Wetland inundation dynamics

Monitoring surface inundation in wetlands is important for understanding the ecosystem services they offer, including the regulation of regional hydrology, water quality and greenhouse gas cycling. Existing methods for detecting wetland inundation using satellite imagery are often inadequate due to the complexity of wetlands in both space and time. Surface water in wetlands is often mixed with vegetation or soils, giving rise to mixed pixels and significant omissions in inundation map products. Additionally, inundation is highly dynamic in most wetland systems and require regular satellite observations to capture their rapid changes. Figure 1 shows the importance of accounting for mixed water pixels when mapping wetland inundation using Landsat imagery. Including only open water (ie. no mixed vegetation or soil) in our inundation map results in a large underestimate in inundated area.

wetland inundation in the Everglades
Figure 1 - Seasonal inundation composites made using all Landsat data over the Everglades wetlands in southern Florida. Accounting for partial (mixed) water pixels as well as open water reveals very different seasonal inundation trends.

Funded by NASA's Land Cover and Land Use Change (LCLUC) programme, my research involves the monitoring of surface water inundation at the sub-pixel level using Landsat and Sentinel-2 data, combined with Sentinel-1 SAR, towards establishing a near-daily record of inundation over much of North America. Read more about this project here.

Collaborators:

  • Chengquan Huang - University of Maryland
  • Megan Lang - US Fish and Wildlife Service - National Wetlands Inventory
  • John Jones - US Geological Survey
  • Wenli Huang - University of Maryland
  • Irena Creed - University of Western Ontario
  • Mark Carroll - NASA Goddard Space Flight Center


    Tropical forest change

    My PhD dissertation describes novel approaches to monitoring change dynamics in tropical forests. New methods are necessary for helping countries in the tropics to advance their monitoring efforts towards initiatives like the UN's Reducing Emissions from Deforestation and Degradation (REDD+) programme. In my research, I explored ways in which time series of Landsat data can be used to monitor deforestation, degradation and regrowth. Figure 2 shows an example of the bfastmonitor method over a site in southern Ethiopia. In this study, I showed that by using all Landsat observations, small-scale deforestation could be tracked fairly well. Without reliable ground-based observations, however, degradation was nearly impossible to track. By partnering with local communities, I showed in another study that high-detail characterization of deforestation and degradation in these complex forest systems is indeed possible with dense Landsat time series.

    forest change in southern Ethiopia

    Figure 2 - Demonstration of the bfastmonitor method over a site in southern Ethiopia, where in-migration caused successive conversion of forest to cropland between 2005 and 2012.

    In addition to my work in southern Ethiopia, I published another of my PhD chapters describing a method for the automated detection of post-disturbance regrowth using Landsat time series. I demonstrated the method in Madre de Dios, southern Peru, an area characterized by rapid forest change in recent years due to conversion to pasture and small-scale gold mining operations.

    Collaborators:

  • Martin Herold - Wageningen University
  • Jan Verbesselt - Wageningen University
  • Arun Pratihast - Wageningen University
  • Mathieu Decuyper - Wageningen University
  • Svane Bender-Kaphengst - Nature and Biodiveristy Conservation Union


    Change monitoring resources

    Developing open-source tools is an important part of my work. Alongside my research activities, I have produced or collaborated on a number of open-source packages. You can read more about them here.

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