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.     

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.     

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

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

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.     

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.     

Spatial scale and pattern dependences of aboveground biomass estimation from satellite images: a case study of the Sierra National Forest,California

Context

Spatial scale and pattern play important roles in forest aboveground biomass (AGB) estimation in remote sensing. Changes in the accuracy of satellite images-estimated forest AGBs against spatial scales and pixel distribution patterns has not been evaluated, because it requires ground-truth AGBs of fine resolution over a large extent, and such data are difficult to obtain using traditional ground surveying methods.

Objectives

We intend to quantify the accuracy of AGB estimation from satellite images on changing spatial scales and varying pixel distribution patterns, in a typical mixed coniferous forest in Sierra Nevada mountains, California.

Methods

A forest AGB map of a 143 km² area was created using small-footprint light detection and ranging. Landsat Thematic Mapper images were chosen as typical examples of satellite images, and resampled to successively coarser resolutions. At each spatial scale, pixels forming random, uniform, and clustered spatial patterns were then sampled. The accuracies of the AGB estimation based on Landsat images associated with varying spatial scales and patterns were finally quantified.

Results

The changes in the accuracy of AGB estimation from Landsat images are not monotonic, but increase up to 60–90 m in spatial scale, and then decrease. Random and uniform spatial patterns of pixel distributions yield better accuracy for AGB estimation than clustered spatial patterns. The corrected NDVI (NDVIc) was the best predictor of AGB estimation.

Conclusions

A spatial scale of 60–90 m is recommended for forest AGB estimation at the Sierra Nevada mountains using Landsat images and those with similar spectral resolutions.

     

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.     

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.     

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.     

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.     

Spatial attenuation of ambient particulate matter air pollution within an urbanised native forest patch

Of interest to researchers and urban planners is the effect of urban forests on concentrations of ambient air pollution. Although estimates of the attenuation effect of urban vegetation on levels of air pollution have been put forward, there have been few monitored data on small-scale changes within forests, especially in urban forest patches. This study explores the spatial attenuation of particulate matter air pollution less than 10 μ in diameter (PM₁₀) within the confines of an evergreen broadleaved urban forest patch in Christchurch, New Zealand, a city with high levels of PM₁₀ winter air pollution. The monitoring network consisted of eight monitoring sites at various distances from the edge of the canopy and was operated on 13 winter nights when conditions were conducive for high pollution events. A negative gradient of particulate concentration was found, moving from higher mean PM10 concentrations outside the forest (mean=31.5 μg m^?3) to lower concentrations deep within the forest (mean=22.4 μg m^?3). A mixed-effects model applied to monitor meteorological, spatial and pollution data indicated temperature and an interaction between wind speed and temperature were also significant (P?0.05) predictors of particulate concentration. These results provide evidence of the potential role that urban forest patches may play in mitigating particulate matter air pollution and should be considered in plans for improving urban air quality.     

The effects of land tenure and land use on the urban forest structure and composition of Melbourne

The urban forest provides valuable ecosystem services for enhancing human well-being. Its structure and composition determine the quantity and quality of these services. There has been little research on the heterogeneity in structure and composition of urban forests in the Australasian region, especially in the centre of a highly dynamic and rapidly urbanizing city. This paper quantifies the structure and the composition of the urban forest of Melbourne, Australia's city centre. The effects of land tenure and land use on the heterogeneity of canopy cover, tree density and canopy size were explored. Species and family composition by land use, land ownership and street type were also analysed using the Shannon–Wiener and Jaccard similarity indices. Most of the canopy cover in the city centre is located on public land and is unevenly distributed across the municipality. The mean canopy cover (12.3%) is similar to that found for whole city studies around the world, which often include peri-urban forests. Similarly to other cities, structure varied across different land uses, and tree size, density and cover varied with land tenure and street type. The diversity index shows that the urban forest is rich in species (H′ = 2.9) and is dominated by native species. Improving the distribution, and increasing tree cover and variety of species will result in a more resilient urban centre, able to provide multiple ecosystem services to their residents and its large population of visitors and workers. The study of the urban centre provides further understanding of compact city morphologies, and allows inter-city comparison independent of the size.     

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.     

Comparison of non-destructive LAI determination methods and optimization of sampling schemes in an open Populus euphratica ecosystem

Populus euphratica (P. euphratica) grows in the water-limited Tarim River Basin in spatially heterogeneous open ecosystems; thus, efforts to quantify the leaf area index (LAI) with optical instruments developed for homogeneous closed canopies have a high probability of failure. In this study, we explored methods for designing an acceptable sampling scheme to quantify the tree LAI for open P. euphratica canopies in arid areas. Field data were collected from three 30 m × 30 m plots and one 100 m × 100 m plot. We compared three indirect methods, i.e. i) allometry, ii) LAI-2000 canopy analyser, iii) Tracing Radiation and Architecture of Canopies (TRAC), and a new semi-direct method combining leaf density and crown volume (SDDV) method for quantifying the isolated tree and canopy LAI of a P. euphratica forest. We also analysed the effects of random and grid sampling designs on the accuracy of the LAI estimates obtained with the LAI-2000. The results showed that the allometric method is applicable to isolated trees with regular shapes; however, because the LAI of P. euphratica was calculated from an allometric equation based on the basal area (at 1.3 m), the allometric equation is prone to failure if the basal area is beyond a specific range. Because there are no significant differences in the plot size between the allometric and the SDDV method predictions, the proposed SDDV method can be used as an alternative for field measurements. The combination of LAI-2000 and TRAC is found to be more reliable than TRAC only, and the field view of the LAI-2000 sensor and the clumping index are important factors for sparse vegetation LAI retrieval. The results from sampling optimization showed that for the LAI-2000 instrument, the best sampling method is grid sampling, and the sampling interval should not be less than 20 m. For random sampling scheme, the number of sampling points in a 100 m × 100 m plot should be greater than 86 with a coefficients of variation of 15% and an allowable error (AE) of 0.15 m² m⁻², respectively.     

Allometric equations for urban ash trees (Fraxinus spp.) in Oakville,Southern Ontario,Canada

Tree growth equations are an important and common tool used to effectively assess the yield and determine management practices in forest plantations. Increasingly, they are being developed for urban forests, providing tools to assist urban forest managers with species selection, placement, and estimation of management costs and ecosystem services. This study describes the development of allometric equations for Fraxinus americana and F. pennsylvanica growing in Oakville, Canada. With data collected from 103 ash trees, five allometric models were tested to develop equations estimating diameter-at-breast-height (dbh), tree height, crown width and crown height, using age and dbh as explanatory variables. Mean annual growth rates are presented to demonstrate species growth performance and were not significantly different over the first 40 years of growth for the two species. Of all the tested random coefficient models for both species, the cubic with weight 1/x provided the best fit for estimating dbh from age. The best models for other parameters were the loglog for crown height from dbh, the quadratic for crown diameter from dbh, and the linear for tree height from dbh for F. americana. Model types showed more consistency for F. pennsylvanica with linear providing the best fit for crown diameter, crown height and tree height from dbh. The number of model types suggests the difficulty of fitting any single model to the vast array of conditions affecting plant growth in urban areas where management practices and environment can significantly influence tree size and growth. These models may be used to estimate the growth of ash tree populations in Oakville and communities with similar climate, soil, planting, and management environments.     

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.     

Unmanned aerial systems for modelling air pollution removal by urban greenery

Urban greenery plays an important role in reducing air pollution, being one of the often-used, nature-based measures in sustainable and climate-resilient urban development. However, when modelling its effect on air pollution removal by dry deposition, coarse and time-limited data on vegetation properties are often included, disregarding the high spatial and temporal heterogeneity in urban forest canopies. Here, we present a detailed, physics-based approach for modelling particulate matter (PM₁₀) and tropospheric ozone (O₃) removal by urban greenery on a small scale that eliminates these constraints. Our procedure combines a dense network of low-cost optical and electrochemical air pollution sensors, and a remote sensing method for greenery structure monitoring derived from Unmanned aerial systems (UAS) imagery processed by the Structure from Motion (SfM) algorithm. This approach enabled the quantification of species- and individual-specific air pollution removal rates by woody plants throughout the growing season, exploring the high spatial and temporal variability of modelled removal rates within an urban forest. The total PM₁₀ and O₃ removal rates ranged from 7.6 g m^-2 (PM₁₀) and 12.6 g m^-2 (O₃) for mature trees of Acer pseudoplatanus to 0.1 g m^-2 and 0.1 g m^-2 for newly planted tree saplings of Salix daphnoides. The present study demonstrates that UAS-SfM can detect differences in structures among and within canopies and by involving these characteristics, they can shift the modelling of air pollution removal towards a level of individual woody plants and beyond, enabling more realistic and accurate quantification of air pollution removal. Moreover, this approach can be similarly applied when modelling other ecosystem services provided by urban greenery.     

GeoAI to implement an individual tree inventory: Framework and application of heat mitigation

Individual Tree Inventory (ITI) is critical for urban planning, including urban heat mitigation. However, an ITI is usually incomplete and costly due to data collection challenges in the dynamic urban landscape. This research developed a methodical GeoAI framework to build a comprehensive ITI and quantify tree species cooling on rising urban heat.The object detection Faster R-CNN model with Inception ResNet V2 was implemented to detect individual trees canopy and seven tree species (Callery pear, Chinese elm, English elm, Mugga ironbark, Plane tree, Spotted gum and White cedar). The land surface temperature (LST) was derived from Landsat 8 surface reflectance imagery. Two models for ITI were further developed for spatial and statistical analysis. Firstly, an ‘Individual tree-based model’ stores the attributes of tree species and its vertical configuration obtained from LiDAR, along with its tree canopy area and surface temperature. Secondly, the ‘LST zone-based model’ stores tree canopy cover and building areas in each zone unit. Pearson correlation, global linear regression, and geographically weighted regression (GWR) were applied to establish the relationship between tree attributes, building areas (explanatory variables) with local temperature (dependent variable). Results showed that English elm has the highest cooling and least by Mugga ironbark in the study area. GWR results demonstrate that 94% of the LST was explained by tree height and tree canopy area. The LST zone-based model showed that 85% of the LST was explained by the percentage of tree species and buildings. Maps of the local R² and coefficients of the independent variables provide spatially explicit information on the cooling of different tree species compared to building areas. The implemented GeoAI approach provides important insights to urban planners and government to monitor urban trees with the enhanced Individual Tree Inventory and strategies mitigation plan to reduce the impact of climate change and global warming.     

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.     

Using plant community uniqueness and floristic quality assessment in management decision-making in an urban park setting

With the densification of cities, it is imperative to identify urban ecosystems that should be protected or restored. We aimed to determine the conservation and restoration needs in a large urban park (1,35 km²) in Quebec City (Canada), based on site history, current species richness, floristic uniqueness, and floristic quality assessment of its diverse ecosystems: forest, swamp, wooded peatland, open peatland, and marsh. We evaluated the cover of all vascular species in 70 plots (400 m²) and assessed 18 environmental variables. We found that forest and swamp plots were the richest while peatland plots were the poorest, with marsh plots showing intermediate values. Ecological uniqueness (LCBD) was not correlated with richness (ρ = 0.17; p > 0.05), with marsh and peatland plots showing the highest and lowest uniqueness, respectively. With a regression tree, we identified canopy openness as the most influential variable explaining plot uniqueness across all ecosystems, especially in the peatland, indicating that future recreational development should be avoided in open ecosystems. By plotting ecological uniqueness (LCBD) with tolerance to disturbance (Mean C) values, we identified areas that could benefit from conservation or restoration, and areas that could sustain future development for recreational use. For each area, floristic composition, site characteristics, and past land-use history were investigated further to identify appropriate actions. The open peatland was identified as the main conservation priority, but actions will be needed to limit rapid tree encroachment. Three marshes were identified as areas that would floristically benefit from restoration actions. Still, since they also act as natural retention basins hosting species adapted to the soil conditions, we suggested monitoring the expansion of exotic and invasive species. Approaches developed and lessons learned from this project will serve as guidelines for municipalities aiming to implement a restoration and management plan in urban parks.