Spatiotemporal variation of surface albedo and its influencing factors in northern Xinjiang, China

Surface albedo is a quantitative indicator for land surface processes and climate modeling,and plays an important role in surface radiation balance and climate change.In this study,by means of the MCD43A3 surface albedo product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS),we analyzed the spatiotemporal variation,persistence status,land cover type differences,and annual and seasonal differences of surface albedo,as well as the relationship between surface albedo and various influencing factors(including Normalized Difference Snow Index(NDSI),precipitation,Normalized Difference Vegetation Index(NDVI),land surface temperature,soil moisture,air temperature,and digital elevation model(DEM))in the north of Xinjiang Uygur Autonomous Region(northern Xinjiang)of Northwest China from 2010 to 2020 based on the unary linear regression,Hurst index,and Pearson's correlation coefficient analyses.Combined with the random forest(RF)model and geographical detector(Geodetector),the importance of the above-mentioned influencing factors as well as their interactions on surface albedo were quantitatively evaluated.The results showed that the seasonal average surface albedo in northern Xinjiang was the highest in winter and the lowest in summer.The annual average surface albedo from 2010 to 2020 was high in the west and north and low in the east and south,showing a weak decreasing trend and a small and stable overall variation.Land cover types had a significant impact on the variation of surface albedo.The annual average surface albedo in most regions of northern Xinjiang was positively correlated with NDSI and precipitation,and negatively correlated with NDVI,land surface temperature,soil moisture,and air temperature.In addition,the correlations between surface albedo and various influencing factors showed significant differences for different land cover types and in different seasons.To be specific,NDSI had the largest influence on surface albedo,followed by precipitation,land surface temperature,and soil moisture;whereas NDVI,air temperature,and DEM showed relatively weak influences.However,the interactions of any two influencing factors on surface albedo were enhanced,especially the interaction of air temperature and DEM.NDVI showed a nonlinear enhancement of influence on surface albedo when interacted with land surface temperature or precipitation,with an explanatory power greater than 92.00%.This study has a guiding significance in correctly understanding the land-atmosphere interactions in northern Xinjiang and improving the regional land-surface process simulation and climate prediction.

Effects of land use and cover change on surface wind speed in China

The surface wind speed(SWS)is affected by both large-scale circulation and land use and cover change(LUCC).In China,most studies have considered the effect of large-scale circulation rather than LUCC on SWS.In this study,we evaluated the effects of LUCC on the SWS decrease during 1979-2015 over China using the observation minus reanalysis(OMR)method.There were two key findings:(1)Observed wind speed declined significantly at a rate of 0.0112 m/(s·a),whereas ERA-Interim,which can only capture the inter-annual variation of observed data,indicated a gentle downward trend.The effects of LUCC on SWS were distinct and caused a decrease of 0.0124 m/(s·a)in SWS;(2)Due to variations in the characteristics of land use types across different regions,the influence of LUCC on SWS also varied.The observed wind speed showed a rapid decline over cultivated land in Northwest China,as well as a decrease in China’s northeastern and eastern plain regions due to the urbanization.However,in the Tibetan Plateau,the impact of LUCC on wind speed was only slight and can thus be ignored.

Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software were used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to 30.9693oC respectively. Results of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from 26.4142oC to -0.898728oC. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.

Land Characterization Analysis of Surface Temperature of Semi-Arid Mountainous City Abha, Saudi Arabia Using Remote Sensing and GIS

This knowledge of land surface temperature and its spatial variations within a city environment is of prime importance to the study of urban climate and human-environment interactions. Few studies have examined the influence of land use and terrain on the surface temperature effects of semi-arid mountainous urban areas. This study investigates the urban environment characterization and its effects on surface temperature using remote sensing. The methodologies adapted for this study are geometric and radiometric corrections of satellite data, extraction of land use/land cover and digital elevation model, estimation of vegetation density using Normalized Difference Vegetation Index (NDVI), and estimation of surface temperature and emissivity using temperature emissivity separation (TES) algorithm. Finally geospatial model and statistical techniques are used for assessing the overall impact of urban environmental characterization on urban climate of semi-arid region of Abha, Kingdom of Saudi Arabia. Herein, results reveal that the spatial distribution of surface temperature was affected by land use/land cover (LULC) and topography. The high dense built-up and commercial/industrial areas display higher surface temperature in comparison with surrounding lands. There is gradual decrease of LULC classes’ surface temperature with the increase in altitude. The cooling effect towards the surrounding urban built-up area is found increasing at the hill located vegetated area, the downward slope and valley terrain inside the recreational park. Therefore the spatial variation in surface temperature also reflected the effects of topography on LULC classes. Suitable mountainous land use utilization would help to expand the cooling effect. In the future, the outcomes of this study could be used to build environmentally sustainable urban planning suitable to semi-arid regions and to create practices that consider the local weather environment in urban planning.

Soil temperature estimation at different depths,using remotely-sensed data

Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature(ST) measured at different depths(0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature(LST) and normalized difference vegetation index(NDVI) derived from AQUA/TERRA MODIS data, and solar declination(Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination(R^2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R^2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors(RMSEs) and R^2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing.

Dynamicity of Land Use/Land Cover(LULC):An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation(DMC)in India

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.

Urban Planning Based on Nature—A Nature-Based Solution

After an international contest announced by the City of Abu Dhabi “Cool Abu Dhabi Challenge”1 and the article published as a digest of a paper titled A Nature-based Solution [1], the decision has been made to take part in improving thermal comfort in public spaces by mitigating the impact of the effect of Urban Heat Islands (UHI)2 in the city of the Belgrade. The basic research aims at achieving the balance between the conflicting impacts when the buildings with their infrastructure and water-green surrounding area are in such correlation that it fulfils acceptable living and heating standards and reduces the use of fossil fuels for cooling the urban areas (buildings). By implementing the remote detection it is possible to analyze and quantify the impact of over-building on the temperature rise in urban areas as well as the disturbance of the heating comfort and the increased demand for additional cooling. Now it is possible to create virtual models that will incorporate this newly-added urban vegetation into urban plans, depending on the evaporation potential that will affect the microclimate of the urban area. Such natural cooling can be measured and adapted and hence aimed at a potential decrease in areas with UHI emissions [2]. Suitable greenery in the summer season can be a useful improvement which concurrently enables and complements several cooling mechanisms—evaporative cooling and evapotranspiration, i.e. natural cooling systems. The remote detection shall establish and map the “healthy” and “unhealthy” greenery zones—that is the vegetation zones with the highest evaporative potential with the “cooling by evaporation” effect and also, by implementing the urban prediction model, it shall propose green infrastructure corridors aimed at a potential decrease in the Urban Heat Island Emission.

山地地貌地表温度的深度学习空间模拟

针对在山地地貌下环境因子与地表温度(land surface temperature,LST)之间存在的空间特征问题,提出利用深度学习方法分析LST在不同植被情景下与环境因子的关系特征。结合山地LST的影响因素和模型特点,构建大青山LST预测模拟模型,利用模型做LST与环境因子变化分析。结果表明:构建的LST深度学习模型预测值与观测值空间分布特征吻合度高(R 2为0.89,MAE为0.60℃,MSE为0.65℃);LST随NDVI、海拔和坡度的增加而降低,随平均气温和地表反照率的增加而增加;随NDVI的增大,LST随各环境因子变化的速率变化不同。研究表明,利用深度学习方法预测山地地貌LST的空间分布具有可行性,该方法有助于理解山地环境因子与LST的空间分布关系。

上海市快速城市化过程中地表温度与地表覆盖的关系研究

城市热岛是城市化过程带来的生态环境效应之一。城市温度升高不仅使得资源消耗增加,而且影响城市居民的生存环境和生活质量。为缓解城市热岛效应,对城市热岛的影响因素进行分析显得尤为重要。采用Landsat TM影像数据,以上海市外环内区域为研究对象,反演了5、11月的城市地表温度、归一化植被指数、改进的归一化差异水体指数和不透水面率,定量分析了它们之间的相互关系。结果表明:(1)城市地表温度与归一化植被指数、改进的归一化差异水体指数具有显著的负相关,与不透水面率具有显著的正相关。(2)城市地表温度的空间分布具有明显的季节特征,5月地表温度较高的区域主要分布在北部区域,而11月地表温度较高的区域在整个研究范围内均有分布。(3)季节因素对城市地表温度与地表覆盖参数的相关性影响较明显。多元回归结果表明,5月城市地表温度与归一化植被指数相关性最大,降温效果绿地大于水体大于城市不透水面;11月城市地表温度与改进的归一化差异水体指数相关性最大,降温效果水体大于城市不透水面大于绿地。(4)城市绿地和水体对于调节城市地表温度具有显著作用,该结果可用于优化城市格局,缓解城市热岛效应。

基于植被指数和土地表面温度的干旱监测模型

干旱是一种周期性发生的自然现象,其发生过程中有关参数如地表覆盖度、温度和土壤表层含水量等可以通过遥感的途径进行定量反演,而这些参数客观地反映了地表的综合特征。综述了运用遥感反演产品———土地表面温度和归一化植被指数在干旱监测中的应用前景和进展,分析了距平植被指数、条件植被指数、条件温度指数和归一化温度指数等干旱监测方法的优缺点,在前人研究的基础上,提出了条件植被温度指数的干旱监测模型,探讨了其应用前景。

基于植被指数和地表反照率影响的北京城市热岛变化

利用TERRA/MODIS遥感反演的地表温度资料,对2000—2006年北京城市热岛季节变化特征进行了研究,结合同期降水量、植被指数和地表反照率变化,分析了该地夏季城市热岛的年际变化成因.结果表明:北京多年四季热岛分布主要以城区为中心向周边郊区延伸,其中夏季城市热岛最强,春、秋和冬季较弱,这种热岛强度的季节性差异主要与太阳辐射强度、地表植被覆盖状况和城市人为热释放等的季节性变化密切相关.北京夏季城市热岛的年际变化特征为:2005和2006年最显著,热岛中心强度分别为10.54和9.61℃;2002和2004年城市热岛最弱,热岛中心强度分别为6.54和7.39℃.2000—2006年北京市夏季城市热岛具有明显增强趋势,热岛强度增温率为0.326℃/a.北京夏季降水对城区地表温度影响大于郊区,降水主要通过影响城区地表温度来影响城市热岛变化;夏季地表植被和地表反照率变化对地表温度和城市热岛也均有较大影响.2000年以来,北京郊区夏季地表植被指数增加率远高于城区,受地表植被和地表反照率变化的影响,郊区降温率明显大于城区,致使城郊温差增大,热岛效应加强.

基于遥感的城市生态环境质量动态变化定量评价——以江苏省宜兴市为例

城市化与人类生存环境改善之间的矛盾日益突出,综合、快速、准确、定量监测城市生态环境质量状况尤为重要.以利用遥感技术反演城市生态系统净初级生产力、植被覆盖度和地表温度等生态参数,提出基于归一化不透水指数的地表裸露度;综合以上参数建立基于遥感参数的生态环境指数模型,并运用该模型定量评价江苏省宜兴市生态环境质量动态变化.结果表明,2000—2013年,宜兴市生态环境质量呈下降和升高趋势的面积分别占总面积的79%和21%,其中,20.56km2呈显著降低趋势,并未监测到显著改善区域.该模型综合各项遥感参数,能快速、准确、定量监测城市生态环境质量变化,具有良好的应用潜力.

基于温度植被旱情指数的青海高寒区干旱遥感动态监测研究

利用MODIS资料提取的归一化植被指数(NDVI)和地表温度(Ts),构建NDVI-Ts特征空间,依据该特征空间设计的温度植被旱情指数作为旱情指标,对青海省东部浅山农业区2004年7月上旬的旱情进行了动态监测,同时利用各气象台站实测的地面数据进行了验证,结果表明利用温度植被旱情指数(TVDI)法对青海高寒区进行干旱动态监测是可行的。

安徽省植被和地表温度季节变化及空间分布特征

卫星遥感广泛应用于宏观、大范围、动态连续的植被和地表温度监测研究。利用2001—2008年MODIS卫星遥感资料,分析了安徽省归一化植被指数(NDVI)和地表温度(LST)的季节、月变化和空间分布特征,探讨了代表城市区域的NDVI和LST时空分布及其相关性。结果表明:安徽省NDVI和LST季节变化显著,具有典型地域特征;受当地气候影响,植被、农作物类型地域差异较大,导致LST季节变化以及空间分布不同;城市中心向郊区过渡时,植被覆盖度在不断增加,伴随着NDVI的增加,LST下降;城市LST明显高于郊区值,呈现热岛效应。研究表明,当地气候和植被分布共同决定了LST的分布状况,这将为安徽省合理进行农业区划、科学监测生态环境以及有效评估土地利用与热岛效应提供重要参考依据。

条件植被温度指数在云南干旱监测中的应用

对干旱进行监测,有助于各级政府及时了解旱情,采取积极有效的防旱、抗旱措施,促进水利设施建设与合理布局,确保农业生产发展与粮食安全。该文采用MODIS多时相归一化植被指数与地表温度遥感影像产品,分析研究区归一化植被指数—地表温度特征空间,应用条件植被温度指数对云南省2009年9月-2010年3月干旱的时间、空间特性进行监测。监测结果表明云南省旱情随时间有所波动,但整体旱情呈发展趋势;监测期内云南省旱情分布较广,受灾面积超过70%,仅西北角小片地区受干旱影响较小,特旱区主要分布于云南省中部、东部和南部地区。应用相关研究成果对干旱监测结果进行验证,结果表明监测结果可信,能够为防灾减灾相关部门提供有力的信息支持。

滇池流域不透水表面扩张监测与时空过程分析

人类活动是影响和改变自然和人文环境最主要的因素,而不透水表面是反映人类活动频度与强度重要指标。近30年来,随着城镇化的不断推进,不透水表面快速扩张,同时伴随着一系列流域水环境问题产生。掌握不透水表面变化的时空过程是改善流域水环境、控制水污染的基础。为此,以滇池流域近25年来的TM影像与OLI影像为数据基础,将改进的NDBI算法及线性光谱混合分析法相结合,得到了滇池流域近25年来不透水表面的空间范围、空间分布、不透水表面指数、不透水表面覆盖度等指标。实验结果表明,本文采用的算法效果较好,0.2 m空间分辨率的航拍影像精度检验结果表明,提取精度达到85.27%;滇池流域不透水表面覆盖率达19.44%,部分子流域不透水表面覆盖率超过40%,其对流域生态系统功能和结构有直接的影响。

基于MODIS产品LST／NDVI／EVI的陕西旱情监测

以陕西省为研究区域,利用2005年4月的MODIS月合成产品数据MODIS11C3和MO-DIS13C3获取的归一化植被指数NDVI、增强型植被指数EVI和陆地表面温度LST,分别构建TS-NDVI和TS-EVI特征空间,从而得到了条件温度植被干旱指数TVDI和旱情等级的空间分布图,以监测评价陕西的旱情,同时将两者进行比较,最后结合94个气象站的气温和降水距平进行了相关性分析。结果表明:利用条件温度植被干旱指数进行陕西省旱情监测,能够较好反映当地旱情。根据地表温度以及增强植被指数之间的关系建立的旱情监测模型与降水距平的线性相关显著,相关系数为0.537,通过了0.05水平的检验。

基于MODIS数据的雪面温度遥感反演

通过对Planck函数在低温范围内进行线性化,改进了针对MODIS数据的实用性分裂窗算法,建立了基于MODIS数据的中纬度地区雪面温度遥感反演方法。以环青海湖地区为研究区进行了算法应用,取得了较理想的效果。验证并分析了雪面温度与海拔高度的负相关关系。通过对下垫面相对均一的3个样区进行分析,讨论了雪面温度与归一化积雪指数的关系,并提出了"NDSI-Ts空间"的概念。

条件植被温度指数在华北平原干旱监测中的应用

以华北平原部分地区为研究区域,应用MODIS多时段卫星遥感数据进行了归一化植被指数和地表温度的计算和反演,应用条件植被温度指数对研究区域2003—2006年每年5月上旬的干旱进行了监测.以监测结果为基础,在时间和空间分布上分析了研究区域的旱情.应用降水量数据和土壤表层含水量数据对干旱监测结果进行了验证,结果表明VTCI与最近1个月的降水量具有显著的线性相关性,VTCI与土壤表层含水量有较好的线性相关性,验证了VTCI是一种近实时的干旱监测方法.

2000-2014年蒙古高原植被覆盖时空变化特征及其对地表水热因子的响应

利用MODIS NDVI数据、同期地表水热组合数据和植被类型数据,对2000-2014年蒙古高原生长季和三季(春、夏、秋季)植被覆盖时空演变特征及其对地表水热因子响应模式进行分析。研究表明:这15 a来,蒙古高原生长季及三季归一化植被指数NDVI均呈增加趋势,且呈显著增加趋势区域主要集中在内蒙古地区,一定程度上反映了该地区生态恢复工程的有效性。研究区植被覆盖变化与地表水分指数LSWI有密切的关系,因此证明研究区植被覆盖的增加归因于自然和人为因素的共同作用。不同类型植被NDVI均呈增加趋势,其中荒漠植被NDVI增加最明显,森林植被增加平缓,且存在季节性差异。此外,不同类型植被NDVI受水热因子影响也存在季节性差异。