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Riparian vegetation planting can be guided by machine learning model
  • Gregory Pasternack,
  • Romina Diaz-Gomez,
  • Hervé Guillon
Gregory Pasternack
University of California Davis

Corresponding Author:gpast@ucdavis.edu

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Romina Diaz-Gomez
University of California Davis
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Hervé Guillon
University of California Davis
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Abstract

Designing where to plant riparian vegetation is a component of many river projects. Several mechanistic models have been developed considering biological, soil, hydrological, and hydraulic requirements that influence riparian vegetation growth. However, many models are not spatial explicit and there remains high uncertainty as to where plantings will survive or die. This study sought to determine if a machine learning (ML) algorithm could be trained to accurately characterize the complex set of site attributes that promote survival, and do so exclusively using metrics derived from airborne LiDAR. Results could then be used to guide planting strategies. The selected testbed river was 34 km of alluvial, regulated, gravel/cobble river where planting projects are common and have high mortality. The lower Yuba River, California, USA was mapped at sub-meter resolution in 2017. Our approach has four steps. First, a set of 32,000 vegetation presence/absence observations were randomly selected from LiDAR-derived polygons of naturally occurring established vegetation. Second, the river was split into 75 training, validation and test areas. Third, a set of 17 LiDAR-derived topographic potential predictors were computed at 0.91-m (3-ft) resolution. Finally, a Random Forest machine learning model was trained to best predict vegetation presence. The model results in a riparian vegetation presence probability map and has a “Area Under the Curve” (AUC) of 0.77. As probability values are difficult to interpret, a forage ratio electivity index analysis was performed with statistical bootstrapping. Results show that points with probability values > 0.8 had ~ 8.5 times more riparian vegetation present than would be likely from random chance at the 95% confidence level. Microtopographic ‘vector ruggedness’ was identified as the main driver for vegetation presence, followed by Terrain Ruggedness Index and Roughness. In conclusion, a ML model can identify where riparian vegetation planting are most likely to succeed and guide design. Our results also suggest that more attention should be paid to creating rugged microtopography under plantings to help cuttings and seedlings establish deposition critical for nutrition.