Active Faulting of Landforms along the Tuosuhu-Maoniushan Fault and Its Seismotectonic Implications in Eastern Qaidam Basin, China

The fold-and-thrust belt along the northern margin of the Qaidam basin is a typical active tectonic belt located in the northeast Tibetan Plateau. This belt is at a high risk of strong earthquakes with magnitudes larger than 6, as shown by multiple recorded events during 1962–2009. The lack of detailed late Quaternary surficial faulting data and systematic seismotectonic studies has posed difficulties in properly assessing the seismic risks and understanding the ongoing geodynamics in this region. In this study, we mapped the geomorphic features and fault traces from high-resolution satellite images and field investigations of the Tuosuhu-Maoniushan fault (TMF). Field photogrammetry was conducted to obtain deformation measurements using a DJI M300 real-time kinematic (RTK) drone. The TMF displaces the Holocene and late Pleistocene alluvial terraces in the eastern Qaidam basin. This fault dips to the south in the west and central segments (as a boundary of the Denan depression) and to the north in the eastern segment along the piedmont of the Maoniushan Mountains. The vertical slip rate is estimated to be 0.37 ± 0.08 mm/yr, which is similar to that of the active southern Zongwulongshan fault. By integrating our investigations with the previously published studies on deep structures and Cenozoic geology of the region, we propose a deep-seated thrust model for the seismotectonics of the northern margin of the Qaidam basin. The Aimunike, Tuosuhu-Maoniushan, southern Zongwulongshan, and Zongwulong faults, along with many folds, form an active compressional zone. The complex across-strike structures and along-strike segmentation could facilitate the release of strain through earthquakes of magnitude 6–7 in this broad seismotectonics belt, rather than through strong surface-rupturing events resulting from a single mature large fault.

Adaptive Enhancement Algorithm of High-Resolution Satellite Image Based on Feature Fusion

Since the traditional adaptive enhancement algorithm of high-resolution satellite images has the problems of poor enhancement effect and long enhancement time, an adaptive enhancement algorithm of high-resolution satellite images based on feature fusion is proposed. The noise removal and quality enhancement areas of high-resolution satellite images are determined by collecting a priori information. On this basis, the histogram is used to equalize the high-resolution satellite images, and the local texture features of the images are extracted in combination with the local variance theory. According to the extracted features, the illumination components are estimated by Gaussian low-pass filtering. The illumination components are fused to complete the adaptive enhancement of high-resolution satellite images. Simulation results show that the proposed algorithm has a better adaptive enhancement effect, higher image definition, and shorter enhancement time.

A Multi-Stage Approach Combining Very High-Resolution Satellite Image, GIS Database and Post-Classification Modification Rules for Habitat Mapping in Hong Kong

Identification and mapping of various habitats with sufficient spatial details are essential to support environmental planning and management. Considering the complexity of diverse habitat types in a heterogeneous landscape, a context-dependent mapping framework is expected to be superior to traditional classification techniques. With the aim to produce a territory-wide habitat map in Hong Kong, a three-stage mapping procedure was developed to identify 21 habitats by combining very-high-resolution satellite images, geographic information system (GIS) layers and knowledge-based modification rules. In stage 1, several classification methods were tested to produce initial results with 11 classes from a WorldView-2/3 image mosaic using a combination of spectral, textural, topographic and geometric variables. In stage 2, modification rules were applied to refine the classification results based on contextual properties and ancillary data layers. Evaluation of the classified maps showed that the highest overall accuracy was obtained from pixel-based random forest classification (84.0%) and the implementation of modification rules led to an average 8.8% increase in the accuracy. In stage 3, the classification scheme was expanded to all 21 habitats through the adoption of additional rules. The resulting habitat map achieved >80% accuracy for most of the evaluated classes and >70% accuracy for the mixed habitats when validated using field-collected points. The proposed mapping framework was able to utilize different information sources in a systematic and controllable workflow. While transitional mixed habitats were mapped using class membership probabilities and a soft classification method, the identification of other habitats benefited from the hybrid use of remote-sensing classification and ancillary data. Adaptive implementation of classification procedures, development of appropriate rules and combination with spatial data are recommended when producing an integrated and accurate map.

Drainage Development in the Dunhuang Basin, NE Tibet, Controlled by Multi-Segment Fault Growth

The Dongbatu Shan (DBTS, also known as the Nanjie Shan), which interrupts the northern Tibetan foreland in the Dunhuang basin, is an active anticline. It has accommodated the northwestern growth of the eastern Altyn Tagh fault system (ATF). Although several thrust faults have been identified around the DBTS, their evolution history and influence on regional landscape have received little attention during the late-Quaternary. In this study, several geomorphic methods are used to investigate the interaction between drainage development and tectonic movement around DBTS. Based on high-resolution satellite images, field investigation, and cosmogenic nuclide 10Be dating method, the fluvial landform sequences around DBTS were constructed. Using quantitative geomorphology methods including landscape relief profile, asymmetry factor (AF), and transverse topographic symmetry factor (T), we hypothesize that drainage deflection is controlled by multi-segment fault growth. Combining the results of the above-mentioned methods, we propose that Yulin He, flowing across the DBTS, had gone through several abandonments since the late mid-Pleistocene due to the lateral propagation of DBTS. Affected by the discharge of channel and multi-segment fault growth, our research confirms that the direction of river abandonment may have decoupled with the mountain range propagation trend. Based on the chronology dating, the DBTS has gone through two severe uplifts since ∼208 ka and the shortening rate across the central DBTS is constrained to be ∼1.47 mm/yr since ∼83 ka. Given the fact that thrust faults are widely developed around DBTS, we propose that the flower-like structure formed by the northward growth of the eastern ATF could better explain the development of the secondary subparallel faults.

Census Mapping in India and Role of GIS: A Look Ahead at Census 2021

The 2021 Census of India for over 1.3 billion population deploying 3 million enumerators, has significant evidence value for 71 countries where census is scheduled during 2021. Census mapping plays a major role in accurate, complete and timely census. It delineates the exact and correct boundaries of all the administrative units. The Indian census has been using Geographic Information System (GIS) technologies over the last three censuses. In this study, we focus on the applications and methodologies being adopted for the census mapping in Census 2021 in India which is going to be the first digital Census of India. Five mobile apps have been developed for data collection and for map-related work. The 2021 Indian census utilises the latest census mapping techniques, namely standardisation of GIS spatial database design, geo-referencing of administrative units and latest mobile mapping application (Arc GIS Quick Capture) for field operations and built-up area digitisation work. We also discuss the various challenges and their solutions for census mapping in India, most prominently a high quality, updated, comprehensive and geo-referenced address registry for accurate data collection and mapping, and the use of geo-referenced high-resolution satellite images at village level for covering the gaps in rural boundary maps.

DCRN: An Optimized Deep Convolutional Regression Network for Building Orientation Angle Estimation in High-Resolution Satellite Images

Recently, remote sensing satellite image analysis has received significant attention from geo-information scientists. However, the current geo-information systems lack automatic detection of several building characteristics inside the high-resolution satellite images. The accurate extraction of buildings characteristics helps the decision-makers to optimize urban planning and achieve better decisions. Furthermore, Building orientation angle is a very critical parameter in the accuracy of automated building detection algorithms. However, the traditional computer vision techniques lack accuracy, scalability, and robustness for building orientation angle detection. This paper proposes two different approaches to deep building orientation angle estimation in the high-resolution satellite image. Firstly, we propose a transfer deep learning approach for our estimation task. Secondly, we propose a novel optimized DCRN network consisting of pre-processing, scaled gradient layer, deep convolutional units, dropout layers, and regression end layer. The early proposed gradient layer helps the DCRN network to extract more helpful information and increase its performance. We have collected a building benchmark dataset that consists of building images in Riyadh city. The images used in the experiments are 15,190 buildings images. In our experiments, we have compared our proposed approaches and the other approaches in the literature. The proposed system has achieved the lowest root mean square error (RMSE) value of 1.24, the lowest mean absolute error (MAE) of 0.16, and the highest adjusted R-squared value of 0.99 using the RMS optimizer. The cost of processing time of our proposed DCRN architecture is 0.0113 ± 0.0141 s. Our proposed approach has proven its stability with the input building image contrast variation for all orientation angles. Our experimental results are promising, and it is suggested to be utilized in other building characteristics estimation tasks in high-resolution satellite images.