About

Professor Matthew C. Hansen is a remote sensing scientist with a research specialization in large area land cover and land use change mapping. His research is focused on developing improved algorithms, data inputs, and thematic outputs that enable the mapping of land cover change at regional, continental, and global scales. Such maps facilitate better-informed approaches to natural resource management, including deforestation and biodiversity monitoring, and can also serve as inputs to carbon, climate, and hydrological modeling studies. His work as an Associate Team Member of NASA's MODIS Land Science Team included the algorithmic development and product delivery of the MODIS Vegetation Continuous Field land cover layers. Currently, his research involves applying the global processing model for MODIS to the Landsat archive, mapping forest disturbance in regions such as the Congo Basin, Indonesia, European Russia, Mexico, Quebec, and the United States. These efforts aim to test global-scale disturbance mapping with Landsat data. Additionally, his research focuses on improving global cropland monitoring capabilities, including estimating soybean cultivated areas using MODIS, Landsat, and RapidEye datasets. His work emphasizes an interdisciplinary approach, combining engineering skills, geographic domain knowledge, and understanding of land information's value to downstream science applications.

Research topics

• Geography
• Environmental science
• Computer Science
• Ecology
• Remote sensing
• Biology
• Environmental resource management
• Artificial Intelligence
• Agroforestry
• Geology
• Physical geography
• Demography
• Mathematics
• Physics
• Business
• Economics
• Geodesy
• Natural resource economics
• Cartography
• Astronomy
• Optics
• Statistics

Selected publications

• An accurate 10 m annual crop map product of maize and soybean across the United States

  Earth system science data · 2026-03-25 · 1 citations

  articleOpen accessSenior author

  Abstract. High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this study, we improved a workflow for crop mapping and developed an openly available, annual, 10 m spatial resolution maize and soybean map product over the Contiguous United States (CONUS) from 2019 to 2022 (available at https://glad.umd.edu/dataset/mapping-crops-10-m-resolution-united-states, last access: 26 December 2025). We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10 d analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10 d ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics and topographic factors, we trained random forest models generalized for annual maize and soybean classification separately. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95 % with standard errors of less than 1 %. User's accuracies (UAs) and producer's accuracies (PAs) for maize were higher than 91 % and 84 % across the years, and UAs and PAs for soybean were greater than 88 % and 82 %, respectively. To illustrate the substantial improvement of the 10 m map over existing datasets, e.g., the 30 m Cropland Data Layer (CDL), we aggregated the 10 m maps to 30 m spatial resolution and quantified the number of mixed pixels that can be reduced by improving the mapping from 30 to 10 m. The counties with the most maize and soybean production in Iowa, Illinois and Nebraska had the lowest reduction in mixed pixels, ranging from 1 % to 7 %, whereas southern counties had a higher reduction in mixed pixels. Overall, the median percentages of mixed maize and soybean pixels reduction across all counties were 8 % and 9 %, respectively. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10 m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales. The dataset is also available at https://doi.org/10.6084/m9.figshare.28934993.v2 (Li et al., 2025).

  PublisherDOI

• Unprecedentedly high global forest disturbance due to fire in 2023 and 2024

  Proceedings of the National Academy of Sciences · 2025-07-21 · 30 citations

  articleOpen accessCorresponding

  Global forests provide key ecosystem services, from climate regulation to biodiversity habitat, but are under increasing pressure from the combined impacts of climate and land use change. Here, we show that forest disturbance due to fire is growing globally, with the most dramatic increases in intact forest landscapes, highlighting an existential threat to remaining high biomass, high biodiversity forests. The global annual area of forest disturbance due to fire for 2023 and 2024 was highest since the beginning of monitoring in 2001. Compared to 2002-2022 average annual forest disturbance due to fire, the 2023-2024 average was 2.2 times higher globally and 3 times higher in the Tropics. More than ¼ of all 2024 forest disturbance from fire occurred in tropical forests. We found a statistically significant increasing trend of forest disturbance due to fire from 2002 to 2024 in all climate domains except Subtropical. High forest, low deforestation tropical countries were not exempt, with Guyana and the Republic of the Congo experiencing record forest disturbance due to fire. Our results agree with recently estimated increases in global forest fire emissions and active fire detections. The unprecedented scale of fires in the world's most remote forests is a potential harbinger of ecosystem tipping points. Protecting these remaining unfragmented high conservation value forests from this threat poses a daunting and as yet undeveloped policy and capacity challenge.

  PublisherOA PDFDOI

• Global annual cropland dynamics 2015 – 2024

  Remote Sensing of Environment · 2025-01-01

  articleOpen access

  Food security worldwide is increasingly threatened by population growth, shifting diets, geopolitical conflicts, and climate change impacts. Annual operational cropland monitoring is required to support the United Nations Zero Hunger Sustainable Development Goal. Landsat satellite data provide a foundation for such global, independent, high-cadence monitoring at 30-m spatial resolution suitable for agricultural policy and management interventions, policy responses, and market adjustments. Here, we used Landsat Analysis Ready Data developed by the Global Land Analysis and Discovery Lab (GLAD-ARD) and machine learning to map global cropland extent annually from 2015 to 2024. We showed that the global cropland area increased by more than 6% over the past decade. By combining sample-based cropland area estimates from our research and the earlier analysis (2003–2019), we estimate that the global cropland area has expanded by nearly 14% since 2003. Between 2015 and 2024, Africa accounted for the largest regional increase (+24.5 Mha). At the national scale, Brazil experienced the largest gain (+16.5 Mha) and Morocco the largest loss (˗0.38 Mha). A third (33.3%) of all new cropland was established through natural vegetation clearing or irrigation expansion within natural drylands. The overall accuracies of the 2015 and 2024 cropland maps were 97.8% (Standard Error 0.3%) and 97.3% (SE 0.4%), respectively. Despite cropland expansion, population growth has outpaced cropland gains; between 2015 and 2024, per-capita cropland area declined from 0.166 to 0.161 ha per person. Our data illustrate the combined effects of changes in land use priorities, climate, water supply, international trade, and armed conflicts on global cropland extent dynamics during the last decade. • Presented global annual cropland maps with overall accuracy >97%. • Global cropland area increased by more than 6% from 2015 to 2024. • Cropland area increased by 14% over the past two decades. • Per-capita cropland area has declined by 3% from 2015 to 2024.

  PublisherDOI

• The importance of distinguishing between natural and managed tree cover gains in the moist tropics

  Nature Communications · 2025-07-02 · 3 citations

  articleOpen access

  Naturally regenerated forests and managed tree systems provide different levels of carbon, biodiversity, and livelihood benefits. Here, we show that tree cover gains in the moist tropics during 1982–2015 were 56% ± 3% naturally regenerated forests and 27% ± 2.6% managed tree systems, with these differences in forest type, not only natural conditions (climate, soil, and topography), driving observed carbon recovery rates. The remaining 17% ± 3% likely represents small, unmanaged tree patches within non-forest cover types. Achieving global forest restoration goals requires robust monitoring, reporting, and verification of forest types established by restoration initiatives. Tree cover gains in the moist tropics (1982–2015) were 56% naturally regenerated forests and 27% managed tree systems, with forest type influencing carbon recovery. Effective forest restoration requires robust tracking of forest types established by restoration efforts.

  PublisherOA PDFDOI

• The Politics of Framing Water Infrastructure: A Topic Model Analysis of Media Coverage of India’s Ken-Betwa River Link

  Journalism and Media · 2025-07-22

  articleOpen access

  The framing of water infrastructure in the news influences how the public perceives future infrastructure development and associated social-environmental risks. This study examines English-language newspaper coverage of the Ken-Betwa river link, the first component of India’s National River Linking Program (INRLP) to receive approval. Data for this analysis comprised 316 newspaper articles, collected via a keyword search in LexisNexis API, from seven Indian English-language newspapers (Free Press Journal (India), Hindustan Times, Indian Express, The Economic Times, The Hindu, The Times of India (TOI), and Times of India (Electronic Edition)) published between 2004 and 2022. By applying LDA topic modeling, a type of generative probabilistic model, to this dataset, this study examines how evolving media narratives frame water infrastructure in India. Our results identify 23 distinct topics and three dominant frames: (1) a government policy frame, (2) INRLP comparative frame, and (3) environmental conservation frame. We find that these frames evolve, with early coverage emphasizing feasibility and government-led negotiations, and later articles highlighting environmental risks. Our analysis shows how media discourse reflects institutional logic and infrastructure milestones. This study demonstrates the value of computational methods for longitudinal media analysis, has the potential to reveal shifts in public discourse, and highlights power dynamics in environmental reporting.

  PublisherOA PDFDOI

• The next Landsat: Mission turning point?

  Remote Sensing of Environment · 2025-10-16 · 3 citations

  articleOpen access

  For over fifty years, the Landsat satellite series has provided continuous and comprehensive data for monitoring changes on the Earth's terrestrial surface. Eight successive missions, carrying progressively more sophisticated sensors, with improved radiometric, geometric, and spatial characteristics, have provided an unbroken series of optical and thermal imagery, unparalleled globally. With limited lifetimes for each Landsat satellite, planning of each mission typically overlaps to ensure continuity. Commencing in 2021, planning of a Landsat-9 successor gathered user needs from across the Earth Observation (EO) community, resulting in the Landsat Next (LNext) mission design of three sun-synchronous satellites to acquire reflective and thermal wavelength observations with two to three times the temporal, spatial, and spectral resolution of previous missions. Proposed 2026 U.S. budgets have significantly reduced NASA Earth Science funding. Alternate architectures are now being investigated for Landsat Next that would only meet Landsat-9 design requirements. While this would provide observation continuity, this implies a revised Landsat Next program launched in the early 2030s with nearly 30 year old capabilities, that may acquire data with lower radiometric quality than the current on-orbit Landsat-8 and 9 missions, and that will not support the new capabilities advocated for by the EO user community. This correspondence serves to raise community awareness that the decision is pending, and outlines the observation requirements originally envisioned for LNext and how they were derived to provide context for evaluating the restructured and descoped capability now being considered. • Landsat Next design: twenty-one 10/20-m VSWIR and five 60-m TIR bands. • Three-satellite constellation, six-day global land surface revisit. • Derivation of Landsat Next observation requirements for science and applications. • Descoped mission will not meet future user needs. • Descoped mission risks less, and lower quality, data than current Landsats.

  PublisherDOI

• Practical global sampling methods for estimating area and map accuracy of land cover and change

  Remote Sensing of Environment · 2025-04-11 · 6 citations

  articleOpen accessSenior author
  PublisherDOI

• BPS2025 - Elucidating interaction nodes created by hydrophobic residues and subsequences in proteins

  Biophysical Journal · 2025-02-01

  article
  PublisherDOI

• The next Landsat: Mission turning point?

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Rapid monitoring of global land change

  Nature Communications · 2025-10-08 · 10 citations

  articleOpen access

  Direct human action, principally land use expansion, and natural dynamics, such as fire and drought, drive global land change. Here we present a global land change monitoring system, DIST-ALERT, that rapidly tracks vegetation loss anomalies with 30 m resolution using imagery from Landsat 8/9 and Sentinel-2A/B/C satellites. The alerts capture agricultural expansion, urbanization, logging, mining, fire, drought, landslides, and other dynamics, but without attribution. Identified through a probability sample, 2023 anthropogenic land use conversions totaled 28.6 ± 7.6 Mha (±standard error), half of which replaced long-lived or secondary natural vegetation. Fires resulting in land cover conversion totaled 14.9 ± 4.3 Mha (±standard error). Combined, these dynamics equal 0.3% of the global land surface, equivalent to the area of the state of California. Annual DIST-ALERT summaries of land use expansion and climate-driven land change can serve as a future long-term global environmental data record. An operational satellite-based monitoring system using NASA/USGS and ESA imagery enables rapid tracking of global land change, with the area of conversion due to direct human action and fire equaling the size of California in 2023.

  PublisherOA PDFDOI

Frequent coauthors

• Peter Potapov

  141 shared

• Ruth DeFries

  Columbia University

  93 shared

• Stephen V. Stehman

  State University of New York

  82 shared

• Alexandra Tyukavina

  University of Maryland, College Park

  57 shared

• Svetlana Turubanova

  56 shared

• John Townshend

  48 shared

• R. A. Houghton

  Woodwell Climate Research Center

  33 shared

• Xiao‐Peng Song

  31 shared

Education

• Ph.D., Geography

  University of California, Santa Barbara

  1991

• M.A., Geography

  University of California, Santa Barbara

  1986

• B.A., Geography

  University of California, Santa Barbara

  1983