Dynamic Cooling Effects of Permanent Urban Green Spaces in Beijing, China
暂无分享,去创建一个
Donghai Wu | Naijing Liu | Ping Liu | Jiacheng Zhao | Haoyu Wang | Shunlin Liang | Xiang Zhao | Ping Liu | S. Liang | Donghai Wu | Jiacheng Zhao | Xiang Zhao | Naijing Liu | Haoyu Wang
[1] V. Muggeo. Estimating regression models with unknown break‐points , 2003, Statistics in medicine.
[2] J. Aryal,et al. Role of geospatial technology in understanding urban green space of Kalaburagi city for sustainable planning , 2019, Urban Forestry & Urban Greening.
[3] Laurie Davies,et al. The identification of multiple outliers , 1993 .
[4] Joanne C. White,et al. Forest Monitoring Using Landsat Time Series Data: A Review , 2014 .
[5] H. Gawrońska,et al. Impact of particulate matter accumulation on the photosynthetic apparatus of roadside woody plants growing in the urban conditions. , 2018, Ecotoxicology and environmental safety.
[6] J. Choumert,et al. Provision of urban green spaces: Some insights from economics , 2008 .
[7] E. S. Krayenhoff,et al. Global multi-model projections of local urban climates , 2021, Nature Climate Change.
[8] P. Fan,et al. Accessibility of public urban green space in an urban periphery: The case of Shanghai , 2017 .
[9] A. Garg,et al. Quantifying the local cooling effects of urban green spaces: Evidence from Bengaluru, India , 2021 .
[10] G. Mills,et al. The impact of green spaces on mental health in urban settings: a scoping review , 2020, Journal of mental health.
[11] Jiansheng Wu,et al. How to quantify the cooling effect of urban parks? Linking maximum and accumulation perspectives , 2021 .
[12] J. Monteith. Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.
[13] Shuguang Liu,et al. Prevalent vegetation growth enhancement in urban environment , 2016, Proceedings of the National Academy of Sciences.
[14] Joe R. McBride,et al. The urban forest in Beijing and its role in air pollution reduction , 2005 .
[15] Shuangcheng Li,et al. Local cooling and warming effects of forests based on satellite observations , 2015, Nature Communications.
[16] A. Danehkar,et al. Applying landscape metrics and structural equation modeling to predict the effect of urban green space on air pollution and respiratory mortality in Tehran , 2020, Environmental Monitoring and Assessment.
[17] M. Shen,et al. Impact of urban greenspace spatial pattern on land surface temperature: a case study in Beijing metropolitan area, China , 2019, Landscape Ecology.
[18] J. Canadell,et al. Greening of the Earth and its drivers , 2016 .
[19] Stefan Zerbe,et al. The influence of tree traits on urban ground surface shade cooling , 2020 .
[20] Lei Ouyang,et al. Canopy transpiration and its cooling effect of three urban tree species in a subtropical city- Guangzhou, China , 2019, Urban Forestry & Urban Greening.
[21] H. Pleijel,et al. Transpiration of urban trees and its cooling effect in a high latitude city , 2015, International Journal of Biometeorology.
[22] Marc Macias-Fauria,et al. Sensitivity of global terrestrial ecosystems to climate variability , 2016, Nature.
[23] Shuguang Liu,et al. Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities. , 2016 .
[24] Hankui K. Zhang,et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. , 2016, Remote sensing of environment.
[25] Shunlin Liang,et al. Assessing the thermal contributions of urban land cover types , 2020 .
[26] E. Ng,et al. Urban tree design approaches for mitigating daytime urban heat island effects in a high-density urban environment , 2016 .
[27] Guo Yu Qiu,et al. Quantifying the Evapotranspiration Rate and Its Cooling Effects of Urban Hedges Based on Three-Temperature Model and Infrared Remote Sensing , 2019, Remote. Sens..
[28] S. Tsoka,et al. Assessing the effects of urban street trees on building cooling energy needs: The role of foliage density and planting pattern , 2021 .
[29] Nandin-Erdene Tsendbazar,et al. Copernicus Global Land Cover Layers - Collection 2 , 2020, Remote. Sens..
[30] Lee Chapman,et al. Quantifying the Daytime and Night-Time Urban Heat Island in Birmingham, UK: A Comparison of Satellite Derived Land Surface Temperature and High Resolution Air Temperature Observations , 2016, Remote. Sens..
[31] Maosheng Zhao,et al. Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .
[32] Christopher E. Holden,et al. Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014) , 2016 .
[33] Michael Dixon,et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .
[34] K. Anderson,et al. Vegetation expansion in the subnival Hindu Kush Himalaya , 2020, Global change biology.
[35] N. Chatterjee,et al. An integrated simulation approach to the assessment of urban growth pattern and loss in urban green space in Kolkata, India: A GIS-based analysis , 2021 .
[36] B. Jia,et al. The roles of landscape both inside the park and the surroundings in park cooling effect , 2020 .
[37] A. R. Ennos,et al. A comparison of the growth and cooling effectiveness of five commonly planted urban tree species , 2014, Urban Ecosystems.
[38] Mohamed Lokman Mohd Yusof,et al. Growth of Samanea saman: Estimated cooling potential of this tree in an urban environment , 2019, Urban Forestry & Urban Greening.
[39] G. Viswanadh,et al. Quantification of flood mitigation services by urban green spaces using InVEST model: a case study of Hyderabad city, India , 2020, Modeling Earth Systems and Environment.
[40] Zhe Zhu,et al. Cloud detection algorithm comparison and validation for operational Landsat data products , 2017 .
[41] M. Claverie,et al. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.
[42] Ying Long,et al. Deciphering the recreational use of urban parks: Experiments using multi-source big data for all Chinese cities. , 2019, The Science of the total environment.
[43] G. Feyisa,et al. Efficiency of parks in mitigating urban heat island effect: An example from Addis Ababa , 2014 .
[44] Irma J. Terpenning,et al. STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .
[45] A. Rosenfeld,et al. Residential cooling loads and the urban heat island—the effects of albedo , 1988 .
[46] S. Pauleit,et al. Comparing the transpirational and shading effects of two contrasting urban tree species , 2019, Urban Ecosystems.
[47] Xinquan Zhao,et al. A Landsat-based vegetation trend product of the Tibetan Plateau for the time-period 1990–2018 , 2019, Scientific Data.
[48] Xiuchen Wu,et al. Aridity change and its correlation with greening over drylands , 2019, Agricultural and Forest Meteorology.
[49] Motoya Koga,et al. Variations in land surface temperature and cooling efficiency of green space in rapid urbanization: The case of Fuzhou city, China , 2018 .
[50] Z. Li,et al. Validation of Collection 6 MODIS land surface temperature product using in situ measurements , 2019, Remote Sensing of Environment.
[51] R. Vargas,et al. Effect of precipitation variability on net primary production and soil respiration in a Chihuahuan Desert grassland , 2011 .
[52] Isabel F. Trigo,et al. Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series , 2020, Remote. Sens..
[53] Michael Veith,et al. Biodiversity in cities needs space: a meta-analysis of factors determining intra-urban biodiversity variation. , 2015, Ecology letters.
[54] Zhiyun Ouyang,et al. Plant species composition in relation to green cover configuration and function of urban parks in Beijing, China , 2006, Ecological Research.
[55] R. Fensholt,et al. The human–environment nexus and vegetation–rainfall sensitivity in tropical drylands , 2020, Nature Sustainability.
[56] T. Kershaw,et al. Utilising green and bluespace to mitigate urban heat island intensity. , 2017, The Science of the total environment.