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RSS FeedsRemote Sensing, Vol. 10, Pages 927: Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine (Remote Sensing)

 
 

18 june 2018 11:00:47

 
Remote Sensing, Vol. 10, Pages 927: Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine (Remote Sensing)
 


Fanjinshan National Nature Reserve (FNNR) is a biodiversity hotspot in China that is part of a larger, multi-use landscape where farming, grazing, tourism, and other human activities occur. The steep terrain and persistent cloud cover pose challenges to robust vegetation and land use mapping. Our objective is to develop satellite image classification techniques that can reliably map forest cover and land use while minimizing the cloud and terrain issues, and provide the basis for long-term monitoring. Multi-seasonal Landsat image composites and elevation ancillary layers effectively minimize the persistent cloud cover and terrain issues. Spectral vegetation index (SVI) products and shade/illumination normalization approaches yield significantly higher mapping accuracies, compared to non-normalized spectral bands. Advanced machine learning image classification routines are implemented through the cloud-based Google Earth Engine platform. Optimal classifier parameters (e.g., number of trees and number of features for random forest classifiers) were achieved by using tuning techniques. Accuracy assessment results indicate consistent and effective overall classification (i.e., above 70% mapping accuracies) can be achieved using multi-temporal SVI composites with simple illumination normalization and elevation ancillary data, despite the fact limited training and reference data are available. This efficient and open-access image analysis workflow provides a reliable methodology to remotely monitor forest cover and land use in FNNR and other mountainous forested, cloud prevalent areas.


 
33 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 10, Pages 928: Real-Time Precise Point Positioning Using Tomographic Wet Refractivity Fields (Remote Sensing)
Remote Sensing, Vol. 10, Pages 926: Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe (Remote Sensing)
 
 
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