ASSESSING URBAN FOREST CANOPY COVER IN GREAT PLAIN CONSERVATION AREA (DÜZCE CITY, TURKEY) BETWEEN 1984 AND 2015

Urban trees and forests are essential components of the urban environment. They can provide numerous ecosystem services and goods, including but not limited to recreational opportunities and aesthetic values, removal of air pollutants, improving air and water quality, providing shade and cooling effect, reducing energy use, and storage of atmospheric CO2. However, urban trees and forests have been in danger of being lost by dense housing resulting from population growth in the cities since the 1950s, leading to increased local temperature, pollution level, and flooding risk. Thus, determining the status of urban trees and forests is necessary for comprehensive understanding and quantifying the ecosystem services and goods. Tree canopy cover is a relatively quick, easy to obtain, and cost-effective urban forestry metric broadly used to estimate ecosystem services and goods of the urban forest. This study aimed to determine urban forest canopy cover areas and monitor the changes between 1984–2015 for the Great Plain Conservation area (GPCA) that has been declared as a conservation Area (GPCA) in 2017, located on the border of Düzce City (Western Black Sea Region of Turkey). Although GPCA is a conservation area for agricultural purposes, it consists of the city center with 250,000 population and most settlement areas. A random point sampling approach, the most common sampling approach, was applied to estimate urban tree canopy cover and their changes over time from historical aerial imageries. Tree canopy cover ranged from 16.0% to 27.4% within the study period. The changes in urban canopy cover between 1984–1999 and 1999–2015 were statistically significant, while there was no statistical difference compared to the changes in tree canopy cover between 1984–2015. The result of the study suggested that an accurate estimate of urban tree canopy cover and monitoring long-term canopy cover changes are essential to determine the current situation and the trends for the future. It will help city planners and policymakers in decision-making processes for the future of urban areas.

The Legacy of Hurricanes, Historic Land Cover, and Municipal Ordinances on Urban Tree Canopy in Florida (United States)

Urban Tree Canopy (UTC) greatly enhances the livability of cities by reducing urban heat buildup, mitigating stormwater runoff, and filtering airborne particulates, among other ecological services. These benefits, combined with the relative ease of measuring tree cover from aerial imagery, have led many cities to adopt management strategies based on UTC goals. In this study, we conducted canopy analyses for the 300 largest cities in Florida to assess the impacts of development practices, urban forest ordinances, and hurricanes on tree cover. Within the cities sampled, UTC canopy ranged from 5.9% to 68.7% with a median canopy coverage of 32.3% Our results indicate that the peak gust speeds recorded during past hurricanes events were a significant predictor of canopy coverage (P-value = <0.001) across the sampled cities. As peak gust speeds increased from 152 km/h (i.e., a lower-intensity Category 1 storm) to 225 km/h (lower-intensity Category 4 and the maximum gusts captured in our data), predicted canopy in developed urban areas decreased by 7.7%. Beyond the impacts of hurricanes and tropical storms, we found that historic landcover and two out of eight urban forest ordinances were significant predictors of existing canopy coverage (P-landcover <0.001; P-tree preservation ordinance = 0.02, P-heritage tree ordinance = 0.03). Results indicate that local policies and tree protections can protect or enhance urban tree canopy, even in the face of rapid development and periodic natural disturbances.

Application and Evaluation of a Deep Learning Architecture to Urban Tree Canopy Mapping

Urban forest is a dynamic urban ecosystem that provides critical benefits to urban residents and the environment. Accurate mapping of urban forest plays an important role in greenspace management. In this study, we apply a deep learning model, the U-net, to urban tree canopy mapping using high-resolution aerial photographs. We evaluate the feasibility and effectiveness of the U-net in tree canopy mapping through experiments at four spatial scales—16 cm, 32 cm, 50 cm, and 100 cm. The overall performance of all approaches is validated on the ISPRS Vaihingen 2D Semantic Labeling dataset using four quantitative metrics, Dice, Intersection over Union, Overall Accuracy, and Kappa Coefficient. Two evaluations are performed to assess the model performance. Experimental results show that the U-net with the 32-cm input images perform the best with an overall accuracy of 0.9914 and an Intersection over Union of 0.9638. The U-net achieves the state-of-the-art overall performance in comparison with object-based image analysis approach and other deep learning frameworks. The outstanding performance of the U-net indicates a possibility of applying it to urban tree segmentation at a wide range of spatial scales. The U-net accurately recognizes and delineates tree canopy for different land cover features and has great potential to be adopted as an effective tool for high-resolution land cover mapping.

Miami-Dade County Urban Tree Canopy Analysis

This assessment focuses on describing urban tree canopy (UTC) within the Urban Development Boundary of Miami-Dade County, as defined by the Miami-Dade County Transportation Planning Organization (Figure 1). The area (intracoastal water areas excluded) encompasses approximately 1147 km2 (443 mi2). A combination of remote sensing and publicly available vector data was used to classify the following land cover classes: tree canopy/shrubs, grass, bare ground, wetland, water, building, street/railroad, other impervious surfaces, and cropland.

REMOTE SENSING FOR URBAN TREE CANOPY CHANGE DETECTION WITH LANDSAT SATELLITE DATA IN NNAMDI AZIKIWE UNIVERSITY AWKA – NIGERIA

Urban tree canopy within a university boundary is a measure of the university's tree cover as a percentage of its total land area. The overall objective of the present study is to conduct a sSpatio-temporal change analysis of urban tree canopy in Nnamdi Azikiwe University Awka-Nigeria. Landsat data of years 1991, 2001, 2011 and 2019 were analysed using Maximum Likelihood Classifier and Confusion Matrix Spatial Analyst in ArcGIS 10.7.1 software. In terms of tree cover loss, there is a steady rate of decrease rate from -31.59 Hectares (ha) between 1991 and 2001; -82.32 ha (2001/2011) and -64.53 ha (2011/2019). Whereas, at an initial land area of 9.40 ha in 1991, physical infrastructural development is progressively increased with 16.92 ha between 1991 and 2001; 43.79 ha 2001/2011 and 12.37 ha between 2011 and 2019. The dominant drivers of tree cover change in the study area related to the expansion of physical infrastructures and sprawling agriculture as a result of encroachers into the study area. In conclusion, tropical forests within university campuses face many threats, such as those posed by unregulated physical infrastructural development and a lack of investment and management of forest relics. As a recommendation, Nigerian universities should invest and conserve their existing forested landscapes towards promoting land resources in line with Sustainable Development Goals number 15 (SDG-15) strategies.

A Case Study Balancing Predetermined Targets and Real-World Constraints to Guide Optimum Urban Tree Canopy Cover for Perth, Western Australia

Trees in urban settings are becoming increasingly important as mediators to emerging challenges that transect social, environmental, and economic factors. Trees provide shade; absorb and store atmospheric carbon and other pollutants; reduce local temperature fluctuations; provide essential inner-city fauna habitat; assist in reducing over-land stormwater flow; provide amenity; and provide many more social, environmental, and economic benefits. To secure these benefits, tree canopy cover targets are commonly employed by land managers; however, such targets are rarely quantified against the characteristics and limitations of individual urban centers. Through the generation and interrogation of qualitative and quantitative data, this case study of Perth, Western Australia presents a new conceptual tool that integrates eleven factors found to influence the capacity and opportunity for a city to support urban tree canopy cover. This tool is designed to capture and causally weigh urban tree canopy considerations based on individual city characteristics, collective values, and identifiable constraints. The output of the tool provides an “optimum” tree canopy cover result (as a percentage of the urban fabric) to better inform canopy cover targets and recommendations for urban tree strategic planning and management. This tool is valuable for urban land managers, city planners, urban designers, and communities in effective planning, management, valuation, and investment regarding urban trees as a sub-set of urban green infrastructure.