Incorporating environmental data to refine the classification and understanding of the mechanisms behind encroachment of a woody species in the Southern Great Plains (USA)

Curtailing encroachment is dependent on effectively identifying where problematic species occur. However, traditional classification methods struggle to distinguish spectrally similar species. New techniques that incorporate environmental variables (edaphic, climatic, and topographic characteristics) into classification can refine predictions and help identify important factors associated with species occurrence. We developed a workflow to improve classification of honey mesquite (Neltuma [=Prosopis] glandulosa) in the Southern Great Plains (USA), examining 70 environmental variables to determine which were most associated with mesquite presence. We used Google Earth Engine to run X-means clustering on high-resolution aerial imagery from 50 replicate 78-km² areas in New Mexico and Texas. We then refined our classification using XGBoost to generate accuracy assessment points for each area to confirm locations of mesquite clusters. Our method improved classification accuracy from 36 % to 83 %. We performed an ex-situ ground-truthed validation study and achieved 74 % accuracy. Inclusion of environmental data increased the accuracy of mesquite classification and allowed us to estimate the influence of each variable in determining whether a given point was classified as mesquite. Shallow, alkaline soils with low water-storage capacity, high electrical conductance, and low cation exchange capacity were associated with mesquite presence; these areas tended to be associated with flat, low-elevation drainages in regions that experience wide annual temperature ranges. These methods provide an easily reproducible and scalable way to assist with image classification of rangeland shrubs from remotely sensed imagery, which may prove useful in managing the further encroachment of problematic species like honey mesquite.