Summary & Purpose
The SOC (Soil Organic Carbon) pool is a large carbon reservoir that is closely linked to climatic drivers. In complex terrain, quantifying SOC storage is challenging due to high spatial variability. Generally, point data is distributed by developing quantitative relationships between SOC and spatially-distributed, variables like elevation. In many ecosystems, remotely sensed information on above-ground vegetation (e.g. NDVI) can be used to predict below-ground carbon stocks. With this research, we evaluated SOC variability in complex terrain and attempt to improve upon SOC models by incorporating hyperspectral and LiDAR datasets.
Date of Publication or Submission
NSF Reynolds Creek CZO: http://criticalzone.org/reynolds/
Single Dataset or Series?
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Will, Ryan M.; Benner, Shawn; Glenn, Nancy F.; Pierce, Jennifer; Lohse, Kathleen A.; Patton, Nicholas; Spaete, Lucas P.; and Stanbery, Christopher. (2017). Mapping Soc Distribution in Semi-arid Mountainous Regions Using Variables From Hyperspectral, Lidar and Traditional Datasets [Data set]. Retrieved from https://doi.org/10.18122/B2Q598
Available for download on Wednesday, March 28, 2018