Coupling UAV and satellite data for tree species identification to map the distribution of Caspian poplar

• Published in: Landscape ecology 2024-02, Vol.39 (2), p.30, Article 30
• Main Authors: Miraki, Mojdeh, Sohrabi, Hormoz, Fatehi, Parviz, Kneubuehler, Mathias
• Format: Article
• Language: English
• Subjects: Accuracy; Biomedical and Life Sciences; Classification; Ecology; Endangered plants; Endangered species; Endemic species; Environmental Management; Forest conservation; Forest management; Geographical distribution; Kappa coefficient; Landscape Ecology; Landscape/Regional and Urban Planning; Life Sciences; Nature Conservation; Plant species; Remote sensing; Research Article; Satellites; Spatial discrimination; Spatial resolution; Species diversity; Sustainable Development; Trees; Wildlife conservation
• ISSN: 1572-9761; 0921-2973; 1572-9761
• DOI: 10.1007/s10980-024-01846-8

Context Mapping the distribution of species, especially those that are endemic and endangered like certain tree species, is a vital step in the effective planning and execution of conservation programs and monitoring efforts. This task gains even more significance as it directly contributes to forest conservation by highlighting the importance of species diversity. Objectives Our study objective was to assess the detection accuracy of a specific tree using different remote sensing sources and approaches. Methods Initially, individual trees were identified and classified using a canopy height model derived from UAV data. Next, we carried out the classification of satellite data within the Google Earth Engine. Lastly, we scaled the UAV-RGB dataset to match the spatial resolution of Sentinel-2, which was then employed to train random forest models using the multispectral data from Sentinel-2. Results For the UAV data, we achieved overall accuracies of 56% for automatically delineated tree crowns and 83% for manually delineated ones. Regarding the second approach using Sentinel-2 data, the classification in the Noor forest yielded an overall accuracy of 74% and a Kappa coefficient of 0.57, while in the Safrabasteh forest, the accuracy was 80% with a Kappa of 0.61. In the third approach, our findings indicate an improvement compared to the second approach, with the overall accuracy and Kappa coefficient of the classification rising to 82% and 0.68, respectively. Conclusions In this study, it was found that according to the purpose and available facilities, satellite and UAV data can be successfully used to identify a specific tree species.