A generalizable NDVI-based wetland delineation indicator for remote monitoring of groundwater flows in the Australian Great Artesian Basin

White, Davina; Lewis, Mervyn K.; Green, Graham; Gotch, T.

doi:10.1016/j.ecolind.2015.01.032

Cited by 45 publications

(24 citation statements)

References 9 publications

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“…These communities are known to retain lower FPC, with short lived greening of vegetation driven by precipitation events. Such patterns in changes in green biomass have been previously documented in other arid environments (White et al, 2015). In contrast, temporal variation in FPC was low and similar between vegetation communities in our humid study area.…”

Section: Discussionsupporting

confidence: 88%

“…In water limited ecosystems, the general assumption is that vegetation communities with access to groundwater produce greater amounts of green biomass (i.e., photosynthetically active leaf area and foliage projective cover) than communities solely reliant on soil moisture (Eamus, Froend, Loomes, Hose, & Murray, 2006;Hatton, Evans, & Merz, 1998). GDV communities have been delineated from surrounding non-GDV communities using remote sensing measures to identify vegetation with greater green biomass (Barron, Emelyanova, Van Niel, Pollock, & Hodgson, 2014;Eamus et al, 2006;White, Lewis, Green, & Gotch, 2015). Smaller seasonal changes in green biomass have also been used to delineate GDV, with the assumption that GDV retains green biomass during dry periods (Tweed, Leblanc, Webb, & Lubczynski, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Changes in foliage projective cover and its implications for mapping groundwater dependent vegetation across a precipitation gradient

et al. 2018

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Adequate protection of groundwater dependent vegetation (GDV) at the landscape scale (>104 km2) is often hampered as their extent is poorly mapped. Remote sensing data sets have previously been used to delineate GDV by analysing both actual and changes, in green biomass over space and time. The rationale behind such approaches is based on the phenological characteristics of GDV, which often retain greater mean green biomass during dry periods, in comparison to vegetation with access to soil moisture only. However, such methods are hampered by inadequate knowledge about how average annual precipitation, seasonality, and differing water regimes of vegetation communities within a landscape influence green biomass. Therefore, we aimed to test for amount of foliage projective cover (FPC) and variation in FPC as indicators of GDV presence. We assessed differences in the FPC of GDV at 3 sites across a 1,500 mm−1 year−1 precipitation gradient, ranging from arid inland to humid coastal environments in Southern Queensland, Australia. Overall, we found that average annual precipitation had the largest influence on predicting FPC followed by seasonality. During winter in the semi‐arid and humid environments (season with least precipitation), FPC for non‐GDV was higher than subsurface GDV. For our study area, GDV delineation on the basis of higher green biomass than surrounding vegetation was effective when average annual precipitation was less than ~600 mm−1 year−1.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Changes in foliage projective cover and its implications for mapping groundwater dependent vegetation across a precipitation gradient

et al. 2018

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“…Springs with laterally small discharge areas (i.e., less than ~0.5 ha) are not well suited to assessment using 30‐m Landsat data but could potentially be assessed with a similar approach using higher‐resolution satellite imagery (White et al. , Pérez Hoyos et al. ).…”

Section: Discussionmentioning

confidence: 99%

“…, White et al. ), such that NDVI‐based approaches can be used to detect decreasing spring discharge resulting from aquifer depletion (Petus et al. , Huntington et al.…”

Section: Discussionmentioning

confidence: 99%

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Springs as hydrologic refugia in a changing climate? A remote‐sensing approach

Cartwright

Johnson

2018

Ecosphere

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Abstract. Spring-fed wetlands are ecologically important habitats in arid and semi-arid regions. Springs have been suggested as possible hydrologic refugia from droughts and climate change; however, springs that depend on recent precipitation or snowmelt for recharge may be vulnerable to warming and drought intensification. Springs that are expected to maintain their ecohydrologic function in a warmer, drier climate may be priorities for conservation and restoration. Identifying such springs is difficult because many springs lack hydrologic records of adequate temporal extent and resolution to assess their resilience to water cycle changes. This study demonstrates proof-of-concept for the assessment of certain spring types (i.e., helocrene, hypocrene, and hillslope springs) in terms of hydrologic and ecological resilience to climatic water stress using freely available remote-sensing and climate data. We used the Normalized Difference Vegetation Index (NDVI) from 1985 through 2011 to delineate surface-moisture zones (SMZs) associated with 39 clusters of 172 springs in a montane sage-steppe landscape in southeastern Oregon, USA. We developed and synthesized seven NDVI-based indicators of SMZ resilience to interannual changes in water availability: (1) mean and (2) standard deviation of July NDVI; (3) mean difference in July NDVI and (4) difference in coefficient of variation for July NDVI between each SMZ and its surrounding watershed; (5) response of SMZ July NDVI to 90-day antecedent precipitation; (6) response of SMZ July NDVI to the previous winter's snowpack; and (7) range of NDVI values from an exceptionally wet year followed by three dry years. Because all resilience indicators were highly inter-correlated, we derived an overall metric of SMZ resilience using principal components analysis that accounted for 66% of total variance. This overall resilience score was positively correlated with SMZ elevation, slope, mean annual precipitation, and with the number of associated springs. Resilience was greater for SMZs on topographically shaded, north-facing slopes. Several high-resilience SMZs were located immediately below persistent snowbanks, suggesting a possible source of steady recharge throughout the growing season. The approach presented here-if combined with field assessments of spring hydrogeology, discharge, and groundwater age-could help identify spring-fed wetlands that are most likely to serve as hydrologic refugia from climate change.

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Available satellite data for monitoring small and seasonally flooded wetlands in semi‐arid environments of southern Africa

Gxokwe,

Dube,

Mazvimavi

2023

Ecohydrology

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Time‐series monitoring of wetland eco‐hydrological dynamics using remote sensing continues to be an attractive and practical tool, mainly due to its ability to overcome challenges related to in situ data availability. However, acquiring seamless and cloud‐free data for accurate and routine wetlands monitoring remains a persistent challenge. In this study, we aimed to evaluate the availability of satellite scenes in the Google Earth Engine (GEE) catalogue that could facilitate the monitoring of eco‐hydrological dynamics in small and seasonally flooded wetlands within the semi‐arid environments of southern Africa. The study covered a 20‐year period from 2000 to 2020, with a specific focus on the Nylsvley floodplain as a case study. The study conducted a comprehensive assessment of available products on the GEE platform, including Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI), Sentinel‐1 and Sentinel‐2. The identified images underwent rigorous filtering and screening based on varying cloud‐cover percentages (0%, 1%–10%, 11%–25% and 26%–50%). The results revealed a considerable number of satellite products (1376) available for the study period. Specifically, there were 492 Landsat images, 394 Sentinel‐1 images and 490 Sentinel‐2 images. Amongst these, Sentinel‐2 and Landsat‐7 had the highest number of images (69% and 76%, respectively) with cloud‐cover percentages ranging from 0% to 20%. However, images with cloud cover exceeding 26% were excluded from the analysis. Further analysis indicated that using satellite images with 0% cloud cover resulted in an overall accuracy (OA) ranging between 69% and 72%, while 1%–10% cloud cover had an OA ranging between 68% and 70%, and 11%–25% cloud cover had an OA ranging between 69% and 80.55% for both the dry and wet seasons. Overall, the classification results demonstrated satisfactory OAs (68%–82%) for all scenes, with some inaccuracies observed for certain classes, notably bare surface and long grass. These inaccuracies were particularly evident when using Landsat‐7 scenes, attributable to the spatial resolution of the data. The findings emphasised the availability of a substantial amount of archival satellite data, capable of monitoring small and seasonally flooded wetlands, providing valuable insights into the eco‐hydrological dynamics of these ecosystems. Moreover, the study highlighted the benefits of cloud‐computing platforms like GEE in addressing challenges associated with big data filtering, processing and analytics, thereby enhancing environmental monitoring and assessments, which may have been limited by the unavailability of advanced processing tools and seamless cloud‐free data.

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A generalizable NDVI-based wetland delineation indicator for remote monitoring of groundwater flows in the Australian Great Artesian Basin

Cited by 45 publications

References 9 publications

Changes in foliage projective cover and its implications for mapping groundwater dependent vegetation across a precipitation gradient

Changes in foliage projective cover and its implications for mapping groundwater dependent vegetation across a precipitation gradient

Springs as hydrologic refugia in a changing climate? A remote‐sensing approach

Available satellite data for monitoring small and seasonally flooded wetlands in semi‐arid environments of southern Africa

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