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

2021 ◽  
Vol 13 (9) ◽  
pp. 1710
Author(s):  
Bianca Molinari ◽  
Ben Stewart-Koster ◽  
Tim J. Malthus ◽  
Stuart E. Bunn

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.

2011 ◽  
Vol 68 (4) ◽  
pp. 651-666 ◽  
Author(s):  
Emmanuel Chassot ◽  
Sylvain Bonhommeau ◽  
Gabriel Reygondeau ◽  
Karen Nieto ◽  
Jeffrey J. Polovina ◽  
...  

Abstract Chassot, E., Bonhommeau, S., Reygondeau, G., Nieto, K., Polovina, J. J., Huret, M., Dulvy, N. K., and Demarcq, H. 2011. Satellite remote sensing for an ecosystem approach to fisheries management. – ICES Journal of Marine Science, 68: 651–666. Satellite remote sensing (SRS) of the marine environment has become instrumental in ecology for environmental monitoring and impact assessment, and it is a promising tool for conservation issues. In the context of an ecosystem approach to fisheries management (EAFM), global, daily, systematic, high-resolution images obtained from satellites provide a good data source for incorporating habitat considerations into marine fish population dynamics. An overview of the most common SRS datasets available to fishery scientists and state-of-the-art data-processing methods is presented, focusing on recently developed techniques for detecting mesoscale features such as eddies, fronts, filaments, and river plumes of major importance in productivity enhancement and associated fish aggregation. A comprehensive review of remotely sensed data applications in fisheries over the past three decades for investigating the relationships between oceanographic conditions and marine resources is provided, emphasizing how synoptic and information-rich SRS data have become instrumental in ecological analyses at community and ecosystem scales. Finally, SRS data, in conjunction with automated in situ data-acquisition systems, can provide the scientific community with a major source of information for ecosystem modelling, a key tool for implementing an EAFM.


1998 ◽  
Vol 22 (1) ◽  
pp. 61-78 ◽  
Author(s):  
Paul J. Curran ◽  
Peter M. Atkinson

In geostatistics, spatial autocorrelation is utilized to estimate optimally local values from data sampled elsewhere. The powerful synergy between geostatistics and remote sensing went unrealized until the 1980s. Today geostatistics are used to explore and describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data; and to increase the accuracy with which remotely sensed data can be used to classify land cover or estimate continuous variables. This article introduces these applications and uses two examples to highlight characteristics that are common to them all. The article concludes with a discussion of conditional simulation as a novel geostatistical technique for use in remote sensing.


1993 ◽  
Vol 33 (5) ◽  
pp. 597 ◽  
Author(s):  
RND Reid ◽  
PJ Vickery ◽  
DA Hedges ◽  
PM Williams

Remote sensing measurements in the visible, near infrared, and short-wave infrared were made on experimental areas of grass-legume pasture with different fertiliser and stocking rate treatments and on commercial pastures with added fertiliser.Divisive classification and ordination analyses of the remotely sensed data were used to allocate the image data to 11-15 classes from measurements in 2 successive years The resultant data were displayed on an image processing system which showed that the fertilised areas belonged to classes different from those without fertiliser. Soil and plant nutrient tests revealed differences between treated and untreated sites as mapped from the remote sensing data.


2010 ◽  
Vol 68 (4) ◽  
pp. 792-799 ◽  
Author(s):  
Robert Williamson ◽  
John G. Field ◽  
Frank A. Shillington ◽  
Astrid Jarre ◽  
Anet Potgieter

Abstract Williamson, R., Field, J. G., Shillington, F. A., Jarre, A., and Potgieter, A. 2011. A Bayesian approach for estimating vertical chlorophyll profiles from satellite remote sensing: proof-of-concept. – ICES Journal of Marine Science, 68: 792–799. A proof-of-concept demonstration is presented using a novel method for estimating vertical distributions of chlorophyll a (Chl a) from archives of data from ships, combined with remotely sensed data of sea surface temperature, surface Chl a, and wind (U and V vectors) from satellites. Our study area has contrasting hydrographic regimes that include the dynamic southern Benguela upwelling system and the stratified waters of the Agulhas Bank. Cluster analysis is used to identify “typical” Chl a profiles from an archive of profiles recorded in 2002–2008. Bayesian networks were then used to relate characteristic profiles to remotely sensed surface features, subregions, seasons, and depths. The proposed method could be used to predict daily Chl a profiles for each pixel of a satellite image to estimate biomass and subsurface light fields, and these combined with a light algorithm to model primary production for the Benguela large marine ecosystem.


Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management


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