Random point sampling to detect gain and loss in tree canopy cover in response to urban densification

Kaspar, Joe; Kendal, Dave; Sore, Rachel; Livesley, Stephen J.

doi:10.1016/j.ufug.2017.03.013

Cited by 35 publications

(22 citation statements)

References 27 publications

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“…Our loss figures are the absolute percentage of a block from which the tree cover has disappeared, not a percentage of the 2005 cover. The net tree cover loss contrasts with the widespread tree density gain recorded for Hobart in an earlier period [16] but is consistent with some other observations from Australia [75][76][77][78] and elsewhere [7,73,[79][80][81]. Tree cover is likely to be predicted by tree density, except where very recent suburbs on previously treeless areas are contrasted with older suburbs or where houses that were built amongst pre-existing trees are contrasted with suburbs of the same age built in treeless areas.…”

Section: Urban Tree Cover Changesupporting

confidence: 85%

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

2020

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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.

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Section: Urban Tree Cover Changesupporting

confidence: 85%

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

2020

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“…Cities may strategically plant a large and increasing number of trees [9,19], but at the same time continue to remove trees to make space for an expanding physical infrastructure [6], or spend more resources maintaining trees in challenging urban conditions [37]. A dynamic equilibrium then emerges where tree numbers and tree canopy often remain stable overall, while increasing in some places and decreasing in others [4,6,38]. These issues translate in a variety of challenges for municipal managers, such as raising costs for urban tree maintenance, difficulty finding space for trees in new developments, or the overestimation of urban tree risk in order to facilitate tree removals (e.g., [29,39,40]).…”

Section: Conflictsmentioning

confidence: 99%

How Urban Forest Managers Evaluate Management and Governance Challenges in Their Decision-Making

Ordóñez

Kendal

Threlfall

et al. 2020

Forests

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Decisions about urban forests are critical to urban liveability and resilience. This study aimed to evaluate the range of positions held by urban forest managers from local governments in the state of Victoria, Australia, regarding the management and governance challenges that affect their decision-making. This study was based on a Q-method approach, a procedure that allows researchers to evaluate the range of positions that exist about a topic in a structured manner based on the experiences of a wide group of people. We created statements on a wide range of urban forest management and governance challenges and asked urban forest managers to rate their level of agreement with these statements via an online survey. Managers generally agreed about the challenges posed by urban development and climate change for implementing local government policies on urban forest protection and expansion. However, there were divergent views about how effective solutions based on increasing operational capacities, such as increasing budgets and personnel, could address these challenges. For some managers, it was more effective to improve critical governance challenges, such as inter-departmental and inter-municipal coordination, community engagement, and addressing the culture of risk aversion in local governments. Urban forest regional strategies aimed at coordinating management and governance issues across cities should build on existing consensus on development and environmental threats and address critical management and governance issues not solely related to local government operational capacity.

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“…Tree canopy COHESION was negatively correlated with both COHESION of roads and buildings. This can be explained by the fact that tree canopy fragmentation and loss is often attributed to urban densification [121]. This is important as urban development lacking urban greenspace and subsequently tree canopy can have many social and physical health implications [122].…”

Section: Importance Of Relationshipsmentioning

confidence: 99%

Showcasing Relationships between Neighborhood Design and Wellbeing Toronto Indicators

et al. 2020

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Cities are the keystone landscape features for achieving sustainability locally, regionally, and globally. With the increasing impacts of urban expansion eminent, policymakers have encouraged researchers to advance or invent methods for managing coupled human–environmental systems associated with local and regional sustainable development planning. Although progress has been made, there remains no universal instrument for attaining sustainability on neither regional nor local planning scales. Previous sustainable urbanization studies have revealed that landscape configuration metrics can supplement other measures of urban well-being, yet few have been included in public data dashboards or contrasted against local well-being indicators. To advance this sector of sustainable development planning, this study had three main intentions: (1) to produce a foundational suite of landscape ecology metrics from the 2007 land cover dataset for the City of Toronto; (2) to visualize and interpret spatial patterns of neighborhood streetscape patch cohesion index (COHESION), Shannon’s diversity index (SHDI), and four Wellbeing Toronto indicators across the 140 Toronto neighborhoods; (3) to quantitatively assess the global collinearity and local explanatory power of the well-being and landscape measures showcased in this study. One-hundred-and-thirty landscape ecology metrics were computed: 18 class configuration metrics across seven land cover categories and four landscape diversity metrics. Anselin Moran’s I-test was used to illustrate significant spatial patterns of well-being and landscape indicators; Pearson’s correlation and conditional autoregressive (CAR) statistics were used to evaluate relationships between them. Spatial “hot-spots” and/or “cold-spots” were found in all streetscape variables. Among other interesting results, Walk Score® was negatively related to both tree canopy and grass/shrub connectedness, signifying its lack of consideration for the quality of ecosystem services and environmental public health—and subsequently happiness—during its proximity assessment of socioeconomic amenities. In sum, landscape ecology metrics can provide cost-effective ecological integrity addendum to existing and future urban resilience, sustainable development, and well-being monitoring programs.

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Random point sampling to detect gain and loss in tree canopy cover in response to urban densification

Cited by 35 publications

References 27 publications

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

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

How Urban Forest Managers Evaluate Management and Governance Challenges in Their Decision-Making

Showcasing Relationships between Neighborhood Design and Wellbeing Toronto Indicators

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