Quantitative dynamic flood monitoring with NOAA AVHRR

The analysis of the spatial extent and temporal pattern of flood inundation from remotely sensed imagery is of critical importance to flood mitigation. With a high frequency of global coverage, NOAA/AVHRR has the advantage of detecting flood dynamics during devastating floods. In this paper, we describe a systematic approach to flood monitoring using AVHRR data. Four critical issues for successful flood monitoring with AVHRR were identified: correct identification of water bodies, effective reduction of cloud contamination, accurate area estimation of flood extent, and dynamic monitoring of flood processes. In accordance with the spectral characteristics of water and land in AVHRR channels, a simple but effective water identification method was developed with the ability to reduce cloud influences. The area of flooded regions was calculated with the consideration of areal distortion due to map projection, and mixed pixels at water/land boundaries. Flood dynamics were analysed from flood distributions in both space and time. The maximum spatial extent of floods, generated by compiling the time series of flood maps, was informative about flood damages. We report a successful use of this approach to monitor the Huaihe river flood, a centennial devastating disaster occurred in the Huaihe river basin of China in the summer of 1991.     

Modelling rainfall intensity from NOAA AVHRR data for operational flood forecasting in Malaysia

Many empirical studies in numerical weather prediction have been carried out that establish the relationship between top‐of‐the‐cloud brightness temperature and rainfall particularly in tropical and equatorial regions of the world. Malaysia is a tropical country that lies along the path of the north‐east and south‐west monsoon rainfall, which sometimes causes extensive flood disasters. Observations have generally shown that heavy cumulonimbus cloud formation and thunderstorms precede the usual heavy monsoon rains that cause flood disasters in the region. In this study, a model has been developed to process National Oceanic & Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) satellite data for rainfall intensity in an attempt to improve quantitative precipitation forecasting (QPF) as input to operational hydro‐meteorological flood early warning. The thermal bands in the multispectral AVHRR data were processed for brightness temperature. Data were further processed to determine cloud height and classification performed to delineate clouds in three broad classes of low, middle, and high. A rainfall intensity of 3–12 mm h^?1 was assigned to the 1‐D cloud model to determine the maximum rain rate as a function of maximum cloud height and minimum cloud model temperature at a threshold level of 235 K. The result of establishing the rainfall intensity based on top of the cloud brightness temperature was very promising. It also showed a good areal coverage that delineated areas likely to receive intense rainfall on a regional scale. With a spatial resolution of 1.1 km, data are course but provide a good coverage for an average river catchment/basin. This raises the opportunity of simulating rainfall runoff for the river catchment through the coupling of a suitable hydro‐dynamic model and GIS to provide early warning prior to the actual rainfall event.     

Land cover mapping and monitoring from NOAA AVHRR data in Bangladesh

NOAA AVHRR HRPT data consisting of two time frames i.e., 1985–86 and 1992–93 were analysed to determine the status of major land cover types of Bangladesh and to monitor change. The data were radiometrically corrected to spectral reflectance and mapped to a consistent Plate Caree projection followed by cloud masking and country masking. The satellite data and the methodology adopted was found to be useful for assessment and monitoring of major land cover types and their dynamics at small scale. The nature and pattern of land cover change derived from the analysis forms a valuable resource for planners and decision-makers in formulating policies, allocating scarce resources and in evaluation of the practical effects of land use policies.     

NOAA AVHRR image referencing

Abstract

A simple and fast algorithm for image referencing of the NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data has been derived to facilitate the identification of geographic co-ordinates corresponding to any pixel on an NOAA image and vice versa. The procedure assumes a spherical Earth and circular orbit and takes into account the effects due to the Earth's rotation and oblateness and the scan skew. Inputs to the procedure are the ascending nodal longitude and lime, the time of the first scan line and one ground control point (GCP). The effects of an ellipsoid Earth and an elliptical orbit are corrected by using the GCP to adjust the spacecraft altitude and inclination angle. No detailed emphemeris data arc required. The average r.m.s. errors obtained by comparing with independent sets of well-distributed GCPs for each image are about 2 pixels and 2 lines or 3 km displacement. Results from the procedure are illustrated by the rectification of NOAA images over France.     

Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India

The Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites has been used for mapping vegetation cover and classification employing the Normalized Difference Vegetation Index (NDVI). Recently, this technique has been improved by converting NDVI with radiation measured in one of the thermal channels and converting brightness temperature into the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). These indices are being used for estimation of vegetation health and monitoring drought. The present study shows the application of vegetation and temperature condition indices for drought monitoring in India.     

NOAA AVHRR land surface albedo algorithm development

The primary objective of this research is to develop a surface albedo model for the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). The primary test site is the Konza prairie, Kansas (U.S.A.), used by the International Satellite Land Surface Climatology Project (ISLSCP) in the First ISLSCP Field Experiment (FIFE). In this research, high spectral resolution field spectrometer data was analyzed to simulate AVHRR wavebands and to derive surface albedos. Development of a surface albedo algorithm was completed by analysing a combination of satellite, field spectrometer, and ancillary data. Estimated albedos from the field spectrometer data were compared to reference albedos derived using pyranometer data. Variations from surface anisotropy of reflected solar radiation were found to be the most significant albedo-related error. Additional error or sensitivity came from estimation of a shortwave mid-IR reflectance (1.3-4.0 mu m) using the AVHRR red and near-IR bands. Errors caused by the use of AVHRR spectral reflectance to estimate both a total visible (0.4-0.7 mu m) and near-IR (0.7-1.3 mu m) reflectance were small. The solar spectral integration, using the derived ultraviolet, visible, near-IR and SW mid-IR reflectivities, was not sensitive to many clear-sky changes in atmospheric properties and illumination conditions.     

Bidirectional reflectance effects in NOAA AVHRR data

The bidirectional reflectance effects in NOAA AVHRR data have been investigated for forest and pasture sites in New Zealand. The impact of surface anisotropy has been examined for channel 1, channel 2, and the derived Normalized Difference Vegetation Index (NDVI) over a 14-day period in the southern hemisphere summer of 1992/1993. Results show a bidirectional effect which persists through atmospheric correction processing and the generation of the NDVI. Comparison is made with previously published results and models, which show consistency for this limited data set.     

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°.     

A new method for high accuracy navigation of NOAA AVHRR imagery

A method to produce rectified Advanced Very High Resolution Radiometer (AVHRR) datasets is proposed, using both the orbital model and identification of ground control points (GCPs), involving a single image transformation from the raw imagery. The ability of the orbital model to determine the geographical co-ordinates of pixels in a raw AVHRR image was tested, using a total of 1098 GCPs in 24 AVHRR images. Five of the 24 images were also rectified using the method proposed, using a newly identified set of GCPs. The differences between the geographical co-ordinates of the existing GCPs and those determined by the new method are calculated and discussed.     

Multi-spectral classification of snow using NOAA AVHRR imagery

Abstract

Problems of accurate discrimination between snow and cloud, together with the detection of the snow pack boundary, have handicapped the use of satellite data in operational snow-cover mapping systems. A technique, involving an unsupervised clustering procedure, is described which allows the removal of cloud areas using NOAA-9 Advanced Very High Resolution Radiometer (AVHRR) channel-1, channel-3 and channel-4 data in conditions of recent snow lie and a difference channel (channel-2 —channel-1 with channel-3 and channel-4) during periods of advanced snow melt. Accurate delineation of snow extent is provided by the techniques if these specified snow conditions are taken into account. A method for the identification of areas of marginal snow melt is also presented, based on comparisons with Landsat Thematic Mapper data. The classifications also enable the determination of snow areas influenced by cloud shadows and conifer forest in addition to separating areas of differing snow depth and percentage cover.     

人体经络的三维数据模型和动画显示方法研究

本文叙述了人体三维形体的空间数据获得方法，提出一种基于齐次坐标变换的人体经络数据模型，为解决计算机人体经络模型的动画演示和经络穴位的透明叠加显示等问题提供了一条新思路，也为今后进一步应用数学方法或从整体综合的角度去研究经络打下了良好基础，并在分析Ｔａｒｇａ图象和ＦＬＩ动画格式的基础上，实现了它们在Ｗｉｎｄｏｗｓ环境下的显示方法。     

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.     

Geometric correction and radiometric normalisation of NOAA AVHRR data for fisheries applications

Sea surface temperature (SST) has been found useful for locating potential fishing grounds (PFGs). Thermal data of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) was acquired using a microcomputer (PC/AT 486) based Direct Reception and Processing Terminal (DRPT) developed in-house to map surface thermal fronts. Geometric correction using orbital ephemeris and ground control points (GCPs) resulted in locational accuracy of 1·73 km by 2·1 km. Besides, the corrections for artificially lowering SST in case of passes with large satellite zenith angles (LSZA) through a radiance normalization based on the mean vectors and dispersion matrices to make it comparable with small satellite zenith angle (SSZA) pass is presented in this letter.     

Cloud detection for the Far East region using NOAA AVHRR images

A new automatic cloud detection method to process the large data sets required for long-term trends in AVHRR time series images is described and tested over the Far East region. In the cloud detection method, a simple NDVI test was used to roughly separate land from ocean. After the NDVI test we carried out two tests: a visible or near-infrared test and an infrared test. Results from the cloud detection method over the Far East region are presented. In order to assess the cloud detection method, a comparison was carried out between the method presented and the Saunders and Kriebel daytime procedure using the N-Land database. We found that in general, the method presented has better performance than the Saunders and Kriebel procedure by about 0.5% to 10.5%.     

AVHRR for monitoring global tropical deforestation

Abstract

Advanced Very High Resolution Radiometer (AVHRR) data have been used to assess the dynamics of forest transformations in three parts of the tropical belt. A large portion of the Amazon Basin has been systematically covered by Local Area Coverage (LAC) data in the 1985-1987 period. The analysis of the vegetation index and thermal data led to the identification and measurement of large areas of active deforestation. The Kalimantan/Borneo forest fires were monitored and their impact was evaluated using the Global Area Coverage (GAC) 4 km resolution data. Finally, High Resolution Picture Transmission (HRPT) data have provided preliminary information on current activities taking place at the boundary between the savanna and the forest in the Southern part of West Africa. The AVHRR approach is found to be a highly valuable means for carrying out deforestation assessments in regional and global perspectives.     

Using NOAA AVHRR imagery in assessing water quality parameters

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) instrument, carried by the NOAA-TIROS/N series of operational meteorological satellites can, on a routine basis, provide observational data which allow interpretation in terms of parameters related to water quality. In principle, some, though not all of the algorithms applied to Coastal Zone Colour Scanner data can be transformed for use with AVHRR observations. In combination with the operational character of the NOAA satellites this opens up the way to applications in monitoring of open sea and inland waters. Algorithms and results for some examples of such potential applications are presented.     

Cover: Fraction images derived from NOAA AVHRR data for global studies

Abstract

Land-use changes in various parts of arid Rajasthan were identified and mapped on reconnaissance, semi-detailed and detailed levels using multidate remotely-sensed data, supported with field check and secondary informations. During the last three decades the net sown area in arid Rajasthan has increased by 36 per cent while current and long fallows have declined by 29 and 41 per cent, respectively. The net irrigated area has increased by 140 per cent. Forest and pastures become highly degraded although their areas have increased to some extent. Land-use changes that occurred during the 1979 and 1990 floods are also discussed. In addition, the advantages and limitations of remote sensing and their comparison with traditional methods are also highlighted.     

运用NOAA AVHRR和Landsat TM数据估算多年水稻种植面积

介绍了综合运用NOAA AVHRR和Landsat TM数据进行多年水稻种植面积监测的一种方法,以湖北省为例,首先运用Landsat TM数据计算了该省1992年的水稻种植面积;接着运用1992年和1994年的NOAA AVHRR数据分别计算这两年的水稻像元数,以这两年水稻像元数的变化来反映水稻种植面积的变化;最后运用线性模型,估算1994年的水稻种植面积。所得的1994年水稻种植面积与湖北省农调队资料相比精度为84.5%。运用同样的方法估算1995年该省的水稻种植面积,精度达90%以上。     

Landsat TM热红外遥感数据定量反演地下水富集带的温度信息——以甘肃河西地区石羊河流域为例

利用ＬａｎｄｓａｔＴＭ６热红外遥感数据定量反演了干旱地区的地表温度,研究结果表明,研究区典型地表覆盖类型的地表亮温比地表真实温度低０．４～１Ｋ,遥感反演的地面真实温度与当地３月下旬的实测温度误差在０．８Ｋ以下,这说明用ＬａｎｄｓａｔＴＭ６定量反演干旱区的地表温度是可行的。研究结果还表明,地下水富集带地表温度具有异常现象,其地表温度比地表水体高５Ｋ左右,而比其它地表类型低７Ｋ以上,据此,可以利用热红外遥感技术有效地探测干旱区地下水富集带的信息。     

A simplified method of accurate geometric correction for NOAA AVHRR 1B data

There are different methods for geometric correction of NOAA AVHRR data, but these methods either pay less attention to the accuracy, or are technically complex, almost unsuitable for most users. To combine AVHRR data with other high spatial resolution satellite data, or with ancillary data in GIS, it is necessary to develop an accurate geometric correction method, which should be easy to use even for non-professional users. After analysing the pixel shape and size of AVHRR 1B data along scan line and evaluating the quality of geographical data of NOAA AVHRR 1B data set, we found that the geographical data was adequately accurate for identifying the pixel size and shape and the method was developed accordingly. The proposed method has two steps. The first step is to correct pixel distortion. The separate program performs the distortion correction, applying the geographical data of AVHRR 1B data set to assigning the value of each pixel of the desired output geographical area with given pixel size, and making logical judgment for unassigned pixel. The second step is to perform conventional polynomial transformation on the results of the first step. An application of this method is presented in the paper. To examine the precision, SAVI images derived from the geometrically corrected NOAA AVHRR band image were used to perform overlay with each other and also with a 1 :50000 river system map. A half-pixel accuracy was achieved.