Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: A remote sensing height-biomass-carbon approach
KAUST DepartmentRed Sea Research Center (RSRC)
Permanent link to this recordhttp://hdl.handle.net/10754/626204
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AbstractMangroves are integral to ecosystem services provided by the coastal zone, in particular carbon (C) sequestration and storage. Allometric relationships linking mangrove height to estimated biomass and C stocks have been developed from field sampling, while various forms of remote sensing has been used to map vegetation height and biomass. Here we combine both these approaches to investigate spatial patterns in living biomass of mangrove forests in a small area of mangrove in north-west Australia. This study used LiDAR data and Landsat 8 OLI (Operational Land Imager) with allometric equations to derive mangrove height, biomass, and C stock estimates. We estimated the study site, Mangrove Bay, a semi-arid site in north-western Australia, contained 70 Mg ha−1 biomass and 45 Mg C ha−1 organic C, with total stocks of 2417 Mg biomass and 778 Mg organic C. Using spatial statistics to identify the scale of clustering of mangrove pixels, we found that living biomass and C stock declined with increasing distance from hydrological features (creek entrance: 0–150 m; y = −0.00041x + 0.9613, R2 = 0.96; 150–770 m; y = −0.0008x + 1.6808, R2 = 0.73; lagoon: y = −0.0041x + 3.7943, R2 = 0.78). Our results illustrate a set pattern of living C distribution within the mangrove forest, and then highlight the role hydrologic features play in determining C stock distribution in arid zone.
CitationHickey SM, Callow NJ, Phinn S, Lovelock CE, Duarte CM (2018) Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: A remote sensing height-biomass-carbon approach. Estuarine, Coastal and Shelf Science 200: 194–201. Available: http://dx.doi.org/10.1016/j.ecss.2017.11.004.
SponsorsThis work was supported by the CSIRO Flagship Marine and Coastal Carbon Biogeochemical Cluster (Coastal Carbon Cluster) with funding from the CSIRO Flagship Collaboration Fund. We thank Dr Jeff Hansen, Prof. Ryan Lowe, and Prof. Jorg Hacker and Airborne Research Australia, for collecting and providing the LiDAR data. We also thank Kathy Murray and DPAW for providing aerial imagery. Partial support for field sampling was provided by Australian Research Council award DP150104437.