Under the influence of anthropogenic and climate change,the problems caused by urban heat island(UHI)has become increasingly prominent.In order to promote urban sustainable development and improve the quality of human...Under the influence of anthropogenic and climate change,the problems caused by urban heat island(UHI)has become increasingly prominent.In order to promote urban sustainable development and improve the quality of human settlements,it is significant for exploring the evolution characteristics of urban thermal environment and analyzing its driving forces.Taking the Landsat series images as the basic data sources,the winter land surface temperature(LST)of the rapid urbanization area of Fuzhou City in China was quantitatively retrieved from 2001 to 2021.Combing comprehensively the standard deviation ellipse model,profile analysis and GeoDetector model,the spatio-temporal evolution characteristics and influencing factors of the winter urban thermal environment were systematically analyzed.The results showed that the winter LST presented an increasing trend in the study area during 2001–2021,and the winter LST of the central urban regions was significantly higher than the suburbs.There was a strong UHI effect from 2001 to 2021with an expansion trend from the central urban regions to the suburbs and coastal areas in space scale.The LST of green lands and wetlands are significantly lower than croplands,artificial surface and unvegetated lands.Vegetation and water bodies had a significant mitigation effect on UHI,especially in the micro-scale.The winter UHI had been jointly driven by the underlying surface and socio-economic factors in a nonlinear or two-factor interactive enhancement mode,and socio-economic factors had played a leading role.This research could provide data support and decision-making references for rationally planning urban layout and promoting sustainable urban development.展开更多
The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods,changing the landscape of regions outside the city an...The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods,changing the landscape of regions outside the city and fostering the growth of physical infrastructure.Using multi-temporal satellite images,the dynamics of Land Use/Land Cover(LULC)changes,the impact of urban growth on LULC changes,and regional environmental implications were investigated in the peri-urban and rural neighbourhoods of Durgapur Municipal Corporation in India.The study used different case studies to highlight the study area’s heterogeneity,as the phenomenon of change is not consistent.Landsat TM and OLI-TIRS satellite images in 1991,2001,2011,and 2021 were used to analyse the changes in LULC types.We used the relative deviation(RD),annual change intensity(ACI),uniform intensity(UI)to show the dynamicity of LULC types(agriculture land;built-up land;fallow land;vegetated land;mining area;and water bodies)during 1991-2021.This study also applied the Decision-Making Trial and Evaluation Laboratory(DEMATEL)to measure environmental sensitivity zones and find out the causes of LULC changes.According to LULC statistics,agriculture land,built-up land,and mining area increased by 51.7,95.46,and 24.79 km^(2),respectively,from 1991 to 2021.The results also suggested that built-up land and mining area had the greatest land surface temperature(LST),whereas water bodies and vegetated land showed the lowest LST.Moreover,this study looked at the relationships among LST,spectral indices(Normalized Differenced Built-up Index(NDBI),Normalized Difference Vegetation Index(NDVI),and Normalized Difference Water Index(NDWI)),and environmental sensitivity.The results showed that all of the spectral indices have the strongest association with LST,indicating that built-up land had a far stronger influence on the LST.The spectral indices indicated that the decreasing trends of vegetated land and water bodies were 4.26 and 0.43 km^(2)/a,respectively,during 1991-2021.In summary,this study can help the policy-makers to predict the increasing rate of temperature and the causes for the temperature increase with the rapid expansion of built-up land,thus making effective peri-urban planning decisions.展开更多
近30年来,复杂的气候变化与剧烈的人类活动造成江苏省海岸带生态演变剧烈,且呈现显著的空间异质性。植被净初级生产力(NPP)和地表温度(LST)是生态系统的2个关键参数,通过将1990−2020年Landsat遥感影像与CASA计算模型和相关性分析等方法...近30年来,复杂的气候变化与剧烈的人类活动造成江苏省海岸带生态演变剧烈,且呈现显著的空间异质性。植被净初级生产力(NPP)和地表温度(LST)是生态系统的2个关键参数,通过将1990−2020年Landsat遥感影像与CASA计算模型和相关性分析等方法结合,分析了江苏海岸带NPP和LST的时空变化及影响因素,结果表明:①由于人类对沿海滩涂资源的利用以及养殖业的发展等,江苏海岸带范围随岸线不断变化,岸线逐步向海推进,且南部向海推进范围大于北部。②近30年来,江苏海岸带NPP和LST呈现出显著的时空异质性特征。时间上1990、2000、2010、2020年代的NPP月均值分别为102.88、88.23、156.62、98.90 g C·m^(−2),呈现下降-上升-下降趋势,而LST月均值分别为32.6、31.7、28.3、37.6℃,呈现先下降后上升的趋势。空间上,NPP与LST在江苏海岸带南北分布呈现出一定差异性。③地表覆盖类型是影响江苏海岸带NPP和LST时空异质性的主要因素。林地的NPP最高,养殖池塘NPP最低;人工建筑的LST值最高,湿地、水域与养殖池塘的LST值相对较低。此外,随着气温升高,NPP和LST有逐渐上升的趋势,而植被覆盖度的升高则导致NPP上升和LST下降。展开更多
Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather sta...Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather station networks is insufficient,especially in sparsely populated regions,greatly limiting the accuracy of estimates of spatially distributed Ta.Due to their continuous spatial coverage,remotely sensed land surface temperature(LST)data provide the possibility of exploring spatial estimates of Ta.However,because of the complex interaction of land and climate,retrieval of Ta from the LST is still far from straightforward.The estimation accuracy varies greatly depending on the model,particularly for maximum Ta.This study estimated monthly average daily minimum temperature(Tmin),average daily maximum temperature(Tmax)and average daily mean temperature(Tmean)over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer(MODIS)LST data(MYD11A2)and some auxiliary data using an artificial neural network(ANN)model.The data from 2003 to 2010 were used to train the ANN models,while 2011 to 2012 weather station temperatures were used to test the trained model.The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean,with root mean square errors(RMSEs)of 1.04℃ and 1.01℃,respectively.Moreover,the best RMSE of Tmax estimation was 1.27℃.Compared with the other two published Ta gridded datasets,the produced 1 km×1 km dataset accurately captured both the temporal and spatial patterns of Ta.The RMSE of Tmin estimation was more sensitive to elevation,while that of Tmax was more sensitive to month.Except for land cover type as the input variable,which reduced the RMSE by approximately 0.01℃,the other vegetation-related variables did not improve the performance of the model.The results of this study indicated that ANN,a type of machine learning method,is effective for long-term and large-scale Ta estimation.展开更多
Local climate zones(LCZs)are an effective nexus linking internal urban structures to the local climate and have been widely used to study urban thermal environment.However,few studies considered how much the temperatu...Local climate zones(LCZs)are an effective nexus linking internal urban structures to the local climate and have been widely used to study urban thermal environment.However,few studies considered how much the temperature changed due to LCZs transformation and their synergy.This paper quantified the change of urban land surface temperature(LST)in LCZs transformation process by combining the land use transfer matrix with zonal statistics method during 2000–2019 in the Xi’an metropolitan.The results show that,firstly,both LCZs and LST had significant spatiotemporal variations and synchrony.The period when the most LCZs were converted was also the LST rose the fastest,and the spatial growth of the LST coincided with the spatial expansion of the built type LCZs.Secondly,the LST difference between land cover type LCZs and built type LCZs gradually widened.And LST rose more in both built type LCZs transferred in and out.Finally,the Xi’an-Xianyang profile showed that the maximum temperature difference between the peaks and valleys of the LST increased by 4.39℃,indicating that localized high temperature phenomena and fluctuations in the urban thermal environment became more pronounced from 2000 to 2019.展开更多
With the rapid development of urban agglomerations in northwest arid and semiarid regions of China, the scope of the urban heat island(UHI) effect has gradually expanded and gradually connected, and has formed a regio...With the rapid development of urban agglomerations in northwest arid and semiarid regions of China, the scope of the urban heat island(UHI) effect has gradually expanded and gradually connected, and has formed a regional heat island(RHI) with a larger range of impact to the regional environment. However, there are few studies on the heat island effect of urban agglomerations in arid and semiarid regions, so this paper selects the urban agglomeration of Hohhot, Baotou and Ordos(HBO) of Inner Mongolia, China as the study area. Based on the 8-day composite Moderate-resolution Imaging Spectroradiometer(MODIS) surface temperature data(156scenes in all) and land use maps for 2005, 2010, and 2015, we analyze the spatiotemporal distributions of regional heat(cool) islands(RH(C)I) and the responses of surface temperatures to land-use changes in the diurnal and interannual surface cities. The results showed that: 1) from 2005 to 2015, urban areas showed the cold island effect during the day, with the area of the cold island showing a shrinking feature;at night, they showed the heat island effect, with the area of the heat island showing a first decrease and then an increase.2) From 2005 to 2015, the land development(unutilized land to building land) brings the greatest temperature increase(ΔT = 1.36°C)during the day, while the greatest temperature change at night corresponds to the conversion of cultivated land to building land(ΔT =0.78°C) exhibited the largest changes at night. From 2010 to 2015, the land development(grassland to building land) bring the greatest temperature increase(ΔT = 0.85°C) during the day, while the great temperature change at night corresponds to the conversion of water areas to building land(ΔT = 1.38°C) exhibited the largest changes at night. Exploring the spatial and temporal evolution of surface urban heat(cool) islands in urban agglomerations in arid and semiarid regions will help to understand the urbanization characteristics of urban agglomerations and provide a reference for the formulation of policies for the coordinated and healthy development of the region and co-governance of regional environmental problems.展开更多
基金Under the auspices of the Social Science and Humanity on Young Fund of the Ministry of Education of China(No.21YJCZH100)the Scientific Research Project on Outstanding Young of the Fujian Agriculture and Forestry University(No.XJQ201920)+1 种基金the Science and Technology Innovation Special Fund Project of Fujian Agriculture and Forestry University(No.CXZX2021032)the Forestry Peak Discipline Construction Project of Fujian Agriculture and Forestry University(No.72202200205)。
文摘Under the influence of anthropogenic and climate change,the problems caused by urban heat island(UHI)has become increasingly prominent.In order to promote urban sustainable development and improve the quality of human settlements,it is significant for exploring the evolution characteristics of urban thermal environment and analyzing its driving forces.Taking the Landsat series images as the basic data sources,the winter land surface temperature(LST)of the rapid urbanization area of Fuzhou City in China was quantitatively retrieved from 2001 to 2021.Combing comprehensively the standard deviation ellipse model,profile analysis and GeoDetector model,the spatio-temporal evolution characteristics and influencing factors of the winter urban thermal environment were systematically analyzed.The results showed that the winter LST presented an increasing trend in the study area during 2001–2021,and the winter LST of the central urban regions was significantly higher than the suburbs.There was a strong UHI effect from 2001 to 2021with an expansion trend from the central urban regions to the suburbs and coastal areas in space scale.The LST of green lands and wetlands are significantly lower than croplands,artificial surface and unvegetated lands.Vegetation and water bodies had a significant mitigation effect on UHI,especially in the micro-scale.The winter UHI had been jointly driven by the underlying surface and socio-economic factors in a nonlinear or two-factor interactive enhancement mode,and socio-economic factors had played a leading role.This research could provide data support and decision-making references for rationally planning urban layout and promoting sustainable urban development.
文摘The availability of better economic possibilities and well-connected transportation networks has attracted people to migrate to peri-urban and rural neighbourhoods,changing the landscape of regions outside the city and fostering the growth of physical infrastructure.Using multi-temporal satellite images,the dynamics of Land Use/Land Cover(LULC)changes,the impact of urban growth on LULC changes,and regional environmental implications were investigated in the peri-urban and rural neighbourhoods of Durgapur Municipal Corporation in India.The study used different case studies to highlight the study area’s heterogeneity,as the phenomenon of change is not consistent.Landsat TM and OLI-TIRS satellite images in 1991,2001,2011,and 2021 were used to analyse the changes in LULC types.We used the relative deviation(RD),annual change intensity(ACI),uniform intensity(UI)to show the dynamicity of LULC types(agriculture land;built-up land;fallow land;vegetated land;mining area;and water bodies)during 1991-2021.This study also applied the Decision-Making Trial and Evaluation Laboratory(DEMATEL)to measure environmental sensitivity zones and find out the causes of LULC changes.According to LULC statistics,agriculture land,built-up land,and mining area increased by 51.7,95.46,and 24.79 km^(2),respectively,from 1991 to 2021.The results also suggested that built-up land and mining area had the greatest land surface temperature(LST),whereas water bodies and vegetated land showed the lowest LST.Moreover,this study looked at the relationships among LST,spectral indices(Normalized Differenced Built-up Index(NDBI),Normalized Difference Vegetation Index(NDVI),and Normalized Difference Water Index(NDWI)),and environmental sensitivity.The results showed that all of the spectral indices have the strongest association with LST,indicating that built-up land had a far stronger influence on the LST.The spectral indices indicated that the decreasing trends of vegetated land and water bodies were 4.26 and 0.43 km^(2)/a,respectively,during 1991-2021.In summary,this study can help the policy-makers to predict the increasing rate of temperature and the causes for the temperature increase with the rapid expansion of built-up land,thus making effective peri-urban planning decisions.
文摘近30年来,复杂的气候变化与剧烈的人类活动造成江苏省海岸带生态演变剧烈,且呈现显著的空间异质性。植被净初级生产力(NPP)和地表温度(LST)是生态系统的2个关键参数,通过将1990−2020年Landsat遥感影像与CASA计算模型和相关性分析等方法结合,分析了江苏海岸带NPP和LST的时空变化及影响因素,结果表明:①由于人类对沿海滩涂资源的利用以及养殖业的发展等,江苏海岸带范围随岸线不断变化,岸线逐步向海推进,且南部向海推进范围大于北部。②近30年来,江苏海岸带NPP和LST呈现出显著的时空异质性特征。时间上1990、2000、2010、2020年代的NPP月均值分别为102.88、88.23、156.62、98.90 g C·m^(−2),呈现下降-上升-下降趋势,而LST月均值分别为32.6、31.7、28.3、37.6℃,呈现先下降后上升的趋势。空间上,NPP与LST在江苏海岸带南北分布呈现出一定差异性。③地表覆盖类型是影响江苏海岸带NPP和LST时空异质性的主要因素。林地的NPP最高,养殖池塘NPP最低;人工建筑的LST值最高,湿地、水域与养殖池塘的LST值相对较低。此外,随着气温升高,NPP和LST有逐渐上升的趋势,而植被覆盖度的升高则导致NPP上升和LST下降。
基金Under the auspices of the‘Beautiful China’Ecological Civilization Construction Science and Technology Project(No.XDA23100203)National Natural Science Foundation of China(No.42071289)Henan Postdoctoral Foundation(No.20180087)。
文摘Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather station networks is insufficient,especially in sparsely populated regions,greatly limiting the accuracy of estimates of spatially distributed Ta.Due to their continuous spatial coverage,remotely sensed land surface temperature(LST)data provide the possibility of exploring spatial estimates of Ta.However,because of the complex interaction of land and climate,retrieval of Ta from the LST is still far from straightforward.The estimation accuracy varies greatly depending on the model,particularly for maximum Ta.This study estimated monthly average daily minimum temperature(Tmin),average daily maximum temperature(Tmax)and average daily mean temperature(Tmean)over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer(MODIS)LST data(MYD11A2)and some auxiliary data using an artificial neural network(ANN)model.The data from 2003 to 2010 were used to train the ANN models,while 2011 to 2012 weather station temperatures were used to test the trained model.The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean,with root mean square errors(RMSEs)of 1.04℃ and 1.01℃,respectively.Moreover,the best RMSE of Tmax estimation was 1.27℃.Compared with the other two published Ta gridded datasets,the produced 1 km×1 km dataset accurately captured both the temporal and spatial patterns of Ta.The RMSE of Tmin estimation was more sensitive to elevation,while that of Tmax was more sensitive to month.Except for land cover type as the input variable,which reduced the RMSE by approximately 0.01℃,the other vegetation-related variables did not improve the performance of the model.The results of this study indicated that ANN,a type of machine learning method,is effective for long-term and large-scale Ta estimation.
基金Under the auspices of National Natural Science Foundation of China(No.42271214,41961027)Key Program of Natural Science Foundation of Gansu Province(No.21JR7RA278,21JR7RA281)+1 种基金the CAS‘Light of West China’Program(No.2020XBZGXBQNXZ-A)Basic Research Top Talent Plan of Lanzhou Jiaotong University(No.2022JC01)。
文摘Local climate zones(LCZs)are an effective nexus linking internal urban structures to the local climate and have been widely used to study urban thermal environment.However,few studies considered how much the temperature changed due to LCZs transformation and their synergy.This paper quantified the change of urban land surface temperature(LST)in LCZs transformation process by combining the land use transfer matrix with zonal statistics method during 2000–2019 in the Xi’an metropolitan.The results show that,firstly,both LCZs and LST had significant spatiotemporal variations and synchrony.The period when the most LCZs were converted was also the LST rose the fastest,and the spatial growth of the LST coincided with the spatial expansion of the built type LCZs.Secondly,the LST difference between land cover type LCZs and built type LCZs gradually widened.And LST rose more in both built type LCZs transferred in and out.Finally,the Xi’an-Xianyang profile showed that the maximum temperature difference between the peaks and valleys of the LST increased by 4.39℃,indicating that localized high temperature phenomena and fluctuations in the urban thermal environment became more pronounced from 2000 to 2019.
文摘With the rapid development of urban agglomerations in northwest arid and semiarid regions of China, the scope of the urban heat island(UHI) effect has gradually expanded and gradually connected, and has formed a regional heat island(RHI) with a larger range of impact to the regional environment. However, there are few studies on the heat island effect of urban agglomerations in arid and semiarid regions, so this paper selects the urban agglomeration of Hohhot, Baotou and Ordos(HBO) of Inner Mongolia, China as the study area. Based on the 8-day composite Moderate-resolution Imaging Spectroradiometer(MODIS) surface temperature data(156scenes in all) and land use maps for 2005, 2010, and 2015, we analyze the spatiotemporal distributions of regional heat(cool) islands(RH(C)I) and the responses of surface temperatures to land-use changes in the diurnal and interannual surface cities. The results showed that: 1) from 2005 to 2015, urban areas showed the cold island effect during the day, with the area of the cold island showing a shrinking feature;at night, they showed the heat island effect, with the area of the heat island showing a first decrease and then an increase.2) From 2005 to 2015, the land development(unutilized land to building land) brings the greatest temperature increase(ΔT = 1.36°C)during the day, while the greatest temperature change at night corresponds to the conversion of cultivated land to building land(ΔT =0.78°C) exhibited the largest changes at night. From 2010 to 2015, the land development(grassland to building land) bring the greatest temperature increase(ΔT = 0.85°C) during the day, while the great temperature change at night corresponds to the conversion of water areas to building land(ΔT = 1.38°C) exhibited the largest changes at night. Exploring the spatial and temporal evolution of surface urban heat(cool) islands in urban agglomerations in arid and semiarid regions will help to understand the urbanization characteristics of urban agglomerations and provide a reference for the formulation of policies for the coordinated and healthy development of the region and co-governance of regional environmental problems.
文摘目前还没有基于国产卫星的1 km分辨率的全天候陆表温度(LST)产品,FY-3D卫星提供了中分辨率成像仪(MERSI)Ⅱ型1 km分辨率晴空LST产品与微波成像仪(MWRI)25 km全天候LST产品,因此可结合两者优势开展全天候1 km分辨率LST的融合研究。基于地理加权回归(GWR)方法,选择海拔、FY-3D归一化植被指数和归一化建筑指数等建立GWR模型对FY-3D/MWRI 25 km LST降尺度到1 km,并与MERSI 1 km LST进行融合;同时针对MWRI轨道间隙,利用前后1天融合后的云覆盖像元1 km LST进行补值,可以得到接近全天候下的1 km LST。基于以上融合算法,选择了中国区域多个典型日期FY-3D/MERSI和MWRI LST官网产品进行了融合试验,并利用公开发布的全天候1 km LST产品(TPDC LST)对FY-3D 1 km LST融合结果进行了评估。研究结果表明,基于GWR法的LST降尺度方法,可以有效避免传统微波LST降尺度方法中存在的“斑块”效应和局地温度偏低等问题;LST融合结果有值率从融合前的22.4%~36.9%可提高到融合后69.3%~80.7%,融合结果与TPDC LST的空间决定系数为0.503~0.787,均方根误差为3.6~5.8 K,其中晴空为2.6~4.9 K,云下为4.1~6.1 K;分析还表明目前官网产品FY-3D/MERSI和MWRI LST均存在缺值较多与精度偏低等问题,显示其存在较大改进潜力,这有利于进一步改进FY-3D LST融合质量。
文摘基于Landsat TM影像中的热红外数据,利用单窗算法和单通道算法对延吉市的地表温度进行了反演,并对反演结果进行了统计.结果表明:2种反演算法所反演出来的地表温度总体趋势比较一致,其中单通道算法所反演出来的温度比单窗算法高一些,平均相差约1.03 K;2种算法的结果与亮度温度相比,单窗算法和单通道算法分别高出约3.32 K和4.35 K.
