Advanced Very High Resolution Radiometer Imagery Research Articles

Ocean Fronts detection over the Bay of Bengal using changepoint algorithms – A non-parametric approach

Oceanic fronts are regions over the oceans where a significant change in the characteristics of the water masses is observed. Advanced Very High Resolution Radiometer (AVHRR) satellite imagery over the Bay of Bengal shows regions that are populated by frontal structures. Over the Bay of Bengal, some of the strongest gradients in temperature and salinity are observed. In recent years, there has been a tremendous growth in the availability of satellite imagery and the necessity of automated fast detection of the frontal features is needed for services like potential fishing zones over open oceans. In this article, an algorithm to infer oceanic fronts over the Bay of Bengal is described using changepoint analysis. The changepoint algorithm is combined in a novel way with a contextual median filter to detect frontal features in AVHRR imagery. The changepoint analysis is a non-parametric technique that does not put thresholds on the gradients of brightness temperatures of the satellite imagery. In the open oceans, the gradients of temperature and salinity are not sharp and changepoint analysis is found to be a useful complementary technique to the existing front detecting methods when combined with contextual median filters.

A Fully Automatic Burnt Area Mapping Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor

The German Aerospace Center’s (DLR) TIMELINE project (“Time Series Processing of Medium Resolution Earth Observation Data Assessing Long-Term Dynamics in our Natural Environment”) aims to develop an operational processing and data management environment to process 30 years of National Oceanic and Atmospheric Administration (NOAA)—Advanced Very High-Resolution Radiometer (AVHRR) raw data into Level (L) 1b, L2, and L3 products. This article presents the current status of the fully automated L3 burnt area mapping processor, which is based on multi-temporal datasets. The advantages of the proposed approach are (I) the combined use of different indices to improve the classification result, (II) the provision of a fully automated processor, (III) the generation and usage of an up-to-date cloud-free pre-fire dataset, (IV) classification with adaptive thresholding, and (V) the assignment of five different probability levels to the burnt areas detected. The results of the AVHRR data-based burn scar mapping processor were validated with the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product MCD64 at four different European study sites. In addition, the accuracy of the AVHRR-based classification and that of the MCD64 itself were assessed by means of Landsat imagery.

A Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor

The German Aerospace Center’s (DLR) TIMELINE project aims to develop an operational processing and data management environment to process 30 years of National Oceanic and Atmospheric Administration (NOAA)—Advanced Very High Resolution Radiometer (AVHRR) raw data into L1b, L2 and L3 products. This article presents the current status of the fully automated L2 active fire hotspot detection processor, which is based on single-temporal datasets in orbit geometry. Three different probability levels of fire detection are provided. The results of the hotspot processor were tested with simulated fire data. Moreover, the processing results of real AVHRR imagery were validated with five different datasets: MODIS hotspots, visually confirmed MODIS hotspots, fire-news data from the European Forest Fire Information System (EFFIS), burnt area mapping of the Copernicus Emergency Management Service (EMS) and data of the Piedmont fire database.

An inter-sensor calibration and atmospheric correction system for long-term time series of AVHRR imagery for coastal waters

The Advanced Very High Resolution Radiometer (AVHRR) data series has more than 30 years of unique and valuable Earth observation imagery available. To use the long-term remote-sensor data, this article presents a system that produces consistent inter-sensor calibration and atmospheric correction for coastal waters. The system can process all five High Resolution Picture Transmission (HRPT) file formats from the twelve (12) AVHRR sensors that operated from the 1980s to the present. The system has been used to process AVHRR data of three Texas estuaries from 1985 to 2010 to document changes in suspended sediment patterns.

Mapping Potential Fishing Grounds in Lake Malawi Using AVHRR and MODIS Satellite Imagery

This paper discusses a procedure that was developed to delineate potential fishing grounds in Lake Malawi using data on chlorophyll-a concentration derived from Moderate-resolution Imaging Spectroradiometer (MODIS/AQUA) in combination with lake surface temperature (LST) data obtained from Advanced Very High Resolution Radiometer (AVHRR) and MODIS/Terra satellite sensors. The paper draws from findings of studies [1,2] on development of algorithms for estimating chlorophyll-a and lake surface temperature in Lake Malawi from satellite imagery, respectively. To estimate chlorophyll concentration (a proxy for phytoplankton) in Lake Malawi using data from MODIS satellite imagery, in situ measurements of chlorophyll concentration were conducted at three selected sampling stations over the southeastern arm of Lake Malawi concurrent with satellite image acquisitions. These were regressed on chlorophyll-a concentration values obtained from Ocean Color (MODIS/AQUA) Data using SeaWIFS Data Analysis System (SeaDAS) software. From this, an equation for estimating chlorophyll-a concentration in Lake Malawi from MODIS satellite imagery was developed and used for mapping the spatial distribution of chlorophyll-a concentration in the lake. Since Lake Malawi is an oligotrophic lake, with an average value of chlorophyll concentration of 1 μg/L, areas in the lake with relatively high chlorophyll-a concentration were identified as potential locations for the development of the fishery industry. Estimation of lake surface temperature using satellite imagery involved two main activities. Firstly, in situ measurements of lake surface temperature were conducted at the three selected sampling stations over Lake Malawi concurrent with satellite image acquisitions. The second activity involved downloading and processing AVHRR and MODIS/Terra satellite imagery. AVHRR data covered the period September 1997 to February 1998 whereas MODIS/Terra data covered the period May to November, 2006. Both MODIS Land Surface Temperature (MOD11A1) and Ocean Color Sea Surface Temperature (SST) were downloaded from EOS Gateway website and processed into lake surface temperature. Two glass thermometers were used to measure temperature directly from the lake surface at a depth of 0 - 7.0 cm (i.e., skin temperature) and the average of the two readings was recorded as the lake surface temperature at a particular sampling station. Observed temperatures were regressed on remotely sensed data. ER Mapper was employed in drawing maps showing the distribution of lake surface temperature using the regression equation that was developed. Upwelling and downwelling zones were demarcated from lake surface temperature maps. Upwelling zones were identified as areas with a high potential for the development of the fishery industry because of their association with primary productivity. Using a simple overlay technique, data from both the spatial and temporal distribution of chlorophyll-a and lake surface temperature were used to delineate potential fishing grounds in Lake Malawi. The zone extending from Salima up to the northern part of Nkhotakota and the area on the northeastern tip of Lake Malawi were identified as areas of high primary productivity and therefore potential fishing grounds. These areas generally exhibit persistent cool surface waters, indicative of upwelling; and have relatively abundant phytoplankton.

A comparison of NOAA–AVHRR fire data with three Landsat data sets in arid and semi-arid Australia

Burnt area data, derived from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery, are validated in 11 regions of arid and semi-arid Australia, using three separate Landsat-derived burnt area data sets. Mapping accuracy of burnt extent is highly variable between areas and from year to year within the same area. Where there are corresponding patches in the AVHRR and Landsat data sets, the fit is good. However, the AVHRR data set misses some large patches. Overall, 63% of the Landsat burnt area is also mapped in the AVHRR data set, but this varies from 0% to 89% at different sites. In total, 81% of the AVHRR burnt area data are matched in the Landsat data set, but range from 0% to 94%. The lower match rates (<50%) are generally when little area has burnt (0–500 km2), with figures generally better in the more northerly sites. Results of regressions analysis based on 10 km × 10 km cells are also variable, with R 2 values ranging from 0.37 (n = 116) to 0.94 (n = 85). For the Tanami Desert scene, R 2 varies from 0.41 to 0.61 (n = 368) over three separate years. Combining the data results in an R 2 of 0.60 (n = 1315) (or 0.56 with the intercept set to 0). The slopes of the regressions indicate that mapping the burnt area from AVHRR imagery underestimates the ‘true’ extent of burning for all scenes and years. Differences in mapping accuracy between low and high fire years are examined, as well as the influence of soil, vegetation, land use and tenure on mapping accuracy. Issues which are relevant to mapping fire in arid and semi-arid environments and discontinuous fuels are highlighted.

Achieving Subpixel Georeferencing Accuracy in the Canadian AVHRR Processing System

Precise geolocation is one of the fundamental requirements for satellite imagery to be suitable for climate applications. The Global Climate Observing System and the Committee on Earth Observing Satellites identified the requirement for the accuracy of geolocation of satellite data for climate applications as 1/3 field of view (FOV). This requirement for the series of the Advanced Very High Resolution Radiometer (AVHRR) on the National Oceanic and Atmospheric Administration platforms cannot be met without implementing the ground control point (GCP) correction, particularly for historical data, because of limited accuracy of orbit modeling and knowledge of satellite attitude angles. This paper presents a new method for precise georeferencing of the AVHRR imagery developed as part of the new Canadian AVHRR processing system (CAPS) designed for generating high-quality AVHRR satellite climate data record at 1-km spatial resolution. The method works in swath projection and uses the following: 1) the reference monthly images from Moderate Resolution Imaging Spectroradiometer at 250-m resolution; 2) orthorectification to correct for surface elevation; and 3) a novel image matching technique in swath projection to achieve the subpixel resolution. The method is designed for processing daytime data as it intensively employs observations from optical solar bands, the near-infrared channel in particular. The application of the developed processing system showed that the algorithm achieved better than 1/3 FOV geolocation accuracy for AVHRR 1-km scenes. It has very high efficiency rate (> 97%) due to the dense and uniform GCP coverage of the study area (5700 × 4800 km2 ), covering the entire Canada, the Northern U.S., Alaska, Greenland, and surrounding oceans.

A Study of Tidal Influences in the North Water Polynya using Short Time Span Satellite Imagery

The North Water Polynya (NOW) is an area of ocean between Greenland and Ellesmere Island that does not freeze completely during the winter months. The mechanism that maintains the polynya during the Arctic winter and early spring is not precisely known, but the presence of open water is a critical factor in allowing oceanic heat to escape into the atmosphere. The northerly location of the NOW permitted the collection of an Advanced Very High Resolution Radiometer (AVHRR) image every 101 minutes for seven consecutive orbits. The unique, short time span imagery allowed thermal features of the NOW to be mapped over a tidal cycle. The combination of AVHRR imagery, Acoustic Doppler Current Profiler data, and the Composite Arctic Sea Surface Temperature Algorithm show the dynamic nature of the NOW over a tidal cycle. Both the amount and configuration of open water can change dramatically over a 12-hour period in response to tidal fluctuations. The evidence suggests that the amount of open water in the NOW during March and April is related to the velocity of the current, which in turn is influenced by the tidal cycle. The open water caused by the tide-induced movement of ice then allows oceanic heat to escape into the environment. During March and April, the considerable temperature difference between the ocean and the atmosphere at their interface results in a high incidence of ice fog near leads and open water in the NOW. The amount of ice fog observed on the satellite imagery fluctuates with the tidal cycle, suggesting that open water within the NOW is influenced by the tide in the short term.

Computing Coastal Ocean Surface Currents From Infrared and Ocean Color Satellite Imagery

Many previous studies have demonstrated the viability of estimating advective ocean surface currents from sequential infrared satellite imagery using the maximum cross-correlation (MCC) technique when applied to 1.1-km-resolution Advanced Very High Resolution Radiometer (AVHRR) thermal infrared imagery. Applied only to infrared imagery, cloud cover and undesirable viewing conditions (gaps in satellite data and edge-of-scan distortions) limit the spatial and temporal coverage of the resulting velocity fields. In addition, MCC currents are limited to those represented by the displacements of thermal surface patterns, and hence, isothermal flow is not detected by the MCC method. The possibility of supplementing MCC currents derived from thermal AVHRR imagery was examined, with currents calculated from 1.1-km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color imagery, which often have spatial patterns complementary to the thermal infrared patterns. Statistical comparisons are carried out between yearlong collections of thermal and ocean color derived MCC velocities for the central California Current. It is found that the image surface patterns and resulting MCC velocities complement one another to reduce the effects of poor viewing conditions and isothermal flow. The two velocity products are found to agree quite well with a mean correlation of 0.74, a mean rms difference of 7.4 cm/s, and a mean bias less than 2 cm/s which is considerably smaller than the established absolute error of the MCC method. Merging the thermal and ocean color MCC velocity fields increases the spatial coverage by approximately 25% for this specific case study

An automatic system for AVHRR land surface product generation

Making products from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar‐orbiting satellites can be time consuming and an automated technique for image processing is required to generate long time series of AVHRR imagery. This paper aims to describe the development of a system for fully‐automated AVHRR image processing, including radiometric calibration, precise geo‐registration and generating land‐surface products, such as vegetation indices, maximum value composites and cloud masks. Tests for crop monitoring purposes were carried out using High Resolution Picture Transmission (HRPT) images between October 2003 and April 2004. The region used to evaluate the system was the State of Paraná, one of the primary soybean producers in Brazil. Results have shown that for severely cloud‐filtered images, the system was effective in generating geometrically precise image products, with geolocation errors less than a pixel. The developed system can be operated with no human intervention and can be used as an important tool for NOAA‐AVHRR image users especially those who need to use long time series.

On comparing multifractal and classical features in minimum distance classification of AVHRR imagery

The ability to distinguish between different types of surfaces is the strength of texture descriptors in the analysis of satellite imagery. Although the most common analytical means are based on co‐occurrence analysis, considerable progress has been made in understanding the role of fractal and multifractal analysis in remote sensing. After indicating the limitations of using fractal dimensions as the only texture descriptor and introducing the concept of multifractal geometry, we consider the effectiveness of using multifractal and second‐order fractal features in image classification. In particular, we present the results of comparing two supervised classifications of an Advanced Very High Resolution Radiometer (AVHRR) image of Scotland using classical texture features and multifractal second‐order fractal ones. In terms of percentage correct and Khat statistics, this study provides evidence, with a confidence limit of 95%, that classifications using multifractal and second‐order fractal features are more accurate than those using classical features. The classification algorithm used for this study is a typical minimum distance classifier.

Relationships among fire frequency, rainfall and vegetation patterns in the wet–dry tropics of northern Australia: an analysis based on NOAA‐AVHRR data

ABSTRACTAim To quantify the regional‐scale spatio‐temporal relationships among rainfall, vegetation and fire frequency in the Australian wet–dry tropics (AWDT).Location Northern Australia: Cape York Peninsula, central Arnhem, central Kimberly, Einasleigh Uplands, Gulf Fall Uplands and northern Kimberley.Methods Monthly ‘fraction of photosynthetic active radiation absorbed by green vegetation’ (fAPAR) was decomposed into monthly evergreen (EG) and monthly raingreen (RG) components using time‐series techniques applied to monthly normalized difference vegetation index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) imagery. Fire affected areas were independently mapped at the same spatio‐temporal resolution from AVHRR imagery. Weather station records were spatially interpolated to create monthly rainfall surfaces. Vegetation structural classes were derived from a digitized map of northern Australian vegetation communities (1 : 1,000,000). Generalized linear models were used to quantify relationships among the fAPAR, EG and RG signals, vegetation structure, rainfall and fire frequency, for the period November 1996–December 2001.Results The fAPAR and EG signals are positively correlated with annual rainfall and canopy cover, notably: EGclosed forest &gt; EGopen heathland &gt; EGopen forest &gt; EGwoodland &gt; EGopen woodland &gt; EGlow woodland &gt; EGlow open woodland &gt; EGopen grassland. Vegetation height and fAPAR are positively correlated, excluding the special case of open heathland. The RG signal is highest where intermediate annual rainfall and strong seasonality in rainfall coincide, and is associated with vegetation structure as follows: RGopen grassland &gt; RGwoodland &gt; RGopen forest &gt; RGopen heathland &gt; RGlow woodland &gt; RGopen woodland &gt; RGlow open woodland &gt; RGclosed forest. Monthly RG tracks monthly rainfall. Annual proportion of area burnt (PB) is maximal where high RG coincides with low EG (open grassland, several woodland communities). PB is minimal in vegetation where both RG and EG are low (low open woodland); and in vegetation where EG is high (closed forest, open heathland).Conclusions The RG–EG scheme successfully reflects digitally mapped tree and grass covers in relation to rainfall. RG–EG patterns are strongly associated with fire frequency patterns. PB is maximal in areas of high RG, where high biomass production during the wet season supports abundant fine fuel during the dry season. PB is minimal in areas with high EG, where relatively moist fuel limits fire ignition; and in areas with low EG and RG, where a relative short supply of fuel limits fire spread.

Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in arctic peripheral seas

An accurate representation of sea ice concentration is valuable to operational ice analyses, process studies, model inputs, and detection of long-term climate change. Passive microwave imagery, such as from the Special Sensor Microwave/Imager (SSM/I), are particularly valuable for monitoring of sea ice conditions because of their daily, basin-scale coverage under all sky conditions. SSM/I-derived sea ice concentration estimates using four common algorithms [Bootstrap (BT), Cal/Val (CV), NASA Team (NT), and NASA Team 2 (N2)] are compared with concentrations computed from Advanced Very High Resolution Radiometer (AVHRR) visible and infrared imagery. Comparisons are made over approximately an eight-month period in three regions of the Arctic and focus on areas near the ice edge where differences between the algorithms are likely to be most apparent. The results indicate that CV and N2 have the smallest mean error relative to AVHRR. CV tends to overestimate concentration, while the other three algorithms underestimate concentration. NT has the largest underestimation of nearly 10% on average and much higher in some instances. In most cases, mean errors of the SSM/I algorithm were significantly different from each other at the 95% significance level. The BT algorithm has the lowest error standard deviation, but none of the considered algorithms was found to have statistically significantly different error standard deviations in most cases. This indicates that spatial resolution is likely a limiting factor of SSM/I in regions near the ice edge in that none of the algorithms satisfactorily resolve mixed pixels. Statistical breakdowns by season, region, ice conditions, and AVHRR scene generally agree with the overall results. Representative case studies are presented to illustrate the statistical results.

Okhotsk Sea Kashevarov Bank polynya: Its dependence on diurnal and fortnightly tides and its initial formation

A powerful resonance in the O1 and K1 diurnal tides occurs over the Kashevarov Bank in the Okhotsk Sea. Model studies show that this resonance creates tidal mixing and an associated vertical heat flux, both with diurnal and fortnightly components. In midwinter this flux controls the behavior of the resultant polynya; in early winter it affects the polynya formation. For the midwinter case, examination of numerical results and of imagery derived from the Special Sensor Microwave Imager (SSM/I), the advanced very high resolution radiometer (AVHRR), and the RADARSAT ScanSAR shows that the polynya area varies diurnally and fortnightly. Specifically, for the 2000 and 2001 winters, daily averaged passive microwave images clearly show a fortnightly cycle in the polynya area. At the diurnal period the model shows that the location of the maximum heat flux oscillates between the northwestern and southwestern edge of the bank and that the polynya oscillates between the western edge of the bank and being elongated in the east‐west direction over the southern edge. Passive microwave swath and AVHRR imagery show a similar polynya behavior. For the early winter case and during a period of northerly winds, synthetic aperture radar imagery shows that the polynya consists of two parts, an upwelling polynya over the bank and a secondary polynya downwind of the bank with properties similar to coastal polynyas. In the case of the secondary polynya the region of upwelled warm water over the bank serves as its northern boundary.

A Comparison of Two 85-GHz SSM/I Ice Concentration Algorithms With AVHRR and ERS-2 SAR Imagery

Sea ice concentrations obtained with two algorithms from Special Sensor Microwave/Imager (SSM/I) data are compared to spaceborne visible/infrared and active microwave imagery for the Greenland Sea in spring. Both algorithms, the ARTIST Sea Ice algorithm (ASI) and the SEA LION algorithm (SLA), utilize 85-GHz SSM/I brightness temperatures with a spatial resolution of 15 km /spl times/13 km. Ice concentrations obtained from Advanced Very High Resolution Radiometer (AVHRR) infrared data in cloud-free areas are underestimated by SLA and ASI ice concentrations by 3.6% and 8.3% (correlation coefficients of 0.90 and 0.91). Ice concentrations estimated from texture classified ERS-2 synthetic aperture radar (SAR) images by assigning experience-based ice concentrations to ice-type classes are overestimated by SLA and ASI ice concentrations by 4.4% and 1.5% (correlation coefficients of 0.84 and 0.77). However, omitting low/high ice concentrations forming up to 80% (AVHRR) and 60% (SAR) of the entire dataset reveals a significantly different statistic. For instance, the correlation between AVHRR and SLA and ASI ice concentrations drops to 0.77 and 0.70, respectively. All presented techniques to obtain ice concentrations need improvement and future developments should involve larger datasets. However, with care, both algorithms can be used to obtain reasonable ice concentration maps with a 12.5 km /spl times/12.5 km grid-cell size.

Characterization of plumes on top of deep convective storm using AVHRR imagery and radiative transfer simulations

Cirrus clouds often form on top of intense convective storms and influence the radiation exchange at the top of the atmosphere. From time to time these cirrus assume a typical plume form, whose origin is yet to be clearly explained. Investigation on plume's structure and composition is necessary using satellite observations and radiative transfer modeling, since in situ measurements are generally not available for the storm's extreme scenarios. Plumes of ice crystals produce a significant increase of cloud top reflectivity in channel 3 (3.55–3.93 μm) of National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR), due to the small size of the crystals (of the order of the channel wavelength). AVHRR measurements over a severe storm are compared with radiative transfer simulations through a 1-D, plane-parallel radiative transfer model (RTM). The hydrometeor vertical distribution of a cumulonimbus tower is assumed, composed of a water droplet layer underlying an ice crystal layer. The presence of the plume on top of the cumulonimbus is simulated by an ice crystal layer whose optical depth and ice crystal size and habit are varied. The results of the comparisons show that channel 3 conveys relevant information on the ice crystal size and habit.

Analysis of satellite imagery for Emiliania huxleyi blooms in the Bering Sea before 1997

The presence of blooms of the coccolithophore Emiliania huxleyi in the Bering Sea shelf has been studied using satellite imagery in order to ascertain whether its first reported appearance in 1997 is really a new phenomenon for the area. Examination for Emiliania huxleyi blooms in Coastal Zone Color Scanner (CZCS) and Advanced Very High Resolution Radiometer (AVHRR) imagery dating from 1978 to 1996 was performed and the relationship between the presence of Emiliania huxleyi and the Pacific Decadal Oscillation and the El Niño Southern Oscillation was investigated. No evidence of the presence of this species was found in CZCS or AVHRR imagery between 1978 and 1995. AVHRR images reveal that a small coccolithophore bloom was present in summer 1996. Although the blooms of 1997 were unprecedented in extension and intensity, it appears that the Bering Sea ecosystem did not respond as abruptly to atmospheric anomalies as initially reported.

Reliability of biomass burning estimates from savanna fires: Biomass burning in northern Australia during the 1999 Biomass Burning and Lightning Experiment B field campaign

This paper estimates the two‐daily extent of savanna burning and consumption of fine (grass and litter) fuels from an extensive 230,000 km2region of northern Australia during August‐September 1999 encompassing the Australian continental component of the Biomass Burning and Lightning Experiment B (BIBLE B) campaign [Kondo et al., 2002]. The extent of burning for the study region was derived from fire scar mapping of imagery from the advanced very high resolution radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) satellite. The mapping was calibrated and verified with reference to one Landsat scene and associated aerial transect validation data. Fine fuel loads were estimated using published fuel accumulation relationships for major regional fuel types. It is estimated that more than 43,000 km2was burnt during the 25 day study period, with about 19 Mt of fine (grass and litter) fuels. This paper examines assumptions and errors associated with these estimates. It is estimated from uncalibrated fire mapping derived from AVHRR imagery that 417,500 km2of the northern Australian savanna was burnt in 1999, of which 136,405 km2, or 30%, occurred in the Northern Territory study region. Using generalized fuel accumulation equations, such biomass burning consumed an estimated 212.3 Mt of fine fuels, but no data are available for consumption of coarse fuels. This figure exceeds a recent estimate, based on fine fuels only, for the combined Australian savanna and temperate grassland biomass burning over the period 1990–1999 but is lower than past estimates derived from classification approaches. We conclude that (1) fire maps derived from coarse‐resolution optical imagery can be applied relatively reliably to estimate the extent of savanna fires, generally with 70–80% confidence using the approach adopted here, over the major burning period in northern Australia and (2) substantial further field assessment and associated modeling of fuel accumulation, especially of coarse fuels, is required.

Land-cover classification of China: Integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.

Multi-temporal analysis of deforestation in Rondônia state in Brazil using Landsat MSS, TM, ETM+ and NOAA AVHRR imagery and its relationship to changes in the local hydrological environment

Deforestation in Rondônia state in the south-western part of the Brazilian Legal Amazon was analysed using Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), National Oceanic & Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and hydrological data. The Landsat sensor data coverage was supplemented with Pathfinder AVHRR Land (PAL) Normalized Difference Vegetation Index (NDVI) datasets. The results from the Landsat-based analysis show that more than 30% of the natural vegetation in the study area was subject to deforestation between 1973 and 1999, a finding reinforced by analysis of the PAL NDVI data. In addition, it was established that trends in the PAL NDVI datasets were coincident with the pattern of deforestation. Apart from imagery analysis, time variations in the hydrological data between 1982 and 1988 were used to estimate the evapotranspiration. A decreasing trend was identified in the rate of evapotranspiration, suggesting that deforestation has a significant impact on the local hydrological cycle.