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Mapping urban tree cover changes using object-based convolution neural network (OB-CNN)

Citation

Timilsina, S and Aryal, J and Kirkpatrick, JB, Mapping urban tree cover changes using object-based convolution neural network (OB-CNN), Remote Sensing, 12, (18) Article 3017. ISSN 2072-4292 (2020) [Refereed Article]


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Copyright Statement

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/licenses/by/4.0/).

DOI: doi:10.3390/RS12183017

Abstract

Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R2 = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R2 = 67.8% and persistence: R2 = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities.

Item Details

Item Type:Refereed Article
Keywords:convolution neural networks (CNNs); deep learning; GEOBIA; object-based CNN; urban tree mapping; socioeconomic predictor variables, trees, urban, remote sensing
Research Division:Biological Sciences
Research Group:Ecology
Research Field:Terrestrial ecology
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Assessment and management of terrestrial ecosystems
UTAS Author:Timilsina, S (Ms Shirisa Timilsina)
UTAS Author:Aryal, J (Dr Jagannath Aryal)
UTAS Author:Kirkpatrick, JB (Professor James Kirkpatrick)
ID Code:143398
Year Published:2020
Web of Science® Times Cited:10
Deposited By:Geography and Spatial Science
Deposited On:2021-03-15
Last Modified:2021-09-22
Downloads:3 View Download Statistics

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