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|Title:||A Combined Approach to Classifying Land Surface Cover of Urban Domestic Gardens Using Citizen Science Data and High Resolution Image Analysis|
Smith, Claire L.
|Citation:||Remote Sensing, 2018, 10(4), 537|
|Abstract:||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.|
|Rights:||Copyright © the authors, 2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
|Description:||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.|
|Appears in Collections:||Published Articles, Dept. of Geography|
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|remotesensing-10-00537-v2.pdf||Published (publisher PDF)||22.7 MB||Adobe PDF||View/Open|
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