Mapping carbon storage in urban trees with multi-source remote sensing data: relationships between biomass, land use, and demographics in Boston neighborhoods

Carbon storage estimation and strategy optimization under low carbon objectives for urban attached green spaces

In the context of rapid urbanization, scarce land resources have highlighted the importance of attached green spaces (AGS), which have received limited attention despite their critical importance in the urban carbon cycle. Analyzing the differences in carbon storage of different AGS types and proposing different optimization strategies can provide a reference for urban managers to scientifically enhance the carbon sinks of greenfield. This study estimated the carbon storage of AGS in central Shanghai using a series of detailed field survey data and remote sensing data. The results showed that AGS stored 296 Gg (1Gg = 109 g) of carbon, accounting for 56 % of the total carbon storage in all urban green spaces. While carbon density in AGS is lower than that in other green space types due to the dominance of small trees, simple vegetation structures and poor maintenance in some areas. Significant differences in carbon storage capacity were also found among different types of AGS, related to individual vegetation attributes and vegetation structure. Our findings emphasized that optimal design strategies should be different for various green space types, and proposed corresponding optimization strategies for different AGS types. Furthermore, the dual role of multi-layered planting structures in carbon sinks and biodiversity was also emphasized.

Advance methodological approaches for carbon stock estimation in forest ecosystems

The forests are a key player in maintaining ecological balance on the earth. They not only conserve biodiversity, reduce soil erosion, and protect watersheds but also promote the above and below-ground ecosystem services. Forests are known as air cleaners on the planet and play a significant role in mitigating greenhouse gas (GHG) emissions into the atmosphere. As per programs launched in the Conference of Parties (COP) 26, there is a need to promote policies and programs to reduce the atmospheric carbon (C) through the forest ecosystem; it is because forests can capture the atmospheric CO₂ for a long time and help to achieve the goals of net-zero emission CO₂ on the earth. Therefore, there is an urgent need to know the advanced technological approaches for estimating C stock in forest ecosystems. Hence, the present article is aimed at providing a comprehensive protocol for the four C stock estimation approaches. An effort has also been made to compare these methods. This review suggests that tree allometry is the most common method used for the quantification of C stock, but this method has certain limitations. However, the review shows that accurate results can be produced by a combination of two or more methods. We have also analyzed the results of 42 research studies conducted for C stock assessment along with the factors determining the amount of C in different types of forests. The C stock in vegetation is affected by temporal and spatial variation, plantation age, land use, cropping pattern, management practices and elevation, etc. Nevertheless, the available results have a large degree of uncertainty mainly due to the limitations of the methods used. The review supports the conclusion that the uncertainty in C stock measurements can be addressed by the integration of the above-mentioned methods.

Biomass and Carbon Stock Quantification in Cork Oak Forest of Maamora Using a New Approach Based on the Combination of Aerial Laser Scanning Carried by Unmanned Aerial Vehicle and Terrestrial Laser Scanning Data

The Mediterranean forests, particularly Cork oak (Quercus suber L., 1927), make a major contribution to the fight against climate change through Carbon sequestration. Hence, there is a great interest in the accurate quantification of biomass and carbon stock. In this context, this study aims at assessing the performance of a new approach, based on the combination of Unmanned aerial vehicle airborne Aerial laser scanning (ALS-UAV) and Terrestrial laser scanning (TLS) data, in the determination of dendrometric parameters (Circumference at 1.30 m and Tree Height), and consequently the estimation of biomass and carbon stock, considering field data as reference. This study takes the Maamora forest in Morocco as an example of a Mediterranean Cork oak forest. The methodology consists of collecting data at three levels: the entire area level for an ALS-UAV scan, the plot and tree levels for TLS surveys, as well as field data collection. Afterwards, dendrometric parameters (Circumference at 1.30 m and the Tree height) were estimated using individual tree segmentation and biomass; the carbon stock (aboveground, belowground, and total) was estimated using allometric equations. The comparison of the estimated dendrometric parameters with those measured in the field shows a strong relationship, with a Pearson coefficient of 0.86 and 0.83, a correlation coefficient (R2) of 0.81 and 0.71, and a Root mean square error (RMSE) of 1.84 cm and 0.47 m, respectively. Concerning the biomass and carbon stock estimation, the proposed approach gives a satisfactory accuracy, with a Pearson coefficient of 0.77, an R2 of 0.83, and an RMSE of 36.40 kg for biomass and 20.24 kg for carbon stock.

A fine spatial resolution modeling of urban carbon emissions: a case study of Shanghai, China

Quantification of fossil fuel carbon dioxide emissions (CEs) at fine space and time resolution is a critical need in climate change research and carbon cycle. Quantifying changes in spatiotemporal patterns of urban CEs is important to understand carbon cycle and development carbon reduction strategies. The existing spatial data of CEs have low resolution and cannot distinguish the distribution characteristics of CEs of different emission sectors. This study quantified CEs from 15 types of energy sources, including residential, tertiary, and industrial sectors in Shanghai. Additionally, we mapped the CEs for the three sectors using point of interest data and web crawler technology, which is different from traditional methods. At a resolution of 30 m, the improved CEs data has a higher spatial resolution than existing studies. The spatial distribution of CEs based on this study has higher spatial resolution and more details than that based on traditional methods, and can distinguish the spatial distribution characteristics of different sectors. The results indicated that there was a consistent increase in CEs during 2000–2015, with a low rate of increase during 2009–2015. The intensity of CEs increased significantly in the outskirts of the city, mainly due to industrial transfer. Moreover, intensity of CEs reduced in city center. Technological progress has promoted the improvement of energy efficiency, and there has been a decoupling between the economic development and CEs in the city was observed during in 2000–2015.

Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality

Enhancing the terrestrial ecosystem carbon sink (referred to as terrestrial C sink) is an important way to slow down the continuous increase in atmospheric carbon dioxide (CO₂) concentration and to achieve carbon neutrality target. To better understand the characteristics of terrestrial C sinks and their contribution to carbon neutrality, this review summarizes major progress in terrestrial C budget researches during the past decades, clarifies spatial patterns and drivers of terrestrial C sources and sinks in China and around the world, and examines the role of terrestrial C sinks in achieving carbon neutrality target. According to recent studies, the global terrestrial C sink has been increasing from a source of (-0.2±0.9) Pg C yr^-1 (1 Pg=10¹⁵ g) in the 1960s to a sink of (1.9±1.1) Pg C yr^-1 in the 2010s. By synthesizing the published data, we estimate terrestrial C sink of 0.20-0.25 Pg C yr^-1 in China during the past decades, and predict it to be 0.15-0.52 Pg C yr^-1 by 2060. The terrestrial C sinks are mainly located in the mid- and high latitudes of the Northern Hemisphere, while tropical regions act as a weak C sink or source. The C balance differs much among ecosystem types: forest is the major C sink; shrubland, wetland and farmland soil act as C sinks; and whether the grassland functions as C sink or source remains unclear. Desert might be a C sink, but the magnitude and the associated mechanisms are still controversial. Elevated atmospheric CO₂ concentration, nitrogen deposition, climate change, and land cover change are the main drivers of terrestrial C sinks, while other factors such as fires and aerosols would also affect ecosystem C balance. The driving factors of terrestrial C sink differ among regions. Elevated CO₂ concentration and climate change are major drivers of the C sinks in North America and Europe, while afforestation and ecological restoration are additionally important forcing factors of terrestrial C sinks in China. For future studies, we recommend the necessity for intensive and long term ecosystem C monitoring over broad geographic scale to improve terrestrial biosphere models for accurately evaluating terrestrial C budget and its dynamics under various climate change and policy scenarios.

Expressing carbon storage in economic terms: The case of the upper Omo Gibe Basin in Ethiopia

Terrestrial carbon storage is important for planning decisions regarding climate change. Therefore, modelling the spatial distribution of carbon storage and valuation can help restore the sustainability of the ecosystems. This study aimed at showing the spatial and temporal variations in carbon storage and valuation in the upper Omo Gibe Basin. Land use/cover and carbon pool data based on field data collection and laboratory analyses supported by GIS and remote sensing were used. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) software was used for modelling carbon storage. The Global voluntary carbon market price and Tropical Economics of Ecosystems and Biodiversity (TEEB) data were used for describing carbon storage in economic terms. ANOVA was carried out to detect significant differences in carbon stock correlation with parameters. The results show that the annual carbon stock declined by 0.37 t/ha and the carbon market declined from USD 25.04 billion in 1988 to USD 24.01 billion in 2018. The highest loss of carbon storage and valuation was found in forest land followed by grazing land. Moreover, carbon stock was positively correlated with NDVI and habitat quality (p < 0.05). Slopes did not affect carbon stock (p > 0.05). This study helps promote and enhance carbon trading.

Literature Review Reveals a Global Access Inequity to Urban Green Spaces

Differences in the accessibility to urban resources between different racial and socioeconomic groups have exerted pressure on effective planning and management for sustainable city development. However, few studies have examined the multiple factors that may influence the mitigation of urban green spaces (UGS) inequity. This study reports the results of a systematic mapping of access inequity research through correspondence analysis (CA) to reveal critical trends, knowledge gaps, and clusters based on a sample of 49 empirical studies screened from 563 selected papers. Our findings suggest that although the scale of cities with UGS access inequity varies between countries, large cities (more than 1,000,000 population), especially in low- and middle-income countries (LMICs), are particularly affected. Moreover, the number of cities in which high socioeconomic status (high-SES) groups (e.g., young, rich, or employed) are at an advantage concerning access to UGS is substantially higher than the number of cities showing better accessibility for low-SES groups. Across the reviewed papers, analyses on mitigating interventions are sparse, and among the few studies that touch upon this, we found different central issues in local mitigating strategies between high-income countries (HICs) and LMICs. An explanatory framework is offered, explaining the interaction between UGS access inequity and local mitigating measures.

A Satellite-Based Model for Estimating Latent Heat Flux From Urban Vegetation

The impacts of extreme heat events are amplified in cities due to unique urban thermal properties. Urban greenspace mitigates high temperatures through evapotranspiration and shading; however, quantification of vegetative cooling potential in cities is often limited to simple remote sensing greenness indices or sparse, in situ measurements. Here, we develop a spatially explicit, high-resolution model of urban latent heat flux from vegetation. The model iterates through three core equations that consider urban climatological and physiological characteristics, producing estimates of latent heat flux at 30-m spatial resolution and hourly temporal resolution. We find strong agreement between field observations and model estimates of latent heat flux across a range of ecosystem types, including cities. This model introduces a valuable tool to quantify the spatial heterogeneity of vegetation cooling benefits across the complex landscape of cities at an adequate resolution to inform policies addressing the effects of extreme heat events.

Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park

Estimation of urban tree canopy parameters plays a crucial role in urban forest management. Unmanned aerial vehicles (UAV) have been widely used for many applications particularly forestry mapping. UAV-derived images, captured by an onboard camera, provide a means to produce 3D point clouds using photogrammetric mapping. Similarly, small UAV mounted light detection and ranging (LiDAR) sensors can also provide very dense 3D point clouds. While point clouds derived from both photogrammetric and LiDAR sensors can allow the accurate estimation of critical tree canopy parameters, so far a comparison of both techniques is missing. Point clouds derived from these sources vary according to differences in data collection and processing, a detailed comparison of point clouds in terms of accuracy and completeness, in relation to tree canopy parameters using point clouds is necessary. In this research, point clouds produced by UAV-photogrammetry and -LiDAR over an urban park along with the estimated tree canopy parameters are compared, and results are presented. The results show that UAV-photogrammetry and -LiDAR point clouds are highly correlated with R2 of 99.54% and the estimated tree canopy parameters are correlated with R2 of higher than 95%.

Assessment of Above-Ground Carbon Storage by Urban Trees Using LiDAR Data: The Case of a University Campus

The biomass represented by urban trees is important for urban decision-makers, green space planners, and managers seeking to optimize urban ecosystem services. Carbon storage by urban trees is one of these services. Suitable methods for assessing carbon storage by urban trees are being explored. The latest technologies in remote sensing and data analyses can reduce data collection costs while improving accuracy. This paper introduces an assessment approach that combines ground measurements with unmanned aerial vehicle-based light detection and ranging (LiDAR) data to estimate carbon storage by urban trees. Methods underpinning the approach were tested for the case of the Vancouver campus of the University of British Columbia (UBC), Canada. The study objectives were (1) to test five automated individual tree detection (AITD) algorithms and select one on the basis of the highest segmentation accuracy, (2) to develop a model to estimate the diameter at breast height (DBH), and (3) to estimate and map carbon storage over the UBC campus using LiDAR heights, estimated DBHs, and an existing tree-level above-ground carbon estimation model. Of the segmentation algorithms tested, the Dalponte AITD had the highest F score of 0.83. Of the five CW thresholds (th) tested in the DBH estimation model, we chose one resulting in the lowest Akaike’s information criterion, the highest log-likelihood, and the lowest root-mean-squared error (19.55 cm). Above-ground carbon was estimated for each tree in the study area and subsequently summarized, resulting in an estimated 5.27 kg C·m−2 over the main campus of UBC, Vancouver. The approach could be used in other urban jurisdictions to obtain essential information on urban carbon storage in support of urban landscape governance, planning, and management.

Influence of landscape management practices on urban greenhouse gas budgets

BACKGROUND

With a lack of United States federal policy to address climate change, cities, the private sector, and universities have shouldered much of the work to reduce carbon dioxide (CO₂) and other greenhouse gas emissions. This study aims to determine how landcover characteristics influence the amount of carbon (C) sequestered and respired via biological processes, evaluating the role of land management on the overall C budget of an urban university. Boston University published a comprehensive Climate Action Plan in 2017 with the goal of achieving C neutrality by 2040. In this study, we digitized and discretized each of Boston University's three urban campuses into landcover types, with C sequestration and respiration rates measured and scaled to provide a University-wide estimate of biogenic C fluxes within the broader context of total University emissions.

RESULTS

Each of Boston University's three highly urban campuses were net sources of biogenic C to the atmosphere. While trees were estimated to sequester 0.6 ± 0.2 kg C m^-2 canopy cover year^-1, mulch and lawn areas in 2018 emitted C at rates of 1.7 ± 0.4 kg C m^-2 year^-1 and 1.4 ± 0.4 kg C m^-2 year^-1, respectively. C uptake by tree canopy cover, which can spatially overlap lawn and mulched landcovers, was not large enough to offset biogenic emissions. The proportion of biogenic emissions to Scope 1 anthropogenic emissions on each campus varied from 0.5% to 2%, and depended primarily on the total anthropogenic emissions on each campus.

CONCLUSIONS

Our study quantifies the role of urban landcover in local C budgets, offering insights on how landscaping management strategies-such as decreasing mulch application rates and expanding tree canopy extent-can assist universities in minimizing biogenic C emissions and even potentially creating a small biogenic C sink. Although biogenic C fluxes represent a small fraction of overall anthropogenic emissions on urban university campuses, these biogenic fluxes are under active management by the university and should be included in climate action plans.

Impact of Urban Expansion and In Situ Greenery on Community-Wide Carbon Emissions: Method Development and Insights from 11 US Cities

Biogenic CO₂ emissions in cities are shaped by urban land cover change which can release carbon stocks, and, carbon sequestration by in situ vegetation. To date, these two processes have not been studied together and compared with transboundary fossil fuel-based CO₂ emissions of urban energy use. We leverage remote sensing and machine learning to quantify biogenic CO₂ emissions between 2006 and 2012, across 11 U.S cities, including central and suburban cities, in different climate zones. Results indicate that in situ carbon sequestration by greenery varied moderately across cities (-2.1 to -0.87 Mg CO₂ ha^-1 yr^-1), while emissions from the carbon stock change due to land conversion varied much more (-3.4 to 9.8 Mg CO₂ ha^-1 yr^-1), indicating that the latter dominates biogenic CO₂ emissions. Net biogenic CO₂ emissions were negative (carbon sink) in four cities, while large net positive emissions were present in rapidly growing suburbs. As a ratio of community-wide energy use for travel and buildings, biogenic CO₂ emissions were a small proportion in the core cities Denver (0.17%) and Minneapolis (0.33%) and as high as 38.2% in growing exurban communities. These results show that land cover change and greenery will be important policy levers in zero-carbon city planning.

The Relationship between Land Cover and Sociodemographic Factors

Multiple social and environmental justice concerns are linked to the urban form such as the distribution of socioeconomic class populations, healthcare spending, air pollution exposure, and human mobility. Because of this, the implications of the relationships between built urban form, sociodemographic factors, and air quality warrant analysis at a high spatial resolution. This study used 1m resolved LiDAR data to characterize land use in Salt Lake County, Utah, and associate it with sociodemographic and air quality data at the census block group and zip code levels. We found that increasing tree cover was associated with higher per capita income and lower minority populations while increasing built cover was linked to lower per capita income and higher minority populations. Air quality showed less strong correlations, however, decreased non-irrigated cover, increased built cover, and higher amounts of households living under poverty were related to higher long-term PM2.5 exposure. Due to regional air pollution concerns, several policy efforts have been undertaken to improve air quality and reduce negative health outcomes in Utah which are being informed by regulatory and research-grade air quality sensors.

Carbon sequestration in the bio-edaphic ecosystem of National Highway-27 in Guwahati, Assam, India

Vehicular pollution in cities is engendering the need to enhance the sequestration of CO₂ through bio-edaphic factors, such as trees and soil. Hence, this pioneering study aimed to analyze the interdependencies of the bio-edaphic ecosystem during carbon sequestration on a national highway in Guwahati, Assam, India. To quantify the tree carbon stock, soil physicochemical properties, soil nutrients, bulk density, organic carbon, and soil carbon stock, trees with diameters at breast height (dbh) ≥ 2 cm and soil samples from different depths (0–15 cm, 15–30 cm, and 30–45 cm) were taken from six areas spread over 36 plots of equal size (2 × 10 m) arranged in a zig-zag manner. The studied parameters were compared among the different areas. It was found that the tree and soil carbon stock, along with soil nutrients, were greater in the Garchuk-Lokhra area (sample area-4). A significantly strong correlation was observed between the soil carbon stock and the above and below-ground biomass of trees (AGB, r = 0.865; BGB, r = 0.847), which indicated the co-dependencies of the bio-edaphic ecosystem in accumulating carbon. Peltophorum ferrugineum (Decne.) Benth is recommended for planting because it has emerged as a tolerant species and has the greatest carbon storage potential. The bio-edaphic ecosystem of the national highway is now on a carbon-friendly trajectory that follows the UNFCCC guidelines and the REDD+ (reducing emissions from deforestation and forest degradation) strategy. However further research on carbon budgeting is required.

Current and future biomass carbon uptake in Boston's urban forest

Ecosystem services provided by urban forests are increasingly included in municipal-level responses to climate change. However, the ecosystem functions that generate these services, such as biomass carbon (C) uptake, can differ substantially from nearby rural forest. In particular, the scaled effect of canopy spatial configuration on tree growth in cities is uncertain, as is the scope for medium-term policy intervention. This study integrates high spatial resolution data on tree canopy and biomass in the city of Boston, Massachusetts, with local measurements of tree growth rates to estimate the magnitude and distribution of annual biomass C uptake. We further project C uptake, biomass, and canopy cover change to 2040 under alternative policy scenarios affecting the planting and preservation of urban trees. Our analysis shows that 85% of tree canopy area was within 10 m of an edge, indicating essentially open growing conditions. Using growth models accounting for canopy edge effects and growth context, Boston's current biomass C uptake may be approximately double (median 10.9 GgC yr^-1, 0.5 MgC ha^-1 yr^-1) the estimates based on rural forest growth, much of it occurring in high-density residential areas. Total annual C uptake to long-term biomass storage was equivalent to <1% of estimated annual fossil CO₂ emissions for the city. In built-up areas, reducing mortality in larger trees resulted in the highest predicted increase in canopy cover (+25%) and biomass C stocks (236 GgC) by 2040, while planting trees in available road margins resulted in the greatest predicted annual C uptake (7.1 GgC yr^-1). This study highlights the importance of accounting for the altered ecosystem structure and function in urban areas in evaluating ecosystem services. Effective municipal climate responses should consider the substantial fraction of total services performed by trees in developed areas, which may produce strong but localized atmospheric C sinks.

A Review of Regional and Global Gridded Forest Biomass Datasets

Forest biomass quantification is essential to the global carbon cycle and climate studies. Many studies have estimated forest biomass from a variety of data sources, and consequently generated some regional and global maps. However, these forest biomass maps are not well known and evaluated. In this paper, we reviewed an extensive list of currently available forest biomass maps. For each map, we briefly introduced the data sources, the algorithms used, and the associated uncertainties. Large-scale biomass datasets were compared across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America. Results showed that these forest biomass datasets were almost entirely inconsistent, particularly in woody savannas and savannas across these regions. The uncertainties in biomass maps could be from a variety of sources including the chosen allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but these uncertainties have not been fully quantified. We suggested the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.

Spatiotemporal Dynamics of Carbon Storage in Response to Urbanization: A Case Study in the Su-Xi-Chang Region, China

Terrestrial ecosystem carbon storage plays an important role in mitigating global warming. Understanding the characteristics and drivers of changes in carbon storage can provide a scientific basis for urban planning and management. The objective of this study was to reveal the ways in which urbanization influences the spatial and temporal variations in carbon storage. In this study, we investigated the changes in carbon storage from 1990–2000, 2000–2010, and 2010–2018 in the Su-Xi-Chang region, which is a typical fast-growing urban agglomeration in China, based on the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model. Moreover, we analyzed the impacts of urbanization-induced land-use changes on carbon storage. The results showed that in terms of space and time, the greatest loss of carbon storage occurred in developing urban areas and during the rapidly urbanizing stage. Our study revealed that the reduction in cultivated land was the greatest contributor to carbon stock losses. In addition, we found that some types of land use conversion can enhance carbon storage. Based on the results, some suggestions are proposed aimed at promoting urban sustainable development. This study also provides insights into enhancing urban sustainability for other urban agglomerations throughout the world.

Hierarchical Classification of Urban ALS Data by Using Geometry and Intensity Information

Airborne laser scanning (ALS) can acquire both geometry and intensity information of geo-objects, which is important in mapping a large-scale three-dimensional (3D) urban environment. However, the intensity information recorded by ALS will be changed due to the flight height and atmospheric attenuation, which decreases the robustness of the trained supervised classifier. This paper proposes a hierarchical classification method by separately using geometry and intensity information of urban ALS data. The method uses supervised learning for stable geometry information and unsupervised learning for fluctuating intensity information. The experiment results show that the proposed method can utilize the intensity information effectively, based on three aspects, as below. (1) The proposed method improves the accuracy of classification result by using intensity. (2) When the ALS data to be classified are acquired under the same conditions as the training data, the performance of the proposed method is as good as the supervised learning method. (3) When the ALS data to be classified are acquired under different conditions from the training data, the performance of the proposed method is better than the supervised learning method. Therefore, the classification model derived from the proposed method can be transferred to other ALS data whose intensity is inconsistent with the training data. Furthermore, the proposed method can contribute to the hierarchical use of some other ALS information, such as multi-spectral information.

An improved method for assessing mismatches between supply and demand in urban regulating ecosystem services: A case study in Tabriz, Iran

Regulating ecosystem services provided by urban forests are of great importance for the quality of life among city dwellers. To reach a maximum contribution to well-being in cities, the urban regulating ecosystem services (URES) must match with the demands in terms of space and time. If we understand the matches or mismatches between the current urban dwellers' desired quality conditions (demand) and the supply of URES by urban forests (UF) in the cities, this will facilitate integrating the concepts of ecosystem services in urban planning and management, but such an assessment has suffered from major knowledge limitations. Since it is complex and problematic to identify the direct demands for URES and the spatiotemporal patterns therein, improving the demand indicators can help to determine the actual requirements. In this paper, a methodological approach based on indicators is presented and demonstrated for two important URES: air quality improvement and global climate change mitigation provided by urban trees and shrubs. Four air quality standards and greenhouse gas reduction targets were used and compared to supplies of the URES in Tabriz, Iran. Our results show that the mean contribution of the URES supply to air quality standards and greenhouse gas reduction targets is modest. Hence, in Tabriz, there is a strong mismatch between demand and supply. Mismatches at the city scale will have to be reduced by both a reduction in pollutant emissions and an increased provisioning of URES supply through urban greenery. The presented assessment approach and the results for Tabriz make it explicit how different the demands and supplies of the two studied URES are, and we expect similar mismatches in many other cities. Therefore, our approach, relatively simple but still realistic and easy-to-apply, can raise awareness about, and the utility of, the ecosystem services concepts for urban planning and policymaking.

Developing General Equations for Urban Tree Biomass Estimation with High-Resolution Satellite Imagery

Urban trees provide various important ecological services, the quantification of which is vital to sustainable urban development and requires accurate estimation of tree biomass. A limited number of allometric biomass equations, however, have been developed for urban species due to the prohibitive cost. Remote sensing has provided cost-effective means for estimating urban forest biomass, although the propagation of error in the estimation process is not well understood. This study aimed to offer a baseline assessment of the feasibility of estimating urban tree biomass with remote sensing-based general equations applicable to broad taxonomic groups by conducting a large urban tree inventory on a university campus. The biomasses of 191 trees of seven species from the inventory, separated into two categories (i.e., evergreen and deciduous), were calculated exclusively with urban-based species-specific allometric equations. WorldView-2 satellite imagery data were acquired to retrieve normalized difference vegetation index (NDVI) values at the location, crown, and stand levels. The results indicated that biomass correlated with NDVI in varying forms and degrees. The general equations at the crown level yielded the most accurate biomass estimates, while the location-level estimates were the least accurate. Crown-level spectral responses provided adequate information for delivering spatially explicit biomass estimation.

Changes in spatio-temporal patterns of urban forest and its above-ground carbon storage: Implication for urban CO2 emissions mitigation under China's rapid urban expansion and greening

BACKGROUND

Understanding spatio-temporal dynamics of UF and its above-ground carbon storage (CS) is important for mitigating urban CO₂ emissions under China's rapid urban expansion and greening.

METHODS

In our study, vegetation index (VI) data obtained from TM image and CS derived from field-based surveys were amalgamated to develop a regression model to predict spatio-temporal patterns of CS. VI correction model was established by normalizing previous imagery (1984, 1995, and 2005) to 2014 image data.

RESULTS

NDVI is better than other VIs for predicting urban forest CS. Both UF area and its CS increased gradually from 1984 to 2014, especially in outer rings of the city. CS showed a definite decreasing trend from outer rings to downtown. Due to urban greening policies, landscape patches of UF or CS recently became larger and more aggregated. The CS by UF class distribution was skewed toward low values in all the years, but the skew gradually decreased over time. It was estimated that the average annual increase of CS by UF could offset 3.9% of the average annual increase in urban carbon emissions.

CONCLUSIONS

Our study proposes that spatio-temporal changes in UF patterns dramatically affected the amount of CS and carbon capture. The increase in UF coverage and quality would mitigate more urban carbon emissions, especially in outer rings.

Remote Sensing in Urban Forestry: Recent Applications and Future Directions

Increasing recognition of the importance of urban forest ecosystem services calls for the sustainable management of urban forests, which requires timely and accurate information on the status, trends and interactions between socioeconomic and ecological processes pertaining to urban forests. In this regard, remote sensing, especially with its recent advances in sensors and data processing methods, has emerged as a premier and useful observational and analytical tool. This study summarises recent remote sensing applications in urban forestry from the perspective of three distinctive themes: multi-source, multi-temporal and multi-scale inputs. It reviews how different sources of remotely sensed data offer a fast, replicable and scalable way to quantify urban forest dynamics at varying spatiotemporal scales on a case-by-case basis. Combined optical imagery and LiDAR data results as the most promising among multi-source inputs; in addition, future efforts should focus on enhancing data processing efficiency. For long-term multi-temporal inputs, in the event satellite imagery is the only available data source, future work should improve haze-/cloud-removal techniques for enhancing image quality. Current attention given to multi-scale inputs remains limited; hence, future studies should be more aware of scale effects and cautiously draw conclusions.

Live fast, die young: Accelerated growth, mortality, and turnover in street trees

Municipalities are embracing greening initiatives as a key strategy for improving urban sustainability and combatting the environmental impacts of expansive urbanization. Many greening initiatives include goals to increase urban canopy cover through tree planting, however, our understanding of street tree ecosystem dynamics is limited and our understanding of vegetation structure and function based on intact, rural forests does not apply well to urban ecosystems. In this study, we estimate size-specific growth, mortality, and planting rates in trees under municipal control, use a box model to forecast short-term changes in street tree aboveground carbon pools under several planting and management scenarios, and compare our findings to rural, forested systems. We find accelerated rates of carbon cycling in street trees with mean diameter growth rates nearly four times faster in Boston, MA, USA (0.78 ± 0.02 cm yr-1) than in rural forest stands of MA (0.21 ± 0.02 cm yr-1) and mean mortality rates more than double rural forested rates (3.06 ± 0.25% yr-1 in street trees; 1.41 ± 0.04% yr-1 in rural trees). Despite the enhanced growth of urban trees, high mortality losses result in a net loss of street tree carbon storage over time (-0.15 ± 0.09 Mg C ha-1 yr-1). Planting initiatives alone may not be sufficient to maintain or enhance canopy cover and biomass due to the unique demographics of urban ecosystems. Initiatives to aid in the establishment and preservation of tree health are central for increasing street tree canopy cover and maintaining/increasing carbon storage in vegetation. Strategic combinations of planting and maintenance will maximize the viability of greening initiatives as an effective climate mitigation tool.

Valuing urban green spaces in mitigating climate change: A city-wide estimate of aboveground carbon stored in urban green spaces of China's Capital

Urban green spaces provide manifold environmental benefits and promote human well-being. Unfortunately, these services are largely undervalued, and the potential of urban areas themselves to mitigate future climate change has received little attention. In this study, we quantified and mapped city-wide aboveground carbon storage of urban green spaces in China's capital, Beijing, using field survey data of diameter at breast height (DBH) and tree height from 326 field survey plots, combined with satellite-derived vegetation index at a fine resolution of 6 m. We estimated the total amount of carbon stored in the urban green spaces to be 956.3 Gg (1 Gg = 10⁹ g) in 2014. There existed great spatial heterogeneity in vegetation carbon density varying from 0 to 68.1 Mg C ha^-1 , with an average density of 7.8 Mg C ha^-1 . As expected, carbon density tended to decrease with urban development intensity (UDI). Likely being affected by vegetation cover proportion and configuration of green space patches, large differences were presented between the 95th and 5th quantile carbon density for each UDI bin, showing great potential for carbon sequestration. However, the interquartile range of carbon density narrowed drastically when UDI reached 60%, signifying a threshold for greatly reduced carbon sequestration potentials for higher UDI. These findings suggested that urban green spaces have great potential to make contribution to mitigating against future climate change if we plan and design urban green spaces following the trajectory of high carbon density, but we should be aware that such potential will be very limited when the urban development reaches certain intensity threshold.

Evaluating the Cooling Potential of Urban Green Spaces to Tackle Urban Climate Change in Lisbon

The increase and optimization of urban vegetation has been considered an effective mitigation measure of an urban heat island (UHI), with positive effects on human thermal comfort. In this study, the cooling potential of all green spaces in Lisbon was estimated. For that, several mobile measurements of air temperature data were made in a single park (Gulbenkian’s Garden). These measurements were used for the interpolation of air temperature. Furthermore, urban biomass was estimated using remote sensing products, namely Landsat satellite images. Ultimately, a linear regression model was built from the relation between vegetation density and air temperature. Results regarding the estimation of biomass (AGB) in the city of Lisbon were higher in winter than in summer. The urban green spaces cooling potential model showed that for every decrease of 1 °C in air temperature between a measuring point and a reference station we need to increase the area covered by vegetation by 50 m2 (planar measure). This methodology can be applied in other urban areas for the quantification of the cooling effect provided by vegetation in order to improve urban climate thermal conditions and human well-being and, consequently, to mitigate some consequences of future climate change.

Developing a framework for stormwater management: leveraging ancillary benefits from urban greenspace

Managing stormwater and wastewater has been a priority for cities for millennia, but has become increasingly complicated as urban areas grow and develop. Since the mid-1800s, cites often relied on an integrated system of underground pipes, pumps, and other built infrastructure (termed gray infrastructure) to convey stormwater away from developed areas. Unfortunately, this gray infrastructure is aging and often exceeds its designed capacity. In an effort to alleviate issues related to excess stormwater, many urban areas across the United States are interested in using green infrastructure as a stopgap or supplement to inadequate gray infrastructure. Green infrastructure and other greenspace promote interception and/or infiltration of stormwater by using the natural hydrologic properties of soil and vegetation. Furthermore, there are numerous ancillary benefits, in addition to stormwater benefits, that make the use of greenspace desirable. Collectively, these ecosystem services can benefit multiple aspects of a community by providing benefits in a targeted manner. In this paper, we present a framework for balancing stormwater management against ancillary benefits of urban greenspace. The framework is structured around the Millennium Ecosystem Assessment ecosystem service categories: provisioning, cultural, regulatory, and supporting services. The purpose is to help communities better manage their systems by 1) allowing stakeholders to prioritize and address their needs and concerns within a community, and 2) maximize the ecosystem service benefits received from urban greenspace.

Evaluating Carbon Sequestration and PM2.5 Removal of Urban Street Trees Using Mobile Laser Scanning Data

Street trees are an important part of urban facilities, and they can provide both aesthetic benefits and ecological benefits for urban environments. Ecological benefits of street trees now are attracting more attention because of environmental deterioration in cities. Conventional methods of evaluating ecological benefits require a lot of labor and time, and establishing an efficient and effective evaluating method is challenging. In this study, we investigated the feasibility to use mobile laser scanning (MLS) data to evaluate carbon sequestration and fine particulate matter (PM2.5) removal of street trees. We explored the approach to extract individual street trees from MLS data, and street trees of three streets in Nantong City were extracted. The correctness rates and completeness rates of extraction results were both over 92%. Morphological parameters, including tree height, crown width, and diameter at breast height (DBH), were measured for extracted street trees, and parameters derived from MLS data were in a good agreement with field-measured parameters. Necessary information about street trees, including tree height, DBH, and tree species, meteorological data and PM2.5 deposition velocities were imported into i-Tree Eco model to estimate carbon sequestration and PM2.5 removal. The estimation results indicated that ecological benefits generated by different tree species were considerably varied and the differences for trees of the same species were mainly caused by the differences in morphological parameters (tree height and DBH). This study succeeds in estimating the amount of carbon sequestration and PM2.5 removal of individual street trees with MLS data, and provides researchers with a novel and efficient way to investigate ecological benefits of urban street trees or urban forests.

Estimating urban above ground biomass with multi-scale LiDAR

BACKGROUND

Urban trees have long been valued for providing ecosystem services (mitigation of the "heat island" effect, suppression of air pollution, etc.); more recently the potential of urban forests to store significant above ground biomass (AGB) has also be recognised. However, urban areas pose particular challenges when assessing AGB due to plasticity of tree form, high species diversity as well as heterogeneous and complex land cover. Remote sensing, in particular light detection and ranging (LiDAR), provide a unique opportunity to assess urban AGB by directly measuring tree structure. In this study, terrestrial LiDAR measurements were used to derive new allometry for the London Borough of Camden, that incorporates the wide range of tree structures typical of an urban setting. Using a wall-to-wall airborne LiDAR dataset, individual trees were then identified across the Borough with a new individual tree detection (ITD) method. The new allometry was subsequently applied to the identified trees, generating a Borough-wide estimate of AGB.

RESULTS

Camden has an estimated median AGB density of 51.6 Mg ha-1 where maximum AGB density is found in pockets of woodland; terrestrial LiDAR-derived AGB estimates suggest these areas are comparable to temperate and tropical forest. Multiple linear regression of terrestrial LiDAR-derived maximum height and projected crown area explained 93% of variance in tree volume, highlighting the utility of these metrics to characterise diverse tree structure. Locally derived allometry provided accurate estimates of tree volume whereas a Borough-wide allometry tended to overestimate AGB in woodland areas. The new ITD method successfully identified individual trees; however, AGB was underestimated by ≤ 25% when compared to terrestrial LiDAR, owing to the inability of ITD to resolve crown overlap. A Monte Carlo uncertainty analysis identified assigning wood density values as the largest source of uncertainty when estimating AGB.

CONCLUSION

Over the coming century global populations are predicted to become increasingly urbanised, leading to an unprecedented expansion of urban land cover. Urban areas will become more important as carbon sinks and effective tools to assess carbon densities in these areas are therefore required. Using multi-scale LiDAR presents an opportunity to achieve this, providing a spatially explicit map of urban forest structure and AGB.

Identification of fine scale and landscape scale drivers of urban aboveground carbon stocks using high-resolution modeling and mapping

Urban areas are sources of land use change and CO₂ emissions that contribute to global climate change. Despite this, assessments of urban vegetation carbon stocks often fail to identify important landscape-scale drivers of variation in urban carbon, especially the potential effects of landscape structure variables at different spatial scales. We combined field measurements with Light Detection And Ranging (LiDAR) data to build high-resolution models of woody plant aboveground carbon across the urban portion of Brisbane, Australia, and then identified landscape scale drivers of these carbon stocks. First, we used LiDAR data to quantify the extent and vertical structure of vegetation across the city at high resolution (5×5m). Next, we paired this data with aboveground carbon measurements at 219 sites to create boosted regression tree models and map aboveground carbon across the city. We then used these maps to determine how spatial variation in land cover/land use and landscape structure affects these carbon stocks. Foliage densities above 5m height, tree canopy height, and the presence of ground openings had the strongest relationships with aboveground carbon. Using these fine-scale relationships, we estimate that 2.2±0.4 TgC are stored aboveground in the urban portion of Brisbane, with mean densities of 32.6±5.8MgCha^-1 calculated across the entire urban land area, and 110.9±19.7MgCha^-1 calculated within treed areas. Predicted carbon densities within treed areas showed strong positive relationships with the proportion of surrounding tree cover and how clumped that tree cover was at both 1km² and 1ha resolutions. Our models predict that even dense urban areas with low tree cover can have high carbon densities at fine scales. We conclude that actions and policies aimed at increasing urban carbon should focus on those areas where urban tree cover is most fragmented.

Measuring urban tree loss dynamics across residential landscapes

The spatial arrangement of urban vegetation depends on urban morphology and socio-economic settings. Urban vegetation changes over time because of human management. Urban trees are removed due to hazard prevention or aesthetic preferences. Previous research attributed tree loss to decreases in canopy cover. However, this provides little information about location and structural characteristics of trees lost, as well as environmental and social factors affecting tree loss dynamics. This is particularly relevant in residential landscapes where access to residential parcels for field surveys is limited. We tested whether multi-temporal airborne LiDAR and multi-spectral imagery collected at a 5-year interval can be used to investigate urban tree loss dynamics across residential landscapes in Denver, CO and Milwaukee, WI, covering 400,705 residential parcels in 444 census tracts. Position and stem height of trees lost were extracted from canopy height models calculated as the difference between final (year 5) and initial (year 0) vegetation height derived from LiDAR. Multivariate regression models were used to predict number and height of tree stems lost in residential parcels in each census tract based on urban morphological and socio-economic variables. A total of 28,427 stems were lost from residential parcels in Denver and Milwaukee over 5years. Overall, 7% of residential parcels lost one stem, averaging 90.87 stems per km2. Average stem height was 10.16m, though trees lost in Denver were taller compared to Milwaukee. The number of stems lost was higher in neighborhoods with higher canopy cover and developed before the 1970s. However, socio-economic characteristics had little effect on tree loss dynamics. The study provides a simple method for measuring urban tree loss dynamics within and across entire cities, and represents a further step toward high resolution assessments of the three-dimensional change of urban vegetation at large spatial scales.

High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments

BACKGROUND

Urban forests reduce greenhouse gas emissions by storing and sequestering considerable amounts of carbon. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging for long-term prognoses. Therefore, this review aims to identify uncertainties in urban forest carbon offset assessment and discuss the extent to which such uncertainties can be reduced by recent progress in high resolution remote sensing. We do this by performing an extensive literature review and a case study combining remote sensing and life cycle assessment of urban forest carbon offset in Berlin, Germany.

MAIN TEXT

Recent progress in high resolution remote sensing and methods is adequate for delivering more precise details on the urban tree canopy, individual tree metrics, species, and age structures compared to conventional land use/cover class approaches. These area-wide consistent details can update life cycle inventories for more precise future prognoses. Additional improvements in classification accuracy can be achieved by a higher number of features derived from remote sensing data of increasing resolution, but first studies on this subject indicated that a smart selection of features already provides sufficient data that avoids redundancies and enables more efficient data processing. Our case study from Berlin could use remotely sensed individual tree species as consistent inventory of a life cycle assessment. However, a lack of growth, mortality and planting data forced us to make assumptions, therefore creating uncertainty in the long-term prognoses. Regarding temporal changes and reliable long-term estimates, more attention is required to detect changes of gradual growth, pruning and abrupt changes in tree planting and mortality. As such, precise long-term urban ecological monitoring using high resolution remote sensing should be intensified, especially due to increasing climate change effects. This is important for calibrating and validating recent prognoses of urban forest carbon offset, which have so far scarcely addressed longer timeframes. Additionally, higher resolution remote sensing of urban forest carbon estimates can improve upscaling approaches, which should be extended to reach a more precise global estimate for the first time.

CONCLUSIONS

Urban forest carbon offset can be made more relevant by making more standardized assessments available for science and professional practitioners, and the increasing availability of high resolution remote sensing data and the progress in data processing allows for precisely that.

Accounting for urban biogenic fluxes in regional carbon budgets

Many ecosystem models incorrectly treat urban areas as devoid of vegetation and biogenic carbon (C) fluxes. We sought to improve estimates of urban biomass and biogenic C fluxes using existing, nationally available data products. We characterized biogenic influence on urban C cycling throughout Massachusetts, USA using an ecosystem model that integrates improved representation of urban vegetation, growing conditions associated with urban heat island (UHI), and altered urban phenology. Boston's biomass density is 1/4 that of rural forests, however 87% of Massachusetts' urban landscape is vegetated. Model results suggest that, kilogram-for-kilogram, urban vegetation cycles C twice as fast as rural forests. Urban vegetation releases (R_E) and absorbs (GEE) the equivalent of 11 and 14%, respectively, of anthropogenic emissions in the most urban portions of the state. While urban vegetation in Massachusetts fully sequesters anthropogenic emissions from smaller cities in the region, Boston's UHI reduces annual C storage by >20% such that vegetation offsets only 2% of anthropogenic emissions. Asynchrony between temporal patterns of biogenic and anthropogenic C fluxes further constrains the emissions mitigation potential of urban vegetation. However, neglecting to account for biogenic C fluxes in cities can impair efforts to accurately monitor, report, verify, and reduce anthropogenic emissions.

Carbon lost and carbon gained: a study of vegetation and carbon trade-offs among diverse land uses in Phoenix, Arizona

Human modification and management of urban landscapes drastically alters vegetation and soils, thereby altering carbon (C) storage and rates of net primary productivity (NPP). Complex social and ecological processes drive vegetation cover in cities, leading to heterogeneity in C dynamics depending on regional climate, land use, and land cover. Recent work has demonstrated homogenization in ecological processes within human-dominated landscapes (the urban convergence hypothesis) in soils and biotic communities. However, a lack of information on vegetation in arid land cities has hindered an understanding of potential C storage and NPP convergence across a diversity of ecosystem types. We estimated C storage and NPP of trees and shrubs for six different land-use types in the arid metropolis of Phoenix, Arizona, USA, and compared those results to native desert ecosystems, as well as other urban and natural systems around the world. Results from Phoenix do not support the convergence hypothesis. In particular, C storage in urban trees and shrubs was 42% of that found in desert vegetation, while NPP was only 20% of the total NPP estimated for comparable natural ecosystems. Furthermore, the overall estimates of C storage and NPP associated with urban trees in the CAP ecosystem were much lower (8-63%) than the other cities included in this analysis. We also found that C storage (175.25-388.94 g/m² ) and NPP (8.07-15.99 g·m^-2 ·yr^-1 ) were dominated by trees in the urban residential land uses, while in the desert, shrubs were the primary source for pools (183.65 g/m² ) and fluxes (6.51 g·m^-2 ·yr^-1 ). These results indicate a trade-off between shrubs and trees in arid ecosystems, with shrubs playing a major role in overall C storage and NPP in deserts and trees serving as the dominant C pool in cities. Our research supports current literature that calls for the development of spatially explicit and standardized methods for analyzing C dynamics associated with vegetation in urbanizing areas.

Soil respiration contributes substantially to urban carbon fluxes in the greater Boston area

Urban areas are the dominant source of U.S. fossil fuel carbon dioxide (FFCO2) emissions. In the absence of binding international treaties or decisive U.S. federal policy for greenhouse gas regulation, cities have also become leaders in greenhouse gas reduction efforts through climate action plans. These plans focus on anthropogenic carbon flows only, however, ignoring a potentially substantial contribution to atmospheric carbon dioxide (CO2) concentrations from biological respiration. Our aim was to measure the contribution of CO2 efflux from soil respiration to atmospheric CO2 fluxes using an automated CO2 efflux system and to use these measurements to model urban soil CO2 efflux across an urban area. We find that growing season soil respiration is dramatically enhanced in urban areas and represents levels of CO2 efflux of up to 72% of FFCO2 within greater Boston's residential areas, and that soils in urban forests, lawns, and landscaped cover types emit 2.62 ± 0.15, 4.49 ± 0.14, and 6.73 ± 0.26 μmolCO2 m(-2) s(-1), respectively, during the growing season. These rates represent up to 2.2 times greater soil respiration than rates found in nearby rural ecosystems in central Massachusetts (MA), a potential consequence of imported carbon amendments, such as mulch, within a general regime of landowner management. As the scientific community moves rapidly towards monitoring, reporting, and verification of CO2 emissions using ground based approaches and remotely-sensed observations to measure CO2 concentrations, our results show that measurement and modeling of biogenic urban CO2 fluxes will be a critical component for verification of urban climate action plans.

Assessing the global warming potential of human settlement expansion in a mesic temperate landscape from 2005 to 2050

Expansion of human settlements is an important driver of global environmental change that causes land use and land cover change (LULCC) and alters the biophysical nature of the landscape and climate. We use the state of Massachusetts, United States (U.S.) to present a novel approach to quantifying the effects of projected expansion of human settlements on the biophysical nature of the landscape. We integrate nationally available datasets with the U.S. Environmental Protection Agency's Integrated Climate and Land Use Scenarios model to model albedo and C storage and uptake by forests and vegetation within human settlements. Our results indicate a 4.4 to 14% decline in forest cover and a 35 to 40% increase in developed land between 2005 and 2050, with large spatial variability. LULCC is projected to reduce rates of forest C sequestration, but our results suggest that vegetation within human settlements has the potential to offset a substantial proportion of the decline in the forest C sink and may comprise up to 35% of the terrestrial C sink by 2050. Changes in albedo and terrestrial C fluxes are expected to result in a global warming potential (GWP) of +0.13 Mg CO2-C-equivalence ha(-1)year(-1) under the baseline trajectory, which is equivalent to 17% of the projected increase in fossil fuel emissions. Changes in terrestrial C fluxes are generally the most important driver of the increase in GWP, but albedo change becomes an increasingly important component where housing densities are higher. Expansion of human settlements is the new face of LULCC and our results indicate that when quantifying the biophysical response it is essential to consider C uptake by vegetation within human settlements and the spatial variability in the influence of C fluxes and albedo on changes in GWP.

Impacts of impervious surface expansion on soil organic carbon--a spatially explicit study

The rapid expansion of impervious surface areas (ISA) threatens soil organic carbon (SOC) pools in urbanized areas globally. The paucity of field observations on SOC under ISA (SOC_ISA), especially in dryland areas has limited our ability to assess the ecological impacts of ISA expansion. Based on systematically measured SOC_ISA (0–80 cm depth) of a dryland city, and land-use and land-cover change data derived from remotely sensed data, we investigated the magnitude and vertical/horizontal patterns of SOC_ISA and mapped the impact of ISA expansion on SOC storage. The mean SOC_ISA in the city was 5.36 ± 0.51 kg C m⁻², lower than that observed in humid cities but much higher than that assumed in many regional carbon assessments. SOC_ISA decreased linearly as the soil depth or the horizontal distance from the open area increased. SOC_ISA accounted for over half of the city’s SOC stock, which decreased by 16% (primarily in the converted croplands) because of ISA expansion from 1990 to 2010. The impacts of the ISA expansion varied spatially, depending on the land- use and converted land-cover type.

Tree Productivity Enhanced with Conversion from Forest to Urban Land Covers

Urban areas are expanding, changing the structure and productivity of landscapes. While some urban areas have been shown to hold substantial biomass, the productivity of these systems is largely unknown. We assessed how conversion from forest to urban land uses affected both biomass structure and productivity across eastern Massachusetts. We found that urban land uses held less than half the biomass of adjacent forest expanses with a plot level mean biomass density of 33.5 ± 8.0 Mg C ha^-1. As the intensity of urban development increased, the canopy cover, stem density, and biomass decreased. Analysis of Quercus rubra tree cores showed that tree-level basal area increment nearly doubled following development, increasing from 17.1 ± 3.0 to 35.8 ± 4.7 cm² yr^-1. Scaling the observed stem densities and growth rates within developed areas suggests an aboveground biomass growth rate of 1.8 ± 0.4 Mg C ha^-1 yr^-1, a growth rate comparable to nearby, intact forests. The contrasting high growth rates and lower biomass pools within urban areas suggest a highly dynamic ecosystem with rapid turnover. As global urban extent continues to grow, cities consider climate mitigation options, and as the verification of net greenhouse gas emissions emerges as critical for policy, quantifying the role of urban vegetation in regional-to-global carbon budgets will become ever more important.