Search

Protected areas play a crucial role in safeguarding biodiversity and ecosystem services. However, they face mounting challenges from environmental pressures. This study examines the Oshtorankooh Protected Area (OPA) in Iran, evaluating its vulnerability to various environmental hazards from 2010 to 2020.A comprehensive set of variables, including physiographic, climatic, geological, edaphic, vegetative, hydrological, and socio-economic factors, were utilized to compute 45 vulnerability indicators across three dimensions: exposure, sensitivity, and adaptive capacity for 2010 and 2020. Maps of these indicators were created and standardized, with weights assigned using the Fuzzy Analytic Hierarchy Process (FAHP). Composite indices for exposure, sensitivity, and adaptive capacity were then developed by integrating these weighted and standardized maps.The vulnerability indices for grid cells within the OPA enabled the identification of spatial vulnerability patterns, highlighting heightened vulnerability in the northern and western sectors influenced by factors such as drought variability, social vulnerability, evapotranspiration rates, spring discharge, and runoff dynamics. Results indicate a notable increase in vulnerability over the study period, with index values rising from 0.76 in 2010 to 2.04 in 2020. This spatially explicit vulnerability assessment underscores the dynamic nature of vulnerabilities within protected areas, emphasizing the necessity for adaptive management strategies. The findings stress the importance of tailored, evidence-based management approaches to mitigate risks and preserve ecological and socio-economic values. Implementing adaptive management strategies informed by this assessment can bolster the resilience of protected areas against environmental threats, thereby advancing global conservation efforts.

The manual extraction of land disturbances associated with oil exploration, which normally includes resource roads, mining facilities, and well pads, presents significant challenges in terms of cost and time. Accurate monitoring and mapping of land disturbances resulting from oil exploration plays a crucial role in conducting comprehensive environmental assessments and facilitating effective land reclamation initiatives. However, prevailing deep learning methodologies in the realm of oil and gas exploration primarily focus on oil spill detection, neglecting the critical aspect of land disturbances resulting from oil exploration, thus overlooking the impact on land. Furthermore, given that the well sites are scattered and relatively diminutive compared to other land covers, their detection poses substantial difficulties. This paper proposes an automatic error-correcting (AEC) algorithm to address deficiencies in ground truth data quality. This AEC method was integrated into the deep-learning framework for land disturbance extraction, specifically tailored for land disturbances analysis associated with oil exploration. The efficacy of our method was validated on a dataset collected in Alberta covering an area of oil sand mining sites. The application of the AEC algorithm significantly enhanced the accuracy of land disturbance analysis, thereby contributing to a more effective hydrocarbon exploration impact analysis and facilitating the timely planning by the Alberta government. The results demonstrate notable improvements in both average pixel accuracy (AA) and mean intersection over union (mIoU), ranging from 8.3% to 15.4% and 0.5% to 5.8%, respectively. These enhancements, which have profound implications for the precision of land disturbance detection, prove that the proposed AEC algorithm can serve a dual purpose: correcting errors in the dataset and efficiently detecting land disturbance features in the oil exploration area.

Many current urban waterlogging emergency scenario constructions overlook the factors that influence users’ concerns, rendering the resulting scenarios less adaptable to various user types and hindering efficient comprehension of waterlogging risks. The objective of this study is to link user concerns with elements of waterlogging emergency scenarios and then proposes a model accommodating various user groups to enhance scenario applicability. Initially, we established a user classification system for urban waterlogging emergencies. Subsequently, we extracted relevant topic keywords from social media and emergency plans, aligning them with different user groups to address their specific concerns. Additionally, we expanded the geographical elements (time, location, people, things, events, and phenomena) of the scenario to encompass its constituent components. Utilising domain ontology, we developed a mapping model between user concerns and scenario elements. Finally, this study employed both prototype system development and questionnaire validation to assess the efficacy of the user model. The validation for mean values, include Accuracy 0.923, Completeness 0.945, Satisfaction 0.879, and model’s effectiveness is 91.7 %. The results indicate that the model has high reliability and could effectively expand the user universality of urban waterlogging emergency scenarios.

Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geo-environmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.

Deep learning methods are widely used in building information extraction from remote sensing images (RSIs). However, this task still faces great challenges. First, it is difficult to perform accurate boundary localization due to the complex contextual relationship of the building boundary area. Second, the heterogeneity caused by different materials on building tops, as well as environmental factors such as climate and vegetation, complicates the extraction of top information. Finally, given the complexity of the RSIs, the accuracy and generalization of the existing method still need to be further enhanced. This study introduces the boundary enhancement and multiscale refinement fusion network to overcome these challenges. The boundary-aware self-attention module is initially proposed to refine the network's boundary detection capabilities. It applies the distance transform algorithm across both channel and spatial dimensions, reducing boundary fluctuation and enhancing the accuracy of building boundary extraction. Subsequently, we present the multiscale refined fusion module to resolve discontinuities within buildings caused by rooftop obstructions. This module effectively merges high and low-level features, overcoming information gaps through the strategic integration of multiscale data. To demonstrate the efficacy of our methodology, we conducted comprehensive experiments and analyses using the WHU Building Dataset and the Inria Aerial Image Labeling Dataset, both known for their complexity. Our method surpassed 16 contemporary state-of-the-art techniques, achieving IoU scores of 91.15% and 79.52% for each dataset, respectively, marking the highest accuracy levels reported. Furthermore, in-depth discussions on efficiency, generalizability, and ablation studies emphasized our method's robustness and adaptability.

Effective restoration strategies play a crucial role in mitigating the environmental impact of mining and colliery activities while promoting ecological resilience and rejuvenating ecosystem services. However, many organizations find it challenging to understand and balance their efforts in restoring degraded lands. For example, their restoration plans lack clarity and overlook relevant ecosystem services. This study reviews and focuses on the potential restoration of ecosystem services at TATA Steel's Noamundi Iron Ore Mine and West Bokaro Colliery to contribute to Sustainable Development Goals (SDGs), particularly SDG-15, for localization. The approach involved assessing the number of preventive measures being implemented to restore a particular ecosystem service. Moreover, the potential of each preventive measure is to restore that ecosystem service. The findings underscore the significance of preventive measures and comprehensive restoration plans in enhancing carbon sequestration, soil fertility, habitat creation, and genetic diversity conservation. Our results showed that the impact scores and ranks of various ecosystem services demonstrate the positive effects of restoration efforts, emphasizing the importance of reestablishing forests, restoring water bodies and wetlands, and allocating land for agriculture and public use. The research provides valuable insights for decision-makers in developing sustainable land management strategies, ensuring biodiversity conservation and local communities' well-being. By prioritizing ecosystem services in restoration initiatives, stakeholders can contribute to the sustainable management of natural resources and foster a harmonious coexistence between human activities and the environment.

Risk communication seeks to develop a shared understanding of disaster among stakeholders, thereby amplifying public awareness and empowering them to respond more effectively to emergencies. However, existing studies have overemphasized specialized numerical modelling, making the professional output challenging to understand and use by non-research stakeholders. In this context, this article proposes a 3D virtual geographic environment for flood representation towards risk communication, which integrates flood modelling, parallel computation, and 3D representation in a pipeline. Finally, a section of the Rhine River in Bonn, Germany, is selected for experiment analysis. The experimental results show that the proposed approach is capable of flood modelling and 3D representation within a few hours, the parallel speedup ratio reached 6.45. The intuitive flood scene with 3D city models is beneficial for promoting flood risk communication and is particularly helpful for participants without direct experience of floods to understand its spatiotemporal process. It also can be embedded in the Geospatial Infrastructure Management Ecosystem (GeoIME) cloud application for intelligent flood systems.

This groundbreaking research makes a contribution to climate change adaptation studies by filling a crucial knowledge gap related to the precise evaluation of mangrove biomass—an essential element influencing the future trends of coastal ecosystems. Specifically, the study concentrates on the Hara Biosphere Reserve (HBR) on the coasts of the Persian Gulf (PG), aiming to generate precise maps of mangrove biomass. The methodological approach involves a comprehensive analysis, including the utilization of Landsat imagery to establish an NDVI map of mangroves, the application of the Cumulative Sum (CUSUM) method to determine a threshold NDVI value for distinguishing between tall and dwarf mangroves, and the subsequent mapping of their distribution in both coastal and island zones. Additionally, the study calculates above-ground biomass (AGB) and below-ground biomass (BGB) values in sample plots, develops a regression relationship between biomass and NDVI values, and integrates maps of tall and dwarf mangrove extent with spatial variations in AGB and BGB. Noteworthy outcomes include the identification of a threshold NDVI value (0.63) for distinguishing mangrove types and revealing distinct biomass values for mangroves in both coastal and island zones. Significantly, tall mangroves positioned on the seaward edges exhibit higher biomass in both zones than dwarf mangroves. These findings shed light on the potential exacerbation of climate change impacts, such as rising sea levels and changing tidal range on mangroves of the coasts of the PG due to the heightened productivity and specific spatial distribution of tall mangroves. Recognizing these structural characteristics and production disparities is crucial for developing effective climate change adaptation programs. Integrating such insights into management strategies is emphasized as pivotal for enhancing the efficiency and success of these programs, presenting a robust solution for protecting mangroves in diverse coastal areas.

Reservoirs are highly relevant infrastructure assets, and now, more than ever, they play an essential role in society’s welfare and national security. Their importance is related to regional socioeconomic development due to their capacity to store water for different uses, such as human consumption, agricultural irrigation, flood control, and hydroelectric energy production, among other important services. However, many reservoirs are reaching the end of their period of life, and others are showing undesired displacements and cracking. Four 3D surveys were conducted on a reservoir that serves the Metropolitan Area of Monterrey City in Mexico. These surveys were carried out over a period of 5 years using GNSS observation to assist in understanding the actual dam kinematics, i.e., the behavior of its longitudinal and transversal displacements and the possible correlation with the reservoir level. The high-precision leveling and close-range remote sensing data were assessed and then mapped. The high-precision geodetic and leveling techniques allowed us to locate and measure 84 established permanent control points with errors of about ± 0.003 m. The mapping of displacements was made possible by modeling the positive and negative translations. The highest uplifts (11 mm) occurred at the left riverbank, and the highest subsidences (−5 mm) occurred along the downstream piers from the middle of the dam crest to the right riverbank. A ground laser scanner (GLS) produced 3D digital models with geometrical and radiometric characteristics, detecting displacements among the dam crest elements. The synergy of GNSS and high-leveling techniques allows the possibility to measure displacements, while the use of geographical information system (GIS) and geomatic techniques allows a better visualization through 2D and 3D maps validated using traditional topographical methods.

To generate an orthophoto mosaic from a collection of aerial images, the original images are first orthorectified individually using a Digital Surface Model (DSM). Then, they are stitched together along some determined seamlines to form the orthophoto mosaic. Determining appropriate seamlines is a critical process, as it affects the visual and geometric quality of the results. The stitching process can usually be done in frame-to-frame or multi-frame modes. Although the latter is more efficient, both still involve a lot of pre-processing, such as creating individual orthophotos, image registration, and overlap extraction. This paper presents a novel coarse-to-fine approach that directly determines the seamline network without such pre-processing. Our method has been specifically applied for UAV photogrammetry projects where, due to the large number of images and the corresponding overlaps, the orthophoto mosaic generation can be very challenging and time-consuming. We established the seamlines simultaneously for all the images through a two-step process. First, a DSM was generated, and a low-resolution grid was overlayed. Then, for each grid point, an optimal image was selected. Then, the grid cells are grouped into polygons based on their corresponding optimal image. Boundaries of these polygons established our seamline network. Thereafter, to generate the orthophoto mosaic, we overlayed a higher/full resolution grid on the top of the DSM, the optimal image of each point of which was quickly identified via our low-resolution polygons. In this approach, not only seamlines were automatically generated, but also were the need for the creation, registration, and overlap extraction of individual orthophotos. Our method was systematically compared with a conventional frame-to-frame (CF) technique from different aspects, including the number of double-mapped areas, discontinuities across the seamlines network, and the amount of processing time. The outcomes revealed a 46% decrease in orthophoto generation time and a notable reduction in the number of double-mapped areas, sawtooth effects, and object discontinuities within the constructed orthophoto mosaic.

Climate change has resulted in severe landslides in Maskun, Iran. This study evaluates and monitors the displacement caused by the landslide mass in Bam using Interferometry Synthetic Aperture Radar (DInSAR) and Persistent Scatterer Interferometry (PSI) techniques, as well as identifies relationships between climatic conditions and mass displacement. Temperature and precipitation data from 2007 to 2019 were combined with satellite images and the DInSAR method was used to determine the mass displacement differences after selecting eighteen radar images from the ASAR sensor of the ENVISAT satellite. Additionally, Sentinel 1 satellite images were acquired and analyzed using the PSI method from November 5, 2014, to June 24, 2019. The highest displacement level at the surface of the Maskun landslide mass was then extracted. The ASAR images show a monthly displacement rate of 7.3 mm. The smallest displacement, on the other hand, occurred between May and September 2009, at a rate of 3.1 mm/month. PSI results also revealed that the maximum Line Of Sight (LOS) velocities detected by PSI are −64.5 mm/yr (away from the satellite) and 32.45 mm/yr (toward the satellite). Rainfall is one of the main triggers for increasing the deformation of the Maskun landslide according to the time-series analysis.

With the continuous advancement of deep learning technology, researchers have made further progress in the hyperspectral image (HSI) classification domain. We propose a double-branch multi-scale residual network (DBMSRN) framework for HSI classification to improve classification accuracy and reduce the number of required training samples. The DBMSRN consists of two branches designed to extract spectral and spatial features from the HSI. Thus, to obtain more comprehensive feature information, we extracted additional local and global features at different scales by expanding the network width. Moreover, we also increased the network depth to capture deeper feature information. Based on this concept, we devise spectral multi-scale residuals and spatial multi-scale residuals within a double-branch architecture. Additionally, skip connections are employed to augment the context information of the network. We demonstrate that the proposed framework effectively enhances classification accuracy in scenarios with limited training samples through experimental analysis. The proposed framework achieves an overall accuracy of 98.67%, 98.09%, and 96.76% on the Pavia University (PU), Kennedy Space Center (KSC), and Indian Pines (IP) datasets, respectively, surpassing the classification accuracy of existing advanced frameworks under identical conditions.

The gravity recovery and climate experiment (GRACE) provides an unprecedented opportunity to detect the spatial and temporal variation of the terrestrial water storage (TWS) for regional to continental scales. However, the GRACE system's coarse temporal resolution (∼monthly) and data discontinuity missing perplexed the TWS research during the operation. In this article, the data assimilation (DA) method was employed to integrate the vertical deformation obtained from the small baseline subset (SBAS) InSAR processing into the NASA catchment land surface model (CLSM), which improved the estimation of the TWS. First, we used a one-dimensional ensemble Kalman filter for DA research to estimate the TWS in Dali Prefecture, southwestern China. Finally, we compared the estimated TWS with the GRACE-based TWS from December 2, 2018 to January 21, 2021. The unbiased root-mean-square of the open loop (OL; without DA) method and the SBAS-InSAR DA method are 61 mm and 30 mm in Dali Prefecture, respectively. Results revealed that the numerical difference between the estimated TWS and the GRACE TWS retrievals was significantly decreased by the SBAS-InSAR DA method than the OL method. In addition, the temporal resolution of the SBAS-InSAR DA-based TWS was improved to 12 days compared with GRACE-based TWS. Furthermore, we recovered the discontinuous deletion and blank of GRACE-based TWS from 2015 to 2018 by the SBAS-InSAR DA method.

Intelligent tunnel engineering requires accurate and comprehensive digital twin models to represent complex geological environments. The digital twin model of tunnel geological environment has multi-level and diversified in-depth applications such as problem diagnosis, risk assessment, trend prediction, emergency response, etc. Since tunnel construction is a long-term continuous dynamic spatial–temporal progress, the tunnel face constantly advances during excavations. Thus, the geological conditions of the construction surface and surrounding areas are continuously revealed and updated, which causes the digital twin model to have a very high update frequency of the geological information in tunnel engineering. The existing methods fail to represent and update the geological environment comprehensively and in real-time, especially for those long deep tunnels with the complicated topology of geological structures and the heterogeneous distribution of internal properties. Due to the digital twin application having a high-level requirement of completeness, accuracy, and timeliness, 3D modeling of tunnel geological environments has remained challenging within digital twin application contexts. This study aims to deliver a solution that efficiently represents and updates the geological structures and internal non-uniform properties of tunnel engineering. Since dense voxels can represent inner intricate heterogeneous property information, sparse voxels can efficiently represent complex structural information. Meanwhile, voxels using Volumetric Dynamic B + trees (VDB) data structure are easy to integrate and update. However, according to the characteristics of long strip distribution of tunnel geological environments, directly using the VDB will still cause an efficiency problem. Therefore, this study proposes an efficient linear segmentation-based multi-level voxel representation method for the prolonged deep tunnel geological environment using the VDB data structure to support dynamic updates. The typical Mountain Tunnel of China is employed for experimental analysis to validate the proposed method, and four representative data with different modalities are integrated into the digital twin model. The results demonstrate that spatial efficiency augments 28.49% after segmentation, and data access with O(1) time complexity supports efficiently dynamic updates.

Abstract This study presents an enhanced analysis of the subsidence rates and their effects on Mexico City. As a result of excess water withdrawal, Mexico City is experiencing subsidence. We integrated and analyzed Interferometric Synthetic Aperture Radar (InSAR), Continuous Global Positioning Systems (CGPS), and optical remote sensing data to analyze Mexico City’s subsidence. This study utilized 52 ENVISAT-ASAR, nine GPS stations, and one Landsat ETM+ image from the Mexico City area to understand better the subsidence rates and their effects on Mexico City’s community. The finding of this study reveals a high amount of correlation (up to 0.98) between two independent geodetic methods. We also implemented the Support Vector Machine (SVM) analysis method based on Landsat ETM+ image to classify Mexico City’s population density. We used SVM to compare Persistent Scatterer Interferometry (PSI) subsidence rates with the buildings’ distribution densities. This integrated study shows that the fastest subsidence zone (i.e., areas greater than 100 mm/yr), which falls into the above-mentioned temporal baseline, occurs in high and moderate building distribution density areas.

Developing a system for emergency response and rescue to find injured people within images has been an interest to many researchers. The key challenge is to define a proper feature to describe the human body's appearance. Various features are often extracted from low-level data, such as texture and colour (Zhang et al., Sensors. 19(5):1005, 2020). One of the strong features is the Sector Ring Histogram of Oriented Gradients (SRHOG) that has been successfully applied to human detection tasks. Despite good accuracy in finding humans, an SRHOG detection method produces a large amount of false-negative labels. Locating an injured body after a disaster in drone images using an imaging camera remained a challenge. This study presents a new extension to SRHOG, so-called AdSRHOG, to reduce the number of false labels. In our approach, the gradient filters used by SRHOG defined adaptively depending on the corresponding pixel location The proposed feature was used the Support Vector Machine (SVM) algorithm to detect humans on drone images. The experiments showed a significant improvement of up to 54.3% in reducing the false labels. It was also found that the overall accuracy of the human detection process had a notable improvement of 13.1% over a traditional SRHOG detection technique.

Algae blooms have been a serious problem in coastal and inland water bodies across Canada. The temporal and spatial variability of algae blooms makes it difficult to use in situ monitoring of the lakes. This study aimed to evaluate the potential of Sentinel-3 Ocean and Land Color Instrument (OLCI) and Sentinel-2 Multispectral Instrument (MSI) data for monitoring algal blooms in Lake Erie. Chlorophyll-a (Chl-a)-related products of these sensors were tested by using the Great Lakes Chl-a NOAA’s monitoring data over summer 2016 and 2017, respectively. Our results show that while fluorescent light height (FLH) algorithm and models are limited to lakes with Chl-a 20 mg/m3 and Sentinel-2 MCI for Chla > 8 mg/m3. Top of atmosphere (TOA) radiances showed a significantly better correlation with in situ data compared to TOA reflectance, which may be related to the poor pixel identification during the process of pixel flagging affected by the complexity of Case-2 water. Sentinel-2 MCI achieves better performance for Chl-a retrieval (R2 = 0.92) than the existing methods. However, the FLH algorithms outperformed negative reflectance due to the shift of reflectance peak to longer wavelengths along with increasing Chl-a values.

Reversible Data Hiding in Encrypted Images (RDH-EI) has gained much popularity in the field of signal processing and cloud computing. In this study, we propose a high-capacity RDH-EI scheme using multi-layer embedding. It ensures that after extraction of the embedded additional data from the marked encrypted image, the original image is recovered completely. Broadly, the method comprises three parties: the content owner, the data hider/ cloud owner, and the receiver. In the beginning, the image is encrypted by the content owner using encryption based on the chaotic behaviour of PWL-memristor and sent to the data hider. At the data hider's side, even-odd value embedding technique is applied in multiple layers of the encrypted image to increase the embedding capacity. Meanwhile, the auxiliary information consisting of location map (LM) for each layer and the number of layers, to be used in the recovery phase, is recorded during the embedding. After completing the embedding procedure, permutation operation is applied on the marked encrypted image to prevent the perceptual information leakage. Finally, at the receiver's side the embedded additional data and the original image is recovered losslessly with the help of valid keys and shared auxiliary information. From the experimental results, it is observed that the quality of the directly decrypted image is 51.0 dB or above. The advantage of the proposed scheme is that the PSNR is almost same for each layer, i.e. even for the n number of layers used, the PSNR is 51.0 dB or above. Moreover, we applied chosen plaintext attack to test the encryption function and in case of any cropping attack on the marked encrypted image during the transfer, the cover image can be recovered with less distortion.

Large-scale crowd evacuation simulation in virtual reality is of great significance for emergency response. However, the existing large-scale crowd evacuation simulation methods are challenging to apply to the virtual reality scene. Thus, the efficiency of large-scale crowd rendering in virtual reality needs to be improved. Here we proposed a chain navigation grid for virtual reality large-scale crowd evacuation simulation. The objective of this study was to apply traditional cellular automata crowd evacuation simulation algorithms to virtual reality scenes through a chain navigation grid. Combined with the characteristics of crowd movement, we proposed the vertex raster rendering method to efficiently visualize large-scale crowds in virtual reality scenes. Finally, we constructed two evacuation scenes and carried out experimental analyses. The results show that the chain evacuation navigation grid can transform the discrete crowd movement of cellular automata into continuous crowd movement in virtual reality and can connect multiple cellular automata corresponding to multi-floors. Compared with the evacuation results of Pathfinder software, the chain evacuation navigation can add the factor of environmental familiarity. Therefore, this study improved evacuation simulation by increasing about 6% efficiency in time and computing process. Compared with the traditional crowd evacuation rendering method, the vertex raster rendering method increases the maximum number of people rendering from 2 times to 31 times.

The Niexia slope, located in Danba County, Sichuan Province, China, with steep slope terrain and dense vegetation coverage, has a height difference of about 3002 m. A traditional manual survey cannot be performed here, and single remote sensing technology is not comprehensive enough to identify potential landslides on such high and steep slopes. In this paper, an integrated approach with multi-remote sensing techniques was proposed to identify potential landslides of the Niexia slope, which combined Interferometry Synthetic Aperture Radar (InSAR), airborne Light Detection and Ranging (LiDAR), and optical remote sensing technologies. InSAR technology was used to monitor the small displacements of the whole slope, and three potential landslides on Niexia slope were identified. The maximum cumulative displacement reached up to 11.9 cm over 1 year. Subsequently, high-resolution optical remote sensing images acquired by remote sensing satellites and a Digital Elevation Model (DEM) without vegetation influence obtained by LiDAR were used to finely interpret the sign of landslide micro-geomorphology and to determine the potential landslide geometry boundaries. As a result, four and nine potential landslides with landslide micro-geomorphic features were identified, respectively. Finally, the identification results of the three techniques were fused and analyzed to assess the potential landslides on the Niexia slope. We compared the results from multi-remote sensing technologies, showing that the three techniques have advantages and disadvantages in terms of monitoring objects, monitoring range, and monitoring accuracy. The integrated use of these three technologies can identify and monitor potential landslides more comprehensively, which could play an important role in the future.

Determining the effects of simultaneously operating climate change and anthropogenic impacts on mangroves remains a challenge for the Persian Gulf and the Gulf of Oman. The objective of this study was to spatially quantify the spatial extents and biomass of dwarf and tall mangroves in response to drought intensities (Standardized Precipitation Index, SPI), sea-level rise (EC), land use/land cover change (LULC), and surface runoff and freshwater inflow to upstream mangrove catchments along the coasts of southern Iran over a 35-year period (1986–2020). The study drew on a long-term time series of 105 Landsat satellite images, maritime data, rainfall data, field surveys, and models to quantify independent variables (i.e., the WetSpass-M model to quantify surface runoff) and mixed models analysis to determine the important drivers of change. Although mixed model analysis indicated that sea-level rise was the main climate change driver of the development of spatial extents and biomass of both tall and dwarf mangroves, its influence was embedded in the larger temporal climate context of the region. Our findings show that spatial extents and biomass development are neither tightly coupled nor developed temporally or directionally synchronously in dwarf and tall mangroves. This study also shows a precipitous decline in rainfall amounts, SPI, and freshwater runoff volumes starting in 1998. The ensuing long-term drought that is still ongoing but decreased slightly in intensity in the last few years shaped the overall spatiotemporal development pattern of the mangrove structures over the entire study period. The correlation between biomass and the independent variables was similar for dwarf and tall mangroves and was positive with SPI and runoff amounts and negative with EC in all three study sites.

Gaofen-3 is the first Chinese spaceborne C-band SAR satellite with multiple polarizations. The Gaofen-3 satellite’s data has few applications for monitoring landslides at present, and its potential for use requires further investigation. Consequently, we must evaluate and analyze the landslide interference quality and displacement monitoring derived from the Gaofen-3 SAR satellite’s data, particularly in high and steep, mountainous regions. Based on the nine Gaofen-3 SAR datasets gathered in 2020–2021, this study used DInSAR technology to track landslide displacement in Mao County, Sichuan Province, utilizing data from Gaofen-3. Our findings were compared to SENTINEL-1 and ALOS-2 data for the same region. This study revealed that due to its large spatial baseline, Gaofen-3’s SAR data have a smaller interference effect and weaker coherence than the SENTINEL-1 and ALOS-2 SAR data. In addition, the displacement sensitivity of the Gaofen-3 and SENTINEL-1 data (C-band) is higher than that of the ALOS-2 data (L-band). Further, we conducted a study of observation applicability based on the geometric distortion distribution of the three forms of SAR data. Gaofen-3’s SAR data are very simple to make layover and have fewer shadow areas in hilly regions, and it theoretically has more suitable observation areas (71.3%). For its practical application in mountainous areas, we introduced the passive geometric distortion analysis method. Due to its short incidence angle (i.e., 25.8°), which is less than the other two satellites’ SAR data, only 39.6% of the Gaofen-3 SAR data in the study area is acceptable for suitable observation areas. This study evaluated and analyzed the ability of using Gaofen-3’s data to monitor landslides in mountainous regions based on the interference effect and observation applicability analysis, thereby providing a significant reference for the future use and design of Gaofen-3’s data for landslide monitoring.

Mobile robots lack a driver or a pilot and, thus, should be able to detect obstacles autonomously. This paper reviews various image-based obstacle detection techniques employed by unmanned vehicles such as Unmanned Surface Vehicles (USVs), Unmanned Aerial Vehicles (UAVs), and Micro Aerial Vehicles (MAVs). More than 110 papers from 23 high-impact computer science journals, which were published over the past 20 years, were reviewed. The techniques were divided into monocular and stereo. The former uses a single camera, while the latter makes use of images taken by two synchronised cameras. Monocular obstacle detection methods are discussed in appearance-based, motion-based, depth-based, and expansion-based categories. Monocular obstacle detection approaches have simple, fast, and straightforward computations. Thus, they are more suited for robots like MAVs and compact UAVs, which usually are small and have limited processing power. On the other hand, stereo-based methods use pair(s) of synchronised cameras to generate a real-time 3D map from the surrounding objects to locate the obstacles. Stereo-based approaches have been classified into Inverse Perspective Mapping (IPM)-based and disparity histogram-based methods. Whether aerial or terrestrial, disparity histogram-based methods suffer from common problems: computational complexity, sensitivity to illumination changes, and the need for accurate camera calibration, especially when implemented on small robots. In addition, until recently, both monocular and stereo methods relied on conventional image processing techniques and, thus, did not meet the requirements of real-time applications. Therefore, deep learning networks have been the centre of focus in recent years to develop fast and reliable obstacle detection solutions. However, we observed that despite significant progress, deep learning techniques also face difficulties in complex and unknown environments where objects of varying types and shapes are present. The review suggests that detecting narrow and small, moving obstacles and fast obstacle detection are the most challenging problem to focus on in future studies.

Atmospheric effects are among the primary error sources affecting the accuracy of interferometric synthetic aperture radar (InSAR). The topography-dependent atmospheric effect is particularly noteworthy in reservoir areas for landslide monitoring utilizing InSAR, which must be effectively corrected to complete the InSAR high-accuracy measurement. This paper proposed a topography-dependent atmospheric correction method based on the Multi-Layer Perceptron (MLP) neural network model combined with topography and spatial data information. We used this proposed approach for the atmospheric correction of the interferometric pairs of Sentinel-1 images in the Baihetan dam. We contrasted the outcomes with those obtained using the generic atmospheric correction online service for InSAR (GACOS) correction and the traditional linear model correction. The results indicated that the MLP neural network model correction reduced the phase standard deviation of the Sentinel-1 interferogram by an average of 64% and nearly eliminated the phase-elevation correlation. Both comparisons outperformed the GACOS correction and the linear model correction. Through two real-world examples, we demonstrated how slopes with displacements, which were previously obscured by a significant topography-dependent atmospheric delay, could be successfully and clearly identified in the interferograms following the correction by the MLP neural network. The topography-dependent atmosphere can be better corrected using the MLP neural network model suggested in this paper. Unlike the previous model, this proposed approach could be adjusted to fit each interferogram, regardless of how much of the topography-dependent atmosphere was present. In order to improve the effectiveness of DInSAR and time-series InSAR solutions, it can be applied immediately to the interferogram to retrieve the effective displacement information that cannot be identified before the correction.

The use of the conventional interferometric synthetic aperture radar (InSAR) to generate digital elevation models (DEMs) always encounters phase unwrapping (PU) errors in areas with a sizeable topographic gradient. Range split-spectrum interferometry (RSSI) can overcome this issue; however, it loses the spatial resolution of the SAR image. We propose the use of the RSSI-assisted In-SAR-derived DEM (RID) method to address this challenge. The proposed approach first applies the RSSI method to generate a prior DEM, used for simulating terrain phases. Then, the simulated terrain phases are subtracted from the wrapped InSAR phases to obtain wrapped residual phases. Finally, the residual phases are unwrapped by the minimum cost flow (MCF) method, and the unwrapped residual phases are added to the simulated phases. Both the simulated and TerraSAR-X data sets are used to verify the proposed method. Compared with the InSAR and RSSI methods, the proposed approach can effectively decrease the PU errors of large gradients, ensure data resolution, and guarantee the DEM’s accuracy. The root mean square error between the topographic phase simulated from the real DEM and the topographic phase generated from the proposed method is 2.22 rad, which is significantly lower than 6.60 rad for InSAR, and the improvement rate is about 66.36%.

Understanding and assessing the landslides is immensely important to scientists and policy-makers alike. Remote sensing conventional methods and modelling approaches in geographical information system (GIS) tend to be limited to authentic quality and spatial coverage. This study aims to identify challenges and quality of landslides assessment based on remotely sensed data by the mean of existing works of the literature and practices we attempted in the Zagros and Alborz Mountains in Iran and the red rock shield Lake, China. Remote sensing data for landslides investigations require a high-resolution digital elevation model (DEM) from either aerial photography, satellite images, airborne laser scanning (ALS) or terrestrial Light Detection and Ranging (LiDAR) derived in order to enable a reliable and valid output performance. This paper presents weaknesses and strengths of the existing remote sensing techniques in the last decades and further provides recommendations for a reliable approach to the future landslide studies. Also, this study estimates the operational use of state-of-the-art technologies (i.e. unmanned airborne vehicle (UAV)) for landslides assessment in the near future that is a realistic ambition if we can continue to build on recent achievements. However, this paper does not deliver a detailed methodology of a DEM generation from the remote sensing approach for landslides assessment.

This study presents a novel workflow for automated Digital Terrain Model (DTM) extraction from Airborne LiDAR point clouds based on a convolutional neural network (CNN), considering a transfer learning approach. The workflow consists of three parts: feature image generation, transfer learning using ResNet, and interpolation. First, each point is transformed into a featured image based on its elevation differences with neighboring points. Then, the feature images are classified into ground and non-ground using ImageNet pretrained ResNet models. The ground points are extracted by remapping each feature image to its corresponding points. Last, the extracted ground points are interpolated to generate a continuous elevation surface. We compared the proposed workflow with two traditional filters, namely the Progressive Morphological Filter (PMF) and the Progressive Triangulated Irregular Network Densification (PTD). Our results show that the proposed workflow establishes an advantageous DTM extraction accuracy with yields of only 0.52%, 4.84%, and 2.43% for Type I, Type II, and the total error, respectively. In comparison, Type I, Type II, and the total error for PMF are 7.82%, 11.60%, and 9.48% and for PTD 1.55%, 5.37%, and 3.22%, respectively. The root means square error (RMSE) for the 1 m resolution interpolated DTM is only 7.3 cm. Moreover, we conducted a qualitative analysis to investigate the reliability and limitations of the proposed workflow.

Interferometric Synthetic Aperture Radar (InSAR) monitors surface change and displacement over a large area with millimeter-level precision and meter-level resolution. Anar fault, with a length of ~200 km, is located in central Iran. Recent seismological studies on the fault indicated that it is approaching the end of its seismic cycle. Although a large earthquake is imminent, the mechanism of the fault is not well understood. Therefore, understanding and discovering the mechanism of Anar fault remains a challenge. Here, we present an approach of displacement fault analysis utilizing a combination of InSAR data obtained from the persistent scatterer interferometry (PSI) method and 178 Sentinel-1 images (ascending and descending) (2017–2020). We incorporated groundwater samples from 40 wells, radon concentration anomaly mapping, Global Positioning System (GPS), and 3D displacement measurement acquired over four years (2016–2020). We investigated and monitored the deformation of the fault plate’s behavior over the last three years (2017–2020) to explore new evidence and signature of displacement. The results show that the time series analysis in the fault range has an increasing displacement rate in all dimensions. We observed that the line-of-sight (LOS) displacement rate varied from −15 mm to 5 mm per year. Our calculations show that the E–W, N–S, and vertical displacement rates of the fault blocks are 2 mm to −2 mm, 6 mm to −6 mm, and 2 mm to −4 mm per year, respectively. An anomaly map of the radon concentration shows that the complete alignment of the high concentration ranges with the fault strike and the radon concentration increased on average from 23.85 Bq/L to 25.30 Bq/L over these three years. Therefore, we predict rising the radon concentration is due to the increase in activity which resulted in a deformation. Finally, our findings show that the Anar fault is an oblique and right-lateral strike-slip with a normal component mechanism. We validated the proposed method and our results by comparing the GPS field data and PSI measurements. The root mean square error (RMSE) of the PSI measurement is estimated to be 0.142 mm. Based on the supporting evidence and signature, we conclude that the Anar fault activity increased between 2017 and 2020.

Abstract This paper expands the previous efforts by other researchers to present a quantitative and deterministic approach for terrain analysis. This study evaluates both spatial and temporal factors contributing landslides utilizing Light Detection and Ranging (LiDAR) point clouds in conjunction with the frequency ratio model (PFR) than has previously been used in the Alborz Mountains. The study area is Marzan Abad of the Alborz Mountain in Iran. The significance of this study is the performance of a high-resolution digital elevation model (DEM) derived from LiDAR point clouds in order to provide detailed information in improving landslide susceptibility evaluation. This study discusses how we improve the quality of landslide susceptibility evaluation. We apply the PFR model to consider the effect of landslide-related factors associated with Google Earth’s high-resolution images and field observations. The LiDAR point cloud data and GIS-based analysis have allowed performing high quality ways of landslide hazard assessments using inventory dataset as compared to previous studies. We contributed an improved landslide inventory map of the Mazandaran Province. We used image elements interpretation from the available ASTER DEM (30 m), LiDAR-DEM (5 m), and the Google Earth high spatial resolution images in conjunction with the field observations. This study evaluates factors such as geology, geomorphology, landuse, soil, slope, and distance from roads and drainage to represent, manipulate, and analyze factors. Also, we evaluated the performance success of the rate curve of landslides susceptibility. The results have indicated an improved landslide susceptibility map from LiDAR-derived DEMs implementing the PFR model with 92.59% of accuracy performance as compared to the existing data and previous studies in the same region. Furthermore, this study reveals that all considering factors have relatively positive effects on the landslides susceptibility mapping in the study, however, the most effective factor on the landslide occurrence is the lithology with 13.7%.

The presence of noise in remote sensing satellite images may cause limitations in analysis and object recognition. Noise suppression based on thresholding neural network (TNN) and optimization algorithms perform well in de-noising. However, there are some problems that need to be addressed. Furthermore, finding the optimal threshold value is a challenging task for learning algorithms. Moreover, in an optimization-based noise removal technique, we must utilize the optimization algorithm to overcome the problem. These methods are effective at reducing noise but may blur some parts of an image, and they are time-consuming. This flaw motivated the authors to develop an efficient de-noising method to discard un-wanted noises from these images. This study presents a new enhanced adaptive generalized Gaussian distribution (AGGD) threshold for satellite and hyperspectral image (HSI) de-noising. This function is data-driven, non-linear, and it can be fitted to any image. Applying this function provides us with an optimum threshold value without using any least mean square (LMS) learning or optimization algorithms. Thus, it is possible to save the processing time as well. The proposed function contains two main parts. There is an AGGD threshold in the interval [−σn, σn], and a new non-linear function behind the interval. These combined functions can tune the wavelet coefficients properly. We applied the proposed technique to various satellite remote sensing images. We also used hyperspectral remote sensing images from AVIRIS, HYDICE, and ROSIS sensors for our experimental analysis and validation process. We applied peak signal-to-noise ratio (PSNR) and Mean Structural Similarity Index (MSSIM) to measure and evaluate the performance analysis of different de-noising techniques. Finally, this study shows the superiority of the developed method as compared with the previous TNN and optimization-based noise suppression methods. Moreover, as the results indicate, the proposed method improves PSNR values and visual inspection significantly when compared with various image de-noising methods.

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

There are frequently interactions between active folds and major rivers (mean annual water discharges > 70 m3s−1). The major river may incise across the fold, to produce a water gap across the fold, or a bevelling (or lateral planation) of the top of the fold. Alternatively, the major river may be defeated to produce a diversion of the river around the fold, with wind gaps forming across the fold in some cases, or ponding of the river behind the fold. Why a river incises or diverts is often unclear, though influential characteristics and processes have been identified. A new scheme for investigating fold-river interactions has been devised, involving a short description of the major river, climate, and structural geology, and 13 characteristics of river and fold geomorphology: (1) Channel width at location of fold axis, w, (2) Channel-belt width at location of fold axis, cbw, (3) Floodplain width at location of fold axis, fpw, (4) Channel sinuosity, Sc, (5) Braiding index, BI, (6) General river course direction, RCD, (7) Distance from fold core to location of river crossing, C-RC, (8) Distance from fold core to river basin margin, C-BM, (9) Width of geological structure at location of river crossing, Wgs, (10) Estimate of erosion resistance of surface sediments/rocks and deeper sediments/rocks in fold, ERs, ERd, (11) Channel water surface slope at location of fold axis, s, (12) Average channel migration rate, Rm, (13) Estimate of fold total uplift rate, TUR. The first 10 geomorphological characteristics should be readily determinable for almost all major rivers using widely available satellite imagery and fine scale geological maps. This use of remote sensing allows a large number of major rivers to be investigated relatively easily, including those in remote or inaccessible areas, without recourse to expensive fieldwork. The last three geomorphological characteristics should be determinable for most major rivers where other data sources are available. This study demonstrates the methodology of this scheme, using the example of the major rivers Karun and Dez interacting with active folds in the foreland basin tectonic setting of lowland south-west Iran. For the rivers Karun and Dez (mean annual water discharges 575 m3s−1 and 230 m3s−1, respectively), it was found that geomorphological characteristics Nos. 2, 3 and 7 had statistically significant differences (p-value ≤ 0.05) between the categories of river incision across a fold and river diversion around a fold. This scheme should be used to investigate a variety of major rivers from across the globe. By comparing the same parameters for different major rivers, a better understanding of fold-river interactions will be achieved.

The world has experienced urban changes rapidly, and this phenomenon encourages authors to contribute to the United Nations sustainable development goals (SDGs) 2030 and geospatial information. This study presents a proposed algorithm of change detection and extracting the borders of buildings. This proposed algorithm provides a set of instructions to describe the method of solving the problem of how extracting the boundary of buildings from the light detection and ranging (LiDAR) input data incorporating with the firefly and ant colony algorithms. The method has used two different epochs to compare buildings and to identify the type of changes in selected buildings. These changes are based on the newly built or demolished buildings. We also used drone images and mask the region-based convolutional neural network (R-CNN) method to compare the results of roof extraction of buildings vs. the proposed algorithm. This study shows that the proposed algorithm identifies the changes of all buildings with higher accuracy of extracting border of buildings than the existing methods, successfully. This study also determines that the amount of root mean square error (RMSE) is 2.40 m2 when we use LiDAR. This proposed algorithm contributes to identifying rapidly changed buildings, and it is helpful for global geospatial information of urban management that can add best practice and solution toward the UN SDGs connectivity dilemma of urban settlement, resilience, and sustainability.

Masjed-i-Soleiman (MIS) is situated in the northern part of the Dezful embayment, which is in the Zagros fold–thrust belt with high seismic activities. MIS faces a shallow buried anticline, formed by the shallowest oilfield with a thick gas cap. The cap rocks of this oilfield are highly fractured, which has resulted in leakages from the gas cap. In this paper, we have used remote sensing techniques and image interpretation for the identification of the Niayesh, Lahbari, Andika and MIS fault zones in the studied area. Further, the study exploited seismic potential mapping using the remote sensing techniques. The relationships between the structural controls and localized gas leakage are assessed within the GIS environment. Additionally, field observation data corroborated that the leakages (and seepages) are smashed within the intersection of Niayesh and MIS fault zone, which belongs to the high fractured hinge zone of the MIS anticline. As a result, the reactivation of these active faults may cause large earthquakes with a maximum magnitude of between 6.23 < Ms < 7.05 (Richter scale) and maximum horizontal acceleration 0.26 < a < 0.55 g. Finally, the authors concluded that this anticipated earthquake may cause large scale fracturing of cap rocks, releasing a large volume of H2S gas from the uppermost layer of the reservoir.