Development of digital interactive processing system for NOAA satellites AVHRR data

The paper discusses the digital image processing system for NOAA/AVHRR data including Land applications — configured around VAX 11/750 host computer supported with FPS 100 Array Processor, Comtal graphic display and HP Plotting devices; wherein the system software for relational Data Base together with query and editing facilities, Man-Machine Interface using form, menu and prompt inputs including validation of user entries for data type and range; preprocessing software for data calibration, Sun-angle correction, Geometric Corrections for Earth curvature effect and Earth rotation offsets and Earth location of AVHRR image have been accomplished. The implemented image enhancement techniques such as grey level stretching, histogram equalization and convolution are discussed. The software implementation details for the computation of vegetative index and normalized vegetative index using NOAA/AVHRR channels 1 and 2 data together with output are presented; scientific background for such computations and obtainability of similar indices from Landsat/MSS data are also included. The paper concludes by specifying the further software developments planned and the progress envisaged in the field of vegetation index studies.     

Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1 km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.     

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.     

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.     

Vegetation cover mapping from NOAA/AVHRR

The visible and near infrared channels, Ch₁ and CH₂ respectively, on the Advanced Very High Resolution Radiometer (AVHRR) provide daily information for monitoring changes in vegetation and crops. Data from these channels are used to create a normalized vegetation index (NVI) that is sensitive to changes in green leaf biomass and is represented mathematically by:

$\text{NVI =}\text{CH}{}_2\text{? CH}{}_1\text{CH}{}_2\text{+ CH}{}_1$

Operational products generated at NOAA include full-scale 1-km resolution images of the NVI covering areas viewed in a single swath of the polar-orbiting NOAA satellite. Global scale NVI images are also produced by compositing over a seven-day period, saving the maximum NVI created daily for each local array (resolution of 15 km at the equator to 30 km at the poles). Such seven-day mapping reduces the effect of cloud contamination. The global vegetation indices are used by foreign and U.S. government agencies for operational and experimental purposes such as assessment of crop conditions, monitoring potential desert locust breeding grounds, forest fire danger models, and monitoring range lands for forage availability. Examples include changes in the NVI in the Lake Chad vicinity, 1981–1982 and 1984; western United States NVI; and seasonal variations of the NVI in the Sahel using the global operational data base, 1982–1983.     

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.     

Long-term changes in indices of geomagnetic activity at the auroral station Sodankylä

Here we compare the traditional analog measure of geomagnetic activity, Ak, with the more recent digital indices of IHV and Ah based on hourly mean data, and their derivatives at the auroral station Sodankylä. By this selection of indices we study the effects of (i) analog vs. digital technique, and (ii) full local-time vs. local night-time coverage on quantifying local geomagnetic activity. We find that all other indices are stronger than Ak during the low-activity cycles 15–16 suggesting an excess of very low scalings in Ak at this time. The full-day indices consistently depict stronger correlation with the interplanetary magnetic field strength, while the night-time indices have higher correlation with solar wind speed. The Ak index correlates better with the digital indices of full-day coverage than with any night-time index. However, Ak depicts somewhat higher activity levels than the digital full-day indices in the declining phase of the solar cycle, indicating that, due to their different sampling rates, the latter indices are less sensitive to high-frequency variations driven by the Alfvén waves in high-speed streams. On the other hand, the night-time indices have an even stronger response to solar wind speed than Ak. The results strongly indicate that at auroral latitudes, geomagnetic indices with different local time coverage reflect different current systems, which, by an appropriate choice of indices, allows studying the century-scale dynamics of these currents separately.     

气象卫星的现况和发展

<正> 一、引言自从1960年发射第一颗气象卫星以来已经25年了。这25年来气象卫星技术及其应用都有了很大发展,在全球天气预报、灾害性天气监视、海洋和水文环境监测、农业和交通中起了越来越重要的作用。气象卫星的功能可以大致概括为: (一)利用遥感探测仪器对卫星下垫面进行探测。探测器主要有两类:一类是成象仪     

Investigations of the Krasnoyarsk Reservoir waters based on the multispectral satellite data

In this work historical investigations and modern results of classification of the Krasnoyarsk Reservoir are presented. The paper presents results of studying the dynamics of phytopigments and other optically active components, using multispectral satellite data. Several approaches to interpreting satellite data for optically complex inland water bodies are offered. Based on results of historical investigations it is shown that the spatial distribution of phytoplankton in the reservoir stems back to the time of its formation. Color index in the red spectral region (CIR) is introduced. A relationship between the color index and chlorophyll concentration is investigated. The CIR, derived from the AVHRR data, has been found to be related to chlorophyll concentration. Based on MODIS data, the waters of the Krasnoyarsk Reservoir have been classified in accordance with their optical spectral variability, using the technique of unsupervised IsoData classification. An empirical relationship between multispectral MODIS data and the ground-truth measurements of chlorophyll concentration has been found.     

Retrieval of crop variables with field-based X-band microwave remote sensing of ladyfinger

Modeling in agriculture has been widely used to retrieve and monitor various soil and crop growth variables. Remote sensing, especially radar sensors can be useful for temporal and spatial monitoring of the soil and plant variables. Therefore, in this paper field measurements of crop ladyfinger were carried out to examine the dependency of radar backscatter on crop–soil variables and to develop a method for monitoring and retrieving crop variables for ladyfinger. A crop-bed was prepared to observe scatterometer response in the angular range of incidence angle 20–70° at 9.89 GHz in the X-band for VV- and HH-polarization. At the same time, soil moisture, plant height, leaf area index and aboveground biomass were measured at various growth stages of crop ladyfinger. The angular variation of scattering coefficient decreases with the age of crop ladyfinger shows the dominance of crop effect on soil moisture effect at the older age. Thus, angular trends are more flat as the plant grows since the effects of soil is masked by developing vegetation. Scattering coefficient increases with the increase of leaf area index for both polarizations (i.e. VV- and HH-). It was found that leaf area index and aboveground biomass of crop ladyfinger are highly correlated with microwave frequency more than with plant height and soil moisture. Leaf area index and biomass of ladyfinger crop were retrieved by polarization based model and non-linear least square optimization model. These two models gave very good results for the retrieval of leaf area index and aboveground biomass.     

Determination of land surface temperature using precipitable water based Split-Window and Artificial Neural Network in Turkey

Land surface temperature (LST) calculation utilizing satellite thermal images is very difficult due to the great temporal variance of atmospheric water vapor in the atmosphere which strongly affects the thermal radiance incoming to satellite sensors. In this study, Split-Window (SW) and Radial Basis Function (RBF) methods were utilized for prediction of LST using precipitable water for Turkey. Coll 94 Split-Window algorithm was modified using regional precipitable water values estimated from upper-air Radiosond observations for the years 1990–2007 and Local Split-Window (LSW) algorithms were generated for the study area. Using local algorithms and Advanced Very High Resolution Radiometer (AVHRR) data, monthly mean daily sum LST values were calculated. In RBF method latitude, longitude, altitude, surface emissivity, sun shine duration and precipitable water values were used as input variables of the structure. Correlation coefficients between estimated and measured LST values were obtained as 99.23% (for RBF) and 94.48% (for LSW) at 00:00 UTC and 92.77% (for RBF) and 89.98% (for LSW) at 12:00 UTC. These meaningful statistical results suggest that RBF and LSW methods could be used for LST calculation.     

Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.     

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.     

Microwave specular scattering response of soil texture at X-band

Soil texture is an important soil parameter that is useful for meteorology, climatology, hydrology, ecology, etc. Therefore, it is important to classify soil based on soil texture (i.e., sand, silt and clay). A lot of studies with radar remote sensing have been carried out to estimate soil moisture and surface roughness, but less attention has been given to study the effect of individual soil texture on radar scattering, especially in specular direction. The main aim of this paper is to check the behavior of specular scattering with change in soil texture. This effect has also been analyzed in presence of soil moisture and surface roughness. Scattering coefficient has been retrieved for various soil texture fields with indigenously designed X-band bistatic scatterometer for a range of incidence angles (from 30° to 70° in steps of 10°) in both like polarizations, i.e., HH-polarization and VV-polarization. Observations were made at 10 GHz frequency. Four different fields were considered on the basis of soil texture variations; especially changes in sand percentage were made. Roughness (smooth soil to 1.4 cm rms surface height) and moisture (dry soil to 0.21 cm³ cm⁻³ volumetric soil moisture) conditions of these fields were varied for observations. Strong change in specular scattering coefficient is observed by changing the sand percentage in soil for HH-polarization, while in case of VV-polarization a lesser change is observed. Also a high change in specular scattering coefficient is noticed once moisture is added to the soil. It is difficult to observe the change in specular scattering coefficient with change in soil texture when surface is considered as rough. Therefore, it is important to minimize the roughness effect while observing the texture with specular scattering. For this purpose, polarization study was carried out to see how polarization can be helpful to minimize the roughness effect. The effect of soil texture on copolarization ratio is critically analyzed, and it is observed that for higher incidence angle (50°), the distinction in soil texture fields are clearly observable on the basis of copolarization ratio. This type of study will be helpful in near future to design the bistatic radar system for soil parameter monitoring, especially for cartwheel satellite system.     

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.     

Variations of the magnetosheath ion flux and geomagnetic activity

The magnetosheath plays a dominant role in the Sun–Earth connection because the magnetosheath field and plasma actually interact with the magnetosphere. The interactions change the magnetospheric magnetic field from its nominal value through a long chain of different processes. The change is usually described by geomagnetic indices and thus it can be expected that these indices would reflect changes in the magnetosheath. The present paper analyzes the relation between geomagnetic activity characterized by changes of the K_p, D_ST and AE indices and ion flux measured in the night-side magnetosheath. The results suggest a weak dependence of the D_ST index on the ion flux in the inner magnetosheath that is connected with a magnetopause displacement. On the other hand, fluctuations of the ion flux in the analyzed frequency range do not correlate with any of the indices.     

Crop parameters estimation by fuzzy inference system using X-band scatterometer data

Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.     

Combining frequency and time domain models to forecast space weather

Space weather series incorporate several distinct components, cycles at multiple frequencies, irregular trends, and nonlinear variability. The cycles are stochastic, i.e., the amplitude varies over time. Similarly, the trend is stochastic: the slope and direction of trending change repeatedly. This study sets out a combined model using both frequency and time domain methods, in two stages. In the first stage, a frequency domain algorithm is estimated and forecasted. In the second stage, the forecast is used as an input in a neural network. The combined model also includes a term enabling the model to react inversely to large deviations between the actual values and forecast. The models are evaluated using two data sets, the hemispheric power data obtained from the Polar Orbiting Environment satellites, and the Aa geomagnetic index. All the series are at a daily resolution. Forecasting experiments are run over horizons of 1–7 days. The models are estimated using a moving window or adaptive approach. The combined model consistently achieves the most accurate results. Among single equation methods, the frequency domain model is more accurate for the geomagnetic index because it is able to capture the underlying cycles more effectively. In the hemispheric power series, the cycles are less pronounced, so that time domain methods are more accurate, except at very short horizons. Nevertheless, in both data sets, the combined model works well because the frequency domain algorithm captures cyclical behavior, while the neural net is better able to capture short-term dependence and trending.     

Meteorological satellites and cooperative programs

Satellite data are available to meteorological centers around the world in two forms: (1) real-time reception directly from the spacecraft to the users; (2) processed data via the Global Telecommunications System (GTS). Real-time data is broadcast by satellites operated by both the U.S. and the U.S.S.R. From the NOAA series, Automatic Picture Transmission (APT) has been in wide use for 19 years. High Resolution Picture Transmission (HRPT) has been available to users with more sophisticated receiving equipment for the past 10 years. The Meteor satellites have been broadcasting for over 10 years. From the geostationary satellites (GOES), specialized products are broadcast via weather facsimile (WEFAX), and for users with very sophisticated ground systems, real-time geostationary images are available. Derived data, i.e., vertical temperature soundings of the atmosphere, etc., are routinely available on the GTS. The characteristics, utility, current utilization and future developments of these services will be reviewed and discussed.     

基于位姿测量不确定度的飞机对接质量评估

针对基于位姿的数字化测量辅助飞机大部件对接技术的发展与应用,对位姿测量不确定度以及基于不确定度的质量评价方法进行了研究.给出了数字化对接环境下大部件位姿的数学表达形式及意义.提出了基于协方差矩阵的位姿测量不确定度解析算法,并通过仿真算例与蒙特卡洛仿真法进行了对比,验证了该算法的有效性.给出了飞机大部件对接过程数字化测量工艺能力指数的概念,用于对测量结果的可信性进行评价;通过构建位姿测量不确定度与对接质量评估指标间的映射关系,提出了一种基于位姿测量不确定度的大部件对接质量评估方法,并以机翼-机身对接过程为案例,对方法的可行性、算法的有效性进行了验证.