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isprs-annals-V-3-2020-541-2020.pdf
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🔗 Source: isprs-annals.copernicus.org
📊 Size: 4.98 MB
📄 Pages: 8 pages
⬇️ Downloads: 121

Summary

Researchers used multi-source remotely sensed data to estimate soil bulk density and carbon in the Kapuskasing region of Ontario, Canada. They collected soil samples in 2018 and 2019 and generated prediction maps for the 0-15 cm depth mineral soil layer. The study found that environmental covariates derived from LiDAR, such as canopy height model and gap fraction, were highly important for predicting bulk density. Random forest models using 12 predictors achieved reasonable results, with coefficients of determination (R2) greater than 0.7 for bulk density and carbon measurements. The study highlights the potential of digital soil mapping using multi-source remotely sensed data to estimate soil properties.

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Researchers used multi-source remotely sensed data to estimate soil bulk density and carbon in the Kapuskasing region of Ontario, Canada.

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  • File Format: PDF
  • File Size: 4.98 MB
  • Pages: 8
  • Language: EN
  • Total Downloads: 121
  • Last Updated: 6 hours ago

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