A growing number of studies have been conducted on urban green spaces (UGSs), given their essential role in mitigating urbanization externalities. The current available methods for acquiring and analyzing UGSs data suffer from several limitations. For example, free satellite imagery exhibits pixel distortion and fails to account for fine-grained structure such as accurate geometry or size. Moreover, official UGSs datasets are typically updated periodically, not continuously. This work provides a Python-derived open-source based analysis for acquiring, modeling and analyzing UGSs change patterns using landscape metrics. To demonstrate the feasibility of the proposed approach, it is applied on the New York City (NYC) region. NYC UGSs data between 2018 and 2022 were analyzed to detect changes and analyze the abundancy, fragmentation and shape of UGSs using 11 metrics. The analysis yields a promising outcome in UGSs studies. Furthermore, the proposed approach can be altered and applied on other land cover types.

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