Detecting Woody Plants in Southern Arizona Using Data from the National Ecological Observatory Network (NEON)

Land cover changes and conversions are occurring rapidly in response to human activities throughout the world. Woody plant encroachment (WPE) is a type of land cover conversion that involves the proliferation and/or densification of woody plants in an ecosystem. WPE is especially prevalent in drylands, where subtle changes in precipitation and disturbance regimes can have dramatic effects on vegetation structure and degrade ecosystem functions and services. Accurately determining the distribution of woody plants in drylands is critical for protecting human and natural resources through woody plant management strategies. Using an object-based approach, we have used novel open-source remote sensing and in situ data from Santa Rita Experimental Range (SRER), National Ecological Observatory Network (NEON), Arizona, USA with machine learning algorithms and tested each model's efficacy for estimating fractional woody cover (FWC) to quantify woody plant extent. Model performance was compared using standard model assessment metrics such as accuracy, sensitivity, specificity, and runtime to assess model variables and hyperparameters. We found that decision tree-based models with a binary classification scheme performed best, with sequential models (Boosting) slightly outperforming independent models (Random Forest) for both object classification and FWC estimates. Mean canopy height and mean, median, and maximum statistics for all vegetation indices were found to have highest variable importance. Optimal model hyperparameters and potential limitations of the NEON dataset for classifying woody plants in dryland regions were also identified. Overall, this study lays the groundwork for developing machine learning models for dryland woody plant management using solely NEON data.

Automated summary

Land cover changes and conversions are occurring rapidly worldwide due to human activities. Woody plant encroachment (WPE) is a common land cover conversion in drylands, with potential negative impacts on vegetation structure and ecosystem functions. Accurately determining woody plant distribution is crucial for effective management strategies. This study used an object-based approach and machine learning algorithms to estimate fractional woody cover (FWC) using remote sensing and in situ data from SRER and NEON. Decision tree-based models performed best, and important variables were identified. The study lays the groundwork for developing machine learning models for dryland woody plant management.