Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city,Delhi-India

The present study is an assessment and identification of urban heat island (UHI) in the environment of one of the fastest urbanizing city of India, Delhi Metropolis, employing satellite image of ASTER and Landsat 7 ETM+ in the thermal infrared region 3–14 μm. Temporal (2001 and 2005) ASTER datasets were used to analyze the spatial structure of the thermal urban environment subsequently urban heat island (UHI) in relation to the urban surface characteristics and land use/land cover (LULC). The study involves derivation of parameters governing the surface heat fluxes, constructing statistics of ASTER thermal infrared images along with validation through intensive in situ measurements. The average images reveal spatial and temporal variations of land surface temperature (LST) of night-time and distinct microclimatic patterns. Central Business District (CBD) of Delhi, (Connaught Place, a high density built up area), and commercial/industrial areas display heat islands condition with a temperature greater than 4 °C compared to the suburbs. The small increase in surface temperature at city level is mainly attributed to cumulative impact of human activities, changes in LULC pattern and vegetation density. In this study the methodology takes into account spatially-relative surface temperatures and impervious surface fraction value to measure surface UHI intensity between the urban land cover and rural surroundings. Both the spatial and temporal variation in surface temperature associated with impervious surface area (ISA) has been evaluated to assess the effect of urbanization on the local climate.     

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.     

Effects of concurrent snow and cloud cover on planetary albedo

The effects of snow and cloud cover on planetary albedo are examined using observations from NOAA polar orbiting satellites. Reflected radiation was measured in the visible range (0.5 – 0.7 μm). Planetary albedos resulting from different cloud/snow cover conditions are compared using Northern Hemisphere snow cover maps, surface weather charts, satellite photos and data on land surface types. None of the cases studied show that concurrent cloud and snow cover produces significantly different planetary albedos than cloud cover alone. Cloud cover alone is found to yield higher planetary albedos than snow cover alone; the difference being greatest over forested areas. With and without snow cover present, clear-sky planetary albedos over farming and grazing lands (snow(0.45), no snow(0.15)) are found to be significantly higher than those over forested regions (snow(0.33), no snow(0.11)). Variations in satellite zenith angle are not found to produce significant effects in most cases studied.     

一种基于现代通信理论的图像质量评价技术

应用现代通信理论对成像系统进行了分析, 建立了模拟和数字图像的目标信息密度模型。利用目标信息密度增量评估了亚像元技术的应用; 讨论了系统调制传递函数(MTF)和信号噪声比(SNR)对目标信息密度的影响, 发现目标信息密度随 SNR提高而提高, MTF的影响与目标统计特性有关。     

Satellite monitoring of surface temperatures in semi-desert: Differences between anthropogenically impacted terrain and protected natural vegetation

The northern Sinai is a sandy semi-desert. Severe overgrazing and other anthropogenic pressures contribute to an extremely sparse vegetative cover. A 6×6 km area was fenced off in the summer of 1974, constituting an exclosure from the grazing herds and from harvesting of plants for firewood. The vegetation in this exclosure recovered rapidly. In this study, radiances and surface temperatures of the vegetated exclosure and of the surrounding anthropogenically impacted terrain were monitored for the period March–September 1981, using NOAA-6 satellite. This satellite carries the Advanced Very High Resolution Radiometer (AVHRR) that measures visible and solar infrared radiances and also radiation temperatures at 11 μm band. In the digital images, the exclosure forms an easily recognized square, darker in the visible and solar infrared AVHRR channels than the surroundings. We concentrated on the corner in which there was no anthropogenic activity. Based on the ratio of the radiance over the exclosure to that over the surrounding terrain, the protrusions parameter s (vertical projection of the protrusions per unit area) has been estimated. The average value of s for the various satellite passes is 0.20 as measured in the visible channel and 0.18 as measured in the solar infrared. The radiation temperatures of the exclosure and of the surrounding terrain were analyzed. The radiation temperatures of the vegetated exclosure (sand with protruding bushes) are higher (by up to 2.5°K) than those of the surrounding terrain (that can be approximately regarded as bare sand). It is concluded that in an arid climate, under the semi-dormant conditions of vegetation (which prevail at all times except for the desert-bloom period, after a rain) the evapotranspiration is low, so that its effect on the surface temperatures is very small. Under these conditions, the surface temperatures are controlled by the surface albedo and the air flow at the surface.     

基于四元数理论的彩色图像脉冲噪声开关滤波

根据四元数单位变换理论定义了一种新的描述彩色图像像素点间差异的距离,该距离可以同时描述两个彩色像素点在强度和色度上的差异.利用该距离和图像边缘的结构性与方向性,构造了一种基于四元数的开关型彩色图像脉冲噪声滤波器.该滤波器对像素点进行4个方向上的噪声检测并仅对检测为噪声的像素点进行滤波.实验表明:该滤波器能够在去除彩色图像中的相关和非相关噪声的同时保护图像细节,同时具有比传统方法更快的执行速度.     

基于天基光学探测图像初析南大西洋异常区影响

文章针对目前在轨运行的高灵敏度探测类可见光遥感器经过南大西洋异常区（SouthAtlanticAnomaly,SAA）时出现的探测能力降低、目标无法识别等现象,通过卫星在轨获取的SAA天基探测图像研究图像中出现的异常现象机理;提出了基于CCD探测器固有暗像元区的图像特征变化开展SAA影响评估的方法,根据在轨图像暗像元区的均值和标准差、图像区的热像素及活像元等特征参数的统计分析,判断经过SAA过程中连续获取的图像中出现显著影响时的拐点,通过曲线拟合估算出该轨道高度的SAA空间分布特征和边界,依此进一步提出利用在轨天基探测图像给出测量影响该轨道高度卫星SAA区域边界的简单方法;最后,基于SAA对天基探测器件的影响分析结果和认识,给出了高灵敏度探测类卫星避免或消除SAA影响的改进措施和建议。文章采用的数据分析和评估方法为提升SAA对天基探测影响的认识,以及下一步有效开展空间碎片探测工作提供参考。     

Extraction of hydrothermal alterations from ASTER SWIR data from east Zanjan,northern Iran

The use of satellite images for mineral exploration has been very successful in pointing out the presence of minerals such as smectite and kaolinite which are important in the identification of hydrothermal alterations. Shortwave infrared (SWIR) bands from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the wavelength of ASTER SWIR bands between 1.65 and 2.43 μm has a good potential for mapping a hydrothermal alteration minerals such as alunite, pyrophyllite, kaolinite, illite–muscovite–sericite, and carbonate. In this range, hydroxide minerals which have been produced by hydrothermal alteration exhibit good absorption compared to shorter or longer wavelengths. In this research which aims to remove atmospheric and topographic effects from ASTER SWIR data, the authors used the log-residual method (LRM) with the minimum noise fraction (MNF) transformation to create a pixel purity index (PPI) which was used to extract the most spectrally pure pixels from multispectral images. Spectral analyses of the clay mineralogy of the study area (east Zanjan, in northern Iran) were obtained by matching the unknown spectra of the purest pixels to the U.S. Geological Survey (USGS) mineral library. Three methods, spectral feature fitting (SFF), spectral angle mapping (SAM), and binary encoding (BE) were used to generate a score between 0 and 1, where a value of 1 indicates a perfect match showing the exact mineral type. In this way, it was possible to identify certain mineral classes, including chlorite, carbonate, calcite–dolomite–magnesite, kaolinite–smectite, alunite, and illite. In this research, two main propylitic and phyllic–argillic zones could be separated using their compositions of these minerals. These two alteration zones are important for porphyry copper deposits and gold mineralization in this part of Iran.     

Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province,Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (?1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.     

Mapping recent agricultural developments in China from satellite data

Landsat data have been employed to study and map agricultural developments in three regions of China: 1) Pearl River delta; 2) Nen River basin; and 3) Xinjiang Autonomous Region. Manual interpretation procedures used in conjunction with multi-date Landsat images and collateral information permitted rice yields to be estimated for the Pearl River delta in 1978. A combination of manual and computer-assisted analyses of Landsat data of Northeast China revealed that more than 15,000 km² of agricultural land in a 184,500 km² study area had been reclaimed from rangeland and marshland. These analyses also indicated a shift in cropping practices, with the foodcrops wheat and corn replacing cash crops such as soybeans. In the arid west, Landsat image data provided valuable input to a geographic information system (GIS). It appears the GIS approach will prove useful for evaluating agricultural land potential in the remote areas of China.     

Determination of surface albedo from satellites

The dynamical features of the climate system result from the interaction of the atmosphere with the surface. The hope for improving climate prediction, on seasonal and interannual time scales, is based on the premise that slowly varying boundary conditions force well defined predictable patterns of general circulation.It is now recognized that in models of surface climate over land, surface properties should not be regarded as constants and that it is important to specify a realistic value of the surface albedo¹ in order to correctly estimate the amount of solar radiation absorbed at the surface. The albedo is also considered by some as an internal dependent parameter of climate, since it time integrates the effects of changes in more variable quantities, such as rainfall.The global nature of the links between forcing and response imply global monitoring of the parameters which control the transfer and feedback of the energy at the surface/atmosphere interface. Satellites can play an important role in resolving the difficult problem of properly representing the average surface albedo over large areas. However, satellites measure only the earth-atmosphere reflectance in narrow spectral intervals, narrow solid angles, and in most cases at a fixed local time. To derive from these observations the effective surface albedo, one has to compute the total reflected planetary flux, integrated over the whole solar spectrum, over all the viewing angles, and over time. A transformation from top of the atmosphere to the surface is also required.In this presentation the implications of atmospheric corrections, bidirectional reflectance, and transformations from narrow spectral bands to the total solar spectrum for albedo derivations will be addressed. Also, the various attempts to derive surface reflectivity and surface albedo from satellites will be reviewed.     

基于小波域清晰度评价的光场全聚焦图像融合

传统全聚焦图像融合以相机多次曝光拍摄的多聚焦图像为基础,光场相机在单次曝光后可计算空间任意深度的重聚焦图像,为后期全聚焦图像的获取提供便利。提出了一种基于小波变换的光场全聚焦图像获取算法,可有效避免传统空域图像融合算法的块效应,获得较高质量的全聚焦图像。该算法通过对微透镜阵列光场相机获得的4D光场数据进行空间变换与投影,得到用于全聚焦图像融合的重聚焦图像,对各帧重聚焦图像进行小波分解提取高、低频子图像集,提出区域均衡拉普拉斯算子、像素可见度函数分别构建融合图像的高、低频小波系数实现图像融合,其性能优于传统的区域清晰度评价函数。实验验证了所提算法的正确性和有效性,采用Lytro光场相机的原始数据计算了融合全聚焦图像,与传统图像融合算法相比,人眼视觉效果更好,客观图像指标也得到了提高。      

The derivation of Halley parameters from observations

A set of nominal model parameters for P/Halley is derived from its light curve and spectra. In those cases where Halley observations are not sufficient, the average value derived from a large set of other comets has been used, or data from comet Bennett, Halley's best analogue has been taken. The derived parameters include nucleus mass, size, density, albedo, rotation period, axial inclination, and surface temperature, the composition of the parent molecules, the total gas and dust production rates, distributions for the dust size and bulk density as well as various other parameters.     

Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios. Image classification by multiple classifier system has been evolving as a promising method for enhancing accuracy and reliability of image classification. Because of the diversity in generalization capabilities of various dimensionality reduction methods, the classifier optimal to the problem and hence the accuracy of image classification varies considerably. The impact of including multiple dimensionality reduction methods in the MCS architecture for the supervised classification of a hyperspectral image for land cover classification has been assessed in this study. Multi-source airborne hyperspectral images acquired over five different sites covering a range of land cover categories have been classified by a multiple classifier system and compared against the classification results obtained from support vector machines (SVM). The MCS offers acceptable classification results across the images or sites when there are multiple dimensionality reduction methods in addition to different classifiers. Apart from offering acceptable classification results, the MCS indicates about 5% increase in the overall accuracy when compared to the SVM classifier across the hyperspectral images and sites. Results indicate the presence of dimensionality reduction method specific empirical preferences by land cover categories for certain classifiers thereby demanding the design of MCS to support adaptive selection of classifiers and dimensionality reduction methods for hyperspectral image classification.     

Retrieval of some climatic elements of Africa from METEOSAT data analysis

A study of some climatic elements of Africa from METEOSAT imagery has been undertaken at our laboratory. The general objectives of this project and its interaction with a Sahelian climate simulation with the LMD GCM are described. The problems relative to the measurement of seasonal land surface albedo variations are outlined. Preliminary results of a simple method comparing January and April 1982 are shown and discussed.     

Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China

Vegetation typically elicits dynamics at the seasonal and annual level. Time-series of normalized difference vegetation index (NDVI) datasets, such as the pathfinder AVHRR land (PAL) NDVI dataset, have proven to be appropriate for the detection of long-term vegetation cover changes. It has been applied in modelling experiments for terrestrial ecosystems at the global, continental, and regional scales. But some PAL NDVI time series remain significant residual effects and noise levels. A simple method, the mean-value iteration filter (MVI), has been developed to reduce the noise and to enable the reconstruction of high quality NDVI time-series. A comparison between the newly developed method and other existing methods (the modified BISE algorithm and a fast Fourier transform algorithm) indicates that the newly developed method is an effective tool for reconstructing high-quality time series of PAL NDVI time series.     

基于学习的图像智能适配显示技术

为适应不同的显示分辨率,出现了各式各样的图像适配显示(IR)的方法.提出了基于图像列的一种快速适配显示方法.在处理过程中,首先,计算一个原始图像的重要性图;其次,根据图像每列的重要性程度为其分配一个比例因子,对不同图像而言,应对比例因子设置不同的上限才可以得到较好的结果;最后,提出通过机器学习方法计算出不同图像的上限,从而可以高效率地得到理想的结果.根据每一列的比例因子采用像素融合的方式处理图片得到目标分辨率.本方法是基于列实现的,其复杂度低、便于计算;设置每列系数的上限控制了图像重要部分的宽度,从而减少了不连贯,处理结果更为自然.      

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.