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.
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.
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.
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.
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.
Utilization of sub-pixel targets for radiometric calibration of airborne and space-borne imaging sensors involves the uncertainty of their contribution to the pixel-integrated radiance. This contribution depends not only on the target area but also on an unknown location of the sub-pixel target within a sensor pixel. A technique is proposed to retrieve both the target radiance and its sub-pixel location from the target image, taking into account the effects of the sensor point spread function. The technique was used for in-flight calibration of the thermal channels of the airborne imaging spectrometer DAIS-7915.
The multi-sensor multi-resolution technique (MMT) was used to unmix simulated ASTER data. The simulation was performed using airborne spectrometer data of the open lignite mine Zwendkau in the Central German lignite mining district. The unmixing of low resolution ASTER thermal IR images with the reflective bands allowed for significant improvement of the spatial resolution. The radiometric accuracy was estimated using reference images and extracted pixel spectra. In comparison to other techniques, the MMT preserves the radiometric information in the TIR. Therefore, the spectral information can be used for a mineralogical analysis of the dumped material.
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.
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.
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.
The space-borne imaging spectrometers to be flown in the near future will have the spatial resolution of a few hundred meters to one kilometer. The usefulness of their data for land- oriented applications is limited due to the problem of 'mixed pixels'. A technique is proposed for 'unmixing' the data of an imaging spectrometer by combined processing of its data with the data of a high resolution multispectral camera. It allows the retrieval of the spatial distribution of classes with the resolution of the multispectral camera and their spectral signatures, with the detail of spectral measurements of the imaging spectrometer. The technique has been tested, using the data of the airborne imaging spectrometer GER-II, obtained over an agricultural area. Various resolutions of the imaging spectrometer were simulated from tens of meters to approximately 1 km. The accuaracy of the retrieved spectra proved to be a few to ten percent in most cases, even when the mean area of 'homogeneous units' was significantly smaller than the pixel area of the imaging spectrometer. The proposed approach makes it possible to use a combination of a high resolution imaging spectrometer and to combine a high resolution information of the camera with the detailed spectral information of the imaging spectrometer during data processing. The advantages of this approach are: simpler and cheaper instruments can be used, including the instruments of the currently planned missions; data fluxes are significantly reduced; the swath width can be increased.