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RSS FeedsRemote Sensing, Vol. 12, Pages 669: Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques (Remote Sensing)

 
 

18 february 2020 15:03:09

 
Remote Sensing, Vol. 12, Pages 669: Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques (Remote Sensing)
 




Coastal woodlands, notable for their floristic diversity and ecosystem service values, are increasingly under threat from a range of interacting biotic and abiotic stressors. Monitoring these complex ecosystems has traditionally been confined to field-scale vegetation surveys; however, remote sensing applications are increasingly becoming more viable. This study reports on the application of field-based monitoring and remote sensing/(Geographic Information System) GIS to interrogate trends in Banksia coastal woodland decline (Kings Park, Perth and Western Australia) and documents the patterns, and potential drivers, of tree mortality over the period 2012–2016. Application of geographic object-based image analysis (GEOBIA) at a park scale was of limited benefit within the closed-canopy ecosystem, with manual digitisation methods feasible only at the smaller transect scale. Analysis of field-based identification of tree mortality, crown-specific spectral characteristics and park-scale change detection imagery identified climate-driven stressors as the likely primary driver of tree mortality in the woodland, with vegetation decline exacerbated by secondary factors, including water stress and low system resilience occasioned by the inability to access the water table and competition between tree species. The results from this paper provide a platform to inform monitoring efforts using airborne remote sensing within coastal woodlands.


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25 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 12, Pages 670: Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager--A Case Study (Remote Sensing)
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