Canadian urban tree canopy cover and carbon sequestration status and change 1990–2012

Urban trees provide numerous ecosystem goods and services by providing shade, habitat for wildlife, removal of air pollutants and the removal and storage of atmospheric CO₂. Carbon removal services provided by Canadian urban trees have previously been assessed using an IPCC 2006 guidelines approach based on the percentage of urban area covered by tree canopy (UTC) for the 2012 time period (Pasher et al., 2014). That work however provided only a single point in time assessment of the national scale UTC and carbon removal services. The research undertaken for this study was a continuation of this earlier work focusing on a 1990 national scale UTC assessment and carbon sequestration estimates for 1990. UTC estimates for 1990 were developed using a point sampling approach with circa 1990 air photos covering a large portion of Canadian urban areas. In total almost 179,000 points were sampled for the 1990 time period, reassessing 83% of the points used for the previous 2012 assessment. Based on the urban area boundary layers for 1991 and 2011, Canada’s urban areas grew by an estimated 6% for this time period. Most of this growth occurred through conversion of agricultural and forested lands to urban. At the national scale the UTC for 1990 was estimated to be 27.6%, as compared to the 2012 UTC estimate of 26.1%, the difference between estimates for the two time periods fell within the uncertainty range. Carbon removal estimates based on the UTC estimates were also very similar for the two dates with 660.2 kt C removed in 1990 and 662.8 kt C removed in 2012. It was noted that urban development in the Prairie regions resulted in an increase in tree cover as compared to the pre-conversion agricultural and natural landscapes and also that in most urban areas across the country UTC increases through time as tree cover matured in newly developed urban areas. These two assessments provide a time series of urban trees for 22 year time period, which will be useful for further studies and analysis.     

Estimating aboveground carbon stocks of urban trees by synergizing ICESat-2 LiDAR with GF-2 data

Accurately mapping carbon stocks of urban trees is necessary for urban managers to design strategies to mitigate climate change. However, the aboveground carbon stocks of urban trees are usually underestimated by passive remote sensing data because of the signal saturation problem. The research is the first attempt to develop a framework to map aboveground carbon density of trees in urban areas by synergizing Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data with Gaofen-2 (GF-2) imagery. The framework consists of three key steps. First, we used a support vector machine classifier to classify GF-2 images and extracted urban tree regions. Second, we estimated the tree carbon density of ICESat-2 strips by developing a ICESat-2 photon feature-based aboveground carbon density estimation model. Third, we mapped the carbon density of urban trees by developing a synergistic model between ICESat-2 and GF-2 data based on an object-oriented method. We tested the approach for the areas within the fifth ring road of Beijing, China. The results showed that the 50th percentile height (PH50) of nighttime photons was a good predictor for estimating carbon density of urban trees, with a R² of 0.69 and a Root Mean Square Error (RMSE) of 2.81 kg C m⁻². Using the spectral features generated by GF-2 imagery, we could further extrapolate the carbon density estimated by ICESat-2 strip data to a full coverage of accurate mapping carbon density by urban trees, resulting in a R² of 0.64 and a RMSE of 2.32 kg C m⁻². The carbon stocks within the fifth ring road of Beijing were 8.28 × 10⁸ kg in total, with the mean carbon density of 3.52 kg C m⁻². Such estimations were larger than that of previous study using passive remote sensing data only, suggesting the integration of spaceborne LiDAR and spectral data could greatly reduce the underestimation of carbon stocks of urban trees. Our approach can more accurately estimate carbon stocks of urban trees and has the potential to be applicable in other cities.     

Quantifying green cover change for sustainable urban planning: A case of Kuala Lumpur,Malaysia

Despite the numerous benefits of urban green cover, urban land development has led to its destruction and degradation, including in Malaysia. In this study, time series Landsat satellite imagery were used to monitor green cover changes in Kuala Lumpur (KL), the largest and capital city of Malaysia. An advanced satellite image processing technique that considers the mixed-pixel problem was employed to determine the fraction of green cover in each Landsat pixel. Results show that the total green coverage in Kuala Lumpur decreased by 3% over the first study period, from 6564 ha in 2001–5,891 ha in 2013. However, it increased by 4% in the second, from 6215 ha in 2014–7,310 ha in 2016, and now green cover is 30% of the total land area of KL. These periods were selected to observe the changes in green cover before and after implementation of the “Greening KL” program, which was aimed to plant 100,000 trees in KL by year 2020. Most of the increase in green cover was contributed by trees planted along streets and in recreational parks. Other findings include a loss of ∼9% of green cover in two public parks compared to their total gazetted area, and a loss of green area in other forested parts of KL. Focus group discussions and structured interviews with public, private and non-governmental organizations indicate that green-cover losses can be partly attributed to weak regulations and their poor enforcement. Opportunities to protect and increase green cover in KL are also explored in this study. Such approaches are urgently needed before most of the green areas disappear from the landscape of KL, exacerbating the existing environmental problems in the city.     

Human and biophysical legacies shape contemporary urban forests: A literature synthesis

Understanding how urban forests developed their current patterns of tree canopy cover, species composition, and diversity requires an appreciation of historical legacy effects. However, analyses of current urban forest characteristics are often limited to contemporary socioeconomic factors, overlooking the role of history. The institutions, human communities, and biophysical conditions of cities change over time, creating layers of legacies on the landscape, shifting urban forests through complex interactive processes and feedbacks. Urban green spaces and planted trees can persist long after their establishment, meaning that today’s mature canopy reflects conditions and decisions from many years prior. In this synthesis article, we discuss some of the major historical human and biophysical drivers and associated legacy effects expressed in present urban forest patterns, highlighting examples in the United States and Canada. The bioregional context – native biome, climate, topography, initial vegetation, and pre-urbanization land use – represents the initial conditions in which a city established and grew, and this context influences how legacy effects unfold. Human drivers of legacy effects can reflect specific historical periods: colonial histories related to the symbolism of certain species, and the urban parks and civic beautification movements. Other human drivers include phenomena that cut across time periods such as neighborhood urban form and socioeconomic change. Biophysical legacy effects include the consequences of past disturbances such as extreme weather events and pest and disease outbreaks. Urban tree professionals play a major role in many legacy effects by mediating the interactions and feedbacks between biophysical and human drivers. We emphasize the importance of historical perspectives to understand past drivers that have produced current urban forest patterns, and call for interdisciplinary and mixed methods research to unpack the mechanisms of long-term urban forest change at intra- and inter-city scales.     

Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur,India

Urban forests play a significant role in carbon cycling. Quantification of Aboveground Biomass (AGB) is critical to understand the role of urban forests in carbon sequestration. In the present study, Machine learning (ML) based regression algorithms (SVM, RF, kNN and XGBoost) have been taken into account for spatial mapping of AGB and carbon for the urban forests of Jodhpur city, Rajasthan, India, with the aid of field-based data and their correlations with spectra and textural variables derived from Landsat 8 OLI data. A total of 198 variables were retrieved from the satellite image, including bands, Vegetation Indices (VIs), linearly transformed variables, and Grey Level Co-occurrence textures (GLCM) taken as independent input variables further reduced to 29 variables using Boruta feature selection method. All the models have been compared where with RF algorithm, R² = 0.83, RMSE = 16.22 t/ha and MAE = 11.86 t/ha. For kNN algorithm R² = 0.77, RMSE = 28.04 t/ha and MAE = 24.24 t/ha and SVM where R² = 0.73, RMSE = 89.21 t/ha and MAE = 74.22 t/ha and the best prediction accuracy has been noted with XGBoost algorithm (R² = 0.89, RMSE = 14.08 t/ha and MAE = 13.66 t/ha) with predicted AGB as 0.51−153.76 t/ha. The study indicates that ML-based regression algorithms have great potential over other linear and multiple regression techniques for spatial mapping of AGB and carbon of urban forests for arid regions.     

Assessing the performance of urban forest carbon sequestration models using direct measurements of tree growth

Across cities worldwide, people are recognizing the value of greenspace in ameliorating the health and well-being of those living there, and are investing significant resources to improve their greenspace. Although models have been developed to allow the quantification of ecosystem services provided by urban trees, refinement and calibration of these models with more accurate site- and species-specific data can increase confidence in their outcomes. We used data from two street tree surveys in Cambridge, MA, to estimate annual tree mortality for 592 trees and diameter growth rates for 498 trees. Overall tree turnover between 2012 and 2015 was relatively low (annualized 3.6% y⁻¹), and mortality rate varied by species. Tree growth rates also varied by species and size. We used stem diameter (DBH) and species identity to estimate CO₂ sequestration rates for each of 463 trees using three different model variations: (1) i-Tree Streets, (2) Urban Tree Database (UTD) species-specific biomass allometries and growth rates, and (3) empirically measured growth rates combined with UTD biomass allometries (Empirical + UTD). For most species, the rate of CO₂ sequestration varied significantly with the model used. CO₂ sequestration estimates calculated using i-Tree Streets were often higher than estimates calculated with the UTD equations. CO₂ sequestration estimates were often the lowest when calculated using empirical tree growth estimates and the UTD equations (Empirical + UTD). The differences among CO₂ sequestration estimates were highest for large trees. When scaled up to the entire city, CO₂ sequestration estimates for the Empirical + UTD model were 49.2% and 56.5% of the i-Tree Streets and UTD estimates, respectively. We suggest future derivations of ecosystem service provision models allow localities to input their own species-specific growth values. By adding capacity to easy-to-use tools, such as i-Tree Streets, we can increase confidence in the model output.     

Estimation of broad-leaved canopy growth in the urban forested area using multi-temporal airborne LiDAR datasets

Inter-annual canopy growth is one of the key indicators for assessing forest conditions, but the measurements require laborious field surveys. Up-to-date LiDAR remote sensing provides sufficient three-dimensional morphological information of the ground to monitor canopy heights on a broad scale. Thus, we attempted to use multi-temporal airborne LiDAR datasets in the estimation of vertical canopy growth, across various types of broad-leaved trees in a large urban park.The growth of broad-leaved canopies in the EXPO '70 urban forest in Osaka, Japan was assessed with 19 plots at the stand level and 39 selected trees at the individual-tree level. Airborne LiDAR campaigns repeatedly observed the park in the summers of 2004, 2008, and 2010. We acquired canopy height models (CHMs) for each year from the height values of the uppermost laser returns at every 0.5 m grid. The annual canopy growth was calculated by the differences in CHMs and validated with the annual changes in field-measured basal areas and tree heights.LiDAR estimations revealed that the average annual canopy growth from 2004 to 2010 was 0.26 ± 0.11 m m⁻² yr⁻¹ at the plot level and 0.26 ± 0.10 m m⁻² yr⁻¹ at the individual-tree level. This result showed that growing trends were consistent at different scales through 2004 to 2010 despite uncertainty in estimating short-term growth for small crown areas at the individual-tree level. This LiDAR-estimated canopy growth shows a moderate relation to field-measured increase of basal areas and average heights. The estimation uncertainties seem to result from the complex canopy structure and irregular crown shape of broad-leaved trees. Challenges still remain on how to incorporate the growth of understory trees, growth in the lateral direction, and gap dynamics inside the canopy, particularly in applying multi-temporal LiDAR datasets to the large-scale growth assessment.     

Private residential urban forest structure and carbon storage in a moderate-sized urban area in the Midwest,United States

In conjunction with urbanization and its importance as a major driver of land-use change, increased efforts have been placed on understanding urban forests and the provisioning of ecosystem services. However, very little research has been conducted on private property and little is known about the structure and function of privately owned urban forests. This research examines the structure of and carbon storage services provided by private residential urban forests in a moderate-sized Midwestern city. The primary research questions are as follows: What is the structure of private urban forests, and how does it vary across parcels? How much carbon is stored in tree and soil pools of private urban forests, and how does carbon vary across parcels? Ecological inventories were conducted on 100 residential parcels within 14 Neighborhood and Homeowners Associations of varying size and development age. Tree species richness, diversity, density, and diameter distribution were determined on a per parcel basis and for the entire tree population sampled. Further, tree and soil carbon storage were determined for each parcel. Results of this research demonstrated large variability in per-parcel tree metrics. Twelve of the parcels sampled had two or fewer trees, while eleven had greater than 50 trees. Further, tree carbon storage ranged from no carbon to 11.22 kg C m^?2. Alternatively, soil carbon storage was less variable and averaged 4.7 kg C m^?2, approximately 1.9 times higher than the average carbon stored in trees (2.5 kg C m^?2). Management efforts aimed at maintaining or enhancing carbon storage and other ecosystem services should focus on both soil protection and maximizing services in living biomass. Our results demonstrate that sustaining tree-produced ecosystem services requires maintenance of large old trees and species diversity, not only in terms of relative abundance, but also relative dominance, and in combination, species–specific size distributions.     

Relationships between urbanization,tree morphology,and carbon density: An integration of remote sensing,allometric models,and field survey

Urban trees store and sequester large amounts of carbon and are a vital component of natural climate solutions. Despite the well-recognized carbon benefits of urban trees, there is limited effort to examine how spatial distribution of carbon density varies across distinctive social, demographic, and built dimensions of urban landscapes. Moreover, it is unclear whether specific aspects of landscape structure and design could help increase carbon densities in urban trees. Here, we produced a fine-resolution carbon density map of urban trees in New York City (NYC) by integrating high-resolution land cover map, LiDAR-derived tree metrics, i-Tree Eco, and field survey data. We then explored spatial variations of carbon density across the gradients of urban development intensity, social deprivation index, and neighborhood age, and we examined the relationships between carbon density, and fragmentation, aggregation, size, and shape of tree canopy cover. We find that carbon stored in urban trees in NYC is estimated as 1078 Gg, with an average density of 13.8 Mg/ha. This large amount of carbon is unevenly distributed, with carbon densities being highest in Bronx and in open parks and street trees. Furthermore, carbon densities are negatively associated with urban development intensity and the social gradient of deprivation. Regarding the impacts of tree morphology on carbon density, our results show that while the amount of tree cover is the most influential factor in determining carbon density, small-sized forest patches and moderate levels of forest edges are also conductive to increasing carbon densities of urban trees. To incorporate urban forestry into developing innovative, effective, and equitable climate mitigation strategies, planners and decision makers need to identify the optimal spatial configuration of urban forests and invest in tree planting programs in marginalized communities.     

Carbon sinks in urban public green spaces under carbon neutrality: A bibliometric analysis and systematic literature review

Urban green spaces play essential roles in regulating the global carbon cycle and reducing carbon dioxide (CO₂) emissions. However, research on the carbon sequestration efficiency of urban public green spaces, which is closely related to the human settlement environment, has not received enough attention. Thus, we systematically analyzed the existing literature in the Web of Science core database using bibliometrics and network analysis combined with the CiteSpace visualization tool. The aim of this review was to elucidate the focus and development trend of research conducted between 2007 and 2022 on carbon sinks in urban public green spaces. Our results demonstrated that: 1) Numbers of related publications are increasing annually, indicating that the subject is receiving increasing global attention. Related research topics primarily focus on two aspects: carbon sink measurement methods and sustainable carbon sink design for urban public green spaces. 2) Methods for measuring and monitoring carbon sinks in the urban public green spaces include sample plot measurement, the assimilation method, the micrometeorological method, the remote sensing (RS) estimation method, and laboratory work. Most methods follow the forestry system approach and lack the methods and techniques to directly assess the carbon sink effectiveness of urban public green spaces. 3) Key factors affecting the carbon sink capacity of urban public green spaces are: plant species selection, plant community structure, green space characteristics, and maintenance management. 4) Future research should focus on encouraging public participation in the design of sustainable carbon sinks in urban public green spaces, advancing biodiversity research on carbon sinks in urban public green spaces, and enhancing the precision of measurements while addressing regional differences. This study contributes to the integration of research on the effectiveness of carbon sinks in urban public green spaces and provides a theoretical reference and guidance reduces prospective urban emission and achieves climate goals.     

Detection of unfavourable urban areas with higher temperatures and lack of green spaces using satellite imagery in sixteen Spanish cities

This paper seeks to identify the most unfavourable areas of a city in terms of high temperatures and the absence of green infrastructure. An automatic methodology based on remote sensing and data analysis has been developed and applied in sixteen Spanish cities with different characteristics. Landsat-8 satellite images were selected for each city from the July-August period of 2019 and 2020 to calculate the spatial variation of land surface temperature (LST). The Normalized Difference Vegetation Index (NDVI) was used to determine the abundance of vegetation across the city. Based on the NDVI and LST maps created, a k-means unsupervised classification clustering was performed to automatically identify the different clusters according to how favourable these areas were in terms of temperature and presence of vegetation. A Disadvantaged Area Index (DAI), combining both variables, was developed to produce a map showing the most unfavourable areas for each city. Overall, the percentage of the area susceptible to improvement with more vegetation in the cities studied ranged from 13 % in Huesca to 64–65 % in Bilbao and Valencia. The influence of several factors, such as the presence of water bodies or large buildings, is discussed. Detecting unfavourable areas is a very interesting tool for defining future planning strategy for green spaces.     

The inequity of distribution of urban forest and ecosystem services in Cali,Colombia

Little is known about urban forest planning, management and its benefits in emerging countries. The uneven distribution of tree canopy cover and parks in urban area is related to environmental justice, especially with disadvantaged socio-economic and marginated communities. However, the inequity of urban forest in many cities of emerging countries where often found irregular and unregulated land use patterns and social and socio-economic inequities, is hardly highlighted. This study explores the inequity of distribution of tree canopy cover and public park in Cali, Colombia. Utilizing the traditional socio-economic indices, the stratification, linear regression analysis is conducted to describe relationship between total tree canopy cover, tree canopy cover of various land use types, number of parks and park area per capita. The result demonstrates that lower income communities have lower tree canopy cover, fewer parks and smaller park area than higher income communities. This paper discusses importance of accounting for urban forests and ecosystem service in city planning efforts and better strategies of reducing inequity in emerging countries. Addressing the inequity of urban forest could be a better strategy to create resilient, sustainable, safe and livable cities in emerging countries.     

Estimating Settlement carbon stock and density using an inventory approach and quantifying their variation by land use and parcel size

A lack of very-high resolution land-cover data and in-situ carbon sampling in Settlement areas has limited the quantification of terrestrial carbon in Canadian Settlements and elsewhere. Without those data, it is difficult to quantify Settlement area terrestrial carbon for United Nations Framework Convention on Climate Change reporting within the Land Use, Land Use Change, and Forestry sector. The presented research takes a step toward filling this gap by first classifying Settlement land cover at a very-high resolution (<=80 cm, 93% overall accuracy). Then, with those data, an inventory approach is used to estimate carbon stocks based on local data, standard rulesets from the IPCC, and practice-based assumptions. Guided by FAIR principles, our approach is operationalized and available in a Jupyter Notebook for distribution, use, and extension by others. Results found that the study Settlement comprised 30% tree cover and 18% turfgrass. When analyzed by parcel size, carbon densities varied little for parcels less than 1.6 ha (3.9–4.2 kg C m^-2), but then increased with larger parcels up to 6.6 Gg C m^-2 for parcels > 8.1 ha. Among different land uses, industrial, commercial, and transportation had the lowest carbon densities (2.4–2.8 kg C m^-2), followed by high, medium, and low-density residential (3.6, 4.0, 8.9 kg C m^-2, respectively) with low-density residential almost achieving carbon densities in protected and recreation areas (10.2 kg C m^-2). Our results suggest that land use is a stronger driver of carbon-storage relative to parcel size, but their combination best represents the variation in carbon-storage in low-density residential land use. Currently, only carbon change in urban trees and deforestation around urban centres is reported in Canada’s National GHG Inventory Report. The present research quantifying carbon densities provides an analysis that could inform carbon change resulting from other land-use conversions and improve deforestation estimates by better defining the final state of a land-use change.