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Article

Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)

1
School of Technology, Environments and Design, Discipline of Geography and Spatial Sciences, University of Tasmania, Hobart, Tasmania 7001, Australia
2
Melbourne School of Engineering, University of Melbourne, Parkville, Victoria 3010, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 3017; https://doi.org/10.3390/rs12183017
Received: 7 August 2020 / Revised: 11 September 2020 / Accepted: 14 September 2020 / Published: 16 September 2020
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. View Full-Text
Keywords: convolution neural networks (CNNs); deep learning; GEOBIA; object-based CNN; urban tree mapping; socioeconomic predictor variables convolution neural networks (CNNs); deep learning; GEOBIA; object-based CNN; urban tree mapping; socioeconomic predictor variables
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MDPI and ACS Style

Timilsina, S.; Aryal, J.; Kirkpatrick, J.B. Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN). Remote Sens. 2020, 12, 3017. https://doi.org/10.3390/rs12183017

AMA Style

Timilsina S, Aryal J, Kirkpatrick JB. Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN). Remote Sensing. 2020; 12(18):3017. https://doi.org/10.3390/rs12183017

Chicago/Turabian Style

Timilsina, Shirisa, Jagannath Aryal, and Jamie B. Kirkpatrick 2020. "Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)" Remote Sensing 12, no. 18: 3017. https://doi.org/10.3390/rs12183017

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