SANA: Sub-pixel automatic navigation of AVHRR imagery

An automatic method (SANA) for sub-pixel navigation of Advanced Very High Resolution Radiometer (AVHRR) imagery is proposed. It progressively corrects satellite attitude and reduces navigation errors all over the scene by using an iterative approach. Tests performed on more than 400 AVHRR passes over Europe, demonstrate the above mentioned method capability to obtain, with no human intervention, a final navigation accuracy within 1 pixel. Main characteristics of such a method are its processing speed as well as its full exportability to other satellite packages.     

Potential operational multi-satellite sensor mapping of snow cover in maritime sub-polar regions

Scottish snow cover as an example of a maritime sub-polar region has two principal problems for an operational monitoring programme: the often ephemeral nature of the snow cover, and the loss of direct access to snow imagery due to clouds. At present only the NOAA AVHRR series provides images with the required temporal and spatial resolutions. Based on the availability of data from the Dundee satellite data receiving station a range of NOAA-12, -14 and -15 day and night passes were collected and processed. Three snow cover products were produced from the NOAA AVHRR/2 data: snow area based on channel 134 ISODATA classifications, percentage snow cover based on multi-temporal Normalized Difference Vegetation Index (NDVI), and daily maximum snow surface temperature maps using split-window combinations of thermal channels. Noted improvements were evident in the accuracy and resolution of snow cover classifications based on provisional testing of AVHRR/3 data. Maximum snow surface temperature maps indicated a potential for mapping areas of snow melt. The principal limitation in the operational snow cover mapping with AVHRR, however, remains the loss of temporal resolution due to cloud cover.     

Linear mixture modelling applied to AVHRR data for crop area estimation

Abstract

A technique for estimating crop coverage using linear mixture modelling of multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data is presented for a study area in northern Greece. This paper identifies some of the problems associated with using satellite sensor data with coarse spatial resolution for crop area estimation. Using satellite sensor imagery with a high spatial resolution to extrapolate ground measurements to AVHRR scales, the paper shows how the mixture model can be applied to AVHRR data in a mixed agricultural system. Crop areas are estimated to an average accuracy of 89 percent on regional scale using this technique. The results show that this linear mixture modelling has potential for operational crop area monitoring on a regional basis.     

The problem of AVHRR image navigation revisited

In this study, Earth location errors in AVHRR satellite data and methods for their correction are examined with particular application to oceanic regions far removed from ground control. A general correction procedure, using landmarks or Ground Control Points (GCPs) and taking into account landmark uncertainties, is presented. Correction functions arc derived as expansions for any complete basis. Operationally-available estimates of Earth location are used as a first-guess in developing the correction procedure. In particular, polynomial expansions are used to represent the correction functions which provide the basis for renavigating the satellite data. The coefficients of the polynomial expansions are obtained using the method of least-squares. The stability of the correction procedure with respect to. local errors in navigation, (i.e. within a scene) and how to select the correct order of the correction polynomials are examined. Uncertainty in extrapolating navigation corrections over remote regions is examined and quantified. The importance of landmark uncertainty in degrading renaviga-tion accuracy is also addressed. Several parameters are introduced to optimize the choice of GCPs and their distributions. The procedures which are developed are then applied to simulated and actual AVHRR imagery. Finally, the impact of local errors in navigation, which most likely arise from rapid variations in spacecraft attitude, on renavigation accuracy is emphasized and one possible solution proposed.     

Improving flood monitoring by the Robust AVHRR Technique (RAT) approach: the case of the April 2000 Hungary flood

In the past, satellite remote sensing techniques have been widely used within the flood risk management cycle. In particular, there have been many demonstrations of the operational use of satellite data for detailed monitoring and mapping of floods and for post-flood damage assessment. When frequent situation reports are requested (e.g. in the emergency phase or for early warning purposes) to assist civil protection activities, high temporal resolution satellites (mainly meteorological, with revisiting times from hours to minutes) can play a strategic role. In this paper, a new Advanced Very High Resolution Radiometer (AVHRR) technique for monitoring flooded areas is presented. Its performances are evaluated in comparison with other well-known approaches, analysing the flood event that occurred in Hungary during April 2000 involving the Tisza and Timis Rivers. The preliminary results seem to indicate the benefits of such a new technique, especially when different observational conditions are considered. In fact, compared with previously proposed techniques, the proposed approach: (a) is completely automatic (i.e. unsupervised with no need for operator intervention); (b) improves flooded-area detection capabilities strongly reducing false alarms; and (c) automatically discriminates (without the need for ancillary information) flooded areas from permanent water bodies. Moreover, it is globally applicable and, because of the complete independence on the specific satellite platform, is easily exportable to different satellite packages.     

Improving volcanic ash cloud detection by a robust satellite technique

Automated and reliable satellite-based techniques are strongly required for volcanic ash cloud detection and tracking. In fact, volcanic ash clouds pose a serious hazard for air traffic and the synoptic (and possibly frequent) coverage offered by satellites can provide exciting opportunities for monitoring activities as well as for risk mitigation purposes.A new, AVHRR-based technique for improved automatic detection of volcanic clouds by means of multi-temporal analysis of historical, long-term satellite records has been recently proposed. The technique basically rests on the Robust AVHRR Techniques (RAT) approach, which is an innovative strategy of satellite data analysis, devoted to a former characterisation of the measured signal, in terms of expected value and natural variability and a further recognition of signal anomalies by an automatic, unsupervised change detection step. In this work, an extension of this method to nighttime observations is presented, by using thermal infrared information coming from AVHRR bands centred approximately at 3.5, 11.0 and 12.0 μm. Results achieved for two recent eruptive events of Mount Etna (occurred in May 2000 and in July 2001) seem to be encouraging, showing clear improvements in terms of ash detection sensitivity as well as in terms of false alarms reduction. The technique performance is also evaluated by comparison with the traditional “split-window” brightness temperature difference method; this exercise revealed a general improvement obtained by the proposed approach, even though some common problems still remain unsolved. The main merits of such an approach are its intrinsic self-adaptability to different environmental/natural/observational conditions and its natural exportability also to different satellite sensors. The results here presented show the benefits of such a technique especially when different observational conditions (time of pass, seasonal period, atmospheric moisture, solar illumination, volcanic cloud composition, satellite angles of view, etc.) are considered.The future prospects, also in terms of possible operational scenarios, coming from the implementation of such an approach on the new generation of satellite sensors (like, for example, SEVIRI aboard Meteosat Second Generation platform) are also discussed.     

卫星导航接收设备健康管理技术研究

针对卫星导航接收设备的技术特点和维护需求,基于故障检测与健康管理技术,提出了一种从射频前端、数字信号处理到算法实现全流程的预测与健康管理方案,搭建了卫星导航接收设备预测与健康管理系统体系结构。基于检测信息设置,完成信号处理与状态监测,开展卫星导航接收设备实时健康评估与寿命预测研究,根据评估与预测结果制定维护方案,实现卫星导航接收设备的健康管理。通过开展仿真试验与实际工程应用,验证了所设计的卫星导航接收设备健康管理方案的正确性与有效性,具有一定的工程推广价值。     

Agreement assessment of NOAA/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas.     

Land cover characterization and change detection for environmental monitoring of pan-Europe

Environmental studies need up-to-date and reliable information on land use and land cover. Such databases, which can be characterized by a high spatial accuracy and that can be updated easily, are currently not available for Europe as a whole. We investigated the applicability of satellite data for Pan-European Land Cover Monitoring (PELCOM). The main objective was to develop a method by which to obtain a 1 km spatial resolution pan-European land cover database that can be updated easily using National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) satellite data. The database will be used as input for environmental impact studies and climate research. The study takes full advantage of both multi-spectral and multi-temporal 1 km AVHRR data. The proposed methodology for land cover mapping has its limitations in monitoring changes due to the spatial resolution and the limited accuracy of AVHRR-derived land cover data. Therefore, a change detection technique based on the use of thematic fraction images highlights those areas where the proportions of the various land cover types have changed.     

GNSS欺骗式干扰检测综述

近年来,随着卫星导航系统在军事监测、精细农业、交通监控、资源勘探、灾害评估等领域的广泛应用,提高卫星导航系统的安全性和鲁棒性成为研究的热点。介绍了卫星导航欺骗式干扰的原理与分类,根据卫星信号从生成到最终实现定位导航这一过程和基于统计学层面将当前的欺骗式干扰检测技术分成基于导航数据信息、基于空间处理、基于射频前端处理、基于基带数字信号处理、基于定位导航运算结果和基于机器学习等六大类,并对每类采用的检测方法进行性能对比,从实时检测和综合检测两个方面对未来的欺骗式干扰检测进行展望。     

Crop condition and yield simulations using Landsat and MODIS

Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6×1.6-km² grid scale were comparable to county yields reported by the USDA-National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.     

Strategy for studying nocturnal aerosol optical depth using artificial lights

In this concept study, we develop a strategy for the monitoring of nighttime aerosol particle optical properties over land using artificial lights on the earth's surface. We demonstrate the possibility of detecting significant aerosol events and deriving variations in aerosol optical depth using the operational linescan system (OLS) on defence meteorological satellite program (DMSP) platforms. Since the OLS instruments have no on‐board calibration, only a qualitative study of the potential to estimate aerosol and cloud properties using city lights at night is shown in this paper. The technique is demonstrated using China and India as test regions. With the launch of the visible/infrared imager/radiometer suite (VIIRS) instrument on the national polar‐orbiting operational environmental satellite system (NPOESS) in the coming decade, fully quantitative retrieval of nighttime aerosol particle optical properties from space observations using such methods will become possible. Such work will benefit aerosol forecasting, safety of navigation, aerosol data assimilation and climate studies.     

The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images

Operational use of remote sensing as a tool for post‐fire, Mediterranean forest management has been limited by problems of classification accuracy arising from confusion of burned and non‐burned areas. Frequently, this occurs as a result of slope illumination and shadowing effects caused by the complex topography encountered in many forested areas. Cloud shadows can also be a problem. The aim of this work was to investigate how image classification results could be improved by removing the illumination effects of topography from satellite images. This was achieved by applying supervised classification to both uncorrected and topographically corrected LANDSAT TM data for a site on the Greek island of Thasos. The classification methodology included atmospheric and geometric correction, field‐based training, seperability/contingency analysis and maximum likelihood processing. The classification scheme was determined on the basis of consultation with the Greek Forest Service. Overlay of the resulting class maps enabled comparison of the total burned area and its spatial extent using the two different approaches to processing. The results of each approach were compared with the forest perimeter map generated by the Forest Service using traditional survey methods. Accuracy assessment and error analysis clearly indicated that the removal of the topographic effect from the satellite image before its classification resulted in more accurate mapping of the burned area. It is concluded that operational use of satellite remote sensing for forest fire management depends on accurate, robust, widely available and proven techniques. Topographic correction should now be regarded as an essential element of any classification methodology which will be used for operational, post‐fire management of forests in complex Mediterranean landscapes.     

Validation and comparison of different techniques for the derivation of digital elevation models and volcanic monitoring (Vulcano Island,Italy)

High accurate digital elevation models (DEM) acquired periodically over a volcanic area can be used for monitoring crustal deformations. Airborne stereoscopic photography is a powerful tool for the derivation of high resolution DEM, especially when combined with Global Positioning System (GPS). We analyse data acquired on Vulcano Island (Italy) to assess the performance of two photogrammetry methods for DEM generation. The first method is based on automatic digital processing of scanned airborne stereo images from a film camera (Wild RC20). In the second method digital stereo data from the multi-spectral High Resolution Stereo Camera-Airborne (HRSC-A) are used. Accuracy assessment through comparison with kinematic GPS height profiles shows that both DEMs have accuracy on the order of few decimetres. Direct comparison of the two DEMs on the La Fossa volcanic cone provides a standard deviation of the residuals of 78 cm. Residuals greater than two metres between the two DEMs acquired at one year interval are locally evidenced in unstable areas with uneven morphology. The application of photogrammetric DEMs is also discussed within a SAR interferometry study carried out on Vulcano Island to evaluate the potentialities of such techniques for ground deformation monitoring. Although accuracy better than 1 m or 2 m is not required for satellite SAR interferometry, we show how the precise photogrammetric DEMs could still significantly improve SAR interferograms of Vulcano Island.     

AVHRR channel selection for land cover classification

Mapping land cover of large regions often requires processing of satellite images collected from several time periods at many spectral wavelength channels. However, manipulating and processing large amounts of image data increases the complexity and time, and hence the cost, that it takes to produce a land cover map. Very few studies have evaluated the importance of individual Advanced Very High Resolution Radiometer (AVHRR) channels for discriminating cover types, especially the thermal channels (channels 3, 4 and 5). Studies rarely perform a multi-year analysis to determine the impact of inter-annual variability on the classification results. We evaluated 5 years of AVHRR data using combinations of the original AVHRR spectral channels (1-5) to determine which channels are most important for cover type discrimination, yet stabilize inter-annual variability. Particular attention was placed on the channels in the thermal portion of the spectrum. Fourteen cover types over the entire state of Colorado were evaluated using a supervised classification approach on all two-, three-, four- and five-channel combinations for seven AVHRR biweekly composite datasets covering the entire growing season for each of 5 years. Results show that all three of the major portions of the electromagnetic spectrum represented by the AVHRR sensor are required to discriminate cover types effectively and stabilize inter-annual variability. Of the two-channel combinations, channels 1 (red visible) and 2 (near-infrared) had, by far, the highest average overall accuracy (72.2%), yet the inter-annual classification accuracies were highly variable. Including a thermal channel (channel 4) significantly increased the average overall classification accuracy by 5.5% and stabilized interannual variability. Each of the thermal channels gave similar classification accuracies; however, because of the problems in consistently interpreting channel 3 data, either channel 4 or 5 was found to be a more appropriate choice. Substituting the thermal channel with a single elevation layer resulted in equivalent classification accuracies and inter-annual variability.     

An automatic system for AVHRR land surface product generation

Making products from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar‐orbiting satellites can be time consuming and an automated technique for image processing is required to generate long time series of AVHRR imagery. This paper aims to describe the development of a system for fully‐automated AVHRR image processing, including radiometric calibration, precise geo‐registration and generating land‐surface products, such as vegetation indices, maximum value composites and cloud masks. Tests for crop monitoring purposes were carried out using High Resolution Picture Transmission (HRPT) images between October 2003 and April 2004. The region used to evaluate the system was the State of Paraná, one of the primary soybean producers in Brazil. Results have shown that for severely cloud‐filtered images, the system was effective in generating geometrically precise image products, with geolocation errors less than a pixel. The developed system can be operated with no human intervention and can be used as an important tool for NOAA‐AVHRR image users especially those who need to use long time series.     

Deriving the operational nonlinear multichannel sea surface temperature algorithm coefficients for NOAA-15 AVHRR/3

The National Oceanic and Atmospheric Administration (NOAA) currently uses Nonlinear Sea Surface Temperature (NLSST) algorithms to estimate sea surface temperature (SST) from NOAA satellite Advanced Very High Resolution Radiometer (AVHRR) data. In this study, we created a three-month dataset of global sea surface temperature derived from NOAA-15 AVHRR data paired with coincident SST measurements from buoys (i.e. called the SST matchup dataset) between October and December 1998. The satellite sensor SST and buoy SST pairs were included in the dataset if they were coincident within 25 km and 4 hours. A regression analysis of the data in this matchup dataset was used to derive the coefficients for the operational NLSST equations applicable to NOAA-15 AVHRR sensor data. An independent matchup dataset (between January and March 1999) was also used to assess the accuracy of these day and night operational NLSST algorithms. The bias was found to be 0.14°C and 0.08°C for the day and night algorithms, respectively. The standard deviation was 0.5°C or less.     

Vegetation classification based on advanced very high resolution radiometer (AVHRR) satellite imagery

Many studies have investigated the potential of Landsat data for vegetation analyses, but there have been few attempts to make similar use of meteorological satellite data. The feasibility of utilizing AVHRR imagery for vegetation classification was tested using a vegetation gradient model based on an experimental climatological variable and AVHRR data along an east-west transect across Texas. The normalized difference vegetation index of AVHRR data, when plotted against vegetation characteristics and moisture values, suggested that a multivariate gradient model incorporating satellite spectral and meteorological data has promise as a technique for vegetation stratification and monitoring.