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June 13, 2022Urban forestry & urban greening77 citationsOpen Access

Remote sensing for the assessment of ecosystem services provided by urban vegetation: A review of the methods applied

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KGKarina Angélica García-PardoDMDavid Moreno-RangelSDSamuel Domínguez-Amarillo

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Abstract

The study and assessment of ecosystem services through remote sensing has increased substantially over the last two decades, as evidenced by the publication of studies that have applied it. The technological development of satellite images has improved in terms of spatial, spectral, radiometric, and temporal resolution, allowing the space-time observation, classification and monitoring of vegetation on the surface of the Earth. However, there are remaining methodological challenges for assessing ecosystem services due to the diversity of applications, the resources used, and its study in complex environments such as cities. This systematic review is based on identifying and analysing the variety of methods concerning the application of remote sensing for the assessment of ecosystem services provided by vegetation in cities, through a classification of these methods according to the data collection source (passive sensors, passive and active sensors and the fusion of other data sources with sensors). The classification of methods has been applied to a selection of existing articles in indexed scientific databases based on a non-statistical meta-analysis that make a direct reference to the topic of interest. The results show the approaches found in every method classified, their relationships with the geographical scale and the image resolutions used, and the advantages and limitations from the data processes that comprise remote sensing. We conclude from this analysis with three key factors to consider in the selection of remote sensing methods for the assessment of ecosystem services provided by urban vegetation: the definition of the approach(es), the urban scale to be assessed, and the image resolution available.

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García-Pardo et al. (2022) studied this question.

synapsesocial.com/papers/6a6215f0f5a6cede3a89c300https://doi.org/10.1016/j.ufug.2022.127636
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