Decision Intelligence Frameworks for Sustainable Fisheries and Forestry Management Using AI and Remote Sensing

Abstract This manuscript develops a decision-intelligence perspective for sustainable fisheries and forestry management using artificial intelligence and remote sensing as complementary, rather than substitutive, instruments of environmental governance. It argues that robust sustainability decisions emerge when observational infrastructures, statistical explanation, machine learning prediction, and scalable analytics lifecycles are designed as an integrated system with explicit assumptions, uncertainty controls, and institutional safeguards. Across the volume, the discussion moves from foundational research design to causal inference, trustworthy predictive modeling, reproducible MLOps, and policy-oriented sectoral application. The book advances a framework in which remote sensing supports spatial–temporal visibility, statistical modeling clarifies mechanisms and intervention effects, machine learning extends anticipatory capacity, and governance protocols define how evidence is translated into action. Particular attention is given to accountability, generalization across regions, and the practical challenge of converting analytic outputs into usable decision tools for managers, cooperatives, regulators, and community institutions. The resulting contribution is neither a purely technical manual nor a purely normative argument, but a research-oriented synthesis that equips scholars and practitioners to build decision systems capable of improving ecological resilience, livelihood stability, and circular resource use across fisheries, forests, and connected landscape economies. Keywords decision intelligence, sustainable fisheries, forestry management, artificial intelligence, remote sensing, geospatial analytics, causal inference, machine learning, environmental monitoring, MLOps, reproducibility, resource governance, circular economy, ecosystem resilience, policy analytics, trustworthiness