Creating a forest fire database for the Far East of Asia using NOAA/AVHRR observation

This study proposed a method for burned area accounting that uses data from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series. As an area of interest, the boreal forest zone located in the Far East region of Asia was used. The burn scar mapping algorithm consists of two parts. The first is a multi‐channel threshold algorithm used for detection of real‐time burning spots in the boreal environment. The second part uses an abrupt change‐detection technique in Normalized Difference Vegetation Index (NDVI) in an 18‐year NDVI time series. Both parts of the algorithm are connected together in a complementary manner, and a forest burn scar mask is obtained for each month and consequently for each year from 1984 to 2001. The validation of the dataset was performed using data from the literature, forestry management organizations and the Landsat Thematic Mapper (TM). The comparison between those validation data and our forest fire dataset shows a satisfactory level of agreement. If the forest fire history is required for other regions in the boreal zone, the proposed methodology could be extended to that region given that a sufficient data archive is available.     

Early detection system of drought in East Asia using NDVI from NOAA/AVHRR data

Abstract

The influence of surface bidirectional reflectance factors, including shadowing, and of atmospheric aerosol variability are modelled for their effects on the remote sensing of desert targets from space in the 0·?μm region at high spatial resolution. The white sand reflectance data of Salomonson are used as the basis for the simulation. The effects of the surface bi-directional reflectance and atmospheric aerosol on the nadir-normalized reflectance measured at the satellite are discussed individually and jointly. The net influence of these two factors is shown to depend on the magnitude of other parameters, such as the surface reflectance and solar zenith angle.     

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°.     

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.     

Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina

We used multiple regression analysis to relate evapotranspiration (ET), computed from a water balance technique, to both thermal infrared and normalized difference vegetation index data obtained from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board on the National Oceanic and Atmospheric Administration (NOAA) satellite. This approach, based on only remotely sensed data, provided a reliable estimate of ET over the Pampas, the main agricultural region of Argentina. The relationship between spectral data and ET was more sensitive to the dates than to the sites used to generate the models.     

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.     

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.     

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.     

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.     

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.     

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.     

A new approach for the detection of major fires caused by industrial accidents,using NOAA/AVHRR imagery

The Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA satellites may be used to detect major fires caused by industrial accidents through the combined use of middle (3.55-3.93 mum) and thermal infrared (10.5-11.3 mum, 11.5-12.5 mum) channels. An algorithm was developed to identify the pixels which correspond to the sites of the industrial accidents where a major fire had developed. The algorithm was applied for two industrial accidents; Lyon, France on 2 June 1987 and Kalohori, Greece on 24 February 1986. The algorithm was based on the analysis of the differences between the brightness temperatures resulting from Channels 3 (3.55-3.93 mum) and 4 (10.5-11.3 mum) of AVHRR and of two masking filters for clouds. The first filter takes advantage of the information provided by Channels 1 (0.58-0.68 mum) and 5 (11.5-12.5 mum) of AVHRR, whereas the second introduces a threshold value for cloud top temperature. The use of the algorithm for the selected industrial accidents demonstrates its capability to detect major fires.     

Improving the estimation of noise from NOAA AVHRR NDVI for Africa using geostatistics

The accuracy of NOAA AVHRR NDVI data can be poor because of interference from several sources, including cloud cover. A parameter of the variogram model can be used to estimate the contribution of noise from the total variation in an image. However, remotely sensed information over large areas incorporates non-stationary (regional) trend and directional effects, resulting in violation of the assumptions for noise estimation. These assumptions were investigated at five sites across Africa for a range of ecological environments over several seasons. An unsupervised spectral classification of multi-temporal NDVI data partially resolved the problem of non-stationarity. Quadratic polynomials removed the remaining regional trend and directional effects. Isotropic variograms were used to estimate the noise contributing variation to the image. Standardized estimates of noise ranged from a minimum of 18.5% in west Zambia to 68.2% in northern Congo. Cloud cover and atmospheric particulates (e.g. dust) explained some of the regional and seasonal variations in noise levels. Image artifacts were also thought to contribute noise to image variation. The magnitude of the noise levels and its temporal variation appears to seriously constrain the use of AVHRR NDVI data.     

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     

Cloud track winds in the polar regions from sequences of AVHRR images

Abstract

A new method is described for obtaining information on the high latitude upper level wind field through the production of cloud motion vectors from sequences of images from polar orbiting satellites. The vectors are produced using sets of three calibrated, 11 μm AVHRR images of the polar regions from successive orbits of the TIROS-N series satellites. Two schemes have been developed for the generation of vectors based on manual and automatic techniques coupled with manual quality control. Case studies have shown that, provided the images are accurately registered and the cloud tracers are carefully selected, useful vectors may be derived.     

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.     

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     

NOAA/AVHRR satellite detection of Siberian silkmoth outbreaks in eastern Siberia

During 1993-1996, in central Siberia, a silkmoth (Dendrolimus superans sibiricus Tschetw.) infestation damaged approximately 700 000 ha of fir, Siberian pine and spruce stands. Temporal (1995-1997) Advanced Very High Resolution Radiometer (AVHRR) images were used for pest outbreak monitoring of this event. Damaged stands were detected, with heavy (50-75% dead and dying trees) plus very heavy (>75%) levels of damage classified. Summer and winter images were used for delineation of the northern border of the region of pest outbreaks. The Siberian taiga insects were classified with respect to their harmfulness to forests, based on the frequency of outbreaks, the size of the damaged territory, and the available food sources based on forest type.     

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.     

Cloud detection in satellite images using multi-objective social spider optimization

Cloud detection algorithms have emerged to automate image data analysis because of its prime influential factor in remote sensing image quality. Cloud detection algorithm still needs domain-expert intervention and large number of training examples to ensure good performance whose acquirement becomes difficult due to unavailability of labeled data as well as the time and process heads involved. The paper puts forward multi-objective social spider optimization (MOSSO) based efficient clustering technique to detect clouds in the visible range. This paper explains the proposed MOSSO algorithm along-with the analysis carried on 14 benchmark two-objective test problems against MOEA/D, MODE, MOPSO and SPEA2 multi-objective algorithms. Further, the strengths and weaknesses of the proposed algorithm are analyzed and have been used for the implementation of an efficient clustering technique named as MOSSO-C. Optimal centroid matrix for clustering is attained in MOSSO-C through environmental selection whose performance evaluation has been done on six synthetic databases and are compared with above mentioned conventional multi-objective algorithms. The obtained results encourage the use of MOSSO-C technique to get labeled data for training process of neural network classifier. This approach efficiently classifies the cloudy pixels against various Earth’s surfaces (water, vegetation and land). The paper also discusses the performance evaluation of proposed technique on four Landsat 8 data which shows on an average 96.37% performance accuracy in detecting cloudy pixels.