Assessing Spatial Variation in Algal Productivity in a Tropical River Floodplain Using Satellite Remote Sensing

Abstract

Studies of tropical floodplains have shown that algae are the primary source material for higher consumers in freshwater aquatic habitats. Thus, methods that can predict the spatial variation of algal productivity provide an important input to better inform management and conservation of floodplains. In this study, a prediction of the spatial variability in algal productivity was made for the Mitchell River floodplain in northern Australia. The spatial variation of aquatic habitat types and turbidity were estimated using satellite remote sensing and then combined with statistical modelling to map the spatial variation in algal primary productivity. Open water and submerged plants habitats, covering 79% of the freshwater flooded floodplain extent, had higher rates of algal production compared to the 21% cover of emergent and floating aquatic plant habitats. Across the floodplain, the predicted average algal productivity was 150.9 ± 95.47 SD mg C m−2 d−1 and the total daily algal production was estimated to be 85.02 ± 0.07 SD ton C. This study provides a spatially explicit representation of habitat types, turbidity, and algal productivity on a tropical floodplain and presents an approach to map ‘hotspots’ of algal production and provide key insights into the functioning of complex floodplain–river ecosystems. As this approach uses satellite remotely sensed data, it can be applied in different floodplains worldwide to identify areas of high ecological value that may be sensitive to development and be used by decision makers and river managers to protect these important ecological assets.

Impact of water resource development on connectivity and primary productivity across a tropical river floodplain

“…The methodological framework of this study was based on integrating the algal productivity map derived by Molinari et al (2021a) Water Resource Assessment (NAWRA; Karim et al, 2018), which comprises 240 daily floodplain inundation maps across six scenarios of inundation. These floodplain inundation data were combined with estimates of the spatial variation in algal productivity (and its predictive intervals) based on turbidity and macrophyte cover (Molinari, Stewart-Koster, Adame, et al, 2021).…”

“…The methodological framework of this study was based on integrating the algal productivity map derived by Molinari et al (2021a) and floodplain inundation maps of the Mitchell River into spatial graph analyses. The framework was based on four steps: (a) data acquisition, (b) processing the inundation map into spatial graphs, (c) calculation of graph theoretic metrics and (d) deriving total inundated and total connected algal productivity.…”

“…The 40‐day duration of the flood events was determined by the model developers and covers the whole flood event, including the inundation and draining periods (Karim et al, 2018). The extent of the floodplain was defined as the same extent used to predict algal primary productivity, which was the portion of the floodplain included in a full Landsat scene (185 × 185 km, path 98, row 71) excluding a buffered area (5 km) along the coastline (Molinari et al, 2021a). Pixels with an estimated depth of water over 10 cm were considered inundated by surface water.…”

“…Algal productivity was measured during a field trip between late April and May 2018 at seven floodplain wetlands, with four different habitats sampled at each wetland including submerged, floating and emergent plants and open water habitats. Based on a statistical model derived from these field data (described in Molinari, Stewart‐Koster, Adame, et al, 2021), algal productivity was predicted across the floodplain using the spectral information of the Landsat 8 image obtained on 25 April 2018 (Molinari et al, 2021a). Predictions were not made for the specific period of the hydrodynamic model because the primary productivity model was built with indices derived from Landsat 8 bands, which were not available for the years modelled by the inundation model.…”

“…Those three dams were chosen for the WRD scenario by the inundation model developers as they were considered to be the most cost-effective to construct under the scenarios derived in the NAWRA study (Petheram et al, 2018). April 2018 (Molinari et al, 2021a). Predictions were not made for the specific period of the hydrodynamic model because the primary productivity model was built with indices derived from Landsat 8 bands, which were not available for the years modelled by the inundation model.…”