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.     

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%.     

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.     

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.     

Technical note - Tracking the Indonesian forest fire using NOAA/AVHRR images

The large scale air pollution episode due to the out-of-control biomass burning for agricultural purposes in Indonesia, which started in June 1997, has become a severe environmental problem for the neighbouring countries. Its impact on the health of the residents and ecology in the affected areas is expected to be substantial, costly and possibly long lasting. In this paper, NOAA/AVHRR images of the fire in September are reported.     

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.     

An intelligent wavelet transform-based framework to detect subsurface fires with NOAA–AVHRR images

Subsurface coal fires (in this article, termed as hotspots), responsible for atmospheric pollution, human fatalities and perilous land subsidence, pose a big threat to major coal-producing countries in the world. The majority of the research performed to date has focused on providing hotspot allocation information for a specific region of interest and most has explored quite expensive high-resolution Landsat Thematic Mapper (TM) satellite images for the same. This article aims to investigate the applicability of a wavelet transform-based model to detect subsurface fires (hotspots) with freely available National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA–AVHRR) images and find out the possibility of extracting novel hotspot features by applying a wavelet transform-based analysis technique. The proposed feature vector consists of wavelet variance coefficients (WVCs) obtained from scale-by-scale decomposition of the AVHRR image variance and builds up a strong base for designing an accurate classification system. Furthermore, the support vector machine (SVM), an efficient machine learning tool, is applied to the proposed feature vector in order to develop a classification model. The demonstrated results successfully prove the effectiveness of the proposed framework as the classified images show a good correspondence with records of subsurface fires mapped by the Bharat Coking Coal Limited (BCCL), India. The effectiveness of the SVM method is also evaluated in comparison with the classical neural network-based approach.     

Fraction images derived from NOAA AVHRR data for studying the deforestation in the Brazilian Amazon

Considerable variation in the performance of passive microwave global rainfall algorithms, both spatially and temporally,was revealed by the first WetNet Precipitation Intercomparison Project, PIP-1, with no one algorithm achieving the best results, in all locations, and all the time. In this paper a Composite Algorithm Procedure is described for the Special Sensor Microwave/Imager (SSM/I) algorithms submitted to PIP-1, that attempts through combining the best algorithm results from different regions of the globe to achieve better overall global rainfall estimates than are possible from any individual algorithm alone. The Composite Algorithm Procedure (CAP) involves the segmentation of the globe into homogeneous regions, the production of validation statistics for the various algorithm results in the different regions, and the identification of combinations of algorithms which perform best globally. The segmentations were based on aspects of the spatial and temporal variability of rainfall, or the microwave properties of the surfaces of the Earth. Initial results for the Composite Algorithm Procedure are presented for a sample month (October 1987): these confirm that improved global rainfall products can be produced in this way. Code detailing a selected Composite Algorithm based on the segmentation method of the microwave properties of the Earth has been supplied to the WetNet Support Group at the Marshall Space Flight Center, Huntsville, Alabama, for experimental, regular production of global rainfall data sets on a near real-time basis.     

A forest fire risk assessment using NOAA AVHRR images in the Valencia area,eastern Spain

The risk of widespread forest fire has been assessed from information supplied by the AVHRR sensor onboard NOAA satellites, for the area of the Autonomous Community of Valencia in eastern Spain, where several major forest fires occurred in the summer of 1994. The burnt surface data were obtained through unsupervised classification of the spectral information of the forest areas, first, from a date previous to the forest fire; and second, from a date following the fire. The methodology for the forest fire risk evaluation is based on the temporal evolution of the NDVI weekly maximum value. Actual forest fires appear to be statistically correlated with the deduced high risk forest fire areas.     

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.     

Use of NOAA AVHRR and Landsat Thematic Mapper images in support of studies of atmospheric composition

Uncertainties in the values of cloud and ground albedo impose considerable errors in the estimation from measured radiances of the vertical profiles of photochemically sensitive chemical species in the upper troposphere and lower stratosphere, especially at lower altitudes and for high cloud coverage. To circumvent this problem, NOAA Advanced Very High Resolution Radiometer (AVHRR) and Landsat Thematic Mapper (TM) images are used to provide spatially and temporally updated information on cloud, land type and cover; information is integrated in an inversion algorithm with considerable success in terms of the reduction of errors in the calculations of the mixing ratios of photochemically active species.     

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.     

Surface current circulation estimation using NOAA/AVHRR images and comparison with HF radar current measurements

The method used to identify sea surface thermal features for estimating surface currents is presented. Results given have been obtained from investigations made in the Central Adriatic Sea off the coast of Monte Conero in the Marche Region, Italy, using Advanced Very High Resolution Radiometer (AVHRR) observations of NOAA-14, made simultaneously with the HF coastal radar system installed there.     

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.     

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.     

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     

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.     

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。     

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.     

NOAA AVHRR and its uses for rainfall and evapotranspiration monitoring

Abstract

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) Global Vegetation Indices (GVI) were used during the 1986 rainy season (June-September) over Senegal to monitor rainfall. The satellite data were used in conjunction with ground-based measurements so as to derive empirical relationships between rainfall and GVI. The regression obtained was then used to map the total rainfall corresponding to the growing season, yielding good results. Normalized Difference Vegetation Indices (NDVI) derived from High Resolution Picture Transmission (HRJT) data were also compared with actual evapotranspiration (ET) data and proved to be closely correlated with it with a time lapse of 20 days.