A comparative analysis of a fixed thresholding vs. a classification tree approach for operational burn scar detection and mapping

The scope of this paper is to demonstrate, evaluate and compare two burn scar mapping (BSM) approaches developed and applied operationally in the framework of the RISK-EOS service element project within the Global Monitoring for Environment and Security (GMES) program funded by ESA (http://www.risk-eos.com). The first method is the BSM_NOA, a fixed thresholding method using a set of specifically designed and combined image enhancements, whilst the second one is the BSM_ITF, a decision tree classification approach based on a wide range of biophysical parameters. The two methods were deployed and compared in the framework of operational mapping conditions set by RISK-EOS standards, based either on sets of uni- or multi-temporal satellite images acquired by Landsat 5 TM and SPOT 4 HRV. The evaluation of the performance of the two methods showed that either in uni- or multi-temporal acquisition mode, the two methods reach high detection capability rates ranging from 80% to 91%. At the same time, the minimum burnt area detected was of 0.9–1.0 ha, despite the coarser spatial resolution of Landsat 5 TM sensor. Among the advantages of the satellite-based approaches compared to conventional burn scar mapping, are cost-efficiency, repeatability, flexibility, and high spatial and thematic accuracy from local to country level. Following the catastrophic fire season of 2007, burn scar maps were generated using BSM_NOA for the entirety of Greece and BSM_ITF for south France in the framework of the RISK-EOS/GMES Services Element project.     

Tsunami Source of the 2010 Mentawai, Indonesia Earthquake Inferred from Tsunami Field Survey and Waveform Modeling

The 2010 Mentawai earthquake (magnitude 7.7) generated a destructive tsunami that caused more than 500 casualties in the Mentawai Islands, west of Sumatra, Indonesia. Seismological analyses indicate that this earthquake was an unusual “tsunami earthquake,” which produces much larger tsunamis than expected from the seismic magnitude. We carried out a field survey to measure tsunami heights and inundation distances, an inversion of tsunami waveforms to estimate the slip distribution on the fault, and inundation modeling to compare the measured and simulated tsunami heights. The measured tsunami heights at eight locations on the west coasts of North and South Pagai Island ranged from 2.5 to 9.3 m, but were mostly in the 4–7 m range. At three villages, the tsunami inundation extended more than 300 m. Interviews of local residents indicated that the earthquake ground shaking was less intense than during previous large earthquakes and did not cause any damage. Inversion of tsunami waveforms recorded at nine coastal tide gauges, a nearby GPS buoy, and a DART station indicated a large slip (maximum 6.1 m) on a shallower part of the fault near the trench axis, a distribution similar to other tsunami earthquakes. The total seismic moment estimated from tsunami waveform inversion was 1.0 × 10²¹ Nm, which corresponded to M_w 7.9. Computed coastal tsunami heights from this tsunami source model using linear equations are similar to the measured tsunami heights. The inundation heights computed by using detailed bathymetry and topography data and nonlinear equations including inundation were smaller than the measured ones. This may have been partly due to the limited resolution and accuracy of publically available bathymetry and topography data. One-dimensional run-up computations using our surveyed topography profiles showed that the computed heights were roughly similar to the measured ones.     

Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support Vector Machines

Information on burnt area is of critical importance in many applications as for example in assessing the disturbance of natural ecosystems due to a fire or in proving important information to policy makers on the land cover changes for establishing restoration policies of fire-affected regions. Such information is commonly obtained through remote sensing image thematic classification and a wide range of classifiers have been suggested for this purpose. The objective of the present study has been to investigate the use of Support Vector Machines (SVMs) classifier combined with multispectral Landsat TM image for obtaining burnt area mapping. As a case study a typical Mediterranean landscape in Greece was used, in which occurred one of the most devastating fires during the summer of 2007. Accuracy assessment was based on the classification overall statistical accuracy results and also on comparisons of the derived burnt area estimates versus validated estimates from the Risk-EOS Burnt Scar Mapping service. Results from the implementation of the SVM using diverse kernel functions showed an average overall classification accuracy of 95.87% and a mean kappa coefficient of 0.948, with the burnt area class always clearly separable from all the other classes used in the classification scheme. Total burnt area estimate computed from the SVM was also in close agreement with that from Risk-EOS (mean difference of less than 1%). Analysis also indicated that, at least for the studied here fire, the inclusion of the two middle infrared spectral bands TM5 and TM7 of TM sensor as well as the selection of the kernel function in SVM implementation have a negligible effect in both the overall classification performance and in the delineation of total burnt area. Overall, results exemplified the appropriateness of the spatial and spectral resolution of the Landsat TM imagery combined with the SVM in obtaining rapid and cost-effective post-fire analysis. This is of considerable scientific and practical value, given the present open access to the archived and new observations from this satellite radiometer globally.     

Characterizing slope morphology using multifractal technique: a study from the western continental margin of India

Here, we present the slope configuration of the submarine gullies, ridges and the adjacent slump zone off Goa, along the western continental margin of India utilizing multibeam bathymetric and single-channel seismic data. The fluid flow migration signature in the form of pockmark seepages, traces of mud volcanoes and enhanced reflectors is observed in the area. Altogether thirty-three depth profiles from the gully, ridge and slump areas depict downslope progression in gully incision and varying gradients in the gullies (1.19–4.07°) and ridges (2.13–3.70°), whereas the profiles of the slump zone are comparatively steady (2.25–2.51°). The scatter plot of the three slope characteristics, viz., gradient, mean depth and root mean square relief, characterizes the profiles of the gullies, ridges and slump zone into three distinct clusters. Principal component analysis as well corroborates the categorization. Furthermore, a stochastic multifractal technique has been employed to understand the nature of the fine-scale seafloor processes active in the slope region. The three estimated parameters of the depth profiles, i.e., the degree of multifractality (α), sparseness (C ₁) and the degree of smoothness (H), substantiate a very high degree of multifractality for all the thirty-three bathymetric profiles. Except for the five adjacent profiles (four from the slump zone and one from the ridge), the remaining twenty-eight depth profiles of the gully, ridges and slump zones show negligible difference. Based on the multifractal study, we conclude that the observed discrimination might be due to the significant interaction between the bottom currents off Goa and the varied seafloor morphological aspects with seepages and faults.     

Investigation of ionospheric TEC precursors related to the M7.8 Nepal and M8.3 Chile earthquakes in 2015 based on spectral and statistical analysis

The assessment of social vulnerability is a requirement for understanding the risk of natural hazards. This paper calculates the social vulnerability index of geological disasters in China with the super-efficiency DEA (data envelopment analysis) model, carries out global and local autocorrelation tests for social vulnerability to geological disasters in each province in China and identifies the characteristics of its spatial distribution pattern. The results show the following. (1) China’s social vulnerability to geological disasters is relatively high and has obvious differences. It represents the pattern of a significant increase by degrees in social vulnerability to geological disasters from east to west and a significantly negative correlation relationship between the vulnerability level and the economic level. (2) Based on the comparative analysis of the mean values of the indexes and the social vulnerability index of geological disasters, it is found that the social vulnerability index of geological disasters in China is directly related to the regional exposure degree and reaction and recovery ability, among which the reaction and recovery ability has great effects on the social vulnerability index. (3) Most of the regions in China are in a high–high clustering area or a low–low clustering area; that is to say, the regions with similar social vulnerability to geological disasters represent the pattern of clustering.     

Delineation of Fractures through Acoustic Televiewer Log

Boreholes are the only sources for direct measurements of geological and geophysical characteristics of the shallow subsurface of the earth. Borehole imaging tool “Acoustic Televiewer (ATV)” is an advanced probe, which records 3D image of the bore hole wall and is used to obtain oriented images of bore hole and provides substantial information regarding lithology, structural information, detection of fractures and casing of the borehole. The images are highly sensitive to the presence of fractures, the delineation of which becomes accurate and reliable based on these acoustic images. Features identified on log-derived images can be correlated with core samples or can be used as substitute in the poor core recovery zones. In the present study, ATV log from a deep borehole drilled at Khadi Kolavan in Koyna-Warna region of Ratnagiri district, Maharashtra located west of the Western Ghat escarpment in the Deccan Volcanic province, India, is used for interpretation of structural characteristics of the formation. The same was correlated with other conventional logging methods such as resistivity and full waveform sonic (FWS) as well as core logging and rock quality designations (RQD) which generated new understanding of the basement and basalt cover in this region.     

Depositional Environments and Diagenesis of the Kuldhar and Keera Dome Carbonates (Late Bathonian–Early Callovian) of Western India

This paper describes 11 microfacies types in late Bathonian–Early Callovian carbonates of the Kuldhar Member of the Jaisalmer Formation (Rajasthan) and the Keera Golden Oolite Member of the Chari Formation (Kachchh Mainland) western India. The different microfacies associations reported in this study reflect an ideal shallowing upward sequence, representing a system of bioclastic bars developed on the lower ramp, evolving into an oolitic bar-to-bank system separating restricted lagoonal—from lower ramp environment. Four main types of cements, i.e. bladed, fibrous, syntaxial overgrowth and blocky cement (characterized in a few cases by ferroan calcite and anhydrite II) occur in these carbonates. The study also reveals that chemical compaction followed the two phases of early mechanical compaction that largely governed porosity of these limestones. However, micritization and neomorphism also contributed significantly in this respect. Diagenetic signatures in these carbonates suggest that marine phreatic and fresh water phreatic environments dominated, but deep burial diagenesis also played its role in shaping these rocks. The early and late diagenetic changes have been controlled by the depositional facies evolving in a basin riddled with rifting in an extensional tectonic regime forcing regional-scale sea level fluctuations.     

An integrated approach for aquifer vulnerability mapping using GIS and rough sets: study from an alluvial aquifer in North India

A modified DRASTIC model in a geographic information system (GIS) environment coupled with an information-analytic technique called ‘rough sets’ is used to understand the aquifer vulnerability characteristics of a segment of the lower Kali watershed in western Uttar Pradesh, India. Since the region is a flat plain, topography (T) is removed as a potential control. Other parameters are the same as in DRASTIC, hence the new model is termed as DRASIC. The rough set technique is employed to provide insight into the relative vulnerabilities of different administrative units (blocks) within the study area. Using rough sets, three important factors are computed: strength, certainty and coverage. Strength indicates how the vulnerability characteristics vary in the entire area, certainty gives the relative fractions of low, medium and high vulnerability regions within a particular block, and coverage computes the percentage of a particular vulnerability state in each block. The purpose of the work is to demonstrate the utility of this integrated approach in classifying different administrative blocks in the study area according to their aquifer vulnerability characteristics. This approach is particularly useful for block-level planning and decision making for sustainable management of groundwater resources.