In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest a...In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest access. Urban Heat Island Effects are measurable phenomenon that are being experienced by the world’s most urbanized areas, including increased summer high temperatures and lower evapotranspiration from having impervious surfaces instead of vegetation and trees. Tree canopy cover is our natural mitigation tool that absorbs sunlight for photosynthesis, protects humans from incoming radiation, and releases cooling moisture into the air. Unfortunately, urban areas typically have low levels of vegetation. Vulnerable urban communities are lower-income areas of inner cities with less access to heat protection like air conditioners. This study uses mean evapotranspiration levels to assess the variability of urban heat island effects across the state of Tennessee. Results show that increased developed land surface cover in Tennessee creates measurable changes in atmospheric evapotranspiration. As a result, the mean evapotranspiration levels in areas with less tree vegetation are significantly lower than the surrounding forested areas. Central areas of urban cities in Tennessee had lower mean evapotranspiration recordings than surrounding areas with less development. This work demonstrates the need for increased tree canopy coverage.展开更多
Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this ...Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this study the impact of using two traditional land use classifications, the United States Geological Survey (USGS) and the Moderate-resolution Imaging Spectroradiometer (MODIS), were evaluated. The Weather Research and Forecasting model (WRF, version 3.2.1) was run for the period 18 - 22 August, 2014 (dry season) at a grid spacing of 3 km centered on the city of Manaus. The comparison between simulated and ground-based observed data revealed significant differences in the meteorological fields, for instance, the temperature. Compared to USGS, MODIS classification showed better skill in representing observed temperature for urban areas of Manaus, while the two files showed similar results for nearby areas. The analysis of the files suggests that the better quality of the simulations favorable to the MODIS file is straightly related to its better representation of urban class of land use, which is observed to be not adequately represented by USGS.展开更多
Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010....Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010. The rapid growth in urban population and urban areas in Sri Lanka may cause serious socioeconomic disparities, if they are not handled properly. Thus, planners in Sri Lanka are in need of information about past and future urban growth patterns to plan a better and sustainable urban future for Sri Lanka. In this paper, we analyzed the characteristics of past land use and land cover trends in Matara City of Sri Lanka from 1980 to 2010 to assess the historic urban dynamics. The land use change detection analysis based on remote sensing datasets reveal that the conversion of homestead/garden and paddy into urban land is evident in Matara City. The historic urban trends are projected into the near future by using SLEUTH urban growth model to identify the hot spots of future urbanization and as well as the urban growth patterns in Matara City up to the basic administrative level, i.e., Grama Niladari Divisions(GND). The urban growth simulations for the year 2030 reveal that 29 GNDs out of 66 GNDs in Matara City will be totally converted into urban land. Whereas, 28 GNDs will have urban land cover from 75% to 99% by 2030. The urban growth simulations are further analyzed with respect to the proposed Matara city development plan by the Urban Development Authority(UDA) of Sri Lanka. The results show that the UDA's city development plan of Matara will soon be outpaced by rapid urbanization. Based on the calibration and validation results, the SLEUTH model proved to be a useful planning tool to understand the near future urbanization of Sri Lankan cities.展开更多
城市用地空间扩张对生态环境的影响映射出人类社会活动和生态环境保护之间的交互作用,系统地研究城市空间无序蔓延所诱发的城市土地利用方式变化对城市生态环境的影响程度,对助推中国生态文明建设目标具有重要现实意义。为探究合肥市城...城市用地空间扩张对生态环境的影响映射出人类社会活动和生态环境保护之间的交互作用,系统地研究城市空间无序蔓延所诱发的城市土地利用方式变化对城市生态环境的影响程度,对助推中国生态文明建设目标具有重要现实意义。为探究合肥市城市扩张对生态安全格局的影响程度,综合运用生态遥感指数、最小累积阻力模型、电路理论和斑块生成土地利用模拟模型,构建合肥市生态安全格局,识别生态夹点和生态障碍点,再从模拟验证的基础上(总体精度为94.71%,Kappa系数为90.04%,Fom值为0.102),预测了2030—2040年的城市扩张,并根据预测结果探讨城市扩张对区域生态安全格局影响程度。研究发现:合肥市生态环境质量整体呈现南高中低的分布格局,识别出合肥市生态源地共计35处,源地间活跃生态廊道70条,非活跃廊道共17条,生态夹点290个,生态障碍点112个。2020—2040年合肥市城乡、工矿居民用地、林地、水域和未利用土地面积将不断增加,而耕地以及草地面积将持续减少。2020—2040年期间城镇建成区分别侵占了生态廊道、源地、夹点、障碍点面积为55.95、10.51、1.04、1.35 km 2。研究结果可为今后快速发展城市的生态环境治理和国土空间生态保护修复工作提供理论依据和技术参考。展开更多
土地利用和土地覆盖变化(Land Use and Land Cover Chang,LUCC)通过影响局地陆面过程及陆气相互作用进而影响局地天气和气候。为探究LUCC产品对陆气相互作用的影响,本文采用了三套LUCC产品,包括USGS、Landsat和MODIS,模拟研究不同LUCC...土地利用和土地覆盖变化(Land Use and Land Cover Chang,LUCC)通过影响局地陆面过程及陆气相互作用进而影响局地天气和气候。为探究LUCC产品对陆气相互作用的影响,本文采用了三套LUCC产品,包括USGS、Landsat和MODIS,模拟研究不同LUCC产品对华东地区土壤和近地面温度、湿度的影响。结果表明,不同LUCC产品的土地利用类型差异主要在城市、农田和以草地、森林为主的自然植被。与USGS产品相比,Landsat和MODIS产品的城市和森林面积分别增加了2%和15%以上,农田面积则减少了17%左右。模拟结果表明,Landsat和MODIS产品的城市面积增加导致该区域的土壤温度和湿度增加,感热通量分别增加了28.1 W·m^(-2)、68.3 W·m^(-2),潜热通量分别减少了28.3 W·m^(-2)、81 W·m^(-2),这使得2 m气温增加了1.5℃左右,相对湿度减小了约9%。USGS产品中的农田和草地在Landsat和MODIS中改变为森林也使得土壤温度、湿度和近地面能量通量、温度和湿度的空间分布随之产生变化,但相比于城市面积改变导致的变化较为复杂。此外,不同LUCC产品之间的城市面积变化对土壤温度、湿度和近地面能量通量、温度和湿度的影响要大于农田和自然植被变化产生的影响。最后,对比三个试验模拟的土壤温度、土壤湿度、2 m气温和相对湿度结果与GLDAS(the Global Land Data Assimilation System)或站点观测资料的相关性、均方根误差、平均偏差和认同指数可以发现,使用更准确、细致的Landsat产品的模式对近地面气象条件的模拟性能要优于USGS和MODIS产品模拟结果。展开更多
Urban expansion is a hot topic in land use/land cover change(LUCC)researches.In this paper,maximum entropy model and cellular automata(CA)model are coupled into a new CA model(Maxent-CA)for urban expansion.This model ...Urban expansion is a hot topic in land use/land cover change(LUCC)researches.In this paper,maximum entropy model and cellular automata(CA)model are coupled into a new CA model(Maxent-CA)for urban expansion.This model can help to obtain transition rules from single-period dataset.Moreover,it can be constructed and calibrated easily with several steps.Firstly,Maxent-CA model was built by using remote sensing data of China in 2000(basic data)and spatial variables(such as population density and Euclidean distance to cities).Secondly,the proposed model was calibrated by analyzing training samples,neighborhood structure and spatial scale.Finally,this model was verified by comparing logistic regression CA model and their simulation results.Experiments showed that suitable sampling ratio(sampling ratio equals the proportion of urban land in the whole region)and von Neumann neighborhood structure will help to yield better results.Spatial structure of simulation results becomes simple as spatial resolution decreases.Besides,simulation accuracy is significantly affected by spatial resolution.Compared to simulation results of logistic regression CA model,Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation.It is found that the proposed model performs well in simulating urban expansion of China.It will be helpful for simulating even larger study area in the background of global environment changes.展开更多
文摘In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest access. Urban Heat Island Effects are measurable phenomenon that are being experienced by the world’s most urbanized areas, including increased summer high temperatures and lower evapotranspiration from having impervious surfaces instead of vegetation and trees. Tree canopy cover is our natural mitigation tool that absorbs sunlight for photosynthesis, protects humans from incoming radiation, and releases cooling moisture into the air. Unfortunately, urban areas typically have low levels of vegetation. Vulnerable urban communities are lower-income areas of inner cities with less access to heat protection like air conditioners. This study uses mean evapotranspiration levels to assess the variability of urban heat island effects across the state of Tennessee. Results show that increased developed land surface cover in Tennessee creates measurable changes in atmospheric evapotranspiration. As a result, the mean evapotranspiration levels in areas with less tree vegetation are significantly lower than the surrounding forested areas. Central areas of urban cities in Tennessee had lower mean evapotranspiration recordings than surrounding areas with less development. This work demonstrates the need for increased tree canopy coverage.
基金This work received funding support from CNPq(National Counsel of Technological and Scientific Development,process 404104/2013-4)CAPES(Coordination for the Improvement of Higher Education Personnel)and Araucária Foundation
文摘Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this study the impact of using two traditional land use classifications, the United States Geological Survey (USGS) and the Moderate-resolution Imaging Spectroradiometer (MODIS), were evaluated. The Weather Research and Forecasting model (WRF, version 3.2.1) was run for the period 18 - 22 August, 2014 (dry season) at a grid spacing of 3 km centered on the city of Manaus. The comparison between simulated and ground-based observed data revealed significant differences in the meteorological fields, for instance, the temperature. Compared to USGS, MODIS classification showed better skill in representing observed temperature for urban areas of Manaus, while the two files showed similar results for nearby areas. The analysis of the files suggests that the better quality of the simulations favorable to the MODIS file is straightly related to its better representation of urban class of land use, which is observed to be not adequately represented by USGS.
文摘Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010. The rapid growth in urban population and urban areas in Sri Lanka may cause serious socioeconomic disparities, if they are not handled properly. Thus, planners in Sri Lanka are in need of information about past and future urban growth patterns to plan a better and sustainable urban future for Sri Lanka. In this paper, we analyzed the characteristics of past land use and land cover trends in Matara City of Sri Lanka from 1980 to 2010 to assess the historic urban dynamics. The land use change detection analysis based on remote sensing datasets reveal that the conversion of homestead/garden and paddy into urban land is evident in Matara City. The historic urban trends are projected into the near future by using SLEUTH urban growth model to identify the hot spots of future urbanization and as well as the urban growth patterns in Matara City up to the basic administrative level, i.e., Grama Niladari Divisions(GND). The urban growth simulations for the year 2030 reveal that 29 GNDs out of 66 GNDs in Matara City will be totally converted into urban land. Whereas, 28 GNDs will have urban land cover from 75% to 99% by 2030. The urban growth simulations are further analyzed with respect to the proposed Matara city development plan by the Urban Development Authority(UDA) of Sri Lanka. The results show that the UDA's city development plan of Matara will soon be outpaced by rapid urbanization. Based on the calibration and validation results, the SLEUTH model proved to be a useful planning tool to understand the near future urbanization of Sri Lankan cities.
文摘城市用地空间扩张对生态环境的影响映射出人类社会活动和生态环境保护之间的交互作用,系统地研究城市空间无序蔓延所诱发的城市土地利用方式变化对城市生态环境的影响程度,对助推中国生态文明建设目标具有重要现实意义。为探究合肥市城市扩张对生态安全格局的影响程度,综合运用生态遥感指数、最小累积阻力模型、电路理论和斑块生成土地利用模拟模型,构建合肥市生态安全格局,识别生态夹点和生态障碍点,再从模拟验证的基础上(总体精度为94.71%,Kappa系数为90.04%,Fom值为0.102),预测了2030—2040年的城市扩张,并根据预测结果探讨城市扩张对区域生态安全格局影响程度。研究发现:合肥市生态环境质量整体呈现南高中低的分布格局,识别出合肥市生态源地共计35处,源地间活跃生态廊道70条,非活跃廊道共17条,生态夹点290个,生态障碍点112个。2020—2040年合肥市城乡、工矿居民用地、林地、水域和未利用土地面积将不断增加,而耕地以及草地面积将持续减少。2020—2040年期间城镇建成区分别侵占了生态廊道、源地、夹点、障碍点面积为55.95、10.51、1.04、1.35 km 2。研究结果可为今后快速发展城市的生态环境治理和国土空间生态保护修复工作提供理论依据和技术参考。
文摘土地利用和土地覆盖变化(Land Use and Land Cover Chang,LUCC)通过影响局地陆面过程及陆气相互作用进而影响局地天气和气候。为探究LUCC产品对陆气相互作用的影响,本文采用了三套LUCC产品,包括USGS、Landsat和MODIS,模拟研究不同LUCC产品对华东地区土壤和近地面温度、湿度的影响。结果表明,不同LUCC产品的土地利用类型差异主要在城市、农田和以草地、森林为主的自然植被。与USGS产品相比,Landsat和MODIS产品的城市和森林面积分别增加了2%和15%以上,农田面积则减少了17%左右。模拟结果表明,Landsat和MODIS产品的城市面积增加导致该区域的土壤温度和湿度增加,感热通量分别增加了28.1 W·m^(-2)、68.3 W·m^(-2),潜热通量分别减少了28.3 W·m^(-2)、81 W·m^(-2),这使得2 m气温增加了1.5℃左右,相对湿度减小了约9%。USGS产品中的农田和草地在Landsat和MODIS中改变为森林也使得土壤温度、湿度和近地面能量通量、温度和湿度的空间分布随之产生变化,但相比于城市面积改变导致的变化较为复杂。此外,不同LUCC产品之间的城市面积变化对土壤温度、湿度和近地面能量通量、温度和湿度的影响要大于农田和自然植被变化产生的影响。最后,对比三个试验模拟的土壤温度、土壤湿度、2 m气温和相对湿度结果与GLDAS(the Global Land Data Assimilation System)或站点观测资料的相关性、均方根误差、平均偏差和认同指数可以发现,使用更准确、细致的Landsat产品的模式对近地面气象条件的模拟性能要优于USGS和MODIS产品模拟结果。
基金supported by the National Key R&D Program of China(Grant No.2017YFA0604404)the National Natural Science Foundation of China(Grant Nos.41801304&41871306)Educational Commission of Guangdong Province of China(Grant No.2016KTSCX045).
文摘Urban expansion is a hot topic in land use/land cover change(LUCC)researches.In this paper,maximum entropy model and cellular automata(CA)model are coupled into a new CA model(Maxent-CA)for urban expansion.This model can help to obtain transition rules from single-period dataset.Moreover,it can be constructed and calibrated easily with several steps.Firstly,Maxent-CA model was built by using remote sensing data of China in 2000(basic data)and spatial variables(such as population density and Euclidean distance to cities).Secondly,the proposed model was calibrated by analyzing training samples,neighborhood structure and spatial scale.Finally,this model was verified by comparing logistic regression CA model and their simulation results.Experiments showed that suitable sampling ratio(sampling ratio equals the proportion of urban land in the whole region)and von Neumann neighborhood structure will help to yield better results.Spatial structure of simulation results becomes simple as spatial resolution decreases.Besides,simulation accuracy is significantly affected by spatial resolution.Compared to simulation results of logistic regression CA model,Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation.It is found that the proposed model performs well in simulating urban expansion of China.It will be helpful for simulating even larger study area in the background of global environment changes.
基金Chinese R&D Program of "Development of a comprehensive monitoring and evaluation system for ecological compensation of typical ecologically vulnerable regions of China (2006BAC08B06)"National Science Fund for Distinguished Young Scholars (40788001)One Hundred Talents Program of the Chinese Academy of Sciences
基金The National Natural Science Foundation of China(31470518)The Project Supported by Institute of Culture and Tourism Development of Beijing Technology and Business University(202106104)。