Accurate information about the extent, frequency and duration of forest inundation is required to inform ecological, biophysical and hydrological models and enables floodplain managers to quantify the efficacy of flood mitigation/modification activities. Open water classifiers derived from optical remote sensing typically underestimate or fail to detect floodplain forest inundation. This paper presents a new method for detecting forest inundation dynamics using freely available Landsat and Sentinel 2 data, referred to as short-wave infrared mapping under vegetation. The method uses a dynamic threshold that accounts for the additional shortwave infrared reflectance caused by the presence of tree canopies over floodwater. The method is demonstrated at five Ramsar listed River Red Gum floodplain forest wetlands in southeastern Australia. Accuracy assessment based on independent drone imagery from a wide range of vegetated wetlands showed an absolute accuracy of 67%–70% and a fuzzy accuracy of 81%–83%. We found the method is conservative, and underestimates inundation (16%–18%) but very rarely misclassifies dry pixels as inundated (0.3%–0.6%). When compared to river gauge data, the method shows similar trends to an open water classifier (i.e., the area of inundated vegetation increases with increasing river height). The method is conservative compared to lidar-based floodplain inundation models but can be applied wherever cloud-free scenes of Landsat or Sentinel 2 have been acquired, thereby enabling floodplain managers with the ability to quantify changes in inundation dynamics in places/time-periods where lidar is unavailable. <b>Citation:</b> Lymburner, L., Ticehurst, C., Adame, M. F., Sengupta, A., & Kavehei, E. (2024). Seeing the floods through the trees: Using adaptive shortwave infrared thresholds to map inundation under wooded wetlands. <i>Hydrological Processes</i>, 38(6), e15174. https://doi.org/10.1002/hyp.15174

<div>Groundwater dependent ecosystems (GDEs) rely on access to groundwater on a permanent or intermittent basis to meet some or all of their water requirements (Richardson et al., 2011). The <a href="https://explorer-aws.dea.ga.gov.au/products/ga_ls_tc_pc_cyear_3">Tasselled Cap percentile products</a> created by Digital Earth Australia (2023) were used to identify potential GDEs for the upper Darling River floodplain study area. These percentile products provide statistical summaries (10th, 50th, 90th percentiles) of landscape brightness, greenness and wetness in imagery acquired between 1987 and present day. The 10th percentile greenness and wetness represent the lowest 10% of values for the time period evaluated, e.g. 10th greenness represents the least green period. In arid regions, areas that are depicted as persistently green and/or wet at the 10th percentile have the greatest potential to be GDEs. For this reason, and due to accessibility of the data, the 10th percentile Tasselled Cap greenness (TCG) and Tasselled Cap wetness (TCW) products were used as the basis for the assessment of GDEs for the upper Darling River floodplain study area. </div><div><br></div><div>This data release is an ESRI geodatabase, with layer files, including:</div><div><br></div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;original greenness and wetness datasets extracted; </div><div><br></div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;classified 10th percentile greenness and wetness datasets (used as input for the combined dataset); </div><div><br></div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;combined scaled 10th percentile greenness and wetness dataset (useful for a quick glance to identify potential groundwater dependent vegetation (GDV) that have high greenness and wetness e.g. river red gums)</div><div><br></div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;combined classified 10th percentile greenness and wetness dataset (useful to identify potential GDV/GDE and differentiate between vegetation types)</div><div><br></div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;coefficient of variation of 50th percentile greenness dataset (useful when used in conjunction with the scaled/combined products to help identify GDEs)</div><div><br></div><div>For more information and detail on these products, refer to <a href="https://dx.doi.org/10.26186/148545">https://dx.doi.org/10.26186/148545</a>.</div><div><br></div><div><strong>References</strong></div><div>Digital Earth Australia (2023). <em><a href="https://docs.dea.ga.gov.au">Digital Earth Australia User Guide</a></em>. </div><div>Richardson, S., E. Irvine, R. Froend, P. Boon, S. Barber, and B. Bonneville. 2011a. <em>Australian groundwater-dependent ecosystem toolbox part 1: Assessment framework.</em> Waterlines Report 69. Canberra, Australia: Waterlines.</div>