Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions

By using both high-resolution orthoimagery and medium-resolution Landsat satellite imagery with other geospatial information, several land surface parameters including impervious surfaces and land surface temperatures for three geographically distinct urban areas in the United States – Seattle, Washington, Tampa Bay, Florida, and Las Vegas, Nevada, are obtained. Percent impervious surface is used to quantitatively define the spatial extent and development density of urban land use. Land surface temperatures were retrieved by using a single band algorithm that processes both thermal infrared satellite data and total atmospheric water vapor content. Land surface temperatures were analyzed for different land use and land cover categories in the three regions. The heterogeneity of urban land surface and associated spatial extents were shown to influence surface thermal conditions because of the removal of vegetative cover, the introduction of non-transpiring surfaces, and the reduction in evaporation over urban impervious surfaces. Fifty years of in situ climate data were integrated to assess regional climatic conditions. The spatial structure of surface heating influenced by landscape characteristics has a profound influence on regional climate conditions, especially through urban heat island effects.     

Using land use change trajectories to quantify the effects of urbanization on urban heat island

This paper proposed a quantitative method of land use change trajectory, which means the succession among different land use types across time, to examine the effects of urbanization on an urban heat island (UHI). To accomplish this, multi-temporal images from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) of Xiamen City in China from 1987 to 2007 were selected. First, the land use change trajectories were extracted based on the classified images from different years. Then the land surface temperatures (LST) were retrieved and the magnitudes of the UHI were evaluated using the UHI intensity (UHII) indicator. Finally, the indices of the contribution to UHI intensity (CUHII) were constructed and calculated to quantify the effects of each land use change trajectory on the UHI during urbanization. The results demonstrated that the land use change trajectories and CUHII are effective and useful in quantifying the effects of urbanization on UHI. In Xiamen City, a total of 2218 land use change trajectories were identified and 530 of them were the existing urban or urbanization trajectories. The UHII presents a trend of continuous increase from 0.83 °C in 1987 to 2.14 °C in 2007. With respect to the effects of urban growth on UHI, the contribution of existing urban area to UHI decreased during urbanization. Prior to 2007, the existing urban area of trajectory NO. 44444 had the most significant effect on UHI with the greatest CUHII, while the value has decreased from 55.00% in 1987 to 13.03% in 2007 because of the addition of new urbanized area. In 2007, the greatest CUHII was replaced by a trajectory from farmland to built-up area (NO. 22224) with the CUHII of 21.98%, followed by the existing urban area of trajectory NO. 44444 with the CUHII of 13.03%. These results provide not only a new methodology to assess the environmental effects of urbanization, but also decision-supports for the planning and management of cities.     

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.     

Remotely sensed thermal information applied to urban climate analysis

Urban heat island (UHI) effects on the ten most populated cities of Hungary, including the Budapest agglomeration area, were analyzed using remotely sensed data. Day- and night-time surface temperature time series observed by sensor MODIS on satellite Terra were used to determine UHI intensities. UHI intensity was defined as the difference in spatially averaged surface temperatures between urban and surrounding rural pixels. Spatial structures of UHIs were determined and compared for different seasons and macrocirculation conditions.     

Modeling the effects of green alternative on heat island mitigation of a meso level town,West Bengal,India

Urban heat island (UHI) is emerged as a ubiquitous phenomenon that affects the outdoor thermal comfort. Hence urban microclimatic studies using numeric simulation software to find out adaption strategies are attracting the great attention of researchers and policymaker. Number of scientists carried out their research on this topic using different tools and technique whereas ENVI-met model is the most widely used simulation tools. The present study intended to examine the implication of green infrastructure of the town on enhanced microclimatic condition and to evaluate the best suitable mitigation strategy modelling cool city with ENVI-met software (V4). Five greening condition such as existing UHI condition (C1), 100% greening of roof (C2), 100% greening of roof and walls (C3), 50% greening of roof and walls (C4) and: Plantation at suitable area with 50% greening of roof and walls (C5) have been taken into consideration for the microclimate simulation for all three selected sites. Among these five conditions, C3 for the open mid-rise and compact low-rise, C5 for the open low-rise are identified as one of the most suitable strategies which can reduce the air temperature of peak hours by 2.6 °C, 1.33 °C and 1.87 °C respectively. These models are validated by simple linear regression between simulated and existing air temperature in case of existing UHI condition (C1) and in all the cases coefficient of determination value is high such as for open mid-rise, compact low-rise and open low-rise, it is 0.92, 0.92 and 0.75 respectively. Therefore it can be concluded that the application of those strategies can improve the urban thermal environment as well as the outdoor thermal comfort of English bazaar Town and its surroundings.     

Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area

It is of great significance to timely, accurately, and effectively monitor land use/cover in city regions for the reasonable development and utilization of urban land resources. The remotely sensed dynamic monitoring of Land use/land cover (LULC) in rapidly developing city regions has increasingly depended on remote-sensing data at high temporal and spatial resolutions. However, due to the influence of revisiting periods and weather, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. In this paper we used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat8 and MODIS data and obtain time-series Landsat8 images. Then, land cover information is extracted using an object-based classification method. In this study, the proposed method is validated by a case study of the Changsha City. The results show that the overall accuracy and Kappa coefficient were 94.38% and 0.88, respectively, and the user/producer accuracies of vegetation types were all over 85%. Our approach provides an accurate and efficient technical method for the effective extraction of land use/cover information in the highly heterogeneous regions.     

Influence of land surface parameters on the spatio-seasonal land surface temperature regime in rural West Bengal,India

The magnitude and causes of changes in the land surface temperature of rural areas have not been extensively studied. The thermal band of Landsat imagery is taken to extract winter, summer, and monsoon season land surface temperature (LST) and relate it to surface parameters over a 30-year period. From the extracted parameters constructed a prospective surface temperature (PST) model using Multivariate Adaptive Regression Splines. The Chandrabhaga river basin in West Bengal of the lateritic Rarh Tract at the Chota Nagpur Plateau fringe was chosen as the study area because it is far from urban influences, to avoid the well-known heat island effect. Over the study period, summer and winter average LST increased linearly by 0.085?°C/y and 0.016?°C/y respectively. These results were validated with air temperature (RMSE?=?x and y, respectively). Over time more of the area is in the higher temperature zones, e.g., in April 2011, 4% area exceeded >32°, whereas in 2015 this proportion reached 52%. PST models of all the seasons were moderate to highly correlate (0.57–0.87) with actual LST, showing the value of this model. It also revealed the relative importance of the regional factors. Based on this information factor management is a scientific step to restrict or minimize the temperature rise effect.     

Built-up land mapping capabilities of the ASTER and Landsat ETM+ sensors in coastal areas of southeastern China

Worldwide urbanization has accelerated expansion of urban built-up lands and resulted in substantial negative impacts on the global environments. Precisely measuring the urban sprawl is becoming an increasing need. Among the satellite-based earth observation systems, the Landsat and ASTER data are most suitable for mesoscale measurements of urban changes. Nevertheless, to date the difference in the capability of mapping built-up land between the two sensors is not clear. Therefore, this study compared the performances of the Landsat-7 ETM+ and ASTER sensors for built-up land mapping in the coastal areas of southeastern China. The comparison was implemented on three date-coincident image pairs and achieved by using three approaches, including per-band-based, index-based, and classification-based comparisons. The index used is the Index-based Built-up Index (IBI), while the classification algorithm employed is the Support Vector Machine (SVM). Results show that in the study areas, ETM+ and ASTER have an overall similar performance in built-up land mapping but also differ in several aspects. The IBI values determined from ASTER were consistently higher than from ETM+ by up to 45.54% according to percentage difference. The ASTER also estimates more built-up land area than ETM+ by 5.9–6.3% estimated with the IBI-based approach or 3.9–6.1% with the SVM classification. The differences in the spectral response functions and spatial resolution between relative spectral bands of the two sensors are attributed to these different performances.     

A generalised methodology for estimating land surface temperature for non-urban areas of Greece through the combined use of NOAA–AVHRR data and ancillary information

The purpose of this study was twofold: to develop a methodology for the estimation of land surface temperature for non-urban areas and to analyze the sensitivity of the methodology. The key element of the methodology was the development of emissivity maps based on CORINE Land Cover and the ASTER spectral library. Land surface temperatures were derived from NOAA/AVHRR data and the methodology was applied at a national scale in Greece, with emphasis given to non-urban areas. A sensitivity analysis was performed in order to determine the variables that mainly affect the estimation of land surface temperature. A varying propagation error was identified depending on the temperature and humidity of the atmosphere, as well as the land cover type. The methodology was applied to a series of 25 AVHRR images and the results were compared to in-situ measurements from representative stations.     

Selection of classification techniques for land use/land cover change investigation

The concerns over land use/land cover (LULC) change have emerged on the global stage due to the realisation that changes occurring on the land surface also influence climate, ecosystem and its services. As a result, the importance of accurate mapping of LULC and its changes over time is on the increase. Landsat satellite is a major data source for regional to global LULC analysis. The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them. The classifiers algorithms are well optimized for the gamma, penalty, degree of polynomial in case of SVM, while for ANN minimum output activation threshold and RMSE are taken into account. The overall analysis shows that the ANN is superior to the kernel based SVM (linear, radial based, sigmoid and polynomial) and MLC. The best tool (ANN) is then applied on detecting the LULC change over part of Walnut Creek, Iowa. The change analysis of the multi temporal images indicates an increase in urban areas and a major shift in the agricultural practices.     

Simulation of the land surface temperature from moon-based Earth observations

The land surface temperature (LST) is a key parameter for the Earth’s energy balance. As a natural satellite of the Earth, the orbital of the moon differs from that of current Earth observation satellites. It is a new way to measure the land surface temperature from the moon and has many advantages compared with artificial satellites. In this paper, we present a new method for simulating the LST measured by moon-based Earth observations. Firstly, a modified land-surface diurnal temperature cycle (DTC) method is applied to obtain the global LST at the same coordinated universal time (UTC) using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The lunar elevation angles calculated using the ephemeris data (DE405) from the Jet Propulsion Laboratory (JPL) were then applied to simulate the Earth coverage observed from the moon. At the same time, the modified DTC model was validated using in situ data, MODIS LST products, and the FengYun-2F (FY-2F) LST, respectively. The results show that the fitting accuracy (root-mean-square error, RMSE) of the modified DTC model is not greater than 0.72?°C for eight in situ stations with different land cover types, and the maximum fitting RMSE of the modified model is smaller than that of current DTC models. By the comparison of the simulated LST with MODIS and FY-2F LST products, the errors of the results were feasible and accredited, and the simulated global LST has a reasonable spatiotemporal distribution and change trend. The simulated LST data can therefore be used as base datasets to simulate the thermal infrared imagery from moon-based Earth observations in future research.     

Exploring the effect of COVID-19 pandemic lockdowns on urban cooling: A tale of three cities

COVID-19 pandemic has had a major impact on our society, environment and public health, in both positive and negative ways. The main aim of this study is to monitor the effect of COVID-19 pandemic lockdowns on urban cooling. To do so, satellite images of Landsat 8 for Milan and Rome in Italy, and Wuhan in China were used to look at pre-lockdown and during the lockdown. First, the surface biophysical characteristics for the pre-lockdown and within-lockdown dates of COVID-19 were calculated. Then, the land surface temperature (LST) retrieved from Landsat thermal data was normalized based on cold pixels LST and statistical parameters of normalized LST (NLST) were calculated. Thereafter, the correlation coefficient (r) between the NLST and index-based built-up index (IBI) was estimated. Finally, the surface urban heat island intensity (SUHII) of different cities on the lockdown and pre-lockdown periods was compared with each other. The mean NLST of built-up lands in Milan (from 7.71 °C to 2.32 °C), Rome (from 5.05 °C to 3.54 °C) and Wuhan (from 3.57 °C to 1.77 °C) decreased during the lockdown dates compared to pre-lockdown dates. The r (absolute value) between NLST and IBI for Milan, Rome and Wuhan decreased from 0.43, 0.41 and 0.16 in the pre-lockdown dates to 0.25, 0.24, and 0.12 during lockdown dates respectively, which shows a large decrease for all cities. Analysis of SUHI for these cities showed that SUHII during the lockdown dates compared to pre-lockdown dates decreased by 0.89 °C, 1.78 °C, and 1.07 °C respectively. The results indicated a high and substantial impact of anthropogenic activities and anthropogenic heat flux (AHF) on the SUHI due to the substantial reduction of huge anthropogenic pressure in cities. Our conclusions draw attention to the contribution of COVID-19 lockdowns (reducing the anthropogenic activities) to creating cooler cities.     

A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape

Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (∼1000 m) thermal images with high revisiting capability (∼1 day) are freely available. To bridge this gap, present study attempts to downscale coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression downscaling (LMS_DS) and (v) Pace regression downscaling (PR_DS) are applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and downscaled to 480 m and 240 m resolution. The downscale accuracy is achieved using LMS_DS and PR_DS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data downscaled from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMS_DS and PR_DS models, respectively. The LMS_DS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. Downscaling model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.     

Studies of optical and biological properties of landcover employing shuttle flights of a multispectral linear array radiometer

Studies to characterize optical and biological properties of land cover as observed from space are planned using a six channel, imaging spectroradiometer employing newly developed multispectral linear array (MLA) detector technology. These studies are to take place by mounting the radiometer on the Shuttle and observing areas with dynamic and diverse types of land cover condition. The radiometer will have 15 meter spatial resolution for four, 20 nanometer bands in the visible and near infrared and 30 meter resolution for similarily narrow bands in the shortwave infrared bands. The instrument will scan ± 45 degrees along the Shuttle orbital path. The principle objective of this experiment is to obtain observations that augment knowledge of the distribution of basic land cover types in regions that are known to be key to questions of biogeochemical cycles, energy balance and climatic change. Another key objective is to quantify the bidirectional reflectance of key land cover conditions in major portions of the visible, near infrared and shortwave infrared as they are observed from space. The initial execution of this experiment is presently scheduled for late 1987.     

Multi-channel improvements to satellite-derived global sea surface temperatures

High-quality multispectral measurements from satellites, and the recent development of multiple-window techniques to correct infrared brightness temperatures for atmospheric attenuation, have enabled marked improvements in global mapping of sea surface temperatures. The 4-km resolution data are in two visual bands and three atmospheric windows in the thermal infrared from the advanced Very High Resolution Radiometer (AVHRR) on NOAA's operational polar satellites. Various threshold and/or spatial homogeniety tests are applied to small data arrays to discriminate nominally cloud-free samples for subsequent processing. Tests of the multi-channel equations against independent buoy data gave bias = 0.42C and scatter = 0.62C. Global statistical comparisons with ships indicate significant improvements in accuracy and coverage over previous satellite-derived surface temperatures.     

烟气覆盖表面红外温度测量的实验分析

为了研究辐射参与性烟气对红外测温法金属壁面温度测量的影响,首先根据实验中红外探测器测量得到的表面温度分布的特点,建立了一维表面红外测温模型;然后根据红外热成像测温原理及辐射传输理论,利用源项多流法分析模型计算了烟气覆盖表面在不同烟气厚度和不同表面温度下红外探测器获得的温度;最后利用马弗炉模拟不同温度的表面,振动筛模拟不同浓度和厚度的烟气环境,在实验室条件下设计搭建了模拟烟气覆盖表面的红外测温实验系统,实验验证了源项多流法分析模型,实验测量得到的温度与分析模型预测的温度吻合较好,并提出了利用反演方法修正红外测温的设想。     

Water surface temperature estimation from Landsat 7 ETM+ thermal infrared data using the generalized single-channel method: Case study of Embalse del Río Tercero (Córdoba,Argentina)

Monitoring of warm distribution in water is fundamental to understand the performance and functioning of reservoirs and lakes. Surface water temperature is a key parameter in the physics of aquatic systems processes since it is closely related to the energy fluxes through the water–atmosphere interface. Remote sensing applied to water quality studies in inland waterbodies is a powerful tool that can provide additional information difficult to achieve by other means. The combination of good real-time coverage, spatial resolution and free availability of data makes Landsat system a proper alternative. Many papers have developed algorithms to retrieve surface temperature (principally, land surface temperature) from at-sensor and surface emissivity data. The aim of this study is to apply the single-channel generalized method (SCGM) developed by Jiménez-Muñoz and Sobrino (2003) for the estimation of water surface temperature from Landsat 7 ETM+ thermal bands. We consider a constant water emissivity value (0.9885) and we compare the results with radiative transfer classic method (RTM).     

临近空间环境下封闭方腔内耦合换热特性

以临近空间浮空器载荷舱为应用背景,对复杂热边界条件下含热源的三维封闭方腔内自然对流、表面辐射和导热的耦合问题进行了数值模拟。综合考虑对流换热、长波辐射、太阳辐射等因素的影响,建立了临近空间热环境模型。通过Fluent软件用户自定义函数（UDF）引入外部非定常的辐射-对流耦合热边界条件,对腔内换热特性的昼夜变化进行研究,并分析了腔壁厚度、发射率和导热系数对其的影响。数值结果表明,腔内平均温度昼夜变化很小,约为12.9 K,但温度场分布随太阳方位变化而变化;腔内对流换热较弱,同一时刻最大温差约为71.3 K;腔壁热阻和发射率增加会削弱自然对流的强度。      

Anomalous increase of chlorophyll concentrations associated with earthquakes

Recent studies have shown land, ocean, atmosphere and ionospheric anomalies prior to earthquakes. The optical and microwave sensors onboard satellites are now capable of monitoring land, ocean, atmosphere and ionosphere which provide changes associated with natural hazards. In this paper, we have analyzed remote sensing data of the ocean coasts lying near the epicenters of recent four major earthquakes (Gujarat of January 26, 2001, Andaman of September 13, 2002, Algeria of May 21, 2002 and Bam, Iran earthquake of December 26, 2003), our detailed analysis shows increase of Chlorophyll-a (Chl-a) concentration associated with these recent earthquakes. The increase of Chl-a concentration is due to the change in sea surface temperature (SST) associated with the changes in stress regime in the epicentral region which is responsible for modifying the in situ thermal structure of the water and enhancing the upwelling of nutrient-rich water. The increase of Chl-a concentration also shows one to one relation with the increase of surface latent heat flux (SLHF) which is found to increase significantly prior to the earthquake events. Due to cloud cover, it has not been possible to quantify the effect of the chlorophyll concentrations associated with the earthquake events for each successive day during an event. However, the limited data from the adjacent oceanic regions provide strong evidence of the increase in Chl-a concentration. The monitoring of chlorophyll concentrations with higher spatial and temporal resolutions may provide early information about impending coastal earthquakes.     

PCB红外热像辐射率校准的非线性滤波方法

在印制电路板(PCB)的红外辐射诊断中,为了获得PCB表面温度的真实分布,须对PCB红外热像进行辐射率校准.将PCB表面温度的测量波形看作真实温度分布与辐射率分布的混合波形,提出一种非线性滤波方法,利用二者连续性的差异将其区分开来.讨论了作为该方法关键步骤的红外热像突变点检测的小波变换实现问题.实验结果表明该方法能够准确地估计PCB表面温度的真实分布波形.该方法还可以用于PCB红外热像的热源辨识以及对未知材料的辐射率估计中.