Search

Accurate measurements of the three-dimensional structure characteristics of urban buildings and their greenhouse effect are important for evaluating the impact of urbanization on the radiation energy budget and research on the urban heat island (UHI) effect. The decrease in evapotranspiration or the increase in sensible heat caused by urbanization is considered to be the main cause of the UHI effect, but little is known about the influence of the main factor “net radiant flux” of the urban surface heat balance. In this study, experimental observation and quantitative model simulation were used to find that with the increase of building surface area after urbanization, the direct solar radiation flux and net radiation flux on building surface areas changed significantly. In order to accurately quantify the relationship between the positive and negative effects, this study puts forward the equivalent calculation principle of “aggregation element”, which is composed of a building’s sunny face and its shadow face, and the algorithm of the contribution of the area to thermal effect. This research clarifies the greenhouse effect of a building with walls of glass windows. Research shows that when the difference between absorption rates of a concrete wall and grass is −0.21, the cooling effect is shown. In the case of concrete walls with glass windows, the difference between absorption rates of a building wall and grass is −0.11, which is also a cooling effect. The greenhouse effect value of a building with glass windows reduces the cooling effect value to 56% of the effect of a building with concrete walls. The simulation of changes in net radiant flux and flux density shows that the greenhouse effect of a 5-story building with windows yields 15.5% less cooling effect than one with concrete walls, and a 30-story building with windows reduces the cooling effect by 23.0%. The simulation results confirmed that the difference in the equivalent absorption rate of the aggregation element is the “director” of cooling and heating effects, and the area of the aggregation element is the “amplifier” of cooling and heating effects. At the same time, the simulation results prove the greenhouse effect of glass windows, which significantly reduces the cold effect of concrete wall buildings. The model reveals the real contribution of optimized urban design to mitigating UHI and building a comfortable environment where there is no atmospheric circulation.

1. This is the dataset described by the data paper "A national urban land use/cover change dataset in China, 2000-2018" submitted to Earth System Science Data (ESSD). 2. The dataset includes the following subsets, i.e. urban boundary (named as "Urban_Year.tif", like "Urban_2000.tif"), urban green space fraction (named as "UGS_Year_Province.tif", like "UGS_2000_Anhui.tif"), urban impervious surface area fraction (named as "UISA_Year_Province.tif", like "UISA_2000_Anhui.tif"). 3. The urban impervious surface area and urban green space datasets are showed by the percentage in a grid. In order for data storage, the data were amplified by 1000 times and converted into integer storage. 4. The detail information of datasets is listed in description.xlsx 5. Some sample mapping were listed in Samples.zip. 6. If you have any questions, please contact Wenhui Kuang (kuangwh@igsnrr.ac.cn). Related publications with this dataset. (1) Kuang W H. 70 years of urban expansion across China: Trajectory, pattern, and national policies. Science Bulletin. 2020. https://doi.org/10.1016/j.scib.2020.07.005 (2) Kuang W H, Dou Y Y. Investigating the Patterns and Dynamics of Urban Green Space in China's 70 Major Cities Using Satellite Remote Sensing. Remote Sensing. 2020, 12, 1929; doi:10.3390/rs12121929 (3) Kuang W H. Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences. 2019, 62(10), 1591-1606. (4) Kuang W H. National urban land-use/cover change since the beginning of the 21st century and its policy implications in China. Land Use Policy. 2020. https:// doi.org/10.1016/j.landusepol.2020.104747, {"references":["Kuang W H. 70 years of urban expansion across China: Trajectory, pattern, and national policies. Science Bulletin. 2020. https://doi.org/10.1016/j.scib.2020.07.005","Kuang W H, Dou Y Y. Investigating the Patterns and Dynamics of Urban Green Space in China's 70 Major Cities Using Satellite Remote Sensing. Remote Sensing. 2020, 12, 1929; doi:10.3390/rs12121929","Kuang W H. Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences. 2019, 62(10), 1591-1606.","Kuang W H. National urban land-use/cover change since the beginning of the 21st century and its policy implications in China. Land Use Policy. 2020. https:// doi.org/10.1016/j.landusepol.2020.104747","Kuang W H, Liu A L, Dou Y Y, et al. Examining the impacts of urbanization on surface radiation using Landsat imagery. GIScience & Remote Sensing. 2019, 56(3): 462-484.","Kuang W H. New Evidences on Anomalous Phenomenon of Buildings in Regulating Urban Climate from Observations in Beijing, China. Earth and Space Science. 2019, 6(5): 861-872."]}

1. This is the dataset described by the report "Global Urban Land Cover Changes in 2000-2020" submitted to Global Ecosystem and Environment Observation Analysis Research Cooperation (GEOARC). 2. The dataset includes the following subsets, i.e. urban agglomeration urban boundary, megacities urban boundary.See the description document for details. 3. The detail information of datasets is listed in Description.xlsx 4. If you have any questions, please contact Wenhui Kuang (kuangwh@igsnrr.ac.cn). 5.Related publications with this dataset. (1)Kuang W H, Du G M, Lu D S, et al. Global observation of urban expansion and land-cover dynamics using satellite big-data. Science Bulletin. 2021, 66(4): 297-300. (2)Kuang W H, Zhang S, Li X Y, et al. A 30 m resolution dataset of China’s urban impervious surface area and green space, 2000–2018. Earth System Science Data. 2021, 13(1): 63-82. (3) Kuang W H. 70 years of urban expansion across China: trajectory, pattern, and national policies. Science Bulletin. 2020, 65(23): 1970-7974. (4) Kuang W H, Dou Y Y. Investigating the patterns and dynamics of urban green space in China’s 70 major cities using satellite remote sensing. Remote Sensing. 2020, 12(12): 1929. (5) Kuang W H. National urban land-use cover change since the beginning of the 21st century and its policy implications in China. Land Use Policy. 2020, 97: 104747. (6) Kuang W H. Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences. 2019, 62(10): 1591-1606. (7) Dou Y Y, Kuang W H. A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Science of the Total Environment. 2020, 706:135828. (8) Zhang S, Fang C L, Kuang W H, et al. Comparison of changes in urban land use/cover and efficiency of megaregions in China from 1980 to 2015. Remote Sensing. 2019, 11(15): 1834. (9) Pan T, Kuang W H, Zhang C, et al. City-level comparison of urban land-cover configurations from 2000-2015 across 65 countries within the global Belt and Road. Remote Sensing. 2019, 11(13): 1515., {"references":["Kuang W H, Du G M, Lu D S, et al. Global observation of urban expansion and land-cover dynamics using satellite big-data. Science Bulletin. 2021, 66(4): 297-300.","Kuang W H, Zhang S, Li X Y, et al. A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018. Earth System Science Data. 2021, 13(1): 63-82.","Kuang W H. 70 years of urban expansion across China: trajectory, pattern, and national policies. Science Bulletin. 2020, 65(23): 1970-7974.","Kuang W H, Dou Y Y. Investigating the patterns and dynamics of urban green space in China's 70 major cities using satellite remote sensing. Remote Sensing. 2020, 12(12): 1929.","Kuang W H. National urban land-use cover change since the beginning of the 21st century and its policy implications in China. Land Use Policy. 2020, 97: 104747.","Kuang W H. Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences. 2019, 62(10): 1591-1606.","Dou Y Y, Kuang W H. A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Science of the Total Environment. 2020, 706:135828.","Zhang S, Fang C L, Kuang W H, et al. Comparison of changes in urban land use/cover and efficiency of megaregions in China from 1980 to 2015. Remote Sensing. 2019, 11(15): 1834.","Pan T, Kuang W H, Zhang C, et al. City-level comparison of urban land-cover configurations from 2000-2015 across 65 countries within the global Belt and Road. Remote Sensing. 2019, 11(13): 1515."]}

1. This is the dataset described by the report "Global Urban Land Cover Changes in 2000-2020" submitted to Global Ecosystem and Environment Observation Analysis Research Cooperation (GEOARC). 2. The dataset includes the following subsets, i.e. global impervious surface area fraction (named as "GISA_Year_Continent.tif", like "GISA_2000_Africa.tif"), global urban green space fraction (named as "GUGS_Year_Continent.tif", like "GUGS_2000_Africa.tif"), See the description document for details. 3. The global impervious surface area and global urban green space datasets are showed by the percentage in a grid. In order for data storage, the data were amplified by 100 times and converted into integer storage. 4. The detail information of datasets is listed in Description.xlsx 5. If you have any questions, please contact Wenhui Kuang (kuangwh@igsnrr.ac.cn). 6.Related publications with this dataset. (1)Kuang W H, Du G M, Lu D S, et al. Global observation of urban expansion and land-cover dynamics using satellite big-data. Science Bulletin. 2021, 66(4): 297-300. (2)Kuang W H, Zhang S, Li X Y, et al. A 30 m resolution dataset of China’s urban impervious surface area and green space, 2000–2018. Earth System Science Data. 2021, 13(1): 63-82. (3) Kuang W H. 70 years of urban expansion across China: trajectory, pattern, and national policies. Science Bulletin. 2020, 65(23): 1970-7974. (4) Kuang W H, Dou Y Y. Investigating the patterns and dynamics of urban green space in China’s 70 major cities using satellite remote sensing. Remote Sensing. 2020, 12(12): 1929. (5) Kuang W H. National urban land-use cover change since the beginning of the 21st century and its policy implications in China. Land Use Policy. 2020, 97: 104747. (6) Kuang W H. Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences. 2019, 62(10): 1591-1606. (7) Dou Y Y, Kuang W H. A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Science of the Total Environment. 2020, 706:135828. (8) Zhang S, Fang C L, Kuang W H, et al. Comparison of changes in urban land use/cover and efficiency of megaregions in China from 1980 to 2015. Remote Sensing. 2019, 11(15): 1834. (9) Pan T, Kuang W H, Zhang C, et al. City-level comparison of urban land-cover configurations from 2000-2015 across 65 countries within the global Belt and Road. Remote Sensing. 2019, 11(13): 1515., {"references":["Kuang W H, Du G M, Lu D S, et al. Global observation of urban expansion and land-cover dynamics using satellite big-data. Science Bulletin. 2021, 66(4): 297-300.","Kuang W H, Zhang S, Li X Y, et al. A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018. Earth System Science Data. 2021, 13(1): 63-82.","Kuang W H. 70 years of urban expansion across China: trajectory, pattern, and national policies. Science Bulletin. 2020, 65(23): 1970-7974.","Kuang W H, Dou Y Y. Investigating the patterns and dynamics of urban green space in China's 70 major cities using satellite remote sensing. Remote Sensing. 2020, 12(12): 1929.","Kuang W H. National urban land-use cover change since the beginning of the 21st century and its policy implications in China. Land Use Policy. 2020, 97: 104747.","Kuang W H. Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences. 2019, 62(10): 1591-1606.","Dou Y Y, Kuang W H. A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Science of the Total Environment. 2020, 706:135828.","Zhang S, Fang C L, Kuang W H, et al. Comparison of changes in urban land use/cover and efficiency of megaregions in China from 1980 to 2015. Remote Sensing. 2019, 11(15): 1834.","Pan T, Kuang W H, Zhang C, et al. City-level comparison of urban land-cover configurations from 2000-2015 across 65 countries within the global Belt and Road. Remote Sensing. 2019, 11(13): 1515."]}

1. This is the dataset described by the report "Global Urban Land Cover Changes in 2000-2020" submitted to Global Ecosystem and Environment Observation Analysis Research Cooperation (GEOARC). 2. The dataset includes the following subsets, i.e. global urban boundary (named as "GUB_Year_Continent.tif", like "GUB_2000_Africa.tif").See the description document for details. 3. The detail information of datasets is listed in Description.xlsx 4. If you have any questions, please contact Wenhui Kuang (kuangwh@igsnrr.ac.cn). 5.Related publications with this dataset. (1)Kuang W H, Du G M, Lu D S, et al. Global observation of urban expansion and land-cover dynamics using satellite big-data. Science Bulletin. 2021, 66(4): 297-300. (2)Kuang W H, Zhang S, Li X Y, et al. A 30 m resolution dataset of China’s urban impervious surface area and green space, 2000–2018. Earth System Science Data. 2021, 13(1): 63-82. (3) Kuang W H. 70 years of urban expansion across China: trajectory, pattern, and national policies. Science Bulletin. 2020, 65(23): 1970-7974. (4) Kuang W H, Dou Y Y. Investigating the patterns and dynamics of urban green space in China’s 70 major cities using satellite remote sensing. Remote Sensing. 2020, 12(12): 1929. (5) Kuang W H. National urban land-use cover change since the beginning of the 21st century and its policy implications in China. Land Use Policy. 2020, 97: 104747. (6) Kuang W H. Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences. 2019, 62(10): 1591-1606. (7) Dou Y Y, Kuang W H. A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Science of the Total Environment. 2020, 706:135828. (8) Zhang S, Fang C L, Kuang W H, et al. Comparison of changes in urban land use/cover and efficiency of megaregions in China from 1980 to 2015. Remote Sensing. 2019, 11(15): 1834. (9) Pan T, Kuang W H, Zhang C, et al. City-level comparison of urban land-cover configurations from 2000-2015 across 65 countries within the global Belt and Road. Remote Sensing. 2019, 11(13): 1515., {"references":["Kuang W H, Du G M, Lu D S, et al. Global observation of urban expansion and land-cover dynamics using satellite big-data. Science Bulletin. 2021, 66(4): 297-300.","Kuang W H, Zhang S, Li X Y, et al. A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018. Earth System Science Data. 2021, 13(1): 63-82.","Kuang W H. 70 years of urban expansion across China: trajectory, pattern, and national policies. Science Bulletin. 2020, 65(23): 1970-7974.","Kuang W H, Dou Y Y. Investigating the patterns and dynamics of urban green space in China's 70 major cities using satellite remote sensing. Remote Sensing. 2020, 12(12): 1929.","Kuang W H. National urban land-use cover change since the beginning of the 21st century and its policy implications in China. Land Use Policy. 2020, 97: 104747.","Kuang W H. Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences. 2019, 62(10): 1591-1606.","Dou Y Y, Kuang W H. A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Science of the Total Environment. 2020, 706:135828.","Zhang S, Fang C L, Kuang W H, et al. Comparison of changes in urban land use/cover and efficiency of megaregions in China from 1980 to 2015. Remote Sensing. 2019, 11(15): 1834.","Pan T, Kuang W H, Zhang C, et al. City-level comparison of urban land-cover configurations from 2000-2015 across 65 countries within the global Belt and Road. Remote Sensing. 2019, 11(13): 1515."]}

Impervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area rema...

This paper looks at the Green for Grain Project in northern Shaanxi Province. Based on remote sensing monitoring data, this study analyzes the locations of arable land in northern Shaanxi in the years 2000, 2010 and 2013 as well as spatio-temporal changes over that period, and then incorporates data on the distribution of terraced fields to improve the input parameters of a RUSLE model and simulate and generate raster data on soil erosion for northern Shaanxi at different stages with a accuracy verification. Finally, combined with the dataset of farmland change, compared and analyzed the characteristics of soil erosion change in the converted farmland to forest (grassland) and the unconverted farmland in northern Shaanxi, so as to determine the project’s impact on soil erosion over time across the region. The results show that between 2000 and 2010, the soil erosion modulus of repur-posed farmland in northern Shaanxi decreased 22.7 t/ha, equivalent to 47.08% of the soil erosion modulus of repurposed farmland in 2000. In the same period, the soil erosion modulus of non-repurposed farmland fell 10.99 t/ha, equivalent to 28.6% of the soil erosion modulus of non-repurposed farmland in 2000. The soil erosion modulus for all types of land in northern Shaanxi decreased by an average of 14.51 t/ha between 2000 and 2010, equivalent to 41.87% of the soil erosion modulus for the entire region in 2000. This suggests that the Green for Grain Project effectively reduced the soil erosion modulus, thus helping to protect the soil. In particular, arable land that was turned into forest and grassland reduced erosion most noticeably and contributed most to soil conservation. Nevertheless, in the period 2010 to 2013, which was a period of consolidation of the Green for Grain Project, the soil erosion modulus and change in volume of soil erosion in northern Shaanxi were significantly lower than in the previous decade.

The cooling and humidifying effects of urban parks are an essential component of city ecosystems in terms of regulating microclimates or mitigating urban heat islands (UHIs). Air temperature and relative humidity are two main factors of thermal environmental comfort and have a critical impact on the urban environmental quality of human settlements. We measured the 2-m height air temperature and relative humidity at the Beijing Olympic Park and a nearby building roof for more than 1 year to elucidate seasonal variations in air temperature and relative humidity, as well as to investigate the outdoor thermal comfort. The results showed that the lawn of the park could, on average, reduce the air temperature by (0.80±0.19)°C, and increase the relative humidity by (5.24±2.91)% relative to the values measured at the building roof during daytime. During the nighttime, the lawn of the park reduced the air temperature by (2.64±0.64)°C and increased the relative humidity by (10.77±5.20)%. The park was cooler and more humid than surrounding building area, especially in night period (more pronounced cooling with 1.84 °C). Additionally, the lawn of the park could improve outdoor thermal comfort through its cooling and humidifying effects. The level of thermal comfort in the park was higher than that around the building roof for a total of 11 days annually in which it was above one or more thermal comfort levels (average reduced human comfort index of 0.92) except during the winter.

The mapping of impervious surface area (ISA) and urban green space (UGS) is essential for improving the urban environmental quality toward ecological, livable, and sustainable goals. Currently, accurate ISA and UGS products are lacking in urban areas at the global scale. This study established regression models that estimated the fraction of ISA/UGS in global 30 cities for validation using MODIS NDVI and DMSP/OLS nighttime light imageries. A global dataset of ISA and UGS fraction with a spatial resolution of 250 m×250 m was developed using the regression model, with a mean relative error of 0.19 for its ISA. The results showed the global urban area of 76.29×104 km2, which was primarily distributed in central Europe, eastern Asia, and central and eastern North America. The urban land area in North America, Europe, and Asia was 66.3×104 km2, accounting for 86.91% of the world’s urban area; the urban land area of the top 50 countries accounted for 59.32% of the total urban land area in the world. The global ISA of 45.26×104 km2 was mainly distributed in central and southern North America, eastern Asia, and Europe, as well as coastal regions around the world. The proportion of ISA situated in built-up areas on the continental scale followed the order of Africa (>70%)>South America>Oceania>Asia (>60%)>North America>Europe (>50%), and these areas were mostly in southeastern North America, southwestern Europe, and eastern and western Asia. North America, Europe, and Asia accounted for 89.44% of the world’s total UGS. The cities of developed countries in Europe and North America exposed a dramatic mosaic of ISA and UGS composites in urban construction. Therefore, the proportion of UGS is relatively high in those cities. However, in developing and underdeveloped countries, the proportion of UGS in built-up areas is relatively low, and urban environments need to be improved for livability.

Drastic rice paddy expansion and rapid upland crop loss have occurred over high latitudes in China, which would affect national food security. Different agro-ecosystems (i.e., state farms guided by the central government for agriculture and private farms guided by individual farmers for agriculture) could lead to different agricultural land use patterns; but this topic has not been investigated, which has limited our understanding of the dynamics of cropping patterns (i.e., rice paddies and upland crops) under different agro-ecosystems and their effect on total grain production. Thus, this study examined these issues over high latitudes in China. The results showed that: the developed methodology for determining cropping patterns presented high accuracy (over 90%). Based on the cropping pattern data, first, a satellite evidence of substantial increase in rice paddies with the loss of upland crops was found, and the large-scale conversion from upland crops to rice paddies has become the principal land use changes during the period of 2000–2015. Second, the new phenomenon was observed with rice paddies in state farms expanding at faster rates (at proportions of 12.98%–70.11%) than those in private farms (4.86%–30.48%). Third, the conversion of upland crops into rice paddies contributed 10.69% of the net increase in grain, which played a significant role in ensuring food security. The study provided new evidence of different changes in cropping patterns under different agro-ecosystems, thereby affecting rice cropping pattern and total grain production. This information is important for understanding and guiding the response to food sustainability and environmental issues.

Cropland redistribution to marginal land has been reported worldwide; however, the resulting impacts on environmental sustainability have not been investigated sufficiently. Here we investigated the environmental impacts of cropland redistribution in China. As a result of urbanization-induced loss of high-quality croplands in south China (∼8.5 t ha–1), croplands expanded to marginal lands in northeast (∼4.5 t ha–1) and northwest China (∼2.9 t ha–1) during 1990–2015 to pursue food security. However, the reclamation in these low-yield and ecologically vulnerable zones considerably undermined local environmental sustainability, for example increasing wind erosion (+3.47%), irrigation water consumption (+34.42%), fertilizer use (+20.02%) and decreasing natural habitats (−3.11%). Forecasts show that further reclamation in marginal lands per current policies would exacerbate environmental costs by 2050. The future cropland security risk will be remarkably intensified because of the conflict between food production and environmental sustainability. Our research suggests that globally emerging reclamation of marginal lands should be restricted and crop yield boost should be encouraged for both food security and environmental benefits., Widespread loss of high-quality cropland induced by urbanization and increasing shift of crop production to low-quality marginal land threaten food security and environmental sustainability.

China’s rural industrialization has been a major driver for its rapid economic growth during the recent decades, but its myriad environmental risks are yet to be fully understood. Based on a comprehensive national land-use data set, our study shows that the area of China’s rural industrial land (RIL) quadrupled during 1990–2015, reaching 39338 km2 in 2015, comparable to urbanization in magnitude but with a much greater degree of landscape fragmentation which implies stronger ecological and environmental impacts. About 91% of the protected areas in the central China were within 50 km from rural industrial land, thus exposed to industrial disturbances. Accelerated rural industrial land expansion, particularly in regions under high geo-hazard risks, led to dramatically increased environmental risks, threatening the safety and health of both rural industrial workers and residents. Moreover, negative effects from rural industrial land expansion could partially offset the crop production growth in recent decades. The underprivileged rural population in the west bears a disproportionally large share of the increased environmental risks. China urgently needs to design and implement sustainable policies to restrict and reshape its rural industrialization. This study aims to inspire policy makers and researchers to rethink the current model of industrial expansion and improve rural industrial land planning, which is important for achieving the sustainable development goals of China.

Urban heat island, a phenomenon that urban temperature is higher than the rural area nearby, affects directly citizens' human health and well-being. However, the cooling effect from urban green space (UGS) and the attribution of the different land processes to surface urban heat island intensity (SUHI) under different background climates remains unclear. The coarse-grained model was used to estimate summer SUHI in three different background climatic zones and for seven agglomerations (BTH, JP, LD, NAAC, NAGL, YZ, UQ). Results indicate that (1) the temperate zone had the highest daytime SUHI (0–10 °C), while the arid zone has the lowest daytime SUHI (−1–2 °C). In both temperate and cold zone, the daytime SUHI was higher than the nighttime SUHI. The SUHI in downtown was higher (more than 2 °C) than in the suburbs. (2) The increasing precipitation can enhance daytime SUHI while can weaken nighttime SUHI in all three climatic zones. The increasing temperature tends to enhance SUHI in both daytime and nighttime (exclude UQ). (3) The cooling effects of UGS in daytime SUHI were highly dependent on the background climate (cold > temperate > arid). (4) The nighttime SUHI could be effectively offset when UGSFs were greater than 0.48, 0.82, 0.97, 0.95 in NAAC, NAGL, YZ, and UQ. This article highlights the different feedback of urban green space to UHII and supports green infrastructure intervention as an effective means of reducing urban heat stress at urban agglomeration scales.

Urban impervious surface area (UISA) and urban green space (UGS) are two core components of cities for characterizing urban environments. Although several global or national urban land use/cover products such as Globeland30 and FROM-GLC are available, they cannot effectively delineate the complex intra-urban land cover components. Here we proposed a new approach to map fractional UISA and UGS in China using Google Earth Engine (GEE) based on multiple data sources. The first step is to extract the vector boundaries of urban areas from China's Land Use/cover Dataset (CLUD). The UISA was retrieved using the logistic regression from the Landsat-derived annual maximum Normalized Difference Vegetation Index (NDVI). The UGS was developed through linear calibration between reference UGS from high spatial resolution image and the normalized NDVI. Thus, the China's UISA and UGS fraction datasets (CLUD-Urban) at 30-meter resolution are generated from 2000 to 2018. The overall accuracy of national urban areas is over 92 %. The root mean square errors of UISA and UGS fractions are 0.10 and 0.14, respectively. The datasets indicate that total urban area of China was 7.10 ×104 km2 in 2018, with average fractions of 70.70 % for UISA and 26.54 % for UGS. The UISA and UGS increased with unprecedented annual rates of 1,492.63 km2/yr and 400.43 km2/yr during 2000–2018. CLUD-Urban can enhance our understanding of urbanization impacts on ecological and urban dwellers’ environments, and can be used in such applications as urban planning, urban environmental studies and practices. The datasets can be downloaded from https://doi.org/10.5281/zenodo.3778424 (Kuang et al., 2020).

Urban green space (UGS) plays a pivotal role in improving urban ecosystem services and building a livable environment for urban dwellers. However, remotely sensed investigation of UGS at city scale is facing a challenge due to the pixels’ mosaics of buildings, squares, roads and green spaces in cities. Here we developed a new algorithm to unmix the UGSF derived from Landsat TM/ETM/8 OLI using a big-data platform. The spatiotemporal patterns and dynamics of UGSs were examined for 70 major cities in China between 2000 and 2018. The results showed that the total area of UGS in these cities grew from 2780.66 km2 in 2000 to 6764.75 km2 in 2018, which more than doubled its area. As a result, the UGS area per inhabitant rose from 15.01 m2 in 2000 to 18.09 m2 in 2018. However, an uneven layout of UGS occurred among the coastal, western, northeastern and central zones. For example, the UGS percentage in newly expanded urban areas in the coastal zone rose significantly in 2000–2018, with an increase of 2.51%, compared to the decline in UGS in cities in the western zone. Therefore, the effective strategies we have developed should be adopted to show disparities and promote green infrastructure capacity building in those cities with less green space, especially in western China.

Climate change and rapid urbanization have severe impacts on urban flood regulation ecosystem services (UFRES). Quantifying the UFRES has attracted increasing attention for urban sustainable development. However, few studies have focused on how to identify urban flood regulation priority areas. In this study, we simulated urban surface runoff by using the soil conservation services-curve number model, and quantified UFRES supply and demand by using relative indicators (i.e., runoff reduction ratio and urban vulnerability) at the subdistrict scale in Beijing, China. Then, an urban flood regulation priority index was developed by integrating UFRES demand and supply, and further used to identify priority areas. The results show that the mean runoff reduction ratio in Beijing decreased from 38.70% (for a 1-year rainfall return period) to 24.74% (for a 100-year rainfall return period). Subdistricts with low UFRES supply were mainly located in the urban central area and the southeastern zone, while subdistricts with high UFRES demand were mainly located in the urban central region. Meanwhile, places with high priority for flood regulation were mainly located in the inner city, and low priority areas were mainly located in northwestern, southwestern, and northeastern Beijing. Our results also imply that the urban flood regulation priority index is an effective indicator to identify urban flood regulation priority areas. These findings could provide urban planners with a comprehensive understanding of UFRES and scientific guidance to improve them.

The recent acute evolution of cropland structure in Cold China could lead to rapid rice paddy expansion, potentially altering land-surface thermal processes and influencing climate. To address the issue, this study investigated the changes in cropland type, land-surface temperature (LST) and heat fluxes in the agricultural region of Cold China during 2000–2015 based on time-series of land-use data and MODIS LST product, using the split-window algorithms (SWA) model and the pixel component arranging and component algorithm (PCACA). The investigation revealed large-scale land transformation from rain-fed farmland to paddy field in Cold China during 2000–2015. Compared to the rain-fed farmland, lower LST was observed in paddy field throughout crop growing seasons, with the highest LST threshold found in June (7.17 ± 1.05 °C) and the lowest value found in August (1.04 ± 0.35 °C). The cooling effect of paddy-field ranged from 0.59 ± 0.06 °C, 0.77 ± 0.07 °C, and 1.08 ± 0.08 °C for the low-, medium-, and high-density paddies, respectively. Compared to other months, stronger cooling effect was found in May and June. Further analysis showed the conversion of a rain-fed farmland to paddy field reduced the sensible heat flux and soil heat flux by 52.94 W/m2 and 15.26 W/m2, respectively, while increased the latent heat flux and net radiation by 115.66 W/m2 and 47.34 W/m2, respectively. The findings from this study indicated the changes in cropland structure and management regime (e.g., irrigation) could profoundly modify land-surface thermal processes and local/regional climate, interfering the signals from global warming. Therefore, instrumental climate data that collected from areas experienced large-scale conversion between rain-fed and paddy farmland should be carefully screened and corrected to prevent land-use induced biases.

As an effect of climate change, cities need detailed information on urban climates at decision scale that cannot be easily delivered using current observation networks, nor global and even regional climate models. A review is presented of the recent literature and recommendations are formulated for future work. In most cities, historical observational records are too short, discontinuous, or of too poor quality to support trend analysis and climate change attribution. For climate modeling, on the other hand, specific dynamical and thermal parameterization dedicated to the exchange of water and energy between the atmosphere and the urban surfaces have to be implemented. Therefore, to fully understand how cities are impacted by climate change, it is important to have (1) simulations of the urban climate at fine spatial scales (including coastal hazards for coastal cities) integrating global climate scenarios with urban expansion and population growth scenarios and their associated uncertainty estimates, (2) urban climate observations, especially in Global South cities, and (3) spatial data of high resolution on urban structure and form, human behavior, and energy consumption.

Metropolitan Beijing is facing many environmental problems such as haze and urban heat island due to the rapid urbanization. Surface shortwave, longwave, and net radiations are key components of th...

In order to increase of husbandry production and to have a sustainable development of pastures, it is important to accelerate the construction of ecological grass-animal husbandry, to promote the grassland ecological carrying capacity and to reach the equilibrium in the grassland-livestock balance. We chose twenty-four farms and ranches in Hulunbuir Agricultural Reclamation Group as study area, satellite imagery from 500-m MODIS, 30-m Landsat, and 4-m GF-2 are used together with annual report data of farms and ranches. We mapped the spatial distribution and the status of degradation of grassland and we computed the net primary productivity (NPP) of farms and ranches in Hulunbuir Agricultural Reclamation Group, using remote sensing images as input data in a Vegetation Photosynthesis Model (VPM). We then analyzed the carrying capacity of grassland and its over-grazing degree and proposed the construction of artificial grassland mode of ecological grass-animal husbandry in farms and ranches. We found that about 66.16% of natural grassland has been subject to serious degradation in different degrees in the twenty-four farms and ranches, mainly manifested in the decline of grassland vegetation productivity and coverage. The grassland fragmentation area was about 1.55% of the total grassland area. Based on remote sensing the result that annual average grassland yield was about 34.07×104 t, ranging from 27.43×104 t in 2001 to 48.89×104 t in 2013 of farms and ranches in Hulunbuir Agricultural Reclamation Group. The actual grazing capacity was 164.19×104 sheep units while the theoretical grazing capacity was only 86.83×104 sheep units in Hulunbuir Agricultural Reclamation Group in 2015. The forage gap was 42.35×104 t and overload rate reached 89% without the consideration of the silage grass and purchased forage. The cultivated land area is 3.95×103 km2 in Hulunbuir Agricultural Reclamation Group. Converting 10% of the cultivated land area into artificial grassland could provide grass yield amounting to 5.86×103 km2 of the natural grassland, which would meet with the forage demand of livestock in Hulunbuir Agricultural Reclamation Group. The implementation of the Grain-Warp-Feed and the Grass-Crop rotation production mode, and the establishment of artificial grassland on cultivated land can improve the grassland quality, grassland-livestock balance, and reduce grassland degradation, which is critical to promoting the ability of the sustainable development of husbandry and ecological protection in pastures. As a measure of agricultural supply side reform, the strategy of transforming grain into forage aims to achieve effective allocation of resources.