Ivan Csiszar

Validation of the MODIS active fire product over Southern Africa with ASTER data

This paper describes the use of high‐spatial‐resolution ASTER data to determine the accuracy of the moderate‐resolution MODIS active fire product. Our main objective was to develop a methodology to use ASTER data for quantitative evaluation of the MODIS active fire product and to apply it to fires in southern Africa during the 2001 burning season. We utilize 18 ASTER scenes distributed throughout the Southern Africa region covering the time period 5 August 2001 to 6 October 2001. The MODIS fire product is characterized through the use of logistic regression models to establish a relationship between the binary MODIS ‘fire’/‘no fire’ product and summary statistics derived from ASTER data over the coincident MODIS pixel. Probabilities of detection are determined as a function of the total number of ASTER fires and Morans I, a measure of the spatial heterogeneity of fires within the MODIS pixel. The statistical analysis is done for versions 3 and 4 of the MODIS fire‐detection algorithm. It is shown that the algorithm changes have a positive effect on the fire‐product accuracy.

Automated monitoring of snow cover over North America with multispectral Satellite data

Current National Oceanic and Atmospheric Administration (NOAA) operational global- and continental-scale snow cover maps are produced interactively by visual analysis of satellite imagery. This snow product is subjective, and its preparation requires a substantial daily human effort. The primary objective of the current study was to develop an automated system that could provide NOAA analysts with a first-guess snow cover map and thus to reduce the human labor in the daily snow cover analysis. The proposed system uses a combination of observations in the visible, midinfrared, and infrared made by the Imager instrument aboard Geostationary Operational Environmental Satellites (GOES) and microwave observations of the Special Sensor Microwave Imager (SSM/I) aboard the polar-orbiting Defense Meteorological Satellite Program platform. The devised technique was applied to satellite data for mapping snow cover for the North American continent during the winter season of 1998/99. To assess the system performance, the automatically produced snow maps were compared with the NOAA interactive operational product and were validated against in situ land surface observations. Validation tests revealed that in 85% of cases the automated snow maps fit exactly the ground snow cover reports. Snow identification with the combination of GOES and SSM/I observations was found to be more efficient than the one based solely on satellite microwave data. Comparisons between the automated maps and the NOAA operational product have shown their good agreement in the distribution of snow cover and its area coverage. The accuracy of the automated product was found to be similar to and sometimes higher than the accuracy of the operational snow cover maps manually produced at NOAA.

Mapping global land surface albedo from NOAA AVHRR

A set of algorithms is combined for a simple derivation of land surface albedo from measurements of reflected visible and near-infrared radiation made by the advanced very high resolution radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites. The system consists of a narrowband-to-broadband conversion and bidirectional correction at the top of the atmosphere and an atmospheric correction. We demonstrate the results with 1 month worth of data from the NOAA National Environmental Satellite, Data, and Information Service (NESDIS) global vegetation index (GVI) weekly data set and the NOAA/NASA Pathfinder Atmosphere (PATMOS) project daily data. Error analysis of the methodology indicates that the surface albedo can be retrieved with 10–15% relative accuracy. Monthly albedo maps derived from September 1989 GVI and PATMOS data agree well except for small discrepancies attributed mainly to different preprocessing and residual atmospheric effects. A 5-year mean September map derived from the GVI multiannual time series is consistent with that derived from low-resolution Earth Radiation Budget Experiment data as well as with a September map compiled from ground observations and used in many numerical weather and climate models. Instantaneous GVI-derived albedos were found to be consistent with surface albedo measurements over various surface types. The discrepancies found can be attributed to differences in areal coverage and representativeness of the satellite and ground data. The present pilot study is a prototype for a routine real-time production of high-resolution global surface albedo maps from NOAA AVHRR Global Area Coverage (GAC) data.

Land and cryosphere products from Suomi NPP VIIRS: Overview and status

[1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA’s Earth Observing System’s Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA’s focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team’s evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS.

Global fire activity from two years of MODIS data

Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Earth Observing System Terra and Aqua satellites provides global fire observations of unprecedented quality. This paper presents spatial and temporal distributions of active fires from 2001 and 2002, the first 2 years of the MODIS active fire data record. Monthly fire counts were analysed globally and within several regions of major fire activity and vegetation type. The global maximum of the annual cycle of fire activity for both years occurred in August; a combined result of burning during the dry season in the Southern Hemisphere tropics and the warm season over the Northern Hemisphere extratropics. The minimum of global fire activity occurred in March in both years. Burning in the tropics occurred mostly in savanna and shrubland areas with a high percentage of herbaceous vegetation. In the extratropics, fires were detected over croplands, grasslands and forests. The global total numbers of fire counts observed in 2001 and 2002 differed by less than 3%, but regionally significant differences were found between the two years in total and relative fire counts and in the timing of burning. Fire counts from daytime MODIS observations from Terra and Aqua also provided evidence of the diurnal cycle of fire activity. This analysis of ‘fire/no fire’ binary indicators is a first-order approximation of global spatio-temporal fire dynamics. For several applications, such as the estimation of pyrogenic emissions, further studies of burned area and fire characteristics are needed.

Using NOAA/AVHRR Products to Monitor El Niño Impacts: Focus on Indonesia in 1997–98

Abstract The development of the El Nino—Southern Oscillation (ENSO) in 1997—98, the most intense in this century, has been monitored by space— and ground—based observations. In this study, the authors present the signatures of ENSO impacts on the surface—atmosphere system, as detected in satellite products that are routinely derived at NOAA from measurements by a single instrument on board NOAA polar—orbiting satellites—the Advanced Very High Resolution Radiometer (AVHRR). The Indonesian archipelago was selected to demonstrate how AVHRR products can be synergistically used to monitor interannual variability, such as caused by ENSO, on regional and global scales. The authors examined month—to—month changes in surface—atmosphere conditions over the region during July 1997—June 1998. The major ENSO impact over the Indonesian archipelago was a prolonged dry period with anomalously low amounts of cloud, precipitation, and water vapor. The net effect of these changes was a significant increase in the absorbed s...

Active fires from the Suomi NPP Visible Infrared Imaging Radiometer Suite: Product status and first evaluation results

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (S-NPP) satellite incorporates fire-sensitive channels, including a dual-gain high-saturation temperature 4 µm channel, enabling active fire detection and characterization. The active fire product, based on the 750 m moderate resolution “M” bands of VIIRS, is one of the standard operational products generated by the Interface Data Processing Segment of the S-NPP ground system. The product builds on an earlier “Collection 4” version of the algorithm used for processing Moderate Resolution Imaging Spectroradiometer (MODIS) data. Following postlaunch quality assessments and corrections in the input VIIRS Sensor Data Record data processing, an initial low detection bias was removed and the product achieved Beta quality in April 2012. Daily spurious detections along-scan lines were also significantly reduced as a result of further processing improvements in October 2012. Direct product comparison with MODIS over 4 months of data in 2013 has shown that VIIRS produces approximately 26% more detections than MODIS within the central 3 pixel VIIRS aggregation zone of approximately ±31° scan angle range and 70% more detections outside of that zone, mainly as a result of the superior VIIRS scanning and sampling characteristics. Further development is in progress to ensure high-quality VIIRS fire products that continue the MODIS data record and better serve the user community by delivering a full image classification product and fire radiative power retrievals. Research is also underway to take advantage of the radiometric signal from the 375 m VIIRS imager “I” bands.

A review of current space-based fire monitoring in Australia and the GOFC/GOLD program for international coordination

Satellite remote sensing of fires provides a unique view of our planet and quantitative information that can inform resource management and policy. Operational and experimental satellite sensing systems have the capability to provide regional and global monitoring of fires. These systems provide different types of fire information for estimation of fire danger, detecting active fires, estimating burned area, quantifying emissions products, estimating fire damage and monitoring post-fire ecosystem recovery. Efforts to extract and provide such information fall largely in the research domain and are in various stages of development. The pressing demand for reliable and up-to-date information, on fire occurrence, extent and emissions, warrants the transition of the mature research methods and experimental sensors into the operational domain. Providing consistent, timely and easily useable fire information of known accuracy for improved resource management is a challenge facing the research and operational communities. As part of the Integrated Global Observing Systems initiative, an international program called Global Observations of Forest Cover/Global Observations of Land Dynamics (GOFC/GOLD) is coordinating a concerted effort to meet this challenge. This paper describes the goals of this international program and provides a case study of the development and current status of satellite-based fire monitoring in Australia. We identify the major obstacles to a broader adoption of the technology by the fire community, the current needs and the relevance of the broader international program to national satellite-based fire monitoring activities.

Hotspot Analysis of Vegetation Fires and Intensity in the Indian Region

In this study, we quantify vegetation fire activity in India using the MODerate resolution Imaging Spectroradiometer (MODIS) active fire datasets. We assessed different fire regime attributes, i.e., fire frequency, seasonality, intensity and the type of vegetation burnt in diverse geographical regions. MODIS data from 2002–2010 revealed an average of 63696 fire counts per year with the highest during 2009. Fire season in India extends from October to June with the peak during March. The K-means algorithm identified hotspot regions of fire clusters in diverse regions of India. We examined fire radiative power (FRP) data in the hotspot regions to address which fires burn intensively than others based on the vegetation type. We first assessed the best statistical fit distributions for the FRP data using the probability density functions (PDFs) and ranked them based on Kolmogorov-Smirnov statistic. We then described the fire intensities using empirical cumulative distribution functions (CDFs). Results suggest diverse pdfs for the FRP data that included Burr, Dagum, Johnson as well as Pearson distribution and they varied based on the vegetation type burnt. Analysis from empirical CDFs suggested relatively high fire intensity for closed broadleaved evergreen/ semi-deciduous forests than the other vegetation types. Although, annual sum of FRP for agricultural fires was less than the closed broadleaved evergreen forests, the values were higher than the mosaic vegetation category and broadleaved deciduous forests. These results on fire hotspots and FRP will be useful to address the impact of vegetation fires on air pollution and climate in India.

On the use of fire radiative power, area, and temperature estimates to characterize biomass burning via moderate to coarse spatial resolution remote sensing data in the Brazilian Amazon

Received 28 December 2009; revised 3 June 2010; accepted 11 June 2010; published 10 November 2010. [1] Spaceborne instruments provide a unique view of global vegetation fire activity many times a day. In this study, we assessed the fire characterization information provided by two major products: the Terra and Aqua MODIS Thermal Anomalies product (MOD14 and MYD14, respectively) and the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product derived from GOES East Imager. Using higher spatial resolution imagery data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Enhanced Thematic Mapper Plus (ETM+) instruments, we analyzed the characterization of subpixel fires detected by MOD14, MYD14, and WF_ABBA over parts of Brazilian Amazonia. Our results suggest that MODIS and GOES fire radiative power (FRP) estimates derived for individual fire‐pixel clusters are subject to errors due to the effects of the point spread function of those instruments (underestimation of up to 75%), improper fire background characterization (overestimation of up to 80% assuming a 10 K cold bias in background temperature), and omission of small fire lines. Detection limits were approximately 11 and 9 MW for MOD14 and MYD14, respectively, and were equivalent to 27 and 19 MW for WF_ABBA data acquired coincidently with MOD14 and MYD14, respectively. We found a positive correlation between FRP and percentage tree cover indicating that FRP is sensitive to biomass density. Fire area and temperature estimates derived from the application of Dozier’s (1981) approach to GOES data did not agree with our reference data (i.e., ASTER and ETM+ active fire masks and in situ fire temperature data), suggesting that large and variable errors could affect the retrieval of those parameters.