Year: 2025
Type: article
Abstract: Urban trees provide essential environmental, health, social, and economic benefits. Consequently, researchers and stakeholders devote considerable effort to characterizing, mapping, and monitoring urban tree species. Traditional identification methods that rely on field surveys are labor-intensive and time-consuming. This study evaluated the potential of field hyperspectral spectroscopy to classify nine common urban tree species at the leaf level. Seven random forest classifiers, each using different combinations of spectral features, were compared for classification accuracy. The model that incorporated both first derivatives of spectral reflectance and vegetation indices achieved the highest overall accuracy (80.4%), whereas the model combining spectral reflectance and vegetation indices had the lowest predictive performance (70.1%). The most influential predictors were spectral bands and first derivatives in the red-edge and SWIR 1 regions; and the vegetation indices Red-edge Vegetation Stress Index (RVSI), Plant Senescence Reflectance Index (PSRI), and Blue Ratio (BR). These results support the use of hyperspectral remote sensing for identifying and classifying urban tree species.
Source: Remote Sensing
DOI: 10.3390/rs18010099
Authors: Rocio R. Duchesne, Alex Krebs, Madelyn Seuser
Institution: University of Wisconsin–Whitewater
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
Award: G349331129
OpenAlex ID: W7117449828