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

3-28-2017

DOI

https://doi.org/10.18122/B2Q598

Funding Citation

NSF Reynolds Creek CZO: http://criticalzone.org/reynolds/

Single Dataset or Series?

Series

Data Format

*.tif

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Data will be provided to all who agree to appropriately acknowledge the National Science Foundation (NSF), Idaho EPSCoR and the individual investigators responsible for the data set. By downloading these data and using them to produce further analysis and/or products, users agree to appropriately acknowledge the National Science Foundation (NSF), Idaho EPSCoR and the individual investigators responsible for the data set. Use constraints: Acceptable uses of data provided by Idaho EPSCoR include any academic, research, educational, governmental, recreational, or other not-for-profit activities. Any use of data provided by the Idaho EPSCoR must acknowledge Idaho EPSCoR and the funding source(s) that contributed to the collection of the data. Users are expected to inform the Idaho EPSCoR Office and the PI(s) responsible for the data of any work or publications based on data provided.

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Available for download on Wednesday, March 28, 2018

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