The Ideal Strategy of Carbon-Neutral for Park Landscape Design: A Proposal for a Rapid Detection Method
Source abstract
Abstract
The primary objective of this study is to investigate the carbon footprint, resilience levels, and optimal landscape area ratios of various parks. Additionally, it explores the relationships between landscape element proportions (LEP), the normalized difference vegetation index (NDVI), resilience indicators (RI), and the carbon reduction benefits associated with carbon neutrality (CN). Six parks were assessed for resilience, NDVI, LEP, and CN values, with Pearson correlation analysis conducted. The results revealed that parks with or without waterbodies exhibited ideal LEP area ratios of 6.5:2:1.5 (Softscape:Waterbody:Hardscape) and 8.3:1.7 (Softscape:Hardscape), respectively. Enhanced Softscape and reduced Hardscape proportions in parks correlated with increased NDVI and CN. NDVI exhibited a positive correlation with Softscape percentage and a negative correlation with Hardscape percentage. Conversely, CN demonstrated a negative correlation with Hardscape percentage and a positive correlation with Softscape percentage. Suggesting Softscape should constitute over 65%, and Hardscape should be under 15% in parks with water bodies. Waterless parks are advised to maintain a Softscape ratio exceeding 83% and a Hardscape ratio below 17%. Finally, the study extended to assess the LEP of 22 additional parks, validating the suitability of the ideal LEP area ratio.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Land Use and Ecosystem Services · Urban Green Space and Health · Urban Heat Island Mitigation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Landscape and Urban Design, Chaoyang University of Technology, Taichung 41349, Taiwan
College of Art Design, College of Landscape Architecture, Nanjing Forestry University, No 159, Longpan Rd., Xuanwu District, Nanjing 210037, China
Doctoral in Architecture and Urban Design Program, Department of Architecture, College of Design, Chaoyang University of Technology, Taichung 41349, Taiwan
Funding
National Science and Technology Council of Taiwan
References (66)
Show source-provided references
1. Carbon sequestration of four urban parks in Rome10.1016/j.ufug.2016.07.007 ↗
2. Estimating realized and potential carbon storage benefits from reforestation and afforestation under climate change: A case study of the Qinghai spruce forests in the Qilian Mountains, northwestern China10.1007/s11027-012-9420-4 ↗
3. (2024, June 17). Development of Low Carbon City. Available online: https://cfp-calculate.tw/cfpc/WebPage/WebSites/docx_detail.aspx?qparentid=61c5ad7f-69aa-4213-9932-cf47660d1330.
4. Tree diversity, distribution, history and change in urban parks: Studies in Bangalore, India10.1007/s11252-010-0148-1 ↗
5. Estimating CO2 balance through the Life Cycle Assessment prism: A case—Study in an urban park10.1016/j.ufug.2020.126869 ↗
6. Carbon storage and sequestration by urban forests in Shenyang, China10.1016/j.ufug.2011.03.002 ↗
7. Promoting sustainable carbon sequestration of plants in urban greenspace by planting design: A case study in parks of Beijing10.1016/j.ufug.2021.127291 ↗
8. UNDRR (2024, June 17). Disasters Data Pathfinder, Available online: https://www.earthdata.nasa.gov/learn/pathfinders/disasters.
9. Soil carbon management and climate change10.4155/cmt.13.31 ↗
10. Lehmann, S. (2021). Growing Biodiverse Urban Futures: Renaturalization and Rewilding as Strategies to Strengthen Urban Resilience. Sustainability, 13.10.3390/su13052932 ↗
11. It is not all bad for the grey city—A crossover study on physiological and psychological restoration in a forest and an urban environment10.1016/j.healthplace.2017.05.007 ↗
12. Characteristics of urban green spaces in relation to aesthetic preference and stress recovery10.1016/j.ufug.2019.03.005 ↗
13. Stress recovery and restorative effects of viewing different urban park scenes in Shanghai, China10.1016/j.ufug.2015.12.003 ↗
14. The urban brain: Analysing outdoor physical activity with mobile EEG10.1136/bjsports-2012-091877 ↗
15. Holly (2024, June 17). What Is The Difference Between Hardscape and Softscape?. Available online: https://paradisescapes.com/what-is-the-difference-between-hardscape-and-softscape/.
16. Huete, A.R., HuiQing, L., and Leeuwen, W.J.D.v. (1997, January 3–8). The use of vegetation indices in forested regions: Issues of linearity and saturation. Proceedings of the IGARSS’97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing—A Scientific Vision for Sustainable Development, Singapore.
17. (2024, June 17). Softscape. Available online: https://www.merriam-webster.com/dictionary/softscape.
18. Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle10.1109/TGRS.2008.2010457 ↗
19. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture10.1016/j.rse.2003.12.013 ↗
20. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass10.1016/j.rse.2008.06.005 ↗
21. The interpretation of spectral vegetation indexes10.1109/TGRS.1995.8746029 ↗
22. León-Tavares, J., Roujean, J.-L., Smets, B., Wolters, E., Toté, C., and Swinnen, E. (2021). Correction of Directional Effects in VEGETATION NDVI Time-Series. Remote Sens., 13.10.3390/rs13061130 ↗
23. Cai, Z., Jönsson, P., Jin, H., and Eklundh, L. (2017). Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data. Remote Sens., 9.10.3390/rs9121271 ↗
24. Pan, H.-Z., Ou, S.-J., and Hsu, C.-Y. (2022). Exploring the Resilience Park Index from the Perspective of Flood and Wind Disasters. Sustainability, 14.10.3390/su14095560 ↗
25. (2024, June 17). What is Carbon Neutrality and How Can It Be Achieved by 2050?. Available online: https://www.europarl.europa.eu/topics/en/article/20190926STO62270/what-is-carbon-neutrality-and-how-can-it-be-achieved-by-2050.
26. Pineda, A.C., Chang, A., and Faria, P. (2020). Foundations for Science-Based Net-Zero Target Setting in The 702 Corporate Sector Version 1.0, Science Based Targets.
27. Zero emission targets as long-term global goals for climate protection10.1088/1748-9326/10/10/105007 ↗
28. An integrated assessment of INDCs under Shared Socioeconomic Pathways: An implementation of C3IAM10.1007/s11069-018-3297-9 ↗
29. Park, H.-M., Jo, H.-K., and Kim, J.-Y. (2021). Carbon Footprint of Landscape Tree Production in Korea. Sustainability, 13.10.3390/su13115915 ↗
30. Carbon footprint reduction from energy-saving measure and green area of Suranaree University of Technology, Thailand10.18178/ijesd.2020.11.4.1246 ↗
31. The Assessment of Carbon Sinks Potential on Landscape Architecture—A Case of House-Style Community Patterns
32. Yang, T. (2016). Landscape Construction Carbon Footprint Evaluation System.
33. Huang, l., Zhang, Y., Deng, Y., Lin, L., Liu, X., and Xiao, R. (2017). Carbon Footprint Accounting and Evaluation of Urban Green Space. For. Resour. Manag.
34. Li, Y., and Yang, W. (2019). Discussion on the Application of Low Carbon Concept in Urban Landscape Design. Mod. Hortic., 132.
35. Sun, Y., and Hu, Y. (2021). Low carbon landscape design under the goal of “carbon peak, carbon neutrality”. Mod. Hortic., 123.
36. Zhao, M., Feng, Y., and Feng, J. (2022). Landscape planning and design of small towns in Kaifeng from the perspective of “double carbon”. Mod. Hortic., 143.
37. Wu, S. (2024, June 17). Research on the Insufficient Proportion and Coverage of Green Space Layout in Parks, Available online: https://www.ly.gov.tw/Pages/Detail.aspx?nodeid=6590&pid=196797.
38. Wang, Y. (2011). The Study on Water Allocation and Area Quantification of Park Waterscape. Chin. Landsc. Archit., 91.
39. Carbon reduction and planning strategies for urban parks in Seoul10.1016/j.ufug.2019.03.009 ↗
40. The carbon sequestration potential of urban public parks of densely populated cities to improve environmental sustainability
41. Carbon storage and stand conversion in a pine-dominated boreal forest landscape10.1016/j.foreco.2014.12.011 ↗
42. Carbon accumulation by native trees and soils in an urban park, Auckland
43. Lin, X. (2018). Architectural industry carbon footprint: Carbon management strategies for construction, landscape, and interior decoration.
44. Yagci, A.L., Di, L., and Deng, M. (2014, January 13–18). The influence of land cover-related changes on the NDVI-based satellite agricultural drought indices. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.10.1109/IGARSS.2014.6946868 ↗
45. Horning, N., Robinson, J.A., Sterling, E.J., Turner, W., and Spector, S. (2020). Remote Sensing for Ecology and Conservation: A Handbook of Techniques, Oxford University Press.
46. Lee, M., Lee, S., Eo, Y., Pyeon, M., Moon, K., and Han, S. Analysis on the effect of Landsat NDVI by atmospheric correction methods. 2016, 375–378.10.1201/b19961-80 ↗
47. Normalized difference vegetation index (ndvi) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia10.1088/1755-1315/126/1/012112 ↗
48. Spatial statistics techniques for SPEI and NDVI drought indices: A case study of Khuzestan Province10.1007/s13762-021-03852-8 ↗
49. Modeling of land surface temperature (LST) in Ardabil plain using NDVI index and Bayesian neural network approach10.1007/s40808-023-01709-2 ↗
50. Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland10.1016/j.ecolind.2018.06.029 ↗
51. Monitoring vegetation systems in the Great Plains with ERTS
52. Spatial and temporal trends of urban green spaces: An assessment using hyper-temporal NDVI datasets10.1080/10106049.2021.1989499 ↗
53. A Study of Green Space and its Changes in Hong Kong Using NDVI10.1080/13615930120086032 ↗
54. Park, J.-H., and Cho, G.-H. (2016). Examining the Association between Physical Characteristics of Green Space and Land Surface Temperature: A Case Study of Ulsan, Korea. Sustainability, 8.10.3390/su8080777 ↗
55. Far from the noisy world? Modelling the relationships between park size, tree cover and noise levels in urban green spaces of the city of Puebla, Mexico
56. No Park Is an Island: Increase in Interference from outside as Park Size Decreases10.2307/3544100 ↗
57. Dustin, M.C. (2015). Monitoring Parks with Inexpensive UAVs: Cost Benefits Analysis for Monitoring and Maintaining Parks Facilities, University of Southern California.
58. Spatio-temporal non-uniformity of urban park greenness and thermal characteristics in a semi-arid region10.1016/j.ufug.2018.05.009 ↗
59. The impact of urban patterns on ecosystem dynamics
60. Accurate prediction of bird species richness patterns in an urban environment using Landsat-derived NDVI and spectral unmixing10.1080/01431160701772534 ↗
61. Efficiency of parks in mitigating urban heat island effect: An example from Addis Ababa10.1016/j.landurbplan.2013.12.008 ↗
62. Quantifying the local cooling effects of urban green spaces: Evidence from Bengaluru, India10.1016/j.landurbplan.2021.104043 ↗
63. Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)
64. Dissecting the species–energy relationship10.1098/rspb.2005.3209 ↗
65. From space to species: Ecological applications for remote sensing10.1016/S0169-5347(03)00071-5 ↗
66. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery10.1016/j.rse.2006.09.003 ↗