1. MODIS diurnal thermal bands analyses for hot spot detection and classification
C. Kuenzer1, J. Zhang1, C. Hecker2, and W. Wagner1
Institute of Photogrammetry and Remote Sensing, IPF, Vienna University of Technology, Austria
International Institute for Geo-Information Science and Earth Observation , ITC, Netherlands
27th EARSeL Symposium, June 2007
Contact: C. Kuenzer,
1. Introduction
MODIS has a great potential for the detection of thermally anomalous areas and hot spots. This is due to two great advantages of MODIS. Firstly, it is possible to acquire morning-, afternoon-, evening-, night- and pre-dawn data on the same day or within the range of only a few days. This allows for the analyses of thermal imagery with varying background temperature conditions, which allows for precise anomaly extraction. Secondly, MODIS has several thermal bands – located in the mid-infrared as well as in the thermal infrared. The analyses of these individual bands as well as the investigation of synthetically created ratio bands allows for the separation of relatively warm anomalies against outstanding hot spots. We present analyses results here for the Jharia coal fire area in India – however, the approach can also be transferred to forest fire analyses or for other heat source investigations in the fields of industry or thermal pollution. a E N N
Band 32, 10:40 b
Band 32, 13:10 c
Band 32, 22:00 d
Band 32, 01:30
2. Concept of Multi-Diurnal Thermal Analyses
MODIS: 36 spectral bands between 0.62 µm to µm. Spatial resolution at nadir: 250 m for bands 1 and 2 (VIS), 500 m for bands 3 to 7 (VIS and NIR), 1000 m for bands 8 to 36 (VIS, NIR, MIR, TIR). Flown on platform TERRA since 1999, and on AQUA since 2002 – this allows for up to five acquisitions per day. For data analysis we chose band 20 ( µm), and band 32 ( µm) from Feb Band 20 captures the shortest wavelengths of the three available mid-infrared bands. Band 32 captures the longest wavelengths of the two available thermal infrared (TIR) bands. Thus, the contrast between the two is best – with band 20 being optimal for outstanding hot spot signals (Wien’s law), while band 32 reflects the temperature pattern of an area in the common thermal domain. Note that band 32 contains purely thermal (=emitted) signals, while band 20 contains reflective and emitted components. In the wavelength region of 3.7 µm some soils still reflect up to 15%. Reflection of vegetated surfaces and other surfaces is near zero. The largest component in the 3.7µm domain are emission peaks at shorter wavelengths from very hot objects.
3. Thermal MIR-TIR Ratio Images
We calculated ratio images of bands 20 over 32. Pixels with similar emission in band 20 and 32 will show values around 1, while pixels containing thermal anomalous areas with relatively greater radiances in band 20 will yield values exceeding 1. Thus, the ratio of the two leads to a ratio image enhancing very strong hot spots. Figure 1 (to the right) shows the differences of band 20, 32 and the ratio image. Light pixels in band 20 (Figure 1 b) indicate mostly extreme hot spots. Some of them are not showing distinct signals in band 32 (see circled white) but are preserved (and enhanced) in the ratio image. The coal fire region shows up in all three images – however, the ratio image enhances the hotter pixels within the coal fire zone and the surrounding area.
Figure 1. Comparison of MODIS radiant temperatures in band 20 (b) and band 32 (c), and ratio image (d) for pre-dawn data of February 17, The LANDSAT subset (a) is for orientation. Both radiant temperature images (b, c) show strong variations in background temperature. Lighter pixel values indicate higher temperatures. The ratio image enhances hot thermal anomalies as high pixel values and suppresses background variation. Extent of image: 172 km x 85 km. Projection: UTM, zone 45 North, WGS 84. Box surrounded in black in upper right corner: Jharia coal mining region.
Figure 4. Thermal anomalies extracted from band 32 (TIR) of a morning (a), afternoon (b), evening (c) and a pre-dawn scene (d). The result is presented half-transparent, overlain on a LANDSAT scene. Coordinate boxes mark the upper left and lower right corner of the MODIS subset, which has an extent of 172 km x 85 km. Only the upper right corner (small white box) shows the Jharia mining area. Projection: UTM, zone 45 North, WGS 84. Most anomalies are extracted in the pre-dawn data, which also represents the coal fire area best. The fact that the fire area does not plot in evening data is related to weather phenomena (strong wind / rain).
4. Automated Regional Anomaly Extraction
For anomaly extraction we employ an automated histogram based algorithm, which grants unbiased and repeatable results. The algorithm is based on a moving window concept, investigating each sub-window for a background temperature (B) and a fire- or anomaly part (F). The separator is the first minimum after the sub-window histogram’s maximum. During the extraction window size varies (11x11 to 35x35). Thus, each center pixel is investigated up to 1225 times. If a pixel belongs to the anomalous area (F) in >70% of the cases the pixel is declared as an anomaly. Adjacent anomalies are clustered.
Figure 3. Pre-dawn detection from ratio band (hottest clusters remain).
Figure 5. Thermal anomalous clusters extracted from ASTER night-time band 14 (orange), MODIS pre-dawn band 32 (grey) and MODIS ratio derived hot spots (white) in February Landsat scene as backdrop. In this figure only the Jharia area (black square in left corner) is presented. MODIS hot spots coincide spatially well with ASTER derived anomalies.
5. Concluding remarks
Suitability of acquisition time for hot spot detection: predawn > nighttime > morning > late afternoon > noontime. Suitability for hot spot detection: band 20 > band 32. Suitability for warm anomalies: band 32 > band 20. Ratio images support the categorization into normal warm anomalies and hot spots, and help to exclude erroneous anomalies. Automated extraction of regional anomalies (not a simple threshold) allows for precise extraction also of subtle anomalies – always with respect to the surrounding background. With this new approach coal fire research based on MODIS is definitely possible. Furthermore, multi-diurnal thermal bands analyses seems interesting for forest fire analyses, industry monitoring and thermal pollution detection.
Table 1. Thermal anomaly extraction in data from 4 different times of the day as extracted from the thermal band 32. Note that most anomalies are extracted in predawn and night-time data.
Figure 2. Work flow of data processing. After a pre-processing including bowtie correction, radiance conversion and band ratioing, regional thermal anomalies are automatically extracted from band 20, band 32 and the band-ratio images. Output images containing thermally anomalous clusters are then compared with higher resolution satellite data.
Thermal anomalies are then extracted from the individual thermal bands (20 and 32) and the ratio band for all time steps (Figs. 3, 4). Warm anomalies and hot spots are differentiated. Furthermore, validation was performed based on ASTER thermal band anomaly extraction. It could be demonstrated that MODIS hot spot anomalies coincide spatially well with anomalies derived from higher resolution thermal data (Fig. 5). Analyses results are shown in the tables and figures to the right – explanations are given in the figure and table captions.
Table 2. Thermal anomaly extraction in data from 4 different times of the day as extracted from the ratio band (20/32). The ratio image is used for the extraction of hottest anomalies; other warmer anomalies are suppressed. Next to coal fire related anomalies further extracted hot spots stem from coal related industry, other industry, as well as human activities, such as biomass- and trash burning. The number of these hot spots is relatively stable during the normal daytime (probably some solar masking around noon time) – however, the number decreases in the middle of the night, which is to be expected.