Boris Zhukov

Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products

A major focus in global change research is to quantify the amount of gaseous and particulate pollutants emitted from terrestrial vegetation fires. Determination of the emitted radiant energy released during biomass combustion episodes (the so-called fire radiative energy or FRE) has been suggested as a new tool for determining variations in biomass combustion rates and the rate of production of atmospheric pollutants. We review the physical principals behind the remote determination of FRE and present an alternative method for its derivation via analysis of ‘fire pixel’ radiances in the middle infrared spectral region. We compare our method to the existing FRE retrieval approach used in the EOS Moderate Resolution Imaging Spectro-radiometer (MODIS) fire products, and to retrievals of FRE based on derived fire temperature and area made via the so-called Bi-spectral method. We test each FRE retrieval method using both simulated data and imagery from a new experimental space mission, the Bi-spectral InfraRed Detection (BIRD) small satellite, which has sensors specifically designed for the study of active fires. We analyse near simultaneous MODIS and BIRD data of the fires that burned around Sydney, Australia in January 2002. Despite the markedly different pixel size and spectral coverage of these sensors, where the spatial extent of the fire pixel groups detected by MODIS and BIRD are similar, the derived values of FRE for these fires agree to within F15 %. However, in certain fires, the lower spatial resolution of MODIS appears to prevent many of the less intensely radiating fire pixels being detected as such, meaning MODIS underestimates FRE for these fires by up to 46% in comparison to BIRD. Though the FRE release of each of these low intensity fire pixels is relatively low, their comparatively large number makes their overall FRE significant. Thus, total FRE release of the Sydney fires on 5 January 2002 is estimated to be 6.5 � 10 9 Js � 1 via BIRD but 4.0 � 10 9 Js � 1 via MODIS. The ability of BIRD to resolve individual fire fronts further allows the first accurate calculation of ‘radiative’ fireline intensity from spaceborne measurements, providing values of 15–75 kJ s � 1 m � 1 for fire fronts that are up to 9 km in length. Finally, we analyse the effectiveness of the satellite-based FRE retrieval methods in estimating the FRE from the active flaming and smouldering components only (FREActive, believed to be proportional to the rate of biomass combustion), despite the sensor receiving additional radiance from the ‘cooling ground’. The MIR radiance method appears particularly strong in this regard, allowing FREActive to be estimated to within F30% in the range 100–100,000 J s � 1 m � 2 . These results provide further confidence in the ability of spaceborne missions to derive physically meaningful values of FRE that could be used to support biomass burning emissions inventories. Future comparisons between FRE derived via MODIS and those from higher spatial resolution BIRD or airborne imagery may allow the MODIS-derived FRE values to be ‘calibrated’ for any systematic underestimation. We therefore expect FRE to become an important tool for enhancing global studies of terrestrial vegetation fires with infrared remote sensing, particularly as the majority of large fires are now imaged four times per day via the MODIS instruments on the Terra and Aqua spacecraft. D 2003 Elsevier Science Inc. All rights reserved.

Unmixing-based multisensor multiresolution image fusion

Constrained and unconstrained algorithms of the multisensor multiresolution technique (MMT) are discussed. They can be applied to unmix low-resolution images using the information about their pixel composition from co-registered high-resolution images. This makes it possible to fuse the low- and high-resolution images for a synergetic interpretation. The constrained unmixing preserves all the available radiometric information of the low-resolution image. On the other hand, the unconstrained unmixing may be preferable in case of noisy data. An analysis of the MMT sensitivity to sensor errors showed that the strongest requirement is the accuracy of geometric co-registration of the data; the co-registration errors should not exceed 0.1-0.2 of the low-resolution pixel size. Applications of the constrained and unconstrained algorithms are illustrated on examples of unmixing and fusion of the multiresolution reflective and thermal bands of a real TM/LANDSAT image as well as of a simulated image of the future ASTER/EOS-AMI sensor.

BIRD detection and analysis of high-temperature events: first results

The primary mission objective of a new small Bi-spectral InfraRed Detection (BIRD) satellite, which was put in a 570 km circular sun-synchronous orbit on 22 October 2001, is detection and quantitative analysis of high-temperature events (HTE) like fires and volcanoes. A unique feature of the BIRD mid- and thermal infrared channels is a real-time adjustment of their integration time that allows a HTE observation without sensor saturation, preserving a good radiometric resolution of 0.1-0.2 K for pixels at normal temperatures. This makes it possible: (a) to improve false alarm rejection capability and (b) to estimate HTE temperature, area and radiative energy release. Due to a higher spatial resolution, BIRD can detect an order of magnitude smaller HTE than AVHRR and MODIS. The smallest verified fire that was detected in the BIRD data had an area of ~12 m2. The first BIRD HTE detection and analysis results are presented including bush fires in Australia, forest fires in Russia, coal seam fires in China, and a time-varying thermal activity at Etna.

Preprocessing for the digital airborne imaging spectrometer DAIS 7915

The digital airborne imaging spectrometer DAIS 7915 is a new hyperspectral scanner developed for scientific and commercial applications. The design of the sensor makes a dedicated preprocessing necessary, prior to any data evaluation. Therefore, a facility is being developed at DLR to fulfill the needs of operational preprocessing. Besides that this facility is used for continuous quality control to support the hardware team in improving the performance of the instrument. The implementation of the software and the algorithms currently used are presented in this paper.

Detection, monitoring, and quantitative analysis of wildfires with the BIRD satellite

Increasing concern about environment and interest to avoid losses led to growing demands on space borne fire detection, monitoring and quantitative parameter estimation of wildfires. The global change research community intends to quantify the amount of gaseous and particulate matter emitted from vegetation fires, peat fires and coal seam fires. The DLR Institute of Space Sensor Technology and Planetary Exploration (Berlin-Adlershof) developed a small satellite called BIRD (Bi-spectral Infrared Detection) which carries a sensor package specially designed for fire detection. BIRD was launched as a piggy-back satellite on October 22, 2001 with ISRO’s Polar Satellite Launch Vehicle (PSLV). It is circling the Earth on a polar and sun-synchronous orbit at an altitude of 572 km and it is providing unique data for detailed analysis of high temperature events on Earth surface. The BIRD sensor package is dedicated for high resolution and reliable fire recognition. Active fire analysis is possible in the sub-pixel domain. The leading channel for fire detection and monitoring is the MIR channel at 3.8 μm. The rejection of false alarms is based on procedures using MIR/NIR (Middle Infra Red/Near Infra Red) and MIR/TIR (Middle Infra Red/Thermal Infra Red) radiance ratio thresholds. Unique results of BIRD wildfire detection and analysis over fire prone regions in Australia and Asia will be presented. BIRD successfully demonstrates innovative fire recognition technology for small satellites which permit to retrieve quantitative characteristics of active burning wildfires, such as the equivalent fire temperature, fire area, radiative energy release, fire front length and fire front strength.

Fusion of airborne hyperspectral and multispectral images

The multi-sensor multi-resolution technique (MMT) was applied to fuse a multispectral image obtained by the multispectral scanner DAEDALUS-1268 with the resolution of 6 m and a hyperspectral image obtained by the imaging spectrometer DAIS-7915. The spatial resolution of the DAIS- 7915 image was additionally degraded to 24 m in order to simulate multi-sensor data fusion with a very different sensor resolution, as is typical for satellite sensors. Both sensors had been operated simultaneously on one aircraft. The MMT algorithm includes: (1) (unsupervised) classification of the multispectral image and mapping the classes with the high resolution of the multispectral scanner, (2) retrieval of the hyperspectral signatures of these classes from the hyperspectral image, and (3) generation of the merged image which combines the pixel size of the multispectral scanner and the spectral bands of the imaging spectrometer. Additional low-pass correction of the merged image allowed us to increase significantly its accuracy. The minimal pixel error of 6.9% was obtained when the classification was performed with 256 spectral classes.

Sensor system for fire detection on-board the small satellite BIRD

With the successful launch of BIRD satellite in October 2001, new possibilities of the observation of hot events like forest fires, volcanic eruptions a.o. from space are opened. The BIRD (Bi-spectral Infrared Detection) is the first satellite which is equipped with space instrumentation dedicated to recognize high temperature events. Current remote sensing systems have the disadvantage that they were not designed for the observation of hot events. Starting with the FIRES Phase A Study, the principle requirements and ideas for a fire recognition system were defined. With the German BIRD demonstrator mission, a feasible approach of these ideas has been realized and work now in space. This mission shall answer technological and scientific questions related to the operation of a compact bi-spectral infrared push-broom sensor and related to the detection and investigation of fires from space. The payload of BIRD is a multi-sensor system designed to fulfil the scientific requirements under the constraints of a micro satellite. The paper describes the basic ideas for fire detection and the estimation of fire temperature, fire size, and energy release in the sub-pixel domain and describes the technical solution for the infrared sensor system on board of BIRD.

TM/LANDSAT thermal image unmixing

The multi-sensor multi-resolution technique (MMT) is applied to unmix a TM/LANDSAT-5 thermal image of a typical agricultural scene using higher-resolution images in the reflective TM channels. The technique allows to retrieve the mean thermal radiance for the multispectral classes which can be recognized in the higher-resolution reflective images. As a result, the unmixed thermal image can be restored with the pixel size of 30 m and merged with the reflective images for combined data analysis. Moving-window processing, as well as low-pass correction are used to reduce the effect of mixing the thermal features which can not be recognized in the reflective images. The accuracy of the technique is tested by comparing the unmixed TM thermal image with the airborne thermal images of the same scene, which were obtained by the DAIS-7915 imaging spectrometer shortly after the LANDSAT-5 fly-by, as well as with on- ground temperature measurements. The technique can be applied for unmixing thermal images of multi-resolution sensors in the near-future spaceborne Earth observation missions.

Objectives and results of the BIRD mission

The DLR small satellite BIRD (Bi- spectral Infrared Detection) is successfully operating in space since October 2001. The main payload is dedicated to the observation of high temperature events and consists mainly of a Bi-Spectral Infrared Push Broom Scanner (3.4-4.2μm and 8.5-9.3μm), a Push Broom Imager for the Visible and Near Infrared and a neural network classification signal processor. The BIRD mission answers topical technological and scientific questions related to the operation of a compact infra-red push-broom sensor on board of a micro satellite. A powerful Payload Data Handling System (PDH) is responsible for all payload real time operation, control and on-board science data handling. The IR cameras are equipped with an advanced real time data processing allowing an autonomously adaptation of the dynamic range to different scenarios. The BIRD mission control, the data reception and the data processing is conducted by the DLR ground stations in Weilheim and Neustrelitz (Germany) and is experimentally performed by a low cost ground station implemented at DLR Berlin-Adlershof. The BIRD on ground data processing chain delivers radiometric and geometric corrected data products, which will be also described in this paper. The BIRD mission is an exemplary demonstrator for small satellite projects dedicated to the hazard detection and monitoring.

Airborne testing of a bispectral infrared pushbroom scanner for hotspot detection

A small Bi-spectral Infrared Detection (BIRD) push broom scanner for a small satellite mission is developed, which is dedicated to the detection and analysis of high temperature events (HTE) including the surrounding background scenario. To avoid the saturation of the detector at high temperatures keeping at the same time a reasonable radiometric resolution for the background a very large dynamic range is required, which will be realized by special adaptive sample techniques. These techniques were proved and verified during special airborne experiments. Using two cameras in different spectral regions (3.4 - 4.2 micrometer and 8.5 - 9.3 micrometer) with a well synchronized sampling mode, it is also possible to detect and analyze hot targets with an extension much less than the nominal ground pixel size. An excellent synchronization of the cameras is required to avoid time expensive matching procedures and therefore to enable a related real time processing. A pre-condition for these sub- pixel techniques is the recognition of the related areas distinguishing them from sun glints and similar false alarm candidates. Analyzing the data of the airborne experiments, the processing algorithms could be tested and improved.