Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors

Retrospective identification of fire severity can improve our understanding of fire behaviour and ecological responses. However, burnt area records for many ecosystems are non-existent or incomplete, and those that are documented rarely include fire severity data. Retrospective analysis using satellite remote sensing data captured over extended periods can provide better estimates of fire history. This study aimed to assess the relationship between the Landsat differenced normalised burn ratio (dNBR) and field measured geometrically structured composite burn index (GeoCBI) for retrospective analysis of fire severity over a 23 year period in sclerophyll woodland and heath ecosystems. Further, we assessed for reduced dNBR fire severity classification accuracies associated with vegetation regrowth at increasing time between ignition and image capture. This was achieved by assessing four Landsat images captured at increasing time since ignition of the most recent burnt area. We found significant linear GeoCBI–dNBR relationships (R2 = 0.81 and 0.71) for data collected across ecosystems and for Eucalyptus racemosa ecosystems, respectively. Non-significant and weak linear relationships were observed for heath and Melaleuca quinquenervia ecosystems, suggesting that GeoCBI–dNBR was not appropriate for fire severity classification in specific ecosystems. Therefore, retrospective fire severity was classified across ecosystems. Landsat images captured within ~ 30 days after fire events were minimally affected by post burn vegetation regrowth.

Precise determination of aquatic plant wet mass using a salad spinner

The reliable assessment of macrophyte biomass is fundamental for ecological research and management of freshwater ecosystems. While dry mass is routinely used to determine aquatic plant biomass, wet (fresh) mass can be more practical. We tested the accuracy and precision of wet mass measurements by using a salad spinner to remove surface water from four macrophyte species differing in growth form and architectural complexity. The salad spinner aided in making precise and accurate wet mass with less than 3% error. There was also little difference between operators, with a user bias estimated to be below 5%. To achieve this level of precision, only 10–20 turns of the salad spinner are needed. Therefore, wet mass of a sample can be determined in less than 1 min. We demonstrated that a salad spinner is a rapid and economical technique to enable precise and accurate macrophyte wet mass measurements and is particularly suitable for experimental work. The method will also be useful for fieldwork in situations when sample sizes are not overly large.