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19 pages, 12404 KiB

Open AccessArticle

Impacts of the Microclimate of a Large Urban Park on Its Surrounding Built Environment in the Summertime

by Majid Amani-Beni, Biao Zhang, Gao-Di Xie and A. Jacob Odgaard

Remote Sens. 2021, 13(22), 4703; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224703 - 21 Nov 2021

Cited by 22 | Viewed by 3434

The cooling effect of green spaces as an ecological solution to mitigate urban climate change is well documented. However, the factors influencing the microclimate in the built environment around forest parks, diurnal variations of their impact and their degree of importance have not [...] Read more.

The cooling effect of green spaces as an ecological solution to mitigate urban climate change is well documented. However, the factors influencing the microclimate in the built environment around forest parks, diurnal variations of their impact and their degree of importance have not been explicitly addressed. We attempted to quantify how much various landscape parameters, including land cover and spatial location, impact the ambient air and surface temperature in the area around Beijing’s Olympic Forest Park. Data were taken along strategically located traverses inside and outside the park. We found: (1) The air temperature during the day was 1.0–3.5 °C lower in the park than in the surrounding area; the surface temperature was 1.7–4.8 °C lower; air humidity in the park increased by 8.7–15.1%; and the human comfort index reduced to 1.8–6.9, all generating a more comfortable thermal environment in the park than in the surrounding area. (2) The distance to the park and the green space ratio of the park’s surrounding area are significant factors for regulating its microclimate. A 1 km increase in distance to the park caused the temperature to increase by 0.83 °C; when the green space ratio increased by 10%, the temperature dropped by 0.16 °C on average. The impact of these two parameters was more obvious in the afternoon than in the middle of the day or in the morning. The green space ratio could be used for designing a more stable thermal environment. (3) Land cover affects surface temperature more than it does air temperature. Our data suggest that an urban plan with an even distribution of green space would provide the greatest thermal comfort. Full article

(This article belongs to the Special Issue Urban Vegetation and Ecology Monitoring Using Remote Sensing)

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24 pages, 2011 KiB

Open AccessArticle

Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy

by Dengkai Chi, Jeroen Degerickx, Kang Yu and Ben Somers

Remote Sens. 2020, 12(15), 2435; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152435 - 29 Jul 2020

Cited by 16 | Viewed by 3960

Abstract

Declining urban tree health can affect critical ecosystem services, such as air quality improvement, temperature moderation, carbon storage, and biodiversity conservation. The application of state-of-the-art remote sensing data to characterize tree health has been widely examined in forest ecosystems. However, such application to [...] Read more.

Declining urban tree health can affect critical ecosystem services, such as air quality improvement, temperature moderation, carbon storage, and biodiversity conservation. The application of state-of-the-art remote sensing data to characterize tree health has been widely examined in forest ecosystems. However, such application to urban trees has not yet been fully explored—due to the presence of heterogeneous tree species and backgrounds, severely complicating the classification of tree health using remote sensing information. In this study, tree health was represented by a set of field-assessed tree health indicators (defoliation, discoloration, and a combination thereof), which were classified using airborne laser scanning (ALS) and hyperspectral imagery (HSI) with a Random Forest classifier. Different classification scenarios were established aiming at: (i) Comparing the performance of ALS data, HSI and their combination, and (ii) examining to what extent tree species mixtures affect classification accuracy. Our results show that although the predictive power of ALS and HSI indices varied between tree species and tree health indicators, overall ALS indices performed better. The combined use of both ALS and HSI indices results in the highest accuracy, with weighted kappa coefficients (Kc) ranging from 0.53 to 0.79 and overall accuracy ranging from 0.81 to 0.89. Overall, the most informative remote sensing indices indicating urban tree health are ALS indices related to point density, tree size, and shape, and HSI indices associated with chlorophyll absorption. Our results further indicate that a species-specific modelling approach is advisable (Kc points improved by 0.07 on average compared with a mixed species modelling approach). Our study constitutes a basis for future urban tree health monitoring, which will enable managers to guide early remediation management. Full article

(This article belongs to the Special Issue Urban Vegetation and Ecology Monitoring Using Remote Sensing)

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Other

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16 pages, 7686 KiB

Open AccessTechnical Note

High-Throughput Remote Sensing of Vertical Green Living Walls (VGWs) in Workplaces

by David Helman, Yehuda Yungstein, Gabriel Mulero and Yaron Michael

Remote Sens. 2022, 14(14), 3485; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143485 - 21 Jul 2022

Cited by 7 | Viewed by 2851

Abstract

Vertical green living walls (VGWs)—growing plants on vertical walls inside or outside buildings—have been suggested as a nature-based solution to improve air quality and comfort in modern cities. However, as with other greenery systems (e.g., agriculture), managing VGW systems requires adequate temporal and [...] Read more.

Vertical green living walls (VGWs)—growing plants on vertical walls inside or outside buildings—have been suggested as a nature-based solution to improve air quality and comfort in modern cities. However, as with other greenery systems (e.g., agriculture), managing VGW systems requires adequate temporal and spatial monitoring of the plants as well as the surrounding environment. Remote sensing cameras and small, low-cost sensors have become increasingly valuable for conventional vegetation monitoring; nevertheless, they have rarely been used in VGWs. In this descriptive paper, we present a first-of-its-kind remote sensing high-throughput monitoring system in a VGW workplace. The system includes low- and high-cost sensors, thermal and hyperspectral remote sensing cameras, and in situ gas-exchange measurements. In addition, air temperature, relative humidity, and carbon dioxide concentrations are constantly monitored in the operating workplace room (scientific computer lab) where the VGW is established, while data are continuously streamed online to an analytical and visualization web application. Artificial Intelligence is used to automatically monitor changes across the living wall. Preliminary results of our unique monitoring system are presented under actual working room conditions while discussing future directions and potential applications of such a high-throughput remote sensing VGW system. Full article

(This article belongs to the Special Issue Urban Vegetation and Ecology Monitoring Using Remote Sensing)

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