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A Combined Approach to Classifying Land Surface Cover of Urban Domestic Gardens Using Citizen Science Data and High Resolution Image Analysis

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journal contribution
posted on 11.07.2019, 08:57 by Fraser Baker, Claire L. Smith, Gina Cavan
Domestic gardens are an important component of cities, contributing significantly to urban green infrastructure (GI) and its associated ecosystem services. However, domestic gardens are incredibly heterogeneous which presents challenges for quantifying their GI contribution and associated benefits for sustainable urban development. This study applies an innovative methodology that combines citizen science data with high resolution image analysis to create a garden dataset in the case study city of Manchester, UK. An online Citizen Science Survey (CSS) collected estimates of proportional coverage for 10 garden land surface types from 1031 city residents. High resolution image analysis was conducted to validate the CSS estimates, and to classify 7 land surface cover categories for all garden parcels in the city. Validation of the CSS land surface estimations revealed a mean accuracy of 76.63% (s = 15.24%), demonstrating that citizens are able to provide valid estimates of garden surface coverage proportions. An Object Based Image Analysis (OBIA) classification achieved an estimated overall accuracy of 82%, with further processing required to classify shadow objects. CSS land surface estimations were then extrapolated across the entire classification through calculation of within image class proportions, to provide the proportional coverage of 10 garden land surface types (buildings, hard impervious surfaces, hard pervious surfaces, bare soil, trees, shrubs, mown grass, rough grass, cultivated land, water) within every garden parcel in the city. The final dataset provides a better understanding of the composition of GI in domestic gardens and how this varies across the city. An average garden in Manchester has 50.23% GI, including trees (16.54%), mown grass (14.46%), shrubs (9.19%), cultivated land (7.62%), rough grass (1.97%) and water (0.45%). At the city scale, Manchester has 49.0% GI, and around one fifth (20.94%) of this GI is contained within domestic gardens. This is useful evidence to inform local urban development policies.

Funding

This work was carried out as part of the Assessing the contribution of domestic gardens to urban ecosystem services project (2016–2018), funded by Natural Environment Research Council NE/N017374/1 and NE/N017374/2. We gratefully acknowledge support from the project team and project partners.

History

Citation

Remote Sensing, 2018, 10(4), 537

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment/Physical Geography

Version

VoR (Version of Record)

Published in

Remote Sensing

Publisher

MDPI

issn

2072-4292

Acceptance date

28/03/2018

Copyright date

2018

Available date

11/07/2019

Publisher version

https://www.mdpi.com/2072-4292/10/4/537

Notes

The following are available online at http://www.mdpi.com/2072-4292/10/4/537/s1. Figure S1. Extent of the True-colour Aerial Imagery (TAI) for Manchester, UK; Figure S2. Thresholds separate initial image segments into superclass groups; Figure S3. Tree classification routine overview; Figure S4. Tree classification region growing; Figure S5. Grass and Shrubs classification—seed placement; Figure S6. Grass and shrubs classification Figure S7. Rules for classification class cleaning; Figure S8. Classification routines to optimise borders between classes.

Language

en