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.     

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.     

Comparison of UAV-based LiDAR and digital aerial photogrammetry for measuring crown-level canopy height in the urban environment

Spatial information on urban forest canopy height (FCH) is fundamental for urban forest monitoring and assisting urban planning and management. Traditionally, ground-based canopy height measurements are time-consuming and laborious, making it challenging for periodic inventory of urban FCH at crown level. Airborne-light detection and ranging (LiDAR) sensor can efficiently measure crown-level FCH; however, the high cost of airborne-LiDAR data collection over large scales hinders its wide applications at a high temporal resolution. Previous studies have shown that in some cases, the Unmanned Aerial Vehicle (UAV)-digital aerial photogrammetry (DAP) approach (i.e., UAV-based structure from motion algorithm) is equivalent to or even outperform airborne-LiDAR in measuring forest structure, but few studies have evaluated their performances in measuring FCH in more complex urban environment, across non-ground coverage (including both canopy and building coverage) and topographical slope gradients. Also, the contribution of multi-angle measurement technique from UAV-DAP to FCH estimation accuracy has rarely been explored in the urban environment. Here, we compared the performances of UAV-LiDAR and UAV-DAP approaches on measuring thousands of crown-level FCH at different non-ground coverage and topographical slope areas in an urban environment. Specifically, UAV-LiDAR-based spatial measurements of crown-level FCH were used as the reference after ground-based validation (R² = 0.88, RMSE = 2.36 m). The accuracy of UAV-DAP approach with/without multi-angle measurement in different non-ground coverage and topographical slope areas were then analyzed. The results showed that although the DAP multi-angle-based approach can improve the accuracy of spatial measurement for crown-level FCH in some cases, non-ground coverage (including both canopy and building coverage) was still the main factor affecting the broad applications of DAP approach in measuring urban FCH: at areas where non-ground coverage < 0.95, no matter how topographical slope varied, the accuracy of DAP approach was high (R² = 0.86∼0.94, RMSE = 1.56∼2.93 m); at areas where non-ground coverage > 0.95, except for the case of flat areas (i.e., topographical slope <= 2°), the accuracy of DAP was poor (R² = 0.20, RMSE = 12.34 m). However, using LiDAR-digital terrain model (DTM) instead of DAP-DTM, at areas where non-ground coverage > 0.95, can significantly improve the accuracy of UAV-DAP approach in measuring crown-level FCH (R² = 0.91, RMSE =1.61 m). Our study thus provides a complete guidance on the usage of cost-effective UAV-DAP approach for measuring crown-level FCH in the urban environment, which will be helpful for precise urban forest management and improving the efficiency of urban environmental planning.     

Homegardens as a modern carbon storage: Assessment of tree diversity and above-ground biomass of homegardens in Matale district,Sri Lanka

Homegardens are principally known as integrated man-made ecosystems with annuals and perennials where trees play a significant role in storing atmospheric carbon in the vegetation as above-ground biomass (AGB). Being one of the agroforestry systems, homegardens could ease the pressure on natural forest cover in the process of carbon sequestration and carbon storage, while reducing the greenhouse gas accumulation (CO₂) in the atmosphere. Thus, they could be described as a promising approach for mitigation of climatic changes. A study was conducted to assess the tree diversity and AGB carbon stock of homegardens in Matale district, Sri Lanka. A total of 122 homegardens ranging from urban dwellings up to natural eco-systems across 05 agro-ecological regions (AER) were surveyed, capturing a vast diversity. A total of 5140 woody trees were recorded from 100 genera and 45 families, covering 16.67 ha of homegardens. Six and three tree species were identified as vulnerable and near threatened, respectively in terms of national-level conservation status. Shannon-Wiener index (SWI) of 1.90 ± 0.49 ranging between 0.49 and 2.83 indicated compositional diversity of the tree species. The AGB was widely varying between 0.8 and 139.4 Mg C/ha (mean AGB of 36.5 ± 27.4 Mg C/ha). AGB and SWI were higher in small-scale (38.8 ± 29.7 Mg C/ha and 1.91 ± 0.50, respectively) than in medium-scale homegardens (28.0 ± 14.9 Mg C/ha and 1.86 ± 0.50, respectively). Species richness and number of species per hectare were higher in small scale (<0.2 ha) homegardens compared to medium scale (0.2–0.8 ha). A considerable variation of tree diversity and AGB was observed among the homegardens in different AERs. The variation of AGB was primarily governed by tree density (trees/ha) and species diversity. Focusing on that, optimizing the potential of homegardens in terms of storing atmospheric carbon as AGB in the study area can be considered as a timely strategy in mitigating impacts of climate change and assisting domestic food security.     

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

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

Assessing above-ground biomass of open-grown urban trees: A comparison between existing models and a volume-based approach

Assessment of the amount of carbon sequestered and the value of ecosystem services provided by urban trees requires reliable data. Predicting the proportions and allometric relationships of individual urban trees with models developed for trees in rural forests may result in significant errors in biomass calculations. To better understand the differences in biomass accumulation and allocation between urban and rural trees, two existing biomass models for silver birch (Betula pendula Roth) were tested for their performance in assessing the above-ground biomass (AGB) of 12 urban trees. In addition, the performance of a volume-based method utilizing accurate terrestrial laser scanning (TLS) data and stem density was evaluated in assessing urban tree AGB. Both tested models underestimated the total AGB of single trees, which was mainly due to a substantial underestimation of branch biomass. The volume-based method produced the most accurate estimates of stem biomass. The results suggest that biomass models originally based on sample trees from rural forests should not be used for urban, open-grown trees, and that volume-based methods utilizing TLS data are a promising alternative for non-destructive assessment of urban tree AGB.     

Comparing the carbon sequestration capacity of temperate deciduous forests between urban and rural landscapes in central Japan

Urbanisation is increasing tremendously in some parts of the world. Consequently, many rural forests may become depleted, although many opportunities exist for urban forests to increase. However, few studies have quantified the carbon (C) sequestration capacities of urban and rural forests in specific climatic zones. The present study compared carbon sequestration in two temperate deciduous forests located in Nagoya and Toyota, central Japan. The Nagoya University forest represented an urban forest, and a site in Toyota represented a rural forest. The urban forest at Nagoya University had comparatively smaller areas of green space and larger areas of buildings and roads. Land uses for building and road, which are typical of urban areas, result in smaller diurnal temperature ranges but higher air temperature, vapour pressure deficit, and atmospheric carbon dioxide (CO₂) concentration. The urban forest in this study exhibited higher gross ecosystem exchange (GEE), especially in the active growing season from May to September, suggesting the possible effect of CO₂ fertilisation. However, higher air temperatures caused comparatively smaller net ecosystem exchange (NEE) because of higher ecosystem respiration (RE). Although both forests functioned as CO₂ sinks at annual time scales, the rural Toyota forest site (5.43 t C ha^–1 yr^–1) had 36% higher net ecosystem production (NEP=–NEE; the negative sign indicates uptake by the forest ecosystem from the atmosphere) than that at the urban forest. The higher normalised respiration (i.e., RE/GPP ratio; GPP=–GEE where GPP represents gross primary production) at the Nagoya University forest might be attributable to factors associated with the degree of urbanisation. Thus, in temperate forests, factors associated with urbanisation may reduce the atmospheric carbon sink function by accelerating respiration. This is an issue of global interest, as many countries are experiencing rapid urbanisation.     

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.     

Feasibility study on the estimation of the living vegetation volume of individual street trees using terrestrial laser scanning

As an important part of urban greening, the canopy of street trees has ecological benefits, such as oxygen production, noise reduction, and dust reduction. The living vegetation volume (LVV) can reflect the spatial structure of the canopy intuitively and enables the estimation of the ecological service value of street trees. Terrestrial laser scanning (TLS) has shown excellent performance for providing three-dimensional data of individual trees with high precision, enabling the accurate quantification of the LVV. In this study, we divided the LVV into the total living vegetation volume (tLVV) and the effective living vegetation volume (eLVV); the latter does not include branches. The eLVV of 40 ginkgo trees separated in two roads in Nanjing was calculated from TLS data. A novel method named LAIM for accurate eLVV calculation based on point cloud data was proposed. The point cloud data of individual tree was segmented along the Z-axis and image processing methods were used. With this, eLVV of each tree was obtained. The results were compared with data obtained from a clustered point cloud generated using convex hulls. The Bland-Altman analysis was used to investigate the consistency of the two methods. Furthermore, we used correlation analysis and all-subsets regression to choose the variables, and the eLVV was fitted using six models. Finally, we evaluated O₂ production, CO₂ and SO₂ absorption by the street trees based on eLVV, the ecological benefits of street trees were quantified. The results showed the following: (1) The number of layers and the dilation size of the point cloud were crucial parameters in the LAIM. (2) For ginkgo trees, the mean difference between the eLVV obtained from the LAIM and the convex hull method was − 0.53–0.19 m³, indicating that the results were highly consistent for the two methods. (3) The eLVV fitting performance was better for the exponential function model (R² =0.8523, RMSE=0.6838 m³) and linear model (R² =0.8361, RMSE=0.7224 m³). The tree height and crown width significantly affected the eLVV estimation. (4) The evaluation about ecological benefits of Zhaoyang Road was better than Cuizhu Road. The quantified ecological benefits were conducive to road ecological evaluation. This study quantified the eLVV of individual trees using TLS, highlighting the importance of live vegetation in urban greening. The results can provide technical support for estimating the ecological service value of urban street trees.     

Evaluation of four methods for estimating leaf area of isolated trees

The accurate modeling of the physiological and functional processes of urban forests requires information on the leaf area of urban tree species. Several non-destructive, indirect leaf area sampling methods have shown good performance for homogenous canopies. These methods have not been evaluated for use in urban settings where trees are typically isolated and measurement may be complicated by proximity to residential areas, buildings, signs, and other infrastructure elements. We evaluated the accuracy, precision, efficiency and other practical considerations associated with four methods of estimating the leaf area of open-grown deciduous trees in urban forests. The methods included color digital image processing (CD), the LAI-2000 Plant Canopy Analyzer, the CI-100 Digital Plant Canopy Imager, and a logarithmic regression equation. Regression coefficients, adjusted R², and confidence intervals were used to determine the best method when using true leaf area of 25 Platanus x acerifolia Willd. and 25 Platanus racemosa Nutt. as an independent variable. Practical considerations included ease of data collection and processing and costs associated with each method. The CD method and LAI-2000 estimates showed good correlation with true leaf area (R² 0.71); however, only the CD method produced estimates within 25 percent of mean true leaf area and met additional requirements for accuracy, precision, and efficient use in urban settings.     

Analysing urban trees on verges and slopes along a highway using machine learning methods

In arboricultural research, data analysis is important to the understanding of the characteristics of urban forest. This study attempted to apply machine learning techniques on a relatively small data set. This research aimed at exploring the biodiversity and structure of tree stands on verges and slopes along a highway, and analysing the influences of habitat characteristics on the tree stands with the aid of machine learning techniques. 53 slopes and 52 verges along San Tin Highway, Hong Kong were surveyed. 7209 trees belonging to 23 species were found. Dimension reduction proved successful in the concise characterisation of urban forest by a biodiversity component and an abundance component. The biodiversity component score of the slopes (0.625) was higher than that of the verges (−0.637). However, the abundance component scores of slopes (−0.059) and verges (0.060) showed slight difference, reflecting comparable tree abundance. A 75–25 train/test split was applied on a data subset consisting of slopes registered under a scheme called Systematic Identification of Maintenance Responsibility of Slopes in the Territory for regression analysis. The scores of the two components were regressed on several slope geophysical variables. Slope height and slope area served as significant predictors explaining biodiversity. Boosting improved the explanatory power and predictive accuracy of the regression model on the biodiversity component, as evidenced by an increase in adjusted R² by 0.23 and a decrease in RMSE by 0.40. This research proved that component scores can serve as inputs for regression models for the explanation of urban forest characteristics by habitat-related variables. In future, small data sets from tree surveys can be analysed using the workflow demonstrated in this study for the generation of more management insights.     

Empirical relationships between land use/cover and estuarine condition in the Northeastern United States

Land–water interactions were examined in three regions in the Virginian Biogeographic Province; the southern shore of Cape Cod, Massachusetts; the Hudson/Raritan region of New York; and the eastern shore of the Delmarva (Delaware/Maryland/Virginia) Peninsula. Cumulative distribution functions were used to evaluate similarity in environmental condition among estuaries. Spatial-setting variables (location in a river, coastal lagoon, or in open waters) were associated with variation for some measures of estuarine condition. Patterns of coastal urban and agriculture gradients were measured and their relationship with indicators of estuarine condition was modeled statistically. When estuaries were pooled, the highest variation explained by spatial-setting variables was found for dissolved oxygen (DO, R ² = 0.44) and salinity (R ² = 0.58), with DO decreasing in river locations and salinity decreasing with rainfall and sampling locations near rivers. The explanatory power for the other indicator variables was low and varied from 6% to 27%. Rainfall explained some of the variation (R ² = 0.23) in total suspended solids. Moderate (0.4 < | r | < 0.7) to strong (| r | ≥ 0.7) linear associations were found between total urban area and measures of estuarine condition. Within regions, total urban area was positively associated with Silver (r = 0.59), Cadmium (r = 0.65), and Mercury (r = 0.47) in Cape Cod, and inversely related to DO (r = −0.65) in the Hudson/Raritan region. No associations were found in the Delmarva Peninsula study area. Total area of agriculture showed a moderate association with Arsenic in Cape Cod, but no other associations were found in the other two regions. Our analyses show a measurable impact of urban land use on coastal ecosystem condition over large areas of the northeastern United States. This pattern was most evident when many different landscapes were considered simultaneously. The relationship between urban development and estuarine condition were weaker within the individual regions studied. The use of land use/cover models for predicting estuarine condition is a challenging task that warrants enhancements in the type, quantity, and quality of data to improve our ability to discern relationships between anthropogenic activities on land and the condition of coastal environments.     

Carbon storage and sequestration by urban forests in Shenyang,China

Urban forests can play an important role in mitigating the impacts of climate change by reducing atmospheric carbon dioxide (CO₂). Quantification of carbon (C) storage and sequestration by urban forests is critical for the assessment of the actual and potential role of urban forests in reducing atmospheric CO₂. This paper provides a case study of the quantification of C storage and sequestration by urban forests in Shenyang, a heavily industrialized city in northeastern China. The C storage and sequestration were estimated by biomass equations, using field survey data and urban forests data derived from high-resolution QuickBird images. The benefits of C storage and sequestration were estimated by monetary values, as well as the role of urban forests on offsetting C emissions from fossil fuel combustion. The results showed that the urban forests in areas within the third-ring road of Shenyang stored 337,000 t C (RMB92.02 million, or $ 13.88 million), with a C sequestration rate of 29,000 t/yr (RMB7.88 million, or $ 1.19 million). The C stored by urban forests equaled to 3.02% of the annual C emissions from fossil fuel combustion, and C sequestration could offset 0.26% of the annual C emissions in Shenyang. In addition, our results indicated that the C storage and sequestration rate varied among urban forest types with different species composition and age structure. These results can be used to help assess the actual and potential role of urban forests in reducing atmospheric CO₂ in Shenyang. In addition, they provide insights for decision-makers and the public to better understand the role of urban forests, and make better management plans for urban forests.     

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.     

Terrestrial photogrammetric stem mensuration for street trees

Much of forest science is dependent on accurate stem measurements, and relatively new photogrammetric techniques may be suitable for modeling stems from the terrestrial perspective. From imagery taken along a windbreak and urban roadways we tested the viability of photogrammetric modeling for producing accurate diameter at breast height measurements. Treatments for different point cloud models differed based on intervals between control points (i.e., every 5 m, 10 m, 25 m, and an absence of target control points) and site conditions (i.e., urban mixed species vs. a windbreak of Pinus taeda) over 100 m sections in the Tampa Bay, FL area. Stem diameter measurements from both the windbreak (n = 53) and the urban sites (n = 93) showed high conformity between field-derived and point cloud model measurements (linear regression showed R² values >0.9 and RMSE values ranging from 7.04 − 12.35%) with the number of control point targets having little influence on modeled DBH accuracy. Modeled stems of larger trees had greater associated error relative to DBH tape measurements, which can be attributed, in part, to problems with estimating diameter from non-circular stems of certain urban species (i.e., Quercus virginiana). Future work will focus on georeferencing these datasets and extracting data on other aspects of stem biometry (e.g., lean angle of stem, stem volume, etc.).     

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.     

Carbon sequestration estimates of indigenous street trees in the City of Tshwane,South Africa

Amelioration of global warming presents opportunities for urban forests to act as carbon sinks, and thereby could possibly be included in the potential future carbon trade industry. The City of Tshwane Metropolitan Municipality provided a strategy in 2002 to plant 115,200 indigenous street trees in the period 2002–2008. These trees hold a monetary carbon value in their potential future growth. In order to calculate the carbon sequestration potential, the growth rates of Combretum erythrophyllum, Searsia lancea and Searsia pendulina were determined. Combined species growth regressions of C. erythrophyllum–S. lancea and S. lancea–S. pendulina are also presented. Combretum erythrophyllum has the fastest growth rate while those of S. lancea and S. pendulina are slower. The results from growth regression relationships were used in a generic allometric biomass regression to calculate the carbon sequestration rate of each species, which was extrapolated to determine the total quantity of carbon to be sequestrated by the street trees over a 30-year period (2002–2032). It is estimated that the tree planting will result in 200,492 tonnes CO₂ equivalent reduction and that 54,630 tonnes carbon will be sequestrated. The carbon dioxide reductions could be valued at more than US$ 3,000,000. But this estimate should also be viewed in the context of the limitations presented in this study. This illustrates that when future carbon trade becomes operational for urban forests these forests could become a valuable source of revenue for the urban forestry industry, especially in developing countries.     

Characterizing the provision and inequality of primary school greenspaces in China’s major cities based on multi-sensor remote sensing

Environmental and green justice problems occur globally, especially in cities with unequal access to urban greenspaces. Recently, inequality in school greenspaces has drawn growing attention, given the importance of campus green environments in young students’ health and academic performance. However, the commonly used Normalized Differences Vegetation Index (NDVI) method for measuring greenspace from satellite imagery is hindered by the saturation issue and tend to underestimate greenspace at high vegetation cover areas, causing large uncertainties in greenspace inequality studies at a national scale. Besides, despite the progress on the inequality of public greenspace exposure, our understandings of primary school greenspace provision and inequality, as well as the driving factors, for young students in a developing world (e.g., China) is still limited. To address these issues, we first adapted a spectral unmixing technique based on multi-sensor remote sensing for more accurate measurements of greenspace provision. Then, we evaluated the provision and inequality of greenspace for 19,681 primary schools in China’s 31 major cities and examined the driving factors using an integrated path analysis. Our findings revealed that: (1) Our proposed multi-sensor remote sensing-based method for greenspace measurement is reliable across our study area with a R² of 0.81 and RMSE of 0.14; in contrast, the traditional NDVI-based greenspace measurement saturated at the range of 0.7–1.0, leading to much lower accuracy (a R² of 0.72 and RMSE of 0.24). (2) Most of the cities under study had low to moderate levels of inequality in primary school greenspace (Gini index < 0.5), but the overall greenspace provision was relatively low; Five cities under study facing high inequality in greenspace exposure (Gini index ≥ 0.5) as well as low greenspace provision (mean fraction cover < 0.25). (3) The monthly maximum temperature and the mean cover of greenspace in primary schools were identified as variables directly affecting the inequality in primary school greenspace (R² = 0.76, p-value < 0.05), whereas the city-level government revenue manifests its effects through the mean cover of greenspace in primary schools and city-level mean greenspace cover. By developing a novel framework for examining the provision and inequality of greenspace in all primary schools in China’s major cities, our study provides valuable insights for designing and evaluating school greening programs in support of healthier learning environment development for next generations.     

A spatially-explicit method to assess the dry deposition of air pollution by urban forests in the city of Florence,Italy

Urban forests (UF) provide a range of important ecosystem services (ES) for human well-being. Relevant ES delivered by UF include urban temperature regulation, runoff mitigation, noise reduction, recreation, and air purification. In this study the potential of air pollution removal by UF in the city of Florence (Italy) was investigated. Two main air pollutants were considered – particulate matter (PM₁₀) and tropospheric ozone (O₃) – with the aim of providing a methodological framework for mapping air pollutant removal by UF and assessing the percent removal of air pollutant.The distribution of UF was mapped by high spatial resolution remote sensing data and classified into seven forest categories. The Leaf Area Index (LAI) was estimated spatially using a regression model between in-field LAI survey and Airborne Laser Scanning data and it was found to be in good linear agreement with estimates from ground-based measurements (R² = 0.88 and RMSE% = 11%). We applied pollution deposition equations by using pollution concentrations measured at urban monitoring stations and then estimated the pollutant removal potential of the UF: annual O₃ and PM₁₀ removal accounted for 77.9 t and 171.3 t, respectively. O₃ and PM₁₀ removal rates by evergreen broadleaves (16.1 and 27.3 g/m²), conifers (10.9 and 28.5 g/m²), and mixed evergreen species (15.8 and 31.7 g/m²) were higher than by deciduous broadleaf stands (4.1 and 10 g/m²). However, deciduous forests exhibited the largest total removal due to the high percentage of tree cover within the city. The present study confirms that UF play an important role in air purification in Mediterranean cities as they can remove monthly up to 5% of O₃ and 13% of PM₁₀.     

A multi-scale assessment of human and environmental constraints on forest land cover change on the Oregon (USA) coast range

Human modification of forest habitats is a major component of global environmental change. Even areas that remain predominantly forested may be changed considerably by human alteration of historical disturbance regimes. To better understand human influences on the abundance and pattern of forest habitats, we studied forest land cover change from 1936 to 1996 in a 25000 km² landscape in the Oregon (USA) Coast Range. We integrated historical forest survey data and maps from 1936 with satellite imagery and GIS data from 1996 to quantify changes in major forest cover types. Change in the total area of closed-canopy forests was relatively minor, decreasing from 68% of the landscape in 1936 to 65% in 1996. In contrast, large-conifer forests decreased from 42% in 1936 to 17% in 1996, whereas small-conifer forests increased from 21% of the landscape in 1936 to 39% in 1996. Linear regression models were used to predict changes in the proportion of large conifer forest as a function of socioeconomic and environmental variables at scales of subbasins (mean size = 1964 km², n=13), watersheds (mean size = 302 km², n=83), and subwatersheds (mean size = 18 km², n=1325). The proportion of land in private ownership was the strongest predictor at all three spatial scales (partial R² values 0.57–0.76). The amounts of variation explained by other independent variables were comparatively minor. Results corroborate the hypothesis that differing management regimes on private and public ownerships have led to different pathways of landscape change. Furthermore, these distinctive trajectories are consistent over a broad domain of spatial scales.