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Developing landscape-scale forest restoration targets that embrace spatial pattern
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AI
DOI：10.1007/s10980-022-01461-5.png)
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Abstract
En 中文
Context Forest restoration plays an important role in global efforts to slow biodiversity loss and mitigate climate change. Vegetation in remnant forests can form striking patterns that relate to ecological processes, but restoration targets tend to overlook spatial pattern. While observations of intact reference ecosystems can help to inform restoration targets, field surveys are ill-equipped to map and quantify spatial pattern at a range of scales, and new approaches are needed. Objective This review sought to explore practical options for creating landscape-scale forest restoration targets that embrace spatial pattern. Methods We assessed how hierarchy theory, satellite remote sensing, landscape pattern analysis, drone-based remote sensing and spatial point pattern analysis could be applied to assess the spatial pattern of reference landscapes and inform forest restoration targets. Results Hierarchy theory provides an intuitive framework for stratifying landscapes as nested hierarchies of sub-catchments, forest patches and stands of trees. Several publicly available tools can map patches within landscapes, and landscape pattern analysis can be applied to quantify the spatial pattern of these patches. Drones can collect point clouds and orthomosaics at the stand scale, a plethora of software can create maps of individual trees, and spatial point pattern analysis can be applied to quantify the spatial pattern of mapped trees. Conclusions This review explored several practical options for producing landscape scale forest restoration targets that embrace spatial pattern. With the decade on ecosystem restoration underway, there is a pressing need to refine and operationalise these ideas.
Keywords:
Restoration
Scale
Reference
Benchmark
Point pattern analysis
Hierarchy theory
Landscape pattern analysis
Drone
UAV
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