2022 Volume 10 Issue 3 Pages 256-279
Urban greenery contributes significantly to enhance the aesthetics of landscapes and further provides best experiences to visitors. Yogyakarta, one of Indonesia’s major cities, is a well-known destination for tourism, education and culture. The purpose of this research therefore was to assess the visual quality of landscape (VQL) of roadside greenery in Yogyakarta City. For that, 30 sample units were selected by using proportional random sampling method. A questionnaire containing questions about respondent’s sociodemographic characteristic and perception on the selected criteria of VQL of the roadside greenery was utilized. The selected respondents were the residents of neighbourhood area around the sample units. The value of the VQL was obtained from 200 respondents’ perception after they has finished to observe 30 photographs of sample units showed to them. The collected data then were analysed by using modified scenic beauty estimation (SBE) method. SBE values were assessed by five criteria comprising complexity, interference to coherence, stewardship, naturalness, and beauty impression of roadside greenery. The three categories of road in Yogyakarta City, namely secondary arterial road (SAR), secondary collector road (SKYSCRAPER/CITY), and local street (Ulrich, Simons et al.) were being classified into three clusters of high, medium and low by using dendrogram analysis method. The research results showed that 30 sample units of roadside greenery confirmed 11 roads in high, 9 roads in medium and 10 roads in low clusters. Because of SBE values various of the five criteria used in the assessment, the VQL of the three roadside greenery should be rearranged and improved. These research results, therefore, would make some contributions to the planner and manager of the Yogyakarta City.
Yogyakarta is described as a city best known for education, culture, and tourism. As a top tourist destination, support in terms of proper maintenance of environmental conditions including visual quality or aesthetics of its urban landscape are required. Visitors often become attracted to various visual landscape elements while enjoying the city experience. Besides, the inhabitants themselves also need a good quality of living environment which is the visual quality of the surrounding landscapes become one of its important indicators. That’s why landscape is described as the relationship between biotic and abiotic components, including human influences. These connections create a complete system and therefore, require integrated analysis and concepts (Krier, 1979). The quality or function of landscape is unique and usually difficult to describe, since physical and human perceptions are involved.
Natural resources and landscape are two interwoven concept, with greenery space and other vegetation’s attributes as the most visible form observed in urban areas. In Indonesia, this regulation is important to equally specified green spaces as places for plants and other landscape elements on the road. In addition to the law, these greeneries’ cover 20 – 30% of road space, and is adopted by categories of roads and urban forests as well. The essential function of urban greeneries includes mainly ecology, alongside socio-culture, economics, and aesthetics. These roles are combined in line with needs, interests, and sustainability of the city, precisely water protection, ecological balance, and biological conservation (Subadyo, Tutuko et al., 2019).
Yogyakarta City is one of the comfort places to live in Indonesia, strengthened by the existence of roadside greenery networks. These networks are also needed to support the system function of urban tourism that includes the tourist destination, beside the tourist attraction, information/marketing, and market/origin area of tourist components (Christie and Morrison, 1985). In Yogyakarta City, the roads are divided into three categories with respect to their hierarchy and include secondary arterial road (SAR), secondary collector road (SCR), and local street (LS), in line with the Yogyakarta Mayor’s Decree no. 214/KEP/2013 about Determination of Road Categories in the City.
The roads in the city are beautified with attractive greenery and attraction resources of the road environment. The SAR has a higher flow of traffic than SCR, while LS covers less, and is located inside settlements. Being compared to other parties including the visitors who come and then go again, the inhabitants living around the roadside greeneries are considered best know about their living environment, including the condition of the roadside greeneries and the visual quality of landscape (VQL) appeared on them. This considers that the community perception formed by people living around roadside greenery is a crucial part for the sustainable development of urban greenery in Yogyakarta City. Furthermore, the municipal community is entitled to have the best environment, usually preserved by the presence of urban greenery. The local government and community should be in coordination and collaboration, therefore, it will develop the efforts to make urban greenery grown and well managed by the vegetations along the roadsides(Raya, Irwan et al., 2020).
Some previous research supported the study background. Productive urban landscape was developed over a long period of time, with urban aesthetics and the scenic beauty provided by nature as one of the values as the pleasure (Kumar, 2012; Viljoen and Katrin, 2005). Study on productive urban landscape in Yogyakarta City is developed by the ecological roadside greenery. On the other hand, the aesthetics of the greenery is influenced by socio-cultural perceptions of the residents living nearby to the roads (Raya, Irwan et al., 2020). As Kim, Rupprecht et al. (2020); Raya, Irwan et al. (2020)identified perception of urban green space using questionnaires and having responses of respondents involved in the study. Cerveny, Biedenweg et al. (2017) revealed recreation as the first value, in addition to all benefits of roadside greeneries.
Being different from the previous research, this current research seeks to allocate the aesthetical assessment of residents living nearby the SAR, SCR, and LS in more details by applying scenic beauty estimation (SBE) method, so that the distribution of the VQL around each category of roadsides can be resulted from the current research. The SBE method has been chosen because of widely used method and effective for evaluating quality of landscape (Daniel and Boster, 1976). This method describes a psychophysical approach for validly and reliably measuring preference of public landscape aesthetic rather than on the subjective evaluations (Blasco, González-Olabarria et al., 2009; Daniel, 2001). Evaluation of plant landscape aesthetic can be more objectively estimated using SBE method. The plant landscape evaluation has been conducted on different types of landscape, such as forest landscapes (Blasco, González-Olabarria et al., 2009), urban parks (Polat and Akay, 2015) and agricultural landscapes (Arriaza, Cañas-Ortega et al., 2004). Based on the last two sentences, it is logical that the visual quality of landscape of roadsides greeneries in urban’s environment, like in Yogyakarta City may be assessed by using SBE method, too.
The results of this research may be followed up by the designer and planner of the roadsides greeneries in Yogyakarta City in order to create a more attractive landscape either for local community or visitors who will be enjoying Yogyakarta City as an urban tourism destination. Landscape aesthetics also affects the regional economy through the roles as a tourist attraction and recreational destinations (Chen, Adimo et al., 2009). Based on above discussions, this research aims to assess the visual quality of landscape of roadside greenery at the three categories of road in Yogyakarta City, Indonesia.accessibility and environmental safety are regarded as the two most important conditions that convince people to walk. The Walk Score, an international web-based walkability measure
This study was conducted in Yogyakarta City, Indonesia on October - November 2019. The tools used include roll meter, questionnaire, video camera, and pictures of 30 road sections as research samples in a photo album, and questionnaires filled by 200 randomly selected respondents settled around the samples. Furthermore, scenic beauty estimation (SBE) was selected as an analysis method to assess the visual quality of landscape (VQL), as stated by aesthetic due to its advantages (Daniel and Boster, 1976).
Source: Google Earth (2021)
In addition, SBE is a relative scale value calculated from the observer's hierarchy of several diverse landscape views. Then, measurements of this scenic beauty were obtained from landscape characteristics and preference of observers-based ranks influenced by a variety of criteria. The variety of criteria used here referred to the nine criteria comprising complexity, coherence, interference to coherence, stewardship, naturalness, impression power, visual scale, history, potential changes due to climate/weather used by Surová, Surový et al. (2013), plus one criteria (impression of beauty) added by the current researchers. After the whole criteria have undergone the validity and reliability tests, then the rest criteria used were complexity, interference to coherence, stewardship, naturalness and impression of beauty.
The current research, hence, was adopted to assess the VQL on roadside greenery in Yogyakarta City. Therefore, the analysis was modified based on five criteria of VQL, including complexity, interference to coherence, stewardship, naturalness and impression of beauty. Figure 2 shows diagrammatic research to clearly describe research schematic.
Yogyakarta city has 558 roads (Irwan, Utami et al., 2019; Sarwadi, Irwan et al., 2019) clusterized in three categories, SARs, SCRs, and LSs. A two-stage cluster random sampling was used in this research. First, 30 roads are randomly and proportionally selected as primary sampling units (PSU). Then, each selected PSU was divided into 100m-length road segemets. From these segments, 30 samples were randomly chosen. We had 30 sampling unit consisting of 6 segments of SAR, 6 segments of SCR, dan 19 segments of LS (Figure 3). The 30 road segments chosen as research sample are as in Table 1.
These samples were determined on each vantage point, and are assumed to be the position of the most attractive landscape view. Moreover, some pictures of these angles were snapped by a professional photographer between 07.00 - 10.00 a.m. Western Indonesia times, when the sun was not fully out. This hour ensures the best picture colour of visual landscape, and one sample of each VQL was then selected from the best vantage points. Therefore, the photos collected were selected and placed into an album, to serve as a research tool.
Source: modified from Sarwadi, Irwan et al. (2019)
| #1 | Bener St. (LS) | #11 | HOS Cokroaminoto 5 St. (SAR) | #21 | Hibrida St. (LS) |
| #2 | Tegalrejo 1 St. (LS) | #12 | KS Tubun 1 St. (LS) | #22 | Gedongkiwo St. (LS) |
| #3 | Tegalrejo 2 St. (LS) | #13 | KS Tubun 2 St. (LS) | #23 | Mangkuyudan St. (LS) |
| #4 | HOS Cokroaminoto 1 St. (SAR) | #14 | KS Tubun 3 St. (LS) | #24 | Kol. Sugiyono St. (SAR) |
| #5 | Tegalrejo 3 St. (LS) | #15 | KS Tubun 4 St. (LS) | #25 | Babaran 1 St. (LS) |
| #6 | HOS Cokroaminoto 2 St. (SAR) | #16 | Kampung Ngadiwinatan St. (LS) | #26 | Babaran 2 St. (LS) |
| #7 | Kompol Bambang Suprapto 1 St. (LS) | #17 | AM Sangaji 1 St. (SCR) | #27 | Babaran 3 St. (LS) |
| #8 | HOS Cokroaminoto 3 St. (SAR) | #18 | AM Sangaji 2 St. (SCR) | #28 | Tegal Turi 1 St. (SCR) |
| #9 | HOS Cokroaminoto 4 St. (SAR) | #19 | Yomodipati St. (LS) | #29 | Tegal Turi 2 St. (SCR) |
| #10 | Kompol Bambang Suprapto St. (LS) | #20 | Ungaran St. (LS) | #30 | Tegal Turi 3 St. (SCR) |
Secondary arterial road (SAR), Secondary collector road (SCR), Local street (LS)
The 30 samples were including of 6 SARs, 5 SCRs, 19 LSs.
The respondents residing along the roadside vegetation were assumed to have a better understanding of the environmental conditions by virtue of their existence. Therefore, the respondents were selected based on proportional random sampling using family population data. This research, therefore, involves 200 respondents for effective data and quantitative analysis. The respondents living near to the roadside were selected not in consideration of duration of long stay. The assessment was human perception of visual landscape which respondent saw on a picture as a photo. The questionnaire provides an assessing sheet to record criteria scores for 30 VQL samples, where some of the components include respondent's age, gender, marital status, education, brief guidance of SBE, and concise instructions on how to answer the survey. Furthermore, the respondent’s perception in evaluating SBE on five criteria were specified by qualitative indicators with scores ranging from 1 – 10. Score 1 indicates a poor VQL, while 10 denotes a very good score.
The ten criteria of VQL conditions were initially analysed by SBE comprising complexity, coherence, interference to coherence, stewardship, naturalness, impression power, visual scale, history, potential changes due to climate/weather, and beauty impression. Surová, Surový et al. (2013) described criteria (1) to (9) as follow. (1) Complexity refers to the abundance of tree species (not shrubs and/or grasses), including size, shape, and landscape canopy colour. (2) Coherence integrates various landscape features (plants, buildings, roads, etc.), vertically, horizontally, and is based on time cycle. (3) Interference to coherence acknowledges elements interfere with landscape components. For instance, elements are known to obstruct scenic beauty, and is influenced by factors including building layout disorder, flags, vandalism on buildings, cars/motorcycles, etc. Less interference is known to increase the quantitative score. (4) Stewardship describes quality maintenance and landscape management. This is indicated by healthy trees due to consistent sprinkling and weeding, hence provides a beautiful and serene environment. (5) Impression power refers to the strength of the visual landscape caused by integrating various elements in total harmony with spectacular, unique, iconic, or historic features, and also serves as a memorable landmark. (6) Visual scale indicates the relative proportion between tree canopy and surrounding landscape. Planting trees at distance shows sufficiently good city view. The trees are not dominant, but appear neat and healthy. (7) Naturalness highlights the natural impression of shape, growth, and tree distribution. (8) History describes the availability of roadside greenery expected to remain relatively unchanged for a long period. This is possibly influenced by change in location, sustainability of the vegetation, historical value of species, and cultural assessment of landscape elements. (9) Potential changes of trees are due to climate/weather fluctuations. For instance, the potencies of colourful flowers, fruits, seeds, and various types of animals (including birds, butterflies and other types of insects) are affected by the tree’s habitat fluctuations, and other potential changes. Then, (10) Beauty impression occurs after directly observing the landscape samples, where the 9 criteria are attached.
The respondent’s perception of the ten SBE criteria was indicated by qualitative scores. In addition, trial questionnaires for 30 VQL samples were examined on 30 respondents selected randomly from the model population data. This evaluation also included 30 visual landscape collections of road segments and instructional videos. First, the respondents were required to watch a two minutes ten seconds video to assist in completing the survey. Second step involves viewing all 30 pictures using eight seconds for each after answering their identity and socio demography. Third, the respondents are expected to decide scores between 1 – 10 for individual photo.
Data were analysed using SPSS to test for data validity and reliability. The results provided ten SBE criteria in order to assess landscape aesthetics, and were further reduced into five criteria comprising (1) complexity, (2) interference to coherence, (3) stewardship, (4) naturalness, and (5) beauty impression. According to Daniel and Boster (1976), assessing visual landscape quality or aesthetics refers to the use of quantitative analysis for SBE. This process then extends to clustering in order to group the SBE values expressed in the following formula.
SBEx = (ZLx – ZLs)
SBEx = xth landscape SBE value
ZLx = mean value of Z of the xth Landscape
ZLs = standard landscape mean value
The value of Z Landscape (ZL) indicates the assessment of visual landscape on 30 pictures of sample from 200 respondents. The ZLx is the Z value average of the xth landscape (x=1, 2, ..., 30) from 30 sample pictures. ZLs is the average Z value of the standard landscape that is closest to the value 0 (zero). The assessment of VQL using the five criteria were statistically processed by calculating SBE. Analysis of Z value for each sample was carried out using a normal distribution.
The description of the sociodemographic characteristics of the 200 respondents involved in this study encompasses male 51%, and female 49%, with nearly similar proportion, where most fit into the productive age, 25-54 years (62%), see Figure 4. In addition, married category is dominant (89,5%) as patriarchs or belong to other family members. Furthermore, education level is conquered by senior high school and college (50.5%), and most have jobs (78%). They all were required to participate in filling out the questioner.
Source: Field observation (2019)
Figure 5 shows an SBE histogram for assessing 6 VQL samples for SARs. The highest value of 30.75 was obtained in Kol. Sugiyono St. . This street was elevated the maximum complexity criteria score to 51.35, then interference to coherence, naturalness and beauty impression criteria estimated at 27.65, 21.46, and 50.32, respectively. The greatest SBE mean value for stewardship criteria on HOS Cokroaminoto 2 St. was approximately 20.88. However, the SBE average spanning 5 criteria and 6 samples for SARs was evaluated as 5.50.
Source: Field observation (2019)
Figure 7 represents the SBE graph for SCRs with 5 samples. Tegal Turi 2 St. (#29) achieved the highest SBE average with complexity criteria score of 88.72 and beauty impression criteria 108.68. Meanwhile, Tegal Turi 3 St. (#30) was maximum for criteria interference to coherence and stewardship at 26.94 and 43.44, respectively. Furthermore, Tegal Turi 2 St and Tegal Turi 3 St. are a component of Tegal Turi St. The highest SBE average for naturalness criteria occurred in AM Sangaji 2 St. with value at 62.03. Tegal Turi 2 St. (#29, Figure 8) possessed the maximum SBE average of 49.04, compared to all 5 criteria. However, for 5 criteria and 5 samples of SCRs, the average highlighted 23.08.rlapping pedestrian traffic accidents and city crimes with Walk Scores calculated in each residential address, we obtained two maps, shown in Figure 5. The common place was that the most concentrated areas of both pedestrian traffic accidents and city crime were in the central commercial area of the Central District, where only a few residential buildings were located. In addition, other relatively concentrated areas were almost distributed in the same area with high Walk Score residential addresses. The difference is that, in the suburban periphery of the city, crime is more dispersed and distributed in different places than pedestrian traffic accidents, especially in the southern area of the city. Comparatively, pedestrian traffic accidents are more aggregated in the southern hub and alongside the main road of the north area.
Source: Field observation (2019)
Figure 9 shows the SBE average of 19 Local Streets (LS’s) samples. The highest SBE for complexity and interference to coherence was reported in Hibrida St. (# 21, Figure 12), and the average values were 101.21 and 30.49, correspondingly. Subsequently, the roadside greenery of Bener St.(# 1, Figure 10) demonstrated the maximum SBE average for stewardship and naturalness at 76.92 and 86.74, respectively. Meanwhile, beauty impression achieved the greatest value of 98.37 in Ungaran St (# 20, Figure 16b). This concludes the SBE average of Bener St. (#1) delivered the highest value of 61.14 from the total criteria. Meanwhile, the street was estimated to extend the highest visual quality in LS category. The results showed the absolute SBE average of 5 criteria and 19 samples of LS roadside greenery at 9.60.
Source: Field observation (2019)
Figure 11 presents the graphs of separate SBE criteria for 30 samples, based on the average value for urban greenery on SARs, SCRs, and LSs. Figures 12, 13 and 14 also indicate the pictures of the roadside conditions. Overall SBE value to assess all 5 criteria is described as follow.
Complexity. Complexity refers to SBE average of 10.71. Hibrida St. is known to have the highest value of 101.21, while the lowest occurred at -55,76 in K.S Tubun 3 St. , with the street as part of the K.S Tubun St. section. Interference to coherence. SBE average for interference to coherence of 30 VQL samples is evaluated at 6.86, and the highest average reported 30.49, also on Hibrida St. (# 21). However, the lowest SBE of -18.71 was present in Tegalrejo 1 St. . Stewardship. The SBE average for stewardship on 30 samples is 6.73, and the highest with 76.92 occurred in Bener St. . Meanwhile, the lowest appears at -43.92 on KS Tubun 3 St. . Naturalness. The SBE average for naturalness is specified at 14.42 with a maximum value of 86.74 discovered in Bener St. . Meanwhile, the lowest of -33.40 was observed in KS Tubun 3 St. . Beauty impression. The SBE average of beauty impression of 30 samples is estimated at 17.78. The highest SBE value of beauty impression is 108.68 occurring in Tegal Turi 2 St. (# 29), while the lowest acquired -39.86 along KS Tubun 3 St. . The overall SBE mean value for all 5 criteria is specified at 10.68, and the highest estimated at 61.14 in Bener St. , while the lowest is -37.59 by KS Tubun 3 St. .
Figure 11(a-f). SBE of five criteria and their average
The SBE mean values (SBEX̅) for each criteria and their average are as follow: a. complexity 10.71; b. interference to coherence 6.86; c. stewardship 6.73; d. naturalness 14.42; e. beauty impression 17.78; f. all criteria 10.68. These numbers are depicted in Table 2.
| Criteria | SBE mean value |
|---|---|
| Complexity | 10.71 |
| Interference to Coherence | 6.86 |
| Stewardship | 6.73 |
| Naturalness | 14.42 |
| Beauty Impression | 17.78 |
| Average | 10.68 |
Source: Field observation (2019)
Source: Field observation (2019)
Source: Field observation (2019)
Based on graphs in Figure 11, Hibrida St. (# 21, Figure 12) has the highest SBE value on complexity (101.21) and interference to coherence (30.49). Bener St. has maximum SBE average (61.14) of 30 VQL samples. The greenery showed full coverage of tree canopy, less interference of building and well maintained. KS Tubun 3 has the lowest SBE average (-37.59) of 30 samples, including lowest SBE value for criteria of complexity (-55.76), stewardship -43.91), naturalness (-33.40) and beauty impression 39.86). This road showed dominant by buildings and unplanned greenery. Tegalrejo 1 St. (# 2, Figure 14) exhibited the lowest SBE for interference to coherence (-18.71) compared to all road samples. Subsequently, KS Tubun 3 St. and Tegalrejo 1 St. presented landscape condition of minimum trees in urban greenery. Roadside without trees indicates more interference known to reduce complexity. Houses, buildings, and surrounding streets are without space for vegetation. However, roadside greenery plays a significant role in improving the city’s visual quality.
Based on hierarchical clustering using Euclid's distance and Ward's method, three clusters of SBE values were obtained out of 30 VQL samples. The clustering method were used as the advanced analysis of SBE. The SBE values were analysed through assessment variables or five criterias including complexity, interference to coherence, stewardship, naturalness, beauty impression, and average of them. These clusters have obtained for all roads without concerning categories of roads. Cluster dendrogram is shown in Figure 15 and mean value in Table 3.
| Cluster | SBE mean value | |||||
| X̅1 | X̅2 | X̅3 | X̅4 | X̅5 | X̅ | |
| 1 | 111.28 | 36.65 | 74.26 | 83.68 | 94.08 | 79.99 |
| 2 | 16.72 | 13.12 | 23.47 | 10.88 | 18.13 | 16.47 |
| 3 | 60.29 | 23.94 | 46.76 | 40.54 | 51.28 | 44.56 |
Note:
X̅1 (complexity), X̅2 (interference to coherence), X̅3 (stewardship),
X̅4 (naturalness), X̅5 (beauty impression), and X̅ (average of X̅1 - X̅5).
Table 4 shows these 30 samples are possible to disintegrate, according to the cluster dendrogram, while Table 3 represents the SBE mean value. Subsequently, the overall average of individual Clusters 1, 3, and 2 denotes high, medium, and low SBE values, respectively. Furthermore, 11 samples on 3 road categories are known to possess large SBE average in Cluster 1 (high), 9 roads in Cluster 2 (low) and 10 in Cluster 3 (medium). The roadside greenery on SCRs were included only in Cluster 1 and 3 indicating a better performance compared to SARs and LSs.
| No. | Cluster 1 (High SBE) | Cluster 2 (Low SBE) | Cluster 3 (Medium SBE) |
| 1 | Sample #1: Bener St. (LS) | Sample #2: Tegalrejo 1 St. (LS) | Sample #6: HOS Cokroaminoto 2 St. (SAR) |
| 2 | Sample #3: Tegalrejo 2 St. (LS) | Sample #4: HOS Cokroaminoto 1 St. (SAR) | Sample #9: HOS Cokroaminoto 4 St. (SAR) |
| 3 | Sample #18: AM Sangaji 2 St. (SCR) | Sample #5: Tegalrejo 3 St. (LS) | Sample #10: Kompol Bambang Suprapto 2 St. (LS) |
| 4 | Sample #19: Yomodipati St. (LS) | Sample #7: Kompol Bambang Suprapto 1 St. (LS) | Sample #11: HOS Cokroaminoto 5 St. (SAR) |
| 5 | Sample #20: Ungaran St. (LS) | Sample #8: HOS Cokroaminoto 3 St. (SAR) | Sample #12: KS Tubun 1 St. (LS) |
| 6 | Sample #21: Hibrida St. (LS) | Sample #13: KS Tubun 2 St. (LS) | Sample #17: AM Sangaji 1 St. (SCR) |
| 7 | Sample #22: Gedongkiwo St. (LS) | Sample #14: KS Tubun 3 St. (LS) | Sample #23: Mangkuyudan St. (LS) |
| 8 | Sample #24: Kol. Sugiyono St. (SAR) | Sample #15: KS Tubun 4 St. (LS) | Sample #25: Babaran 1 St. (LS) |
| 9 | Sample #27: Babaran 3 St. (LS) | Sample #16: Kampung Ngadiwinatan St. (LS) | Sample #26: Babaran 2 St. (LS) |
| 10 | Sample #29: Tegal Turi 2 St. (SCR) | Sample #28: Tegal Turi 1 St. (SCR) | |
| 11 | Sample #30: Tegal Turi 3 St. (SCR) |
Secondary arterial road (SAR), Secondary collector road (SCR), Local street (LS)
The 30 samples were including of 6 SARs, 5 SCRs, 19 LSs.
Urban greenery significantly contributes to aesthetics value, microclimate enhancement, recreational experiences and various other productive functions (Daniel Terry, Muhar et al., 2012). These are important for the tourism city and comfort living place of Yogyakarta. The community perception on VQL formed by people living around roadside greenery is a crucial part for the sustainable development of urban greenery in Yogyakarta City. Besides, the community have considered aesthetics as an urgent landscape and environmental management issue (De Vries, Lankhorst et al., 2007), because of the contribution to life quality, health and vitality, through the provision of inspiration, harmony, and peace (Millennium ecosystem assessment, 2005). This research is to be considered as a relevant reference for urgency of green space development of planning, particularly the aesthetically valuable roadside greenery, known to be one of the tourists’ view objects in urban areas of Yogyakarta. This survey results also provide the resources required by urban green space designers and managers to develop an in-depth knowledge on human interaction with urban nature, consequently improving the environmental responsibility of people (Qureshi, Breuste et al., 2013).
Implementation of VQL AssessmentAccording to histograms in Figure 5, 7 and 9, the average SBE scores for SARs, SCRs, and LSs were 5.50, 23.08, and 9.60, respectively. While SCRs have the highest SBE value. However, the SAR’s category comprises of a bigger road, compared to the others. The highest SBE mean value in the SCR’s category was recorded in Tegal Turi 2 St. (#29), and Bener St. for Ls, and Kol. Sugiyono St. for SAR. Nevertheless, Ungaran St. achieved the highest SBE for beauty impression. All criteria of VQL assessment (complexity, interference to coherence, stewardship, naturalness, beauty impression) were calculated to get SBE value of the roadside greenery. Identifying SBE of a roadside greenery in the similar road category is important, assuming each road category has a standard quality from the highest value.
Once the VQL of roadside greenery is well maintained according to the stewardship criteria, together with the other four criteria used in this research, i.e. complexity, interference to coherence, naturalness and beauty impression. People passing through roadside greenery will be provided with various important benefits, including comfort, recreation, physical and psychological well-being (Chan, Satterfield et al., 2012). Particularly, complexity simply means the arrangement of trees on roadside greenery in an urban area, required to comply with the peoples’ impression of beauty, while stewardship is the concern to maintain a visually aesthetics landscape with maintained trees. Coherence without interference performs the greenery looks natural and beautiful.
Based on the assessment of VQL, the research results showed a correlation between the best assessment and the highest SBE mean value of the criteria on all road categories for roadside greenery. This is useful as a reference or guideline for future and urban planning and landscape, alongside policymaking and management in Yogyakarta City, a destination for education, tourism and culture. However, roadside greenery and urban forest compose the green open space that are managed by Environment Office, Yogyakarta Municipality Government. ity quickly, but its single measure of facility convenience also indicates the limitations of the method. In future research, we suggest that more aspects of urban design be included. Of course, this does not mean using a series of formulas to calculate the numerical level but to extract some regulations from the complex and huge system of urban form.
Roadside greenery in the Cluster AnalysisFigure 15 shows three clusters of 30 samples VQL. The best visual quality was indicated in the SBE mean value of cluster 1 (Table 3 and 4). Complexity (X̅1, 111.28) and beauty impression (X̅5, 94,08) are the important factors for potential improvement. The five criteria, including complexity, interference to coherence, stewardship, naturalness, and beauty impression used in this research for VQL assessment with SBE method are relevant and inter-correlated in analysis. Figure 16 and 17 contain pictures of VQL of roadside greenery with high SBE (Cluster 1) and low SBE (Cluster 2). Low SBE of VQL in some samples in Figure 17 are characterized by disorganised trees, or disorder in steward assumption. Whereas, high SBE covers abundant of trees and better landscape plan.
These criteria of assessment are needed to improve VQL by redesigning the landscape plan. So, Yogyakarta City has better performance and high VQL in average. However, all the information collected are important to urban landscape planners and policymakers, for the development of urban greenery guideline, based on the five criteria for further improvements in Yogyakarta City.
Source: Field observation (2019)
in Cluster analysis 2 (low SBE)
Source: Field observation (2019)
Irwan, Utami et al. (2019) described the tree identification on roadside greenery in Yogyakarta City. The existing roadside greenery indicated less productive value for urban greenery. Table 3 shows list of some roads with existing trees in cluster 1 which present the highest SBE. Bener St. (# 1, LS), was dominated (96.8%) by mast trees that planted in a row to form a green corridor. This is resulting to high stewardship and naturalness scores. In addition, the road satisfies naturalness due to well-maintained vegetation. Tegal Turi 2 St. (# 29, best for SCR) consisted of spanish cherry and mast trees, while Kol Sugiono St. (# 24, best for SAR)showed diverse species (Irwan, Utami et al., 2019). Being the maximum SBE for SAR, particularly in complexity and beauty impression, sample #24 were predominantly planted by angsana and spanish cherry trees.
Hibrida St. (# 21, LS)achieved the best complexity criteria that comprised of diverse trees. This result is supported by the definition of complexity as the richness and abundance of tree species (not shrubs and/or grasses), including size, shape, and landscape canopy colour (Surová, Surový et al., 2013). Beauty impression criteria were performed by Ungaran St. (# 20, LS), where weeping fig tree was estimated at 60.9%, and spanish cherry showed 34.8%. However, field observation revealed more species were planted serially or orderly one line of the roadside to achieve beauty impression and complexity.
| Roads | SBE value | Botanical name | Common name | |
|
Kol Sugiono St. SAR |
Highest SBE for SAR |
Pterocarpus indicus Mimusops elengi Tamarindus indicus Terminalia catappa Hibiscus tiliaceus Artocarpus heterophyllus Veitchia merrilii |
Angsana Spanish cherry Tamarind Indian almond Cottonwood Jackfruit Manila palm |
|
|
Tegal Turi St. (#29) SCR |
Highest SBE for SCR |
Mimusops elengi Polyalthia longifolia Araucaria heterophylla, Wodyetia bifurcata Spatodhea campanulata Gmelina arbore, Pitelelobium dulce Lagerstroemia speciosa Pterocarpus indicus Terminalia catappa Muntingia calabura Annona squamosa Leucaena leucocephala |
Spanish cherry Mast Norfolk Island pine Foxtail palm African tulip Gmelina Madras thorn Pride of India Angsana Indian almond Calabur Sugar apple Lead |
|
|
Bener St. LS |
Highest SBE for LS, Highest SBE mean value |
Polyalthia longifolia Pterocarpus indicus Hibiscus tiliaceus Mimusops elengi Mangifera indica |
Mast Angsana Cotton wood Spanish herry Mango |
|
|
Hibrida St. LS |
Highest SBE for complexity Highest SBE for interference to coherence |
Triphasia trifolla Psidium guajava Syzygium malaccense Mangifera indica |
Lime berry Guava Malay apple Mango |
|
|
Ungaran St. LS |
Highest SBE for beauty impression | Ficus benjamina Mimusops elengi | weeping fig tree spanish cherry | |
Source: Irwan, Utami et al. (2019)
This research shows LS contained high SBE values. This is caused by biodiversity of flora and fauna present. In addition, LS involves greenery grouped into three clusters of high, medium and low SBE values. Heterogenity of visual landscape was known to instigate unplanned greenery, and the tree characteristics are improbable to determine the aesthetic value of the unpreserved landscape.
Another interesting result was observed on a long road. For instance, Tegalrejo St., was divided into Tegalrejo 1 St. and Tegalrejo 2 St. However, Tegalrejo St., was not arranged in a good landscape plan or grouped into a cluster. This showed the absence of a standard guideline, although roadside greenery applied aesthetic quality. Subsequently, the lowest SBE occurred in LS, KS Tubun St. often traversed by visitors from other cities, and was also experienced in SAR. HOS Cokroaminoto 3 St. (# 8, 17c)showed a poor visual quality as a result of inadequate landscape elements, e.g., trees, hence, the roads were dominated by buildings in terms of interference to coherence. This condition requires improvement in landscape planning of roadside greenery, specifically low SBE landscape observed in cluster 2.
Furthermore, smaller road size, such as LS, showed a decline in vehicular movement or walking speed. Therefore, more persons are expected to engage in biking and walking. This requires the support to enhance aesthetic quality in order to ensure people feel comfortable and happy about the city view. The analogous, however, concurs with the statement, where landscape aesthetics are defined as the enjoyment and pleasure felt through observing environmental scenic elements (Swaffield and McWilliam, 2013).
The results of this research extend to landscape management programme. Aesthetic elements are covered by the provision of tree canopy regarding to the highest SBE criteria. Furthermore, landscape architect has proven to develop the criteria for assessing visual quality intercorrelated to aesthetics, particularly for urban roadside greenery, based on this study. Subsequently, improved visual quality of landscape demonstrated the aesthetic value is known to promote roadside greenery in Yogyakarta city in order to support the development of productive urban landscape.
Estimating the roadside aesthetics is significant in assessing the 5 criteria for visual quality of landscape, including complexity, interference to coherence, stewardship, naturalness and beauty impression. Aesthetic variations based on assessing visual quality landscape on roadside greenery in Yogyakarta City was presented by the SBE mean values and clusters. These conditions showed variations in SBE values grouped into high (cluster 1, 11 samples), medium (cluster 3, 10 samples), and low (cluster 2, 9 samples). The highest SBE value belonged to secondary collector roads (SCRs, X̅ 23.08), followed by local streets (LSs, X̅ 9.60), while the lowest occurred in secondary arterial road (SARs, X̅ 5.50). However, Bener St. (# 1, LS) has achieved highest SBE mean value for all criteria (X̅ 61.34) and the lowest SBE (-37.59) is KS Tubun 3 St. (# 14, LS). Therefore, the need to improve on visual quality using a proper landscape plan is very important, and these research results can be beneficial for further consideration.
Some suggestions which may be involved in the plan as follow. Observing all conditions of the landscape segments in the roadside greenery with the low to the medium conditions, and then improve them to cover the five criterias of visual quality of landscape. The criterias consist of complexity, interference to coherence, stewardship, naturalness and beauty impression. The intended improvements consist of to increase the complexity by adding the tree species’s variety, to increase the integration between the landscape’s features which are various (plants, buildings, and roads) so that it may reduce the inference to the landscape’s coherence, to enhance the stewardship of the landscape shown by the presence of the vegetations in roadside greenery that look well-maintained, to improve the naturalness indicated by the presence of trees that looked natural through their shape and distribution in the local environment, and to increase the beauty impression of the roadside greenery by implementing the all criteria simultaneously.
Research design, R.N.U, S.N.R.I. and Y.S.; methodology, R.N.U, S.N.R.I. and Y.S.; field orservation, R.N.U and S.N.R.I.; results analysis, R.N.U, S.N.R.I. and Y.S.; statistics, Y.S.; writing manuscript, R.N.U and S.N.R.I.; revised manuscript, R.N.U, S.N.R.I and Y.S.; proof read, S.N.R.I. The authors have committed and agreed for the content of manuscript to be published.
The authors declare that this paper is the authors’ own original work, which has not been published elsewhere. There are no conflicts of interest in the team of authors regarding the publication of this paper.
We would also like to show our gratitude to our colleagues and research team for assistance, comments and sharing their pearls of wisdom with us during the course that greatly improved the manuscript.
This study was a part of multiyear research supported by Ministry of Research, Technology, and Higher Education of the Republic of Indonesia in funding of the University grant-in-aid scheme, 2018-2020.
