Kexin Song

Understanding resilience in a changing world

I bring together satellite remote sensing, field measurements, causal inference, and geospatial AI to understand how ecosystems and societies respond to disturbance and to turn those insights into better environmental decisions.

Theme 01

Forest resilience & ecosystem dynamics

How do forests respond to disturbance, recover from stress, and gain or lose resilience under global environmental change?

Lag-1 temporal autocorrelation (TAC) is widely used as an ecosystem-resilience indicator because increasing TAC can signal a declining recovery rate. Yet its ecological meaning has rarely been tested directly in real ecosystems.

By combining satellite time series with field-based physiological measurements in the Amazon, we showed that Landsat-derived TAC captures meaningful variation in community-level hydraulic safety margin. The work also provides guidance on temporal frequency, rolling-window length, and vegetation state variables for robust satellite-based TAC estimation.

Conceptual basin-stability diagram
Fig. 1. How observational frequency influences estimates of ecosystem resilience.

I also use dense satellite time series to detect forest-disturbance timing, severity, and recovery under diverse natural and anthropogenic stressors.

Animated map of disturbance timing
Disturbance timing inferred from detected onset.
Animated map of disturbance severity
Disturbance severity represented by detected magnitude.

Related publications

  1. Song, K. et al. (2026). Physiological fidelity of a satellite-derived forest resilience indicator in the Amazon. Nature Ecology & Evolution. Paper
  2. Qiu, S. et al. (2025). A shift from human-directed to undirected wild land disturbances in the USA. Nature Geoscience, 18, 989–996.

Selected presentations

  • Song, K. & Zhu, Z. (2024). Can We Reliably Measure Forest Resilience from Space? AGU Fall Meeting.
  • Song, K. & Zhu, Z. (2024). Unveiling Forest Resilience Changes in Response to Insect Disturbance. AAG Annual Meeting.

Theme 02

Human–environment systems

How do human activities, infrastructure, and environmental change interact to reshape landscapes and coupled-system resilience?

I investigate how conflict, human displacement, infrastructure expansion, and environmental interventions reshape coupled human–natural systems. Current projects combine dense satellite time series with causal inference and geospatial AI to evaluate land-system changes associated with conflict and refugee displacement, emerging infrastructure such as data centers, and environmental management.

Related work reveals the spiky, cyclical, and asynchronous nature of urbanization and tracks increasing volatility in nighttime human activity.

Land-disturbance trajectories in Myanmar and Bangladesh
Fig. 2. Land-disturbance trajectories before, during, and after conflict and displacement.
Estimated effects of conflict damage on land cover
Fig. 3. Estimated effects of conflict-damage exposure on agricultural, forest, and impervious land-cover trajectories.

Related publications

  1. Zhu, Z. et al., including Song, K. (2026). The Urban Pulse: Diagnosing the urbanization process as spiky, cyclical, and asynchronous. PNAS, 123(24), e2537770123. Paper
  2. Li, T. et al., including Song, K. (2026). Satellite imagery reveals increasing volatility in human night-time activity. Nature, 652, 379–386.
  3. Song, K. & Sanford, L. (in prep). Seeing like a satellite: How to effectively use remote sensing data for social science inquiry.

Theme 03

Earth observation & geospatial AI

How can we develop more rigorous, reliable, and interpretable methods to observe, understand, and predict land-system change?

My methodological research spans multi-sensor satellite data fusion, dense time-series analysis, change detection, product evaluation, explainable machine learning, and geospatial AI.

Multi-sensor satellite data fusion

I developed the Time-series-based Image Fusion (TIF) algorithm to harmonize Landsat and Sentinel-2 imagery into dense 10-m optical time series. Published in Remote Sensing of Environment, TIF supports high-resolution monitoring of insect disturbance, crop phenology, flash floods, and other rapid land changes.

Workflow of the TIF satellite image-fusion algorithm
Fig. 4. Workflow of the Time-series-based Image Fusion algorithm.
Comparison of land-change maps from image-fusion methods
Fig. 5. Change maps generated from Sentinel-2, TIF, and comparison methods.

Related publications

  1. Song, K. et al. (2025). TIF: A time-series-based image fusion algorithm. Remote Sensing of Environment, 331, 115035. Paper
  2. Song, K. & Minnett, P. J. (2024). Evaluation of summertime passive microwave and reanalysis sea-ice concentration in the central Arctic. Earth and Space Science, 11(1).

From evidence to action

Stakeholder-engaged research

I work closely with stakeholders to translate Earth observation and geospatial data science into actionable decision support.

Vegetation risk & infrastructure vulnerability

In collaboration with StormWise and the Eversource Energy Center, I developed machine-learning models that integrate satellite, aerial, LiDAR, and infrastructure data to quantify vegetation-related power-outage risks.

Predicted roadside tree-failure risk map
Fig. 6. Predicted roadside tree-failure risk. Select the figure to explore the application.

Related publication: Worthley, T. et al. (2024). Stormwise: Innovative Forest Management to Promote Storm Resistance in Roadside Forests. Journal of Forestry, 122(4), 398–409.