Applied Geomatics最新文献

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.

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.

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 (p < 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.

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.

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.

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.

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.

Rapid urbanization processes have significantly increased freshwater consumption, prompting the need for precise predictions of snowmelt-derived streamflow in glacierized Himalayan basins, which are highly susceptible to climate change. However, understanding snow characteristics, such as snow cover and snow grain size, remains a challenge due to inaccessibility of these terrains and the limitations of in-situ data collection. Hyperspectral remote sensing datasets offer a promising solution for monitoring and retrieving snow properties at both micro and macro levels. In this study, the PRISMA hyperspectral dataset was employed to estimate snow grain sizes in the Bhilangana basin of the Upper Himalayan region through the Spectral Angle Mapper (SAM) and Snow Grain Size Index (SGSI) methods. The SGSI approach uses visible and near-infrared wavelengths to classify snow grains, while the SAM method applies endmember spectral signatures validated against the USGS spectral library. The results shows that both SGSI and SAM effectively classified snow grains into fine (< 0.5 mm), medium (0.5–1.0 mm), and coarse (1.0–2.0 mm) categories, achieving a classification accuracy of approximately 88%. The SGSI method utilized the bi-spectral reflectance ratio of PRISMA bands 6 (441.63 nm) and 69 (1028.79 nm) to classify snow grains with spatial variability. The outcomes of the study disclose the competency of PRISMA data for spatial representation of snow grain size variability. The spatial analysis shows that fine and medium grain sizes dominate the snowpack, particularly during the seasonal accumulation observed in February. The findings indicate that fine-grained snow distribution at higher altitudes is crucial for assessing avalanche risks assessment and predicting snowmelt timing. This research demonstrates PRISMA data’s effectiveness in detailed snow grain size mapping, offering valuable insights for applications in climatology, hydrology, and mountain hazard management. Enhanced snow grain size mapping contributes to improved avalanche forecasting and resource planning, ultimately supporting the safety and resilience of the Himalayan mountain regions.

Urban footprint extraction is used for the extraction or classification of various land use classes like water bodies, urban areas, vegetation, and others over any region. But this is quite difficult to perform in the hilly terrains. The work recognises the optimal threshold value for the extraction of urban features is based on the coherence properties of the processed SAR dataset. The work utilises two Sentinel-1 A satellite images acquired on 7th January 2020 and 31st January 2020 respectively. The work of urban footprint is accomplished with (a) the creation of a coherence image with a pair of SAR imageries; (b) further pre-processing of the coherence image to apply multi-looking and terrain correction; (c) the derived coherence image is stacked to create a false colour composite image to provide an input for feature extraction; (d) feature extraction is performed by masking out the urban areas at different thresholds levels. The results of the extracted urban footprint are authenticated with a comparison to the optical dataset. Some sample locations are selected for validating the results from Google Earth historical imagery. Results indicate that the urban features extracted at a threshold value of 0.5 provide improved results in comparison to the threshold values of 0.4, 0.6, and 0.7. The pixels of urban features at a coherence threshold of 0.5 are lying at the same position where urban areas are present. The work can be further propagated for the identification and monitoring of other urban features regardless of any weather conditions for several other applications.

This paper explores new technologies that can advance the state-of-the-practice in safety assessment and health monitoring of existing infrastructures. In this context, multi-temporal interferometric Synthetic Aperture Radar techniques combined with the use of digital models of infrastructures represent a powerful integration to conventional approaches in the monitoring and assessment of structural safety of infrastructures. Although the interferometric method is widely used for ground deformation investigations, using displacement data from satellite observation in structural monitoring is less investigated. The joint use of multi-frequency satellite radar data provided by the European Space Agency Copernicus project and Italian Space Agency will be explored. The paper introduces the workflow implemented for processing satellite radar data from the X-band COSMO-SkyMed constellation by the Italian Space Agency over the municipality of Modena (Italy). An open-source workflow based on Multi-Temporal Interferometric technique and Persistent Scatterers Interferometry is adopted, enabling the detection of displacements of stable targets and the generation of corresponding time series. Radar data products, derived from the processing of both COSMO-SkyMed and Sentinel-1 data, are analyzed in a Geographic Information System alongside the available geospatial dataset of infrastructures. This approach enables the extraction of displacement components related to the ground and infrastructures. The method’s potential for characterizing infrastructures behaviour is assessed through the analysis of selected case studies. The results aim to establish the foundations for a method capable of assessing infrastructure safety.