Remote Sensing and Integrated Systems Frameworks for Decision Support in Sustainable Development

Abstract

Local leaders in sustainable development face challenging decisions due to complex environmental phenomena, intersecting socioeconomic factors, diverse stakeholders, data scarcity, and constrained financial resources. Decision Support Systems (DSS) software can aid these stakeholders by improving understanding of systems dynamics and interrelated societal factors. However, flaws in existing DSS development and functionality often produce DSS that do not meet the objectives of local stakeholders, leading to DSS disuse. This research implements a novel DSS development process to address these issues in two case studies: flood resilience in Pekalongan, Indonesia, and natural resource management for the Yurok Tribe in California.

First, System Architecture Framework (SAF) uses inputs of stakeholder interviews to translate stakeholder objectives into DSS functions and forms. Targeted satellite remote sensing (SRS) of permanent water, shoreline change, and mangrove trends are conducted in Pekalongan, and forest trends and above ground biomass are analyzed for the Yurok Tribe. Classification analyses achieve high overall accuracy (>= 84%) and trend analyses have correlations to high resolution data at a significance level of 𝛼 > 0.05. The Environment-Vulnerability-Decision-Technology (EVDT) integrated modeling framework is used to integrate local infrastructure and land use data towards insights for environmental impact mitigation decisions and community aid allocation. DSS user evaluations with Boston-area (n = 20), Indonesian (n = 37), and Yurok Tribe users (n = 9), are conducted to assess DSS utility and verify the mapping of SRS analyses to specific stakeholder decisions and economic metrics. \

High user information-relevancy (<= 94%) and information-sufficiency (<= 81%) ratings, 5 specific decisions mapped to the SRS analyses via dedicated stakeholder interviews, and 57 actionable comments from user studies, provide strong support for the use of SAF and user studies to improve DSS usefulness and accessibility. Higher understanding scores achieved by DSS users compared to control-briefing users on environmental (p = 0.0012), socioeconomic (p = 0.0093), and policy (p = 0.0043) questions, analyses of integrated SRS and local data that provide concrete insights for stakeholder decisions (such as inundation trends for agricultural adaptation budget allocation and forest trends for carbon sequestration project management), and positive stakeholder comments regarding DSS capabilities, support the theory that SRS data and EVDT can improve DSS functionality.

Demonstrating the utility of a novel DSS design process in overcoming previous roadblocks to DSS use in sustainable development is this work’s core contribution. SAF to target stakeholder objectives, integration of accessible SRS analyses and local socioeconomic data via EVDT for actionable insights, and user studies to gather stakeholder feedback are the core elements of this novel design process. The DSS developed also provide tangible benefits to users, with local stakeholders expressing a strong desire for DSS institutionalization. Future work includes ensuring DSS longevity and the application of the DSS design process to other relevant case studies. Overall, this research collaborates directly with communities to confront environmental impacts, address challenging decisions, and advance sustainable development.