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Remote sensing for restoration planning: how the big picture can inform stakeholders
- Cordell, Susan, Questad, Erin J., Asner, Gregory P., Kinney, Kealoha M., Thaxton, Jarrod M., Uowolo, Amanda, Brooks, Sam, Chynoweth, Mark W.
- Restoration ecology 2017 v.25 Suppl S2 pp. S147
- arid lands, case studies, digital elevation models, ecological restoration, ecosystem management, ecosystems, endangered species, invasive species, land use, landscapes, managers, monitoring, planning, plant growth, remote sensing, risk, satellites, stakeholders, vegetation, wildland fire management
- The use of remote sensing in ecosystem management has transformed how land managers, practitioners, and policymakers evaluate ecosystem loss, gain, and change at multiple spatial and temporal scales. Less developed is the use of these spatial tools for planning, implementing, and evaluating ecosystem restoration projects and especially so in multifunctional landscapes. We use a case study approach in a multistakeholder tropical dryland restoration project to highlight the potential of remotely sensed products to quantitatively and economically guide often conflicting restoration priorities with stakeholder objectives. High‐resolution digital elevation models derived from an airborne remote sensing platform informed land managers tasked with endangered species restoration by guiding their efforts to highly suitable areas of the landscape where plant growth, performance, and survival should be greater. In turn, satellite‐based monitoring offered a temporal approach to broadly quantify vegetation fire risk in order to restrict fire promoting activities in dry landscapes most modified by fire promoting invasive grasses. Together, the delineation of high suitability areas for plant‐based restoration and low suitability areas for wildfire management ultimately releases moderate suitability land for alternative land uses deemed important in multistakeholder landscapes. We review the benefits of using remotely sensed data for restoration planning, and highlight the costs and benefits of various data sources.