Applied Geomatics最新文献

{"title":"Harnessing geoinformatics and AHP techniques to assess the groundwater potential zones in Uttar Pradesh, India","authors":"Sushil Chandra,&nbsp;Ajay Pratap Singh","doi":"10.1007/s12518-025-00640-8","DOIUrl":"10.1007/s12518-025-00640-8","url":null,"abstract":"<div><p>Water is an essential source for our daily needs and occurs in groundwater or surface water. In the present climatic manifestation, their occurrence, usage, recharge, and future sustainability are indispensable for mankind, agriculture, industries and ecological environmental equilibrium. Integrated studies of RS (Remote Sensing) satellite data and GIS (Geographic Information System) applications effectively evaluate GPZ (Groundwater Potential Zones) and provide vitality to futuristic scenario development. The present study is aimed to delineate the GPZ of Uttar Pradesh using MCDM (Multi-Criteria Decision Making) and AHP (Analytical Hierarchy Process) taking seven parameters such as rainfall, geomorphology, LULC (Land Use-Land Cover), drainage density, slope, soils and lineament density along with field survey. Using the free web-based AHP, all parameters were assigned a weightage, and weightage overlay analysis (WOA) using ArcGIS was performed. The results identified a total of 8% area in the piedmont region under the excellent potential zone followed by 46% of area of the Ganga River left bank. At the same time, the southern part of Uttar Pradesh indicated good potential zones. Similarly, the intermediate zone (45%) along the Ganga River right bank and the southern zone of Uttar Pradesh demarcate fair potential zone. The poor potential zone (1%) is predicted in the southwestern part. These results were tested with the pre and post monsoon well water levels of the last 11 years (2010- 2021) which satisfies our findings of the AHP method. Hence, AHP is a new method for identifying GPZ maps that can be used to carry out extensive ground-based hydrological studies that aid in the identification of potential bore well/dug well locations.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 3","pages":"589 - 604"},"PeriodicalIF":2.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering groundwater potential zones using integrated approach of remote sensing, GIS, and AHP in a reservoir-induced seismicity (RIS) region in western India","authors":"Venkatarao Ajaykumar,&nbsp;Nepal Chandra Mondal","doi":"10.1007/s12518-025-00636-4","DOIUrl":"10.1007/s12518-025-00636-4","url":null,"abstract":"<div><p>This study aims to decipher groundwater potential zones using an integrated approach of remote sensing, geographical information system, and analytical hierarchy process in the Koyna-Warna region of western India, an area affected by the reservoir-induced seismicity (RIS). This region serves as a key example of the RIS, primarily due to the construction of the Koyna dam in 1964. The filling of the reservoir water behind the dam has been associated with a significant increase in seismic activity, particularly in the surrounding area. This seismicity is thought to be triggered by the weight of the water, which induces stress on the Earth’s crust, leading to the faults slipping. Moreover, the groundwater potential zones in this region are crucial for understanding the dynamics of seismic events. Thus, multiple important factors affecting groundwater such as geology, geomorphology, soils, land use and land cover, slope, lineaments density, drainage density, rainfall, normalized vegetation index, and topography wetness index were considered for deciphering the groundwater potential zones. Spatially distributed thematic layers of all these factors were generated using remotely sensed data and ground-based data in GIS platform. The assigned weights of all these layers and their attributes were then normalized by using analytical hierarchy process technique. The deciphered groundwater potential zones of this RIS area were categorized as very good (15.68%), good (27.34%), moderate (29.25%), poor (19.54%), and very poor (8.19%). These assessed groundwater potentialities were positively correlated with the well specific yields with a correlation coefficient of R = 0.90, and was found reasonable. It was also observed that the very good to good potential zones were in the upstreams. Most of the very good groundwater potential zones (~ 16.79%) were found in the northern part, namely Koyna region (which was more the seismically active) than the Warna region (~ 14.57%) located in the southern part. It indirectly indicated that the groundwater potentially also induced the seismicity of earthquakes along with both Koyna and Warna reservoir waters. The deciphered groundwater potential zones in this RIS area will aid in better study of the earthquake seismicity in future.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 4","pages":"605 - 625"},"PeriodicalIF":2.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grid-based soil erosion assessment and vulnerability mapping using integrated GIS, remote sensing, and RUSLE Model: a case study in Rarh region of West Bengal, India","authors":"Kabirul Islam","doi":"10.1007/s12518-025-00632-8","DOIUrl":"10.1007/s12518-025-00632-8","url":null,"abstract":"<div><p>The Rarh region of West Bengal, India, faces a critical challenge to its agricultural sustainability and environmental well-being due to soil erosion. This study aims to develop a scalable model for estimating soil erosion rates and identifying vulnerable areas within the region. By employing the Revised Universal Soil Loss Equation in conjunction with GIS and remote sensing techniques, the research integrates factors such as rainfall erosivity, soil erodibility, topography, land cover, and conservation practices to create a comprehensive soil erosion risk map. Findings reveal that annual soil loss in the study area ranges from 0.53 to 955.39 t ha⁻¹ yr⁻¹, with an average of 13.014 t ha⁻¹ yr⁻¹, exceeding the tolerable soil loss limit of 11.2 t ha⁻¹ yr⁻¹. The erosion map classifies the region into zones of varying soil loss severity from very low to very severe. Approximately 2.67% of the area falls under severe to very severe categories, 10.63% is classified as high to very high risk, while 48.92% and 17.60% are in moderate and low-risk zones, respectively. The western and central parts of the Rarh region, characterized by steep slopes, intensive agriculture, and high rainfall intensity, are identified as areas with high soil loss potential. The model's performance was validated using an Area under the Curve (AUC) analysis, achieving a score of 0.832, indicating good predictive capability. This study's results offer valuable insights for policymakers and land managers, enabling the implementation of targeted soil conservation measures and sustainable land use practices in the Rarh region of West Bengal.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 3","pages":"567 - 587"},"PeriodicalIF":2.3,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intensity correction of multi-return signal from airborne laser scanning to improve land cover interpretation","authors":"Magdalena Pilarska-Mazurek","doi":"10.1007/s12518-025-00638-2","DOIUrl":"10.1007/s12518-025-00638-2","url":null,"abstract":"<div><p>Airborne laser scanning technology is widely used in photogrammetry and remote sensing, enabling three-dimensional information about objects located on the Earth’s surface to be obtained. In addition, the intensity of the reflected signal is received, which records the power with which the laser beam is reflected from objects. Moreover, unique to laser scanning is its ability to penetrate vegetation. As a result, more than one return may be acquired for a laser beam regarding vegetation. With each return, there is a loss of laser beam power, which can be problematic when classifying and interpreting land cover under trees, especially in urban areas. This article presents a methodology for correcting the intensity values of multiple returns on the ground to improve the interpretation of land cover under trees. For this purpose, methods for calculating transmittance and methods based on the Beer-Lambert law were examined. The effectiveness of the developed methodology was evaluated through statistical analyses and intensity images before and after correction were generated. The results of the studies showed that it is possible to effectively correct the intensity of signal multiple returns, thus improving the interpretation of land cover under trees. The accuracy of intensity image classification before and after intensity correction improved from 0.57 to 0.79 in the leaf-on season and from 0.44 to 0.62 in the leaf-off season.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 3","pages":"547 - 565"},"PeriodicalIF":2.3,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-025-00638-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decision tree machine learning algorithm for pegmatites mapping using remote sensing data (Anti-Atlas, Morocco)","authors":"Soufiane Maimouni,&nbsp;Yousra Morsli,&nbsp;Youssef Zerhouni,&nbsp;Saida Alikouss,&nbsp;Zouhir Baroudi","doi":"10.1007/s12518-025-00633-7","DOIUrl":"10.1007/s12518-025-00633-7","url":null,"abstract":"<div><p>In the past few years, the use of Machine learning (ML) to classify remotely sensed data has increased, offering new opportunities for geological mapping. Conventional remote sensing classification methods often rely on spectral information, but distinguishing between lithological classes with similar spectral signatures remains a persistent challenge. In particular, accurately mapping and extracting pegmatites from other lithological classes, especially granite, presents a difficulty. The objectives of this study are to map the lithological units in the Angarf region (Zenaga, Central Anti-Atlas, Morocco) and to extract pegmatite outcrops, with a particular focus on separating the pegmatite from the granite, as this challenge has been considered in several previous studies. The methodology developed is innovative and based on a Decision Tree (DT) approach of ML, which is applied to spectral indices derived from ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) images. The interpretation and analysis of spectroradiometric measurements have enabled us to understand the behavior of spectral information of pegmatites compared to other geological formations. The achieved overall accuracy of the DT classification was 96.28 %. Also, the comparison of the produced map, particularly the pegmatite classes, with the field data highlighted the potential of the adapted methodology. The DT algorithm is a fast, reliable, robust, and highly accurate mapping model that is simple to configure, uses few parameters, and handles input data heterogeneity effectively. The obtained pegmatite maps provide a support and can be used as a preliminary step in mineral exploration.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 3","pages":"535 - 546"},"PeriodicalIF":2.3,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial analysis of the fire incidents and burned areas induced by Russia-Ukraine war in 2022 using MODIS and VIIRS data","authors":"Mahlatse Kganyago,&nbsp;Portia Tshigoli,&nbsp;Lerato Shikwambana","doi":"10.1007/s12518-025-00634-6","DOIUrl":"10.1007/s12518-025-00634-6","url":null,"abstract":"<div><p>Wildfire incidents and their impact on the environment and socio-economic factors have been of major concern globally. Consequently, several studies sought to understand the influence of climate change-related extreme conditions and anthropogenic activities on wildfire occurrence and regimes and their subsequent impact on biodiversity, ecosystems, soil sustainability, air quality, and atmospheric processes. The current study particularly focuses on the additional pressure exerted by armed conflicts and wars, often overshadowed by more immediate concerns such as saving lives. Specifically, we explored the influence of the Russia-Ukraine war, that began in February 2022, on fire incidents and burned areas in Ukraine. We conducted a comparative analysis of MODIS and VIIRS active fire products to characterise spatio-temporal patterns of fire incidence hotspots between 2021 (pre-war) and 2022 (during the war). The results revealed a higher number of significant fire incident hotspots at a 95% confidence level and higher burning in 2022, particularly in croplands and forests, which has implications for food security and environmental sustainability in Europe. The forests were impacted as part of the war-related activities near Chornobyl Nuclear Power Station in northern Ukraine, while most croplands were burned in the eastern parts. The study also revealed that MODIS and VIIRS varied spatially and temporally in detecting fire incidents and hotspots, with VIIRS exhibiting significantly more fire incidents per land cover class (<i>p</i> &lt; 0.02), and hotspots across all seasons. This finding is consistent with previous studies that found that VIIRS detects significantly more fires than MODIS. Furthermore, the spatio-temporal distributions of fire hotspots were mostly consistent with reports of war-related activities by Armed Conflict and Location Dataset. By evaluating the MODIS and VIIRS fire products, this study underscores the potential of remote sensing data in assessing war-induced fire incidents and their environmental consequences, which may persist for a long time after the war.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 3","pages":"519 - 533"},"PeriodicalIF":2.3,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-025-00634-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of flooding on rice biomass in Hanoi city on the basis of deep learning application and Sentinel-1A data","authors":"Anh Ngoc Thi Do,&nbsp;Tuyet Anh Thi Do","doi":"10.1007/s12518-025-00631-9","DOIUrl":"10.1007/s12518-025-00631-9","url":null,"abstract":"<div><p>Despite being Vietnam's largest city, Hanoi's economy still relies on agriculture. Recent weather events, like floods, have significantly impacted rice biomass. Mapping and monitoring rice growth using synthetic aperture radar (SAR) data and the Artificial Bee Colony—Deep Neural Network (ABC-DNN) can provide reliable data on rice production affected by floods. Sentinel-1 satellite images from January to October 2022 showed that VH polarization yielded more detailed information than VV polarization. Field data and Support Vector Machine (SVM) classification estimated rice cultivation areas at approximately 81 ha for Winter-Spring and 77 ha for Summer-Autumn crops, with over 90% accuracy. The ABC-DNN model predicted aboveground biomass (AGB) with coefficients of determination (R2) ranging from 0.722 to 0.745. The model effectively identified flood-prone areas, aiding policymakers in developing strategies to mitigate agricultural damage, particularly in lowland regions of Hanoi.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 3","pages":"501 - 518"},"PeriodicalIF":2.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Studying the spatial spectral fusion models for remote sensing images","authors":"Ali Ebrahim,&nbsp;Mahmoud El-Mewafi,&nbsp;Mohamed Zhran","doi":"10.1007/s12518-025-00629-3","DOIUrl":"10.1007/s12518-025-00629-3","url":null,"abstract":"<div><p>Image fusion is the tactic of collecting two or more distinct imagery to produce a modern imagery using a model to learn more and good details about a subject. For many applications, the usage of freely available satellite imagery as Landsat 8 (L8) and Sentinel 2 (S2) is yet essential. In this study, the port said governorate was covered by the fusion of a 30 m spatial resolution L8 level- 2 and a 10 m spatial resolution S2 level 2 A and the ismailia city was covered by the fusion of a 43 cm spatial resolution high resolution (HR) and a 10 m spatial resolution S2 Level 2 A. Applying the Gram-Schmidt (GS), nearest neighbor diffuse, brovey, intensity-hue-saturation, and simple mean algorithms. The main aim of this paper to improve the spatial resolution of L8 (by pan sharpening with S2) and the spatial resolution of S2 (by pan sharpening with HR). The fused images are assessed using high-quality image techniques as error relative global average squared, root mean squared error, entropy, structural similarity index measure, and correlation coefficient. The outcomes demonstrated that the GS method based on the red band of S2 (band 4) has the preferable results for fusion between L8 and S2 for port said governorate and brovey method has the preferable results for fusion between HR and S2 for ismailia city. Following these results, the study's following phase examined how various scale (S) parameters affected the image segmentation process. Segmentation is an essential step in the conversion of pixel-depended image analysis to object-depended image analysis. The outcomes demonstrate that the preferable values for the GS fusion method, depend on the S2 red band (band 4), are about S factor 70 for fusion between L8 and S2 and about S factor 50 and 60 for fusion between HR and S2.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 3","pages":"483 - 500"},"PeriodicalIF":2.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-025-00629-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A potential interaction-based approach for appraising robustness and identifying critical links of regional road networks exposed to repeated flooding: case study of Dibrugarh district, Assam, India","authors":"Gopal Chandra Banik,&nbsp;Subrata Kumar Paul,&nbsp;Sudip Kumar Roy","doi":"10.1007/s12518-025-00630-w","DOIUrl":"10.1007/s12518-025-00630-w","url":null,"abstract":"<div><p>The article presents a methodology for appraising the robustness and identifying critical links of regional road networks exposed to recurring flooding. A set of indices termed the Network Robustness Index is introduced to appraise the robustness of a regional road network by comparing its performance between normal and disrupted conditions due to inundation. The performance indicator in the Network Robustness Index is the aggregate 'potential interaction' within a study region, estimated with the inputs of centrality, population size and spatial separation of constituent settlements, based on the theoretical framework of the Gravity Model. A diminution of 'potential interaction' in disruption conditions quantifies the network's robustness. K-means cluster analysis technique is applied to identify the 'very critical', 'critical' and 'less critical' flood zones based on the criteria of relative diminution of aggregate ‘potential interaction’ resulting from inundation-induced serviceability loss of road links. The criticality of a road link corresponds to the criticality of its associated flood zone. The GIS platform is utilised for data extraction, processing, mapping and other analyses. The suggested methodology is demonstrated in Dibrugarh, one of the worst flood-affected districts in Assam, India. The findings indicate that approximately 34.67% of the study area experiences regular inundation, and the regional road network may suffer an estimated 18.23% performance loss in the worst possible flood scenario. Flood zones are categorised, and critical road links are identified. The study provides essential insights for prioritising pre-disaster mitigation, post-disaster retrofitting and disaster management planning. It also highlights opportunities for further research.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 3","pages":"465 - 481"},"PeriodicalIF":2.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Groundwater artificial recharge indexing using fuzzy catastrophe membership functions","authors":"Masoumeh Khorasani Alamdari,&nbsp;Sina Sadeghfam,&nbsp;Ali Ehsanitabar,&nbsp;Ata Allah Nadiri,&nbsp;Sahar Darvishi,&nbsp;Mohamad Alizadeh Noughani,&nbsp;Rokhshad Hejazi","doi":"10.1007/s12518-025-00628-4","DOIUrl":"10.1007/s12518-025-00628-4","url":null,"abstract":"<div><p>Water shortages have resulted from the unsustainable exploitation of aquifers, the increased need for agricultural and drinking water, the pollution of surface water resources, and reduced water resources. Replenishment of groundwater resources through artificial or natural recharge (from rainfall and runoff) is one of the ways to compensate for this issue. The data layers used in site selection for Groundwater artificial recharge (GWR) are heterogeneous and, therefore, cannot be directly integrated. Catastrophe Fuzzy Membership (CFM) functions are among the latest advances in this field, making it possible to integrate various types of data layers. However, the type of catastrophe function and fuzzy membership intervals are determined based on expert opinion. This study determined the final weights of criteria and sub-criteria, and 16 indicators and 76 sub-criteria were selected to evaluate potential sites for artificial recharge in Tabriz Plain, Iran. The results showed that the areas with gentle slopes in the center of the study area have great potential for groundwater recharge, while the mountainous areas in the north and South are unsuitable. The final suitability map was created using remote sensing (RS) and Geographic Information System (GIS) software.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 3","pages":"449 - 463"},"PeriodicalIF":2.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}