Automatic registration of SeaWiFS and AVHRR imagery

An automatic approach for integrating images from multitemporal and multisensor remote sensing is outlined based on coastlines derived from satellite images. One point on a coastline is taken as a candidate point of ground control points (GCPs). A correlation-relaxation (CR) technique is used to search for the corresponding point in the second image. A decision rule is used to guarantee the correctness of GCPs which are used to compute a polynomial equation for registering two images. The relationship between the accuracy of registration and the number of GCPs indicates that a large number of GCPs will lead to more accurate image registration. The correctness of GCPs can also improve the accuracy of geometric registration. The approach can be used particularly well to register images of coastal areas. Examples are given for registration of SeaWiFS and AVHRR imagery.     

Hurricane Georges and vegetation change in Puerto Rico using AVHRR satellite data

This study assesses natural disturbances at Puerto Rico resulting from hurricane Georges in September 1998. The study was done using data from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) 14 satellite. Specifically, the Luquillo Experimental Forest, the Guanica Dry Forest and five cities were examined using AVHRR data. It was found that although there is probably no significant relationship between Georges and the before/after temperature data, there is a statistically significant relationship between distance to the location affected by the hurricane and the extent of changes in NDVI, a fact that suggests that it is possible to measure hurricane impacts on vegetation by using AVHRR data.     

Assessing the relation between emissivity and vegetation with AVHRR

The present study relates to the climatologic component of the Global Change Program and uses Advanced Very High Resolution Radiometer (AVHRR) data. The analysis of the radiative transfer process between the surface and space shows that the Thermal Infra-Red Emsissivity (TIRE) can be expanded with respect to a non-dimensional small parameter related to the difference between atmospheric absorptions of AVHRR channels 11 and 12μm, respectively. The discussion shows that retrieved values of TIRE remain in good accordance with those expected. The land surface temperature can be expanded in a similar manner, with respect to the split window technique which indicates an inverse relation between temporal variables of the Normalized Difference Vegetation Index (NDVI) and TIRE.     

Neural network based methods for cloud classification on AVHRR images

Artificial neural networks trained on spectral and textural features extracted from Advanced Very High Resolution Radiometer (AVHRR) images have been used to develop an automated cloud classification system. Selection of the optimum combination of features was achieved by using statistical methods presented in earlier work by Gu et al. and by running large numbers of neural network simulations on test datasets. The performance of these methods surpasses that of other approaches such as the use of Gabor filters for texture segmentation and the maximum likelihood classifier. A particular architecture for an operational classification system is presented based on a two-stage multiple network configuration which is shown to segment complex images to a high degree of accuracy and achieves an overall accuracy on an independent, representative test set of 91%.     

基于聚类分析的SAR图像变化检测

针对传统变化检测方法用于合成孔径雷达(SAR)图像时不能有效地降低背景变化导致的检测虚警,提出了基于聚类分析的SAR图像变化检测方法。该方法对聚类后SAR图像像素类间变化引入Mahal-anobis距离(M距离),结合变化阈值的选择来分析图像中变化像素的M图,进而实现SAR图像变化检测。仿真结果说明:该方法不仅能克服自然和背景变化给检测带来的困难,而且,不受传感器位置的影响,能有效地对不同时刻的SAR图像进行变化检测。     

Treeline vegetation composition and change in Canada's western Subarctic from AVHRR and canopy reflectance modeling

Climate change is expected to have significant impacts on northern vegetation, particularly along transition zones such as the treeline. Studies of vegetation composition and change in this ecotone have largely focussed on local analysis of individual trees using labour intensive stand reconstruction techniques, which are spatially limited and do not consider vegetation types other than trees. Remote sensing may be well suited to monitoring recent changes across the treeline because it captures integrated changes of all vegetation life forms over large spatial extents. This research examines treeline vegetation composition and change along the western subarctic treeline mapped by Timoney et al. (1992) using a 1 km resolution, 22-year AVHRR archive from 1985-2006. While most remote sensing studies on vegetation change in arctic and subarctic regions only exploit information contained in the Normalized Difference Vegetation Index (NDVI), we examine long-term reflectance trends in AVHRR bands 1 and 2 in addition to NDVI. The GeoSail canopy reflectance model is used to map treeline composition by combining information from 22-year summertime and early springtime composite images. A set of spectral change vectors are then generated from GeoSail simulations and used to classify trends in AVHRR along the treeline to estimate vegetation change. Evaluation of vegetation composition against the MODIS Vegetation Continuous Fields (VCF) product that has been recently validated along the treeline reveals good spatial correspondence. Temporal trends are shown to agree with literature on tundra-taiga vegetation dynamics in recent decades. Evidence is presented that suggests replacement of bare surfaces with herb, conifer decline along the southern treeline, increased shrubiness, and increased conifer recruitment and growth in the north.     

Modelling corn production in China using AVHRR‐based vegetation health indices

Weather‐related crop losses have always been a concern for farmers, governments, traders and policy makers for the purpose of balanced food supplies, demands, trade, and distribution of aid to nations in need. Therefore, early crop loss assessment in response to weather fluctuations is an important issue. This paper discusses the utility of Advanced Very High Resolution Radiometer (AVHRR)‐based vegetation health indices as a proxy for modelling corn yield and for early warning of drought‐related losses of agricultural production in China. The indices were tested in Jilin province on corn yield during 1982–2001 using correlation and regression analysis. A strong correlation between corn yield and the vegetation health indices were found during the critical period of corn growth, which starts 2–3 weeks before and 2–3 weeks after corn tassel. Following the results of correlation analysis, several regression equations were constructed where vegetation health indices were used as independent variables. The estimates of corn yield can be carried out well in advance of harvest and the errors of the estimates are 7–10%. The errors become smaller when the estimations are related to losses in corn yield due to drought.     

Exploring relationships between ENSO and vegetation vigour in the south-east USA using AVHRR data

This research explores the relationship between El Nino/Southern Oscillation (ENSO), captured by equatorial Pacific Ocean Sea Surface Temperature (SST), and interannual variation in vegetation vigour in the southeast USA, captured by Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI), for the period 1982-1992. The moving average and 'baseline' methods (anomaly from the long term mean) were used to extract interannual patterns in the NDVI signature for croplands, deciduous forests and evergreen forests. The ENSO cycle was measured using mean SST anomalies and the percentage of SST cells above certain threshold values (e.g. 1.0° C above the long term mean). The baseline method indicated a weak, yet persistent, negative correlation between ENSO warm phase events and vegetation vigour in the south-east USA. The moving average method yielded similar results but produced higher correlation values (-0.45 to-0.76, significant at the 0.01 level). Use of the 2.0° C threshold SST anomaly was found to yield the highest correlation values as it captures not only the presence but also the intensity of ENSO warm phase events. These results indicate that there is a clear and recognizable, though inconsistent, relationship between ENSO and vegetation vigour in the south-east USA.     

Estimating bidirectional angles in NOAA AVHRR images

In studies concerning the surface bidirectional reflectance distribution function (BRDF) and thermal-infrared multiangular emissions, Sun-sensor geometry must be known. This Letter provides a potential and simple method for NOAA Advanced Very High Resolution Radiometer (AVHRR) users to estimate the imaging configuration of each pixel in a geometrically corrected image. Our formulas were tested with example AVHRR data and their precision was shown to be comparatively high with a maximum error of either the satellite zenith or azimuth angle less than 4°. The standard deviation for the zenith is 2.07° and azimuth is 2.47°.     

Scale considerations in vegetation monitoring using AVHRR data

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) is currently the only operational remote sensing system capable of providing global daily data which can be used for vegetation monitoring. These data are available with resolution cell sizes ranging from around one to 20 km on a side, though the temporal and spatial extent of cover at each resolution is variable. In this paper Normalized Difference Vegetation Index temporal curves derived from AVHRR at different resolutions are compared over both agricultural and natural tropical vegetation types. For the agricultural regions the length of growing season and major breaks of slope associated with key crop development events are equally well shown at coarse and fine resolution. Detailed examination of the curves reveals differences thought to result from temporal changes in landscape structure. Temporal curves derived from AVHRR data at dilTerent spatial resolutions shows that the spatial organization of both agricultural and natural landscapes, tropical forest in this case, changes throughout a single season. Transitions across major ecological zones are detected across a range of resolutions, though the undersampling employed in the generation of the coarser resolution products is found to exert some limitations on the spatial representivity of these data; this varies both with geographical area and time. These observations highlight the importance of a consideration of scale when using AVHRR data for vegetation monitoring, and emphasize the need for dilTerent scales of observation (both in temporal and spatial terms) for different problems and at different times of the year.     

Identification of carbon quantifiable regions in the former Soviet Union using unsupervised classification of AVHRR global vegetation index images

Global Vegetation Index (GVI) data from the Advanced Very High Resolution Radiometer (AVHRR) was used to identify macro-scale vegetation/ land cover regions in the former Soviet Union (FSU). These regions are a better representation of surface vegetation and land cover than can be obtained from existing thematic maps of the FSU. Image classes were identified through cluster analysis using the ISODATA clustering algorithm and a maximum likelihood classifier. Qualitative analysis of the image variants produced with different input parameters indicated that an image with 42 classes best represented significant details in vegetation and land cover patterns without producing uninterpretable levels of details that represent artefacts of the clustering algorithm. Initial identification of image classes has been made by considering the weight of evidence provided by quantitative and qualitative analysis of existing maps, analytical tools from class statistics, ancillary data from a variety of sources and expert assessment by Russian scientists with extensive field experience in the FSU. Overall, this method of image classification using GVI data appears to describe accurately regions with similar vegetation and hind cover across the FSU. Some questions regarding the identification of wetlands and potential problems with classification in the Russian high arctic are discussed. The products of this research will help improve carbon budget estimates of the FSU by providing accurate delineation and definition of carbon quantifiable regions.     

Noise estimation in NOAA AVHRR maximum-value composite NDVI images

Noise has been estimated in Advanced Very High Resolution Radiometer (AVHRR) maximum-value composite Normalized Difference Vegetation Index (NDVI) products using a geostatistical method calculating the noise as the square root of the nugget variance in a semi-variogram. High noise values (20-50 per cent of the standard deviation) were found in individual scenes, whereas values close to zero were found in a nine-year average image. The higher levels of noise in individual images were present in humid areas particularly during the wet season, indicating that atmospheric effects and cloud contamination may be among the main contributing factors.     

New method and error analysis of lake retrieval with MetOp-A AVHRR images on the Tibetan Plateau

Land-surface water is an important factor influencing the regional environment and climate and is a key factor in the Tibetan Plateau, which is one of the most sensitive regions to global changes. Because of the high elevation, complex topography, and erratic weather of the Tibetan Plateau, direct measurement of the area of every lake is largely unfeasible. Moreover, complex natural geographic conditions increase the difficulty of image processing and information extraction with remote sensing because they enhance the uncertainty of quantitative data retrieved with satellites. Methods based on spectral features do not generate the expected results of lake area over the Tibetan Plateau due to a lack of distinction between water and other land objects, especially snow, vegetation, and low cloud cover. Therefore, a new method to extract lake area from satellite images in the Tibetan Plateau is needed. In this article, an automatic method was proposed to evaluate lake area during the wet season (from 1 September to 31 October) on the Tibetan Plateau with multi-day Advanced Very High Resolution Radiometer (AVHRR) remote-sensing images on board the Meteorological Operational satellite-A (MetOp-A) satellite. The method considers both spectral and textural features of lakes and does not need a cloud mask as an input. In addition, the Mixture Tuned Matched Filtering (MTMF) algorithm was applied to decompose the mixed pixels to better identify lakes and estimate the lake area. Based on daily lake identifications, the wet season’s lake data were composited with the maximum value composition (MVC) method to determine the lake area. A comparison of our work with the manually interpreted results from Landsat Thematic Mapper (TM) images and observational reports demonstrates the accuracy and reliability of our approach. However, certain factors, i.e. the sensor zenith angle of the polar-orbit satellite and the topography, can affect the lake area extracted from the remote-sensing images.     

一种基于植被指数的遥感影像决策树分类方法

以江苏省徐州市为研究区,采用2000年ETM+多光谱影像作为遥感信息源,选择影像的光谱特征和归一化植被指数（NDVI）、绿度植被指数（GVI）、比值植被指数（RVI）等10种植被指数作为分类特征,基于See5决策树学习软件构建分类决策树,实现了研究区景观格局的遥感分类。研究结果表明,决策树分类法易于综合多种特征进行遥感影像的分类,植被指数参与到决策树分类中能够提高分类的总体精度。     

Coastally trapped atmospheric gravity waves on SAR,AVHRR and MODIS images

Alternative dark–bright patterns on two ENVISAT Advanced Synthetic Aperture Radar (ASAR) images of the east coast of the Korean Peninsula acquired on 18 and 19 May 2004 are interpreted as atmospheric gravity waves (AGWs) on the basis of simultaneous multi‐satellite observations and atmospheric gravity wave theory. The AGWs appeared in the form of a wave packet containing several waves located between 50 and 200 km offshore. The wavelengths were ranging from 13 to 20 km. The lengths of AGW crests were from 20 to 150 km. An NOAA‐17 pass was received about 30 min after the ASAR pass. Its channel 4 infrared (IR) image clearly shows wave‐like moisture patterns. However, the sea surface temperature (SST) image derived after applying nonlinear calibration and split‐window atmospheric correction shows no wave patterns. A daytime true‐colour MODIS image taken about 14 h later still shows a cloud band of same AGWs, indicating the lifespan of the standing AGWs can be over half a day. Although oceanic internal waves (IWs) may also cause similar wave patterns imaged by spaceborne SAR as they modulate the ocean surface roughness, we provide evidence to eliminate this possibility in this case. The characteristics of satellite observed AGWs are in good agreement with these simulated by a linear coastal AGW model.     

基于MODIS和AVHRR数据源的东北地区植被NDVI变化及其与气温和降水间的相关分析 

基于逐像元一元线性回归模型,应用MODIS NDVI数据对AVHRR-GIMMS NDVI进行时间序列拓展,拓展序列通过一致性检验,基于所建立的1982~2009年植被年最大NDVI数据集,在GIS平台上进行了植被NDVI变化和NDVI与年平均气温、年降水量之间的相关分析。研究结果表明:过去28 a间,植被年最大NDVI呈3个变化阶段:1982~1992年呈小幅上升趋势,1992~2006年呈缓慢下降趋势,2006~2009年呈缓慢回升态势。由空间变异分析得出NDVI变化相对大的区域主要分布在内蒙干旱和半干旱区。21世纪初和20世纪90年代相对于80年代NDVI值升高,3个阶段平均NDVI变化幅度为±0.3。 20世纪初,赤峰地区以及松嫩平原西部地区植被NDVI呈轻度增加的面积占全区6.45%。植被年最大NDVI与年平均气温、年降水量相关性空间差异明显。偏相关系数绝对值,气温大于降水的像元数占54%;综合分析,较降水而言,气温是东北全区植被年最大NDVI的主控影响因子。对于不同植被类型年最大NDVI,受气温影响强度由大到小依次为:森林>草地>沼泽湿地>灌丛>耕地;受降水影响按草地>耕地>灌丛>沼泽湿地>森林依次减弱。     

Gradual land cover change detection based on multitemporal fraction images

This study proposes a new approach to change detection in remote sensing multi-temporal image data. Rather than allocating pixels to one of two disjoint classes (change, no-change) which is the approach most commonly found in the literature, we propose in this study to define change in terms of degrees of membership to the class change. The methodology aims to model images depicting the natural environment more realistically, taking into account that changes tend to occur in a continuum rather than being sharply distinguished. To this end, a sub-pixel approach is implemented to help detect degrees of change in every pixel. Three experiments employing the proposed approach using synthetic and real image data are reported and their results discussed.     

Critical assessment of vegetation indices from AVHRR in a semi-arid environment

The most frequently used vegetation index (VI), the Normalized Difference Vegetation Index (NDVI) and its variants introduced recently to correct for atmospheric and soil optical response such as Global Environment Monitoring Index (GEMI) and Modified Soil-Adjusted Vegetation Index (MSAVI) are evaluated over a Sahelian region. The usefulness and limitations of the various vegetation indices are discussed, with special attention to cloud contamination and green vegetation detection from space. The HAPEX Sahel database is used as a test case to compare these indices in arid and semi-arid environments. Selected sites are characterized by sparse vegetation cover and day-to-day variability in atmospheric composition. Simulated indices values behaviour at the surface level shows that these VIs were all sensitive to the presence of green vegetation but were affected differently by changes in soil colour and brightness. We showed that GEMI is less sensitive to atmospheric variations than both NDVI and MSAVI since it exhibits a high atmospheric transmissivity over its entire range for various atmospheric aerosol loadings and water vapour contents. These results were first tested on a vegetation gradient, and secondly evaluated on a transect which encompasses various soils formations. On the vegetation gradient, it was found that GEMI computed from measurements at the top of the atmosphere is invariable from one day to the next. On the bare soils transect, MSAVI calculated at the surface level, has shown a great insensitivity to soil optical responses modifications, while GEMI exhibits from space noticeable variability in this bright soil context. Finally, it was illustrated that GEMI exhibits interesting properties for cloud detection because of the strong decrease of its value on cloudy pixels.     

Polynomial trend surface analysis applied to AVHRR images to improve definition of arctic leads

Polynomial trend surface analysis was applied to three AVHRR images to determine whether regional trends in image radiance can be removed with this procedure. Results suggest that trend surface techniques can be effective in removing region-scale variation in image radiances that are related to uneven illumination, intermittent cloud cover, and variation in the surface temperature field. The dominant effects of illumination in Channel 2 (visible) data, caused by variable sun angle and proximity of the scene to the terminator, can be minimized by removing (subtracting) the first- and second-order trend surfaces from the raw image. These low-order surfaces also remove regional variation in the surface temperature field, which leads to marginal improvement in binary images derived from Channel 4 (infrared) data. Optimum results for both Channel 2 and Channel 4 data are achieved when the third- and fourth-order surfaces are subtracted to remove local temperature and illumination anomalies that occur at smaller spatial scales, primarily in the vicinity of clouds. Application of higher order surfaces fails to improve image quality. There is some indication that the topography of these higher-order surfaces in part maps regional variation in lead density. Use of a best-fit criterion based on a strict variance technique (such as the least-squares method) to define the trend surface limits the effectiveness of the technique in this application. Criteria that allow for data to be weighted based on their distance from the plane about which they cluster are more appropriate to the structure of AVHRR radiance data typical of images that show sea ice. A criterion that incorporates a rule system based on fuzzy logic offers an alternative means of assessing goodness-of-fit that might prove appropriate in this application.     

Validation of the Geocoding and Compositing System (GEOCOMP) using contextual analysis for AVHRR images

Mapping of remotely-sensed data to a common projection can potentially degrade the quality of the data. The Canada Centre for Remote Sensing (CCRS) has developed the Geocoding and Compositing System (GEOCOMP) to map Advanced Very High Resolution Radiometer (AVHRR) data to a 1 km2 equal area projection. Using data from the Boreal Ecosystem-Atmosphere Study (BOREAS), we tested GEOCOMP's processing of individual pixels and its ability to preserve the spatial integrity of the original AVHRR observations. Our study shows that GEOCOMP processing does not significantly degrade the ability to derive environmental variables and indices using a contextual approach. Errors produced by the GEOCOMP system are largest for scenes with high radiometric contrast and the parts of scenes with large satellite view angles.