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Japanese knotweed (Fallopia japonica) is listed among 100 of the World's worst invasive alien species and poses an increasing threat to ecosystems and agriculture in Northern America, Europe, and Oceania. This study proposes a remote sensing method to detect local occurrences of F. japonica from low-cost digital orthophotos taken in early spring and summer by concurrently exploring its temporal, spectral, and spatial characteristics. Temporal characteristics of the species are quantified by a band ratio calculated from the green and red spectral channels of both images. The normalized difference vegetation index was used to capture the high near-infrared (NIR) reflectance of F. japonica in summer while the characteristic texture of F. japonica is quantified by calculating gray level co-occurrence matrix (GLCM) measures. After establishing the optimum kernel size to quantify texture, the different input features …