Matthew Marcus
- Assistant Professor of Practice
- Member of the Graduate Faculty
- Assistant Professor, Remote Sensing / Spatial Analysis - GIDP
Contact
Bio
No activities entered.
Interests
No activities entered.
Courses
2025-26 Courses
-
MA Project in GIST
GIST 909 (Spring 2026) -
Raster Spatial Analysis
GIST 602A (Spring 2026) -
Remote Sensing Science
GIST 501B (Spring 2026) -
Remote Sensing Science
GIST 501B (Fall 2025) -
Web GIS
GEOG 414 (Fall 2025) -
Web GIS
GIST 414 (Fall 2025) -
Web GIS
GIST 514 (Fall 2025)
2024-25 Courses
-
Raster Spatial Analysis
GIST 602A (Summer I 2025) -
Vector Spatial Analysis
GIST 602B (Summer I 2025) -
Intro. to Map Science
GIST 214 (Spring 2025) -
Raster Spatial Analysis
GIST 602A (Spring 2025) -
Remote Sensing Science
GIST 501B (Spring 2025) -
Remote Sensing Science
GIST 501B (Fall 2024) -
Senior Capstone
GIST 498 (Fall 2024) -
Stat Tch Geog,Reg Dev+Pl
GEOG 457 (Fall 2024) -
Stat Tch Geog,Reg Dev+Pl
GEOG 557 (Fall 2024) -
Stat Tch Geog,Reg Dev+Pl
GIST 457 (Fall 2024) -
Stat Tch Geog,Reg Dev+Pl
PLG 457 (Fall 2024)
2023-24 Courses
-
MA Project in GIST
GIST 909 (Summer I 2024) -
Raster Spatial Analysis
GIST 602A (Summer I 2024) -
Geo-Databases
GEOG 470 (Spring 2024) -
Geo-Databases
GIST 470 (Spring 2024) -
Raster Spatial Analysis
GIST 602A (Spring 2024) -
Remote Sensing Science
GIST 501B (Spring 2024) -
Remote Sensing Science
GIST 601B (Fall 2023) -
Stat Tch Geog,Reg Dev+Pl
GEOG 457 (Fall 2023) -
Stat Tch Geog,Reg Dev+Pl
GIST 457 (Fall 2023) -
Stat Tch Geog,Reg Dev+Pl
PLG 457 (Fall 2023)
Scholarly Contributions
Journals/Publications
- Marcus, M. S., Hergoualc???h, K., & Guti??rrez-V??lez, V. H. (2025). To climb or to fell? Identification of social-ecological conditions that promote sustainable fruit harvesting in Lowland Amazon palm swamps. Environmental Research: Food Systems, 2(2), 025005.
- Marcus, M. S., Hergoualc’h, K., & Gutiérrez-Vélez, V. H. (2025). To climb or to fell? Identification of social-ecological conditions that promote sustainable fruit harvesting in Lowland Amazon palm swamps. Environmental Research: Food Systems, 2(Issue 2). doi:10.1088/2976-601x/adcf2aMore infoA well-documented environmental threat in the Amazonian region of Loreto, Peru involves harvesting the fruit from the dominant palm Mauritia flexuosa by chopping fruit-bearing females growing in carbon-dense peat swamps. Numerous conservation interventions have been proposed to protect the swamps, such as encouraging harvest of fruits by climbing the palms to preserve the resource instead of cutting them. These efforts have produced mixed success; some communities have embraced sustainable harvest methods, while others have not, despite the obvious benefits of climbing and the simple technology required. In this study, we aim to understand why some communities opt to harvest fruit sustainably while others do not, and to assess whether the experience of communities sustainably harvesting offers broader lessons for ecosystem management. Nine communities were visited in Loreto, where in-depth interviews were performed to identify economic, institutional, and cultural elements linked to fruit extraction practices. Field measurements were produced to evaluate ecological characteristics in harvested swamps nearby communities that mostly climb or mostly chop. The five communities that mostly climb placed high importance on the fruit, had resource management rules, and derived significant material benefit from the fruit. In contrast, the four communities that mostly chop derived only a marginal economic benefit from the fruit and tended to face obstacles to building systems of sustainable management of common property, such as poverty and problems associated with pollution from the oil industry. One community embraced climbing 30 years ago and observed remarkable social, economic, and environmental benefits. Through sustained support from NGOs and the regional government, this community was empowered to build a system of sustainable resource management on its own terms. Its capacity to develop and enforce its rules of harvest, ensured through robust communal trust, was key to its success.
- Marcus, M. S., Hergoualc'h, K., Honorio Coronado, E. N., & Gutiérrez-Vélez, V. H. (2024). Spatial distribution of degradation and deforestation of palm swamp peatlands and associated carbon emissions in the Peruvian Amazon. Journal of Environmental Management, 351(Issue). doi:10.1016/j.jenvman.2023.119665More infoThe vast peat deposits in the Peruvian Amazon are crucial to the global climate. Palm swamp, the most extensive regional peatland ecosystem faces different threats, including deforestation and degradation due to felling of the dominant palm Mauritia flexuosa for fruit harvesting. While these activities convert this natural C sink into a source, the distribution of degradation and deforestation in this ecosystem and related C emissions remain unstudied. We used remote sensing data from Landsat, ALOS-PALSAR, and NASA's GEDI spaceborne LiDAR-derived products to map palm swamp degradation and deforestation within a 28 Mha area of the lowland Peruvian Amazon in 1990–2007 and 2007–2018. We combined this information with a regional peat map, C stock density data and peat emission factors to determine (1) peatland C stocks of peat-forming ecosystems (palm swamp, herbaceous swamp, pole forest), and (2) areas of palm swamp peatland degradation and deforestation and associated C emissions. In the 6.9 ± 0.1 Mha of predicted peat-forming ecosystems within the larger 28 Mha study area, 73% overlaid peat (5.1 ± 0.9 Mha) and stored 3.88 ± 0.12 Pg C. Degradation and deforestation in palm swamp peatlands totaled 535,423 ± 8,419 ha over 1990–2018, with a pronounced dominance for degradation (85%). The degradation rate increased 15% from 15,400 ha y−1 (1990–2007) to 17,650 ha y−1 (2007–2018) and the deforestation rate more than doubled from 1,900 ha y−1 to 4,200 ha y−1. Over 1990–2018, emissions from degradation amounted to 26.3 ± 3.5 Tg C and emissions from deforestation were 12.9 ± 0.5 Tg C. The 2007–2018 emission rate from both biomass and peat loss of 1.9 Tg C yr−1 is four times the average biomass loss rate due to gross deforestation in 2010–2019 reported for the hydromorphic Peruvian Amazon. The magnitude of emissions calls for the country to account for deforestation and degradation of peatlands in national reporting.
- Minasny, B., Adetsu, D. V., Aitkenhead, M., Artz, R. R., Baggaley, N., Barthelmes, A., Beucher, A., Caron, J., Conchedda, G., Connolly, J., Deragon, R., Evans, C., Fadnes, K., Fiantis, D., Gagkas, Z., Gilet, L., Gimona, A., Glatzel, S., Greve, M. H., , Habib, W., et al. (2024). Mapping and monitoring peatland conditions from global to field scale. Biogeochemistry, 167(Issue 4). doi:10.1007/s10533-023-01084-1More infoPeatlands cover only 3–4% of the Earth’s surface, but they store nearly 30% of global soil carbon stock. This significant carbon store is under threat as peatlands continue to be degraded at alarming rates around the world. It has prompted countries worldwide to establish regulations to conserve and reduce emissions from this carbon rich ecosystem. For example, the EU has implemented new rules that mandate sustainable management of peatlands, critical to reaching the goal of carbon neutrality by 2050. However, a lack of information on the extent and condition of peatlands has hindered the development of national policies and restoration efforts. This paper reviews the current state of knowledge on mapping and monitoring peatlands from field sites to the globe and identifies areas where further research is needed. It presents an overview of the different methodologies used to map peatlands in nine countries, which vary in definition of peat soil and peatland, mapping coverage, and mapping detail. Whereas mapping peatlands across the world with only one approach is hardly possible, the paper highlights the need for more consistent approaches within regions having comparable peatland types and climates to inform their protection and urgent restoration. The review further summarises various approaches used for monitoring peatland conditions and functions. These include monitoring at the plot scale for degree of humification and stoichiometric ratio, and proximal sensing such as gamma radiometrics and electromagnetic induction at the field to landscape scale for mapping peat thickness and identifying hotspots for greenhouse gas (GHG) emissions. Remote sensing techniques with passive and active sensors at regional to national scale can help in monitoring subsidence rate, water table, peat moisture, landslides, and GHG emissions. Although the use of water table depth as a proxy for interannual GHG emissions from peatlands has been well established, there is no single remote sensing method or data product yet that has been verified beyond local or regional scales. Broader land-use change and fire monitoring at a global scale may further assist national GHG inventory reporting. Monitoring of peatland conditions to evaluate the success of individual restoration schemes still requires field work to assess local proxies combined with remote sensing and modeling. Long-term monitoring is necessary to draw valid conclusions on revegetation outcomes and associated GHG emissions in rewetted peatlands, as their dynamics are not fully understood at the site level. Monitoring vegetation development and hydrology of restored peatlands is needed as a proxy to assess the return of water and changes in nutrient cycling and biodiversity.
