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Article

Effect of Photovoltaic Panel Coverage Rate in Mountainous Photovoltaic Power Stations on the Ecological Environment of Mountainous Landscapes

by
Le Chang
1,2,
Yukuan Dong
1,3,*,
Jiatong Liu
1,
Juntong Cui
2 and
Xin Liu
2
1
School of Architecture and Urban Planning, Shenyang Jianzhu University, Shenyang 110168, China
2
School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China
3
Key Lab of City Information and Spatial Perception, Liaoning Province, Shenyang 110168, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(18), 10068; https://doi.org/10.3390/app151810068
Submission received: 30 July 2025 / Revised: 12 September 2025 / Accepted: 12 September 2025 / Published: 15 September 2025
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Abstract

Facing the severe challenge of global warming, the construction of photovoltaic (PV) power stations has been increasing annually both in China and worldwide, with mountainous areas gradually becoming preferred sites for such projects. Mountain landscapes are ecologically sensitive, and the large-scale installation of PV panels may lead to destruction of the mountain landscape ecological environment. In this study, soil physicochemical properties were measured in 160 soil test plots, and vegetation community conditions were assessed in 26 vegetation test plots at a mountain PV power station in Damiao Town, Chaoyang County, Liaoning Province, China, using a combination of field sampling and laboratory testing. Based on mean values of 15 soil and vegetation indicators under different PV panel coverage rates, calculated via ANOVA in SPSS 27.0 software with Bonferroni-corrected p-values, the effects of various coverage rates on the mountain landscape ecological environment were investigated through multiple comparisons of the mean values. Using the Euclidean distance principle, the similarity ranking between the ecological environment under different PV coverage intervals and the control point was determined as follows: 0% > 0–5% > 15–20% > 5–10% > 10–15% > over 20%. Ultimately, considering the power generation requirements of the PV power station, the 15–20% PV panel coverage rate was identified as the optimal range that minimizes impact on the mountain landscape ecological environment while meeting electricity production demands. Therefore, construction stakeholders should fully consider the influence of PV panel coverage rate on the mountain landscape ecological environment and control the coverage within the 15–20% range according to the power generation needs of mountain PV power stations, so as to mitigate the environmental impact of PV panel installation.

1. Introduction

In the face of the severe challenge of global warming, countries around the world1 are gradually attaching importance to the use of green energy as they steadily advance the “dual carbon” goals. Solar energy, an important renewable energy source, has become a key development direction owing to its unique advantages [1]. According to the breakdown of photovoltaic installations in 2024 released by China’s National Energy Administration, as of the end of November 2024, the cumulative installed capacity of photovoltaic reached 820 million kilowatts, and the new installed capacity of photovoltaic reached 206.3 million kilowatts. As the number of photovoltaic power stations has been increasing year by year, mountainous areas have gradually become the site for the construction of photovoltaic power stations [2]. Because of the high sensitivity of mountainous areas and the fact that the construction team only considers the power generation of the photovoltaic panels without comprehensively considering the damage to the original ecological environment or the most suitable restoration measures [3], the effect of construction of mountain photovoltaic power stations on the landscape ecological environment is far greater than that of the ecological restoration in the later stages of construction [4], which results in severe damage to the original landscape ecological environment [5]. The photovoltaic panel coverage rate is an indicator of the ratio of the installed area of the photovoltaic power generation system to the total land area [6]. Different photovoltaic panel coverage rates result in different patterns of water and heat resources in photovoltaic fields, and their ability to affect the mountain landscape ecological environment also differs [7]. Therefore, in large-scale construction of photovoltaic power stations in mountainous areas [8], the coverage rate of photovoltaic panels should be reasonably determined to minimize the impact of photovoltaic panels on the ecological environment of the mountain landscape and promote the mountain photovoltaic industry to achieve more efficient and sustainable development while protecting the ecological environment of the landscape.
The effect of photovoltaic power stations on the surrounding ecological environment has drawn increasing attention. Yang [9] conducted a sampling survey of vegetation in a large-scale photovoltaic base in the desert of the Ningxia Hui Autonomous Region, and found that the vegetation biomass between photovoltaic panels was significantly higher than that under the panels. Ren [10] studied the meadow photovoltaic area in Daqing City, Heilongjiang Province, and found that Shannon–Wiener and Simpson indices in areas between photovoltaic panels were significantly higher than those in areas under panels. Quentin [11] sampled soil between and under photovoltaic panels in a semi-natural grassland in Europe and found that the laying of photovoltaic panels would change the physicochemical properties of the soil and effect soil microorganisms. Chong [12] studied the effect of photovoltaic panels on soil carbon and nitrogen content in an arid prairie in the United States, and found that the total nitrogen (TN) content under the panels was significantly lower than that at the control point. From the above-mentioned literature, it can be seen that in terms of the selection of research subjects, previous domestic and international studies have mostly focused on flat-terrain desert photovoltaics, grassland photovoltaics, etc., and there remains a lack of research on more complex terrain mountain photovoltaic systems. In terms of the selection of independent variables, domestic and international scholars mostly use the area under and between photovoltaic panels as the entry point for the effect of photovoltaic panels on the ecological environment, without considering the more detailed independent variable of the photovoltaic panel coverage rate. Regarding selection of dependent variables, existing studies have mostly focused on a single indicator, lacking a comprehensive analysis of the effect of photovoltaic power stations on the ecological environment by integrating multiple dependent variables, such as soil physicochemical properties and vegetation community characteristics. Therefore, the analysis of the effect of the photovoltaic panel coverage rate in mountain photovoltaic power stations on the mountain landscape ecological environment in the present study not only addresses the research gap regarding the environmental effect of the photovoltaic panel coverage rate in mountain photovoltaic power stations, but also provides theoretical support for future deployment of large-scale construction of photovoltaic power stations in mountains.
This study aims to combine field sampling with laboratory testing to determine the soil physicochemical properties and vegetation community conditions under different photovoltaic panel coverage rates in mountain photovoltaic power stations. By analyzing the characteristics of soil properties and vegetation communities across varying coverage rates, the research seeks to thoroughly investigate the impact of different photovoltaic coverage rates on the mountain landscape ecological environment. Furthermore, based on the Euclidean distance principle, the optimal range of photovoltaic panel coverage will be identified, thereby revealing, from a practical perspective, the effects of different coverage rates on the mountain landscape ecological environment.

2. Materials and Methods

2.1. Study Area

The study area is located in Damiao Town, Chaoyang County, Chaoyang City, Liaoning Province, China (127°27″ E, 41°35″ N), characterized by a temperate continental monsoon climate. As a typical representative of China’s low mountainous and hilly regions, the area exhibits high sensitivity in its mountain landscape ecological environment. Simultaneously, as a national-level green energy demonstration county, it is vigorously promoting large-scale mountain photovoltaic projects, which can provide regional insights for green and sustainable development in ecologically sensitive mountainous areas both in China and globally. Damiao Town possesses excellent solar energy conditions, with an average annual total sunshine duration of 2861.7 h and a sunshine rate of 65% [13]. According to the classification system of Chinese Soil Taxonomy and Flora of China, the soil types in Damiao Town are primarily cinnamon soil and brown soil, with a sandy texture and relatively thin soil layers. Overall soil fertility is medium to low, with some areas containing gravel [14]. Simultaneously, the vegetation types belong to temperate deciduous broad-leaved forests, shrubs, and meadows. The dominant species include Vitex negundo–Lespedeza spp. shrubland and Setaria viridis–Digitaria sanguinalis meadow, with common associated species such as those from the genus Artemisia and Clematis. The mountain photovoltaic power station project examined in this study was officially completed in July 2020. It covers an area of 13,000 mu, with a total installed capacity of 300 megawatts. The station utilizes 800,000 monocrystalline silicon photovoltaic modules of 450 W and 455 W, generating an annual electricity output of 520.553 million kWh. The research area was defined near Daizhangzi Power Station. Considering factors such as ridge line distribution patterns, spatial settlement patterns, and ecological sensitive zone boundaries, a plot area of 137.28 hectares containing 2801 photovoltaic units was delineated as the study area (Figure 1a–c). The study area spans approximately 8 km from east to west and 7 km from north to south, covering a total area of about 56 km2. At the same time, a comparison map of vegetation coverage before and after the construction of the photovoltaic power station in the study area was drawn, as shown in Figure 2a,b. The blue line area in the figure represents the study area, and the colors indicate the normalized difference vegetation index (NDVI) values, where green indicates higher vegetation coverage, yellow indicates lower vegetation coverage, and red indicates bare soil or surfaces without vegetation coverage. It can be seen from the figure that after the construction of the photovoltaic power station in the study area, the NDVI values decreased, indicating a reduction in vegetation and an increase in bare soil. To quantitatively calculate the photovoltaic panel coverage rate, the study area was divided into 137 test plots of 100 m × 100 m, as shown in Figure 1c, where the number indicates the test plot serial number (n = 137). This facilitates the analysis of the impact of different photovoltaic panel coverage rates in mountainous photovoltaic power stations on the mountain landscape ecological environment.

2.2. Research Methods

2.2.1. Soil Data Collection and Measurement

Given the long daylight hours and vigorous vegetation growth in August [15], field investigations and on-site sampling of soil and vegetation were conducted in the study area in August 2024, with the aim of comprehensively analyzing the impact of the mountain photovoltaic power station on the mountain landscape ecological environment from two aspects: soil physicochemical properties and vegetation community conditions. Using Equation (1), the photovoltaic coverage rate for each plot was calculated. The calculated coverage rates within the area ranged from 0% to 63.04%. Considering factors such as mountain slope aspect, altitude, and soil type, the 137 soil test plots were divided into six mutually exclusive groups: 0% (18 plots), 0–5% (41 plots), 5–10% (23 plots), 10–15% (21 plots), 15–20% (13 plots), and over 20% (21 plots). Note that the intervals are exclusive; for example, the 0–5% group does not include plots with 0% coverage, and so forth. Soil sampling was conducted for the different groups. In addition, 23 points were randomly selected as control points for sampling from plots without photovoltaic coverage adjacent to the study area. Since soil bulk density (BD), total nitrogen (TN), total phosphorus (TP), total potassium (TK), soil organic matter (SOM), available potassium (AK), available phosphorus (AP), alkali-hydrolyzable nitrogen (AN), and soil pH are commonly used as typical indicators of soil fertility, chemical properties, and health status in the mountain landscape ecological environment [16,17], these nine indicators were selected for this study to represent the soil physicochemical properties. Physical and chemical soil properties were determined using a combination of on-site sampling and laboratory testing [18]. Undisturbed soil cores were collected from the field at a depth of 20 cm using a ring knife and transported to the laboratory. The unprocessed soil was air-dried, ground, and then tested in the laboratory for the following nine soil physicochemical properties: BD, TN, TP, TK, SOM, AK, AP, AN, and pH [19,20]. The specific testing methods, instruments and sources used are detailed in Table 1.
Coverage   rate = S p r o j e c t i o n S t o t a l a r e a × 100 %
Here: Sprojection represents the total vertical projection area of photovoltaic panels in a plot, and Stotal area represents the total area of that plot.

2.2.2. Vegetation Data Collection and Measurement

To clarify the impact of different photovoltaic panel coverage rates on vegetation community conditions in mountainous photovoltaic power stations, 18 vegetation test plots (100 m × 100 m) and 2 control points within the photovoltaic area were selected as representative plots based on the vegetation characteristics of the 137 soil test plots. In addition, six control points outside the photovoltaic area were established at the first, second, and third talas of the photovoltaic field area (Figure 3). The number indicates the test plot serial number (n = 137), the flag position is the location of the vegetation test plot. Vegetation community conditions were determined using a combination of field sampling and laboratory testing [18]. Since the species richness index, Shannon–Wiener diversity index, Simpson dominance index, Pielou evenness index, as well as above-ground and below-ground biomass are commonly used as typical indicators of vegetation community structural stability, biodiversity function, and ecosystem productivity in the landscape ecological environment, these six indicators were selected for this study to represent the vegetation community conditions of the mountain landscape ecological environment [21]. For the vegetation community diversity index assessment, vegetation in each plot was cut at ground level to record species type, number of species, coverage, height, frequency, etc. [22]. The species importance value [23], species richness index [24], Shannon–Wiener diversity index [25], Simpson dominance index [26], and Pielou evenness index [27] were calculated using Equations (2)–(5). For biomass measurement, ten 1 m × 1 m quadrats were established in each plot, and three were randomly selected. The above-ground parts of the vegetation in the quadrats were placed in an 80 °C constant-temperature oven for 24 h until a constant weight was achieved to determine the above-ground biomass. Soil columns with depths of 0–20 cm were collected; stones were removed from the samples, and fine soil was sieved through a 0.25 mm sieve. Roots were wrapped in gauze, rinsed in water, and dried in an 80 °C oven for 24 h to a constant weight to determine the below-ground biomass. This process was repeated three times [28]. The underground biomass of the vegetation was divided by the ratio of the cross-sectional area of the soil column to the area of the sample quadrat to calculate the underground biomass of the vegetation in the plot.
The species importance value was calculated as:
P = ( A + B + C ) / 3
where A is relative height, B is relative frequency, and C is relative coverage.
The Shannon–Wiener index was calculated as:
S W I = − ∑ ( P i / ln ( P i ) )
The Simpson index was calculated using:
S I = 1 − ∑ P i 2
The Pielou index was calculated as:
P I = S W I / ln S
In the formula: Pi is the ratio of the importance value of the i-th species to the sum of the importance values of all species in the sample plot, and S is the total number of species in each sample plot, the species richness index (SRI).

3. Results

3.1. Data Processing and Analysis of Soil Physicochemical Properties and Vegetation Community Conditions

3.1.1. Data Processing

Table 2 presents the research design for collecting soil physicochemical properties and vegetation community conditions under different photovoltaic panel coverage rates. To mitigate the impact of imbalanced sample sizes across different coverage rates in the mountainous area, this study utilized mean values method via ANOVA in SPSS 27.0 software to compare the mean values of 15 soil and vegetation indicators under varying photovoltaic panel coverage rates. The Bonferroni method was applied to adjust the p-values for significance. Based on multiple comparisons of means, the influence characteristics of photovoltaic panel coverage rates on soil physicochemical properties and vegetation community conditions were analyzed. The Bonferroni method was used to correct the significance p-values among different photovoltaic panel coverage rate groups, thereby determining whether significant differences existed in the mountain landscape ecological environment across these groups. Means values of soil and vegetation indicators in the study area in Table 3 and multiple comparison of means with Bonferroni test p values in Table 4 are results from multiple comparison of means. Meanwhile, the results of the quadrat survey are shown in Table 5.

3.1.2. Data Analysis

Based on the multiple comparisons of mean values presented in Table 3 and Table 4, it can be observed that for BD and SOM, significant differences (p < 0.05) exist between the “over 20%” photovoltaic coverage rate interval and the “0%”, “0–5%”, and “15–20%” coverage rate intervals. For SPI, significant differences (p < 0.05) are found between the “over 20%” coverage rate interval and the “control point”, “0%”, and “15–20%” intervals. Regarding SWI, PI, AGB, and BGB, the “over 20%” photovoltaic coverage rate interval shows significant differences (p < 0.05) compared to the “15–20%” interval. These results indicate that photovoltaic installation in mountainous areas has a significant impact on certain indicators of the mountain landscape ecological environment. Specifically, an excessively high photovoltaic coverage rate exceeding 20% leads to notable differences compared to other coverage rate groups. According to the vegetation quadrat survey statistics in the study area summarized in Table 5, the vegetation community in the research area comprises a total of 21 species, belonging to 19 genera. The plant species composition is diverse, including annual herbs, annual and biennial herbs, xeric, greening, and afforestation tree species, densely tufted perennial herbs, perennial small herbs, trees, Rosaceae species, sub-shrubby herbs, and perennial herbs. This diversity provides substantial data support for subsequent analysis of the impact of photovoltaic coverage rates on vegetation community conditions.

3.2. Impact Analysis of Photovoltaic Coverage Rate on the Mountain Landscape Ecological Environment

3.2.1. Effect of Photovoltaic Coverage Rate on Soil Physicochemical Properties

Changes in soil physicochemical properties under different photovoltaic coverage rates in the mountainous area are shown in Figure 4. According to the mean values of soil physicochemical property indicators across coverage rates, the soil bulk density (BD) was the highest in the “over 20%” group, followed in decreasing order by: 10–15% > 5–10% > control point > 0–5% > 0% > 15–20%. The total nitrogen (TN) content was highest under the 15–20% coverage rate, followed by: 0% > control point > 5–10% > 0–5% > 10–15% > over 20%. The total phosphorus (TP) content was highest at the control point, then decreased in the order: 15–20% > 5–10% > 0–5% > 0% > 10–15% > over 20%. The soil organic matter (SOM) content peaked under 15–20% coverage, with subsequent values ranked as: control point > 5–10% > 0–5% > 0% > 10–15% > over 20%. The total potassium (TK) content was highest at the control point, followed by: 15–20% > 5–10% > 0% > 0–5% > 10–15% > over 20%. The alkali-hydrolyzable nitrogen (AN) content was greatest under 15–20% coverage, decreasing in the sequence: control point > 5–10% > 0% > 10–15% > 0–5% > over 20%. The available potassium (AK) content reached its maximum under 15–20% coverage, with the order: 0% > 10–15% > control point > 0–5% > 5–10% > over 20%. The soil pH was highest at the control point, then followed by: 15–20% > 0–5% > 0% > 10–15% > 5–10% > over 20%. The available phosphorus (AP) content was highest under 15–20% coverage, with values decreasing as: 10–15% > control point > 0% > 0–5% > 5–10% > over 20%.

3.2.2. Effect of Photovoltaic Coverage Rate on Vegetation Community Conditions

Variations in vegetation community diversity indices under different photovoltaic coverage rates in the mountainous area are shown in Figure 5. The species richness index (SRI) was highest at the 0% coverage rate, followed in decreasing order by: control point > 15–20% > 5–10% > 0–5% > 10–15% > over 20%. The Shannon–Wiener diversity index (SWI) reached its maximum under the 15–20% coverage rate, with subsequent values ranked as: 0% > control point > 0–5% > 10–15% > 5–10% > over 20%. The Simpson dominance index (SI) was highest in the 15–20% group, decreasing in the order: 0% > control point > 5–10% > 0–5% > 10–15% > over 20%. The Pielou evenness index (PI) peaked under 15–20% coverage, followed by: 0% > control point > 0–5% > 5–10% > 10–15% > over 20%. Both above-ground biomass (AGB) and below-ground biomass (BGB) were highest under the 15–20% coverage rate, and decreased in the sequence: 0% > control point > 0–5% > 5–10% > 10–15% > over 20%.

3.3. Determination of Optimal Interval for Mountain Photovoltaic Panel Coverage Based on Euclidean Distance

3.3.1. Euclidean Distance Principle

The Euclidean distance calculates the true distance between two points in an N-dimensional space. This distance is derived from the Euclidean geometry formula for the distance between two points, obtained by calculating the sum of the squares of the differences between the two points in each dimension and then taking the square root [29], as shown in Equation (6). The Euclidean distance is commonly used to determine the degree of similarity between individuals, with a closer distance indicating higher similarity [30,31]. In this study, Equation (1) was combined to calculate the Euclidean distance of the mean values of 15 soil and vegetation indicators between the control point and the six photovoltaic coverage rate interval plots. By comparing the magnitudes of the Euclidean distances, the similarity ranking of the landscape ecological environment between the six interval plots and the control point was determined, thereby identifying the optimal range of photovoltaic coverage rate in mountainous areas.
X g c o m p a r i s o n − X a v g = ∑ n = 1 15 ( X g c o m p a r i s o n n − X a v g n ) 2
In the formula, Xgcontrol points represents the index content mean values of the reference point, Xavg represents the index content mean values of a certain photovoltaic panel coverage plot, and n represents the n-th index of soil and plants. The indicators are sorted in the order from BD to PI in the columns of the indicators in Table 3.

3.3.2. Determination of Optimal Range of Photovoltaic Panel Coverage

To eliminate the dimensional influence between data variables, the data must first be normalized before calculating the Euclidean distance [32]. The combined data of soil physical and chemical properties and vegetation community in the sample plots with different photovoltaic panel coverage rates in Table 3 (7) were normalized, and the normalization results were shown in Table 6. The results of the normalization process are shown in Table 6. The normalized data from Table 6 were substituted into Equation (6) to obtain the Euclidean distances between the soil and vegetation conditions of plots in different photovoltaic coverage rate intervals and the control point, as summarized in Table 7. The similarity ranking of the landscape ecological environment between plots with different photovoltaic coverage rates and the control point was as follows: 0% > 0–5% > 15–20% > 5–10% > 10–15% > over 20%. These results indicate that the installation of photovoltaic panels in mountainous areas has a certain impact on the mountain landscape ecological environment. Except for the 15–20% photovoltaic coverage rate interval, as the photovoltaic coverage rate gradually increases, the similarity between soil and vegetation conditions in the sample plots and the control point gradually decreases. This suggests that the impact of mountain photovoltaic power stations on the mountain landscape ecological environment also increases with higher photovoltaic coverage rates. Considering the current photovoltaic installation situation in the study area, the overall photovoltaic coverage rate is 9.66%. The 0–5% photovoltaic coverage rate interval cannot meet the power generation demand, while the 15–20% interval can satisfy the electricity production requirements of the region. Therefore, the 15–20% photovoltaic coverage rate is identified as the optimal range that minimizes the impact on the mountain landscape ecological environment while meeting the power generation needs of the photovoltaic panels.
Z = X − μ σ
In Equation (7), μ is the mean values and σ is the standard deviation.

4. Discussion

4.1. Reasons for Effect of Mountain Photovoltaic Panel Coverage on Soil Physicochemical Properties

The effects of different photovoltaic panel coverage rates in mountain photovoltaic power stations on soil physicochemical properties mainly originate from soil disturbance and compaction due to panel installation [33,34,35], as well as the shading effect of the panels and their interception of rainfall, soil exposure to light, temperature, and moisture content [36,37,38,39]. When the photovoltaic coverage rate in mountainous areas is 0–5%, the soil is less affected by compaction from the panel supports, retains its natural structure and porosity, and experiences normal water evaporation [40], resulting in relatively low soil bulk density (BD). At the same time, sufficient light exposure and rainfall provide suitable soil temperature and humidity [41], promoting the decomposition of organic matter such as plant residue and organic fertilizers by soil microorganisms, which releases elements such as nitrogen, phosphorus, and potassium [42,43], thereby maintaining the overall content of soil chemical properties at a relatively high level. When the photovoltaic coverage rate reaches 5–15%, photovoltaic panels post installation effect intensifies the disturbance of the soil and changes the structure and level of the soil [44], the soil particles are compressed, resulting in denser soil per unit volume, resulting in an increase in soil bulk density [45]. Meanwhile, the shading effect of the panels inhibits plant photosynthesis [46], resulting in reduced root exudates and decreased activity of soil microorganisms that depend on these exudates. This impedes processes such as nitrogen, phosphorus, and potassium cycling [47,48], causing an overall decreasing trend in the content of soil chemical properties. When the photovoltaic coverage rate is 15–20%, the further increase in panel installation exacerbates soil compaction by the supports. However, under suitable shading conditions, soil water evaporation decreases, and the soil becomes relatively loose [49], mitigating the impact of support compaction on soil BD. Additionally, moderate shading from the panels maintains suitable soil temperature and humidity conditions, promoting the involvement of soil microorganisms in the transformation and cycling of soil nitrogen and phosphorus [50]. For example, during the nitrogen cycle, suitable soil temperature and humidity facilitate the conversion of organic nitrogen to ammonium nitrogen and nitrate nitrogen through nitrification and denitrification by soil microorganisms, increasing the soil total nitrogen (TN) content [51]. In this environment, lush vegetation growth contributes a large amount of litter to the soil, and microbial decomposition increases soil organic matter (SOM) content [52], leading to an overall increasing trend in soil chemical properties. When the photovoltaic coverage rate over 20%, although the shading effect reduces soil water evaporation [53], the continuous soil compaction caused by panel supports significantly increased soil density, hence soil BD content peaks. Furthermore, large-scale shading by the panels results in poor soil aeration. Under anaerobic conditions, organic matter is broken down by soil microorganisms into acidic substances such as organic acids, intensifying soil acidification and lowering pH [54]. Plant root secretions and litter input decrease significantly, and soil microbial activity is inhibited [55], ultimately minimizing the content of soil chemical properties.

4.2. Reasons for Effect of Mountain Photovoltaic Panel Coverage Rate on Vegetation Community

The impact of different photovoltaic (PV) panel coverage rates in mountain PV power stations on vegetation community diversity indices and biomass primarily stems from alterations in the vegetation’s growing environment—such as light, moisture, and nutrient conditions—caused by the installation of PV panels [56,57].When the PV panel coverage rate is 0–5%, the soil experiences less compaction from the panel supports. Soil structure, light intensity, and water evapotranspiration remain close to natural conditions [58]. The PV panels have minimal influence on the local microclimate and soil properties, thereby providing a natural environment conducive to vegetation growth and development [59]. As a result, both the species richness index and biomass of the vegetation remain relatively high. When the coverage rate increases to 5–15%, soil compaction from the PV supports leads to uneven soil aeration and nutrient cycling [60], restricting vegetation growth. Simultaneously, the shading effect of the panels modifies light availability and water evapotranspiration [61], hindering photosynthesis and resulting in an overall declining trend in vegetation community diversity indices and biomass. At a coverage rate of 15–20%, although the increase in the number of photovoltaic panels has exacerbated earth pressure; however, appropriate PV panel installation reduces direct solar radiation on the ground, creating a microclimate characterized by lower soil evaporation and suitable temperature and humidity levels [62]. In this scenario, moderate shading not only mitigates excessive solar radiation that would intensify soil moisture evaporation [63] but also prevents insufficient photosynthetic energy due to weak light, thereby alleviating the impact of panel supports on soil compaction. Furthermore, the panels act as a physical barrier that reduces damage to vegetation from strong winds [64], enabling plants to perform relatively stable photosynthesis under suitable temperature and humidity conditions. Consequently, both the diversity indices and biomass of the vegetation community show an overall increasing trend. When the PV panel coverage rate over 20%, although the shading effect reduces surface solar radiation [65] and soil water evaporation, the excessive installation causes large areas of soil to become compacted, resulting in poor aeration and water permeability. Moreover, the shading from overly dense panel arrays alters soil hydrothermal conditions [66]. Severe insufficiency in ground-level light inhibits photosynthesis and restricts vegetation growth [67], ultimately minimizing the vegetation community diversity indices and biomass.

4.3. Reasons for Ranking of Photovoltaic Panel Coverage Rate in Mountainous Areas and Similarity of the Landscape Ecological Environment of Control Point

Based on the Euclidean distance principle, the similarity ranking of the landscape ecological environment between plots with different photovoltaic panel coverage intervals and the control point was determined as follows: 0% > 0–5% > 15–20% > 5–10% > 10–15% > over 20%. This order can be attributed to the following reasons: 0% coverage plot was not disturbed by the laying of photovoltaic panels, the soil structure remained loose, and the soil light, water and nutrient cycles were sufficient [68]. As a result, soil and vegetation conditions were closest to the natural state, showing the highest similarity to the control point. In plots with 0–5% coverage, the limited number of panels resulted in minimal effects of compaction and shading on soil and vegetation, leading to relatively high similarity to the control point. Plots with 15–20% coverage exhibited balanced light, temperature, and humidity due to appropriate shading [69], which promoted microbial activity. However, differences remained in total phosphorus, total potassium, and species richness index compared to the natural state, placing this interval third in similarity. In plots with 5–15% coverage, the gradual increase in panels inhibited plant photosynthesis [70], reduced root exudates, and decreased the activity of soil microorganisms involved in nitrogen, phosphorus, and potassium cycling. Consequently, the contents of soil physicochemical properties and vegetation community indicators were lower than those under natural conditions, resulting in lower similarity to the control point. Plots with over 20% coverage experienced severe soil compaction due to excessive PV installation, along with extensive shading and rainfall interception that intensified soil acidification and hindered photosynthesis. These factors led to significant deviations in soil properties and vegetation indicators from the natural state, yielding the lowest similarity to the landscape ecological environment of the control point.

5. Conclusions

This study measured the soil physicochemical properties and vegetation community conditions in the research area through a combination of field sampling and laboratory testing. By conducting multiple comparisons of mean values of soil and vegetation indicators under different photovoltaic panel coverage rates in mountainous areas via ANOVA, it was concluded that the installation of photovoltaic panels leads to an overall increasing trend in soil bulk density due to soil disturbance and compaction, the shading effects of the panels, and the interception of rainfall. Meanwhile, a general decreasing trend was observed in soil chemical property indicators, vegetation community diversity indices, and biomass. Furthermore, the mean values of soil and vegetation indicators under different photovoltaic panel coverage rates were normalized. Based on the Euclidean distance principle, the similarity ranking between plots with different coverage rates and the control point was determined as follows: 0% (2.6474) < 0–5% (2.9783) < 15–20% (3.2726) < 5–10% (3.5467) < 10–15% (3.8948) < over 20% (9.1492). Combined with the current status of photovoltaic installation in the study area, the 15–20% photovoltaic panel coverage rate was identified as the optimal range that minimizes the impact on the mountain landscape ecological environment while meeting the electricity production demands of the photovoltaic panels. Although scholars domestically and internationally have not yet conducted studies on photovoltaic coverage rates in mountainous areas or other regions such as deserts and meadows, the conclusions of this study align with the existing consensus that large-scale photovoltaic power station construction affects soil and vegetation conditions. Moreover, it provides a more refined photovoltaic coverage rate scale to supplement this perspective. Therefore, it is recommended that in future large-scale construction of photovoltaic power stations in mountainous areas, stakeholders should fully consider the impact of photovoltaic coverage rate on the mountain landscape ecological environment. Depending on the power generation requirements of mountain photovoltaic power stations, the photovoltaic coverage rate should be controlled within the 15–20% range to mitigate its environmental impact. This study provides an important theoretical basis for the field of landscape ecology and offers practical significance for the future large-scale construction of photovoltaic power stations in mountainous regions both in China and worldwide. Similar studies should be extended to different landscape types such as deserts and wetlands to evaluate the impact of photovoltaic coverage rates on the landscape ecological environment. This will provide scientific support for promoting the coordinated development of green energy utilization and ecological environmental protection globally, thereby contributing to the achievement of the “Dual Carbon” goals.

Author Contributions

Conceptualization, Y.D. and L.C.; methodology, L.C.; validation, L.C.; formal analysis, L.C.; investigation, L.C. and J.L.; resources, Y.D. and L.C.; data curation, J.C. and X.L.; writing—original draft preparation, L.C.; writing—review and editing, L.C.; visualization, L.C.; supervision, Y.D.; project administration, Y.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Liaoning Provincial Department of Education Basic Research Program for Colleges and Universities (Nos. LJ112510153001) and Liaoning Province Science and Technology Innovation Think Tank Project (Nos. LNKX2025QN32).

Data Availability Statement

Data are contained within the article.

Acknowledgments

We acknowledge Liaoning Province Chaoyang County Damiao Town Photovoltaic Power Station for providing the research site.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BDBulk density
TNTotal nitrogen
TPTotal phosphorus
TKTotal potassium
ANAlkali-hydrophobic nitrogen
AKAvailable potassium
SOMSoil organic matter
APAvailable phosphorus
SRISpecies Richness index
SWIShannon–Wiener index
SISimpson index
PIPielou index
AGBAbove-ground biomass
BGBUnderground biomass

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Figure 1. (a–b) Orientation map of the study area; (c) satellite image of the test plots (n = 137).
Figure 1. (a–b) Orientation map of the study area; (c) satellite image of the test plots (n = 137).
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Figure 2. Comparison of vegetation coverage in the study area before and after mountain photovoltaic panel installation: (a) Before installation; (b) After installation.
Figure 2. Comparison of vegetation coverage in the study area before and after mountain photovoltaic panel installation: (a) Before installation; (b) After installation.
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Figure 3. Satellite image of vegetation detection plots.
Figure 3. Satellite image of vegetation detection plots.
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Figure 4. Statistical chart of soil physical and chemical properties in plots with different photovoltaic panel coverage rates.
Figure 4. Statistical chart of soil physical and chemical properties in plots with different photovoltaic panel coverage rates.
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Figure 5. Statistical chart of vegetation communities with different photovoltaic panel coverage rates.
Figure 5. Statistical chart of vegetation communities with different photovoltaic panel coverage rates.
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Table 1. Soil indicators testing methods, instruments and sources.
Table 1. Soil indicators testing methods, instruments and sources.
Determination of IndicatorsMethodsExperimental InstrumentsInstrument Source
Total nitrogen (TN)Kjeldahl MethodDRK-K616
Automatic Kjeldahl Nitrogen Analyzer		Shandong Drickinstruments Co., Ltd., Jinan, China
Total phosphorus (TP)Sodium hydroxide fusion–molybdenum–antimony anticolorimetric methodUV-Vis Spectrophotometer
TU-1900 GLLS-JC-059		Qingdao Optical Electronic Technology Co., Ltd., Qingdao, China
Total potassium
(TK)		Inductively Coupled Plasma Optical Emission SpectrometryInductively Coupled Plasma Spectrometer
Agilent 5110 ICP-OES GLLS-JC-493		Agilent Technologies, Santa Clara, CA, USA
Organic matter (SOM)Potassium dichromate external heating methodAnalytical Balance (0.0001 g),
HH-S Oil Bath, etc.		Changzhou Guohua Electric Appliance Co., Ltd., Changzhou, China
Available potassium (AK)Ammonium acetate extraction–flame photometryFlame Atomic Absorption Spectrophotometer
Agilent 280FS GLLS-JC-163		Agilent Technologies, Santa Clara, CA, USA
Available phosphorus (AP)Sodium bicarbonate extraction–molybdenum-antimony anti-spectrophotometric methodUV-Vis Spectrophotometer
TU-1900 GLLS-JC-059		Qingdao Optical Electronic Technology Co., Ltd., Qingdao, China
Alkali-hydrolyzed nitrogen (AN)Alkali hydrolysis diffusion methodGerman Seal AA3
Automated Continuous Flow Analyzer		SEAL Analytical (Shanghai) Co., Ltd., Shanghai, China
pHPotentiometryIon Meter PXS-270
GLLS-JC-054		INASE Scientific Instrument Co., Ltd., Shanghai, China
Soil bulk density
(BD)		Ring knife methodSoil Bulk Density Tester
YDRZ-4L		Zhejiang Top Cloud-Agri Technology Co., Ltd., Zhejiang, China
Table 2. Research Design for Collecting Soil Physicochemical Properties and Vegetation Community Conditions under Different Photovoltaic Panel Coverage Rates.
Table 2. Research Design for Collecting Soil Physicochemical Properties and Vegetation Community Conditions under Different Photovoltaic Panel Coverage Rates.
Photovoltaic Panel Coverage
Rate Plot		Number of Soil Physicochemical
Properties Test Plots		Number of Vegetation Community
Condition Test Plots
0%183
0–5%413
5–10%233
10–15%213
15–20%133
Over 20%213
Control points238
Total16026
Table 3. Mean values of soil and vegetation indicators in plots with different photovoltaic coverage rates.
Table 3. Mean values of soil and vegetation indicators in plots with different photovoltaic coverage rates.
IndicatorControl Point0%0–5%5–10%10–15%15–20%Over 20%
BD (g/cm3)1.1231.1161.1221.1321.1321.1041.156
TN (g/kg)1.81681.86671.75651.75671.67991.98751.6248
TP (%)0.0370.0310.0320.0320.0270.0370.020
TK (g/kg)25.53224.26723.91824.30923.81925.57821.083
AN (g/kg)0.16840.16220.15190.16430.15650.18480.1339
AK (g/kg)0.23840.25220.22290.21590.25310.23920.2138
SOM (g/kg)28.95025.93127.88828.50923.64832.88614.465
PH7.0016.86786.88786.81526.86816.94696.7729
AP (g/kg)0.00800.00790.00770.00750.008950.01000.00415
AGB (g)409.241432.414380.251379.042377.86482.517297.292
BGB (g)4.6134.6584.3674.2394.2375.4052.558
SPI4.2864.4003.5623.6253.3334.2502.500
SWI1.0951.2370.9270.8820.8911.3260.495
SI0.6440.6610.5550.550.5380.6790.341
PI0.7670.8610.7380.7530.7310.9280.35
Table 4. Mean values from Bonferroni post hoc tests for soil and vegetation indicators.
Table 4. Mean values from Bonferroni post hoc tests for soil and vegetation indicators.
IndicatorPaired SamplesMean Values DifferenceStandard ErrorSignificance p95% Confidence Interval
Lower LimitUpper Limit
BD0% vs. over 20%−0.03980.01150.0151−0.0755−0.0042
0–5% vs. over 20%−0.03380.00960.0132−0.0635−0.0040
15–20% vs. over 20%−0.05210.01270.0011−0.0913−0.0130
SOMcontrol point vs. over 20%13.78813.99600.01611.424026.1532
0% vs. over 20%10.76903.34010.03300.432021.1061
0–5% vs. over 20%12.72602.79100.00024.091021.3620
15–20% vs. over 20%18.01503.67000.00016.658029.3721
AGB15–20% vs. over 20%185.22656.90630.04422.4444368.007
BGB15–20% vs. over 20%2.84680.70350.00400.58715.1060
SPIcontrol point vs. over 20%1.78510.53500.03500.06653.5049
0% vs. over 20%1.90000.57290.03710.06103.7400
15–20% vs. over 20%1.75010.52290.03410.07033.4301
SWI15–20% vs. over 20%0.83150.23670.02080.07111.5919
PI15–20% vs. over 20%0.57790.17280.03410.02291.1329
Table 5. Research Area Plant Species Quadrat Survey.
Table 5. Research Area Plant Species Quadrat Survey.
Genus NamePlant NamePlant Latin NameGenera CountSpecies Count
VitexNegundo ChastetreeVitex negundo11
LespedezaKorean LespedezaRhamnus aurea12
Shrub LespedezaLespedeza bicolor
SetariaGreen BristlegrassSetaria viridis11
DigitariaHairy CrabgrassDigitaria sanguinalis11
ClematisChinese BushcloverLespedeza cuneata11
ArtemisiaFoetid WormwoodArtemisia anethifolia12
Sweet WormwoodArtemisia annua
LeontopodiumEdelweissLeontopodium leontopodioides11
SanguisorbaGreat BurnetSanguisorba officinalis11
DianthusChinese PinkDianthus chinensis11
RhamnusLittleleaf BuckthornRhamnus parvifolia Bunge11
PotentillaChinese CinquefoilPotentilla chinensis11
RobiniaBlack LocustRobinia pseudoacacia11
PlatycladusOriental ArborvitaePlatycladus orientalis11
PhragmitesCommon ReedPhragmites australis11
UlmusSiberian ElmUlmus pumila11
AstragalusMongolian MilkvetchAstragalus membranaceus11
PopulusBlack PoplarPopulus nigra11
KummerowiaCommon LespedezaKummerowia striata11
ReynoutriaJapanese KnotweedReynoutria japonica11
Total 1921
Table 6. Normalized mean values of soil and vegetation indicators in plots with different photovoltaic coverage rates.
Table 6. Normalized mean values of soil and vegetation indicators in plots with different photovoltaic coverage rates.
IndicatorControl Points0%0–5%5–10%10–15%15–20%over 20%
BD−0.2113−0.6427−0.27290.34340.3434−1.38231.8225
TN0.27130.6854−0.2297−0.2277−0.86551.6891−1.3229
TP1.03620.02410.19280.1928−0.65071.0362−1.8315
TK0.97280.1296−0.10300.1576−0.16871.0032−1.9916
AN0.520320.1215−0.53540.2555−0.24271.5678−1.6870
AK0.29311.1364−0.6562−1.08691.19030.3393−1.2160
SOM0.4985−0.01860.31660.4229−0.40961.1725−1.9823
PH1.5817−0.15890.1025−0.8462−0.15490.8748−1.3990
AP0.142230.0869−0.0237−0.13430.66771.2485−1.9873
AGB0.26530.6711−0.2423−0.2635−0.28411.5484−1.6949
BGB0.36550.41750.0812−0.0664−0.06901.2806−2.0091
SPI0.85611.0250−0.2163−0.1229−0.55540.8028−1.7893
SWI0.42150.9374−0.1889−0.3524−0.31971.2607−1.7585
SI0.66950.8170−0.1029−0.1463−0.25040.9733−1.9602
PI0.18730.69880.02950.1112−0.00851.0633−2.0815
Table 7. Euclidean distance between soil and vegetation conditions and control points of plots with different photovoltaic panel coverage rates.
Table 7. Euclidean distance between soil and vegetation conditions and control points of plots with different photovoltaic panel coverage rates.
Photovoltaic Panel Coverage RateEuclidean Distance from Control PointSorted by Similarity to Control Point
0%2.64741
0–5%2.97832
5–10%3.54674
10–15%3.89485
15–20%3.27263
over 20%9.14926
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Chang, L.; Dong, Y.; Liu, J.; Cui, J.; Liu, X. Effect of Photovoltaic Panel Coverage Rate in Mountainous Photovoltaic Power Stations on the Ecological Environment of Mountainous Landscapes. Appl. Sci. 2025, 15, 10068. https://doi.org/10.3390/app151810068

AMA Style

Chang L, Dong Y, Liu J, Cui J, Liu X. Effect of Photovoltaic Panel Coverage Rate in Mountainous Photovoltaic Power Stations on the Ecological Environment of Mountainous Landscapes. Applied Sciences. 2025; 15(18):10068. https://doi.org/10.3390/app151810068

Chicago/Turabian Style

Chang, Le, Yukuan Dong, Jiatong Liu, Juntong Cui, and Xin Liu. 2025. "Effect of Photovoltaic Panel Coverage Rate in Mountainous Photovoltaic Power Stations on the Ecological Environment of Mountainous Landscapes" Applied Sciences 15, no. 18: 10068. https://doi.org/10.3390/app151810068

APA Style

Chang, L., Dong, Y., Liu, J., Cui, J., & Liu, X. (2025). Effect of Photovoltaic Panel Coverage Rate in Mountainous Photovoltaic Power Stations on the Ecological Environment of Mountainous Landscapes. Applied Sciences, 15(18), 10068. https://doi.org/10.3390/app151810068

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