Understanding the terrestrial carbon metabolism is critical because terrestrial ecosystems play a major role in the global carbon cycle. More accurate quantification of net CO2 exchange at global scales can improve our understanding of the feedbacks between the terrestrial biosphere and the atmosphere. There has been an intensive global effort in recent years to measure and model carbon exchanges between the terrestrial biosphere and the atmosphere using a combination of modelling, remote sensing (RS), and a global network of eddy covariance (EC) flux tower.
EC flux towers have been used for measuring the exchange of CO2 at diurnal, seasonal, and inter-annual time scales at ecosystem level since the 1990s. However, current observations of C-flux based measurements via EC data are too sparse in time and space to allow inferences of terrestrial carbon sources and sinks with sufficient accuracy To examine the terrestrial carbon cycle over regions or continents, EC measurements need to be up-scaled to these larger areas. RS provides means for the mapping of ecosystem properties at larger scales capturing essential ecosystem processes. In addition, RS has the potential to provide information on disturbance history over decadal time scales; in particular since the USGS Landsat archive is now freely available in a pre-processed format. It is, therefore, relevant to better understand connections between RS data and EC data.
The aim of this research topic is to enhance understanding of post disturbance carbon dynamics in forest ecosystems by understanding the mechanistic connections between remotely sensed reflectance dynamics and forest physiology at fluxnet sites levels. This will be done by analysing the linkage between Landsat spectral trends of disturbances and recovery (DR) and EC data. For this purpose, EC data were acquired/processed and Landsat data were acquired for each fluxnet site included in the study.
- Identify and characterize forest resilience trajectories since disturbance at fluxnet site levels by using time series algorithms such as BFASTMonitor, LandTendr or Vegetation Change Tracker.
- Explore spectral parameters (e.g. disturbance indices, vegetation indices, biomass, Land Surface Temperature, and surface reflectance, etc.) to link spectral data and EC data for EC measurements up-scaling.
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- Robert E. Kennedy, Zhiqiang Yang, Warren B. Cohen, (2010), Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms, Remote Sensing of Environment, Volume 114, Issue 12, Pages 2897–2910.
- Jan Verbesselt, Achim Zeileis, Martin Herold, (2012), Near real-time disturbance detection using satellite image time series, Remote Sensing of Environment, Volume 123, Pages 98–108.
- P. Campbell1, E. Middleton, K. F. Huemmrich, S. Bernardes, Qingyuan Zhang and L. Ong, (2015), EO-1 Hyperion reflectance time series for remote sensing of vegetation carbon flux dynamics.
- Petya Entcheva Campbell, Elizabeth M. Middleton, Kurt J. Thome, Nathaniel A. Brunsell, (2013)EO-1 Hyperion Reflectance Time Series at Calibration and Validation Sites: Stability and Sensitivity to Seasonal Dynamics, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2), p276-290.
- GRS–32306 - Advanced Earth Observation
- Knowledge in tropical forest ecology
- Good knowledge in scripting is an asset (e.g. R)
Theme(s): Modelling & visualisation, Integrated Land Monitoring