Bio

Diana A. Moanga, PhD, is a Lecturer in the Earth Systems Program at Stanford University's Doerr School of Sustainability and serves as Manager of the Spatial Analysis Center. She teaches a comprehensive suite of geospatial courses including Remote Sensing of Land, Fundamentals of Geographic Information Science, Advanced Concepts in Geospatial Information Science, and Independent Study classes, and has been recognized with Stanford Doerr School of Sustainability's Excellence in Teaching Award in 2025.

Dr. Moanga's research centers on coastal resilience, land system science, and conservation, with expertise in GIS, remote sensing, and spatial analysis. Her work focuses on understanding land use and land cover change processes, particularly examining the effects of environmental and anthropogenic stressors on coastal systems. She is especially passionate about advancing our understanding of coupled socio-ecological systems, mapping coastal hazards dynamics and developing resilience metrics.

She earned her PhD in Environmental Science Policy and Management from UC Berkeley in 2020, where her dissertation research employed geospatial techniques to study land use and land cover changes across California. Her doctoral work explored management impacts on California's coastal lands, agricultural transitions in the Central Valley, and wildfire activity under future climate regimes. Prior to her doctoral studies, Diana completed a Master's in Marine Affairs and Policy from the University of Miami in 2015, where she examined the spatial and temporal characteristics of harmful algal blooms and studied coastal zone management and coral conservation.

Before joining Stanford as a lecturer in 2023, Dr. Moanga served as a postdoctoral researcher at Stanford University's Department of Earth System Science and previously at Florida International University's Sea Level Solutions Center.

Academic Appointments

  • Lecturer, Earth Systems Program

Honors & Awards

  • Excellence in Teaching Award, Stanford Doerr School of Sustainability (May 2025)
  • Community Engaged Teaching Fellowship, Haas Center for Public Service (2024-2025)

Professional Education

  • BA, University of Miami, Marine Affairs (2013)
  • MS, University of Miami, Marine Affairs and Policy (2015)
  • PhD, University of California Berkeley, Environmental Science Policy and Management (2020)

Contact

Additional Info

Links

Research Interests

  • Data Sciences
  • Environmental Education

2025-26 Courses

All Publications

  • Exploring state-level messaging toward US water reuse: a media analysis across time and space ENVIRONMENTAL RESEARCH: INFRASTRUCTURE AND SUSTAINABILITY Fu, S., Moanga, D., Hacker, M. E., Scruggs, C., Osman, K. K. 2025; 5 (3)

    View details for DOI 10.1088/2634-4505/adf664

    View details for Web of Science ID 001550084800001

  • Evaluating perceptions of green stormwater infrastructure (GSI) through a community-based participatory research (CBPR) approach ENVIRONMENTAL RESEARCH LETTERS Medina, C. Y., Shrivatsa, S., Stone, M., Moanga, D., White Jr, E., Awais, M., Cardenas, A., Revels, K., Nieto, Y., Osman, K. K. 2025; 20 (5)

    View details for DOI 10.1088/1748-9326/adc9c5

    View details for Web of Science ID 001472569700001

  • Advancing the understanding of coastal disturbances with a network-of-networks approach ECOSPHERE Myers-Pigg, A. N., Moanga, D., Bond-Lamberty, B., Ward, N. D., Megonigal, J., White Jr, E., Bailey, V. L., Kirwan, M. L. 2025; 16 (1)

    View details for DOI 10.1002/ecs2.70156

    View details for Web of Science ID 001391757300001

  • A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land-use change. Global change biology Clare, J. D., de Valpine, P., Moanga, D. A., Tingley, M. W., Beissinger, S. R. 2023: e17019

    Abstract

    Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi- or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within-time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site-specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long-term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.

    View details for DOI 10.1111/gcb.17019

    View details for PubMedID 37987241

  • Hyperlocal Observations Reveal Persistent Extreme Urban Heat in Southeast Florida Journal of Applied Meteorology and Climatology Clement, A., Troxler, T., Keefe, O., Arcordia, M., Cruz, M., Moanga, D., Hernandez, A., Adefris, Z., Jacobson, S. 2023

    View details for DOI 10.1175/JAMC-D-22-0165.1

  • Farm consolidation and turnover dynamics linked to increased crop diversity and higher agricultural input use. Agricultural Systems Olivia, H., Butsic, V., Moanga, D., Wartenberg, A. 2023

    View details for DOI 10.1016/j.agsy.2023.103708

  • The threat of wildfire is unique to cannabis among agricultural sectors in California ECOSPHERE Dillis, C., Van Butsic, Moanga, D., Parker-Shames, P., Wartenberg, A., Grantham, T. E. 2022; 13 (9)

    View details for DOI 10.1002/ecs2.4205

    View details for Web of Science ID 000850311700001

  • Identifying drivers of change and predicting future land-use impacts in established farmlands JOURNAL OF LAND USE SCIENCE Wartenberg, A. C., Moanga, D., Butsic, V. 2022; 17 (1): 161-180

    View details for DOI 10.1080/1747423X.2021.2018061

    View details for Web of Science ID 000733661800001

  • Limited Economic-Ecological Trade-Offs in a Shifting Agricultural Landscape: A Case Study From Kern County, California FRONTIERS IN SUSTAINABLE FOOD SYSTEMS Wartenberg, A. C., Moanga, D., Potts, M. D., Butsic, V. 2021; 5

    View details for DOI 10.3389/fsufs.2021.650727

    View details for Web of Science ID 000641262300001

  • A System for Resilience Learning: Developing a community-driven, multi-sector research approach for greater preparedness and resilience to long-term climate stresses and extreme events in the Miami metropolitan region Journal of Extreme Events. Troxler, T., et al 2021
  • The space-time cube as an approach to quantifying future wildfires in California INTERNATIONAL JOURNAL OF WILDLAND FIRE Moanga, D., Biging, G., Radke, J., Butsic, V. 2021; 30 (2): 139-153

    View details for DOI 10.1071/WF19062

    View details for Web of Science ID 000588770100001

  • "Sealed in San Jose:" Paving of front yards diminishes urban forest resource and benefits in low-density residential neighborhoods URBAN FORESTRY & URBAN GREENING Lacan, I., Moanga, D., McBride, J. R., Butsic, V. 2020; 54

    View details for DOI 10.1016/j.ufug.2020.126755

    View details for Web of Science ID 000569421200017

  • Avoided land use conversions and carbon loss from conservation purchases in California JOURNAL OF LAND USE SCIENCE Moanga, D., Schroeter, I., Ackerly, D., Butsic, V. 2018; 13 (4): 391-413

    View details for DOI 10.1080/1747423X.2018.1533043

    View details for Web of Science ID 000455153200002

  • Using InVEST to assess ecosystem services on conserved properties in Sonoma County, CAYY CALIFORNIA AGRICULTURE Butsic, V., Shapero, M., Moanga, D., Larson, S. 2017; 71 (2): 81-89

    View details for DOI 10.3733/ca.2017a0008

    View details for Web of Science ID 000400939800009

  • Eastern Pacific Coral Reef Provinces, Coral Community Structure and Composition: An Overview CORAL REEFS OF THE EASTERN TROPICAL PACIFIC: PERSISTENCE AND LOSS IN A DYNAMIC ENVIRONMENT Glynn, P. W., Alvarado, J. J., Banks, S., Cortes, J., Feingold, J. S., Jimenez, C., Maragos, J. E., Martinez, P., Mate, J. L., Moanga, D. A., Navarrete, S., Reyes-Bonilla, H., Riegl, B., Rivera, F., Vargas-Angel, B., Wieters, E. A., Zapata, F. A. edited by Glynn, P. W., Manzello, D. P., Enochs, I. C. 2017; 8: 107-176

    View details for DOI 10.1007/978-94-017-7499-4_5

    View details for Web of Science ID 000400151600007

https://orcid.org/0000-0001-7269-8418