A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Urban green space classification using Object-Based Image Analysis (OBIA) and LiDAR fusion: Accuracy evaluation and landscape metrics assessment
Tekijät: Siljander, Mika; Männistö, Sameli; Kuoppamäki, Kirsi; Taka, Maija; Ruth, Olli
Kustantaja: ELSEVIER GMBH
Kustannuspaikka: MUNICH
Julkaisuvuosi: 2025
Journal: Urban Forestry and Urban Greening
Tietokannassa oleva lehden nimi: URBAN FORESTRY & URBAN GREENING
Lehden akronyymi: URBAN FOR URBAN GREE
Artikkelin numero: 128997
Vuosikerta: 112
Sivujen määrä: 15
ISSN: 1618-8667
eISSN: 1610-8167
DOI: https://doi.org/10.1016/j.ufug.2025.128997
Verkko-osoite: https://doi.org/10.1016/j.ufug.2025.128997
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/499786100
With over two-thirds of the global population projected to live in cities by 2050, accurately mapping urban green spaces is increasingly important for sustainable development. This study integrates Object-Based Image Analysis (OBIA) and LiDAR data fusion to improve green space classification in three urban catchments in Helsinki, representing high (Ita-Pasila), intermediate (Pihlajamaki), and low (Verajamaki) land-use intensities. Using highresolution color-infrared (CIR) aerial orthophotographs enhanced by LiDAR-derived vegetation height data, the method effectively identified vegetated areas. Results were validated against a reference dataset using standard accuracy metrics and landscape structure indices. The results show that the OBIA method yielded green space area estimates within 1-4 % of the reference, but tended to produce more fragmented landscape configurations in high land-use intensity urban areas, resulting in higher numbers of patches and lower aggregation indices. Conversely, results in less urbanized Verajamaki closely matched the reference data both spatially and structurally. These discrepancies underscore the inherent challenges in interpreting spatial patterns within complex urban morphologies, particularly where spectral information is limited by shading, like in Ita-Pasila. Nevertheless, the OBIA-LiDAR fusion approach demonstrated strong reliability in less structurally complex environments and provides valuable data for watershed-scale hydrological and ecological modeling.
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The authors would like to acknowledge the funding from the Academy of Finland (project URCA, grant number 263308). This work was also supported by the KONE Foundation (grant number 202101976).
