Toward a Remote Sensing Solution for Regional Sustainability Assessment and Monitoring

• Main Author: James K. Lein
• Format: Article
• Language: English
• Published: MDPI AG 2014-04-01
• Series: Sustainability
• Subjects: sustainability assessment; remote sensing; development intensity; landscape metrics; environmental monitoring; principal components analysis
• Online Access: http://www.mdpi.com/2071-1050/6/4/2067

Regional sustainability encourages a re-examination of development programs in the context of environmental, social and economic policies and practices. However, sustainability remains a broadly defined concept that has been applied to mean everything from environmental protection, social cohesion, economic growth, neighborhood design, alternative energy, and green building design. To guide sustainability initiatives and assess progress toward more sustainable development patterns, a need exists to place this concept into a functional decision-centric context where change can be evaluated and the exploitation of resources better understood. Accepting the premise that sustainable development defines a set of conditions and trends in a given system that can continue indefinitely without contributing to environmental degradation, answers to four critical questions that direct sustainability over the long-term must be addressed: (1) What is the present state of the environmental system, (2) Is that pattern sustainable, (3) Are there indications that the environmental system is degrading, and (4) Can that information be incorporated into policy decisions to guide the future? Answers to these questions hinge on the development of tractable indices that can be employed to support the long-term monitoring required to assess sustainability goals and a means to measure those indices. In this paper, a solution based on the application of remote sensing technology is introduced focused on the development of land use intensity indices derived from earth-observation satellite data. Placed into a monitoring design, this approach is evaluated in a change detection role at the watershed scale.