal fuel treatments are more equitably distributed across vulnerable groups. • CWPPs and state fuel treatments, placed near urban, link to lower vulnerability. High-latitude regions are experiencing larger, longer, and more severe wildfires, leading to significant impacts on ecosystems and human societies. However, quantitative assessments of wildfire risk that consider both social and ecological characteristics are still lacking in these remote regions. Using Alaska as a case study, we quantified and mapped the association between social vulnerability, wildfire hazard potential, and selected wildfire mitigation activities (federal and state fuel treatment projects and Community Wildfire Protection Plans) to address this gap. We observed great variation in the associations. Remote regions in southcentral and interior Alaska displayed moderate-to-high social vulnerability and wildfire hazard potential, while urban areas exhibited lower social vulnerability regardless of wildfire hazard potential. Notably, state fuel treatments and CWPPs, which are concentrated near urban areas, generally showed a negative association with social vulnerability, though the CWPP–vulnerability association turned positive under high wildfire hazard in urban regions. In contrast, federal fuel treatment projects, which were widespread across the landscape, showed a consistent positive association with social vulnerability regardless of wildfire hazard potential and urban/rural divisions. Our results provide critical context for the policy challenges posed by escalating wildfire risk and inform the environmental justice implications of wildfire mitigation activities. This study contributes to larger-scale, global wildfire management assessments, offering guidance for equitable, context-specific wildfire management strategies in other regions facing increasing wildfire risks.

  PublisherDOI

• Enhancing hydrogen evolution reaction performance of ZnIn2S4-Based single atom catalysts via surface vacancies and interlayer charge transfer

  International Journal of Hydrogen Energy · 2025-10-01 · 1 citations

  article
  PublisherDOI

• Rational design of photoswitches based on chiroptical dimethylcethrene at the single-molecule level

  Physica B Condensed Matter · 2025-04-24 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

Recent grants

• Collaborative Research: Hydrologic and Permafrost Changes Due to Tree Expansion into Tundra

  NSF · $180k · 2017–2022

• Collaborative Research: NNA Research: Interactions of natural and social systems with climate change, globalization, and infrastructure development in the Arctic

  NSF · $172k · 2022–2026

Frequent coauthors

• Xiaofang Wei

  Research Institute of Petroleum Exploration and Development

  36 shared

• Cadance A. Lowell

  Central State University

  36 shared

• Ning Zhang

  Shanghai Ocean University

  36 shared

• Bin Cui

  State Key Laboratory of Crystal Materials

  21 shared

• Dongqing Zou

  Ludong University

  20 shared

• Mingsheng Li

  Bowling Green State University

  19 shared

• Wenkai Zhao

  Nankai University

  18 shared

• Changfeng Fang

  17 shared

Education

• PhD, Environmental Science, Policy, and Management

  University of California Berkeley

  2006