Matthew Clark

Professor and Director, Center for Interdisciplinary Geospatial Analysis (CIGA)

Matthew Clark

Contact

707-664-2558
matthew.clark@sonoma.edu

Office

Stevenson Hall 3503

Office Hours

Wed: 11:00 am-12:00 pmvia Zoom

Thu: 11:00 am-12:00 pmIn Person

Available by appointment only.

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I am broadly interested in the conservation of Earth's biological diversity in the face of many interrelated threats, such as climate change, altered disturbance regimes, and land-use conversion. My research interests are in using remote sensing, geospatial technology, and machine learning to map and monitor our changing coupled human and natural systems at multiple spatial and temporal scales, with the goal to deepen our understanding the drivers of change and to find practical solutions.

I am currently working locally in California with wildfire science and citizen science-driven biodiversity applications, including sensors on unoccupied aerial systems (e.g., drones) to satellites. I also have a new international research project in South Africa focused on understanding patterns of animal diversity from ground-based acoustic analysis and spatial information from advanced hyperspectral and lidar remote sensing technology.

Full CV

Education

PhD Geography. 2005. University of California, Santa Barbara USA

Dissertation: An assessment of hyperspectral and lidar remote sensing for the monitoring of tropical rain forest trees

MS Ecosystem Analysis and Conservation. 1998. University of Washington, Seattle USA

Thesis: An analysis of Western Olympic Peninsula forest structure using combined synthetic aperture radar and Landsat thematic mapper images

BA— Integrative Biology and Environmental Science, University of California, Berkeley, 1993

Academic Interests

Remote Sensing
Geographic Information Systems
Biogeography

Selected Publications & Presentations

Reilly, S., Clark, M. L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L. P., & Menor, I. O. (2024). Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment, 312, 114310. https://doi.org/10.1016/j.rse.2024.114310

Quinn, C.A., Jantz, P., Salas, L., Goetz, S., Clark, M. (2024). Soundscape mapping: understanding regional spatial and temporal patterns of soundscapes in the context of remotely-sensed predictors and wildfire disturbance. Environmental Research: Ecology. https://iopscience.iop.org/article/10.1088/2752-664X/ad4bec/meta

Krause, P., Forbes, B., Barajas-Ritchie, A. Clark, M., Disney, D., Wilkes, P., Patrick Bentley, L. (2023). Using terrestrial laser scanning to evaluate non-destructive aboveground biomass allometries in diverse Northern California forests. Frontiers in Remote Sensing. 4. https://doi.org/10.3389/frsen.2023.1132208

Quinn, C., Burns, P., Hakkenberg, C. R., Salas, L., Pasch, B., Goetz, S., & Clark, M. (2023). Soundscape components inform acoustic index patterns and refine estimates of bird species richness. Frontiers in Remote Sensing, 4, 35. https://doi.org/10.3389/frsen.2023.1156837

Clark, M.L., Salas, L., Baligar, S., Quinn, C., Snyder, R.L., Leland, D., Schackwitz, W., Goetz, S.J., Newsam, S. (2023). The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2023.102065

Quinn, Q.A., Burns, P., Gill, G., Baligar, S., Snyder, R.L., Salas, L., Goetz, S.J., Clark, M.L. (2022). Soundscape classification with convolutional neural networks reveals temporal and geographic patterns in ecoacoustic data. Ecological Indicators,138. https://doi.org/10.1016/j.ecolind.2022.108831.

Snyder, R., Clark, M., Salas, L., Schackwitz, W., Leland, D., Stephens, T., Erickson, T., Tuffli, T., Tuffli, M., Clas, K. (2022). Citizen Science: Theory and Practice. 7(1), p.24. DOI: http://doi.org/10.5334/cstp.391

López-Carr, D., Ryan, S. J., Clark, M. (2022). Global economic and diet transitions drive Latin American and Caribbean forest change during the first decade of the century: a multi-scale analysis of socioeconomic, demographic, and environmental drivers of local forest cover change. Land, 11(3), 326. https://doi.org/10.3390/land11030326

Forbes, B., Reilly, S., Clark, M., Ferrell, R., Kelly, A., Krause, P., Matley, C., O’Neil, M., Villasenor, M., Disney, M., Wilkes, P., Bentley, LP. (2022) Comparing remote sensing and field-based approaches to estimate ladder fuels and predict wildfire burn severity. Frontiers in Forests and Global Change, 5. https://doi.org/10.3389/ffgc.2022.818713

Clark, M.L., Ruiz,J., Fandino, M.C., López-Carr, D. (2021). Conservation priorities in terrestrial protected areas for Latin America and the Caribbean based on an ecoregional analysis of woody vegetation change, 2001–2010. Land, 10(10):1067. https://doi.org/10.3390/land10101067

Reilly, S., Clark, M., Bentley, L.B., Matley, C., Piazza, E., Oliveras, I. (2021). The potential of multispectral imagery and 3D point clouds from unoccupied aerial systems (UAS) for monitoring forest structure and the impacts of wildfire in Mediterranean-climate forests. Remote Sensing, 13, 3810. https://doi.org/10.3390/rs13193810

Okujeni, A., Jänicke, C., Cooper, S., Frantz, D., Hostert, P., Clark, M., Segl, K., van der Linden, S. (2021). Multi-season unmixing of vegetation class fractions across diverse Californian ecoregions using simulated spaceborne imaging spectroscopy data. Remote Sensing of Environment, 264, 112558. https://doi.org/10.1016/j.rse.2021.112558

Green, K., Tukman, M., Loudon, D., Schichtel, A., Gaffney, K., Clark, M. (2020). Sonoma County Complex Fires of 2017: Remote sensing data and modeling to support ecosystem and community resiliency. California Fish and Wildlife 2020 Special Fire Issue. https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=184827&inline

Cooper, S., Okujeni, A., Jänicke, C., Clark, M., van der Linden, S., Hostert, P. (2020). Disentangling fractional vegetation cover: regression-based unmixing of simulated spaceborne imaging spectroscopy data. Remote Sensing of Environment. 246, 111856. https://doi.org/10.1016/j.rse.2020.111856

Burns, P., Clark, M., Salas, L., Hancock, S., Jantz, P., Leland, D., Dubayah, R., Goetz, S. (2020). Incorporating canopy structure from simulated GEDI lidar into bird species distribution models. Environmental Research Letters. 15, 095002. https://doi.org/10.1088/1748-9326/ab80e

Ackerly, D.D., Kling, M.M., Clark, M.L., Papper, P., Oldfather, M.F., Flint, A.L., Flint, L.E. (2020). Topoclimates, refugia, and biotic responses to climate change. Frontiers in Ecology and Environment. https://doi.org/10.1002/fee.2204

Ackerly, D.D., Kozanitas, M., Papper, P. Oldfather, M., Clark. M. (2019). Mortality and resprouting in California oak woodlands following mixed-severity fire. Pp. 23-30. Proceedings of the International Oak Society, Davis CA

Clark, M. L. (2020). Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 228-245

Jänicke, C., Okujeni, A., Cooper, S., Clark, M., Hostert, P., & van der Linden, S. (2020). Brightness gradient-corrected hyperspectral image mosaics for fractional vegetation cover mapping in northern California. Remote Sensing Letters, 11(1), 1-10.

Clark, M. L., Buck-Diaz, J., Evens, J. (2018). Mapping of forest alliances with simulated multi-seasonal hyperspectral imagery and machine learning classifiers. Remote Sensing of Environment, 210, 490–507.

Blundo, C., Gasparri, N. I., Malizia, A., Clark, M., Gatti, G., Campanello, P. I., ... & MacDonagh, P. (2018). Relationships among phenology, climate and biomass across subtropical forests in Argentina. Journal of Tropical Ecology, 34(2), 93-107.

Clark, M. L. (2017). Comparison of simulated hyperspectral HyspIRI and multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal, regional land-cover mapping. Remote Sensing of Environment, 200, 311-325.

Guidici, D., & Clark, M. L. (2017). One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California. Remote Sensing, 9(6), 629.

Clark, M. L., & Kilham, N. E. (2016). Mapping of land cover in northern California with simulated hyperspectral satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 228-245.