Coal fire quantification using ASTER, ETM and BIRD satellite instrument data

Coal fires cause severe environmental and economic problems. Although satellite remote sensing has been used successfully to detect coal fires, a satellite data based concept that can quantify the majority of the detected coal fires is still missing. Recently, the determination of fire radiative energy (FRE) has been introduced as a new remote sensing tool to quantify forest and grassland fires. This thesis tests the concept of remotely measured FRE, with a view to ascertaining its potential applicability to coal fires. It contains an investigation of a new generation of satellite instruments, including the operational Enhanced Thematic Mapper (ETM) instrument, the experimental Bi-spectral InfraRed Detection (BIRD) satellite sensor and the experimental Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which explores the potential of these sensors to determine coal fire radiative energy (CFRE). Additionally, based on the results of this analysis, the thesis presents a new, automated ETM and ASTER data based algorithm, adapted to quantify coal fires in semi-arid to arid regions in northern China. Field observations carried out in September 2002 and 2003 in three coalfields in northern China (the Wuda, Gulaben and Ruqigou coalfields) demonstrate that coal fire related, surface anomalies are significantly cooler than forest and grassland fires. The theoretical investigation of the ASTER, ETM and BIRD instruments outlines the fact that the thermal infrared (TIR) or mid infrared (MIR) spectral channels of the ASTER, ETM and BIRD instrument are particularly effective in registering these ‘warm spots’, whilst the short wave infrared (SWIR) spectral range is, however, insufficiently sensitive to be able to register spectral coal fire radiances. The commonly used bi-spectral fire quantification method (Dozier, 1981) can be applied to BIRD data in order to quantify relatively large and / or hot coal fires. However, existing FRE retrieval approaches fail to quantify coal fires via ASTER and ETM instrument data. In this thesis, a new CFRE retrieval method is presented, which links the fire and background TIR spectral radiances to the CFRE through an empirical relationship. This newly developed TIR method is applied to visually detected fire clusters from night-time ASTER data, and from both day- and night-time ETM data, taken from the three study coalfields in northern China. The ASTER and ETM CFRE values, calculated via the TIR method, are compared to CFRE estimates from BIRD data, calculated via the existing bi-spectral method. Despite the different spatial resolution and spectral properties of the ETM, ASTER and BIRD instruments, CFRE computed from ASTER, ETM and BIRD data show good correlations with one another. However, CFRE retrievals from daytime data appear to be very undependable to background temperature variations, while CFRE, estimated from night-time data, appears to be relatively stable. A comparison between night-time ETM-derived CFRE and the figures given by local mining authorities for total coal fire induced, coal loss estimates in the Wuda coalfield gives a clear indication that the overall dimension of the coal fire problematic can in fact be approximated via satellite data CFRE retrievals. It is thus expected that CFRE derived from night-time satellite data will become a crucial tool in obtaining reliable, quantitative information for coal fires. A multi-temporal comparison of CFRE retrievals from night-time BIRD and ETM data, covering the Ruqigou and Wuda coalfields, indicates that only major shifts or activity changes in coal fire induced, surface anomalies can be observed by means of these data. These results, which could only partially be verified by field observations, indicate that ETM or BIRD data can be used to monitor major changes in coal fire related, surface anomalies. These data however cannot entirely replace detailed field observations, especially in case of smaller and / or cooler coal fire related, surface anomalies

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Prototyping an improved PPS cloud detection for the Arctic polar night

under noll grader och snö och regn är vanligt förekommande, vilket bl.a. skapar hala vägar och behov avA new Polar Platform Systems (PPS) Cloud Mask (CM) test sequence is required for improving cloud detection during Arctic winter conditions. This study introduces a test sequence, called Ice Night Sea (INS), that to a greater extent successfully detects clouds over ice surfaces and which is less sensitive to cloud free misclassification.The test sequence uses a combination of Numerical Weather Prediction (NWP) fields and Advanced Very High Resolution Radiometer (AVHRR) satellite data. Only the infrared (IR) AVHRR channels can be exploited during night conditions. Training target data from winter 2001-2002, collected over a large area north of the Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska, were used to assess the general atmospheric state of the Arctic and to perform a qualitative validation of CM test sequences. Results clearly show that the atmospheric conditions during Arctic winter severely hamper cloud detection efforts. Very cold surface temperatures and immense surface temperature inversions lead to a diminished separability between surfaces and clouds. One particular problem is that the IR brightness temperatures for the shortest wavelength (3.7μm - henceforth T37) are strongly affected by noise. The use of an IR noise filter was shown to improve results significantly. In addition, the problem of misclassifying cracks in the pack ice as Cirrus clouds was basically solved by using a dedicated filter using the local variance of T37.Using an inverse version of a typical daytime Cirrus test (based on just two IR channels and normally applied successfully outside the Arctic region), it is shown that we can detect a substantial part of the warmsemi-transparent clouds commonly found in the Arctic. Running the test sequences on training target data revealed an improvement in correct cloud free target classification of around 30% but only a marginal improvement for cloudy training targets. However, visual inspection of results obtained for about 50 scenes covering a large part of the Arctic region in January 2007 clearly indicated improvements also for the cloudy portion of the scenes. The INS CM test sequence awaits a more rigorous and quantitative validation, e.g. based on comparisons with CLOUDSAT/CALIPSO satellite data sets

Detecting coal fires in China using differential interferometric synthetic aperture radar (InSAR)

In this study we investigate the feasibility of detecting subsurface in subsurface coal deposits through InSAR observations of surface displacements. Uncontrolled burning of subsurface coal seams have been reported from different locations around the world. In northern China alone, more than 10 Million tons of coal are estimated to burn every year, with massive implications for the economy and ecology. In fighting these fires and controlling burning coal seams the timely and reliable detection and mapping of the affected regions is critical. This has proven to be extremely difficult in the remote regions of northern China, where many of the fires have been caused by uncontrolled, small-scale mining operations. Both the volume change of the burning coal and thermal effects in the adjacent rock mass are expected to cause measurable surface displacements and collapses of the earth's surface have often been reported. Unfortunately, reliable data on surface deformation accompanying the fires have not been acquired. However, theory and individual reports suggest that subsidence mapping using differential InSAR may be a suitable tool to detect burning regions and map the spatial extent of the affected areas. Though topography, temporal decorrelation, and poor data coverage complicate the analysis we have identified several localized areas of subsidence in the region. Here we discuss the promise and limitations of using InSAR for coal-fire detection in northern China

The Impact of Time Difference between Satellite Overpass and Ground Observation on Cloud Cover Performance Statistics

Cloud property data sets derived from passive sensors onboard the polar orbiting satellites (such as the NOAA’s Advanced Very High Resolution Radiometer) have global coverage and now span a climatological time period. Synoptic surface observations (SYNOP) are often used to characterize the accuracy of satellite-based cloud cover. Infrequent overpasses of polar orbiting satellites combined with the 3- or 6-h SYNOP frequency lead to collocation time differences of up to 3 h. The associated collocation error degrades the cloud cover performance statistics such as the Hanssen-Kuiper’s discriminant (HK) by up to 45%. Limiting the time difference to 10 min, on the other hand, introduces a sampling error due to a lower number of corresponding satellite and SYNOP observations. This error depends on both the length of the validated time series and the SYNOP frequency. The trade-off between collocation and sampling error call for an optimum collocation time difference. It however depends on cloud cover characteristics and SYNOP frequency, and cannot be generalized. Instead, a method is presented to reconstruct the unbiased (true) HK from HK affected by the collocation differences, which significantly (t-test p &lt; 0.01) improves the validation results

Snow cover maps from MODIS images at 250 m resolution, part 1: Algorithm description

A new algorithm for snow cover monitoring at 250 m resolution based on Moderate Resolution Imaging Spectroradiometer (MODIS) images is presented. In contrast to the 500 m resolution MODIS snow products of NASA (MOD10 and MYD10), the main goal was to maintain the resolution as high as possible to allow for a more accurate detection of snow covered area (SCA). This is especially important in mountainous regions characterized by extreme landscape heterogeneity, where maps at a resolution of 500 m could not provide the desired amount of spatial details. Therefore, the algorithm exploits only the 250 m resolution bands of MODIS in the red (B1) and infrared (B2) spectrum, as well as the Normalized Difference Vegetation Index (NDVI) for snow detection, while clouds are classified using also bands at 500 m and 1 km resolution. The algorithm is tailored to process MODIS data received in real-time through the EURAC receiving station close to Bolzano, Italy, but also standard MODIS products are supported. It is divided into three steps: first the data is preprocessed, including reprojection, calculation of physical reflectance values and masking of water bodies. In a second step, the actual classification of snow, snow in forested areas, and clouds takes place based on MODIS images both from Terra and Aqua satellites. In the third step, snow cover maps derived from images of both sensors of the same day are combined to reduce cloud coverage in the final SCA product. Four different quality indices are calculated to verify the reliability of input data, snow classification, cloud detection and viewing geometry. Using the data received through their own station, EURAC can provide SCA maps of central Europe to end users in near real-time. Validation of the algorithm is outlined in a companion paper and indicates good performance with accuracies ranging from 94% to around 82% compared to in situ snow depth measurements, and around 93% compared to SCA derived from Landsat ETM+ images

Snow cover maps from MODIS images at 250 m resolution, part 2: Validation

The performance of a new algorithm for binary snow cover monitoring based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images at 250 m resolution is validated using snow cover maps (SCA) based on Landsat 7 ETM+ images and in situ snow depth measurements from ground stations in selected test sites in Central Europe. The advantages of the proposed algorithm are the improved ground resolution of 250 m and the near real-time availability with respect to the 500 m standard National Aeronautics and Space Administration (NASA) MODIS snow products (MOD10 and MYD10). It allows a more accurate snow cover monitoring at a local scale, especially in mountainous areas characterized by large landscape heterogeneity. The near real-time delivery makes the product valuable as input for hydrological models, e.g., for flood forecast. A comparison to sixteen snow cover maps derived from Landsat ETM/ETM+ showed an overall accuracy of 88.1%, which increases to 93.6% in areas outside of forests. A comparison of the SCA derived from the proposed algorithm with standard MODIS products, MYD10 and MOD10, indicates an agreement of around 85.4% with major discrepancies in forested areas. The validation of MODIS snow cover maps with 148 in situ snow depth measurements shows an accuracy ranging from 94% to around 82%, where the lowest accuracies is found in very rugged terrain restricted to in situ stations along north facing slopes, which lie in shadow in winter during the early morning acquisition

Improving Land Surface Temperature Retrievals over Mountainous Regions

Algorithms for Land Surface Temperature (LST) retrieval from infrared measurements are usually sensitive to the amount of water vapor present in the atmosphere. The Satellite Application Facilities on Climate Monitoring and Land Surface Analysis (CM SAF and LSA SAF) are currently compiling a 25 year LST Climate data record (CDR), which uses water vapor information from ERA-Int reanalysis. However, its relatively coarse spatial resolution may lead to systematic errors in the humidity profiles with implications in LST, particularly over mountainous areas. The present study compares LST estimated with three different retrieval algorithms: a radiative transfer-based physical mono-window (PMW), a statistical mono-window (SMW), and a generalized split-windows (GSW). The algorithms were tested over the Alpine region using ERA-Int reanalysis data and relied on the finer spatial scale Consortium for Small-Scale Modelling (COSMO) model data as a reference. Two methods were developed to correct ERA-Int water vapor misestimation: (1) an exponential parametrization of total precipitable water (TPW) appropriate for SMW/GSW; and (2) a level reduction method to be used in PMW. When ERA-Int TPW was used, the algorithm missed the right TPW class in 87% of the cases. When the exponential parametrization was used, the missing class rate decreased to 9%, and when the level reduction method was applied, the LST corrections went up to 1.7 K over the study region. Overall, the correction for pixel orography in TPW leads to corrections in LST estimations, which are relevant to ensure that long-term LST records meet climate requirements, particularly over mountainous regions