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.     

Current and potential carbon stocks of trees in urban parking lots in towns of the Eastern Cape,South Africa

Greening of shopping centre parking lots is a potentially important strategy that can contribute to urban carbon mitigation efforts, improve aesthetics and the shopping experience of consumers, whilst adding to urban biodiversity. Twenty-eight shopping centre parking lots in six Eastern Cape urban centres, South Africa, were sampled to determine tree species composition, density and annual carbon sequestration potential. The best case parking lot found during the study was used as a benchmark to display the difference between current tree density and above-ground carbon stocks relative to the potential optimum. The highest tree density was 66 trees ha^?1, whereas the average density across all sampled parking lots was less than half that (27.2 ± 22.6 trees ha^?1). The average annual carbon sequestration potential per parking lot was 1390 ± 2503 kg ha^?1. Planting density was positively related to annual sequestration rates, whilst parking lot age and the mean annual rainfall of the town had no influence. Mean tree species richness per parking lot was 2.3 ± 1.8 species, with a positive relationship to parking lot size, but not to mean annual rainfall of the site. The majority of trees (62.5%) in parking lots were alien species, although newer parking lots had significantly greater proportions of indigenous species. There was no difference in mean annual carbon sequestration rate per tree between indigenous and alien trees species. Low tree densities and small parking lot areas constrained the potential for earning carbon credits from trees in parking lots. Nonetheless, planners and designers need to be more aware of the potential contribution of trees towards urban sustainability.     

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.     

Allometric equations for estimating the aboveground volume of five common urban street tree species in Daegu,Korea

Quantifying urban tree biomass and carbon (C) storage by using allometric equations is required for various studies such as assessing the inventory, modelling, and measuring ecosystem services of urban trees. However, the lack of urban-specific allometric equations leads to uncertainty when estimating urban tree biomass and C storage. Therefore, we followed a nondestructive approach and developed allometric equations specifically for Acer buergerianum Miq., Ginkgo biloba L., Platanus orientalis L., Prunus yedoensis Matsum., and Zelkova serrata (Thunb.) Makino in Daegu, Korea. Diameter at breast height (DBH)-based and DBH-and-height-based allometric equations were highly accurate at estimating the aboveground volume (R² > 0.92), while the allometric equations for P. orientalis and Z. serrata developed for traditional forests overestimated volume by 68% and 427%, respectively. The addition of a height variable into the DBH-based allometric equations did not increase the reliability of the allometric equations at a local level. The mean aboveground C storage of urban street trees was 24.9 Mg C/ha except for P. orientalis with a mean of 69.7 Mg C/ha, and the total aboveground C storage of urban street trees in Daegu was 10.6 Gg C. Alternatively, a generalized allometric equation which compiled species-specific equations can be applied for large-scale estimation. The generalized equations developed in this study and those found in the literature may suggest a constant value (～2.3–2.4) for the scaling exponent in the generalized equations. Allometric equations developed from natural or artificial stands may overestimate the volume of urban street trees; therefore, estimating urban tree biomass and C storage requires urban-specific allometric equations.     

Green roof cooling and carbon mitigation benefits in a subtropical city

Green roofs are promoted as an effective nature-based urban heat island mitigation strategy. Green roof cooling and energy-saving benefits have been simulated for various climatic zones, but mainly at the building scale. Due to a lack of fact-based information on neighborhood cooling benefits, green roof construction lags and has rarely been incorporated into urban planning actions. This study investigated the thermal benefits and energy savings of green roofs for the central area of the Xianlin Campus of Nanjing University at the neighborhood scale. Three scenarios were simulated for a hot summer day using a validated ENVI-met model: a base case (S0), extensive green roofs (EGRs) (S1), and intensive green roofs (IGRs) (S2). The air temperature cooling benefit from green roofs extended downwards to the pedestrian level. The EGR scenario achieved a maximum 0.29 °C air temperature reduction at the pedestrian level and 0.37 °C at the rooftop level. The IGR scenario achieved a maximum 0.35 °C air temperature reduction at the pedestrian level and 0.45 °C at the rooftop level. EGRs and IGRs reduced energy demands for air-conditioning by 0.39 kWh·m⁻²·d⁻¹ and 0.56 kWh·m⁻²·d⁻¹ and CO₂ emissions by 31,997 kg·d⁻¹ and 45,967 kg·d⁻¹, respectively. These results confirm that green roofs yield substantial cooling and carbon mitigation benefits. Our study provides essential data to establish green roofs as mainstream cooling technology in subtropical cities. The results also imply that urban planners and policymakers may need to embrace the implementation of green roofs in long-term planning and building design practices to improve urban thermal environments, reduce building energy demand, and curb carbon emissions.     

Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds

Urban forest is a crucial part of urban ecological environment. The accurate estimation of its tree aboveground biomass (AGB) is of significant value to evaluate urban ecological functions and estimate urban forest carbon storage. It has a high accuracy to estimate the forest AGB with field measured canopy structure parameters, but unsuitable for large-scale operations. Limited by low spatial resolution or spectral saturation, the estimated forest AGBs based on various satellite remotely sensed data have relatively low accuracies. In contrast, Unmanned Aerial Vehicle (UAV) remote sensing provides a promising way to accurately estimate the tree AGB of fragmented urban forest. In this study, taking an artificial urban forest in Ma'anxi Wetland Park in Chongqing City, China as an example, we used UAVs equipped with a digital camera and a LiDAR to acquire two point cloud data. One was produced from overlapping images using Structure from Motion (SfM) photogrammetry, and the other was resolved from laser scanned raw data. The dual point clouds were combined to extract individual tree height (H) and canopy radius (R_c), which were then input to the newly established allometric equation with tree H and R_c as predictor variables to obtain the AGBs of all dawn redwood trees in study area. In accuracy assessment, the coefficient of determination (R²) and Root Mean Square Error (RMSE) of extracted H were 0.9341 and 0.59 m; the R² and RMSE of extracted R_c were 0.9006 and 0.28 m; the R² and RMSE of estimated AGB were 0.9452 and 17.59 kg. These results proved the feasibility and effectiveness of applying dual-source UAV point cloud data and the new allometric equation on H and R_c to accurate AGB estimation of urban forest trees.     

The feasibility of remotely sensed data to estimate urban tree dimensions and biomass

Accurately measuring the biophysical dimensions of urban trees, such as crown diameter, stem diameter, height, and biomass, is essential for quantifying their collective benefits as an urban forest. However, the cost of directly measuring thousands or millions of individual trees through field surveys can be prohibitive. Supplementing field surveys with remotely sensed data can reduce costs if measurements derived from remotely sensed data are accurate. This study identifies and measures the errors incurred in estimating key tree dimensions from two types of remotely sensed data: high-resolution aerial imagery and LiDAR (Light Detection and Ranging). Using Sacramento, CA, as the study site, we obtained field-measured dimensions of 20 predominant species of street trees, including 30–60 randomly selected trees of each species. For each of the 802 trees crown diameter was estimated from the aerial photo and compared with the field-measured crown diameter. Three curve-fitting equations were tested using field measurements to derive diameter at breast height (DBH) (r² = 0.883, RMSE = 10.32 cm) from the crown diameter. The accuracy of tree height extracted from the LiDAR-based surface model was compared with the field-measured height (RMSE = 1.64 m). We found that the DBH and tree height extracted from the remotely sensed data were lower than their respective field-measured values without adjustment. The magnitude of differences in these measures tended to be larger for smaller-stature trees than for larger stature species. Using DBH and tree height calculated from remotely sensed data, aboveground biomass (r² = 0.881, RMSE = 799.2 kg) was calculated for individual tree and compared with results from field-measured DBH and height. We present guidelines for identifying potential errors in each step of data processing. These findings inform the development of procedures for monitoring tree growth with remote sensing and for calculating single tree level carbon storage using DBH from crown diameter and tree height in the urban forest.     

Carbon storage in a peri-urban neotropical forest: Assessing its potential and patterns of change over half a century

Forest ecosystems are associated with environmental regulation services, such as carbon storage, which is an outstanding service. Carbon fluxes in cities are difficult to estimate due to the scale at which they are addressed, particularly at the local level. In this work, we were interested in determining the carbon stored in the aboveground biomass of the tropical montane cloud forest tree species located on the western periphery of the city of Xalapa, Veracruz, Mexico. With these data, we interpolated the storage and sequestration over five decades through image satellites and aerial photography of this unique forest. Additionally, to assess this potential as a basis for a biodiversity contribution to city resilience, we conducted phytosociological sampling. Native species such as Quercus xalapensis, Liquidambar styraciflua var. mexicana and Q. lancifolia showed the most significant values of 72.92, 58.79, and 49.14 Mg ha⁻¹, respectively, of carbon. We used phytosociological sampling to better understand structural and functional features of urban forest biodiversity that can contribute to management practices for adaptation to climate change. In addition, the native species currently studied offer an opportunity for the city to implement better-targeted reforestation and ecological restoration programs for integrated landscaping in urban planning. Our results suggest that between 1966 and 2022, there was a forest recovery of 52.4 ha and an increase in urban areas of 63.4 ha, which is equivalent to an increase from 7,700.86 Mg ha⁻¹ in 1966 to 12,620.00 Mg ha⁻¹ in 2022. In this context, it is possible to both recover the vegetation cover and expand the city, thus avoiding part of the loss of ecosystem services that urbanization usually implies. This should be promoted among decision-makers and citizens in urban planning. Recovery processes can take place successfully in some cases even as urban areas expand. This possibility is relevant due to the many contributions that vegetation provides to citizens, including carbon storage. The opportunity to study five decades allows us to know the history, monitor the processes and make a projection to conserve the vegetation and improve management.     

Mapping leaf area of urban greenery using aerial LiDAR and ground-based measurements in Gothenburg,Sweden

Leaf area of urban vegetation is an important ecological characteristic, influencing urban climate through shading and transpiration cooling and air quality through air pollutant deposition. Accurate estimates of leaf area over large areas are fundamental to model such processes. The aim of this study was to explore if an aerial LiDAR dataset acquired to create a high resolution digital terrain model could be used to map effective leaf area index (L_e) and to assess the L_e variation in a high latitude urban area, here represented by the city of Gothenburg, Sweden. L_e was estimated from LiDAR data using a Beer-Lambert law based approach and compared to ground-based measurements with hemispherical photography and the Plant Canopy Analyser LAI-2200. Even though the LiDAR dataset was not optimized for L_e mapping, the comparison with hemispherical photography showed good agreement (r² = 0.72, RMSE = 0.97) for urban parks and woodlands. Leaf area density of single trees, estimated from LiDAR and LAI-2200, did not show as good agreement (r² = 0.53, RMSE = 0.49). L_e in 10 m resolution covering most of Gothenburg municipality ranged from 0 to 14 (0.3% of the values >7) with an average L_e of 3.5 in deciduous forests and 1.2 in urban built-up areas. When L_e was averaged over larger scales there was a high correlation with canopy cover (r² = 0.97 in 1 × 1 km² scale) implying that at this scale L_e is rather homogenous. However, when L_e was averaged only over the vegetated parts, differences in L_e became clear. Detailed study of L_e in seven urban green areas with different amount and type of greenery showed a large variation in L_e, ranging from average L_e of 0.9 in a residential area to 4.1 in an urban woodland. The use of LiDAR data has the potential to considerably increase information of forest structure in the urban environment.     

Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model,LiDAR and digital hemispherical photography

The article proposes methods for combining Airborne Laser Scanning (ALS) with Digital Hemispherical Photography (DHP) data required by the Urban Forest Biomass (UFB) model to predict the aboveground biomass (AGB) of Scotch pine (Pinus sylvestris L.) in urban forests of Lublin (Poland). The article also demonstrates the potential of ALS and DHP data in urban AGB estimation. ALS and Leaf Area Index (LAI) data were calculated using a voxels-vector approach based on the measurements taken at eight permanent sample plots (PSPs). The research was conducted in 2014 and the prediction was made until 2030. It was found that the determination coefficients (R²) for the Basal Area (BA) of the trees are 0.97, and the BA modeling parameters have a high correlation with those observed in the field (model efficiency (ME) 0.94). 83 % growth trajectory based on the measured BA was appropriately modeled using the UFB model (P > 0.9). The results for AGB show that the degree of fitting and accuracy are greatest for the Monte Carlo (MC) simulation technique based on ALS and DHP data (UBF with ALS and DHP) where R² = 0.98, RMSE = 2.97 t/ha, MAE = 2.35 t/ha, rRMSE = 1.28 %, which performed better than MC simulation technique without ALS and DHP (UBF without ALS and DHP) where R² = 0.94, RMSE = 4.58 t/ha, MAE = 3.64 t/ha, rRMSE = 3.29 %. The results indicate that the proposed method based on combining the UFB model, LiDAR and DHP allows us to improve the accuracy of the AGB prediction.     

Enhancing plant diversity and mitigating BVOC emissions of urban green spaces through the introduction of ornamental tree species

Promoting the plant diversity of urban green spaces is crucial to increase ecosystem services in urban areas. While introducing ornamental plants can enhance the biodiversity of green spaces it risks environmental impacts such as increasing emissions of biogenic volatile organic compounds (BVOCs) that are harmful to air quality and human health. The present study, taking Qingdao City as a case study, evaluated the plant diversity and BVOC emissions of urban green spaces and tried to find out a solution to increase biodiversity while reducing BVOC emissions. Results showed that: (1) the species diversity and phylogenetic diversity of trees in urban green spaces were 22% and 16% lower than rural forest of this region; (2) urban areas had higher BVOC emission intensity (2.6 g C m⁻² yr⁻¹) than their rural surroundings (2.1 g C m⁻² yr⁻¹); (3) introducing the selected 11 tree species will increase 15% and 11% of species diversity and phylogenetic diversity, respectively; and (4) the BVOC emissions from green spaces will more than triple by 2050, but a moderate introduction of the selected low-emitting trees species could reduce 34% of these emissions. The scheme of introducing low-emitting ornamental species leads to a win–win situation and also has implications for the sustainable green space management of other cities.     

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.     

PM2.5 reduction capacities and their relation to morphological and physiological traits in 13 landscaping tree species

Fine particulate matter (PM_2.5) is emerging as a serious environmental problem worldwide with the increase in anthropogenic emission sources, such as fossil fuels, transportation, and industries. In urban areas, where industrial complexes and human activities are concentrated, PM_2.5 poses a threat to human health. Recently, because of their ability to reduce PM_2.5, the introduction of landscaping trees as an environment-friendly solution has become popular; however, there remains a lack of research on the selection of species and their management. In this study, we quantified and compared the PM_2.5 reduction capacities of 13 major landscaping tree species and analyzed their relationship with the morphological and physiological characteristics of each species. The results showed that the amount of PM_2.5 reduction per leaf area differed among species and was the highest in Ginkgo biloba (28 165 ± 5353 # cm⁻² min⁻¹) and the lowest in Pinus strobus (1602 ± 186 # cm⁻² min⁻¹). Moreover, PM_2.5 reduction by the broadleaf species (18 802 ± 1638 # cm⁻² min⁻¹) was approximately 8.6-fold higher than that of the needleleaf species (2194 ± 307 # cm⁻² min⁻¹). Correlation analysis revealed that differences in PM_2.5 reduction were explained by differences in specific leaf area between species (P = 0.004) and by the length of margin per leaf area among individual trees (P < 0.05). Additionally, reduction in PM_2.5 correlated with photosynthetic properties such as maximum assimilation and carboxylation rates (P < 0.001), indicating that PM_2.5 is reduced not only by physical adsorption but also by physiological processes. These findings emphasize that for effective reduction in PM_2.5 using landscaping trees, comprehensive consideration of the morphological and physiological characteristics of the species is essential during species selection, and that continuous management is also necessary to maintain the active physiological conditions of the trees.     

Seasonal nitrogen and phosphorus leaching in urban agriculture: Dominance of non-growing season losses in a Southern Swedish case study

Urban agriculture, as most agriculture, can potentially contribute to eutrophication via losses to ground and surface water. Few published studies have empirically measured nitrogen and phosphorus losses (including leaching) from urban agriculture, and even fewer have examined losses in real-world settings throughout the year. Here we investigated year-round (May 2020–2021) weekly nitrogen and phosphorus leaching from allotment gardens in Linköping, southern Sweden. We installed eight lysimeters (8 plots) and collected water 0.3 m below the soil surface in four gardens (2 plots per garden), each with their own gardening practices (organic fertilizers, irrigation, and crops). The gardens exhibited large nutrient leaching per area cultivated compared to observed nutrient leachate in rural agriculture in similar climates. There was a large variability among studied plots, where nitrogen leaching reached 39–191 kg ha⁻¹ y⁻¹ and phosphorus 0.9–2.4 kg ha⁻² y⁻¹. Importantly, the non-growing season, especially snowmelt, was a key period for leaching. Most of the nitrogen (78–91 %) and phosphorus (45–97 %) leaching occurred from November to April when the soil was bare, suggesting that mineralization of organic matter was important. Three of the gardens received high amounts of organic fertilizers, though no clear relation between inputs and leaching could be discerned. One plot deviated from the pattern, with less than 40 % of the nutrient leaching occurring in the non-growing season. This gardener had a fine net covering the plot to deter insects. This protected from precipitation as the water volume collected was the lowest, with only 26 % collected in the non-growing season, and nitrogen leaching was also the lowest. Our results illustrate that additional monitoring studies should occur year-round and in several gardens to account for high temporal and spatial heterogeneity and avoid under-estimating leaching losses from urban agriculture. Providing guidance on fertilization, irrigation, and soil covering may be a way to minimize leaching.     

In situ emission of BVOCs by three urban woody species

Controlling and monitoring air quality in cities requires understanding anthropogenic sources, but also natural sources must be considered. This is because beneficial Biogenic Volatile Organic Compounds (BVOCs) can exacerbate air pollution by reacting with anthropogenic pollutants. Although these compounds help trees survive, they may have negative effect on human life in polluted cities. In this study we measured terpenoid emissions of urban trees early and late in the growing season, using Solid Phase Micro-extraction (SPME) in a branch enclosure system. Results showed that Robinia pseudoacacia and Platanus orientalis emitted significant amounts of isoprene throughout the season. Isoprene emission early in the season was roughly the same for both species. Late in the season, the standardized emission rate increased to 17.8 and 45 μg g⁻¹ dw h⁻¹ for R. pseudoacacia and P. orientalis, respectively. Furthermore, all trees emitted significant amounts of 2-ethylhexanol late in the season (7.3, 7.9, and 9.2 μg g⁻¹ dw h⁻¹ for Fraxinus rotundifolia, R. pseudoacacia, and P. orientalis, respectively). In conclusion, trees that are typically planted in urban Tehran, emit significant amounts of isoprene. Planting more F. rotundifolia and fewer P. orientalis trees would help improve air quality in Tehran and the cities like Tehran.     

The overlooked carbon loss due to decayed wood in urban trees

Decayed wood is a common issue in urban trees that deteriorates tree vitality over time, yet its effect on biomass yield therefore stored carbon has been overlooked. We mapped the occurrence and calculated the extent of decayed wood in standing Ulmus procera, Platanus × acerifolia and Corymbia maculata trees. The main stem of 43 trees was measured every metre from the ground to the top by two skilled arborists. All trees were micro-drilled in two to four axes at three points along the stem (0.3 m, 1.3 m, 2.3 m), and at the tree’s live crown. A total of 300 drilling profiles were assessed for decay. Simple linear regression analysis tested the correlation of decayed wood (cm²) against a vitality index and stem DBH. Decay was more frequent and extensive in U. procera, than P. acerifolia and least in C. maculata. Decay was found to be distributed in three different ways in the three different genera. For U. procera, decay did appear to be distributed as a column from the base to the live crown; whereas, decay was distributed as a cone-shape in P. acerifolia and was less likely to be located beyond 2.3 m. In C. maculata decay was distributed as pockets of variable shape and size. The vitality index showed a weak but not significant correlation with the proportion of decayed wood for P. acerifolia and C. maculata but not for U. procera. However, in U. procera, a strong and significant relationship was found between DBH and stem volume loss (R² = 0.8006, P = 0.0046, n = 15). The actual volume loss ranged from 0.17 to 0.75 m³, equivalent to 5%–25% of the stem volume. The carbon loss due to decayed wood for all species ranged between 69–110 kg per tree. Based on model’s calculation, the stem volume of U. procera trees with DBH ≥ 40 cm needs to be discounted by a factor of 13% due to decayed wood regardless of the vitality index. Decayed wood reduces significantly the tree’s standing volume and needs to be considered to better assess the carbon storage potential of urban forests.     

Mapping the urban forest in detail: From LiDAR point clouds to 3D tree models

Trees are an integral component of the urban environment and important for human well-being, adaption measures to climate change and sustainable urban transformation. Understanding the small-scale impacts of urban trees and strategically managing the ecosystem services they provide requires high-resolution information on urban forest structure, which is still scarce. In contrast, there is an abundance of data portraying urban areas and an associated trend towards smart cities and digital twins as analysis platforms. A GIS workflow is presented in this paper that may close this data gap by classifying the urban forest from LiDAR point clouds, detecting and reconstructing individual crowns, and enabling a tree representation within semantic 3D city models. The workflow is designed to provide robust results for point clouds with a density of at least 4 pts/m² that are widely available. Evaluation was conducted by mapping the urban forest of Dresden (Germany) using a point cloud with 4 pts/m². An object-based data fusion approach is implemented for the classification of the urban forest. A classification accuracy of 95 % for different urban settings is achieved by combining LiDAR with multispectral imagery and a 3D building model. Individual trees are detected by local maxima filtering and crowns are segmented using marker-controlled watershed segmentation. Evaluation highlights the influences of both urban and forest structure on individual tree detection. Substantial differences in detection accuracies are evident between trees along streets (72 %) and structurally more complex tree stands in green areas (31 %), as well as dependencies on tree height and crown diameter. Furthermore, an approach for parameterized reconstruction of tree crowns is presented, which enables efficient and realistic city-wide modeling. The suitability of LiDAR to measure individual tree metrics is illustrated as well as a framework for modeling individual tree crowns via geometric primitives.     

Effects of aspect and altitude on carbon cycling processes in a temperate mountain forest catchment

Context

Varying altitudes and aspects within small distances are typically found in mountainous areas. Such a complex topography complicates the accurate quantification of forest C dynamics at larger scales.

Objectives

We determined the effects of altitude and aspect on forest C cycling in a typical, mountainous catchment in the Northern Limestone Alps.

Methods

Forest C pools and fluxes were measured along two altitudinal gradients (650–900 m a.s.l.) at south-west (SW) and north-east (NE) facing slopes. Net ecosystem production (NEP) was estimated using a biometric approach combining field measurements of aboveground biomass and soil CO₂ efflux (SR) with allometric functions, root:shoot ratios and empirical SR modeling.

Results

NEP was higher at the SW facing slope (6.60?±?3.01 t C ha^?1  year^?1), when compared to the NE facing slope (4.36?±?2.61 t C ha^?1 year^?1). SR was higher at the SW facing slope too, balancing out any difference in NEP between aspects (NE: 1.30?±?3.23 t C ha^?1 year^?1, SW: 1.65?±?3.34 t C ha^?1 year^?1). Soil organic C stocks significantly decreased with altitude. Forest NPP and NEP did not show clear altitudinal trends within the catchment.

Conclusions

Under current climate conditions, altitude and aspect adversely affect C sequestering and releasing processes, resulting in a relatively uniform forest NEP in the catchment. Hence, including detailed climatic and soil conditions, which are driven by altitude and aspect, will unlikely improve forest NEP estimates at the scale of the studied catchment. In a future climate, however, shifts in temperature and precipitation may disproportionally affect forest C cycling at the southward slopes through increased water limitation.

     

Relationships between bole and crown size for young urban trees in the northeastern USA

Knowledge of allometric equations can enable urban forest managers to meet desired economic, social, and ecological goals. However, there remains limited regional data on young tree growth within the urban landscape. The objective of this study is to address this research gap and examine interactions between age, bole size and crown dimensions of young urban trees in New Haven, CT, USA to identify allometric relationships and generate predictive growth equations useful for the region. This study examines the 10 most common species from a census of 1474 community planted trees (ages 4–16). Regressions were applied to relate diameter at breast height (dbh), age (years since transplanting), tree height, crown diameter and crown volume. Across all ten species each allometric relationship was statistically (p < 0.001) significant at an α-level of 0.05. Consistently, shade trees demonstrated stronger relationships than ornamental trees. Crown diameter and dbh displayed the strongest fit with eight of the ten species having an R² > 0.70. Crown volume exhibited a good fit for each of the shade tree species (R² > 0.85), while the coefficients of determination for the ornamentals varied (0.38 < R² < 0.73). In the model predicting height from dbh, ornamentals displayed the lowest R² (0.33 < R² < 0.55) while shade trees represented a much better fit (R² > 0.66). Allometric relationships can be used to develop spacing guidelines for commonly planted urban trees. These correlations will better equip forest managers to predict the growth of urban trees, thereby improving the management and maintenance of New England's urban forests.     

Assessing regulating ecosystem services provided by the Ege University Rectorship Garden

Urban ecosystem services are generated in a diverse set of natural and managed urban green areas, including parks, urban forests, cemeteries, vegetated corridors, vacant lots, gardens, yards, and campus areas. Private gardens are generally undervalued for the ecosystem services they provide along with the other urban green areas.This paper aims to calculate three regulating ecosystem services; runoff retention, carbon storage and sequestration generated by the Ege University Rectorship Garden, which is one of the few former Levantine gardens remaining in the highly urbanized Bornova district in İzmir. The carbon storage and sequestration capacity of the trees in the area was calculated based on allometric equations. Runoff retention was computed by using the SCS-CN method. Findings show that pervious surfaces cover approximately two-thirds of the garden with 1203 trees. The estimated carbon storage of both the above and below-ground parts of the trees in the garden is 648.25 t. The total annual carbon sequestration rate is estimated to be 7.87 t year⁻¹ (0.10 kg m⁻²). The potential storm water runoff value was predicted to be approximately 7,018.9 m³. This indicates that the garden has a high value of runoff retention and substantial capacity carbon storage and sequestration.It can be concluded that private gardens and associated ecosystem services in urban landscapes can play an important role in enhancing the quality of life in cities. Therefore, an integral approach is needed where all types of green areas are planned and managed in a systematic way, so that they can provide maximum services.