Document Type : ORIGINAL RESEARCH ARTICLE
Authors
1 University of Connecticut
2 Former fellow of the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, Ecuador
Abstract
Geospatial data analytics is an essential tool in the toolbox of contemporary forest engineering and natural resource management. Beyond its application in estimating wood and fiber production, geospatial data analytics also proves indispensable in conservation planning. By leveraging a myriad of geospatial datasets, forest engineers and natural resource managers make well-informed decisions regarding forest restoration and carbon sequestration that foster environmental sustainability. However, one often-underestimated aspect of geospatial data analytics is its potential to help identify and address issues of distributive justice relating to forest resources and associated benefits. Thus, this article outlines a roadmap for forest engineers and natural resource managers to harness geospatial data effectively to simultaneously promote environmental sustainability and distributive justice – that is, the fair and equitable allocation of natural resources, nature’s benefits, and environmental burdens. The approach involves defining local concerns and priorities through community engagement to guide spatial data gathering, determining spatial and temporal scales of assessment, accessing and preprocessing data sources, developing prioritization indexes, performing relevant analytical tests, and creating opportunities for data return prior to decision making. Through this methodological approach, forest engineers and natural resource managers can harness the power of geospatial data to model and synthesize information, assess ecosystem services, evaluate community risks, and identify environmental hazards. In a world where data is abundant but its transformation into actionable insights is often lacking, this overview aims to illuminate the potential of geospatial data analytics as a tool that can simultaneously advance environmental sustainability and distributive justice.
Keywords
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