Ecosystem Service Assessment of Campus Street Trees for Urban Resilience: A Case Study from Guangxi Arts University

Abstract

Ecosystem-based adaptation (EbA) provides a practical framework for enhancing urban resilience. This study had three objectives: (i) to quantify the structural attributes and ecosystem services (ESs) of campus street trees, (ii) to integrate LiDAR-derived metrics with the i-Tree Eco model to improve assessment accuracy, and (iii) to evaluate how quantified ESs contribute to climate resilience and inform localized EbA strategies. Field surveys were complemented with LiDAR data to enhance estimation of leaf area index (LAI), canopy dimensions, and tree height. Results show that 2643 street trees representing 29 species provide substantial ESs, including carbon storage of 508,230 kg, annual carbon sequestration of 48,580.5 kg, removal of major air pollutants totaling 2132 kg/year, and stormwater runoff reduction of 2351.8 m³/year, with a combined annual economic value of USD 202,822.10. A small number of species dominated ES delivery, with C. camphora and M. indica contributing disproportionately to canopy structure and ecological benefits. These findings highlight the critical role of urban vegetation in carbon mitigation, air-quality regulation, and flood adaptation at the parcel scale. The study provides a replicable framework for integrating LiDAR-enhanced i-Tree assessments into urban greening policies. It also emphasizes the need for species diversification and the inclusion of omitted services (e.g., biodiversity support, microclimate regulation) in future work to deliver more comprehensive EbA planning.

1. Introduction

In the context of rapid urbanization, urban green spaces (UGSs) have increasingly become a critical pathway to mitigating environmental degradation, enhancing human well-being, and achieving sustainable urban development [1,2]. UGSs deliver a wide range of ecosystem services (ESs), including regulatory, provisioning, supporting, and cultural functions [3,4]. As noted by Pineda-Pinto et al. [5], the ecological and social benefits of UGSs have been extensively recognized across global urban contexts. Importantly, ESs also play a vital role in enhancing urban preparedness and resilience, especially under the growing threats of climate change, public health crises, and environmental instability.

Street trees, as one of the most prominent and accessible components of UGSs, are particularly valuable for their ecological functionality and spatial pervasiveness [6]. Through photosynthesis, trees sequester atmospheric carbon dioxide (CO₂) and release oxygen (O₂), while simultaneously removing nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), carbon monoxide (CO), and fine particulate matter (PM_2.5 and PM₁₀), thereby improving air quality and mitigating climate change [7,8]. Furthermore, their canopy intercepts rainfall and their root systems facilitate infiltration, playing a vital role in reducing surface runoff and alleviating urban flooding [9]. Such functions are not only ecologically valuable but also contribute to climate adaptation and disaster risk reduction, aligning with the goals of ecosystem-based adaptation (EbA) frameworks. Growing evidence has highlighted the economic value of these ESs, reinforcing their importance in urban planning and greening strategies [6,10,11,12].

Numerous studies in the United States, Europe, and Japan have examined the ecosystem services (ESs) of urban street trees [13,14,15,16]. In contrast, far fewer efforts have quantified their multifunctional benefits in rapidly urbanizing contexts, where green infrastructure planning is often fragmented. In contrast, developing countries are still in the early stages of exploring such topics in the context of rapid urban expansion [17].

In China, several urban-focused studies have already evaluated the ecosystem services (ES) of street trees. For example, Wang et al. (2018) conducted an evaluation of street tree structure and associated ecosystem service benefits in Dalian, concluding that the financial returns from these services far surpassed the costs of maintenance [18]. Similarly, Chen et al. (2021) analyzed carbon mitigation contributions of street trees across an urban–suburban continuum in Shanghai, revealing substantial variations and underscoring the importance of tailoring greening strategies to match local urbanization levels [19]. The capacity of UGSs to provide ESs is affected by spatial type, size, structure, and governance frameworks [20,21]. For example, Zhou et al. [22] found a positive relationship between green space area and economic development across 283 Chinese cities, suggesting that a finer-scale analysis could offer valuable insights into localized planning interventions. University campuses are ideal study sites due to their defined boundaries, mixed land uses, and concentrated tree cover, making them representative units for parcel-scale ES research [23,24,25].

To support ecosystem-based adaptation planning, this study also introduces an important methodological enhancement for assessing urban green infrastructure. While traditional ground-based surveys are effective for species identification, they are time-consuming and often limited in accurately capturing tree structural attributes such as diameter at breast height (DBH), height, and canopy width. In contrast, Light Detection and Ranging (LiDAR) offers a remote sensing alternative capable of efficiently providing high-resolution, three-dimensional vegetation data. Although widely applied in forest ecosystems, LiDAR remains underutilized in urban settings for evaluating ecosystem functions [26]. As noted by Salas et al. [27], LiDAR has transformed forest structural analysis, but its potential in assessing urban green infrastructure, especially in institutional parcels, is still emerging.

In this study, we integrated LiDAR-derived structural metrics into the i-Tree Eco model to improve the evaluation of street-tree ecosystem services at Guangxi Arts University (GXAU) in Nanning, China. Notably, while LiDAR offers valuable input for tree structural characterization, the core emphasis of this research lies in quantifying ESs—such as carbon sequestration, pollution removal, and runoff regulation—and analyzing their contributions to climate adaptation, disaster mitigation, and urban resilience. This approach aligns with the principles of ecosystem-based adaptation (EbA) and offers a replicable framework for localized green infrastructure planning in similar urban institutions.

The specific objectives of this study are to:

• Quantify the ESs provided by street trees at the parcel level, including carbon storage, carbon sequestration, air pollution removal, and stormwater runoff mitigation;
• Analyze how tree species composition and structural characteristics influence ES provision;
• Evaluate how quantified ecosystem services contribute to climate resilience and inform localized ecosystem-based adaptation strategies in small-scale urban units.

2. Materials and Methods

2.1. Study Area

The research took place at Guangxi Arts University (GXAU), located in Nanning, the capital city of Guangxi Zhuang Autonomous Region, China (22°50′ N, 108°19′ E). The campus is located in the subtropical zone of southern China. The study focused specifically on the main road network within the campus boundary, where street trees were densely planted for both functional and aesthetic purposes. To delineate the study boundary and road system, high-resolution satellite imagery and open-source road vector data were integrated using ArcGIS Pro 3.1.1 (https://www.arcgis.com/). The total length of campus roads assessed was approximately 13,560 m. All street trees aligned along these roadsides were surveyed for data collection, including both sides of dual-carriage roads and tree-lined pedestrian pathways Nanning experiences a humid subtropical monsoon climate characterized by hot, wet summers and mild, dry winters. The mean annual temperature is 22.3 °C, with the highest monthly average reaching 28.8 °C in July and the lowest averaging 12.5 °C in January. The city receives an average annual precipitation of around 1300–1600 mm, with peak rainfall typically occurring between May and August (Figure 1).

2.2. Structural Tree Data Acquisition: A Dual-Modality Survey Approach

To comprehensively capture the structure and ecological attributes of street trees, this study adopted a dual-modality data acquisition method integrating field-based assessments and LiDAR-enabled remote measurement. This approach enabled the acquisition of both standardized urban forestry data and high-resolution geometric parameters that traditional surveys may overlook.

2.2.1. Field-Based Tree Census

To reconcile differences between field and LiDAR measurements, we matched individual trees by location and crown footprint, compared DBH, total height, and crown width, and applied simple bias-correction functions where needed. Corrected LiDAR metrics were used in i-Tree inputs when agreement with field values was acceptable; otherwise, field measurements were retained [28,29].

2.2.2. LiDAR-Enhanced 3D Structural Mapping

To enhance vertical and horizontal resolution and reduce field labor intensity, the study employed a two-tiered LiDAR system:

• Aerial LiDAR System (ALS):

A Pegasus D2000 drone (DJI, Shenzhen, China) equipped with the Lidar2000 scanner (GreenValley International Inc., Berkeley, USA) was deployed at 150 m altitude using terrain-following flight to collect high-density top-down canopy data. Overlap was set at 70%, yielding a complete 3D profile of street tree coverage and canopy volume.

• Backpack LiDAR System (BLS):

A Hi-Target RS10 backpack system (Hi-Target Surveying Instrument Co., Ltd., Guangzhou, China) was utilized to scan from ground level, ensuring trunk and under-canopy structure (often blocked in ALS) were captured. This was especially useful in dense tree corridors and narrow road sections.

• Post-Processing Pipeline:

  ○

  Raw point cloud data were preprocessed using LiDAR360 (version 6.3; GreenValley International Inc., Berkeley, CA, USA):

  ○

  Noise removal and ground filtering

  ○

  Tree segmentation (individual crown isolation)

  ○

  DBH and height modeling via circle and surface fitting algorithms

All extracted metrics (DBH, TH, CS) were aligned with field data and formed the primary inputs for ecosystem service modeling.

2.3. Ecosystem Service Value Estimation Based on the i-Tree Eco Model

To evaluate the ecosystem services (ES) provided by street trees on campus, the i-Tree Eco model version 6 was employed. Developed by the USDA Forest Service, i-Tree Eco is a widely recognized and peer-reviewed tool designed to assess urban forest structure and quantify associated environmental benefits such as carbon storage and sequestration, air pollutant removal, and stormwater runoff reduction (www.itreetools.org). Previous studies have validated its robustness and applicability across global regions [25,30,31]. In this study, tree structural data collected from GXAU were processed with the i-Tree Eco model, combined with localized weather and pollution data, to derive quantitative and economic evaluations of ecosystem services.

2.4. Hourly Weather and Pollution Data Acquisition and Substitution Strategy

To support the global application of the i-Tree Eco model, the system allows users to upload localized environmental datasets, including hourly meteorological and air pollution data, to quantify ecosystem services (ES). Although some areas in Guangxi, China, are included in the i-Tree Eco database, Nanning City is currently not covered. Because Nanning is not covered by the current i-Tree database, we used Liuzhou environmental data as the nearest available substitute. To reassure validity, we provide a brief descriptive comparison of key climatic and air-quality indicators for the two cities (Table S1), showing broadly similar ranges in recent years [32].

Meteorological data were sourced from the Longzhou Meteorological Station (NCEI ID: 594170-99999), located at 22.367° N, 106.750° E, with an elevation of 129.0 m. This station is officially registered with the National Centers for Environmental Information (NCEI) of the United States. While the hourly precipitation data for the year 2023 is incomplete (recorded as 0 mm), the cumulative 6-h precipitation data reached 1926.08 mm, which is comparable to the average annual precipitation in Nanning. These data are sufficient to support calculations related to stormwater runoff reduction and PM_2.5 interception benefits.

Air pollution data, including concentrations of CO, NO₂, O₃, PM₁₀, PM_2.5, and SO₂, were retrieved from the built-in dataset for Liuzhou in the i-Tree system. These datasets have been peer-reviewed and verified through the official i-Tree Database, ensuring high reliability and comparability.

3. Structure and Function

3.1. Street Tree Structure

3.1.1. Importance Value

To evaluate species dominance within the study area, the Importance Value (IV) for each tree species was computed by averaging three parameters: the percentage of total tree count, total leaf area contribution, and canopy cover proportion. The IV ranges from 0 to 100, where a value of 100 denotes exclusive reliance on a single species [33].

3.1.2. Age Structure

In order to assess the distribution of tree age classes, all street trees were categorized into four groups, young (0–15 cm), semi-mature (15–30 cm), mature (30–60 cm), and over-mature (>60 cm), based on their diameter at breast height (DBH) [34]. This classification facilitates the evaluation of the population’s structural balance and regeneration potential.

3.2. Assessment of Ecosystem Benefits Provided by Urban Street Trees

3.2.1. Quantifying Stored and Sequestered Carbon

Carbon storage was estimated using field-measured tree parameters and genus-specific biomass equations sourced from existing literature. Given that our study area is located in an urban setting, all biomass values were conservatively reduced by 20% to account for environmental stress and reduced growth potential in urban trees. The gross annual carbon sequestration was calculated by adding the average diameter increment (by genus, DBH class, and tree condition) to existing DBH values from year x, estimating tree growth and associated carbon gain for year x + 1) [35,36].

The carbon storage value was estimated at $178 per metric ton of carbon, based on current market rates. The total carbon-related benefits thus included both stored and sequestered carbon.

The specific formula applied for estimating total carbon storage was

$$\mathrm{Carbon} \mathrm{Storage}=\mathrm{Carbon} \mathrm{Factor}\times \mathrm{Tree} \mathrm{Cover} \mathrm{Rate}\times \mathrm{Study} \mathrm{Area}$$

(1)

3.2.2. Air Pollutant Removal

Air pollutant removal by campus street trees was modeled by combining tree attributes (canopy cover, leaf area index, and percent evergreen) with hourly local meteorological and pollutant concentration data. The target pollutants included CO, NO₂, O₃, SO₂, PM_2.5, and PM₁₀ [35,37].

The removal rate for each pollutant was determined using a dry deposition model, expressed by the following formula:

$$\mathrm{F}={\mathrm{V}}_{\mathrm{d}}\times \mathrm{C}$$

(2)

where

• F—flux of pollutant removal (g·m⁻²·s⁻¹).
• V_d—deposition velocity (m·s⁻¹).
• C—atmospheric pollutant concentration (g·m⁻³).

Pollution removal values were monetized using pollutant-specific pricing: CO: $1508/t; O₃: $1061/t; NO₂: $10,619/t; SO₂: $2599/t; PM_2.5 and PM₁₀: $7090/t) [38].

3.2.3. Runoff Reduction

Stormwater regulation services were calculated by assessing the volume of rainfall intercepted by tree canopies and the associated reduction in surface runoff. While branches and bark also contribute to rainfall interception, this study conservatively focused on leaf-based interception only [39].

To estimate annual runoff reduction, the following formula was applied:

$$\mathrm{RD}=\mathrm{V}\times {\mathrm{C}}_{\mathrm{is}}\times \mathrm{P}$$

(3)

where

• RD—annual runoff avoided (m³).
• V—study area (km²).
• C_is—percent impervious surface area (%).
• P—mean annual precipitation (m³).

The economic value of avoided runoff was calculated at $2.30 per cubic meter.

4. Results

4.1. Structure of Street Trees at GXAU

This subsection describes the composition and structural attributes of campus street trees, including species dominance, age distribution, and canopy cover. These metrics provide the baseline for evaluating ecosystem service provision.

4.1.1. Species Composition

A total of 2643 street trees were recorded across the main roads of Guangxi Arts University (GXAU), encompassing 29 species, 22 genera, and 18 families. Evergreen trees accounted for 61.8%, while 38.2% were deciduous. The ten most common species accounted for 86.2% of the total trees sampled (

Table 1. Top 10 street tree species at GXAU and their composition.

Rank	Scientific Name	Family	Number of Trees	Percentage (%)
1	Mangifera indica	Anacardiaceae	513	19.4
2	Cinnamomum camphora	Lauraceae	444	16.8
3	Bauhinia spp.	Fabaceae	336	12.7
4	Koelreuteria paniculata	Sapindaceae	219	8.3
5	Prunus dulcis	Rosaceae	209	7.9
6	Styphnolobium japonicum	Fabaceae	161	6.1
7	Ginkgo biloba	Ginkgoaceae	143	5.4
8	Platanus orientalis	Platanaceae	122	4.6
9	Pandanus spp.	Pandanaceae	66	2.5
10	Prunus subhirtella	Rosaceae	66	2.5

).

The most abundant species was Mangifera indica (19.4%), followed by Cinnamomum camphora (16.8%), Bauhinia spp. (12.7%), Koelreuteria paniculata (8.3%), Prunus dulcis (7.9%), Styphnolobium japonicum (6.1%), Ginkgo biloba (5.4%), Platanus orientalis (4.6%), Pandanus spp. (2.5%), and Prunus subhirtella (2.5%).

4.1.2. Importance Values

The combined Importance Value (IV) for the ten most abundant street tree species at Guangxi Arts University (GXAU) was 94.3, indicating a high concentration of dominant species across campus. Among these, Cinnamomum camphora exhibited the highest IV (25.7), as it ranked first in both total leaf area (32.1%) and total canopy cover (28.3%), and second in total tree count (16.8%). This demonstrates a strong ecological and spatial dominance of the species.

Mangifera indica, though ranked first in number of trees (19.4%), had slightly lower contributions to total canopy (23.6%) and leaf area (16.4%), resulting in the second-highest IV (19.8). Following that, Koelreuteria paniculata and Bauhinia spp. had IVs of 11.4 and 10.5, respectively, contributing significantly to the vegetation structure.

Other notable species included Prunus dulcis (IV = 7.8), Platanus orientalis (IV = 6.6), and Styphnolobium japonicum (IV = 5.7), each of which played supporting roles in providing canopy cover and foliage. In contrast, Pandanus spp. and Prunus subhirtella had relatively low IVs (both below 2.0), due to their limited abundance and lower structural contribution (Figure 2).

4.1.3. Age Structure of Trees

The age structure of campus street trees at Guangxi Arts University was classified into four diameter at breast height (DBH) categories. Trees with a DBH of 0–15 cm (young trees) accounted for 20.0% of the total. The proportion of trees with DBH between 15 and 30 cm (maturing trees) was 56.0%, while trees with DBH between 30 and 60 cm (mature trees) made up 18.0%. The remaining 6.0% of trees had a DBH greater than 60 cm and were classified as old trees (

Table 2. Age structure of street trees at GXAU based on DBH classification.

DBH Class	Number of Trees	Percentage (%)
Young (0–15 cm)	529	20
Maturing (15–30 cm)	1480	56
Mature (30–60 cm)	476	18
Old (>60 cm)	159	6

).

4.2. Ecosystem Services Provided by Street Trees at GXAU

Here we present the quantified ecosystem services (ESs) provided by campus street trees, including carbon storage, annual sequestration, air pollutant removal, and stormwater runoff reduction. The following subsections detail the magnitude of each ES.

4.2.1. Carbon Storage and Sequestration

• Carbon Storage

Street trees at Guangxi Arts University were estimated to store a total of 508,230 kg of carbon in their biomass, with an associated economic value of $101,646.00. The species with the highest carbon storage was C. camphora, contributing 26.2% of the total storage (133,200 kg), followed by M. indica (22.2%), B. spp. (11.9%), and K. paniculata (11.2%). The average carbon storage per tree among the top 10 species ranged from 85 kg (P. subhirtella) to 410 kg (P. orientalis). Species with higher total storage values also exhibited higher monetary values, with C. camphora reaching $26,640.00, M. indica $22,572.00, and B. spp. $12,096.00 (

Table 3. Carbon storage and sequestration of dominant street trees on campus at GXAU.

	Carbon Storage (kg)				Carbon Sequestered (kg/Y)				Total Value ($)
Species	Avg.	Total	Percent Total	Value ($)	Avg.	Total	Percent Total	Value ($)
C. camphora	300	133,200	26.2	26,640	32	14,208	29.3	17,760	44,400
M. indica	220	112,860	22.2	22,572	18.5	9490.5	19.5	11,863.12	34,435.12
K. paniculata	260	56,940	11.2	11,388	26	5694	11.7	7117.5	18,505.5
B. spp.	180	60,480	11.9	12,096	15	5040	10.4	6300	18,396
P. orientalis	410	50,020	9.8	10,004	38	4636	9.5	5795	15,799
P. dulcis	210	43,890	8.6	8778	19	3971	8.2	4963.75	13,741.75
S. japonicum	160	25,760	5.1	5152	17	2737	5.6	3421.25	8573.25
G. biloba	90	12,870	2.5	2574	8	1144	2.4	1430	4004
P. subhirtella	85	5610	1.1	1122	14	924	1.9	1155	2277
P. spp.	100	6600	1.3	1320	11	726	1.5	907.5	2227.5

and Figure 3).

• Carbon Sequestration

The total annual carbon sequestration by street trees was estimated at 48,580.5 kg, valued at $60,725.37. C. camphora again led in sequestration contribution, accounting for 29.3% of the total, followed by M. indica (19.5%), K. paniculata (11.7%), and B. spp. (10.4%). Average annual sequestration per tree varied from 8.0 kg/year (G. biloba) to 38.0 kg/year (P. orientalis). These values translated into the highest sequestration-related economic returns for C. camphora ($17,760.00) and M. indica ($11,863.12) (Table 3 and Figure 4).

4.2.2. Air Pollutant Removal of Trees

Street trees on the Guangxi Arts University campus removed approximately 2132 kg of air pollutants annually, generating an associated economic value of $17,963.6 (

Table 4. Air pollution mitigation by the top 10 most common street tree species at GXAU.

Species	Pollutants Removed (kg/y)	Percent Total Removed	Removal Value ($/y)	Percent Total Value
C. camphora	835.1	39.2	7058.6	39.4
K. paniculata	478.4	22.4	4481.8	25
B. spp.	247.8	11.6	1986.9	11.1
M. indica	182.5	8.6	1311.9	7.3
P. dulcis	123.7	5.8	937.8	5.2
S. japonicum	99.9	4.7	779.3	4.4
G. biloba	65.7	3.1	562.1	3.1
P. orientalis	47.3	2.2	379.1	2.1
P. subhirtella	25.4	1.2	206	1.2
P. spp.	25.7	1.2	200.1	1.1

). Among the various pollutants, C. camphora contributed the most to pollutant removal, accounting for 835.1 kg/year (39.2%) and yielding a removal value of $7058.6/year (39.4%). K. paniculata followed, removing 478.4 kg/year (22.4%) with a value of $4481.8/year (25.0%). Other notable contributors included B. spp., M. indica, and P. dulcis, together making up a significant share of both quantity and value. These results highlight the functional importance of dominant species in campus air quality improvement and provide a basis for future planting and species selection strategies (Figure 5).

4.2.3. Reduction in Surface Runoff by Campus Street Trees

Street trees at Guangxi Arts University were estimated to intercept approximately 2351.8 m³ of stormwater runoff annually, resulting in an economic value of approximately $5576.10. Among the ten most abundant species, K. paniculata contributed the most to runoff reduction (21.3%), followed by P. orientalis (17.6%) and P. stenoptera (5.0%). The remaining species each contributed less than 5% to the total reduction (

Table 5. Stormwater Interception Capacity of the Predominant Street Tree Species at GXAU.

Species Name	Avoided Runoff (m3/y)	Avoided Runoff Value ($/y)	Avg. m3/Tree	Avg. $/Tree
C. camphora	840.5	248.9	0.9	0.27
K. paniculata	492.8	1187.4	1.4	3.38
B. spp.	276.1	678.1	1.1	2.73
M. indica	200.3	429.2	0.75	1.61
P. dulcis	160.4	376.1	0.92	2.15
S. japonicum	102.5	218.5	0.58	1.24
G. biloba	90.3	192.8	0.55	1.17
P. orientalis	138.4	315.2	2.2	7.84
P. subhirtella	13.9	32.2	0.12	0.31
P. spp.	9.6	18.5	0.08	0.15

).

On a per-tree basis, the average runoff reduction was 0.91 m³/tree, with an associated value of $2.17/tree. Among the dominant species, P. orientalis provided the highest per-tree value ($8.06/tree), followed by P. stenoptera ($6.24/tree) and K. paniculata ($3.47/tree), all exceeding the campus average.

Radar charts (Figure 6) illustrate the performance of each dominant species across four metrics: total runoff reduction, total economic value, average runoff reduction per tree, and average economic value per tree. This visual representation provides a comprehensive understanding of the species’ contributions to mitigating urban stormwater runoff.

4.3. Integrated Assessment of Ecosystem Services Provided by Street Trees

The total ecosystem service value provided by street trees on the Guangxi Arts University (GXAU) campus was estimated to be $202,822.10 annually, which corresponds to an average value of $76.74 per tree. Among the various services, carbon storage contributed the largest portion of value ($103,900.70), followed by gross carbon sequestration ($75,300.00), pollution removal ($17,963.6), and runoff reduction ($5582.60). Excluding carbon storage, sequestration and pollution removal dominated the economic value, whereas runoff reduction contributed the least (

Table 6. Annual Ecosystem Service Value of Leading Street Tree Species at GXAU.

Benefits	Total Y ($)	Y ($)/Tree
Carbon storage	103,900.7	39.32
Gross carbon sequestration	75,300	28.49
Pollution removal	17,963.6	6.82
Avoided runoff	5582.6	2.11
Total value	202,822.1	76.74

and Figure 7).

5. Discussion

5.1. Evaluation and Planning Guidance for Optimizing Street Tree Deployment

In this study, a total of 2643 street trees were recorded along the main roads of Guangxi Arts University (GXAU), encompassing 29 species. The ten most common species accounted for 86.2% of all recorded trees, indicating a high level of species concentration and a lack of sufficient ecological and landscape diversity [40]. Among them, Mangifera indica (19.4%) and Prunus dulcis (7.9%) were the most abundant. Although Cinnamomum camphora ranked second in tree count (16.8%), it exhibited the highest ecological dominance, with the largest canopy cover (28.3%) and leaf area contribution (32.1%), resulting in the highest Importance Value (IV) of 25.7. In contrast, Mangifera indica, despite being the most abundant species, had a lower IV of 19.8.

According to the “10–20–30” rule of urban tree diversity—which states that no single species should exceed 10%, no genus over 20%, and no family over 30% [34]—the current composition at GXAU significantly exceeds these thresholds, suggesting latent ecological vulnerability. As noted by Chen et al. (2021), rapid urban expansion often leads to centralized planting strategies, resulting in “high species redundancy and low ecological resilience” in urban tree communities [19]. To operationalize resilience, we recommend phasing toward the 10–20–30 diversity guideline by under-planting stress-tolerant native and regionally adapted species, mixing leaf habits (evergreen/deciduous) to spread phenological risk, and staggering age classes to avoid cohort failure. This reduces vulnerability to pests and extreme events while maintaining campus character. These findings directly address Objective 1 by quantifying both structural composition and ES provision at the campus scale. A comparable study at Jiangsu University [41] recorded 36 species, with the top ten accounting for 84.3%, closely paralleling GXAU’s 29 species and 86.2%. This indicates that species dominance is a widespread challenge across Chinese university campuses.

From a spatial pattern perspective, Mangifera indica, Prunus dulcis, Bauhinia spp., and Artocarpus heterophyllus were repeatedly planted along multiple main roads, typically arranged in symmetrical paired rows. This has created a uniform greening structure marked by “frequent repetition, low spatial stratification, and weak visual variation”, leading to monotonous and unvaried streetscapes. The root cause of this lies in earlier landscaping decisions that prioritized cost-efficiency and ease of maintenance, often at the expense of landscape aesthetics and biodiversity planning [42].

While Mangifera indica is a native tropical species well-suited to local conditions [43]—being heat- and drought-tolerant, with a large canopy and both ecological and cultural value—overreliance on this species carries certain risks. On the one hand, it contributes little to species-level biodiversity; on the other hand, it is highly sensitive to biotic and abiotic stressors. Earlier research has indicated that mango trees are susceptible to various biological threats—such as quick wilt, anthracnose, and fruit fly infestations—as well as environmental stressors including high temperatures and irregular precipitation patterns [44]. These stressors can negatively influence mango tree physiology, including growth, flowering, fruiting, and overall ecological function, thereby posing risks to their long-term viability and sustainability [45].

Therefore, future planting strategies should adopt a dual approach of “preserving local character while enhancing structural diversity” [46]. It is recommended to introduce regionally adaptive native species with strong stress resistance, diverse ecological functions, and high ornamental value (e.g., Lagerstroemia indica, Ginkgo biloba, and Sapindus mukorossi) [47]. Evergreen and deciduous species should be combined to create seasonal color variation and complementary ecological benefits [48]. Furthermore, the current rigid symmetrical planting layout should be replaced with spatial patterns that incorporate “rhythmic spacing, vertical layering, and functional mixing” to enhance both ecological resilience and visual appeal [49].

In addition, considering that mature trees make up a large proportion of the street tree population at GXAU, while young and old trees account for only 20% and 6%, respectively, there is a potential risk of canopy gaps in the future due to tree mortality or road expansion. Therefore, while ensuring the health of existing vegetation, it is essential to continuously plant appropriately aged saplings to optimize the age structure and ensure the continuity and stability of ecological services. As emphasized by Nowak (2002), mature trees contribute far more to carbon sequestration than young ones, and maintaining a balanced age distribution is crucial for sustaining ecosystem services (ES) over the long term [50].

In summary, to enhance the ecological services and landscape quality of campus street trees, the following planting strategies are recommended:

• Introduce heterogeneous, multi-species compositions to avoid dominance by a single species;
• Select native species with both ecological and aesthetic value, based on i-Tree Eco assessments;
• Adjust age structure and establish continuous renewal mechanisms to ensure generational succession;
• Break linear planting patterns and adopt spatial layouts featuring “vertical layering, varied density, and multi-tiered integration” to improve quality as well as quantity.

5.2. Benchmarking Street Tree Ecosystem Functions

Reducing atmospheric CO₂ levels is a vital strategy in addressing the global climate crisis [51]. In this study, carbon storage and sequestration represented the most significant portion of ecosystem services provided by street trees on the Guangxi Arts University (GXAU) campus. The total carbon stored in tree biomass was estimated at 508,230 kg, with annual carbon sequestration reaching 48,580.5 kg—equivalent to approximately 1,864,189 kg of CO₂ stored and 178,432 kg of CO₂ sequestered each year. On a per-tree basis, the average carbon stored was 194 kg, and annual CO₂ sequestration was 18.5 kg, indicating a moderate carbon performance under the humid subtropical monsoon climate of South China [52].

Compared to studies from other university campuses, the per-tree carbon sequestration rate at GXAU is lower than that of Zhenjiang, China (256 g/tree/year) [41], but higher than those reported from campuses in India, Northeast China, and South Korea [53,54,55]. This variation is primarily due to differences in climate, species composition, and maintenance intensity. Unlike some campuses that prioritize ornamental species with low ecological function, GXAU has planted a substantial proportion of structurally dominant species with well-developed canopies, contributing to both strong growth and ecological functionality. Notably, 57% of the street trees on campus are mature, with healthy crowns and high leaf area index—traits that are strongly positively correlated with biomass accumulation and carbon sequestration capacity [56].

In terms of economic value, based on the standard rate of $178 per metric ton of CO₂, the annual value of carbon sequestration by GXAU’s street trees is estimated at $30,200, while the total carbon storage value reaches $101,646. These findings not only quantify the hidden ecological value of urban green infrastructure but also provide a cost–benefit rationale for future investments in urban greening and tree management programs.

Street trees on the Guangxi Arts University (GXAU) campus removed approximately 2132 kg of air pollutants annually, generating an associated ecological value of USD 17,963.6. Among all species, Cinnamomum camphora exhibited outstanding pollutant reduction performance, contributing 835.1 kg/year (39.2%) and generating an annual economic value of USD 7058.6. It played a central role in campus air quality improvement. Koelreuteria paniculata followed with 478.4 kg/year (22.4%) and a corresponding economic value of USD 4481.8. Together, these two dominant species accounted for over 60% of the total quantity and value of pollutant removal, highlighting their pivotal role in urban air purification [57].

The total avoided runoff attributed to street trees at Guangxi Arts University (GXAU) was estimated at 2352.7 m³ annually, yielding an associated economic value of $5582.6, which reflects the cost savings from reduced reliance on grey infrastructure and flood remediation efforts. Among the dominant species, P. orientalis showed the highest per-tree performance, intercepting 2.2 m³ of runoff and contributing $7.84/tree/year, despite its relatively low planting density. In contrast, C. camphora and M. indica, the most frequently planted species on campus, intercepted only 0.9 m³ and 0.75 m³ per tree, with respective values of $0.27 and $1.61 per tree per year, suggesting a mismatch between planting frequency and hydrological effectiveness.

These disparities emphasize the need to strategically adjust planting schemes by incorporating tree species with superior rainfall interception capacities. Species with larger crowns, denser foliage, and higher leaf area index (LAI) are particularly suitable for enhancing stormwater regulation. The strong positive relationship between LAI and rainfall retention capacity has been confirmed by numerous studies. For instance, Ji et al. and Ross et al. [58,59] demonstrated that well-developed canopy structures significantly delay surface runoff and reduce peak flows. Furthermore, Orta-Ortiz and Geneletti [9] identified LAI as one of the most influential variables in urban runoff mitigation.

Importantly, the hydrological performance of green infrastructure is also influenced by local rainfall characteristics, including intensity, duration, and seasonal patterns. Therefore, a site-specific understanding of precipitation regimes is essential to fully evaluate and optimize the role of street trees in stormwater management and urban water quality improvement. Moving forward, urban vegetation planning at GXAU should prioritize the integration of functionally resilient species and structurally optimized planting layouts, especially in flood-prone zones, to maximize the ecological and economic benefits of green infrastructure. These quantified benefits provide essential evidence to support climate adaptation and resilience planning at the local scale, aligning with the principles of ecosystem-based adaptation. This analysis addresses Objective 3 by demonstrating the contribution of campus vegetation to carbon mitigation, public health, and flood adaptation, consistent with recent findings on the climate resilience role of urban forests [60,61].

5.3. LiDAR-Assisted Assessment for Enhanced Accuracy

Recent advances in remote sensing technologies have provided new opportunities for accurately assessing the ecological functions of urban forests. Among them, LiDAR (Light Detection and Ranging) has proven particularly valuable due to its capacity to capture fine-scale, three-dimensional vegetation structures. In this study, LiDAR-derived structural metrics—such as tree height, crown width, and leaf area index (LAI)—were integrated into the i-Tree Eco model to improve the accuracy of ecosystem service assessments.

LiDAR’s high spatial resolution enables more precise estimation of canopy architecture, which is essential for quantifying key functions such as stormwater interception, carbon storage, and air pollutant removal [62]. Specifically, the incorporation of LiDAR-derived LAI data substantially enhanced the accuracy of rainfall interception modeling. Furthermore, vertical canopy profile data allowed better differentiation of overlapping tree layers, supporting more reliable estimations of multi-tiered vegetation benefits.

Given the increasing demand for high-resolution, spatially explicit data in urban forest management, the integration of LiDAR with ecosystem service models represents a promising direction. For campus- and municipal-scale assessments, the adoption of LiDAR-assisted approaches is recommended to enhance the scientific basis and scalability of urban forest planning. This study underscores the importance of remote sensing tools in evaluating the ecological functions of fragmented, heterogeneous urban green spaces. Such enhanced accuracy is particularly relevant for ecosystem-based adaptation strategies, where spatially explicit and reliable data are crucial for targeted urban resilience planning. This fulfills Objective 2 by showing the added accuracy of LiDAR-enhanced modeling, extending previous applications in forest ecosystem analysis [63] to the campus scale.

5.4. Research Limitations

Despite the strengths of combining field measurements with LiDAR data, this study has several limitations. First, the workflow was developed and tested in a humid, subtropical campus setting. While the approach is likely transferable to other urban and campus contexts, parameter calibration may be necessary in arid or strongly monsoonal regions where rainfall intensity, pollutant profiles, or species pools differ markedly. Second, we focused on a subset of ecosystem services—carbon storage and sequestration, air-quality regulation, and stormwater runoff reduction. Other important services, such as biodiversity support and microclimate regulation, were not explicitly quantified, although they are relevant for urban resilience. Finally, the substitution of Liuzhou environmental data for Nanning introduces potential bias, even though our comparative analysis suggests broadly similar climatic and air-quality conditions. Future studies should integrate structural diversity indices, microclimate monitoring or simulation, and city-specific environmental data to provide a more holistic and robust evaluation of urban tree contributions. Acknowledging these limitations ensures transparency and situates our findings within a broader research agenda, directly contextualizing Objectives 1–3.

6. Conclusions

(1)

Objective 1—Quantify structure and ESs: Street trees at GXAU are dominated by a few species, with the top ten accounting for 86.2% of the total population. Cinnamomum camphora and Mangifera indica disproportionately contribute to ecological benefits due to their wide canopy spread and mature crowns. However, overreliance on Mangifera indica and Prunus dulcis is not recommended, as they increase vulnerability to pests, diseases, and environmental stress.

(2)

Objective 2—LiDAR integration: The hybrid field–LiDAR workflow improved the accuracy of canopy and LAI estimations, showing clear methodological value for campus- and municipal-scale urban forestry assessments. This demonstrates the added accuracy of LiDAR-enhanced modeling compared to field surveys alone.

(3)

Objective 3—Climate resilience and EbA: The quantified ESs highlight the role of campus trees in supporting carbon mitigation, air-quality improvement, and stormwater regulation. These findings directly contribute to climate resilience and provide parcel-scale evidence to inform localized ecosystem-based adaptation strategies in urban environments.

Overall, we recommend introducing stress-tolerant native species such as Lagerstroemia indica, Ginkgo biloba, and Sapindus mukorossi while avoiding excessive reliance on species like Mangifera indica and Prunus dulcis. Ensuring age-class diversification and structural heterogeneity will enhance the long-term stability, ecological function, and climate adaptation potential of campus street trees.