Exploring Applicability of Deep Learning for Restorative Urban Forest Landscape Evaluation: Focused on Related Literature and Methodology Review

2021 ◽  
Vol 10 (2) ◽  
pp. 277-291
Author(s):  
Eujin Julia Kim ◽  
Youngeun Kang
1988 ◽  
Vol 12 (1) ◽  
pp. 83-107 ◽  
Author(s):  
George V Profous ◽  
Rowan A Rowntree ◽  
Robert E Loeb

2014 ◽  
Vol 18 (1) ◽  
pp. 223-238 ◽  
Author(s):  
Sanna Mäkeläinen ◽  
Marko Schrader ◽  
Ilpo K. Hanski

Author(s):  
Wei Wang ◽  
Rongyuan Liu ◽  
Huiyun Yang ◽  
Ping Zhou ◽  
Xiangwen Zhang ◽  
...  

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1691
Author(s):  
Jie Zhang ◽  
Zhi Yang ◽  
Zhuo Chen ◽  
Mengyuan Guo ◽  
Peng Guo

Interacting with urban spaces that are green and blue is believed to promote mental well-being and positive emotions. Therefore, there is an incentive to strategically design urban forest landscapes in a given space to evoke more positive emotion. In this study, we conducted a pilot study in Northeast China with 24 parks from 11 cities across 3 provinces. The subjects of the study are the visitors and a total of 1145 photos and selfies were collected from open micro-twitters in Sino Weibo (~50 individuals per park). Facial expressions of happy and sad emotions were recognized and rated as percent scores by FireFACE v1.0. Demographically, male adolescents smiled more than male visitors in other age groups and female teens. Females expressed more positive emotions than males according to their positive response index (PRI; happy-sad). Multivariate linear regression indicated positive contribution of green space to happy scores (estimate of 0.0040) and a stronger negative contribution of blue area to sad scores (estimate of −0.1392). Therefore, an urban forest landscape can be optimized by mapping green- and blue-spaces to predict spatial distributions of positive emotions. Male teens are recommended more as frequent visitors than people in other age ranges.


2018 ◽  
Vol 10 (12) ◽  
pp. 4397 ◽  
Author(s):  
Yang Li ◽  
Chunyan Xue ◽  
Hua Shao ◽  
Ge Shi ◽  
Nan Jiang

The landscape patterns of urban forests not only reflect the influence of urbanization on urban forests, but also determines its function in urban ecosystem services. In the case of mastering the overall forest landscape pattern of a city, a study of the structure of urban forest landscapes at different scales and in urbanized regions is beneficial to a comprehensive understanding of the forest characteristics of a city. In the present study, an attempt was made to map and monitor the spatio-temporal dynamics of an urban forest in Shanghai from 2004 to 2014 using remote sensing techniques. Methods of landscape ecology analysis are followed to quantify the spatiotemporal patterns of an urban forest landscape by urban and rural gradient regionalization. The results show that the spatial structure of an urban forest landscape is essentially consistent with an urban landscape pattern. Due to strong interference from human activities, the ecological quality of forest landscapes is low. At the landscape level, the urban forest coverage rate increased from 11.43% in 2004 to 16.02% in 2014, however, the number of large patches decreased, there was a high degree of urban forest landscape fragmentation, landscape connectivity was poor, landscape patch boundaries were uniform, and weak links were present between ecological processes. Different urban and rural gradient division methods exhibit obvious gradient characteristics along the urban–rural gradient in Shanghai. The regional differences in the urban forest landscape ecological characteristics have further increased as a result of urban planning and zoning. The total amount of urban forest is located closer to the urban center, which has the smallest total amount of forest; however, in terms of urban forest coverage, the suburbs have more coverage than do the outer suburbs and the central urban areas. The urban forest landscape’s spatial distribution area is evidently different. Urbanization affects the areas closest to urban residential areas, which are markedly disturbed by humans, and the urban forest landscape has a high degree of fragmentation. The forest patches have become divided and unconnected, and the degree of natural connectivity has gradually decreased over the past 10 years. At the landscape class level, broadleaf forests are dominant in Shanghai, and their area exhibits an increasing trend; shrublands and needleleaf forests, however, show a decreasing trend. Compared with other forest types, the spatial distribution of broadleaf forest is concentrated in the suburbs, and the aggregation effect is relatively apparent. From the perspective of urban forest landscape pattern aggregation characteristics in Shanghai, the spatial distribution of urban forest landscape point patterns in the study area exhibit extremely uneven characteristics. The point density of urban forest patches larger than 1 ha in Shanghai increased from 2004 to 2014. However, the total number of patches with areas larger than 5 ha decreased, and this decrease plays an important role in the ecological environment. In the past 10 years, the concentration characteristics of urban forests with large patches has gradually decreased. In 2014, the urban forest landscapes decreased by 5 km compared to the intensity of aggregates in 2004, which also indicates that urban forests in Shanghai tend to be fragmented. The results of this study can be useful to help improve urban residents’ living environments and the sustainable development of the urban ecosystem, and they will also be vital to future management.


2021 ◽  
Vol 13 (9) ◽  
pp. 1749
Author(s):  
Zhe Wang ◽  
Chao Fan ◽  
Min Xian

Urban forest is a dynamic urban ecosystem that provides critical benefits to urban residents and the environment. Accurate mapping of urban forest plays an important role in greenspace management. In this study, we apply a deep learning model, the U-net, to urban tree canopy mapping using high-resolution aerial photographs. We evaluate the feasibility and effectiveness of the U-net in tree canopy mapping through experiments at four spatial scales—16 cm, 32 cm, 50 cm, and 100 cm. The overall performance of all approaches is validated on the ISPRS Vaihingen 2D Semantic Labeling dataset using four quantitative metrics, Dice, Intersection over Union, Overall Accuracy, and Kappa Coefficient. Two evaluations are performed to assess the model performance. Experimental results show that the U-net with the 32-cm input images perform the best with an overall accuracy of 0.9914 and an Intersection over Union of 0.9638. The U-net achieves the state-of-the-art overall performance in comparison with object-based image analysis approach and other deep learning frameworks. The outstanding performance of the U-net indicates a possibility of applying it to urban tree segmentation at a wide range of spatial scales. The U-net accurately recognizes and delineates tree canopy for different land cover features and has great potential to be adopted as an effective tool for high-resolution land cover mapping.


Author(s):  
Ernest Bielinis ◽  
Emilia Janeczko ◽  
Krzysztof Janeczko ◽  
Lidia Bielinis

Rubbish in a forest environment is a great threat to this ecosystem, but this threat may also apply to the lost benefits for visitors to the forest. Previous studies proved that forest areas have a positive effect on obtaining psychological relaxation in the people visiting them. However, it was not known whether this restorative experience could be disturbed in any way by the presence of an open dump in the forest. To check how the presence of a landfill affects the visitors, an experiment was planned in which the respondents observed a forest area with a landfill and a forest landscape without a landfill for 15 minutes (control). The respondents then assessed the landscape using the semantic differential method and the Perceived Restorativeness Scale (PRS). An analysis of these observations showed that the presence of a landfill in the forest significantly changed the appreciation of the landscape by the respondents, the values of positive experiences decreased, and the negative experiences increased. Restorativeness was also reduced. Based on the results, it can be concluded that the presence of garbage in the forest may interrupt the restorative experience of its visitors.


2021 ◽  
Author(s):  
Yang Yang ◽  
Xiaolan Tang

Abstract In recent years, the construction of urban forest parks has run into the fast lane in China. As an indispensable natural landscape resource for urban forest parks, forest landscape has been paid increasing attentions by the public, in contrast, less effort has been made in the field of aesthetic evaluation of forest landscape. Based on the theories of landscape esthetics and psychology, this paper aims to present methods for the aesthetic evaluation, and understand citizen’s aesthetic perceptions of forest landscape using Semantic Differential (SD) and Principal Component Analysis (PCA) methods. Moreover, further suggestions will be put forward for a better development of the forest landscape, thereby giving full play to their landscape and recreation functions. As per the findings of this paper, the vegetation element diversity (PC 1 ), the magnificent feel (PC 2 ), the nature-pastoral feel (PC 3 ) and the sense of space (PC 4 ) present the critical comprehensive indexes affecting the aesthetic values of the forest landscape. The relationship between the comprehensive indexes and the landscape aesthetic value is revealed by multiple regression analyses. PC 3 and PC 4 are found to be less influencing on aesthetic values than PC 1 and PC 2 . At last, three suggestions for the construction and protection of forest landscape are put forward. The results of this study will contribute to the preservation of the forest landscape aesthetic, and the integration of these conclusions into the sustainable development strategies of urban forest parks.


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