Measuring ecosystem recovery with spectral indices, fractional cover and semantic categories

Background: After a disturbance such as a landslide, a fire, an avalanche or a wind-throw, satellite data are often used to track how quickly vegetation returns. A common measure is vegetation cover, but it can be described in very different ways. A spectral index such as NDVI gives a unitless value, spectral unmixing gives the share of vegetation in each pixel, and semantic approaches assign a knowledge-based vegetation category. These approaches exist side by side but are rarely compared. Depending on which one is used, the same surface may appear recovered after three years or only after ten.

Schematic figure of ecosystem recovery, here with tree cover as a proxy. How do recovery intervals differ for spectral indices, fractional cover and a semantic approach? Figure source: Mandl et al., 2024

Short description: The thesis tracks recovery on disturbed areas in a region of choice with Sentinel-2 or Landsat time series. Vegetation cover is described in three ways, as NDVI, as vegetation fraction from unmixing, and as the corresponding semantic vegetation category. For each area, recovery is measured as the time until vegetation reaches its pre-disturbance level or a fixed threshold. Very high resolution imagery from different years serves as reference. The result shows how strongly recovery estimates depend on the chosen measure and where the approaches disagree, for example with sparse, dry or patchy vegetation.

Suggested reading

Mandl, L., Viana-Soto, A., Stritih, A., Seidl, R., & Senf, C. (2024). Spectral-unmixing based forest recovery indicators for predicting long-term recovery success. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2024.114194

Okujeni, A., van der Linden, S., Tits, L., Somers, B., & Hostert, P. (2013). Support vector regression and synthetically mixed training data for quantifying urban land cover. Remote Sensing of Environment, 137, 184–197. https://doi.org/10.1016/j.rse.2013.06.007

Sudmanns, M., Augustin, H., van der Meer, L., Baraldi, A., & Tiede, D. (2021). The Austrian semantic EO data cube infrastructure. Remote Sensing, 13(23), 4807. https://doi.org/10.3390/rs13234807

Start: Anytime

Prerequisites/qualification: 

·        Remote Sensing & GIS

·        Basic programming

·        Interest in ecosystem dynamics