AVHRR Images Research Articles

SST, thermohaline structure, and circulation in the southern Gulf of California in June 2004 during the North American Monsoon Experiment

The thermohaline structure, circulation, and heat fluxes in the Gulf of California entrance during June 2004 are described based on conductivity‐temperature‐depth and Lowering Acoustic Doppler Current Profiler data collected in a 14‐day survey, supported by satellite data. The AVHRR images show extensive mesoscale structures in the region, the most striking being (1) a cool filament extending from the California Current domain and (2) a warm intrusion along the mainland shelf. On the warm side of the thermal front created by the cool filament there was a strong current flowing into the Gulf, with speeds up to 0.70 ms−1 in the surface; this current, which the SST images suggest was associated with a decaying eddy, carried 6 Sv into the Gulf. Associated with the second structure, there was an ingoing coastal current on the mainland shelf, with weak surface currents but with speeds ∼0.25 ms−1 at its core, between 70 and 200 m; this coastal current transported 2 Sv into the Gulf. The two ingoing currents appear to join inside the Gulf, forming a very strong (speeds 0.40–0.80 ms−1) narrow (∼30 km) coastal current between the surface and 500 m depth. Changes in the thermohaline structure of the upper layers observed by repeat sampling of three cross sections were dominated by advection. However, it was found that the advective heat flux is very variable in space and time. For the period of observation it was estimated that the lateral heat input was 4.8 ± 3.0 × 105 Wm−2 as estimated with LADCP currents and 5.7 ± 2.20 × 105 Wm−2 with geostrophic velocities.

Seasonal reflectance trends of hemiboreal birch forests

MODIS, AVHRR and SPOT VEGETATION satellite images have recently been used to track coarse scale seasonal vegetation dynamics of boreal and temperate forests. However, the understanding of driving factors of reflectance seasonality at forest stand level is still in its infancy, and has only preliminarily been linked to, for example, forest structure or site fertility. We present results from a study on the seasonal reflectance trends of 145 hemiboreal birch stands in Estonia from budburst to initial senescence. A time series comprising 32 high resolution Landsat ETM+, TM and SPOT HRVIR, HRV images from April to September was assembled for analyzing empirical reflectance courses of birch stands. The most noteworthy seasonal reflectance dynamics were observed in the red and NIR channels, changes in the green and SWIR spectral channels were relatively small. The most stable period in stand reflectance in all the spectral channels occurred in midsummer i.e. when stand leaf area index (LAI) reached its highest level and changes in solar angle were the smallest. A twenty-day difference was observed between the reflectance development of birch stands growing on infertile and fertile sites. Next, to provide an explanation for the observed reflectance changes, we simulated the mean seasonal reflectance trajectories of the study stands at 10 day intervals for the same period using a radiative transfer model (FRT). Simulated seasonal reflectance courses for the different site fertility classes followed the general pattern of the measured courses. Simulation results indicated that the main driving factors for reflectance seasonality for all the site fertility classes in the red and green bands were stand LAI and leaf chlorophyll content, in the NIR band stand LAI, and in the SWIR band LAI and general water content. Finally, we discuss current limitations related to applying forest radiative transfer models in investigating the driving factors of seasonal reflectance changes in the boreal zone.

Mediterranean desertification and land degradation: Mapping related land use change syndromes based on satellite observations

In past decades, the European Mediterranean has undergone widespread land use transformations. These are largely driven by changes of socio-economic conditions, such as accession to the European Community, and had strong effects on the way the land is being used. Aiming at a systematic description of such change processes on a global level, the syndrome concept was proposed to describe archetypical, co-evolutionary patterns of human–nature interactions, and has been specifically linked to the desertification issue. In this study, we present an adaptation of the syndrome approach to the Iberian Peninsula. We suggest a data processing and interpretation framework to map the spatial extent of specific syndromes. The mapping approach is based on the time series analysis of satellite data. We have characterized vegetation dynamics using NDVI estimates from the coarse scale, hyper-temporal 1-km MEDOKADS archive, which is based on calibrated NOAA–AVHRR images. Results indicate that local patches of abrupt disturbance, mainly caused by fire, are contrasted by a widespread increase in vegetation, which is in large parts attributed to the abandonment of rural areas. Although this questions the dominance of classical desertification traits, i.e. decline of productivity after disturbance, it is concluded that the recent greening presents a different sort of environmental risk, as it may negatively impact on fire regimes and the hydrological cycle.

Near‐surface ocean velocity from infrared images: Global Optimal Solution to an inverse model

We address the problem of obtaining ocean surface velocities from sequences of thermal (AVHRR) space‐borne images by inverting the heat conservation equation (including sources of surface heat fluxes and vertical entrainment). We demonstrate the utility of the technique by deriving surface velocities from (1) The motion of a synthetic surface tracer in a numerical model and (2) a sequence of five actual AVHRR images from 1 day. Typical formulations of this tracer inversion problem yield too few equations at each pixel, which is often remedied by imposing additional constraints (e.g., horizontal divergence, vorticity, and energy). In contrast, we propose an alternate strategy to convert the underdetermined equation set to an overdetermined one. We divide the image scene into many subarrays and define velocities and sources within each subarray using bilinear expressions in terms of the corner points (called knots). In turn, all velocities and sources on the knots can be determined by seeking an optimum solution to these linear equations over the large scale, which we call the Global Optimal Solution (GOS). We test the accuracy of the GOS by contaminating the model output with up to 10% white noise but find that filtering the data with a Gaussian convolution filter yields velocities nearly indistinguishable from those without the added noise. We compare the GOS velocity fields with those from the numerical model and from the Maximum Cross Correlation (MCC) technique. A histogram of the difference between GOS and numerical model velocities is narrower and more peaked than the similar comparison with MCC, irrespective of the time interval (Δt = 2 or 4 h) between images. The calculation of the root mean square error difference between the GOS (and MCC) results and the model velocities indicates that the GOS/model error is only half that of the MCC/model error irrespective of the time interval (Δt = 2 or 4 h) between images. Finally, the application of the technique to a sequence of five NOAA AVHRR images yields a velocity field, which we compare with that from a Coastal Ocean Dynamics Radar (CODAR) array. We find that the GOS velocities generally agree more closely with those from the CODAR than they do with those from the MCC. Specifically, the root mean square error obtained by differencing GOS and CODAR velocities is smaller than that from the similar calculation with MCC velocities. The magnitude of the complex correlation between GOS and CODAR is larger than that between MCC and CODAR. The phase of the complex correlation indicates that both MCC and GOS on average yield velocity vectors biased in the clockwise direction relative to the CODAR vectors for the period examined.

A generalised methodology for estimating land surface temperature for non-urban areas of Greece through the combined use of NOAA–AVHRR data and ancillary information

The purpose of this study was twofold: to develop a methodology for the estimation of land surface temperature for non-urban areas and to analyze the sensitivity of the methodology. The key element of the methodology was the development of emissivity maps based on CORINE Land Cover and the ASTER spectral library. Land surface temperatures were derived from NOAA/AVHRR data and the methodology was applied at a national scale in Greece, with emphasis given to non-urban areas. A sensitivity analysis was performed in order to determine the variables that mainly affect the estimation of land surface temperature. A varying propagation error was identified depending on the temperature and humidity of the atmosphere, as well as the land cover type. The methodology was applied to a series of 25 AVHRR images and the results were compared to in-situ measurements from representative stations.

Motion Estimation Techniques to Automatically Track Oceanographic Thermal Structures in Multisensor Image Sequences

The ocean involves a complex set of physical, chemical, biological, and geological processes, interacting with each other to influence our climate and natural environment. One of the most important disciplines in oceanography is the study of the ocean dynamics and, particularly, the ocean surface circulation. One can estimate this by the automated tracking of thermal infrared features in pairs of sequential satellite imagery. In this context, an extensive analysis of different motion estimation techniques has been performed by employing databases with synthetic sequences, real sequences, and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situ</i> measurements. Four region- based metrics and two differential algorithms are proposed to estimate surface currents in multitemporal and multisensor AVHRR and MODIS image sequences. Once the appropriate motion estimation techniques have been selected, a new methodology to compute ocean currents is proposed. It includes a preliminary step to precisely segment the oceanographic structures and a second step to track its motion using additional modules (initialization, preprocessing, and postprocessing) to increase effectiveness. The information provided by the segmentation step reduces computing times, initializes the motion estimation parameters with appropriate values, and increases the overall performance. In summary, this two-stage approach combines image processing tools and physical oceanography knowledge to achieve a good ocean current estimation.

Is the meander growth in the Brazil Current system off Southeast Brazil due to baroclinic instability?

Temporally-growing frontal meandering and occasional eddy-shedding is observed in the Brazil Current (BC) as it flows adjacent to the Brazilian Coast. No study of the dynamics of this phenomenon has been conducted to date in the region between 22 ° S and 25 ° S. Within this latitude range, the flow over the intermediate continental slope is marked by a current inversion at a depth that is associated with the Intermediate Western Boundary Current (IWBC). A time series analysis of 10-current-meter mooring data was used to describe a mean vertical profile for the BC-IWBC jet and a typical meander vertical structure. The latter was obtained by an empirical orthogonal function (EOF) analysis that showed a single mode explaining 82% of the total variance. This mode structure decayed sharply with depth, revealing that the meandering is much more vigorous within the BC domain than it is in the IWBC region. As the spectral analysis of the mode amplitude time series revealed no significant periods, we searched for dominant wavelengths. This search was done via a spatial EOF analysis on 51 thermal front patterns derived from digitized AVHRR images. Four modes were statistically significant at the 95% confidence level. Modes 3 and 4, which together explained 18% of the total variance, are associated with 266 and 338-km vorticity waves, respectively. With this new information derived from the data, the [Johns, W.E., 1988. One-dimensional baroclinically unstable waves on the Gulf Stream potential vorticity gradient near Cape Hatteras. Dyn. Atmos. Oceans 11, 323–350] one-dimensional quasi-geostrophic model was applied to the interpolated mean BC-IWBC jet. The results indicated that the BC system is indeed baroclinically unstable and that the wavelengths depicted in the thermal front analysis are associated with the most unstable waves produced by the model. Growth rates were about 0.06 (0.05) days −1for the 266-km (338-km) wave. Moreover, phase speeds for these waves were low compared to the surface BC velocity and may account for remarks in the literature about growing standing or stationary meanders off southeast Brazil. The theoretical vertical structure modes associated with these waves resembled very closely to the one obtained for the current-meter mooring EOF analysis. We interpret this agreement as a confirmation that baroclinic instability is an important mechanism in meander growth in the BC system.

Assessment of waterlogging risk in Lixiahe region of Jiangsu Province based on AVHRR and MODIS image

Four images of 1991 AVHRR, 2003 and 2007 MODIS were used to extract waterlogging inundated water of three years, and three inundated water maps were overlaid to estimate waterlogging affected frequency. Based on waterlogging affected frequency, waterlogging hazard of pixel scale was assessed. According to the weighed score of area percentage of different waterlogging affected frequency in 13 counties/cities of Lixiahe region, waterlogging hazard rank of every county/city was assessed. Waterlogging affected frequency map and 1km×1km grid landuse map were used to assess waterlogging risk of pixel scale; and then waterlogging risk rank of every county/city was assessed by the similar method by which waterlogging hazard rank of every county/city was assessed. High risk region is located mainly in core zone of Lixiahe hinterland, medium risk region is adjacent to high risk region, and low risk region is located in the most outlying area of risk zone and mainly in south to middle part of Lixiahe region. Xinghua and Gaoyou belong to high risk city, Jiangyan belongs to medium risk city, and the other counties/cities have low or lower waterlogging risk. The method of assessing waterlogging risk in this paper is simple and applicable. This paper can provide guidance for the waterlogging risk analysis in broader area of Huaihe River Basin.

Uso de dados multi-temporais do sensor AVHRR/NOAA-14 para avaliar a degradação do solo

This paper presents a methodology for the use of AVHRR/NOAA-14 (Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Administration) sensor data in USLE (universal soil loss equation) for the identification of soil degradation and the mapping of erosion risks at regional level. To apply USLE equation, remote sensing and GIS (Geographic Information System) techniques were used to define sugarcane plantation areas. NDVI (Normalized Difference Vegetation Index) data acquired from seven different AVHRR images were transformed from gray scale levels to percent of reflectance, because reflectance data are more adequate to get NDVI values. A linear correlation analysis was performed between soil losses ratio and the age of sugarcane to derive the factor C (land use and management) which indicates the soil protection provided by the vegetative cover and it changes gradually as the amount of biomass increases. NDVI data derived from AVHRR/NOAA-14 images were adequate to characterize biomass increase in sugarcane plantations in a 16-month period covering two harvests

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.

Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran

In order to evaluate the capability of NOAA-AVHRR data for drought monitoring in the northwest of Iran having cold semi-arid climate, a study plan was designed involving the production of normalized difference vegetation index (NDVI) and vegetation condition index (VCI) indices and correlating their values to precipitation data. Raw AVHRR images were processed and geometric and radiometric corrections were performed. Seven-day maximum NDVI maps were produced and VCI was calculated using the maximum and minimum NDVI values for the same time period. Precipitation statistics from 19 synoptic meteorological stations were collected. The study covered a five-year time period with three consecutive months in the growing season. Pearson correlation was performed to correlate NDVI and VCI values to precipitation data. Different time lag schemes were tried and the highest correlation coefficients (r values) were obtained while correlating NDVI and VCI to three-month (current plus last two months) precipitation. Better agreement was observed between NDVI and precipitation as compared with that between VCI and precipitation in individual stations. Good correlations were also obtained between average NDVI and VCI of the study area and average three-month precipitation. The results indicated that NOAA-AVHRR derived NDVI well reflects precipitation fluctuations in the study area promising a possibility for early drought awareness necessary for drought risk management.

Uncertainty analysis for satellite derived sensible heat fluxes and scintillometer measurements over Savannah environment and comparison to mesoscale meteorological simulation results

Three methods for estimating instantaneous sensible heat flux ( H) over Savannah environment in West Africa were compared: first, satellite derived estimations using the Surface Energy Balance Algorithm for Land (SEBAL) method [Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M., 1998a. A remote sensing energy balance algorithm for land, SEBAL: 1. Formulation. J. Hydrol. 212–213, 198–212]; secondly, measurements at two test sites in Ghana with a large-aperture scintillometer (LAS); third, high resolution mesoscale meteorological simulations using the MM5 (5th-Generation Penn State/NCAR) mesoscale modelling system. Satellite-derived sensible heat flux was based on seven NOAA-16 AVHRR images that were processed for a 2-week period in December 2001 (dry season) and were compared to LAS-data and MM5 simulation results. A methodology based on Gaussian Error Propagation is presented to derive uncertainties in satellite derived sensible heat flux due to (a) input data, (b) coefficients to determine leaf area index (LAI) and (c) methodological differences in estimating surface temperature T 0. Total computed relative uncertainty in H was 15% for the Tamale test site and 20% for the Ejura site. Uncertainties in instantaneous evapotranspiration λ E, however, are much smaller than uncertainties of H. This results due to the same bias in H and R n − G. For LAS-data, an uncertainty analysis due to input data was performed which showed relative uncertainty of 8% for the Tamale site and 7% for Ejura. Satellite derived net radiation ( R n) was underestimated in comparison to ground measurements which finally caused an underestimation of H. Satellite estimates of H using spatially interpolated ground based measurements of net radiation showed good agreement to LAS data. MM5-computed latent heat flux showed very low values for the entire region. This caused a serious relative MM5-overestimation of sensible heat flux in comparison to LAS and satellite derived estimates. It could be shown that Gaussian Error Propagation can serve as an essential tool to asses the reliability of satellite derived sensible heat fluxes. The resulting uncertainties give information on sensitivities in estimating H and therefore provide a tool for validation purposes.

Assessment of Regional Variations in Biomass Production Using Satellite Image Analysis between 1992 and 2004

AbstractAs a consequence of the increasing aridity experienced in Hungary in the past decades, any future spatial planning decisions should consider the threat of an arid environment. For identifying sensitive and endangered areas, suitable methods should be developed. These could help those responsible for decision making in providing quick and suitable actions in planning. Through the investigation of vegetation, regional scale variations in the landscape along with the responses of forests to climatic changes can be easily detected. Our primary aim was the evaluation of the natural water supply on the Danube Tisza Interfluve by studying the forests, meadows and pastures of the area in the summers of 1992 and 2004. We investigated with the help of remote sensing the short‐term response of vegetation to the altered environmental conditions. According to the spatial and temporal analyses of vegetation indices (NDVI) based on AVHRR and MODIS images, a considerable decrease in the activity of vegetation can be observed throughout the entire vegetation period. Due to the suspected climatic changes, 33% of the studied vegetation might be endangered.

Assessment of climate change impact on snowmelt in the two mountainous watersheds using CCCma CGCM2

The purpose of this paper is to evaluate climate impact on snow hydrology using SLURP (Semi-distributed Land Use-based Runoff Process) model for two mountainous watersheds (A; 6661.5 km2 and B; 2694.4 km2) of South Korea. The climate change results of CCCma CGCM2 based on SRES A2 and B2 were adopted. Three sets of NOAA AVHRR images (1997–1998, 1999–2000, 2000–2001) were analyzed to prepare snow-related data of the model. Snow cover areas were extracted using channels 1, 3 and 4, and the snow depth was spatially interpolated using snowfall data of ground meteorological stations. For 4 years (1998–2001) discharge comparison, the average Nash-Sutcliffe efficiencies of A and B watersheds during the snow period (January to April) were 0.60 and 0.68, respectively. In case of A2 scenario, the 2100 runoff of A and B watersheds for 44.4% and 85.9% increase of snowfall increased 67.3% and 19.6% comparing with the 2000 runoff. In case of B2 scenario, the 2100 runoff for 10.3% and 41.3% increase of snowfall increased 38.8% and 9.8% for A and B watersheds, respectively. The results showed that the time of snowmelt runoff was advanced about one month.

준 분포형 수문모형 SLURP에서 융설매개변수 적용 및 영향 평가

본 연구는 충주댐 유역을 대상으로 SLURP 모형에서 RS, GIS를 이용한 융설매개변수 적용 및 영향을 평가하고자 한다. 모형의 음설 관련 매개변수 준비를 위해 3 set (1998-1999, 2000-2001, 2001-2002)의 NOAA AVHRR 위성영상을 분석하였다. 적설분포면적은 채널 1번, 3번, 4번을 이용하여 추출하였고, 적설심은 지상기상관측소의 적설심 자료를 이용하여 공간적으로 내삽하여 추출하였다. 융설 매개변수와 DEM, 토지피복도, NDVI, 수문기상자료를 이용하여 3개년도(1998, 2000, 2001)의 일별유출량을 모의하여 보정하였다 그리고 보정된 매개변수를 이용하여 1개년도(1999)를 검증하였다. 4년(1998-2001)동안의 유량 비교 결과, 평균 Nash-Sutcliffe의 모형 효율은 0.76이고 적설 및 융설 기간(1월<TEX>$\sim$</TEX>5월)동안의 평균 모형 효율은 0.57이다. 융설매개변수 미고려시 평균 Nash-Sutcliffe의 모형 효율은 0.73이고 적설 및 융설 기간(1월<TEX>$\sim$</TEX>5월)동안의 평균 모형 효율은 0.19이다. 융설매개변수를 포함한 유출량이 융설매개변수를 포함하지 않은 경우보다 관측유량의 수문시계열적 특성을 잘 표현하는 결과를 보였다. The purpose of this paper is to prepare snowmelt parameters using RS and GIS and to assess the snowmelt impact in SLURP (Semi-distributed Land Use-based Runoff Process) model for Chungju-Dam watershed <TEX>$(6,661.5km^2)$</TEX>. Three sets of NOAA AVHRR images (1998-1999, 2000-2001, 2001-2002) were analyzed to prepare snow-related data of the model during winter period. Snow cover areas were extracted using 1, 3 and 4 channels, and the snow depth was spatially interpolated using snowfall data of ground meteorological stations. With the snowmelt parameters, DEM (Digital Elevation Model), land cover, NDVI (Normalized Difference Vegetation Index) and weather data, the model was calibrated for 3 years (1998, 2000, 2001), and verified for 1 year (1999) using the calibrated parameters. The average Nash-Sutcliffe efficiencies for 4 years (1998-2001) discharge comparison with and without snowmelt parameters were 0.76 and 0.73 for the full period, and 0.57 and 0.19 for the period of January to May. The results showed that the spatially prepared snow-related data reduced the calibration effort and enhanced the model results.

Application of principal component analysis and information fusion technique to detect hotspots in NOAA/AVHRR images of Jharia coalfield, India

Present paper proposes an algorithm for hotspot (sub-surface fire) detection in NOAA/AVHRR images in Jharia region of India by employing Principal Component Analysis (PCA) and fusion technique. Proposed technique is very simple to implement and is more adaptive in comparison to thresholding, multi-thresholding and contextual algorithms. The algorithm takes into account the information of AVHRR channels 1, 2, 3, 4 and vegetation indices NDVI and MSAVI for the required purpose. Proposed technique consists of three steps: (1) detection and removal of cloud and water pixels from preprocessed AVHRR image and screening out the noise of channel 3, (2) application of PCA on multichannel information along with vegetation index information of NOAA/AVHRR image to obtain principal components, and (3) fusion of information obtained from principal component 1 and 2 to classify image pixels as hotspots. Image processing techniques are applied to fuse information in first two principal component images and no absolute threshold is incorporated to specify whether particular pixel belongs to hotspot class or not, hence, proposed method is adaptive in nature and works successfully for most of the AVHRR images with average 87.27% detection accuracy and 0.201% false alarm rate while comparing with ground truth points in Jharia region of India.

Application of a simple algorithm to estimate daily evapotranspiration from NOAA–AVHRR images for the Iberian Peninsula

Evapotranspiration (ET) is a key process in land surface–atmosphere studies. It mainly depends on water availability and incoming solar radiation and then reflects the interactions between surface water processes and climate. In this paper, a methodology for retrieving ET from low spatial resolution remote sensing data is presented. It is based on the evaporative fraction concept, and it has been applied to Advanced Very High Resolution Radiometer (AHVRR) data acquired over the Iberian Peninsula. The methodology does not require other data than the data provided by the satellite and may be applied to areas with almost spatially constant atmospheric conditions and which include wet and dry sub-areas. The comparison with high resolution ET estimation shows a root mean square error (RMSE) of 1.4 mm d − 1 which is in agreement with the sensitivity analysis of the method. Finally, the methodology has been applied to temporal NOAA–AVHRR images acquired from 1997 to 2002 in order to analyze the seasonal evolution of the daily ET. The temporal study of the ET values realized in this paper shows that the highest ET values are associated with the higher development crops, while the lowest values are related with lower development or null crop. As a conclusion, it is shown that the ET values obtained with the proposed model evolve according to the variations presented in parameters such as surface temperature or vegetation index.

An improved algorithm for the detection of plumes caused by natural or technological hazards using AVHRR imagery

Fires caused by natural or technological disasters emit large amounts of smoke which, once formed into plumes, may affect the human health and the environment. Satellite remote sensing data provide an effective tool to achieve detection and monitoring of these plumes over large areas on a routine basis. Discrimination of plumes on satellite images is a prerequisite to study and retrieve physical, chemical and optical properties of emitted smoke. An improved algorithm for the detection of plumes caused by natural or technological hazards using AVHRR imagery is presented in this study. The method is based on a multi-temporal and multi-spectral change detection algorithm. It is performed in two main steps: a) appropriate spectral and spatial filters are applied on the images acquired before and after a fire event in visible and near-infrared ranges in order to extract the core of the plume; b) a criterion on spectral information is defined as an homogeneity measure that enables, through a modified version of the region-growing method, the spatial expansion of the detected core to include the complete area covered by the plume. Through this approach, a pixel is identified as a plume pixel if it is “close” to the core plume pixels in both spatial and spectral spaces. The algorithm was developed and calibrated using AVHRR images acquired over Spain before and during a major forest fire event on July 16, 2005. It was applied using past events of natural and technological hazards in several locations to ensure its global applicability and robustness. The algorithm produced accurate results in all cases of plumes, either in natural or in technological fire events. Three application cases are presented in this study: A major fire in an industrial installation in London (December 11, 2005), a major fire in Baghdad during the recent war in Iraq (April 1, 2003) and a forest fire in California (September 29, 2005).

Evaluating the Impact of Drought Using Remote Sensing in a Mediterranean, Semi-arid Region

This paper analyses monthly differences in drought impact on vegetation activity in a semi-arid region in the north-east of the Iberian Peninsula between 1987 and 2000. The study determines spatial differences in the effects of drought on the natural vegetation and agricultural crops by means of the joint use of vegetation indexes derived from AVHRR images, a drought index (standardized precipitation index), and Geographic Information Systems. The results show that the effect of drought on vegetation varies noticeably between areas, a pattern that is determined mainly by the location of land-cover types. The influence also varies each month and, in general, is higher during the spring and summer. Aridity and vegetation characteristics similarly account, in part, for spatial differences in the impact of drought on vegetation. In general, the most arid areas, where vegetation cover and activity are low, are those in which the interannual variability of vegetation activity is more determined by the drought occurrence. In assessing drought impact, this analysis takes into account the effects of drought on the vegetation and also considers spatial and seasonal differences. The results should be useful for the management of natural vegetation and crops and for the development of better drought mitigation strategies.

A new algorithm for forest fire detection method with statistical analysis using NOAA AVHRR images

Various researchers have carried out forest fire analysis using NOAA satellite images. There are several methods of doing this, and most can detect a fire. However, many false fires were also detected, and, in some cases, actual fires were missed. We analysed four satellite‐based fire detection methods using data from AVHRR of NOAA‐16 over a period of three to six months for the Sakhalin region and the Japan region. Considering the fundamental differences, problems, and effectiveness of these methods, we have constructed an improved fire detection method with statistical analysis. Our method has reduced false fire detection significantly, as well as detected actual fire with accuracy.