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International Journal of Remote Sensing Vol. 30, No. 1, 10 January 2009, 9 22 Automatic smoke detection using satellite imagery: preparatory to smoke detection from Insat-3D BIPASHA PAUL SHUKLA* and P. K. PAL Atmospheric Sciences Division, Meteorology and Oceanography Group Space Applications Centre, ISRO, Ahmedabad-380015, India (Received 24 July 2007; in final form 9 October 2007 ) Identification of smoke on satellite imagery is a prerequisite to study and retrieve physical, chemical, and optical properties of smoke and also forms a crucial part in fire-management systems. Automatic detection of smoke is a challenge in itself, owing to the large overlap in the spectral signatures of smoke and other scene types, and it becomes all the more complex over the Indian region owing to less contrast between background and target. In this study, an algorithm, which is based on multiband thresholding technique, employing a conventional as well as a pseudo-channel, is developed for the Indian region with the help of radiative transfer simulation and is a preparatory exercise for setting up algorithms for application of INSAT 3D imager data. The algorithm has been executed using MODIS data on agricultural fire spread over north-western India for the year 2005. The outputs are validated using MODIS AOD and Cloud Fraction products, and the results suggest that the algorithm is able to isolate smoke pixels in the presence of other scene types such as clouds, although it performs better in identifying fresh dense smoke as compared with highly diffused smoke. 1. Introduction Each year, more than 100 million tons of smoke aerosols are released into the atmosphere as a result of biomass burning. More than 80% of this burning is in the tropical regions (Christopher et al. 2002). Smoke plumes produced as a consequence of such events can travel over hundreds or even thousands of kilometres horizontally and also reach up to the stratosphere under certain atmospheric circulation conditions. Thus, smoke can have an impact far beyond the region of fire activity. Smoke plays a major role on the radiation balance of the Earth atmosphere system. Smoke particles scatter and absorb incoming solar radiation, thereby having a twofold impact, i.e. a cooling effect at the surface but a warming effect on the atmosphere (Kondratyev and Varotsos 1995). Since the magnitude of the scattering effect outweighs that of absorption, smoke has a net cooling effect at the top of the atmosphere surface system, often called the direct effect of smoke aerosols (Christopher et al. 2002). Smoke can also modify the shortwave reflective properties of clouds by acting as cloud condensation nuclei (Kaufman et al.1990). Under a limited supply of water vapour, an increased number of nuclei result in smaller cloud droplets that have a higher reflectivity than larger cloud droplets. This effect, called indirect radiative *Corresponding author. Email: bipasha@sac.isro.gov.in International Journal of Remote Sensing ISSN 0143-1161 print/issn 1366-5901 online # 2009 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/01431160802226059

10 B. P. Shukla and P. K. Pal forcing, is difficult to quantify and has large uncertainties associated with the sign and magnitudes. Furthermore, an optically thick, subcontinental smoke plume can have a significant impact on regional air quality due to large amounts of trace gases, including CO, NO x, and ozone typically associated with smoke plumes (Taubman et al. 2004). Understanding such numerous and complex effects of smoke on weather and climate requires a good knowledge of the spatial and temporal variation of smoke and its optical properties, which is only feasible by means of satellite observation. Recently, the infield observations of aerosols revealed the nonlinear nature of the aerosol properties, or in other words, that the temporal and spatial variability of the aerosol concentration obey a power-law (scaling effect). The estimation of the scaling exponents of the power-law relationship has recently been achieved by the employment of the detrended fluctuation analysis (DFA) (Varotsos 2005a, b). This method has already proved its usefulness in the aerosol content at the Earth s surface (Varotsos et al. 2005) and the aerosol index (Varotsos et al. 2006). Identification of aerosol, smoke in particular, on satellite imagery is a prerequisite to initiate the above-mentioned studies. Besides, smoke detection forms a principal component in fire-associated disaster management. The detection of fire and smoke in satellite imagery has been documented by Matson et al. (1987), Kaufman et al.(1990) and Prins and Menzel (1992). The detection of smoke is more difficult than fire detection (Baum and Trepte 1999), since most schemes rely on a dark background such as dense vegetation (Kaufman et al. 1990) or water (Rao et al. 1989). The most commonly used method of identifying smoke is to assign different colours to different channels or channel combinations. The resulting false-colour images can provide visual separation of smoke from other objects. For example, Kaufman et al. (1990) assigned AVHRR channel 1 to red, channel 2 to green, and inverse channel 4 to blue, generating a composite image showing smoke plumes. The Hazard Mapping System (HMS) developed in 2001 by the National Oceanic and Atmospheric Administrations (NOAA) and National Environmental Satellite and Data Information Service (NESDIS) also generated the smoke outlines manually, primarily utilizing animated visible band satellite imagery. Chung (2002) demonstrated the role of satellite imagery in tracking the transport of a massive smoke plume produced from the wildfires of Canada, travelling up to Ontario, Wisconsin, while Huang and Siegert (2004) discussed the capabilities of MERIS sensor onboard ENVISAT to detect smoke plumes. Smoke plumes and their path can also be traced using temporal satellite imagery through image-processing techniques (Randriambelo et al. 1998). However, the majority of these methods rely on human intervention and can hardly be used for automatic detection. In the recent past, considerable efforts have been made to automate the process of distinguishing smoke in satellite imagery. Some of these methods include using artificial neural networks and threshold approaches applied to AVHRR imagery made by Li et al. (2001) and multiband threshold classifier technique like the Automated Smoke/Aerosol Detection Algorithm (ASADA) applied on Geostationary Operational Environmental Satellite GOES-8 imager data to monitor biomass burning in South America (Prins et al. 1998), the group-threshold approach (Baum and Trepte 1999). Innovative methodologies were developed by Chrysoulakis and Cartalis (2000, 2003) and Chrysoulakis and Opie (2004) to detect smoke plumes caused by manmade disasters and technological accidents. Chrysoulakis et al. (2007) improved

Automatic smoke detection using satellite data 11 these methodologies to propose a flexible, automated, and globally applicable algorithm for the detection of plumes caused by natural or technological hazards using AVHRR images. The advent of MODerate resolution Imaging Spectro-radiometer (MODIS) sensors onboard the Earth Observing System (EOS) Terra and Aqua polar orbiting satellites steered a new era in global aerosol remote sensing from space (Ichoku et al. 2004, Levy et al. 2007) The MODIS aerosol algorithm infers aerosol characteristics such as the aerosol optical thickness (AOT) and particle size parameters. Although AOT is derived for all aerosols integrally, it is possible to distinguish smoke aerosols from others (such as dust and sulphates) based on their size differences (Kaufman et al. 2003) In the perspective of the proposed launch of INSAT-3D, the next ISRO meteorological satellite (Katti et al. 2006), this study focuses on developing an automatic algorithm for identification of smoke especially for the Indian region. INSAT 3D is an exclusive mission designed for enhanced meteorological observations and monitoring of land and ocean surfaces for weather forecasting and disaster warning, with the help of its six-channel imager (VHRR) and 19- channel sounder. VHRR produces images in six spectral channels (mm): 0.55 0.75 (visible), 1.55 1.70 (shortwave infrared), 3.80 4.00 (mid-infrared), 10.2 11.3 (thermal-infrared 1), and 11.5 12.5 (thermal infrared 2). The method proposed here is applied to smoke generated from agricultural fires, although the governing principles are applicable to smoke produced from forest-fire events as well. 2. Development of smoke detection algorithm over the Indian region The Indian region is geographically divided into the Himalayan, Indo-Gangetic basin, and peninsular regions. The land-cover characteristics of the Indian region have diverse features. The major fire prone locations are concentrated in the Himalayan forests and North-eastern regions. In addition, agricultural fire due to field clearing for new plantation is also a source of smoke. In general, smoke is composed of many small particles suspended in the air. These particles scatter and absorb (attenuate) different spectra of electromagnetic radiation. The effect of smoke aerosol as a function of wavelength forms the crux of its satellite detection algorithm. Smoke has a significant effect in the visible part of the spectrum, decreasing in magnitude with wavelength from the blue to the red region. In the near IR, the effect is smaller than the variation in the surface reflectance between the smoke-free area and the area affected by smoke. The smoke effect is not observable in the mid-ir (2.2 mm) region due to the large ratio of the wavelength of radiation to the size of particles. Thus, theoretically, using visible channel it is possible to distinguish smoke plumes over land. But in actual cases, the detection of smoke over land continues to be difficult due to highly reflective backgrounds. The contrast between the target and the background is usually more important in determining how well a target can be sensed than is the target s energy flux. For instance, the densely vegetated areas of Brazil provide an appropriate background for smoke as well as fire detection, which was captured aptly by the Smoke, Clouds and Radiation in Brazil (SCAR-B), using the GOES-8 imager data (Prins et al. 1998). But the detection of smoke over the Indian region poses a real challenge due to relatively less dense undergrowth resulting in low contrast. Furthermore, the temperature contrast of the plume with

12 B. P. Shukla and P. K. Pal the surface background is unlikely to support clear identification except in exceptional circumstances of very cold ground. Although discriminating smoke and land pixels can be a tough job, it can be resolved putting up stringent thresholds. But the real trial is detection of smoke in the presence of clouds, since diffusion processes associated with them lead to fuzzy boundaries. Hence, although the reflectance of smoke is generally less than that of clouds, the latter has so large a range of variation that it is difficult to use it to discriminate smoke pixels from cloudy pixels. Distinction between smoke and clouds is generally made on the basis of cloud classification. Addressing all these issues, algorithm development is carried out in two separate trees. One is a module for smoke detection, and another is component for picking probable hot spots. Probable hot spots are selected by carrying out the following tests (Kaufman et al. 1990, Li et al. 2000): T 3:9 wt 1 T 3:9 {T 11 wt 2 T 11 wt 5 T 11 {T 12 vt 11, where T 3.9, T 11, and T 12 are the brightness temperature corresponding to 3.9 mm, 11 mm, and 12 mm channels, respectively, and t 1, t 2,...t 11 are threshold values of brightness temperatures. Detection of smoke is carried out as an elimination process, in which probable land and cloud pixels are screened out, and the remaining pixels are treated as smoke pixels (Prins et al. 1998). The surface pixels are screened out using doublesided thresholds a 1, a 2, on the reflectance (R 0.65 ) of the 0.65-mm visible channel. a 1 wr 0:65 wa 2 : Cloud pixel exclusion is done on basis of cloud classification, utilizing the IR channels, as follows: t 3 vt 3:9 vt 4 T 11 wt 5 ð1þ ð2þ ð3þ t 6 vt 3:9 {T 11 vt 7 T 11 wt 8 T 12 wt 9 t 10 vt 11 {T 12 vt 11, ð4þ where t 1 >t 4, while t 2 >t 7.t 6. Equation (3) represents the classifier to screen out opaque cirrus and stratus clouds, since the large difference in brightness temperatures between 3.9 mm and 11 mm channels due to the reflection of solar energy at 3.9 mm is successful in screening out water clouds. Finally, 11 mm, 12 mm, and the difference between these two channels are utilized in equations (4) to screen out low-level moisture and cirrus clouds, and the method is often referred to as the split window technique.

Automatic smoke detection using satellite data 13 Even after applying the tests (2 4), misclassification between smoke and clouds is possible. For further screening, pseudo-channel CLD (Chrysoulakis and Cartalis 2000, 2003) is made use of and defined as: CLD~ ðtir2 Þ{ ð Visible Þ ðtir2þzðvisibleþ : ð5þ Owing to their different signatures in the pseudo-channel, smoke plumes and clouds can be discriminated using a CLD threshold. Although in earlier studies CLD and Normalized Difference Vegetation Index (NDVI) feature space analysis was used for categorization of smoke and cloud, it was found to generate false alarms and was eventually improved using the multitemporal change detection technique (Chrysoulakis et al. 2007). However, in this study, it has been found that combining CLD with a conventional multithreshold test also improves the robustness of smoke detection. Thus, pixels are further required to pass the following test: CLDwC 1, where C 1 is the threshold value for smoke plumes. Earlier studies had determined C 1 with respect to AVHRR channels, and as such, for this study CLD was needed to be recalibrated and was done using radiative transfer calculations. Outputs from equation (1) and equations (2), (3), (4) and (6) are used to generate probable hot spots index y F and smoke index y S, which are defined as: 1, Pixelðx,yÞ passes test 1 y F ðx,yþ~ ð Þ ð7þ 0, Otherwise 1, Pixelðx,yÞ passes all test 2{4,6 y S ðx,yþ~ ð Þ ð8þ 0, Otherwise: Thereafter, a spatial uniformity test is implemented to eliminate any false alarms, which is mathematically represented as: A Integers i,j[ ½{n,nŠ, ð8þ such that y S ðxzi,yzjþ~1 ORy F ðxzi,yzjþ~1, where n denotes the window size. When a probable smoke pixel passes the spatial continuity test (8), it is affirmatively classified as a smoke pixel. ð6þ 3. Data used and study region Since this study has been undertaken as a preparatory for applications of INSAT 3D imager data, the proposed algorithm was tested using equivalent channel data. For this, MODIS channels No. 1 (0.65 mm), 22 (3.9 mm), 31 (11 mm), and 32 (12 mm), which have their corresponding counterparts in the INSAT-3D imager, have been used. Due to analogous bands being used, the algorithm will be executed in a similar fashion with the INSAT 3D data, albeit fine tuning of thresholds may be required. MODIS was put into orbit in December 1999 onboard the TERRA satellite, which has a heliosynchronous polar orbit close to 700 km. MODIS has excellent radiometric resolution of 12 bits with 36 different spectral bands in 250 m for bands 1 and 2 (0.6 0.9 mm), 500 m for bands 3 7 (0.4 2.1 mm) and 1000 m for bands 8 36 (0.4 14.4 mm). All data are downloaded from the Level 1 and Atmosphere Archive

14 B. P. Shukla and P. K. Pal and Distribution System (LAADS Web) including the MODIS Level1B Radiance product (MOD02) and geolocation dataset (MOD03) for five passes covering the study region, for the period from 11 October 2005 to 16 October 2005. The developed algorithm was tested on the agricultural fires spread at the base of the Himalayas Mountains in north-western India, in mid-october 2005, in the states of Punjab (closest to Pakistan) and Haryana (to the south-east), which are considered to be two of India s agricultural powerhouses. Although Punjab occupies less than 2% of the area of the country, it produces about two-thirds of the food grains in India. In these regions, wheat and rice are the two most commonly grown food crops. Farmers use fire to clear fields and prepare them for new plantings. The fire and smoke can be clearly visualized in the true colour high-resolution image taken by MODIS on 12 October 2005 (http://earthobservatory.nasa.gov/naturalhazards/ Archive/Oct2005/FAS_India1.AMOA2005285_lrg.jpg). 4. Results and discussion The radiance data were first converted to reflectance for visible channel using MODIS data scale and offset factor, provided with level 1B product. Similarly radiance data for the IR channels were converted to brightness temperature using Planck s inversion. A key task in the process was setting thresholds for the event. The initial values of the thresholds were chosen from the SCAR-B experiment (Prins et al. 1998) and were tuned in for the Indian region using visual inspection techniques, false-colour composite and histogram analysis. Ascertaining the threshold for CLD was a trickier job, and this was done using the Santa Barbara DISORT Radiative Transfer (SBDART) code (Ricchiazzi et al. 1998). For a range of input parameters of aerosol optical depths (Christopher et al. 2002), and different aerosol models in SBDART, CLD values for smoke aerosol for the study region were simulated between 0.45 and 0.73, while the CLD values for clouds were found in the range of 20.48 to 0.37. These range of values were further put through different visual analysis techniques to finally arrive at the CLD cut-off for smoke. The different thresholds used in this study are given in table 1. In order to remove any false alarms, which are the biggest challenge in this study, a conservative Table 1. Threshold values used in the algorithm. Threshold Value a 1 0.1 a 2 0.4 t 1 350 t 2 50 t 3 350 t 4 285 t 5 285 t 6 25 t 7 50 t 8 285 t 9 285 t 10 0 t 11 6 C 1 0.53

Automatic smoke detection using satellite data 15 approach was adopted to set the thresholds, which may result in underestimation of smoke pixels. Based on these predetermined thresholds, the algorithm was executed on the set of MODIS images. The results are illustrated in figures 1 5, which are plotted for the domain 30 33u N in latitude and 72 78u E in longitude, covering the region of interest. Image (a) in all the figures is the reflectance image, followed by (b), which gives fire hot spots depicted by a black asterisk, and (c) which gives the smoke map, Figure 1. Reflectance image for the 0.65-mm channel (a), hot spots (b) and smoke pixels (c) for 11 October 2005.

16 B. P. Shukla and P. K. Pal Figure 2. Reflectance image for the 0.65-mm channel (a), hot spots (b), smoke pixels (c), MODIS AOD product (d), and MODIS Cloud Fraction image with superimposed smoke pixels (e) for 13 October 2005. where the smoke pixels are depicted by black square markers. The black line in (a) (blue in (b) and (c), white in (d), (e)), represents the political boundary of India. In figure 1, which corresponds to the image acquired on 11 October 2005, a large number of hot spots are visible, spread over the north-western part of India, and smoke pixels are also discernible, although fewer in number compared with fire spots. Some nascent smoke plume can also be noticed in the reflectance image. This marks the beginning of the fire activity in the area, where farmers clear the fields for new plantings. In the subsequent image (figure 2), for 13 October 2005, the fire is in full bloom, as evidenced by widespread hot spots, although there is a decrease in identified hot pixels due to engulfing smoke. Here, numerous smoke pixels have been detected, supported by the reflectance image. Figure 2(d) shows the MODIS AOD product for the same image, which matches well with the derived smoke pixels detected through the algorithm, although some discrepancy is observed owing to the different resolutions of the two products.

Automatic smoke detection using satellite data 17 Figure 3. Reflectance image for the 0.65-mm channel (a), hot spots (b) and smoke pixels (c) for 14 October 2005. Figure 2(e) shows the image of MODIS Cloud Fraction product, upon which smoke pixels detected have been superimposed in black square markers. It can be seen that although there is a portion of the cloud cover within the region of interest, having a high cloud fraction, the algorithm has not picked any smoke pixels corresponding to cloud fraction.0.75, thereby proving its efficiency in isolating smoke pixels in the presence of cloud. In figures 3 and 4, which correspond to 14 and 15 October 2005, due to dispersion of smoke and encompassing haze, almost negligible fire hot spots are observed, although few isolated smoke pixels have been picked up. It can be aptly construed from here that due to the generation of categorized output, which is an inherent

18 B. P. Shukla and P. K. Pal Figure 4. Reflectance image for the 0.65-mm channel (a), hot spots (b) and smoke pixels (c) for 15 October 2005. characteristic of thresholding techniques (Li et al. 2001), fresh and dense smoke can be captured by this algorithm, while thin dispersed smoke pixels may be left out. On 16 October 2005 (figure 5), plenty of smoke and fire pixels can be seen, which mark the start of new fire activity, after haze has receded. The reflectance image also shows a similar trend. Table 2 gives the statistics of pixels detected during this study. It can be seen that the inclusion of CLD technique helps eliminate any false alarms, mainly in the presence of clouds. Figure 6 shows the variation of CLD values for total smoke pixels for these 5 days with mean 0.64 (red line), and standard deviation 0.045. The

Automatic smoke detection using satellite data 19 Figure 5. Reflectance image for the 0.65-mm channel (a), hot spots (b) and smoke pixels (c) for 16 October 2005. mean value corresponds to the SBDART simulated CLD value of soot aerosol having an aerosol optical depth t equal to 0.30. 5. Conclusions This paper discusses the development of smoke detection algorithm over the Indian region using MODIS data. The study was done as a preparatory study for the application of INSAT 3D data for automatic identification of smoke, which is a prerequisite in understanding the effects of smoke on weather and climate, and also for fire-related disaster management. Additionally, this kind of study can be used to establish the feasibility of using remote-sensing techniques for quantifying

20 B. P. Shukla and P. K. Pal Day Table 2. Detection statistics for the whole event. No. of fire pixels No. of smoke pixels (without CLD) No. of smoke pixels (with CLD) 11 October 2005 39 21 21 13 October 2005 16 20 17 14 October 2005 0 1 1 15 October 2005 1 2 2 16 October 2005 11 16 11 agricultural burning (McCarty et al. 2007). The algorithm discussed in this regard uses a multiband threshold classifier, along with a pseudo-channel (CLD) and spatial continuity test to segregate smoke pixels and was implemented on agricultural fire in north-western India during mid October 2005. The incorporation of the CLD approach along with the conventional classifier technique is effective in screening out any false alarms, especially those due to cloudy pixels. The outputs were validated with MODIS AOD and Cloud Fraction products. It has been found that the algorithm works reasonably well and is able to isolate smoke pixels in the presence of different scene features such as clouds. However, it performs better in identifying fresh dense smoke as compared with thin dispersed smoke. This study lays a foundation for an operational satellite smoke-monitoring system to provide and supplement timely fire and smoke statistics across India. The efficiency and reliability of the algorithm may improve if augmented with the Figure 6. Variation of CLD for detected smoke pixels.

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