Multisource remotely sensed data,field checks and seismicity for the definition of active tectonics in Ecuadorian Andes

Abstract

The active tectonics of the Ecuadorian Andes were analysed by the correlation of remotely sensed features, and microtectonic and seismological data. Landsat and radar imagery, and airborne stereophotos, were employed to investigate the fault pattern of scarcely accessible areas, with special reference to those lineaments showing young morphology, linear continuity and dragging effect on streams of post-glacial age. Some NNE-SSW long lineaments characterized by right-lateral strike-slip motions, were identified in the eastern Andes as probably very recent faults. Field microtectonic analysis was guided by remotely sensed data and allowed the reconstruction of the active tectonic compressional stress field characterized by a WSW-ENE trending σ. It also confirmed the recent motions along the NNE-SSW set. These data are consistent with published focal mechanism solutions in the same area. The seismicity is explained by a kinematic model of the upper crust, in which west dipping oblique thrusts and strike-slip faults coexist in the eastern Andes. Following the remotely sensed data and the available literature this model is extended southwards to complete the regional geodynamic framework.     

Classifying depth‐layered geological structures on Landsat TM images by gravity data: a case study of the western slope of Songliao Basin,northeast China

Geological structures on remotely sensed images, Landsat Thematic Mapper (TM) images in this case, can be classified by quantitative depth information on the basis of the comparison of results from Landsat TM images and geophysical data. Although the lineaments with different depths can be visually interpreted together on Landsat TM images, the depth information and geological significance of these lineaments are however hard to obtain solely from the Landsat TM images of a study area under a thick cover, and it is of much importance for hydrocarbon exploration in the Western Slope Belt of Songliao Basin, northeast China. During the present study, the 3‐dimensional field source information, including location and depth information, is derived from 3‐dimensional Euler deconvolution of gravity data in particular. As an example, it may be quantitatively classified into four groups of depth range: <100?m, 100–500?m, 500–1000?m, >1000?m. It is then superimposed onto the lineaments map from Landsat TM images using a geographical information system (GIS). With a comprehensive analysis of the superimposed maps, we obtain validation and quantitative depth information of the geological structures delineated on the Landsat TM images. Four depth‐layered maps of geological structures with different depths are presented here. It is concluded that the number of structures with depth greater than 1000?m on the Landsat TM images is fewer than those at the other three depth ranges. The detection of geological structures on Landsat TM images attributed to depth information derived from the geophysical data may also be possible by this approach.     

Integration of Landsat imagery interpretation and geomagentic data on verification of deep‐seated transverse fault lineaments in SE Zagros,Iran

In this paper, Landsat images are used in the mapping of transverse fault lineaments in Zagros, Iran, and the origin of the fault lineaments as the result of basement reactivation faults is examined using geomagnetic maps. The area under investigation is located in the SE part of the Zagros Fold Belt (ZFB) and was affected by influence of concealed faults, or fault lineaments, during the late Alpine Zagros orogeny. Image interpretation of geological structures such as curvilinear geometry of fold hinges, en echelon pattern of surficial lineaments and younger folds, and breached salt plugs, has been used to map the fault lineaments. Two sets of strike‐lateral transverse fault lineaments have been detected. The first set, NW‐trending, was identified by right lateral curved geometry of the main fold hinges, generation of younger folds, and en echelon pattern of surficial lineaments. The second set, NE‐trending, was verified based on left lateral displacement of the structures and alignment of salt plugs pierced anticline hinges. The placement of Upper Proterozoic salt plugs on the surface along the fault lineaments, together with their correlation with the magnetic‐driven lineaments, implies that the fault lineaments have a basement origin. The trend of these fault lineaments can be correlated with the trend of basement faults in the northern margin of the Arabian plate. The key result of this study is that the fault lineaments identified by integration of remotely sensed data and geomagnetic maps are generated as first order wrench faults by reactivation of the N–S‐trending basement faults. The thick Hormoz Salt formations overlaying the basement do not allow the fault lineaments to emerge at the surface. Therefore, they can be mapped through regional remote sensing and their basement origin can only be recognized by their correlation with magnetic faults. It is also concluded that satellite imagery can greatly contribute to the structural mapping of the ZFB and is therefore a valuable aid for oil and gas exploration in analogous tectonic environments.     

Application of wavelet analysis to the study of spatial pattern of morphotectonic lineaments in digital terrain models. A case study

Tectonic faults are often associated with characteristic geomorphological features such as linear valleys, ridgelines and slope breaks that can be identified as lineaments in remotely sensed images or digital terrain models. Lineaments of tectonic origin are often characterised by periodicity and characteristic spatial pattern. Unlike traditional methods of autocorrelation, variogram, lineament density and Fourier analysis, wavelet analysis is capable of capturing and describing both periodicity and spatial pattern of lineaments. In this paper, a case study is shown for the application of wavelet analysis to morphotectonic lineament investigation. Results of wavelet analysis are compared to traditional methods. Although this study involves DEM-derived morphological lineaments, the presented wavelet analysis can be also used for lineaments derived from remotely sensed images. These results hold for this case study and provide a good assessment of the relative abilities of wavelet analysis, but it remains to be seen how effective it is for other data sources, areas and geological terrain.     

遥感地质线性体与化探信息数字复合处理提取金矿控矿构造信息

从遥感图像获取的地质线性体与土壤岩石化探异常数据处理来进行定量分析提取具有控矿构造意义的遥感地质线性体,并从遥感地质环形体影像与化探金异常数据的关系,间接地分析环形体在成矿、控矿中的作用,为地面地质工作者提供找矿构造数值分析依据。研究结果与过去做过的大量地面成矿、控矿地质构造分析结果非常吻合。     

遥感地质线性体与化探信息数字复合处理提取金矿控矿构造信息

从遥感图像获取的地质线性体与土壤岩石化探异常数据处理来进行定量分析提取具有控矿构造意义的遥感地质线性体,并从遥感地质环形体影像与化探金异常数据的关系,间接地分析环形体在成矿、控矿中的作用,为地面地质工作者提供找矿构造数值分析依据。研究结果与过去做过的大量地面成矿、控矿地质构造分析结果非常吻合。     

Neotectonic study of Ganga and Yamuna tear faults,NW Himalaya,using remote sensing and GIS

The Ganga and Yamuna rivers emerge from the Himalayas along two major faults known as the Ganga and Yamuna Tear Faults respectively. The two major strike-slip faults transverse to the Siwalik range are clearly seen in satellite imagery of the Dehradun area. Earthquake records, landslide and recent changes in geomorphological features indicate that the area between the Main Boundary Thrust and the Main Frontal Thrust is tectonically active. An effort has been made to study the tectonic evolution and neotectonism of the Ganga and Yamuna tear faults. Spectral and spatial enhancement techniques have been employed to the digital data of IRS-1B LISS-I to delineate the lineaments and major faults of the area. Based on Mohr's theory, failure criteria and statistical analysis of remotely sensed lineament data, horizontal compressive stress values (S_Hmax) have been estimated at various sites of the study area. These data are found to be consistent with the published S_Hmax orientation determined from earthquake focal mechanism solutions. Active faults and lineaments have been extracted from the remotely sensed lineament data. Past earthquake data and depth to basement contour data have been used in an integrated approach with available Geographic Information System (GIS) techniques to reconstruct a present-day regional geodynamic model. Attempts have been made to investigate the genesis of Ganga and Yamuna Tear Faults and possible causes of recent tectonic activities of the area with the help of the proposed geodynamic model.     

Revealing the geology of the Great Nicobar Island,Indian Ocean,by the interpretation of airborne synthetic aperture radar images

A number of new geological structures have been revealed in the Great Nicobar Island, Indian Ocean, from the analysis of airborne synthetic aperture radar (SAR) data. The advantages of SAR images for mapping geological structures over other images for the Great Nicobar Island, the southern most island of Andaman-Nicobar arc, have been highlighted. A visual analysis of lineaments in the southern part of the Great Nicobar Island using SAR and Landsat TM colour composites, TM bands 2, 3, and 4, and TM bands 4, 2 and SAR, reveals a wealth of structural information not shown on previous maps. Other identified features include lithological units derived from landforms, northwest-southeast trending faults, faults trending towards the mouth of Galathea river, the graben valley, and many other new faults. The geological features reported in this work were verified using limited ground checking.     

Remote sensing techniques for mangrove mapping

Different approaches to the classification of remotely sensed data of mangroves are reviewed, and five different methodologies identified. Landsat TM, SPOT XS and CASI data of mangroves from the Turks and Caicos Islands, were classified using each method. All classifications of SPOT XS data failed to discriminate satisfactorily between mangrove and non-mangrove vegetation. Classification accuracy of CASI data was higher than Landsat TM for all methods, and more mangrove classes could be discriminated. Merging Landsat TM and SPOT XP data improved visual interpretation of images, but did not enhance discrimination of different mangrove categories. The most accurate combination of sensor and image processing method for mapping the mangroves of the eastern Caribbean islands is identified.     

Digital terrain analysis using Landsat‐7 ETM+ imagery and SRTM DEM: a case study of Nevsehir province (Cappadocia), Turkey

A three‐dimensional (3D) model of land‐use/land‐cover (LULC) and a digital terrain model of Nevsehir province (Cappadocia), Turkey, were generated and analysed using a Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) multispectral image set and a Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM). Stream drainage patterns, lineaments and structural‐geological features (landforms) were extracted and analysed. In the process of analysing and interpreting the multispectral images of geological features, criteria such as colour and colour tones, topography and stream drainage patterns were used to acquire information about the geological structures of the land, including as geomorphological, topographic and tectonic structures. Landsat‐7 ETM+ multispectral imagery and an SRTM DEM of the study region were used experimentally for classification and analysis of a digital terrain model. Using the multispectral image data, the LULC types were classified as: settlement (1.2%); agricultural land (70.1%); forest (scrubland, orchard and grassland) (2.9%); bare ground (25.5%); and water bodies (lakes and rivers) (0.3%) of the study area (5434 km²). The results of the DEM classification in the study area were: river flood plain (11.3%); plateau (52.3%); high plateau (28.4%); mountain (7.6%); and high mountain (0.3%). Lineament analysis revealed that the central Kizilirmak River divides the region into two nearly equal parts: the Kirsehir Plateau in the north and the Nevsehir Plateau in the south. In terms of the danger of catastrophe, the settlements of Kozakli, Hacibektas and Acigol were found to be at less risk of earthquake and/or flooding than those of Avanos, Gulsehir, Urgup, Nevsehir, Gumuskent and Derinkuyu, which are located on river flood plains and/or the main stream drainage channels, particularly stream beds, where the lineaments are deep valleys or fracture or fault‐line indicators.     

Extraction of linear and anomalous features using ERS SAR data over Singhbhum Shear Zone,Jharkhand using fast Fourier transform

Digital filtering of ERS‐2 SAR data using the fast Fourier transform (FFT) has been attempted over Singhbhum shear zone (SSZ) and its surroundings for extraction of linear and anomalous patterns. The results show that numerous lineaments as well as drainage patterns could be identified and demarcated by FFT digital filtering method. Major as well as several minor drainage patterns are easily detectable from the filtered image, which are structurally controlled and not observed in the original map. Comparison of the present interpretation of the study area to existing geological map/earlier interpretation has been done effectively. This technique was found to be more effective in identifying the lineaments using ERS SAR data compared with using Landsat imagery over the study area. The present study reveals that maximum lineaments occurring in the north of SSZ are NNE, NNW and NW trending, while maximum lineaments occurring in the south of SSZ are NE, ENE, WNW, and NW trending. The demarcated geological structures may have a great significance to locate the hidden ore/mineral occurrences. The existences of various mines, such as Baharagora, Mosaboni, Surda, Narwa, Bhatin, Jadugoda, Rakha, and Tatanagar along the shear zone, correlate well with the interpreted results.     

Landsat Thematic Mapper data analysis for Quaternary tectonics in parts of the Doon valley,NW Himalaya,India

Identification of active faults in Himalaya is extremely significant as they directly reflect the Himalayan continental collision. They have moved repeatedly during the Quaternary, resulting in the dislocation of many landforms, such as streams, alluvial and piedmont fans and river terraces, etc. In the present study, Landsat Thematic Mapper (TM) data has been used to identify signatures of Quaternary tectonics in parts of the Doon valley, NW Himalaya. The Siwalik zone is being squeezed between the very active Main Boundary Thrust and the Himalayan Frontal Thrust and there is differential uplift and subsidence in the Doon valley. The study has helped to extract certain subtle geological information which has played a significant role in the present geomorphic configuration of the valley. Apart from the many lineaments identified it was observed that a NNE-SSW trending lineament, identified distinctly on the satellite image, separates the Doon valley fan sequence from the Yamuna terraces in the west. It is inferred that the fan sequence has been uplifted and tilted towards the west. The analysis of remotely-sensed data along with selected field checks has yielded interesting results of the Quaternary tectonics experienced in the Doon valley.     

基于MPI的遥感影像高效能并行处理方法研究

采用基于不同尺度下的面向特征基元的影像分析方法对高分辨率遥感影像进行基于MPI的处理,即在对常规的影像数据划分方法进行总结分析的基础上,提出了基于特定环境下的非均匀数据划分策略;在进行基于影像数据库的MPI并行处理时,提出了一种新的数据流分配方法。处理结果表明,这两种方法均能够在一定环境下取得比常规方法更高的效率。     

Street orientation detection and recognition in Landsat TM and SPOT HRV imagery

Building (street) orientation is one of the important parameters for estimation of building bulk size (height and width) from corner reflector effects using remotely sensed radar image data. However, this parameter is difficult to obtain directly from radar data. Other sensor data such as optical and near infrared data may provide possibilities. This paper reports on a method for detection and recognition of street orientation in remotely sensed Landsat TM and/or SPOT HRV imagery. The methodology includes two steps: (1) multiscale wavelet transform techniques are employed to detect edges; (2) the predominant street orientation for each 20 × 20 pixel block is then recognised by applying a simple algorithm to the detected edges which contain most of the information about street orientations.     

Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis

Classification tree analysis (CTA) provides an effective suite of algorithms for classifying remotely sensed data, but it has the limitations of (1) not searching for optimal tree structures and (2) being adversely affected by outliers, inaccurate training data, and unbalanced data sets. Stochastic gradient boosting (SGB) is a refinement of standard CTA that attempts to minimize these limitations by (1) using classification errors to iteratively refine the trees using a random sample of the training data and (2) combining the multiple trees iteratively developed to classify the data. We compared traditional CTA results to SGB for three remote sensing based data sets, an IKONOS image from the Sierra Nevada Mountains of California, a Probe-1 hyperspectral image from the Virginia City mining district of Montana, and a series of Landsat ETM+ images from the Greater Yellowstone Ecosystem (GYE). SGB improved the overall accuracy of the IKONOS classification from 84% to 95% and the Probe-1 classification from 83% to 93%. The worst performing classes using CTA exhibited the largest increases in class accuracy using SGB. A slight decrease in overall classification accuracy resulted from the SGB analysis of the Landsat data.     

Parallel spatiotemporal autocorrelation and visualization system for large-scale remotely sensed images

Current studies on large-scale remotely sensed images are of great national importance for monitoring and evaluating global climate and ecological changes. In particular, real time distributed high-performance visualization and computation have become indispensable research components in facilitating the extraction of remotely sensed image textures to enable mining spatiotemporal patterns and dynamics of landscapes from massive geo-digital information collected from satellites. Remotely sensed images are usually highly correlated with rich landscape features. By exploiting the structures of these images and extracting their textures, fundamental insights of the landscape can be derived. Furthermore, the interdisciplinary collaboration on the remotely sensed image analysis demands multifarious expertise in a wide spectrum of fields including geography, computer science, and engineering.     

Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization

Forest dynamics are characterized by both continuous (i.e., growth) and discontinuous (i.e., disturbance) changes. Change detection techniques that use optical remotely sensed data to capture disturbance related changes are established and commonly applied; however, approaches for the capture of continuous forest changes are less mature. Optical remotely sensed imagery is well suited for capturing horizontally distributed conditions, structures, and changes, while Light Detection And Ranging (LIDAR) data are more appropriate for capturing vertically distributed elements of forest structure and change. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to fully characterize forest canopy attributes and dynamics.The study described in this paper captures forest conditions along a corridor approximately 600 km long through the boreal forest of Canada. Two coincident LIDAR transects, representing 1997 and 2002 forest conditions respectively, are compared using image segments generated from Landsat ETM+ imagery. The image segments are used to provide a spatial framework within which the attributes and temporal dynamics of the forest canopy are estimated and compared. Segmented and classified Landsat imagery provides a context for the comparison of sufficiently spatially related LIDAR profiles and for the provision of categories to aid in the application of empirical models requiring knowledge of land cover.Global and local approaches were employed for characterizing changes in forest attributes over time. The global approach, emphasized the overall trend in forest change along the length of the entire transect, and indicated that key canopy attributes were stable, and transect characteristics, including forest canopy height, did not change significantly over the five-year period of this study (two sample t-test, p = 0.08). The local approach analyzed segment-based changes in canopy attributes, providing spatially explicit indications of forest growth and depletion. The local approach identified that 84% of the Landsat segments intercepted by both LIDAR transects either have no change, or have a small average increase in canopy height (0.7 m), while the other 16% of segments have an average decrease in canopy height of 1.6 m. As expected, the difference in the magnitude of the changes was markedly greater for depletions than it was for growth, but was less spatially extensive. Growth tends to occur incrementally over broad areas; whereas, depletions are dramatic and spatially constrained. The approach presented holds potential for investigating the impacts of climate change across a latitudinal gradient of boreal forest.     

An improved neural network technique for data dimensionality reduction in remotely sensed imagery

An approach for non-linear projection of multidimensional data is discussed with application to remotely sensed imagery. The approach uses a multi-layer neural network in auto-associative mode with an improved updating rule, based on a conjugate gradient. To evaluate the usefulness of the proposed approach, we used the maximum likelihood classifier applied to a Landsat TM image of Kénitra region (Morocco).     

Land‐cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data

Land‐cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land‐cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet‐merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey‐level co‐occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum‐likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land‐cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8–6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land‐cover classification accuracies.     

Statistical techniques using NURE airborne geophysical data and NURE geochemical data

Some standard techniques in multivariate analysis are used to describe the relationships among remotely sensed observations (Landsat and airborne geophysical data) and between these variables and hydrogeochemical and stream sediment analyses. Gray-level pictures of such factors make the analytic results more accessible and easier to interpret.