Bio

I'm currently a postdoc at Stanford Woods Institute of the Environment working on combining remote sensing and econometric to assess the environmental and social outcomes of natural climate solutions and forest management interventions. I completed my PhD at the Department of Geographical Sciences at UMD in May 2024. During my PhD, I developed remote sensing techniques utilizing multi-source remote sensing data (e.g,. GEDI, ICESat2, Landsat archive, PlanetScope) for monitoring long-term carbon sequestration in forest restoration areas in East Africa. Seeking to understand how to use Earth Observation to improve the sustainability of human-environment interaction is both a passion of mine and the research agenda during my PhD and onwards. Moreover, I have developed skills in forest inventory and Terrestrial Laser Scanner (TLS) data collection from working on field campaigns in Mozambique and Uganda. Developing web-based interactive map dashboards is another set of technical expertise that I have been practicing (see http://mliang8.github.io/ for map portfolio ) and want to employ in future projects to enhance communications with various stakeholders.

Professional Education

  • Master of Science, Technische Universitat Munchen (2019)
  • Bachelor of Science, University of Wisconsin Madison (2017)
  • Doctor of Philosophy, University of Maryland College Park (2024)
  • PhD, University of Maryland, College Park, Geographical Sciences (2024)
  • MS, Technical University of Munich, Cartography (2019)
  • BS(w/ Honors), University of Wisconsin, Madison, Geography, Cartography/GIS (2017)

Stanford Advisors

Contact

  • Academic
    mliang77@stanford.edu
    University - Scholar Department: Department of Earth System Science Position: Postdoctoral Scholar

Lab Affiliations

All Publications

  • A meta-analysis of carbon losses and gains from tropical moist forest degradation and regeneration. Science advances Heinrich, V., Holcomb, A., Besnard, S., Requena Suarez, D., Cook-Patton, S., Bourgoin, C., Chazdon, R., Gibbs, D. A., Souza Mendes, F., McNicol, I., Wheeler, C., Silva-Junior, C. H., Amani, B. H., Bastin, J. F., Besisa Nguba, T., Chen, N., Chen, H., Ciais, P., Dalagnol, R., Dou, X., Duan, Q., Gao, X., Goodarzi, A. N., Hérault, B., House, J., Lapola, D. M., Liang, M., Meng, Z., Nabuurs, G. J., Poor, E., Parsons, L., Reiche, J., Sitch, S., Valbuena, R., Welsink, A. J., Wiltshire, S., Wu, C., Xu, Y., Zhao, J., Aragão, L., Herold, M. 2026; 12 (27): eadz1923

    Abstract

    Aboveground carbon (AGC) fluxes from deforestation and subsequent regrowth in tropical moist forest (TMF) are increasingly well characterized, but carbon losses and gains following partial disturbance are uncertain. We synthesized 146 studies quantifying postdisturbance AGC changes relative to undisturbed forests across TMF. Immediate AGC losses (mean ± 1 SD; 2.5 ± 2.3 years after disturbance) following partial anthropogenic disturbances were greatest for forest fires (49 ± 26%), selective logging (34 ± 20%), and edge effects (31 ± 19%). Higher-frequency and -intensity disturbances significantly increased carbon loss. After 20 years of regeneration, AGC stock was higher in recovering degraded forests (41 to 117%) compared to secondary regrowth forests after complete deforestation (1 to 74%), indicating greater regeneration potential when forest structure is preserved. Our compiled database and associated meta-analysis improve accuracy and completeness for carbon inventory reporting and modeling. Substantial AGC losses and gains from distinct degradation and recovery processes are now better characterized, serving as an evidence base for policies to halt degradation and foster recovery for climate mitigation.

    View details for DOI 10.1126/sciadv.adz1923

    View details for PubMedID 42397928

    View details for PubMedCentralID PMC13330863

  • Comparing the spatial effects and longevity of key fuel treatments in California using spaceborne lidar data. Journal of environmental management Liang, M., Hakkenberg, C., Villa, L. M., Duncanson, L., Field, C. B. 2025; 396: 128044

    Abstract

    Systematically comparing fuel treatment strategies' effects on vegetation and fuel structure is essential for mitigating wildfire risk, supporting forest management, and reducing climate-related impacts. While prescribed fire and mechanical activities have been compared using field data, large-scale comparisons across treatment types have been limited by inconsistent vegetation structure measurements. To address this gap, we integrate total of 29 million observations from NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar mission with 2870 records from two fuel treatment datasets for California. We assessed spatial and temporal effects of fuel treatments in California forests. We compared broadcast burns, mechanical fuel reduction, fuel breaks, right-of-way clearance, and forest stewardship to untreated zones using GEDI-derived metrics: aboveground biomass density (AGBD), canopy cover, ladder fuels, canopy height, and layering. Broadcast burns produced significant reductions across all metrics, with the largest decrease in AGBD and canopy height (13-18 %). Mechanical fuel reduction significantly reduced all metrics except canopy height, though with smaller magnitude (6-16%). Fuel breaks yielded the greatest reductions in canopy cover, layering, and ladder fuels (19-26 %) but showed limited effects on canopy height and no significant reduction in AGBD. Using a space-for-time substitution analysis, we found mechanical treatments maintain reductions for 9-15 years across crown fuel metrics, lasting 6-9 years longer than prescribed fire. Managed wildfires showed heterogeneous recovery, while forest stewardship exhibited gradual regrowth. These findings underscore the value of GEDI and future active remote sensing missions for monitoring fuel treatment outcomes. Understanding ecological responses to treatments supports optimizing forest management to reduce wildfire risk in fire-prone regions.

    View details for DOI 10.1016/j.jenvman.2025.128044

    View details for PubMedID 41275782