Floodplain inundation and vegetation dynamics in the Alligator Rivers region (Kakadu) of northern Australia assessed using optical and radar remote sensing

Ward, Douglas; Petty, Aaron M.; Setterfield, Samantha A.; Douglas, Michael M.; Ferdinands, Keith B.; Hamilton, Stephen K.; Phinn, Stuart

doi:10.1016/j.rse.2014.02.009

Cited by 101 publications

(86 citation statements)

References 38 publications

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“…For example, the ability to map areas of inundation at regional scales has recently provided information on extent of inundation for different flow return intervals (Chojnacki et al 2012, Theiling andBurant 2013). In ungaged areas, remotely sensed data are being increasingly used to understand temporal inundation dynamics (Ward et al 2014, Thomas et al 2015. Spatially-explicit inundation information can be linked to known species responses to identify suitable habitat for species of interest (Jacobson et al 2011) and evaluate changes in habitat availability under different management and climate change scenarios (Matella and Merenlender 2015).…”

Section: Follow-up Surveymentioning

confidence: 99%

Stakeholder-led science: engaging resource managers to identify science needs for long-term management of floodplain conservation lands

Bouska¹,

Lindner²,

Paukert³

et al. 2016

E&S

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Section: Follow-up Surveymentioning

confidence: 99%

Stakeholder-led science: engaging resource managers to identify science needs for long-term management of floodplain conservation lands

Bouska¹,

Lindner²,

Paukert³

et al. 2016

E&S

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“…In low-gradient floodplains there is a general positive relationship between discharge and the area of floodplain inundation (Murray et al, 2006), where larger discharges inundate more floodplain area and therefore connect a greater area under flood (Mertes et al, 1995;Hughes, 1997). The hydrological character of flood events, that is the timing, magnitude and duration of floodplain inundation, is consistently identified as a prominent influence on landscape patterns of floodplain vegetation (Mertes et al, 1995;Capon, 2005;Ward et al, 2014). The results of this study revealed an inconsistent influence of flood size on vegetation productivity response through the adaptive-cycle phases.…”

Section: Discussionmentioning

confidence: 99%

Adaptive cycles of floodplain vegetation response to flooding and drying

Thapa¹,

Thoms²,

Parsons³

et al. 2016

Earth Surf. Dynam.

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Abstract.Flooding is a key driver of floodplain vegetation productivity. Adaptive cycles provide a model for examining the productivity of semi-arid floodplain vegetation in response to hydrology. We examined the response of vegetation productivity (measured as NDVI) through a hypothesised adaptive cycle to determine whether the cycle repeats over time and how it is affected by differently sized flood events. The area of floodplain inundation was associated with an adaptive cycle that repeated in four flood events through the following phases: wetting (exploitation phase), wet (conservation phase), drying (release phase) and dry (reorganisation phase). Vegetation productivity responses corresponded to these phases. The area and quality of floodplain vegetation productivity followed the hypothesised pattern of higher-quality vegetation vigour in the wetting and wet phases, lower vigour in the drying phase and lowest vigour in the dry phase. There were more transitions between NDVI classes in the wet phase, which was dominated by two-way transitions. Overall, the wetting, wet and drying phases were dominated by smaller-probability class changes, whereas in the dry phase, higher-probability class changes were more prominent. Although the four flood events exhibited an adaptive cycle the duration of the adaptive-cycle phases, and the nature of vegetation productivity response, differed with the character of the flood event. Vegetation response in two of the adaptive-cycle phases -the release and reorganisation phases -were as hypothesised, but in the exploitation and conservation phases, changes in vegetation productivity were more dynamic. The character of vegetation response through the adaptive cycle also indicates that semi-arid floodplain vegetation productivity is more vulnerable to changing state during the conservation and release phases and not during the exploitation and reorganisation phases as resilience theory suggests. Overall, the adaptive cycle represents a new model to improve our understanding of the complexity of change in semi-arid floodplain vegetation productivity through cycles of flooding and drying. Changes in vegetation productivity could initiate structural changes in floodplain vegetation communities, with commensurate influences on floodplain sediment dynamics.

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“…Synthetic aperture radar (SAR) data has also been used to map wetland vegetation and is often preferred due to the sensitivity of microwave energy to soil moisture and its ability to penetrate vegetative canopies [4]. L-band SAR data has been successful in discriminating inundated areas that are vegetated, such as Melaleuca swamps [12]. In addition, the fusion of optical and radar data has been shown to be effective in mapping long term vegetation and inundation dynamics of tropical floodplains [12].…”

Section: Introductionmentioning

confidence: 99%

“…L-band SAR data has been successful in discriminating inundated areas that are vegetated, such as Melaleuca swamps [12]. In addition, the fusion of optical and radar data has been shown to be effective in mapping long term vegetation and inundation dynamics of tropical floodplains [12].…”

Section: Introductionmentioning

confidence: 99%

Mapping Aquatic Vegetation in a Tropical Wetland Using High Spatial Resolution Multispectral Satellite Imagery

Whiteside¹,

Bartolo²

2015

Remote Sensing

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Vegetation plays a key role in the environmental function of wetlands. The Ramsar-listed wetlands of the Magela Creek floodplain in Northern Australia are identified as being at risk from weeds, fire and climate change. In addition, the floodplain is a downstream receiving environment for the Ranger Uranium Mine. Accurate methods for mapping wetland vegetation are required to provide contemporary baselines of annual vegetation dynamics on the floodplain to assist with analysing any potential change during and after minesite rehabilitation. The aim of this study was to develop and test the applicability of geographic object-based image analysis including decision tree classification to classify WorldView-2 imagery and LiDAR-derived ancillary data to map the aquatic vegetation communities of the Magela Creek floodplain. Results of the decision tree classification were compared against a Random Forests classification. The resulting maps showed the 12 major vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010. The decision tree classification method provided an overall accuracy of 78% which was significantly higher than the overall accuracy of the Random Forests classification (67%). Most of the error in both classifications was associated with confusion between spectrally similar classes dominated by grasses, such as Hymenachne and Pseudoraphis. In addition, the extent of the sedge Eleocharis was under-estimated in both cases. This suggests the method could be useful for mapping wetlands where statistical-based supervised classifications have achieved less than satisfactory results. Based upon the results, the decision tree method will form part of an ongoing operational monitoring program.

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Floodplain inundation and vegetation dynamics in the Alligator Rivers region (Kakadu) of northern Australia assessed using optical and radar remote sensing

Cited by 101 publications

References 38 publications

Stakeholder-led science: engaging resource managers to identify science needs for long-term management of floodplain conservation lands

Stakeholder-led science: engaging resource managers to identify science needs for long-term management of floodplain conservation lands

Adaptive cycles of floodplain vegetation response to flooding and drying

Mapping Aquatic Vegetation in a Tropical Wetland Using High Spatial Resolution Multispectral Satellite Imagery

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