Residential greenspace has great intracity variations. Its driving factors have been widely investigated in Western countries (e.g., in Europe and North America), where residential areas are mostly ...privately owned, but few studies have examined the determinants of residential greenspace in China, where land is owned by the government. Taking the subtropical city of Changsha, China, as an example, this study mapped the percent green cover (PGC) in residential quarters with a fine-resolution satellite image. We also investigated its driving factors measured from multisource geospatial data. The results show that (1) the PGC in the residential quarters showed great spatial variation, with an average of 36.6% (0–85.7%) and a standard deviation of 18.3%. (2) Urban form, biophysical context, and socioeconomic factors together explained 49.18% of the variation in the PGC, and they independently explained 22.87%, 11.17%, and 2.31% of the variation, respectively. (3) Residential quarter size, PGC in the surrounding buffer zone, residential quarter age, housing value, and population density significantly and positively impact the PGC, while percent building cover, floor area ratio, and distance to city center had significant negative impacts. The strongest positive and negative impacts came from PGC in the surrounding buffer zone and percent building cover, respectively. Socioeconomic factors, the dominant driving factors in Western countries, show little impact on residential PGC. These findings expand our understanding of the intracity variation in greenspace coverage and the driving factors, which sheds light on the effective planning and management of urban greenspaces in China.
The urban heat storage flux, <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula>, is one of the main drivers of the nocturnal urban heat island effect. However, ...the complex 3-D building structure makes observations and simulations of <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S }} </tex-math></inline-formula> difficult. This study observes the 3-D surface radiant temperature (<inline-formula> <tex-math notation="LaTeX">T_{\mathrm {s}} </tex-math></inline-formula>) of a building in Beijing, China. The element surface temperature method (ESTM) and the half-order (HO) method are compared for <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula> simulation using <inline-formula> <tex-math notation="LaTeX">T_{\mathrm {s}} </tex-math></inline-formula> observations. The impact of building structure on <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula> and urban heat island intensity (UHII) are also studied. Results show the following. First, <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula>'s simulated by ESTM and HO are nearly the same for walls. However, the HO method only needs one-layer exterior surface temperature, which has great potential for regional <inline-formula> <tex-math notation="LaTeX">\Delta Q_{\mathrm {S}} </tex-math></inline-formula> simulation by satellite remote sensing data. Second, during the daytime, <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula>'s of each facet are significantly different from each other. The maximum observed difference of <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula> is up to 452 W/m 2 between the roof and north wall in May 2019. Third, complete <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S, c}} </tex-math></inline-formula>) is calculated by each facet <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula> and area fraction. The relationships between UHII and both 2-D <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula>(roof <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula>) and 3-D <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S }}(Q_{\mathrm {S, c}}) </tex-math></inline-formula> are studied. <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula> is positively correlated with nocturnal UHII, and 3-D <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula> corresponds more closely to UHII with a larger Spearman's coefficient (<inline-formula> <tex-math notation="LaTeX">p < 0.05 </tex-math></inline-formula>). This study presents the effect of building structure on heat flux and could provide an insight for future <inline-formula> <tex-math notation="LaTeX">Q_{\mathrm {S}} </tex-math></inline-formula> and urban heat island (UHI) studies.
Land surface temperature (LST) is one of the most important and widely used parameters for studying land surface processes. Moderate Resolution
Imaging Spectroradiometer (MODIS) LST products (e.g., ...MOD11A1 and MYD11A1) can provide this information with moderate spatiotemporal resolution with
global coverage. However, the applications of these data are hampered because of missing values caused by factors such as cloud contamination,
indicating the necessity to produce a seamless global MODIS-like LST dataset, which is still not available. In this study, we used a spatiotemporal
gap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020
based on standard MODIS LST products. The method includes two steps: (1) data pre-processing and (2) spatiotemporal fitting. In the data
pre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In the
spatiotemporal fitting, first we fitted the temporal trend (overall mean) of observations based on the day of year (independent variable) in each
pixel using the smoothing spline function. Then we spatiotemporally interpolated residuals between observations and overall mean values for each
day. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values in
the original MODIS LST were effectively and efficiently filled with reduced computational cost, and there is no obvious block effect caused by large
areas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation with
different missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error
(RMSE) of 1.88 and 1.33∘, respectively, for mid-daytime (13:30) and mid-nighttime (01:30). The seamless global daily (mid-daytime and
mid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling,
and terrestrial ecosystem studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (T. Zhang
et al., 2021).
Microclimate (e.g., temperature and humidity) in urban green space (UGS) patches changes nonlinearly from the edge to the interior, displaying a microclimatic edge effect (MEE). Reducing the MEE ...width is an effective and practical strategy for UGS planning and management to enhance climatic benefits (e.g., cooling). However, cost-effective approaches are not available to quantify the MEE width, let alone explore its driving factors. This study proposes a novel and cost-effective method to quantify the MEE width, based on the relationship between climatic variables (e.g., temperature and humidity) and the percentage of remaining UGS after successive edge removals. The method was tested in the subtropical city of Changsha, China, considering air temperature (AT) and humidity. Its effectiveness was also tested using widely available land surface temperature (LST) datasets while considering different spatial resolutions of the UGS map (i.e., 1–10 m) and different analytical units (i.e., 1 km2 grid and township census tract). Finally, the generalizability of this method was validated with data on six other representative Chinese cities. The results show that: (1) The estimated MEE widths for temperature and humidity in Changsha are about 8 m and 6 m, respectively. (2) The approach obtains consistent MEE widths irrespective of temperature types and analytical units. (3) A high spatial resolution UGS map (i.e., 1 m) is recommended for higher accuracy. (4) This approach effectively identifies MEE width in six other representative Chinese cities (Beijing, Chengdu, Shanghai, Shenyang, Wuhan, Xi’an), demonstrating its generalizability. This novel approach provides an easy and fast method to identify the MEE width at the landscape scale, which can help (1) better understand the relationship between UGS fragmentation and the urban microclimate, and (2) better plan and manage UGS to improve the urban microclimate.
•The microclimatic edge effect (MEE) width of urban green spaces is quantified.•A novel and cost-effective approach is proposed.•This approach works well for temperature and humidity.•This approach has high generalizability and can be applied to other cities•High spatial resolution of urban green space map is required.
Urban greenspace planning plays a crucial role in improving the quality of human settlements and the living standard of citizens. Urban public greenspace (UPGS) is an important part of urban ...greenspaces. Existing literature rarely includes a scientific evaluation of greenspace plans (including of UPGS) and plan implementation effects. To bridge this gap, this study evaluated and monitored the UPGS plan enacted in 2010 in Kunming, China. Object-based image classification and visual interpretation of satellite images and Google Earth imagery were used to quantify the different periods of UPGS implementation. Six indicators and monitoring at four classic sites were applied to explore the change at two scales (overall scale and district scale) for monitoring the UPGS plan execution. The results showed that UPGS structure greatly improved after plan implementation. However, UPGS provision per capita has not reached the level of greenspace planning and the connectivity was poor. Significant implementation inequalities existed in each district and implementation has lagged behind schedule. This study contributes to a better understanding of greenspace planning and urban planning in general, which can help improve future planning and planning decisions.
High spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. Moderate ...Resolution Imaging Spectroradiometer (MODIS) daily LST is one of such high spatiotemporal datasets that are widely used. But, it has a large amount of missing values primarily because of clouds, shadows, and other atmospheric conditions. Gapfilling the missing values is an important approach to create seamless high spatiotemporal LST datasets. However, current gapfilling methods have limitations in terms of accuracy and efficiency to assemble the data over large areas (e.g., national and continental levels). In this study, we developed a 3-step hybrid method by integrating daily merging (gapfilling missing values at one overpass using values at the other three overpasses each day), spatiotemporal gapfilling (estimating missing values based on values of their spatial and temporal neighbors), and temporal interpolation (gapfilling missing values based on values of their neighboring days), to create a seamless high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. We applied this method in urban and surrounding areas in the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates its root mean squared error (RMSE) to be 3.3K for mid-daytime (1:30pm) and 2.7K for mid-nighttime (1:30am) observations. The method can be easily extended to other years and regions and is also applicable to other satellite products for large areas. This seamless daily (mid-daytime and mid-nighttime) LST product with 1km spatial resolution is of great value for studying urban climate (e.g., quantifying surface urban heat island intensity, creating seamless high spatiotemporal air temperature dataset) and the related impacts on people (e.g., health and mortality), ecosystems (e.g., phenology), and energy systems (e.g., building energy use).
•A three-step hybrid gapfilling method was proposed to fill gaps in MODIS LST data.•A gapless LST dataset in urban and surrounding areas in the U.S. was developed.•The method can be extended spatiotemporally to build gapless LST for urban studies.
PDF
