DEM Data Research Articles

Strain and Deformation Analysis Using 3D Geological Finite Element Modeling with Comparison to Extensometer and Tiltmeter Observations

This study verifies the practicality of using finite element analysis for strain and deformation analysis in regions with sparse GNSS stations. A digital 3D terrain model is constructed using DEM data, and regional rock mass properties are integrated to simulate geological structures, resulting in the development of a 3D geological finite element model (FEM) using the ANSYS Workbench module. Gravity load and thermal constraints are applied to derive directional strain and deformation solutions, and the model results are compared to actual strain and tilt measurements from the Jiujiang Seismic Station (JSS). The results show that temperature variations significantly affect strain and deformation, particularly due to the elevation difference between the mountain base and summit. Higher temperatures increase thermal strain, causing tensile effects, while lower temperatures reduce thermal strain, leading to compressive effects. Strain and deformation patterns are strongly influenced by geological structures, gravity, and topography, with valleys experiencing tensile strain and ridges undergoing compression. The deformation trend indicates a southwestward movement across the study area. A comparison of FEM results with ten years of strain and tiltmeter data from JSS reveals a strong correlation between the model predictions and actual measurements, with correlation coefficients of 0.6 and 0.75 for strain in the NS and EW directions, and 0.8 and 0.9 for deformation in the NS and EW directions, respectively. These findings confirm that the 3D geological FEM is applicable for regional strain and deformation analysis, providing a feasible alternative in areas with limited GNSS monitoring. This method provides valuable insights into crustal deformation in regions with sparse strain and deformation measurement data.

A Case Study on the Integration of Remote Sensing for Predicting Complicated Forest Fire Spread

Forest fires can occur suddenly and have significant environmental, economic, and social consequences. The timely and accurate evaluation and prediction of their progression, particularly the spread speed in difficult-to-access areas, are essential for emergency management departments to proactively implement prevention strategies and extinguish fires using scientific methods. This paper provides an analysis of models for predicting forest fire spread in China and globally. Incorporating remote sensing (RS) technology and forest fire science as the theoretical foundation, and utilizing the Wang Zhengfei forest fire spread model (1983), which is noted for its broad adaptability in China as the technical framework, this study constructs a forest fire spread model based on remote sensing interpretation. The model improves the existing model by adding elevation an factor and optimizes the method for acquiring certain parameters. By considering regional landforms (ridge lines, valley lines, and slopes) and vegetation coverage, this paper establishes three-dimensional visual interpretation markers for identifying hotspots; the orientation of the hotspots can be identified to simulate the spread of the fire uphill, downhill, in the direction of the wind, left-level slope, and right-level slope. Then, the data of Sentinel-2 and DEM were used to invert the fuel humidity and slope of pixels in the fire line areas. The statistical inversion data from pixels, which replaced fixed-point values in traditional models, were utilized for predicting forest fire spread speed. In this paper, the model was applied to the case of a forest fire in Mianning County, Sichuan Province, China, and verified using high-time-resolution Himawari-8 data, Gaofen-4 data, and historical data. The results demonstrate that the direction and maximum speed of fire spread for the fire lines in Baifen Mountai, Jiaguer Villageand, Muchanggou, Xujiabaozi, and Zhaizigou are uphill, 16.5 m/min; wind direction, 17.32 m/min; wind direction, 1.59 m/min; and wind direction, 5.67 m/min. The differences are mainly due to the locations of the fire lines, moisture content of combustibles, and maximum slopes being different. Across the entire fire line area, the average rate of increase in the area of open flames within one hour was 3.257 hm2/10 min (square hectares per 10 min), closely matching the average increase rate (3.297 hm2/10 min) monitored by the Himawari-8 satellite in 10 min intervals. In contrast, conventional fixed-point fire spread models predicted an average rate of increase of 3.5637 hm2/10 min, which shows a larger discrepancy compared to the Himawari-8 satellite monitoring results. Moreover, when compared to the fire spot monitoring results from the Gaofen-4 satellite taken 54 min after the initial location of the fire line, the predictions from the RS-enabled fire spread model, which integrates remote sensing interpretations, closely matched the actual observed fire boundaries. Although the predictions from the RS-enabled fire spread model and the traditional model both align with historical data in terms of the overall fire development trends, the RS-enabled model exhibits higher reliability and can provide more accurate information for forest fire emergency departments, enabling effective pre-emptive measures and scientific firefighting strategies.

ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data

Extreme precipitation events often present significant risks to human life and property, making their accurate prediction an essential focus of current research. Recent studies have primarily concentrated on exploring the formation mechanisms of extreme precipitation. Existing prediction methods do not adequately account for the combined terrain and atmospheric effects, resulting in shortcomings in extreme precipitation forecasting accuracy. Additionally, the satellite data resolution used in prior studies fails to precisely capture nuanced details of abrupt changes in extreme precipitation. To address these shortcomings, this study introduces an innovative approach for accurately predicting extreme precipitation: the multimodal attention ConvLSTM-GAN for extreme rainfall nowcasting (ER-MACG). This model employs high-resolution Fengyun-4A(FY4A) satellite precipitation products, as well as terrain and atmospheric datasets as inputs. The ER-MACG model enhances the ConvLSTM-GAN framework by optimizing the generator structure with an attention module to improve the focus on critical areas and time steps. This model can alleviate the problem of information loss in the spatial–temporal convolutional long short-term memory network (ConvLSTM) and, compared with the standard ConvLSTM-GAN model, can better handle the detailed changes in time and space in extreme precipitation events to achieve more refined predictions. The main findings include the following: (a) The ER-MACG model demonstrated significantly greater predictive accuracy and overall performance than other existing approaches. (b) The exclusive consideration of DEM and LPW data did not significantly enhance the ability to predict extreme precipitation events in Zhejiang Province. (c) The ER-MACG model significantly improved in identifying and predicting extreme precipitation events of different intensity levels.

Integrating sentinel-2a imagery, DEM data, and spectral feature analysis for landslide detection via fully convolutional networks

Integrating sentinel-2a imagery, DEM data, and spectral feature analysis for landslide detection via fully convolutional networks

Geo-spatial assessment of geomorphic characteristics of Swat Valley, Pakistan

Morphometric analysis is essential for understanding drainage networks and landform evolution, particularly in complex mountainous regions like the Swat Valley. Accurate characterization of these features aids in water resource management and mitigating climate-induced challenges. The study employed GIS and remote sensing technologies, utilizing SRTM-30m DEM data to perform a comprehensive morphometric and hypsometric analysis. Key morphometric parameters such as basin size, stream order, bifurcation ratio, drainage density, circularity ratio, and elongation ratio were calculated by using Whitebox tool. Additionally, three-dimensional parameters including basin relief, roughness number, ruggedness index, and slope were analyzed. Hypsometric analysis was conducted to examine area-elevation relationships, using empirical formulas and contour data. The basin was found to be irregularly shaped, with an elongation ratio of 6.97 and a circulatory ratio of 0.3091, indicating an elongated form. Flow accumulation data highlighted areas of rapid and moderate water flow. Hypsometric analysis showed that 14 sub-basins were in an erosional stage, 8 were mature, and the remainder were older and more resistant to erosion. The overall hypsometric integral (HI) of 0.406 reflects moderate elevation variation across the basin. The study provides critical insights into the geomorphometric controls of the Swat Valley's drainage system, contributing to improved water resource management strategies. Understanding morphometric and hypsometric characteristics is vital for adapting to climate-induced risks and safeguarding water resources in mountainous landscapes.

Morphological Analysis of River Characteristics in Musi Rawas Utara Regency

Effective watershed management requires the efficient collection, storage, and use of runoff water, as well as the recharge of groundwater. This study will employ morphometric analysis to determine the characteristics of the rivers flowing through one of the districts of the Province of South Sumatera, Musi Rawas Utara (Muratara). Using DEM data and GIS software, this study conducted a quantitative-descriptive analysis to obtain quantitative information about the watershed. The acquired numbers are interpreted descriptively to provide a description of the watershed's characteristics and their implications for rivers in Muratara.The eighth-order rivers of Muratara Regency are classified as major rivers. A mean bifurcation ratio (Rb) of 3.98 indicates that geological features have no effect on the typical flat drainage pattern. On average, the drainage density of the watersheds was moderate, but a few locations had a very high drainage density range, indicating their erodibility. The basins of rivers with low circularity and elongation ratios (0.19 and 0.48, respectively) are elongated and have a steep slope. The morphometric research reveals that the Muratara rivers have abundant water resources and are also predicted to experience numerous water-related disasters. Using morphometric knowledge, however, the local government could develop mitigation and planning systems for river management. Future measures must include ensuring that adequate drainage systems are in place and that all construction adheres to the principles of proper land management.

Wetland mapping in the Liaohe River Estuary using multi-source remote sensing image feature selection

ABSTRACT As a crucial node in the East Asia–Australasia migratory bird network, the Liaohe River Estuary Wetland (LREW) contributes significantly to coastal ecological balance and biodiversity. Accurate and timely mapping of the LREW is essential for monitoring ecological changes and understanding shifts in the wetland’s ecosystem structure and functions. In this study, an innovative approach that integrates radar (Sentinel-1), optical (Sentinel-2), and DEM data on Google Earth Engine (GEE) is proposed to achieve a more optimized and accurate wetland mapping. Recursive Feature Elimination (RFE) is employed to improve feature collection, facilitating the construction of an optimal feature set for Random Forest (RF) classification to extract detailed LREW information. Mapping conducted from 2017 to 2023 using the proposed approach demonstrates its effectiveness and stability in mapping the LREW, achieving overall accuracies ranging from 89.84% to 93.73% and Kappa from 0.85 to 0.91. Compared to single-source methods, the integration of multi-source data substantially enhances mapping accuracy, while RF-RFE effectively eliminates redundant features, further refining classification precision. Texture features (Sum Average, SAVG) and the Water Index 2019 (WI2019) were found to significantly influence wetland mapping. The importance of different feature types is ranked as follows: spectral features > vegetation/water body index > PCA features > SAR texture features > red-edge index > other spectral index > spectral texture features > backscatter coefficient > H-Alpha. During the study period, the wetland area increased from 849.60 km2 to 979.49 km2. This expansion was caused primarily by wetland protection policies that led to the dismantling of coastal aquaculture ponds, resulting in a reduction of 90.28 km2 in waterbodies over 7 years, with many areas transitioning to tidal flats and Suaeda salsa habitats. In conclusion, the proposed method is effective in mapping the LRE wetlands and provides reliable data support for wetland ecological protection and restoration.

Land use & land cover dynamics and its relationship with nitrate pollution in groundwater around inactive mine areas using geospatial techniques, SW part of Cuddapah basin, Southern India

Geospatial maps can show how the ineffective operations of inactive mines affect water and aquifer quality. As such, the purpose of this study is to assess the impact of mining and irrigation on the aquifer ecosystem through the evaluation of LULC and slope maps through the application of Landsat 8 OLI/TIRS and DEM data. A total of 50 groundwater samples were prepared from villages in the close proximity to inactive mines during pre and post monsoon periods in 2021. The results of the analysis revealed alarming statistics, that 14% of groundwater samples exceeded the WHO nitrate limit in pre & post monsoon season, indicating a high-risk in the study area. According to guidelines (USEPA, 2014), 34% in pre-monsoon and 26% post-monsoon of samples exceeded the THI levels for adults and children respectively, indicating non-carcinogenic health risks. In addition, 80% of the samples in both seasons exhibited high NPI values, indicating nitrate contamination associated with blue baby syndrome. From the Geospatial analysis the findings from the LULC classification indicate that there has been a significant increase in cropland area from 2016 to 2021 due to changes in forest land, fallow land, and water resources. These problems have been exacerbated by the expansion of cultivated land, which has increased from 71.1 square kilometers in 2016 to 118 square kilometers in 2021, accounting for 13.1% of the total area. This expansion, coupled with elevated water body resource availability, has compounded the nitrate pollution including in intensely irrigated regions. The slope map analysis revealed that the inactive mines occur at low slope, high rainfall areas and these are compounded by runoff from other sources such as domestic and agricultural wastes. For these matters, sealing and remediating these inactive mines is essential so as to prevent further nitrate leakage.

Monitoring and Evaluation of Debris Flow Disaster in the Loess Plateau Area of China: A Case Study

The Loess Plateau area, with complex geomorphological features and geological structure, is highly prone to geologic disasters such as landslides and debris flow, which cause great losses. To investigate the initiation mechanism of landslide and debris flow disasters and their spreading patterns, historical satellite images in the Laolang gully were collected and digitized to generate three-dimensional topographic and geomorphological maps. Typical landslides were selected for landslide thickness measurement using a standard penetrometer and high-density electrical method. Numerical models were established to simulate the occurrence and development of landslides under different working conditions and to evaluate the spreading range based on the propagation algorithm and friction law. The results show that the 10 m resolution DEM data are well matched with the potential hazard events observed in the field site. The smaller the critical slope threshold, the greater the extent and distance of landslide spreading. The larger the angle of arrival, the greater the energy loss, and therefore the smaller the landslide movement distance. The results can provide scientific theoretical guidance for the prevention and control of rainfall-induced landslide and debris flow disasters in the Loess Plateau area.

ESTIMATION OF LENGTH AND SLOPE FACTOR (LS) AS EROSION PREDICTION IN WAY PUBIAN SUB WATERSHED

Erosion caused by land degradation is one of the main challenges to soil quality around the world. One way to prevent erosion is by analyzing the length and slope factor (LS). The increasing length of the slope causes the higher amount of cumulative runoff and also the increasing steepness of the soil slope causes the higher speed of runoff that contributes to erosion. The purpose of this research is to analyze the LS factor value of Moore and Burch (1986) method and LS Nomograph for Way Pubian Subwatershed. The research method was done with Moore and Burch method and Nomograf LS. Moore and Burch (1986) method used the flow accumulation on DEM data as the main input in ArcGIS and the result shows the LS value ranged from 0 to 765.625 with an average (mean) value of 4.427. Meanwhile, the nomograph LS method resulted in high LS values and is not compatible with the Jakarta RTL-RLKT Preparation Implementation Guidelines (1986). Slope conditions affect the amount of surface flow so that it also affects the contribution of erosion and LS value. Way Pubian sub-watershed also classified as steep, resulting in a high LS value. Based on the method of Moore and Burch (1986) using ArcGIS software, we can conclude that the results of LS factor values can be used in Indonesia compared to the nomograph LS method which the results are less suitable due to topographic factors. As a result, the LS value from Moore and Burch (1986) method can be used in erosion prediction calculation.

Cold Region River Flood Mapping and Scour Potential Prediction: Insights from Hydraulic Model Using Advanced Autonomous Surface Vehicles

This study aimed to map the 2022 flood with a 16.5-year return period near a bridge on the Red River, close to Grafton City, North Dakota, and evaluate the scour potential around the bridge. The Red River Basin (RRB) near Grand Forks, ND, and Emerson, ND, is a cold region river vulnerable to floods. Local scouring around bridge piers during floods can lead to hydraulic structure failure. An Autonomous Surface Vehicle (ASV) equipped with LiDAR DEM data from the ND DWR’s LiDAR dataset was used to collect comprehensive bathymetry and discharge data, including the 2022 flood. The HEC-RAS model was used to create flood maps, and the Colorado State University (CSU) methodology was employed to assess local scour around the bridge pier. The study area recorded maximum velocities of 1.71 m/s, 1.87 m/s, and 1.56 m/s for discharge values of 368 m3/s, 784 m3/s, and 1335 m3/s, respectively, with higher velocities recorded upstream of the bridge. The maximum water depth reached 13.14 m during the peak discharge of 1335 m3/s. Higher discharge resulted in increased Froude number and contraction scour depth, with the latter continuing to increase even when the Froude number decreased as water reached the bridge deck. The study highlights the effectiveness of integrating ASVs, bathymetry, and LiDAR data to comprehensively understand flood dynamics and bridge scour in cold region rivers, offering the way for the development of effective flood control measures and strategies to safeguard critical infrastructure.

Integrating terrain structure characteristics into generative adversarial nets for hillshade generation

A hillshade is a visualization technique that represents three-dimensional terrain in a two-dimensional plane by illumination mapping. The digital relief shading promotes the visualization of the terrain efficiently using DEM data. However, compared with manual shading, the digital algorithm-based method still has a gap in the visual effect of illumination strategy and terrain generalization. The typical landform characteristics and micro-geomorphic properties usually are destroyed. The reason is that the complexity of illumination rules is hard to summarize for different terrain phenomena. In this study, namely the data-driven artificial intelligent method, an alternative strategy based on generative adversarial networks (GANs) is proposed rather than the rule-based method. The DEM and the terrain skeleton lines are input to the model and part of the manual relief shading of Swisstopo is used for the training samples. Through the GAN training and learning, the manual skill imbedded in the hillshade is discovered in the generation model. The results show that the proposed model performs better than the digital relief shading on various landforms in aesthetic visualization and geo-scientific representation. Compared to other models, including other convolution neural network (CNN) based methods, terrain structure is maintained more significantly through the proposed model.

Assessing the potential of multi-source remote sensing data for cropland soil organic matter mapping in hilly and mountainous areas

Cropland soil organic matter (SOM) is recognized as a significant carbon reservoir in terrestrial ecosystems. Digital mapping of SOM in croplands is essential for comprehending the global carbon cycle. Accurately mapping cropland SOM using multi-source remote sensing data has been effectively incorporated into prediction models across various scales. However, the impact of multi-source remote sensing data on cropland SOM mapping outcomes in hilly and mountainous regions remains insufficiently understood. In this study, Jiangyou City, located in Sichuan Province, China, was chosen as a representative example of hilly and mountainous regions. Fifteen distinct feature combinations were devised using three remote sensing variables (Sentinel-1, Sentinel-2, and Landsat-8) along with DEM data. Feature selection was conducted using the Boruta algorithm. Subsequently, the RF, SVR, Cubist, and INLA-SPDE models were adopted to create spatially detailed distribution maps of cropland SOM for the region. Additionally, an uncertainty analysis was performed on the cropland SOM mapping results. The results indicate the following: (1) The INLA-SPDE model, which integrates both data information and spatial structure, achieves the highest accuracy and the less uncertainty in cropland SOM mapping, with an R2 of 0.647 and an RMSE of 4.227 g/kg. (2) Optical imagery is more important than SAR images, but their combination enhances model accuracy. Specifically, Sentinel-2 data has a significant impact cropland SOM prediction in hilly and mountainous areas, followed by Landsat-8 data. (3) The predicted spatial distribution patterns of cropland SOM by the four models show consistency, indicating lower SOM content in the southwest and higher SOM content in the central and northeast regions. This study provides valuable references for future large-scale and high-spatial cropland SOM prediction, highlighting the importance of spatial resolution for precise SOM prediction accuracy in hilly and mountainous regions.

Forest Fire Detection Based on Spatial Characteristics of Surface Temperature

Amidst the escalating threat of global warming, which manifests in more frequent forest fires, the prompt and accurate detection of forest fires has ascended to paramount importance. The current surveillance algorithms employed for forest fire monitoring—including, but not limited to, fixed threshold algorithms, multi-channel threshold algorithms, and contextual algorithms—rely primarily upon the degree of deviation between the pixel temperature and the background temperature to discern pyric events. Notwithstanding, these algorithms typically fail to account for the spatial heterogeneity of the background temperature, precipitating the consequential oversight of low-temperature fire point pixels, thus impeding the expedited detection of fires in their initial stages. For the amelioration of this deficiency, the present study introduces a spatial feature-based (STF) method for forest fire detection, leveraging Himawari-8/9 imagery as the main data source, complemented by the Shuttle Radar Topography Mission (SRTM) DEM data inputs. Our proposed modality reconstructs the surface temperature information via selecting the optimally designated machine learning model, subsequently identifying the fire point through utilizing the difference between the reconstructed surface temperatures and empirical observations, in tandem with the spatial contextual algorithm. The results confirm that the random forest model demonstrates superior efficacy in the reconstruction of the surface temperature. Benchmarking the STF method against both the fire point datasets disseminated by the China Forest and Grassland Fire Prevention and Suppression Network (CFGFPN) and the Wild Land Fire (WLF) fire point product validation datasets from Himawari-8/9 yielded a zero rate of omission errors and a comprehensive evaluative index, predominantly surpassing 0.74. These findings show that the STF method proposed herein significantly augments the identification of lower-temperature fire point pixels, thereby amplifying the sensitivity of forest surveillance.

Erosive wear and particle attrition in multi-stage solar particle receivers and screw conveyors: A CFD-DEM approach with machine learning and artificial neural networks

A coupled finite volume and discrete element method (CFD-DEM) numerical model is developed to unravel the fundamental mechanisms of granular transport, surface erosive wear and particle attrition in multistage solar particle receivers (MSPR) and screw conveyors (SC). Additionally, various regression-based supervised machine learning (ML) and artificial neural networks (ANN) are trained and optimized with the goal of quickly quantifying erosion and attrition thereby overcoming (a) the inherent challenges of high computational expense of CFD-DEM simulations and (b) exorbitant time required to post-process and extract DEM results. This study is the first of its kind to harness a synergistic numerical framework (CFD-DEM, ML, ANN) to unravel the fundamental mechanisms of erosion and attrition in MSPRs, non-vibrationally induced SCs, and vibrationally-induced SCs. It is found that the packing distribution, particle curtain width, and severity of particle scattering in a MSPR exhibits a subtle variation with particle diameter. In the context of SCs, the magnitudes of erosion and attrition are contingent on the particle diameter and flow operating conditions such as screw blade velocity, vibrational frequency and vibrational amplitude. Interestingly, a vibrationally-induced SC (VI-SC) exhibits superior performance in terms of low erosion and attrition unlike non-vibrationally induced SC (NVI-SC). It is found that MSPRs with 750–1000 µm particles and SCs with 750–1000 µm coupled with vibrational frequencies and vibrational amplitudes of 5 Hz and 0.007 m, respectively, are the preferred configurations due to minimum particle scattering, low erosive wear and low particle attrition. The trained and optimized ML models such as XGBoost, CatBoost, and Multilayer Perceptron (MLP) exhibit superior performance in predicting erosive wear and particle attrition on unseen DEM data, whereas the Multiple Linear Regression (MLR) and Gaussian Process Regression (GPR) ML exhibit poor performance prediction. For the first time, new information on fundamental granular flow dynamics, erosive wear and particle attrition in MSPRs and SCs is discerned. The methodology allows scientists to quickly examine material degradation for a myriad of industrial applications such as concentrated solar thermal (CST), among others.

Lineaments Extraction and Analysis Using Sentinel 2B, Sentinel 1A Data and ALOS PALSAR DEM: A Case Study of the Igoudrane Region, Jbel Saghro, Eastern Anti-Atlas, Morocco

The remote sensing techniques have been used several years ago in many geological studies, and have proved their utility especially in lineament extraction. During this research, we extracted the lineaments automatically as well as manually in the Igoudrane region, Imiter inlier, eastern Anti-Atlas. The purpose of this research is to extract and map lineaments in the study area, and then compare the results provided, where the VNIR bands of Sentinel-2B, Sentinel-1A and the ALOS PALSAR DEM (12.5 m) were used. The directional filters were applied to the VNIR bands of Sentinel-2B into six directions, in the interest of attaining a significant lineament contrast. The VH and VV radar data provided the greatest number of lineaments (7243) by LINE module algorithm, while the shaded relief and slope were applied to the ALOS PALSAR DEM, which gave a higher number of lineaments than the VNIR bands of Sentinel-2B (4699, 4647 respectively). The manual extraction on the VV and VH polarizations yielded 441 lineaments, while the VNIR bands of Sentinel-2B provided 453 lineaments. Furthermore, the main orientations of the lineaments are NE-SW, ENE-WSW and NNE-SSW. The lineaments extracted from the VNIR bands (Sentinel-2B) and ALOS PALSAR’s DEM exhibited an optimal match along with the fractures digitized manually and from the geological maps of the research area. The results revealed that Sentinel-1 data is less efficient than ALOS PALSAR DEM data, especially in arid regions where the capacity of DEM radar waves to penetrate rises for thinner sedimentary layers, offering deeper information.

The Ecological Environmental Effects and Topographic Gradient Analysis of Transformation in the Production–Living–Ecological Spaces in the Northern Slope of the Tianshan Mountains

Taking the northern slope of the Tianshan Mountains (NSTM) in Xinjiang as the research area, this study analyzes the changes in the Production–Living–Ecological (P-L-E) Spaces and their Ecological Environmental Effects, providing a basis for optimizing the spatial pattern of the P-L-E Spaces and regional ecological environment protection in this area. Based on land use data and DEM data, various methods were used for analysis. These methods include the land use transfer matrix, ecological environment quality index, hot spot analysis, ecological contribution rate, and Terrain Position Index. The analysis focused on changes in the spatial pattern of the P-L-E Spaces from 1980 to 2020. It also examined the spatiotemporal distribution of ecological environment quality (EEQ). Furthermore, it explored the differentiation characteristics of EEQ in terrain gradients. The conclusions are as follows: (1) On the NSTM, Ecological Space decreased while Production and Living Space expanded. From 1980 to 2020, Agricultural Production Space increased rapidly. Industrial Production Space also saw rapid growth during this period. Urban Living Space expanded significantly from 1980 to 2020. Rural Living Space experienced steady growth over the same period. Forest Ecological Space initially increased but later decreased. Water Ecological Space showed an initial increase followed by a decrease from 1980 to 2020. (2) The EEQ first remained stable, declined slightly from 2000 to 2010, improved significantly, and then deteriorated from 2010 to 2020. The distribution of EEQ exhibits a “high in the northwest, low in the southeast” pattern. EEQ hot spots on the NSTM are concentrated in the Tianshan Mountains, with clustering increasing in both northern and southern areas. Cold spots are found in the southern, eastern, and northern NSTMs, with aggregation strengthening in the south and north and slightly weakening in the east. Hot spots of EEQ changes on the NSTM show stable distribution, with stronger aggregation from 2000 to 2020. However, aggregation of cold spots has gradually weakened, yet noticeable aggregation persists throughout the study period. (3) There is a significant gradient difference in EEQ distribution. Higher terrain gradients have a higher EEQ. From 1980 to 2020, lower terrain gradients saw improvement, while higher gradients experienced deterioration. The EEQ on the NSTM has declined, showing significant spatial differences, with better quality on the northern side than the southern side. Future efforts should focus on restoring the environment at lower gradients, mitigating deterioration at higher gradients, and enhancing water conservation in the Tianshan Mountains.

Simulation and Prediction of Snowmelt Runoff in the Tangwang River Basin Based on the NEX-GDDP-CMIP6 Climate Model

In this study, the future snowmelt runoff in the chilly northeast region’s Tangwang River Basin was simulated and predicted using the SWAT model, which was built and used based on the NEX-GDDP-CMIP6 climate model. This study conducted a detailed analysis of the spatial and temporal distribution characteristics of snowmelt runoff using high-resolution DEM, land use, and soil data, along with data from historical and future climatic scenarios. Using box plots and the Bflow digital filtering approach, this study first determined the snowmelt runoff period before precisely defining the snowmelt periods. Sensitivity analysis and parameter rate determination ensured the simulation accuracy of the SWAT model, and the correlation coefficients of the total runoff validation period and rate period were 0.75 and 0.76, with Nashiness coefficients of 0.75 for both. The correlation coefficients of the snowmelt runoff were 0.73 and 0.74, with Nashiness coefficients of 0.7 and 0.68 for both, and the model was in good agreement with the measured data. It was discovered that while temperatures indicate an increasing tendency across all future climate scenarios, precipitation is predicted to increase under the SSP2-4.5 scenario. The SSP2-4.5 scenario predicted a decreasing trend regarding runoff, while the SSP1-2.6 and SSP5-8.5 scenarios showed an increasing trend with little overall change and the SSP5-8.5 scenario even showed a decrease of 6.35%. These differences were evident in the monthly runoff simulation projections. Overall, the findings point to the possibility that, despite future climate change having a negligible effect on the hydrological cycle of the Tangwang River Basin, it may intensify and increase the frequency of extreme weather events, creating difficulties for the management of water resources and the issuing of flood warnings. For the purpose of planning water resources and studying hydrological change in this basin and other basins in cold regions, this study offers a crucial scientific foundation. An in-depth study of snowmelt runoff is of great practical significance for optimizing water resource management, rational planning of water use, spring flood prevention, and disaster mitigation and prevention, and provides valuable data support for future research on snowmelt runoff.

Analysis of Devanur and Manamedu Ophiolite Complexes in SGT, India: A detailed examination employing remote sensing techniques and Laboratory Spectral Signature investigations

This study employs advanced satellite imagery from ASTER and Sentinel-2A to conduct detailed lithological mapping of the Devanur and Manamedu ophiolite complexes in the southern Central Shear Zone (CSZ). The primary focus is on the Manamedu Ophiolite Complex (MOC) and the Devanur Ophiolitic Complex (DOC). Image enhancement techniques such as Color composites, Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were utilized to differentiate various rock types. RGB band combinations derived from PCA and MNF outputs demonstrated effective discrimination of rock units. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods were employed on ASTER and Sentinel-2A images, yielding classified lithologies that closely matched existing maps from the Geological Survey of India (GSI) and other studies, validating the accuracy of the findings. Additionally, Laboratory Spectral Signature Studies were conducted on 10 rock samples using an ASD FieldSpec Pro® spectroradiometer, providing reflectance spectra from 350 nm to 2500 nm. These spectra, particularly the continuum-removed reflectance, revealed diagnostic absorption features that were corroborated by geochemical analyses. A detailed analysis investigated how elemental compositions and key minerals influenced absorption bands. Major oxide geochemical compositions of DOC and MOC samples were identified using XRF methods. The aim of this research is to characterize DOC and MOC through remote sensing and spectral signature analysis. Sentinel-2A data proved more effective in lithological discrimination compared to ASTER, with spectral signatures indicating the presence of iron (Fe) and magnesium (Mg) contents. Notably, SVM classification of Sentinel-2A MNF + DEM data achieved an overall accuracy of more than 90% when compared with field investigations. This study underscores the efficacy of processing VNIR and SWIR bands from ASTER and Sentinel-2A satellite imagery alongside DEM data and ground surveys for mapping mafic-ultramafic rocks in the DOC and MOC regions of the CSZ.

Identification of the active faults and seismotectonic zonation of Laos territory

In this study, the main active faults in the territory of Laos were identified by analyzing the spatial relationship between the distributions of neo-tectonic faults and earthquake epicenters. The map of neo-tectonic faults was built by integrating the results of neo-tectonic faults research using geological-geomorphological data together with the lineament map obtained from remote sensing analysis. Nontectonic lineaments were eliminated by correlating the spatial distribution of the lineament field with a topographic map, DEM, and geological-geomorphological data. The earthquake data, including 4416 events in Laos and its surroundings, were collected from different sources: the International Seismological Center (ISC), the earthquakes recorded by the local seismic network in Laos, the seismic data in Vietnam, and the earthquake catalog provided by the Thailand Meteorological Department (TMD). Among these, 820 events were located using the hypocenter method, and the local network recorded the data. The magnitude conversion was applied to get a unified scale Mw. The catalog of 1617 main shocks obtained after eliminating foreshocks and aftershocks using the declustering technique was used for a spatial correlation with the neotectonic fault distribution to identify active faults. A total of 14 main active fault zones in the Laos territory were defined. Most are also seismogenic faults with Mw ≥ 5.0 occurring along their trace. Considering the characteristics of seismic activity and the active and neotectonic faults, the territory of Laos can be divided into six seismotectonic zones according to the decreasing level of seismic activity: the Western, the Northeastern Samnua, the Phongsali, the South Truong Son, the North Truong Son, and the Khorat zones. Each zone is characterized by relative homogeneity in the seismic activity and the characteristics of active and neotectonic faults.