Natural ecosystems within urban settings are necessary for providing ecosystem services ( Göktuğ et al., 2015 ; Jang-Hwan et al., 2020 ). The urban forest ecology is one of these ecosystems that is widely recognised as being an important component ( Göktuğ et al., 2015 ; Muslih et al., 2022 ; Roeland et al., 2019 ; Wang et al., 2022 ). The need for more sustainable approaches to planning and managing wildlife in urban areas is become increasingly urgent as the percentage of the world’s population living in cities rises ( Dobbs et al., 2011 ). The process of urbanisation affects urban ecology ( de Lourdes Saavedra-Romero et al., 2021 ; Dharma & Zakaria, 2022 ; Jang-Hwan et al., 2020 ). Therefore, the absence of ecological attention throughout the planning stages of development is the cause of environmental issues. It is also noteworthy that, internationally, only a small number of urban development plans give environmental integrity, sustainability, biological diversity, and conservation of urban landscapes the attention they deserve to protect urban nature and sustain biodiversity ( Barron et al., 2016 ; Paracchini et al., 2014 ). But in numerous nations and towns, thorough investigations on the diversity of plant species and their interactions with anthropogenic and environmental factors have been carried out ( Ordóñez & Duinker, 2010 ).

Other recent studies have examined and highlighted the significance of urban forests, their importance in the urbanising world and the urgent need for their conservation. These studies include ( Baumeister et al., 2022 ; Devisscher et al., 2022 ; Dharma & Zakaria, 2022 ; Jang-Hwan et al., 2020 ; Roeland et al., 2019 ; Tuffour-Mills et al., 2020 ). These studies demonstrated the importance of urban forests, their value and the urgent need to protect them as a shared natural resource.

In this study, questionnaires developed through literature review on urban forests and analysis of user attitudes were applied using field survey instruments that consisted of attitudinal statements questionnaire items. An attitudinal survey gives information on respondent perceptions (emotions, feeling, attitudes) of their experience in each study. In such a study, a scale consisting of a set of selected statements or manifest items expected to assess or reflect theoretical latent variables, constructs, or factors is used to rate the attitudes. An attitudinal survey questionnaire was adopted for assessing attitudinal preferences among urban forest visitors towards urban forests in Nairobi. This data collection tool comprised 40 attitudinal statements designed to understand attitudes towards the urban forest environment better. Additionally, this study employed a methodological evaluation to assess Nairobi residents’ perceptions of urban forests. Najd et al. (2015) contend that the preferred approach has been frequently applied in studies on people’s perceptions of and attitudes about various surroundings. This strategy has been demonstrated to be a legitimate and appropriate way to identify the underlying components influencing perceptions, such as attitude, content, and the spatial configuration of the landscapes in each setting.

Mansfield et al. (1977) concurs further that people’s emotive response and judgment are related to how they perceive their environment. Therefore, research on environmental perception can explain how people’s behaviour is affected by their surroundings and vice versa. According to the model and theory of preference, Herzog et al. (1982) and Kaplan (1982) , preference is one of the finest indicators of human perception. This is because it is a by-product of perception and people regularly make preference judgements. Furthermore, information, natural reaction, cognitive process, motivation, emotion, and perception have a greater influence on preference assessment than anything else.

Considering this, it can be said that preference is the gathering of data regarding how individuals perceive their surroundings. Therefore, the preference survey method is used in this study to identify the underlying variables influencing perceptions, such as attitude, user characteristics, usage patterns, and spatial configurations in Nairobi’s urban forests.

To analyze the data, this study used factor analysis to condense the larger number of qualities into a more manageable collection of variables that were then used to infer conclusions. To understand the underlying reason these factors (also known as latent or component factors) capture a significant amount of information about a group of variables in the dataset, factor analysis is performed. Some of the features in data with many predictors may share a common pattern when analysed. The elements that share a common meaning at their core might be affecting the target variable via sharing this causation, hence these features are joined to form a factor. As a result, a factor (or latent) is a fundamental component that unifies or underlies all the other variables. Additionally, because these latent variables (or latent constructs) cannot be measured independently using a single variable, they cannot be directly observed.

Therefore, a factor (or latent) is a core element that unifies or underlying all other variables. Moreover, these latent variables (or latent constructs) cannot be directly seen because they are incapable of being independently quantified by a single variable. For this study, a total of forty (40) attitude statements were looked at, with their analysis being condensed into six (6) components.

In sampling, a heterogeneous cluster-based frame was adopted for this study, necessitating that all five urban forests be selected from which six visitor clusters could be selected and simple random sampling adopted. Additionally, the demographic makeup of the respondents was checked to ensure a good representation of variables such as gender, ethnicity, and educational background.

Non-willingness of every respondent from the sampling units to respond made the study use non-probability voluntary sampling to identify sample units which were visitors in forests from the study areas. According to ( Kothari et al., 2009 ), results obtained from the analysis of such a deliberately selected sample are still tolerably reliable as the investigator was impartial, worked without bias, and had the necessary experience to make sound judgments.

Stratified probability sampling was used to create strata in each study area. The number of strata in each urban forest was determined based on the sizes of these study areas. City Park, Karura, and Ngong Road forests were divided into four strata, while Nairobi Arboretum and Michuki Park were divided into two strata. Equal samples were then collected from each of the strata. This sampling method helps increase precision where a bigger population requires a smaller sample size.

The sampling unit was the visitors in the selected urban forests of City Park, Nairobi Arboretum, Karura, Ngong Road, and Michuki Park in Nairobi at the time of the study interviewed on their perception of the urban forests and preference attitudes of their urban forest environment. This unit was achieved by selecting all the willing participants that were present in the forest at the date and time of the data collection.

SPSS and Excel were used in data processing and analysis of variables. Data entry, coding, and graphs projection were made using Excel spreadsheets, while SPSS was used in the descriptive analysis to calculate frequencies, mean, mode, medians, and Analysis of Variance (ANOVA).

Principal Component Analysis (PCA) was used in SPSS version 21 along with Kaiser Normalization and Varimax Rotation. To achieve a relatively strong coefficient and effective loading of variables, the loading factor was adjusted at a maximum of 0.5. Statements from these variables were loaded into component groupings known as preference dimensions. The mean, mode, and median of each dimension were determined to rank the dimensions and further analyse the level of preference and likeability.

The study area of this research was in Nairobi County, which has many urban forests of public and private nature. The study’s complexity required the selection of public urban forests of significant acreage. Hence through non-probability purposive sampling, five gazetted urban forests of City Park, Nairobi Arboretum, Karura and Ngong Road, were sampled to represent urban forests with similar urban forest characteristics Nairobi County Kenya ( Figure 1 ).

Measurement scales normally include equal numbers of positively and negatively stated items to avoid agreement bias. In this study, a 40-item standard questionnaire for rating attitude statements on a 7-point Likert scale comprised both positive statements in favour of urban forest resources and negative statements opposed to urban forest resources.

To identify the underlying issues affecting user attitudes, six components were extracted from the 40 attitude items that can provide a meaningful correlation amongst users’ attitudes towards Nairobi’s urban forest ecosystems. In this study, the user attitudes with a factor loading greater than 0.5 show that the factor can significantly interpret the component. The six components constitute the concepts that can provide a framework for conserving urban forests.

The study findings established that if the benefits and functions of urban forests are recognised and promoted, the attitudes of the city residents and urban forest users will be changed positively. Secondly, the study also established that proper management of urban forests is key in predicting the quality and state of urban forests. Thirdly, urbanization and land use strategies, policies, and patterns affect and threaten urban forests through competing needs in a capitalistic environment. Fourthly, accessibility is key in promoting the utilization and enjoyment of urban forest resources. The fifth finding is biodiversity and its role and place in the urban environment. This is the key attraction to urban forests which directly affects the meaning and value attached to the urban forest by the users and city residents. An increase in biodiversity will positively affect the user’s attitudes. Lastly, in the sixth finding, if problems and threats to urban forests are reduced, the attitudes of the city residents and urban forest users will be changed positively too.

The first finding of the study was that the sentiments people had about urban woods were most strongly influenced by their benefits. An average score of 5.41 confirms this. The respondents’ general appreciation and like of nature in the city can be credited for this. Additionally, it demonstrates how much Nairobi’s citizens value urban trees’ advantages.

Thirdly, biodiversity was the third attributer of attitudes associated with urban forests. This is supported by a mean score of 4.86. This confirms the role of flora and fauna in urban forest ecosystems. The first objective’s findings on urban forest components further support this finding. Biodiversity is the source of ecosystem services and the main attraction to urban forests.

Fourthly, urbanization and land use were the fourth attributer of attitudes associated with urban forests. This is supported by a mean score of 4.59. This confirms the challenges and threats of urbanization to the urban forests in Nairobi. Urbanization causes put pressure on urban forests through competing land use demands.

Fifthly, accessibility to urban forests was the fifth attributer of attitudes associated with urban forests. This is supported by a mean score of 4.31. This finding provides a case for enhancing access to urban forests and sufficient amenities to ensure ample utilization of urban forests. The respondents in this study, who were derived from visitors in the urban forests surveyed, accessed these forests through different means from residential neighbourhoods across Nairobi.

Sixth, managing urban forests was the sixth characteristic of attitudes related to urban forests. A 3.82 mean score corroborates this. The perceived poor management of Nairobi’s urban woodlands is evidence in favour of this conclusion. A thorough resource inventory and monitoring should serve as the foundation for managing urban forests, much like policies and planning should.

In summary, participants generally had favourable attitudes towards urban forests in Nairobi, with most residents believing that urban forests are beneficial and need protection. Interestingly, the residents are willing to participate in the protection of these urban forests as they recognise them as a beneficial shared resource. People protect what they deem valuable, and this can be reported to be true in this study.

In conclusion, the summarised interpretation of the users’ attitudes is that there is a universal appreciation of the role and benefits of urban forests. There is also a great appetite for these benefits as illustrated by the quest for universal access and general improvement of the forest’s environment. In addition, available scientific literature, evidence, and numerous policy instruments have emphasized the importance of urban green spaces in urban social-ecological systems to mitigate several problems of urban dwellers in the last two decades. This study has further brought out the attitudes that users of Nairobi have towards its environment. These attitudes summarise the values attached to the natural environment and its presence in the city. These values provide a basis for protection since it is generally accepted that a society cannot protect what it does not value.