Welcome to the Remote Sensing and Ecosystem Change Lab!

Our research focuses on ecosystem responses to changing climate, fire disturbances, and management practices, as well as the subsequent consequences for energy, water, and carbon cycles. We integrate remote sensing imagery and other geospatial data, via machine learning techniques, to study the processes and feedbacks associated with ecosystem dynamics. Our studies cover diverse ecosystems, ranging from croplands, rangelands, savannas, to forests, with a particular focus on landscape phenomena. The primary goal is to develop improved ecosystem monitoring capabilities and provide data-driven information support for more effective resource management, mitigation and adaptation decisions.

Most Recent Publications

Chen, B., Y. Jin, E. Scaduto. M. Mortitz, M. L. Goulden, and J. T. Randerson, Climate, fuel, and land use controls on the spatial pattern of wildfire in California’s Sierra Nevada, JGR-Biogeosciences, in press.

Liu, H., Y. Jin, L. M. Roche, A. O’ Geen, and R. Dahlgren, Understanding spatial variability in Mediterranean forage production: delineating climate, topography and soil controls,  Environmental Research Letters, in press.

Scaduto, E., B.Chen, and Y. Jin, Satellite-based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California, JSTARS, 2020.

Huang, Y., Y. Jin, M. Schwartz, and J. Thorne, Intensified burn severity in California’s northern coastal mountains by drier climatic condition, Environmental Research Letter, 2020.

Jin, Y., B. Chen, B. Lampinen, and P. Brown, Advancing Agricultural Production with Machine Learning Analytics: Yield Determinants for California’s Almond Orchards, Frontiers in Plant Science, 2020.

Zhang, Zhou, B. Chen, Y. Jin, and P. Brown (2019), California Almond Yield Prediction at the Orchard Level with a Machine Learning Approach, Frontiers in Plant Science, 18, 2019.

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