Forestry, Faculty of
Non UBC
DSpace
University of British Columbia. Collaborative for Advanced Landscape Planning
Zhao, Yujie
Xu, Shaowei
Huang, Ziluo
Fang, Wenqiang
Huang, Shanjun
Huang, Peilin
Zheng, Dulai
Dong, Jiaying
Chen, Ziru
Yan, Chen
Zhong, Yukun
Fu, Weicong
We explored the spatial and temporal characteristics of the urban forest area soundscape by setting up monitoring points (70 × 70 m grid) covering the study area, recorded a total of 52 sound sources, and the results showed that: (1) The soundscape composition of the park is dominated by natural sounds and recreational sounds. (2) The diurnal variation of sound sources is opposite to that of temperature, 6:00–9:00 is the best time for the public to perceive birdsong, and after 18:00, the park is dominated by insect chirps. (3) The PSD (power spectral density) and the SDI (soundscape diversity index) of the park are greatly affected by public recreation behaviors, and some recreation behaviors may affect the vocal behavior of organisms such as birds. (4) Spaces with high canopy density can attract more birdsong and recreational sounds in summer, and the combination of “tree + lake” can attract more birdsong. Vegetation has a significant dampening effect on traffic sound. (5) Landscape spatial elements, such as the proportion of hard ground, sky, trees, and shrubs, have a significant impact on changes in the PSD, the SDI and different kinds of sound sources. The research results provide effective data support for improving the soundscape of urban forests.
Citation: Zhao, Y.; Xu, S.; Huang, Z.;Fang, W.; Huang, S.; Huang, P.;Zheng, D.; Dong, J.; Chen, Z.; Yan, C.;et al. Temporal and SpatialCharacteristics of SoundscapeEcology in Urban Forest Areas andIts Landscape Spatial InfluencingFactors. Forests 2022, 13, 1751.https://doi.org/10.3390/f13111751Academic Editor: PalomaCariñanosReceived: 30 September 2022Accepted: 19 October 2022Published: 24 October 2022Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2022 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).ArticleTemporal and Spatial Characteristics of Soundscape Ecology inUrban Forest Areas and Its Landscape SpatialInfluencing FactorsYujie Zhao 1, Shaowei Xu 1, Ziluo Huang 1, Wenqiang Fang 1, Shanjun Huang 1, Peilin Huang 1, Dulai Zheng 1,Jiaying Dong 2 , Ziru Chen 3, Chen Yan 1, Yukun Zhong 1 and Weicong Fu 1,4,*1 College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, 15 Shangxiadian Rd.,Fuzhou 350000, China2 School of Architecture, Clemson University, Clemson, SC 29634, USA3 College of Architecture and Urban Planning, Fujian University of Technology, 33 Xuefunan Rd.,Fuzhou 350118, China4 Collaborative for Advanced Landscape Planning, Faculty of Forestry, The University of British Columbia,Vancouver, BC V6T 1Z4, Canada* Correspondence: weicongfufj@163.comAbstract: We explored the spatial and temporal characteristics of the urban forest area soundscape bysetting up monitoring points (70 × 70 m grid) covering the study area, recorded a total of 52 soundsources, and the results showed that: (1) The soundscape composition of the park is dominated bynatural sounds and recreational sounds. (2) The diurnal variation of sound sources is opposite tothat of temperature, 6:00–9:00 is the best time for the public to perceive birdsong, and after 18:00, thepark is dominated by insect chirps. (3) The PSD (power spectral density) and the SDI (soundscapediversity index) of the park are greatly affected by public recreation behaviors, and some recreationbehaviors may affect the vocal behavior of organisms such as birds. (4) Spaces with high canopydensity can attract more birdsong and recreational sounds in summer, and the combination of“tree + lake” can attract more birdsong. Vegetation has a significant dampening effect on traffic sound.(5) Landscape spatial elements, such as the proportion of hard ground, sky, trees, and shrubs, have asignificant impact on changes in the PSD, the SDI and different kinds of sound sources. The researchresults provide effective data support for improving the soundscape of urban forests.Keywords: sound source composition; ecosystem acoustics; temporal and spatial change; land-scape space1. IntroductionUrban forests are important places for the public to get a close experience with nature,and the ways to improve the quality of urban forest recreation have received increasingattention and become the focus of many scholars [1,2]. As the construction of urban forestshas entered the stage of quality improvement, managers have shifted from the creation ofvisual landscapes to a comprehensive improvement of the sensory experience [3–6], andsoundscape is an important part of the senses. Ecosystem sounds create a soundscape com-prised of acoustic periodicities and frequencies emitted from the ecosystem’s biophysicalentities. These sounds are acoustic signals that reflect the dynamics of biological, social,and physical systems of a landscape. Soundscape ecology is “the study of systematicrelationships between humans, organisms, and their sonic environment”. As an importantresource ranked only second to the visual landscape, the soundscape has gradually receivedattention in the optimization design of urban forests [7,8]. Understanding and makingproper use of the sounds in the environment around us are directly related to the livabilityof living environments and the quality of life of residents.Forests 2022, 13, 1751. https://doi.org/10.3390/f13111751 https://www.mdpi.com/journal/forestsForests 2022, 13, 1751 2 of 18The soundscape varies in different landscape spaces. Scholars have conducted variousstudies on urban forests and other public open spaces, which mainly focused on soundscapechanges [9–13] and quality evaluation [14–20]. Ali Jahani et al. assessed the role of birdsongcomponents in the psychological recovery of urban visitors and developed a decisionsupport system as a practical tool [21]. Hong Xinchen et al. explored the impact ofsoundscape-driving factors on the perception of birdsong and established a perceivedbirdsongs model (PBM) that effectively simulated the process from soundscape informationto perceived birdsongs in urban forests. The methodology developed could be useful forsoundscape assessment and conservation in urban forests [22]. Liu Jiang et al. showed thatlandscape features greatly affect the public’s perception of soundscapes and human-madesounds dominate urban soundscapes in the time-space dimension, but birdsongs andcertain geophysical sounds also play an important role in soundscape [23]. Hao Zezhouet al. studied the variation characteristics and influencing factors of birdsongs and insectchirps in urban forests, as well as analyzed the activity patterns of urban biomes based onsoundscape records [24]. Scholars have achieved fruitful achievements in the protection ofgreen space biodiversity, improvement of public recreational benefits, and the reductionin noise; however, the existing research mainly uses the soundwalk method or sets upmonitoring points in typical recreational areas to collect and analyze the acoustic data, andtherefore, the results of studies are of weak guiding significance to the whole park.We record sounds autonomously at multiple places in the park throughout the dayand providing a window on the ecological spatiotemporal continuum, thereby provide atool to capture the data needed to assess ecological integrity over time. This survey wasable to visualize temporal patterns of landscape acoustic signals throughout the day toexamine changes in the soundscape over time. Our study uses Hot Spring Park, locatedin the core area of Fuzhou city, during the summer as an example. To provide referencesfor the management and optimization of the public space soundscape in urban core areas,monitoring sites in Hot Spring Park were selected by dividing grids and we focusedon solving the following problems: (1) identify the structural features of urban forestsound sources, (2) analyze the spatial and temporal variation characteristics of the parksoundscape, and (3) explore the differences in soundscape characteristics and influencingfactors of various landscape spaces.2. Materials and Methods2.1. Study LocationFuzhou was awarded the title of the “Chinese National Forest City” in 2017 andthe “National Forest Tourism Model City” in 2018. Hot Spring Park is located in the citycenter of the intersection of “Riding Greenway” and “Inner River Greenway” in Fuzhou,which belongs to the core of urban forest [25]. The Hot Spring Park covers an area ofabout 10 hm2(119◦18′34′′ E~119◦18′55′′ E, 26◦5′43′′ N~26◦5′59′′ N). The park containshigh species richness, with over 2000 big trees and 200 kinds of plants being plantedhere, and contains dozens of plant community structures, such as palm forest, evergreenconiferous forest, evergreen broad-leaved forest and deciduous broad-leaved forest [26].With the increase in development in the city, Hot Spring Park has become a place forexercise, leisure, and entertainment for the public. In recent years, Hot Spring Park hasundergone continuous upgrades and transformations, which provides the possibility forfuture soundscape integration.2.2. Soundscape Monitoring and Data Acquisition2.2.1. Soundscape MonitoringAccording to previous studies, the feasible distance between recording equipmentis 100 m [27–29]. We divided Hot Spring Park into grids that were equally distanced at70 × 70 m [27] and selected 31 monitoring points (Figure 1). After our pre-experimentin July 2021, we randomly selected 3 sunny and breezy days for acoustic data collection(contains the weekend) so that our experiment would be more consistent with the actualForests 2022, 13, 1751 3 of 18situation of the park soundscape. Monitoring points were collected by placing a SonyPCM-D100 recording device in the center of a grid; if the monitoring point was inaccessible,an appropriate offset was made. Sampling occurred between 6:00 and 22:00 (park openinghours), and the sampling frequency was set to 44,100 Hz. The data resolution is set to 16 bitand the audio format was WAV. After sampling, we randomly intercepted 2 min of soundfiles within one hour [11], and had an average of 992 min of sound clips per day.Forests 2022, 13, x FOR PEER REVIEW 3 of 19 2.2. Soundscape Monitoring and Data Acquisition 2.2.1. Soundscape Monitoring According to previous studies, the feasible distance between recording equipment is 100 m [27–29]. We divided Hot Spring Park into grids that were equally distanced at 70 × 70 m [27] and selected 31 monitoring points (Figure 1). After our pre-experiment in July 2021, we randomly selected 3 sunny and breezy days for acoustic data collection (contains the weekend) so that our experiment would be more consistent with the actual situation of the park soundscape. Monitoring points were collected by placing a Sony PCM-D100 recording device in the center of a grid; if the monitoring point was inaccessible, an ap-propriate offset wa made. Sampling occurred betw en 6:00 a d 22:00 (park opening , d the sampling frequency was set to 44,100 Hz. The data resoluti n is set to 16 bit and the audi format was WAV. After sampling, r ly intercepted 2 min of sound files within one hour [11], and had an average of 992 min of sound clips per day. Figure 1. Grid and location of monitoring points (the numbers represent our monitoring points). 2.2.2. Panoramic Photos Collection To analyze the landscape spatial characteristics of the monitoring site, a panoramic camera (Insta360 One X, panoramic image example shown in Figure 2) was placed during sound collection. Figure 1. Grid and location of monitoring points (the numbers represent our monitoring points).anoramic Ph tos Collectionanalyze the landscape spatial characteristics of the monitoring site, a panoramicca era (Insta360 One X, panoramic image example shown in Figure 2) was placed duringsound collection.Forests 2022, 13, x FOR PEER REVIEW 4 of 19 Figure 2. Panoramic overview of 31 monitoring point. 2.3. Data Processing and Analysis 2.3.1. Sound Source Analysis To identify and classify the types of sound sources in urban parks, Adobe Audition was used in combination with spectrograms. Sound events can be visualized through the spectrogram, where brighter and more intense colors indicate a sound with greater power [30]. In our experiment, each acoustic signal appeared at the end of vocalization as one vocalization event [31] (Figure 3). In the statistics of audio, different kinds of sound sources in the same audio can be counted separately in frequency and time length, and sound types can overlap with each other. Figure 3. Schematic diagram of sound spectrum recognition in each frequency band. Figure 2. Panoramic overview of 31 monitoring point.Forests 2022, 13, 1751 4 of 182.3. Data Processing and Analysis2.3.1. Sound Source AnalysisTo identify and classify the types of sound sources in urban parks, Adobe Auditionwas used in combination with spectrograms. Sound events can be visualized throughthe spectrogram, where brighter and more intense colors indicate a sound with greaterpower [30]. In our experiment, each acoustic signal appeared at the end of vocalization asone vocalization event [31] (Figure 3). In the statistics of audio, different kinds of soundsources in the same audio can be counted separately in frequency and time length, andsound types can overlap with each other.Forests 2022, 13, x FOR PEER REVIEW 4 of 19 Figure 2. Panoramic overview of 31 monitoring point. 2.3. Data Processing and Analysis 2.3.1. Sound Source Analysis To identify and classify the types of sound sources in urban parks, Adobe Audition was used in combination with spectrograms. Sound events can be visualized through the spectrogram, where brighter and more intense colors indicate a sound with greater power [30]. In our experiment, each acoustic signal appeared at the end of vocalization as one vocalization event [31] (Figure 3). In the statistics of audio, different kinds of sound sources in the same audio can be counted separately in frequency and time length, and sound types can overlap with each other. Figure 3. Schematic diagram of sound spectrum recognition in each frequency band. Figure 3. Schematic diagram of sound spectrum recognitio in each frequency band.2.3.2. Power Spectral DensityDifferent entities in the soundscape produce sounds at different frequencies [11].Sound signals depict frequency on the vertical axis of the spectrum and time on thehorizontal axis. The shading indicates the intensity (the power spectral density, PSD)of the sound signal at a particular frequency and time. Calculating the power spectraldensity can reflect the intensity of all the collected sound signals more objectively, andcan also calculate the intensity of sound signals in different frequency intervals. The PSD,measured in w/kHz, was created by Welch in 1967 and represents the physical quantity ofthe power of an acoustic signal in relation to its frequency [32]. It is usually used to studyacoustic signals of random vibration. The power of the sound can reflect the change ofsound amplitude; that is, the greater the power, the greater the amplitude, the greater theamplitude, the greater the loudness, the greater the loudness, the stronger the sound isperceived by people [33]. Matlab was used to calculate the PSD in the range of 1–11 kHzto explore its temporal and spatial variations. In this study, the frequency of the soundsources observed was concentrated above 1 kHz, and data in the lowest interval, 0–1 kHz,was excluded as this can be largely attributed to human operation.2.3.3. The Soundscape Diversity IndexThe soundscape diversity index (SDI), used to evaluate the diversity of sound elementsin the environment, was developed as an improved Simpson Diversity Index by scholars [34,35].The main expression is as follows:SDI = 1−S∑i=1(nN)2(1)Forests 2022, 13, 1751 5 of 18where n and N are the total number of perceived occurrences of a particular sound, I, andall sounds, S, in the soundscape sample, respectively. The SDI ranges between 0 and 1, andthe greater the value, the more diverse the soundscape.The SDI could “objectively” reflect soundscape characteristics and illustrates the overallsoundscape perception characteristics [34], we calculated the SDI of each monitoring pointto explore the spatial and temporal variation characteristics of park soundscape diversity.2.3.4. Image Semantic SegmentationTo begin image semantic segmentation, a total of 770 pictures of Fuzhou city parkwere collected by camera (Contains both regular and panoramic photos) and randomlydivided into training and test set according to the ratio of 10:1. Based on the classificationof ADE20K dataset, images (including ordinary images and panoramic images) weremanually annotated and input into DeeplabV3+ for training. The segmentation accuracy ofthe model reached 66.022% (MIoU = 66.022%), among which the segmentation accuracyof the water surface reached 84.53%. DeeplabV3+ is a mainstream network model in thefield of image semantic segmentation, with multi-scale convolution layer and encoding-decoding dual modules [36]. It can achieve fine segmentation of the structure contained inthe images [37]. As an encoding module, the DeeplabV3+ network can achieve hierarchicalnested extraction of target features and multi-scale context information extraction, whereasthe decoding module can integrate the low-level features and high-level abstract featuresgenerated in the DeeplabV3+ backbone network, and its structure is shown in Figure 4 [38].Based on the DeepLabV3+ network structure, the urban park landscape labels are dividedinto 9 categories: water, trees, shrubs, ground cover, people, garden sketches, buildings,hard ground, and sky.Forests 2022, 13, x FOR PEER REVIEW 6 of 19 Figure 4. DeepLab V3+ model structure diagram [36]. 3. Results 3.1. Composition Characteristics of the Urban Forest Soundscape in Summer Through the discrimination of audio files by manually listening combined with Adobe Audition software. A total of 52 types of sound sources were identified and then, based on the methods of previous studies, divided into four primary classifications: nat-ural sound, traffic sound, management sound, and recreation sound (Table 1.) [2,39–41]. The data of sound source types (including frequency and duration of different types of sound sources) in the monitoring points were averaged to represent the composition of sound sources in the park. A total of 60,527.6 s of sound source data were counted, with a total of 11,202 times. In terms of duration, natural sounds accounted for 28.51% of the overall, with birdsong and insect chirps being the majority (87.94%). Traffic sounds ac-counted for 9.63%, and consisted mainly of car driving sounds (78.49%). Management sounds accounted for 19.99%, with engine, fountain machinery and park radio sounds being the majority (88.90%). Recreational sounds accounted for 41.87%, and consisted mainly of music and conversation (70.13%). In terms of frequency, natural sounds ac-counted for 43.29% of the overall, with birdsong being the majority (96.74%); traffic sounds accounted for 5.27%; management sounds accounted for 10.40%; and recreational sounds accounted for 41.04%. Table 1. Classification of park sound source types. primary Classifica-tion Expound Secondary Classi-fication Sound Element natural sound produced by natural elements, and no in-terference from hu-man activities. plant sound leaves animal sounds birdsong, insects, pets, birds flapping wings, frogs natural phenome-non sound wind traffic sound traffic sound around the park traffic sound car driving, car engine, tram driving, horn, airplane roaring, motorcycle, bus arrival, bus announcement, brake, siren, ambulance, bicycle bell management sound The sound of park management maintenance sound engine, steel plate, digging, electric drill, sweeping, door opening, trailer, beat, shovel, weed, fountain machinery, sprinkler Figure 4. eepLab 3+ odel structure diagra [36].3. Results3.1. Composition Characteristics of the Urban Forest Soundscape in SummerThrough the discrimination of audio files by manually listening combined with AdobeAudition software. A total of 52 types of sound sources were identified and then, basedon the methods of previous studies, divided into four primary classifications: naturalsound, traffic sound, management sound, and recreation sound (Table 1.) [2,39–41]. Thedata of sound source types (including freq ency and durati of different types of soundsources) in the monitoring points were averaged to represent the compositio of soundrces in th park. A total of 60,527.6 s of sound sourc data were count d, with a total f11,202 times. In terms of dura ion, natural sounds accounted for 28.51% of the ov rall, withbirds ng and insect chirps being the m jority (87.94%). Traffic sounds acc unted for 9.63%,Forests 2022, 13, 1751 6 of 18and consisted mainly of car driving sounds (78.49%). Management sounds accountedfor 19.99%, with engine, fountain machinery and park radio sounds being the majority(88.90%). Recreational sounds accounted for 41.87%, and consisted mainly of music andconversation (70.13%). In terms of frequency, natural sounds accounted for 43.29% of theoverall, with birdsong being the majority (96.74%); traffic sounds accounted for 5.27%;management sounds accounted for 10.40%; and recreational sounds accounted for 41.04%.Table 1. Classification of park sound source types.Primary Classification Expound Secondary Classification Sound Elementnatural soundproduced by natural elements,and no interference fromhuman activities.plant sound leavesanimal sounds birdsong, insects, pets, birdsflapping wings, frogsnatural phenomenon sound windtraffic sound traffic sound around the park traffic soundcar driving, car engine, tramdriving, horn, airplane roaring,motorcycle, bus arrival, busannouncement, brake, siren,ambulance, bicycle bellmanagement sound The sound of parkmanagementmaintenance soundengine, steel plate, digging, electricdrill, sweeping, door opening,trailer, beat, shovel, weed, fountainmachinery, sprinklerdevice sound park radiorecreation sound sound from park activitiesand social interactionsactivity soundplay badminton, clap and stomp,skateboard, jump rope, key shaking,slap the ball, portable radio, music,run and jump, walk,social soundconversation, children playing, sing,reunion, cry, laugh, sneeze, cough,whistle, shout3.2. Diurnal Variation Characteristics of the Soundscape3.2.1. Diurnal Variation Characteristics of Sound SourcesOverall, the soundscape presented a “U”-shaped change feature (Figure 5). Naturalsounds occurred more frequently between 6:00 and 9:00 and lasted for a long time. Birdsongdominated the park soundscape, with Heron calls being the main bird soundscape presentat noon. Natural sounds lasted longer at night and consisted mainly of insect chirps. Trafficsound dominated the park soundscape between 12:00 and 14:00 in duration. From 15:00to 16:00, management sounds occupied a dominant position in the park soundscape, withthe sounds of engines, park radio, sweeping, and mechanical fountains accounting for alarge proportion. The sound of recreation appeared more frequently and lasted longerbefore 12:00 and after 16:00. The sounds of music and conversation in the morning made agreater contribution to the duration, and the sounds of people clapping, stomping, playingbadminton, and walking made a greater contribution to the frequency. The sound ofchildren playing, running and jumping accounted for a considerable proportion after 16:00.Forests 2022, 13, 1751 7 of 18Forests 2022, 13, x FOR PEER REVIEW 7 of 19 device sound park radio recreation sound sound from park ac-tivities and social in-teractions activity sound play badminton, clap and stomp, skateboard, jump rope, key shaking, slap the ball, portable radio, music, run and jump, walk, social sound conversation, children playing, sing, reunion, cry, laugh, sneeze, cough, whistle, shout 3.2. Diurnal Variation Characteristics of the Soundscape 3.2.1. Diurnal Variation Characteristics of Sound Sources Overall, the soundscape presented a “U”-shaped change feature (Figure 5). Natural sounds occurred more frequently between 6:00 and 9:00 and lasted for a long time. Bird-song dominated the park soundscape, with Heron calls being the main bird soundscape present at noon. Natural sounds lasted longer at night and consisted mainly of insect chirps. Traffic sound dominated the park soundscape between 12:00 and 14:00 in dura-tion. From 15:00 to 16:00, management sounds occupied a dominant position in the park soundscape, with the sounds of engines, park radio, sweeping, and mechanical fountains accounting for a large proportion. The sound of recreation appeared more frequently and lasted longer before 12:00 and after 16:00. The sounds of music and conversation in the morning made a greater contribution to the duration, and the sounds of people clapping, stomping, playing badminton, and walking made a greater contribution to the frequency. The sound of children playing, running and jumping accounted for a considerable pro-portion after 16:00. (a) (b) (c) (d) Figure 5. Diurnal variation characteristics of duration, frequency and relative proportion of various sound sources; (a) diurnal variation of sound duration; (b) diurnal variation characteristics of rela-tive proportion of sound duration; (c) diurnal variation of sound frequency; (d) diurnal variation characteristics of relative proportion of sound frequency. 3.2.2. Diurnal Variation Characteristics of the PSD 00:00.014:24.028:48.043:12.057:36.012:00.06–77–88–99–1010–1111–1212–1313–1414–1515–1616–1717–1818–1919–2020–2121–22Time(t)Time(t)Nature TrafficManagement and protection Recreation0.00%20.00%40.00%60.00%80.00%6–77–88–99–1010–1111–1212–1313–1414–1515–1616–1717–1818–1919–2020–2121–22Duration relative proportion(%)Time(t)Nature TrafficManagement and protection Recreation0200400600800100012006–77–88–99–1010–1111–1212–1313–1414–1515–1616–1717–1818–1919–2020–2121–22frequency(freq)Time(t)Nature TrafficManagement and protection Recreation0.00%20.00%40.00%60.00%80.00%100.00%6–77–88–99–1010–1111–1212–1313–1414–1515–1616–1717–1818–1919–2020–2121–22Frequency relative proportion(%)Time(t)Nature TrafficManagement and protection RecreationFigure 5. Diurnal variation characteristics of duration, frequency and relative proportion of varioussound sources; (a) diurnal variation of sound duration; (b) diurnal variation characteristics ofrelative proportion of sound duration; (c) diurnal variation of sound frequency; (d) diurnal variationcharacteristics of relative proportion of sound frequency.3. .2. Diurnal Variation Characteristics of the PSDThe PSD of the soundscape during the summer in Hot Spring Park were distributedbetween 0 and 1.2 w/kHz (Figure 6). Except for the sounds from birds, insects, horns,and brakes, most of the sound sources were included in the range of 1–3 kHz, mainlymanagement and recreation sound. We found that power of the sound in the range of1–3 kHz fluctuated and rose after sunrise. The peak in power between 16:00 and 17:00and was greatly affected by the sounds of children playing, running, jumping, engine andwind. Between 20:00 and 22:00, the PSD decreased and gradually stabilized. The soundof birds, insects, and frogs were mainly in the 3–9 kHz frequency band. Some artificialsounds, such as horns, music, people playing badminton, clapping and stomping, wouldalso reach frequencies above 3 kHz. Sound power in the 3–9 kHz band exhibited a verysimilar pattern to 1–3 kHz.Forests 2022, 13, x FOR PEER REVIEW 8 of 19 The PSD of the soundscape during the summer in Hot Spring Park were distributed between 0 and 1.2 w/kHz (Figure 6). Except for the sounds from birds, insects, horns, and brakes, most of the sound sources were included in the range of 1–3 kHz, mainly manage-ment and recreation sound. We found that power of the sound in the range of 1–3 kHz fluctuated and rose after sunrise. The peak in power between 16:00 and 17:00 and was greatly affected by the sounds of children playing, running, jumping, engine and wind. Between 20:00 and 22:00, the PSD decreased and gradually stabilized. The sound of birds, insects, and frogs were mainly in the 3–9 kHz frequency band. Some artificial sounds, such as horns, music, people playing badminton, clapping and stomping, would also reach frequencies above 3 kHz. Sound power in the 3–9 kHz band exhibited a very similar pattern to 1–3 kHz. Figure 6. Diurnal variation of acoustic power spectral density in 10 frequency ranges. The relative proportion and diurnal variation characteristics of each frequency sound are shown in Figure 7. Sound power decreases with the increase in the frequency. The sound power of 1–3 kHz, mainly from recreation and management sound, accounted for more than 50% of the park’s power. The relative proportion of sound power fluctuated and rose after sunrise, and gradually decreased after reaching the maximum between 13:00 and 14:00 noon. However, the proportion of sound power above 3 kHz showed the opposite trend. Figure 7. Daily variation of the relative proportion of sound power in each frequency band. 3.2.3. Diurnal Variation Characteristics of the SDI The SDI was relatively high during the morning and night, and reached a low level between 12:00 and 15:00. After 15:00, the SDI gradually increased, and reached a peak of 0.827 between 17:00 and 18:00, and then gradually decreased once again (Figure 8). 00.20.40.60.811.21.46–77–88–99–1010–1111–1212–1313–1414–1515–1616–1717–1818–1919–2020–2121–22PSD (w/kHz)Time(t)1–2kHz 2–3kHz 3–4kHz 4–5kHz 5–6kHz6–7kHz 7–8kHz 8–9kHz 9–10kHz 10–11kHz0%20%40%60%80%100%6–77–88–99–1010–1111–1212–1313–1414–1515–1616–1717–1818–1919–2020–2121–22Relative proportion of sound power(%)Time(t)1–2kHz 2–3kHz 3–4kHz 4–5kHz 5–6kHz6–7kHz 7–8kHz 8–9kHz 9–10kHz 10–11kHzFigure 6. Diurnal variation of acoustic power spectral density in 10 frequency ranges.Forests 2022, 13, 1751 8 of 18The relative proportion and diurnal variation characteristics of each frequency soundare shown in Figure 7. Sound power decreases with the increase in the frequency. Thesound power of 1–3 kHz, mainly from recreation and management sound, accounted formore than 50% of the park’s power. The relative proportion of sound power fluctuatedand rose after sunrise, and gradually decreased after reaching the maximum between13:00 and 14:00 noon. However, the proportion of sound power above 3 kHz showed theopposite trend.Forests 2022, 13, x FOR PEER REVIEW 8 of 19 The PSD of the soundscape during the summer in Hot Spring Park were distributed between 0 and 1.2 w/kHz (Figure 6). Except for the sounds from birds, insects, horns, and brakes, most of the sound sources were included in the range of 1–3 kHz, mainly manage-ment and recreation sound. We found that power of the sound in the range of 1–3 kHz fluctuated and rose after sunrise. The peak in power between 16:00 and 17:00 and was greatly affected by the sounds of children playing, running, jumping, engine and wind. Between 20:00 and 22:00, the PSD decreased and gradually stabilized. The sound of birds, insects, and frogs were mainly in the 3–9 kHz frequency band. Some artificial sounds, such as horns, music, people playing badminton, clapping and stomping, would also reach frequencies above 3 kHz. Sound power in the 3–9 kHz band exhibited a very similar pattern to 1–3 kHz. Figure 6. Diurnal variation of acoustic power spectral density in 10 frequency ranges. The relative proportion and diurnal variation characteristics of each frequency sound are shown in Figure 7. Sound power decreases with the increase in the frequency. The sound power of 1–3 kHz, mainly from recreation and management sound, accounted for more than 50% of the park’s power. The relative proportion of sound power fluctuated and rose after sunrise, and gradually decreased after reaching the maximum between 13:00 and 14:00 noon. However, the proportion of sound power above 3 kHz showed the opposite trend. Figure 7. Daily variation of the relative proportion of sound power in each frequency band. 3.2.3. Diurnal Variation Characteristics of the SDI The SDI was relatively high during the morning and night, and reached a low level between 12:00 and 15:00. After 15:00, the SDI gradually increased, and reached a peak of 0.827 between 17:00 and 18:00, and then gradually decreased once again (Figure 8). 00.20.40.60.811.21.46–77–88–99–1010–1111–1212–1313–1414–1515–1616–1717–1818–1919–2020–2121–22PSD (w/kHz)Time(t)1–2kHz 2–3kHz 3–4kHz 4–5kHz 5–6kHz6–7kHz 7–8kHz 8–9kHz 9–10kHz 10–11kHz0%20%40%60%80%100%6–77–88–99–1010–1111–1212–1313–1414–1515–1616–1717–1818–1919–2020–2121–22Relative proportion of sound power(%)Time(t)1–2kHz 2–3kHz 3–4kHz 4–5kHz 5–6kHz6–7kHz 7–8kHz 8–9kHz 9–10kHz 10–11kHzFigure 7. Daily variation of the relative proportion of sound power in each frequency band.3.2.3. Diurnal Variation Characteristics of the SDIThe SDI was relatively high during the morning and night, and reached a low levelbetween 12:00 and 15:00. After 15:00, the SDI gradually increased, and reached a peak of0.827 between 17:00 and 18:00, and then gradually decreased once again (Figure 8).Forests 2022, 13, x FOR PEER REVIEW 9 of 19 Figure 8. Characteristics of daily variation of the SDI. 3.3. Spatial Variation Characteristics of the Soundscape 3.3.1. Spatial Variation Characteristics of Soundscape Composition The spatial variation characteristics of soundscape composition are shown in Figure 9. Natural sounds were evenly distributed in the park between 6:00 and 8:00. The fre-quency and duration of natural sounds were relatively low at monitoring points 12, 13, 27, 28 (square), and 29 (lawn along the street), these monitoring points experienced more recreational sounds during this period. Between 8:00 and 10:00, natural sounds were con-centrated in the southeast area of the park, with monitoring point 14 near the lake experi-encing the most sounds. Between 10:00 and 16:00, the frequency of natural sounds were highest in the children’s activity area, Ficus microcarpa by the lake, and the monitoring points with high canopy closure around them, mainly birdsong. The natural sound at the relatively open lakeside promenade in the southern part of the park and the monitoring points in the northern bamboo garden had a longer duration and consisted of mainly in-sect chirps. Between 16:00 and 18:00, the natural sounds at monitoring point 13 (Ficus mi-crocarpa by the lake) and the southern lakeside promenade had high frequency and long duration. After 18:00, the frequency of natural sounds in most areas of the park decreased, and the vocal behavior of birds decreased., mainly insect chirps. 0.718 0.776 0.606 0.646 0.788 0.648 0.380 0.542 0.339 0.700 0.696 0.872 0.835 0.832 0.801 0.754 00.10.20.30.40.50.60.70.80.916–77–88–99–1010–1111–1212–1313–1414–1515–1616–1717–1818–1919–2020–2121–22SDITime(t)Figure 8. Characteristics of daily variation of the SDI.3.3. Spatial Variation Characteristics of the Soundscape3.3.1. Spatial Variation Characteristics of Soundscape CompositionThe spatial variation characteristics of soundscape composition are shown in Figure 9.Natural sounds were evenly distributed in the park between 6:00 and 8:00. The frequencyand duratio of natural soun were relatively low at monitoring points 12, 13, 27, 28(square), and 29 (lawn long the street), thes monitoring points experienced more recre-ational sounds uring this period. Between 8:00 and 10:00, natural sounds w re c ncen-trated in the southeast area of the park, with monitoring point 14 ne r the lake exp rie cingthe most sounds. B tween 10:00 and 16:00, t e frequency of natural sounds wer highest inthe c ildren’s activity area, Ficus microcarpa by t lake, a d the monitoring points withigh canopy closure around them, mainly birdsong. The natural sound at the relativelyopen lakeside promenade in the southern part of the park and the monitoring points inthe norther bamboo garden had a longer duration and consisted of mainly insect chirps.Forests 2022, 13, 1751 9 of 18Between 16:00 and 18:00, the natural sounds at monitoring point 13 (Ficus microcarpa by thelake) and the southern lakeside promenade had high frequency and long duration. After18:00, the frequency of natural sounds in most areas of the park decreased, and the vocalbehavior of birds decreased., mainly insect chirps.Forests 2022, 13, x FOR PEER REVIEW 10 of 19 Figure 9. Spatial distribution of natural, traffic, management, and recreational sounds. The frequency of traffic sound was higher at the park boundary. These monitoring points were mostly open spaces with single vegetation structure, were close to the main road and were greatly affected by traffic sound. The Aquarius Square had a high fre-quency of traffic sounds due to the entry of an external tram. Traffic sound affected an area up to 140 m from the park boundary, and the sound of horns could reach up to 210 m in the park. Monitoring points 16, 30, 26, and 27 were separated from surrounding roads by buildings or complex vegetation structure, even though the road was closer, the mon-itoring points were less affected by traffic sound. Management sound was relatively random in the park. Between 6:00 and 12:00, the monitoring sites of the arched fountains have a longer sound duration, the sound of foun-tain machinery accounted for a large proportion. Between 14:00 and 18:00, the sound du-ration in the central square and the surrounding lake area was longer and consisted mainly of the engine. After 18:00, the main sound at the entrance of the park was the sound of the park radio. Figure 9. Spatial distribution of natural, traffic, management, and recreational sounds.The frequency of traffic sound was higher at the park boundary. These monitoringpoints were mostly open spaces with single vegetation structure, were close to the mainroad and were greatly affected by traffic sound. The Aquarius Square had a high frequencyof traffic sounds due to the entry of an external tram. Traffic sound affected an area up to140 m from the park boundary, and the sound of horns could reach up to 210 m in the park.Monitoring points 16, 30, 26, and 27 were separated from surrounding roads by buildingsor complex vegetation structure, even though the road was closer, the monitoring pointswere less affected by traffic sound.Management sound was relatively random in the park. Between 6:00 and 12:00,the monitoring sites of the arched fountains have a longer sound duration, the sound offountain machinery accounted for a large proportion. Between 14:00 and 18:00, the soundduration in the central square and the surrounding lake area was longer and consistedForests 2022, 13, 1751 10 of 18mainly of the engine. After 18:00, the main sound at the entrance of the park was the soundof the park radio.Recreation sound was relatively evenly distributed between 6:00 and 8:00, and waseither concentrated in the central square or a large hard area of the node. Areas that hada higher frequency of sound from badminton being played or the sound of clapping andstomping were Ficus microcarpa Garden, the south entrance square, the central Vase Square,and the north Bamboo Garden. Sound from the boulevard and children’s activity arealasted for a long time and mainly included the sound of music, children’s playing, andconversation. Between 8:00 and 16:00, the sound of music and conversation accounted for alarge proportion, and their distribution was concentrated in a certain hard ground and treecovered environment. After 18:00, the sound of recreation covered the entire park again.The duration of the recreational sound increased significantly. The water features in thepark did not play a significant role in attracting recreational sound.3.3.2. Distribution Characteristics of the PSDFigure 10 shows the distribution of the PSD in Hot Spring Park during the summer.The maximum sound power of the park (PSD = 1.09 w/kHz) was located at the riversidepromenade on the east side of the park, which was affected by the beating sound of theexternal residential area, the sound of electric drills and the sound of traffic. Meanwhile,due to the open space, the monitoring point was located at the tuyere, which was greatlyaffected by the wind. The entrance on the north side of the park was close to the mainroad and was affected by traffic sounds such as cars driving and horns, the sound of parkradio in entrance, and the sound of children playing during school closing time at dusk.The sounds of children playing also contributed a large amount of power, with a PSD of0.8 w/kHz.Forests 2022, 13, x FOR PEER REVIEW 11 of 19 Recreation sound was relatively evenly distributed between 6:00 and 8:00, and was either concentrated in the central square or a large hard area of the node. Areas th t had a hig frequency of sound from badminton bein played or the sou d of clapping nd stomping were Ficus microcarpa Garden, the south entrance square, the central Vase Square, a d the north Bamboo G rden. Sound from the boulevard and children’s activity area lasted f a long time an mainly included the s nd of music, children’s playing, and conversation. Betw en 8:00 nd 16:00, th sound f music and onversation accounted for a large proportion, and their distribution was concentr ted in a certain hard ground and tree covered environment. After 18:00, the sound of r creation covered the entire park again. The duration of the rec eational sound increased sig ificantly. The water features in the park did not play a significant role in attr cting recreational sound. 3.3.2. Distribution Characteristics of the PSD Figure 10 shows the distribution of the PSD in Hot Spring Park during the summer. The maximum sound power of the park (PSD = 1.09 w/kHz) was located at the riverside promenade on the east side of the park, which was affected by the beating sound of the external residential area, the sound of electric drills and the sound of traffic. Meanwhile, due to the open space, the monitoring point was located at the tuyere, which was greatly affected by the wind. The entrance on the north side of the park was close to the main road and was affected by traffic sounds such as cars driving and horns, the sound of park radio in entrance, and the sound of children playing during school closing time at dusk. The sounds of children playing also contributed a large amount of power, with a PSD of 0.8 w/kHz. (a) (b) Figure 10. (a) Spatial distribution characteristics of the PSD; (b) spatial distribution characteristics of the SDI. The PSD of monitoring point 12—Aquarius square, monitoring point 18—circular fountain and monitoring point 5—shady path were relatively high. In Aquarius square and circular fountain, the landscape space had the following characteristics: low basal plants, high basal constructions, without covering, both open space, plant enclosure was relatively low, and greatly affected by the sounds of engine and various recreational ac-tivities. Shady path in the basement of higher plants, the basal construction surface of low, high degree of vegetation cover, it is a semi-enclosed space, where the sound of children playing is of high power. In the southern part of the park, the sound energy was less than 0.2 W/kHz, and was mainly from the sounds of footsteps and conversation. 3.3.3. Distribution Characteristics of the SDI As shown in Figure 10, monitoring points 12 and 13 were in open spaces with a large proportion of the basal construction and no coverage, which created a diverse sound en-vironment for recreation. Monitoring points 2 and 4 were close to the north entrance of Figure 10. (a) Sp tial distribution ch racteristics of the PSD; (b) spatial distribution c ar t i ti ofthe SDI.The PSD of monitoring point 12—Aquarius square, monitoring point 18—circularfountain and monitoring point 5—shady path were relatively high. In Aquarius square andcircular fountain, the landscape space had the following characteristics: low basal plants,high basal constructions, without covering, both open space, plant enclosure was relativelylow, and greatly affected by the sounds of engine and various recreational activities. Shadypath in the basement of higher plants, the basal construction surface of low, high degree ofvegetation cover, it is a semi-enclosed space, where the sound of children playing is of highpower. In the southern part of the park, the sound energy was less than 0.2 W/kHz, andwas mainly from the sounds of footsteps and conversation.3.3.3. Distribution Characteristics of the SDIAs shown in Figure 10, monitoring points 12 and 13 were in open spaces with alarge proportion of the basal construction and no coverage, which created a diverse soundenvironment for recreation. Monitoring points 2 and 4 were close to the north entranceForests 2022, 13, 1751 11 of 18of the park and were in the main passage for the public to enter the park. The sound ofrecreation was abundant at monitoring points 2 and 4. In addition, management soundssuch as floor sweeping, were incorporated due to the daily management of the park.Monitoring point 24 was close to the lake, with complex vegetation and a superior naturalacoustic environment. Monitoring point 31 was an open entrance plaza on the south sideof the park, which was rich in recreational sound. Monitoring points 28 and 29 were closeto the road and the traffic sound was rich. Meanwhile, monitoring point 28 was locatednext to the running track in the park, so the recreational sound was relatively rich and theSDI was high.Monitoring point 14 was at the lakeside rest node based on the construction and water.The surrounding vegetation structure was simple, contained less area for movement, andthe attraction of natural sound and recreational sound was insufficient. There was anartificial bird’s nest with prominent bird singing at monitoring point 17, and most of thepublic gathered to bird watch. The lack of other recreational behaviors led to a low SDI.3.4. Influencing Factors of Landscape Space of the SoundscapeSPSS 26.0 software was used for correlation analysis and regression analysis to explorethe relationship between landscape elements and the SDI, the PSD, and the frequency andduration of various sounds.As shown in Table 2., the proportion of hard ground had a significant positive correla-tion with the SDI and the PSD, while the proportion of shrubs had a significant negativecorrelation with the PSD. Through linear regression analysis, the listed landscape factorscan explain 55.5% of the change in the PSD (R2 = 0.555), and there was no collinearitybetween variables (VIF < 10), and the samples are independent of each other (DW = 2.336)(Table 3). Our results showed that the proportion of hard ground, sky, trees, and shrubs hada significant impact on the change in the PSD (p < 0.05) (Table 4). The greater the proportionof hard ground, the lower the proportion of sky, trees, and shrubs, the higher the PSD.PSD = 0.776 + 0.01× X1 − 0.031× X6 (2)Table 2. Correlation between the PSD, the SDI and the proportion of landscape spatial elements.IndicatorsHardGroundRatioBuildingRatio Sky RatioArborRatioTouristsRatioShrubRatioGroundCoverRatiogardenOrna-mentsRatioWaterRatioSDI 0.364 * 0.008 −0.069 0.147 −0.160 0.136 −0.002 0.233 −0.227PSD 0.361 * 0.133 0.084 −0.272 0.032 −0.362 * −0.062 0.122 0.008* At the 0.05 level (two tailed), the correlation is significant.Table 3. Summary of multiple linear regression models of the PSD and proportion of landscape elements.Model R R2 Adjusted R2EstimatedStandard Error Durbin-Watson1 0.745 a 0.555 0.355 0.16873 2.336R2 reflects the variance explained by the regression equation as a percentage of the variance of the dependentvariable. Durbin-Watson reflects autocorrelation of independent variables.Listed landscape factors can explain 43.2% of the change in the SDI (R2 = 0.432), andthe samples were independent of each other and there was no collinearity (VIF < 10, DW = 2.152)(Table 5). It was found that with the higher proportion of hard ground, the higher thediversity of the spatial soundscape and richer the soundscape (Table 6).SDI = −0.053 + 0.013× X1 (3)Forests 2022, 13, 1751 12 of 18Table 4. Analysis of landscape elements affecting the PSD.ModelUnstandardizedCoefficientsStandardizedCoeffi-cients t SignificanceCollinearity StatisticsB StandardError Beta Tolerance VIF1(constant) 0.766 0.355 2.157 0.043X1 Hardgroundratio0.010 0.005 0.520 2.123 0.046 0.371 2.698X2 Buildingratio 0.001 0.012 0.012 0.068 0.947 0.680 1.471X3 Skyratio −0.017 0.006 −1.161 −2.826 0.010 0.132 7.585X4 Arborratio −0.015 0.005 −1.153 −3.066 0.006 0.157 6.359X5 Touristsratio −0.043 0.074 −0.115 −0.585 0.565 0.579 1.727X6 Shrubratio −0.031 0.013 −0.405 −2.420 0.025 0.793 1.261X7 Groundcover ratio 0.009 0.007 0.309 1.237 0.230 0.357 2.804X8 Gardenornamentsratio0.234 0.231 0.187 1.015 0.322 0.657 1.521X9 Waterratio 0.012 0.007 0.317 1.670 0.110 0.617 1.620Table 5. Summary of multiple linear regression models of the SDI and proportion of landscape elements.Model R R2 Adjusted R2EstimatedStandard Erro Durbin-Watson1 0.657 a 0.432 0.176 0.152 2.152R2 reflects the variance explained by the regression equation as a percentage of the variance of the dependentvariable. Durbin-Watson reflects autocorrelation of independent variables.Table 6. Analysis of landscape elements affecting the SDI.IndicatorsUnstandardizedCoefficientsStandardizedCoefficientst SignificanceCollinearity StatisticsB StandardError Beta Tolerance VIF(constant) −0.053 0.320 −0.167 0.869X1 Hard ground ratio 0.013 0.004 0.804 2.905 0.009 0.371 2.698X2 Building ratio 0.008 0.010 0.167 0.818 0.423 0.680 1.471X3 Sky ratio 0.005 0.005 0.426 0.917 0.370 0.132 7.585X4 Arbor ratio 0.008 0.004 0.711 1.672 0.110 0.157 6.359X5 Tourists ratio −0.122 0.066 −0.409 −1.847 0.080 0.579 1.727X6 Shrub ratio 0.013 0.012 0.210 1.110 0.280 0.793 1.261X7 Ground cover ratio 0.006 0.007 0.280 0.992 0.333 0.357 2.804X8 Garden ornaments ratio −0.119 0.208 −0.119 −0.574 0.572 0.657 1.521X9 Water ratio 0.000 0.006 0.017 0.079 0.938 0.617 1.620Table 7 further explores the relationship between the proportion of each landscapespace element and various sound sources. Our results showed that, in terms of soundfrequency, the proportion of hard ground had an extremely significant negative correlationwith the frequency of natural sound. There was a significant negative correlation betweenthe proportion of buildings and the frequency of recreational sound, while the proportionof trees has a significant positive correlation with the frequency of recreational sound.Forests 2022, 13, 1751 13 of 18There was a significant positive correlation between the proportion of trees, shrubs, gardensketches and the frequency of management sound.Table 7. Correlation between various sound sources and the proportion of landscape space elements.IndicatorsHardGroundRatioBuildingRatioSkyRatioArborRatioTouristsRatioShrubRatioGroundCoverRatioGardenOrnamentsRatioWaterRatioNatural frequency −0.537 ** −0.043 −0.109 0.157 −0.191 −0.079 0.318 0.018 0.287Traffic frequency −0.007 0.330 −0.301 0.217 0.154 0.023 0.249 −0.058 −0.233Management frequency −0.099 −0.213 −0.295 0.366 * −0.030 0.521 ** 0.173 0.413* −0.060Recreational frequency −0.023 −0.427 * −0.208 0.381 * −0.299 −0.146 0.236 0.161 −0.032Natural duration −0.574 ** −0.432 * −0.156 0.252 −0.443 * 0.002 0.572 ** −0.086 0.233Traffic duration −0.047 0.637 ** −0.370 * 0.314 0.218 −0.248 0.227 −0.243 −0.155Management duration 0.285 0.193 0.424 * −0.619 ** 0.276 −0.190 −0.456 * −0.203 −0.026Recreational duration −0.021 −0.250 −0.540 ** 0.503 ** 0.066 0.050 0.402 * 0.195 −0.305* At the 0.05 level (two tailed), the correlation is significant; ** at the 0.01 level (two tailed), the correlation is significant.In terms of duration of vocalization, the proportion of hard ground, buildings, andpeople had a significant negative correlation with the duration of natural sound. Likewise,the proportion of ground cover had a significant positive correlation with the durationof natural sound. There was a significant positive correlation between the proportion ofbuildings and the duration of traffic sound, and a significant negative correlation betweenthe proportion of sky and the duration of traffic sound. There was a significant positivecorrelation between the proportion of sky and the duration of the sound of managementand protection, while the proportion of trees and ground cover had a significant negativecorrelation with the duration of the sound of management and protection. There was a sig-nificant negative correlation between the proportion of sky and the duration of recreationalsound, but the proportion of trees and ground cover had a significant positive correlationwith the duration of recreational sound.4. Discussion4.1. Diurnal Characteristics of the SoundscapeWe found that natural sounds appeared most frequently between 6:00 and 9:00 andmainly consisted of birdsong. This verified the dawn chorus behavior of birds after sun-rise [11,42]. However, the dusk chorus was not found in our monitoring, which may havebeen related to the different urban environments where the monitoring site was located.The increase in sound of recreation at dusk may also impact on the vocalization of birds.The long duration of nocturnal insect chirping was consistent with previous studies [43].Traffic sounds dominated the park soundscape at noon as the high temperatures limitedthe frequency of natural and recreational sounds. Recreational sounds dominated thepark soundscape before 12:00 and after 16:00, which was consistent with previous stud-ies [44], and showed that the summer temperatures had a great influence on the recreationalbehavior of the public.In the summer park, sounds in the 1–3 kHz frequency band accounted for more than50% of the overall sound power of the park. The overall daily change was in the shapeof “U”, with the valley appearing between 14:00 and 15:00 at noon. The diurnal variationcharacteristics of sound power were in good agreement with the temperature variationcharacteristics. In this study, playing, running, and jumping in the park during the lateafternoon, when children were walking through or staying in the park after school, resultedin high levels of sound power in the park, and increased levels of sound power at duskwhen the park engine and wind were affected. We found that the nighttime sound powerof urban parks was lower, which was different from previous research results [31]. Thiscould possibly be due to the prohibition of activities such as square dancing during theepidemic period, as well as children’s activities at night were distributed in fewer areas(mostly concentrated in children’s activity areas), the frequency and duration of childrenplaying sound are low, and the power presented is small, which lead to lower sound powerat night. Similarly, previous studies found that biological sounds were mainly distributedabove 2 kHz [45,46], with vocal peaks appearing in the early morning [13]. We found thatForests 2022, 13, 1751 14 of 18high-frequency and low-frequency sounds exhibited a similar diurnal variation pattern, wealso found that many recreational sounds, such as the sound of people playing badminton,clapping, stomping, the sound of children playing, occupied the high-frequency band ofbiological sound and showed previously unseen higher power on the spectrogram, theserecreational behaviors may have an impact on biological vocalizations. It was speculatedthat previous studies were mostly in the suburban forest and the sound of recreation hadless influence on it. In terms of park soundscape diversity, the SDI showed a trend ofhigher in the morning and evening and lower in the noon, which was the same as the timedistribution trend of recreational sound. The recreational behavior of the public had a greatimpact on the diversity of the park soundscape. Therefore, the SDI can be used as one ofthe indicators to measure the diversity of recreation behaviors in urban parks, and can beused as the basis for the evaluation of park recreation activities in subsequent studies.4.2. Spatial Variation Characteristics of the SoundscapeThe frequency and duration of natural sounds were lower in squares and parks alongthe street, but higher in forests with high canopy closure and lakeside forest areas. Ourresults confirmed that trees are an important factor in determining urban biodiversity [47–49],and that the physical attributes of tree, such as larger canopies and side branches, make bigtrees more attractive to birds [50]. Therefore, areas with high vegetation canopy closureand the combination of “big trees + lake water” can contribute to a suitable habitats forbirds in summer, also enables the public to hear birdsong at noon in summer (such as egretand ardeola bacchus).Traffic sounds had a higher frequency and longer duration at the park boundary. Mostareas within 140 m from the park boundary were affected by traffic sounds, and vehiclehorns were able to reach as far as 210 m in the park. The relatively open area close tothe traffic road was influenced by the traffic sounds, and the open terrain was conduciveto sound transmission, so the sound power is higher. By comparing monitoring pointsclose to the road, we found that multi-layered plant configuration (especially shrub layerconstruction and utilization of plants with low branching points) and increased densityof vegetation and structures can help reduce the noise of traffic outside. At the same time,previous studies have shown that structures can also affect the transmission of traffic sound,and the impact of traffic can be effectively reduced by placing structures on the side closestto the traffic road [51]. Increasing the density of façade elements near the road area helps toreduce the impact of traffic sound on the interior of the park.The sounds of recreation during the day were mainly distributed at monitoring siteswith high canopy closure. Since summer is different from the other seasons, light intensityand temperature were higher, and the spaces with higher canopy closure were most suitablefor public activities [52]. Our study found that fountains and other water features did notbring significant attraction to recreational sounds during the opening hours of park’s waterfeatures. However, past studies have shown that the public has higher expectations forwater sound [2], and it is speculated that the waterscape at the node does not producepleasant soundscapes, such as water flow sound and water droplet sound [15]. At thesame time, the waterscape in the park did not have hydrophilic functions, and the area ofthe venue provided for public activities was reduced. There were also fewer recreationalfacilities in the surrounding areas, which resulted in the lower frequency and duration ofrecreational sounds and the mechanical sound of the fountain in the square muffled thesound of water, therefore making the area less attractive to the public.The SDI in the open space and the park entrance is higher, the recreational sounds inthe two types of spaces is diverse, and the vegetation structure is rich around the lake toattract more natural sound. If the facilities in the site are strongly attractive to the public,there will be fewer recreational activities in the site, and the types of recreational soundswill be reduced. For example, the public will be attracted by the artificial bird nest in thefield, and conduct birdwatching behavior, and only generate conversation soundsForests 2022, 13, 1751 15 of 184.3. Influence of Landscape Space Elements on the SoundscapeAmong all landscape spatial elements, the larger the proportion of hard ground,the larger the soundscape power (the higher the PSD) and the higher the soundscapediversity (the higher the SDI). In parks, there are more types of recreational sounds thannatural sounds, management sounds and traffic sounds, and the size of hard ground is animportant factor related to public activities. In the two types of spaces with low proportionof trees and shrubs and low proportion of sky, the soundscape power is larger and the PSDis higher. It is found that spaces with low sky coverage (mostly covered by vegetation)will attract more recreational activities in summer, resulting in more recreational soundsand greater sound power. At the same time, in open spaces with large hard ground andlow enclosed degree of trees and shrubs, it is more conducive to the public to carry outvarious activities and sound transmission, and the public will have a stronger perceptionof sound in this type of space. However, areas with large amounts of hard ground faced areduction in natural sounds, and the increase in number of buildings and tourists will causea decrease in natural sounds, especially birdsong. The lager the ground cover, the longerthe natural, this kind of space was conducive to insect vocalizations. It was areas that wererich in ground-covering plants and shrubs that were conducive to insect habitat [39], and,as the park restricted visitors from entering the grassland, recreational behavior had lessimpact on insects. We found that recreation sounds increase when the proportion of treesand ground cover were larger, and the proportion of sky was smaller. We also found thatthe proportion of hard ground had no significant effect on the duration and frequency ofrecreational sounds, which further indicated that shaded environments and ventilationof the site were the main factors for the public to consider during summer recreation. Wenoticed that buildings, vegetation, and sky were commonly ignored factors in previousstudies, but they, especially the proportion of sky, should be considered as key factors forfuture soundscape construction [23]. More space enclosed by buildings will reduce thefrequency of recreational sound, and such space will be distributed in the boundary area ofthe park, which will restrict the recreation behavior of the public.5. ConclusionsOur study drew the following main conclusions: (1) a total of 52 types of sound sourceswere identified in the forest environment of the urban core area in summer, and the parksoundscape was dominated by natural sounds and recreational sounds. (2) Sound sourcespresented a “U”-shaped diurnal variation characteristic, and the variation characteristicwas opposite to the temperature change. The best time for the public to perceive bird songis between 6:00 and 9:00; and after 18:00, the natural sound of the park is dominated byinsect chirps. (3) The soundscape power and the soundscape diversity of the park aregreatly affected by public recreation behaviors, and some recreation behaviors occupythe high-frequency band of biological vocalations, which may affect the vocal behaviorof organisms such as birds (4) Building spaces with high canopy density can attract morebirdsong and recreational sounds in summer, and the combination of “tree + lake” canattract more birdsong. As a negative sound, traffic sound can affect the space within140 m, while horn sound can reach a further area. Enriching vegetation structure andincreasing vegetation width can effectively shorten the influence range of traffic sound inurban forest. (5) In all kinds of landscape space, spaces with large hard ground and lowdegree of containment will attract public recreation, in such space the public perceptionof the soundscape will be more intense. In summer, the shade and ventilation conditionof site is the main factor of public leisure to consider, this kind of space will attract morebirds, at the same time, increase the cover area as well as forbid public access to the lawnis also conducive to insect habitat, increasing the proportion of insects chirping in thepark. Compared to previous studies, the grid method was used to set monitoring points torefine the spatial variation characteristics of the soundscape in urban parks. The landscapespatial factors that affected the soundscape were determined based on factor analysis,Forests 2022, 13, 1751 16 of 18and our research results provided effective data support for improving the soundscape ofurban forests.Author Contributions: Conceptualization, Y.Z. (Yujie Zhao) and W.F. (Weicong Fu); methodology,Y.Z. (Yujie Zhao), S.X. and Z.H.; software, S.X. and Z.C.; validation, Z.H., C.Y. and Y.Z. (Yukun Zhong);formal analysis, Y.Z. (Yujie Zhao), S.X. and Z.C.; investigation, Y.Z. (Yujie Zhao), S.X., Z.H., W.F.(Weicong Fu), S.H., P.H., D.Z. and W.F. (Weicong Fu); resources, J.D.; data curation, Y.Z. (Yujie Zhao),W.F. (Wenqiang Fang) and S.H.; writing—original draft preparation, Y.Z. (Yujie Zhao); writing—review and editing, S.X., Z.H. and W.F. (Weicong Fu). All authors have read and agreed to thepublished version of the manuscript.Funding: This research was funded by (1) Green Urbanization across China and Europe: Collabora-tive Research on Key technological Advances in Urban Forests, grant number 2021YFE0193200; (2)Horizon 2020 strategic plan: CLEARING HOUSE—Collaborative Learning in Research, Informationsharing, and Governance on How Urban tree-based solutions support Sino-European urban futures,grant number 821242; (3) Fujian Social Science Planning Project “Restorative Outcomes of UrbanWaterfront Space Based on Aquaphilia Perception”, grant number FJ2021C038.Data Availability Statement: The data used to support the findings of this study are available fromthe corresponding author upon request.Conflicts of Interest: The authors declare no conflict of interest.References1. Liu, J.; Yan, Z.F.; Zhao, X. The Research of Soundscape Ecological Creativing Wisdom in Urban Relics Park. Chin. Landsc. Archit.2018, 34, 73–75.2. 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sound source composition
ecosystem acoustics
temporal and spatial change
landscape space
Temporal and Spatial Characteristics of Soundscape Ecology in Urban Forest Areas and Its Landscape Spatial Influencing Factors
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