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.     

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.     

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.     

Fast coastal algorithm for automatic geometric correction of AVHRR images

A coastal algorithm for fully automatic geometric correction of Advanced Very High Resolution Radiometer (AVHRR) images is presented. Inputs are the AVHRR image and updated ephemeris data and outputs are the georeference image and a cloud image mask. Its principal advantage and novelty is that it requires only manual control in the first stage of the process. Particularly, the detection of Ground Control Points (GCPs), usually rather time consuming, is performed with this method in an automatic way. The procedure only requires the previous existence of a coastal reference-windows database. To find the exact location of the GCPs, the routine searches the best match of these referencewindows with the image. The complete automation of the process makes the routine very fast, then allowing its operative application on a large volume of images. The process provides accuracies to within 1-1.5 AVHRR pixels.     

A simple cloud masking approach using NOAA AVHRR daytime data for tropical areas

A simple method for cloud detection for AVHRR daytime data is presented and checked for equatorial/tropical areas based on a study area in northeastern Brazil. Five different cloud masking techniques based on visible and infrared spectral information for cloud detection are calibrated. The significant differences between the equatorial threshold obtained in this work and the midlatitude thresholds given by Saunders and Kriebel and by Thiermann and Ruprecht are compared and discussed. Results from the cloud masking algorithm are presented and comments are made about the problems related to the automatic cloud detection algorithm presented in this study.     

图像几何畸变精校正研究

卫星传感器在成像过程中会受到诸多因素的影响,使得所获取的影像在几何位置上发生畸变,几何校正的目的就是尽可能消除这些畸变的影响。本文主要研究了用于消除图像几何畸变的两个几何校正模型：多项式模型和MQ模型（Multiquadric Fuctions）,对MQ模型中的系数R进行分析实验提出了一种新的R值的确定方法,与原来的R值相比新的R值能够进一步提高模型的精度;将多项式模型和MQ模型结合使用的Göpfert’s算法引入到卫星影像几何精校正中,实验表明,能够取得比较高的校正精度.     

Compression of NOAA/AVHRR data with a wavelet transform

An attempt has been made to assess the efficiency of image data compression by wavelet transform encoding using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images. Raw and derived images were compressed to various levels and a number of parameters in the decompressed images compared with those obtained using raw data as a yardstick against which to measure the loss of information due to compression. Unsupervised classification, Normalized Difference Vegetation Index (NDVI) values and brightness temperatures appeared to suffer little degradation and only for fractal dimensions was there significant loss of integrity at compression rates of up to a factor of 32. The general conclusion from a visual inspection of the effect of such compressions on artificially generated geometrical imagettes confirms the effectiveness of this method of compression.     

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.     

Characterizing tropical forest regeneration in Cameroon using NOAA AVHRR data

To facilitate estimation of the carbon sink associated with tropical forests in Cameroon, regenerating and mature forests were mapped using an unsupervised classification of AVHRR channels 1, 2 and 3. Stages of regeneration were defined using nonlinear relationships between AVHRR channel 3 radiance and basal area, estimated using data collected from 183 plots (1 ha in size) in an area south-east of the capital, Yaounde. The overall extent and patterns of regenerating forest were comparable to those generated in previous studies. Older stages of regeneration could not, however, be discriminated adequately from mature forest, suggesting that areas of tropical forest disturbance may be underestimated when mapped using AVHRR data. closed tropical forests were regenerating and that their rate of expansion million ha y 1. These regenerating forests accumulate biomass more rapidly     

A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data

In this paper, a new methodology to estimate the biomass (organic matter) of conifer-dominated boreal forests is developed. It aims to estimate biomass of extensive areas where ground data are limited. First, the principal models are computed using ground measurements and high resolution satellite images. Spectral models are then applied directly to a calibrated AVHRR image mosaic covering the entire area of interest. This methodology was tested quantitatively in Finland, where detailed forest measurement data are available, on an area reaching from the west coast of Norway to the Ural mountains. The methodology appeared to perform beyond pre-test expectation.     

Location of tuna resources in Indian waters using NOAA AVHRR data

Abstract

The world demand for tuna resources is ever increasing and there is scope for better economic returns in terms of foreign exchange earnings. It is one of the least exploited resources of the Indian seas. Remote sensing based studies on the tuna environment began in the seventies in the Gulf of Guinea. This study helped to establish a fishing strategy during the eighties. But so far this has not been attempted in Indian waters. With the basic understanding that most of the species of tuna respond directly to temperature, a study using NOAA AVHRR data was carried out to locate tuna resources. Thermal data of NOAA AVHRR for eight dates in the 1989-90 season were analysed to generate sea surface temperature (SST) images. Catch per unit effort (CPUE) of tuna longline data acquired from the Fishery Survey of India pertaining to fishing conducted by chartered vessels, was plotted on the SST images. Yellowfin tuna (YFT) comprises the maximum catch plus small quantities of marlins. It was observed that almost all the data points were located near the edge of warm water (27°-29°C). A relation between average CPUE of YFT and multi-channel sea surface temperature (MCSST) charts generated by OPC (the Ocean Products Centre) of NOAA was established. It shows on an average an increasing trend in the CPUE of YFT from 26° C (hooking rate ～ 1 per cent) to 29°C (hooking rate ～3 65 per cent) and then shows a drop with further rise in temperature. Since YFT is known to be present in a wide range of temperatures, it can be concluded that the location of warm water edges having a gradient of about 1°C and the above mentioned range of temperature will be desirable in locating tuna potential areas.     

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.     

Correction of the effect of Sun-sensor-target geometry in NOAA AVHRR data

Models of determining the effects of the bidirectional reflectance distribution function (BRDF) of different surfaces and of eliminating the effect of Sun-sensor-target geometry from the remotely sensed satellite data are actual. The objective of this study is to develop a simple relation between the Sun-sensor-target geometry and the remotely sensed vegetation index. In this investigation 238 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images were used over Hungary during the period 1996-98. The greenness vegetation index (the difference between the reflectance values of near-infrared and visible channels) was used between days of the year 140-200, because the greenness values can be considered as constant in this period over the agricultural areas. The so-called 'hot spot effect' can be observed in the variation of reflectance values with different viewing zenith angles of the sensor. A simple quadratic relation was found between the raw AVHRR greenness values and the angle enclosed by the Sun-target and target-sensor directions over the agricultural areas, forests and grasslands. A correction method was developed to eliminate the effect of the Sun-sensor-target geometry, which it is hoped will improve the accuracy of yield forecasting and estimation procedures using NOAA AVHRR data.     

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.     

Calibration of NOAA16 AVHRR over a desert site using MODIS data

This paper presents a new approach to AVHRR-sensors cross-calibration in the visible to shortwave-infrared spectral domain using an a-priori, well calibrated sensor (MODIS). The approach has been tested over a stable Sahara desert site and was initially applied to compare the absolute calibration coefficients of three different bands of the Terra and Aqua MODIS instruments. The observed agreement was better than 1% for bands 1 (0.67 μm), 2 (0.87 μm) and 7 (2.13 μm). The approach was then applied to cross-calibrate the AVHRR sensor onboard NOAA16. The absolute calibration coefficients derived for bands 1 and 2, using the Terra MODIS as a reference, were compared to the vicarious coefficients derived using the ocean and clouds method [Vermote E.F. and Kaufman Y.J. (1995). Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views, International Journal of Remote Sensing, 16, 13, 2317-2340.]. The coefficients were consistent within less than 1%.     

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.     

图像几何畸变校正方法

在机器视觉检测中大视场短焦距摄像机镜头一般都存在一定程度的光学畸变,在高精度测量中必须对摄像机镜头畸变进行校正。提出利用光学成像规律和镜头畸变校正模型相结合的畸变校正方法求出初始畸变系数,然后通过优化目标函数求出最优畸变系数,最后采用三次B样条插值对畸变图像进行灰度重建。实验结果表明该方法在不依赖摄像机内部参数的前提下,校正后径向均方根误差为0.45个像素,灰度重建后径向均方根误差为0.36个像素。     

Monitoring the grasslands of the Sahel using NOAA AVHRR data: Niger 1983

Normalized difference vegetation index data derived from the Advanced Very High Resolution Radiometer on board the NOAA-7 satellite for the 1983 growing season for the Sahelian Zone of Niger are compared with biomass estimates derived from an empirical grassland productivity model. The model used daily rainfall data to estimate the potential biomass production for fourteen meteorological stations through the growing season. A good general correspondence (r = 0·75) was seen between the productivity model and the satellite-derived integrated NDV1, although specific differences were apparent between actual and potential biomass. The study shows the utility of high-temporal-resolution satellite data for monitoring grassland conditions at a local and regional scale and emphasizes the importance of a maximum value compositing approach to the analysis. The study also shows the potential of the satellite data for quantifying phenological characteristics of vegetation     

A method for operational calibration of AVHRR reflective time series data

We have developed an automatic method to monitor the AVHRR instrument sensitivity over time in the short-wave reflective channels to ensure that trends in the data series obtained by this instrument are real and not sensor artefacts.Our radiometric calibration method uses the Multivariate Alteration Detection (MAD) algorithm to statistically select invariant features over land areas from multiple image-pairs that are compared to assess changes in the instrument's calibration. This method requires no a priori regional knowledge and is globally applicable. A calibrated time series from Pseudo-Invariant Features located in central Australia are shown to have long-term trends removed. The resulting MAD-based calibration has a root mean squared error of ∼ 5-6% for both channels 1 and 2 and is in alignment with other approaches.     

Distortion correction of NOAA image by statistical method

Remote sensing technique by satellite has been well-developed in recent years. In this paper two kinds of statistical correction methods are proposed to clarify observation data by NOAA with atmospheric scattering. The first method is the deletion of the path radiance effect by the least squares method. The second uses a smoothing filter. These methods are applied to the original remote sensing data collected on 26 July 1987.