The biomass represented by urban trees is important for urban decision-makers, green space planners, and managers seeking to optimize urban ecosystem services. Carbon storage by urban trees is one of these services. Suitable methods for assessing carbon storage by urban trees are being explored. The latest technologies in remote sensing and data analyses can reduce data collection costs while improving accuracy. This paper introduces an assessment approach that combines ground measurements with unmanned aerial vehicle-based light detection and ranging (LiDAR) data to estimate carbon storage by urban trees. Methods underpinning the approach were tested for the case of the Vancouver campus of the University of British Columbia (UBC), Canada. The study objectives were (1) to test five automated individual tree detection (AITD) algorithms and select one on the basis of the highest segmentation accuracy, (2) to develop a model to estimate the diameter at breast height (DBH), and (3) to estimate and map carbon storage over the UBC campus using LiDAR heights, estimated DBHs, and an existing tree-level above-ground carbon estimation model. Of the segmentation algorithms tested, the Dalponte AITD had the highest F score of 0.83. Of the five CW thresholds (th) tested in the DBH estimation model, we chose one resulting in the lowest Akaike’s information criterion, the highest log-likelihood, and the lowest root-mean-squared error (19.55 cm). Above-ground carbon was estimated for each tree in the study area and subsequently summarized, resulting in an estimated 5.27 kg C·m−2 over the main campus of UBC, Vancouver. The approach could be used in other urban jurisdictions to obtain essential information on urban carbon storage in support of urban landscape governance, planning, and management.

The advent of technology and its implications on especially remote sensing image processing using High Resolution Satellite Images (HRSI) to map land cover provide researchers to monitor land changes, make landscape analyses, and manage land transformation. One of land dynamics that should be mapped for the sustainability of urban area is green spaces. Urban green spaces, such as parks, playgrounds, and residential greenery may promote both mental and physical health. Besides, they contribute to ecosystem services such as reducing heat island effect and carbon storage, aiding water regulation etc. Therefore, mapping urban green infrastructure from a high-resolution satellite image provides an important tool to conduct studies, researches, and projects for sustainable development of urban areas. As the material of this research, one of the orthophotos of Aydin urban area exemplifies the park, the green cover in the agricultural area, the playground, and the residential garden, was used. For classifying land cover from the orthophoto with Object-Based Image Analysis (OBIA), eCognition Developer 9.0 software was utilized. To combine spectral and shape features, multiresolution segmentation was implemented. Additionally, features as brightness and ratio green were used for the extraction of urban green areas. In this research, urban green areas were successfully extracted from the orthophoto and accuracy assessment was performed on the classified image. OBIA of high resolution imagery enables to extract detailed information of various targets on urban areas. The result of accuracy assessment of the classification achieved 84.68% overall accuracy. To increase the accuracy via manual interventions, manual classification tool of eCognition Developer 9.0 may be used if needed.

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There is an interactive relationship between humans and landscapes. Humans inherently assess landscapes by creating spontaneous preferences based on surrounding stimuli. Vision plays a key role in these preferences. Visual preferences are relevant for understanding visual aesthetic liking (VAL), which needs to be evaluated objectively. This study was carried out in Herakleia ad Latmos, comprising Lake Bafa Natural Park and the Latmos-Beşparmak Mountains. The aim of this paper is to predict people's VAL of historical sites (HS) by applying processing fluency theory to social media data. Among fluency theory metrics, four metrics -visual simplicity, visual symmetry, visual contrast, and visual self-similarity, were used to develop an ordinary least squares (OLS) regression model. Two primary questions are explored in this study: (1) How to quantify spontaneous visits of people near historical sites, and (2) how to estimate preferences of people based on distances to HS regardless of landscape types (either cultural or natural). Results show that people mostly visited three HS out of thirteen historical sites between 2004 and 2020: Kapıkırı Island (HS 1), and the ancient cities of Herakleia (HS 2) and Latmos (HS 3). According to the findings of the OLS regression model, year (t = 8.99, p <.0001), visual simplicity (t = -4.64, p ≤ 0.0001), and visual contrast (t = -2.01, p = 0.04) of the geotagged photos were all statistically significant predictors of VAL. HS 2 had the highest VAL value, followed by HS 1, and HS 3.

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Walkability is a modern concept that has become important in recent years due to the doubtless effects it has on aspects such as health and wellbeing, sustainable development, climate change, and tourism. It is necessary, therefore, that urban development strategies aim to achieve walkable cities. The main objective of this study is to define a methodology to calculate the walkability index in tourist cities and to predict the effects of climate change on this index, which is applied to three World Heritage cities in central Spain: Salamanca, Ávila, and Segovia. The methodology is developed in three phases. Phase I focus on the calculation of walkability, considering the following factors: facilities and services, accessibility, sidewalk width, population density, green areas, and urban trees. In Phase II, walkability in 2020, climate-related variables were added to the previous result: temperatures, solar radiation, and shadows. Finally, the third phase, walkability under climate change pressure in 2030, 2050, and 2100, establish predictions for different climate scenarios. The results show excellent walkability indices (higher) in city centers and newly built neighborhoods and low values in the rest of the peripheral areas, industrial estates, and neighborhoods. Climate predictions showed a generalized decrease in walkability over time, even higher in the scenario with high greenhouse gas emissions. Likewise, the models can be an excellent tool for the tourist management of cities since they show the most walkable areas and, therefore, the most suitable for tourist routes.

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