Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of UAV aerial imagery offer an alternative to tedious ground surveys. However, the timing (season) of the aerial surveys, input variables considered for classification, and the model type affect the classification accuracy. This work evaluates how the seasons and input variables considered in the species classification model affect the accuracy of species classification in a temperate broadleaf and mixed forest. Among the considered models, a Random Forest (RF) classifier demonstrated the highest performance, attaining an overall accuracy of 83.98% and a kappa coefficient of 0.80. Simultaneously using input data from summer, winter, autumn, and spring seasons improved tree species classification accuracy by 14–18% from classifications made using only single-season input data. Models that included vegetation indices, image texture, and elevation data obtained the highest accuracy. These results strengthen the case for using multi-seasonal data for species classification in temperate broadleaf and mixed forests since seasonal differences in the characteristics of species (e.g., leaf color, canopy structure) improve the ability to discern species.

Accurate microtopography data are an important input for characterizing small-scale rill erosion and its progression following disturbances. UAV–LiDAR systems are increasingly accessible and have successfully been used to measure microtopography data for several applications. Yet, the use of UAV–LiDAR systems for rill erosion studies in post-landslide landscapes have not been well investigated. Therefore, the purpose of this study was to implement and evaluate a UAV–LiDAR-based workflow to capture the microtopography of a post-landslide landscape, and by doing so, to help to determine best practices for UAV–LiDAR-based rill analysis. A commercial UAV–LiDAR system was used to map three post-landslide slopes and generate digital elevation models with a 1 cm-per-pixel ground resolution. Using data captured over multiple years, temporal rill development was assessed by comparing rill cross-sections and calculating changes to rill density and erosion volume. A flow-accumulation algorithm was adopted to automatically extract the rill network. We found that a flow accumulation algorithm with a threshold value of 5000 detected the rill network with overall accuracies of >88% and F1-scores of >93%. Vertical cross-sections of individual rills revealed an increase in the depth and width of rills over a one-year period. This study demonstrates that a commercial UAV–LiDAR system can effectively describe microtopography in a post-landslide landscape and facilitate analysis of small-scale rill characteristics and the progression of rill erosion.