Capability evaluation of 3–5 µm and 8–12.5 µm airborne thermal data for underground coal fire detection

Based on ground temperature measurement, thermal anomalies induced by underground coal fires were extracted from airborne night-time 8–12.5?µm, daytime 8–12.5?µm, and daytime 3–5?µm images by density slicing. The thermal anomalies extracted from these images had different sizes and were partially overlapped. Integration of the three processed images revealed detailed structures of the surface thermal anomalies. The temperature difference (ΔT) between a threshold temperature and the background temperature can be taken as a criterion for the evaluation of detection capabilities of different thermal data. The ΔTs for night-time, daytime 8–12.5?µm data and 3–5?µm data are 2.7, 4.4 and 7.1°C, respectively. The detection capabilities for sub-pixel sized coal fires from different thermal data were evaluated by assuming these fires cause the equivalent spectral radiance as a full pixel thermal anomaly does. The 3–5?µm data are more sensitive to sub-pixel-sized high-temperature (i.e. >165°C) thermal anomalies.     

Landsat-TM data for estimating ground temperature and depth of subsurface coal fire in the Jharia coalfield,India

Coal fires are a ubiquitous problem in coal-mines, the world over. They burn our prime energy resource, lead to atmospheric pollution and render mining of coal hazardous. Processes leading to coal combustion and spread of subsurface fires are briefly examined in this paper and the role of remote sensing in surveillance of coal fires is presented.

The present study aims at developing a quick method for estimating the temperature of the ground surface directly above subsurface coal fires. Utility of TM6 and TM7-band data for temperature estimation is briefly reviewed. It is argued that temperature calculations of surface anomalies related to subsurface fires can only be done on the basis of 8–14 μm band data, due to the low temperatures involved.

In the Jharia coalfield, it is noted that subsurface fires in various coal-mines are associated with surface thermal anomalies, as has also been confirmed by ground checks. The pattern of TM6 data distribution and ground truth is used to isolate thermal anomalies, and the TM6 digital numbers are converted into kinetic temperature values. It is observed that for the Landsat-TM scene ( 28 November 1990) the kinetic temperatures range from 16·0°C to 31·6°C in the Jharia coalfield, with a threshold value of 25·6°C associated with the anomalies. Depth estimation of fire has been carried out using field structural geology data and pixel locations of thermal anomalies. It is inferred that the depth of subsurface coal fire ranges between 45–55 m, in most cases, which is in general agreement with the field data. Limitations of the method are indicated.     

ASTER‐derived emissivity and coal‐fire related surface temperature anomaly: a case study in Wuda,north China

Subsurface and surface coal fires form serious environmental, economic and safety problems in coal‐producing countries like China and India. Remote sensing offers the possibility of detecting and studying thermal anomalies due to coal fires. Emissivity plays an important role in determining the surface temperature of a body using remotely sensed data. In the present study an attempt is made to use satellite‐derived emissivity to estimate the surface temperature in Wuda, north China. With the use of multispectral thermal Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (five bands in 8.125–11.65 µm region) in combination with a Temperature/Emissivity Separation (TES) algorithm, the anomalous pixels due to coal fires can be extracted from the background to achieve a qualitative study of coal fires. In the present study, during night‐time overpass of ASTER, satellite images have been recorded and simultaneous field measurements were collected. These field measurements were used to process the satellite thermal data and to validate the results obtained. Using the TES approach, satellite‐based temperature corresponded well with actual field measurements at selected locations.     

Detecting unknown coal fires: synergy of automated coal fire risk area delineation and improved thermal anomaly extraction

This paper presents two complementing algorithms for remote sensing based coal fire research and the results derived thereof. Both are applicable on Landsat, ASTER and MODIS data. The first algorithm automatically delineates coal fire risk areas from multispectral satellite data. The second automatically extracts local coal fire related thermal anomalies from thermal data. The presented methods aim at the automated, unbiased retrieval of coal fire related information. The delineation of coal fire risk areas is based on land cover extraction through a knowledge based spectral test sequence. This sequence has been proven to extract coal fire risk areas not only in time series of the investigated study areas in China, but also in transfer regions of India and Australia. The algorithm for the extraction of thermal anomalies is based on a moving window approach analysing sub‐window histograms. It allows the extraction of thermally anomalous pixels with regard to their surrounding background and therefore supports the extraction of very subtle, local thermal anomalies of different temperature. It thus shows clear advantages to anomaly extraction via simple thresholding techniques. Since the thermal algorithm also does extract thermal anomalies, which are not related to coal fires, the derived risk areas can help to eliminate false alarms. Overall, 50% of anomalies derived from night‐time data can be rejected, while even 80% of all anomalies extracted from daytime data are likely to be false alarms. However, detection rates are very good. Over 80% of existing coal fires in our first study area were extracted correctly and all fires (100%) in study area two were extracted from Landsat data. In MODIS data extraction depends on coal fire types and reaches 80% of all fires in our study area with hot coal fires of large spatial extent, while in another region with smaller and ‘colder’ coal fires only the hottest ones (below 20%) can be extracted correctly. The success of the synergetic application of the two methods has been proven through our detection of so far unknown coal fires in Landsat 7 ETM+ remote sensing data. This is the first time in coal fire research that unknown coal fires were detected in satellite remote sensing data exclusively and were validated later subsequently during in situ field checks.     

ENVISAT multisensor data for fire monitoring and impact assessment

The European ENVISAT satellite provides both optical and radar measurements of the Earth's surface. In this Letter, three ENVISAT instruments were used to investigate the extent and impact of the forest and peatland fires that devastated large areas in Central Kalimantan, Indonesia in 2002. Reduced spatial resolution MERIS imagery was used to identify simple land cover features and smoke plumes. Fire hotspots were detected by band 3.7?µm of Advanced Along Track Scanning Radiometer (AATSR) night-time acquisitions, and burnt areas were detected by Advanced Synthetic Aperture Radar (ASAR) wide swath radar imagery acquired before and after the fire event. The capability of ENVISAT to acquire data from different sensors simultaneously or within a short period of time greatly enhances the possibilities to monitor fire occurrence and assess fire impact.     

Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site

In this paper, a methodology using a single-channel and a two-channel method is presented to estimate the land surface temperature from the DAIS (Digital Airborne Imaging Spectrometer) thermal channels 74 (8.747?µm), 75 (9.648?µm), 76 (10.482?µm), 77 (11.266?µm), 78 (11.997?µm) and 79 (12.668?µm). The land surface temperature retrieved with both methods has been validated over the Barrax site (Albacete, Spain) in the framework of the DAISEX (Digital Airborne Imaging Spectrometer Experiment) field campaigns. Prior to the validation an analysis of the DAIS data quality has been performed in order to check the agreement between in situ data and the values extracted from the DAIS images supplied by the DLR (German Optoelectronic Institute). Suitable differences between in situ and DAIS data have been found. To solve this problem a linear re-calibration of the DAIS thermal channels has been applied using two ground calibration points (bare soil and water). For the land surface temperature retrieved, rms deviations of 0.96?K using a single-channel method and 1.46?K using a two-channel method with the DAIS thermal channels 77 and 78 have been obtained considering re-calibrated data.     

Hydrocarbon Index – an algorithm for hyperspectral detection of hydrocarbons

Previous studies have shown that spectral signatures of hydrocarbon-bearing materials are characterized by prominent absorption features at 1.73 and 2.31?µm. Many other materials also show absorption features at wavelengths in the interval from 2.0 to 2.5?µm, yielding a mixed response in spectral signatures. In contrast to this wavelength range, most materials show similar spectral characteristics in the 1.73?µm range. Mainly hydrocarbon-bearing materials produce an absorption feature which is unique and prominent at 1.73?µm. A Hydrocarbon Index (HI) was developed and tested for the direct detection of hydrocarbons. The HI transforms multispectral data into a single image band that shows the distribution of hydrocarbons on the ground surface. The HI takes advantage of reflection differences around the 1.73?µm feature in hydrocarbon spectra. The HI indicates the presence of the 1.73?µm hydrocarbon absorption feature in a pixel spectrum. HI values can be easily calculated from radiance and reflectance data recorded by high signal-to-noise ratio hyperspectral scanners.     

Active forest fire monitoring in Uttaranchal State,India using multi‐temporal DMSP‐OLS and MODIS data

This paper gives an account of day–night active forest fire monitoring conducted over the sub‐tropical and moist temperate forests of the Uttaranchal State, India, during 2005 using the Defence Meteorological Satellite Program – Operational Line Scan system (DMSP‐OLS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The state experienced heavy fire episodes during May–June 2005 and daily datasets of DMSP‐OLS (night‐time) and selected cloud‐free MODIS (daytime) datasets were used in mapping active fire locations. DMSP‐OLS collects data in visible (0.5 to 0.9 µm) and thermal (10.5 to 12.5 µm) bands and detects dim sources of lighting on the earth's surface, including fires. The enhanced fire algorithm for active fire detection (version 4) was used in deriving fire products from MODIS datasets. Fire locations derived from DMSP‐OLS and MODIS data were validated with limited ground data from forest department and media reports. Results of the study indicated that the state experienced heavy fire episodes, most of them occurring during night‐time rather than daytime. Validation of satellite‐derived fires with ground data showed a high degree of spatial correlation.     

DART: a 3D model for simulating satellite images and studying surface radiation budget

DART (Discrete Anisotropic Radiative Transfer) is a radiative transfer model that simulates remotely acquired images. It was originally developed to work in the short wavelengths (0.3–3?µm) within 3D natural scenes that are represented as matrices of rectangular cells containing trees, shrubs, grass, soil, etc. DART was recently modified to extend its domain of application and to improve its accuracy. This paper summarizes the major features of DART and presents the changes that were implemented for improving its accuracy. Presently, this model works with natural and urban landscapes, on the whole optical domain (thermal infrared included) and with a multispectral approach that uses optical data bases from 0.3?µm up to 15?µm. It simulates radiative transfer in the whole ‘atmosphere–Earth’ system and it accounts for the instrumental transfer function. Three major changes allowed us to improve DART accuracy by a factor of three: more accurate simulation of single and multiple scattering, use of a scheme that oversamples DART cells and a better account of the direction of radiation that gives rise to multiple scattered radiation.     

Spectral and radiometric requirements for the airborne thermal imaging spectrometer ARES

ARES (Airborne Reflective/Emissive Spectrometer) is an airborne imaging spectrometer for remote sensing of land surfaces covering the wavelength regions 0.45–2.45?µm and 8–13?µm with 160 channels. The instrument is being built by Integrated Spectronics, financed by DLR and GFZ, and will be available to the scientific community from 2005 on.

This contribution presents the design of the thermal spectrometer covering the 8–13?µm region with 32 channels of 150?nm bandwidth while a separate paper treats the instrument specifications in the solar reflective region. The spectro‐radiometric design is based on scientific requirements derived from application scenarios comprising vegetation, soils of different compositions, and mineral exploration. The corresponding emissivity spectra are input for a simulation model that calculates at‐sensor radiance spectra, resamples them with the channel‐specific response functions, adds different amounts of sensor noise to the signal, and performs a retrieval to get the corresponding noisy surface emissivity spectra. The results of the simulation study indicate that a spectral wavelength accuracy of 3?nm and a sensor noise equivalent temperature of 0.05–0.1?K are required for an accurate retrieval of emissivity spectra.     

Recent coal-fire and land-use status of Jharia Coalfield,India from satellite data

The Jharia Coalfield (JCF) in India is known for its high grade coal and associated coal fires. Before it can be exploited, valuable coal reserves are destroyed in the sub-surface due to fire. The combined act of fire and subsidence has endangered the environmental safety of the JCF, although several methods have been adopted to control the coal fires. Coal fire is a dynamic phenomenon, hence, its location and extent changes with time. To control the coal fires effectively, the status of the fires and the landscape must be assessed periodically. In this study, the thermal band in Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data (daytime) of 29 March 2003 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (night-time) of 9 October 2006 are used to delineate the coal-fire areas. The kinetic temperature of the coal fire-affected areas is calculated from Landsat-7 ETM+ data using a Normalized Difference Vegetation Index (NDVI)-derived emissivity model, and from band 13 of ASTER data with a fixed emissivity value. The study showed that the eastern part of the JCF is more affected by coal fires than the western part. The affected collieries in the eastern part are Kusunda, Lodna, Bararee, Gonudih and Ena. Among all collieries, Kusunda is the most affected by coal fires (29% of the area) and showed a 0.56 km² increase in fire area from the year 2003 to 2006. During this period, a total increase in coal-fire area of 0.51 km² occurs in the JCF. The land-use map prepared from Indian Remote Sensing (IRS) Satellite-P6 Linear Imaging Self-scanning Sensor (LISS)-III data showed that 6.9% of the area in the JCF is occupied by mining-related activities, which indicates its vulnerability to environmental degradation.     

Fire and smoke observed from the Earth Observing System MODIS instrument--products,validation, and operational use

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, launched on the National Aeronautics and Space Administration Terra satellite at the end of 1999, was designed with 36 spectral channels for a wide array of land, ocean, and atmospheric investigations. MODIS has a unique ability to observe fires, smoke, and burn scars globally. Its main fire detection channels saturate at high brightness temperatures: 500 K at 4 µm and 400 K at 11 µm, which can only be attained in rare circumstances at the 1 km fire detection spatial resolution. Thus, unlike other polar orbiting satellite sensors with similar thermal and spatial resolutions, but much lower saturation temperatures (e.g. Advanced Very High Resolution Radiometer and Along Track Scanning Radiometer), MODIS can distinguish between low intensity ground surface fires and high intensity crown forest fires. Smoke column concentration over land is for the first time being derived from the MODIS solar channels, extending from 0.41 µm to 2.1 µm. The smoke product has been provisionally validated both globally and regionally over southern Africa and central and south America. Burn scars are observed from MODIS even in the presence of smoke, using the 1.2 to 2.1 µm channels. MODIS burned area information is used to estimate pyrogenic emissions. A wide range of these fire and related products and validation are demonstrated for the wild fires that occurred in northwestern USA in Summer 2000. The MODIS rapid response system and direct broadcast capability is being developed to enable users to obtain and generate data in near real-time. It is expected that health and land management organizations will use these systems for monitoring the occurrence of fires and the dispersion of smoke within two to six hours after data acquisition.     

Monitoring coal fires using multi-temporal night-time thermal images in a coalfield in north-west China

China has the largest coal resources in the world but these are seriously endangered by coal fires. Though the problem of coal fires is long standing and not only limited to China, little has been done for regular monitoring of these fires. This Letter proposes the use of multi-temporal night-time thermal images acquired from Landsat Thematic Mapper band 6 for establishing a coal fire monitoring system for a coalfield in north-west China. Other images and map data are fused with the thermal images to provide a comprehensive picture of the fires through the years. Finally the fires are classified into different categories based on multi-temporal changes.     

Data fusion for investigating land subsidence and coal fire hazards in a coal mining area

Coal mining areas all over the world are often threatened by serious environmental hazards such as the occurrence of coal fires, land subsidence, etc. Coal fires burn away the natural non-renewable coal resources, locally raise the temperature of the area, emit polluting gases such as oxides of carbon, sulphur and nitrogen, and when present underground are even the cause of land subsidence. Mining-induced subsidences, on the other hand, cause horizontal and vertical movements in the land surface, and open cracks and fissures that serve as inlets for oxygen, which in turn aggravate the problem of coal fires. These inter-related phenomena often render the mining areas unfit for human inhabitation and the commercial exploitation of coal nearly impossible in some parts. In this study, satellite data acquired in three regions of the electromagnetic spectrum, namely optical, thermal and microwave, along with field data, are used to identify the areas affected by coal fires and land subsidence in a coalfield in north-west China. Data fusion techniques are used for an integrated analysis of this complex problem.     

Potential of small-baseline SAR interferometry for monitoring land subsidence related to underground coal fires: Wuda (Northern China) case study

Uncontrolled coal fires can result in massive surface displacements due to the change in volume of burning coal and thermal effects in the adjacent rock mass; simultaneously, the resultant surface breakings provide greater access to air and water that in turn can aggravate the problem of underground coal seam burning. In this case study, we have investigated the feasibility and potential of detecting the land subsidence accompanying coal fires by means of satellite InSAR observations. Three groups of small-baseline InSAR approaches (PSI, stacking and 2-passDInSAR) were applied to the Wuda coalfield (Northern China) to reveal the spatial and temporal signals of the land subsidence in the areas affected by the coal fires. The interferometric results agree well with GPS observations and coal fire data obtained by field investigation, which demonstrates that the small-baseline InSAR techniques have remarkable potential to detect this land subsidence of interest. In particular, our results show that the development of coal fires can lead to new subsiding areas and also accelerate the ongoing surface subsidence, typically within the areas of mature coal fires, through a comparison of the interferometric observations and the multi-temporal coal fire maps. This timely and reliable information on land subsidence will be useful for the detection and mapping of the coal fire affected regions and thereby assist in fighting and controlling coal seam burning.     

MODIS and NOAA-AVHRR l and surface temperature data detect a thermal anomaly preceding the 11 March 2011 Tohoku earthquake

Geospatial data, coupled with ground-based observations where available, enable scientists to survey pre-earthquake signals in areas of strong tectonic activity. On 11 March 2011, at local time 14:46 JST (05:46 UTC), a mega-earthquake of moment magnitude (M _w) 9.0 and shallow focus (24 km), known as the Tohoku-Oki earthquake, occurred on the Japan Trench plate boundary off the eastern shore of northern Honshu, followed by a large tsunami on the Pacific coast of Japan. This article is an attempt to analyse the development of thermal anomalies in land surface temperature (LST) preceding the 11 March 2011 Tohoku earthquake. In order to correlate LST variations and the Tohoku earthquake, we analysed time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua satellite daytime/night-time and National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA-AVHRR) data. A clear rise of LST (1–10°C), which is apparently related to pre-seismic activity, was observed 2 weeks before the major event in all analysed satellite data around the earthquake epicentre.     

Thermal–infrared emissivities of natural surfaces: improvements on the experimental set-up and new measurements

Ground measurements of thermal infrared emissivities of terrestrial surfaces are required to derive accurate temperatures from radiometric measurements, and also to apply and validate emissivity models using satellite sensor observations. This paper focuses on the demanding aspects that are involved in the field measurement of emissivity using the box method and a hand-held radiometer. Measuring emissivities in field conditions can be hampered by external factors such as wind and solar irradiance. This can increase the time spent on the field campaign but, most importantly, it can cause no-sense fluctuations between consecutive observations. Here we propose original developments for the experimental instrumentation to ensure consistency of measurements. Moreover, we present a dataset of emissivity values for different soils, rocks and vegetation samples measured in the 8–14, 8.2–9.2, 10.5–1 1.5 and 11.5–12.5 µm wavebands.     

An in situ study of the effects of surface anisotropy on the remote sensing of burned savannah

This Letter presents field‐based evidence of the perturbing effects of surface anisotropy on the remote sensing of burned savannah. The analysis is based on bidirectional spectral reflectance data collected at different solar illumination angles and convolved to Moderate‐resolution Imaging Spectroradiometer (MODIS) reflective bands. Results from a grass savannah site show that burning reduces the anisotropy of the surface compared to its pre‐burn state. In contrast, at a shrub savannah site, burning reduces or increases surface anisotropy. Spectral indices defined from 1.240 µm and 2.130 µm reflectance, and 1.640 µm and 2.130 µm reflectance, provided stronger diurnal separation between burned and unburned areas than individual reflectance bands but do not eliminate anisotropic effects. The Normalized Difference Vegetation Index (NDVI) provides weak diurnal separation relative to these near‐ and mid‐infrared based indices. Implications of the findings are discussed for burned area mapping.     

Feasibility of airborne imaging spectrometry for lake monitoring—a case study of spatial chlorophyll a distribution in two meso-eutrophic lakes

The spatial distribution of the sum of chlorophyll a and phaeophytin a concentrations (chl-a) under light wind (0–2 m s^?1) conditions was studied in two lakes with an AISA airborne imaging spectrometer. Chl-a was interpreted from AISA radiance data using an algorithm based on the near-infrared (700–710 nm) to red (660–665 nm) ratio. The results of Lake Lohjanjärvi demonstrate that the use of one monitoring station can result in over- or underestimation by 29–34% of the overall chl-a compared with an AISA-based estimation. In Lake Hiidenvesi, the AISA-based estimation for the mean chl-a with 95% confidence limits was 25.19±2.18 µg l^?1. The use of AISA data together with chl-a measured at 15 in situ sampling stations decreased the relative standard error of the mean chl-a estimation from 20.2% to 4.0% compared with the use of 15 discrete samples only. The relative standard error of the mean chl-a using concentrations at the three routine monitoring stations was 15.9 µg l^?1 (63.1%). The minimum and maximum chl-a in Lake Hiidenvesi were 2 and 101 µg l^?1, 6 and 70 µg l^?1 and 11 and 66 µmg l^?1, estimated using AISA data, data from 15 in situ stations and data from three routine in situ stations, respectively.     

Retrospective analysis of Suomi NPP VIIRS radiometric bias for reflective solar bands due to operational calibration changes

A comprehensive analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) radiometric bias relative to Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 data has been performed since early 2012 for selected reflective solar bands. The study suggests that VIIRS bias trends changes over time mainly due to calibration updates and anomalies. Results show nearly consistent biases of 1.7% for M5 (0.672 µm) and 2% for M7 (0.865 µm) throughout the mission. However, M1 (0.412 µm) and M4 (0.555 µm) biases are less consistent. While biases for both M1 and M4 fluctuates mostly around 0%, M1 shows most frequent short-term changes in bias trends, as high as 4%. When the bias trends are compared with VIIRS on- board-calibration-based gain trends, there exists a high correlation. In addition, the operational VIIRS data product of NOAA and the reprocessed NASA Land Product Evaluation and Test Element (PEATE) data were compared by trending the radiance ratio. The ratio trends show calibration differences that agree well with bias trends. The comparison of bias with F-factors and ratio trends indicates that the frequent changes observed in VIIRS bias trends are primarily caused by calibration updates and anomalies in VIIRS operational calibration. The study suggests that even though the operational VIIRS data archive meets the specification of ±2% radiometric uncertainty, reprocessing can improve data quality needed for rigorous scientific applications.