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Open AccessEditor’s ChoiceArticle

Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services

1
Department of Agriculture, Food, Environment and Forestry University of Florence, Piazzale delle Cascine 18, 50144 Firenze, Italy
2
Department of Architecture, University of Florence, Via della Mattonaia, 14, 50121 Firenze, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 329; https://doi.org/10.3390/rs12020329
Received: 7 December 2019 / Revised: 6 January 2020 / Accepted: 14 January 2020 / Published: 19 January 2020
(This article belongs to the Special Issue Remote Sensing in Applications of Geoinformation)
There is an urgent need for holistic tools to assess the health impacts of climate change mitigation and adaptation policies relating to increasing public green spaces. Urban vegetation provides numerous ecosystem services on a local scale and is therefore a potential adaptation strategy that can be used in an era of global warming to offset the increasing impacts of human activity on urban environments. In this study, we propose a set of urban green ecological metrics that can be used to evaluate urban green ecosystem services. The metrics were derived from two complementary surveys: a traditional remote sensing survey of multispectral images and Laser Imaging Detection and Ranging (LiDAR) data, and a survey using proximate sensing through images made available by the Google Street View database. In accordance with previous studies, two classes of metrics were calculated: greenery at lower and higher elevations than building facades. In the last phase of the work, the metrics were applied to city blocks, and a spatially constrained clustering methodology was employed. Homogeneous areas were identified in relation to the urban greenery characteristics. The proposed methodology represents the development of a geographic information system that can be used by public administrators and urban green designers to create and maintain urban public forests. View Full-Text
Keywords: urban forest; landscape metrics; LiDAR; aerial images; street view images; semantic segmentation; convolutional neural network (CNN); spatial clustering urban forest; landscape metrics; LiDAR; aerial images; street view images; semantic segmentation; convolutional neural network (CNN); spatial clustering
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MDPI and ACS Style

Barbierato, E.; Bernetti, I.; Capecchi, I.; Saragosa, C. Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services. Remote Sens. 2020, 12, 329. https://doi.org/10.3390/rs12020329

AMA Style

Barbierato E, Bernetti I, Capecchi I, Saragosa C. Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services. Remote Sensing. 2020; 12(2):329. https://doi.org/10.3390/rs12020329

Chicago/Turabian Style

Barbierato, Elena; Bernetti, Iacopo; Capecchi, Irene; Saragosa, Claudio. 2020. "Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services" Remote Sens. 12, no. 2: 329. https://doi.org/10.3390/rs12020329

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