A cyber-enabled spatial decision support system to inventory Mangroves in Mozambique: coupling scientific workflows and cloud computing

ABSTRACT

Mangroves are an important terrestrial carbon reservoir with numerous ecosystem services. Yet, it is difficult to inventory mangroves because of their low accessibility. A sampling approach that produces accurate assessment while maximizing logistical integrity of inventory operation is often required. Spatial decision support systems (SDSSs) provide support for integrating such a sampling design of fieldwork with operational considerations and evaluation of alternative scenarios. However, this fieldwork design driven by SDSS is often computationally intensive and repetitive. In this study, we develop a cyber-enabled SDSS framework to facilitate the computationally challenging fieldwork design that requires the efficacious selection of base camps and plots for the inventory of mangroves. Our study area is the Zambezi River Delta, Mozambique. Cyber-enabled capabilities, including scientific workflows and cloud computing, are integrated with the SDSS. Scientific workflows enable the automation of data and modeling tasks in the SDSS. Cloud computing offers on-demand computational support for interoperation among stakeholders for collaborative scenario evaluation for the fieldwork design of mangrove inventory. Further, this framework allows for harnessing high-performance computing capabilities for accelerating the fieldwork design. The cyber-enabled framework provides significant merits in terms of effective coordination among science and logistical teams, assurance of meeting inventory objectives, and an objective basis to collectively and efficaciously evaluate alternative scenarios.

KEYWORDS:

• Mangrove inventory
• Zambezi Delta
• spatial decision support system
• scientific workflow
• cloud computing

Acknowledgments

The authors would like to thank Temilola E. Fatoyinbo and Marc Simard from NASA for providing LiDAR-drived canopy height data in this study. This work was supported with funding from USAID – Mozambique through the US Forest Service International Programs Office. Special thanks is given to Dr. Yonggang Wang for his assistance on statistical analysis in this study, Jing Deng, and Dr. Eric Delmelle.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by United States Agency for International Development – Mozambique through the International Programs, US Forest Service.