Applied Geomatics最新文献

{"title":"The agro-ecological capacity of north and central Sistan-Baluchestan Province, Iran, for canola cultivation determined by GIS and analytical hierarchical process","authors":"Ebrahim Moradi,&nbsp;Hamid Reza Mobasser,&nbsp;Ahmad Mehraban,&nbsp;Hamid Reza Ganjali","doi":"10.1007/s12518-024-00571-w","DOIUrl":"10.1007/s12518-024-00571-w","url":null,"abstract":"<div><p>Identification of potential lands on the basis of their environmental benefits and constraints can greatly contribute to the stability of canola production in different parts of the world including Sistan-Baluchestan province, Iran. Accordingly, using GIS, the scores of environmental factors affecting canola production including temperature, rainfall, slope, altitude, organic matter, soil salinity, pH and soil nutrients (N, P, K, Fe and Zn) were integrated along with the weights of analytical hierarchical process (AHP) for the production of canola suitability maps. The zoning maps of climate, topography, and soil as well as the canola suitability maps and the current production maps of canola were prepared. According to the AHP results canola cultivation was affected the most by climate (rainfall and water sources) compared with topography and soil. ArcGIS results indicated southern lands of Zahedan had the highest organic matter, and excluding Hirmand, other parts of the area had appropriate salinity for canola production. The most appropriate areas in terms of acidity for canola production are Hirmand and the central part of Zahedan. In arable soil, the nitrogen level was not maximum in the region, and Nimrooz and Zabol had the highest phosphorus. Potassium was average in the research area, and Zabol, Zahak, Nimrooz and Hamoon had the highest Fe. The output maps obtained from the combination of various ecological factors indicated that the moderate and non-suitable classes of land for canola cultivation are located in the northern parts of Zahak, Hamon, Nimzoz and the total lands of Hirmand and Zabol.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 3","pages":"529 - 544"},"PeriodicalIF":2.3,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141679873","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":"Mapping the thermal footprint of a municipal solid waste landfill using remote sensing and artificial intelligence","authors":"Nawras Shatnawi,&nbsp;Munjed Al-Sharif,&nbsp;Majd A. Briezat","doi":"10.1007/s12518-024-00570-x","DOIUrl":"10.1007/s12518-024-00570-x","url":null,"abstract":"<div><p>This work demonstrates the value of combining remote sensing, regression models, random forest (RF) algorithms, and artificial neural networks (ANN) to provide crucial information for landfill management in Jordan. The process of predicting land surface temperature (LST) using linear and nonlinear regression models, ANN, and RF depended on past LST time series retrieved from Landsat images for the years 2000 to 2018. Additionally, the study utilized the normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), as well as data on humidity, wind velocity, and ambient air temperature. The deployed ANN model exhibited a coefficient of determination of 0.87 and a mean squared error of 6.40*10^-8. Similarly, the RF model accurately identified 93.88% of the LST values. The findings revealed that the LST at landfills was consistently higher than the summer air temperature, and that the LSTs of open landfill cells exceeded those of closed cells. Moreover, the predicted LST values from ANN and RF models surpassed those from linear and nonlinear regression models. Notably, the R^2 value of 0.81 indicates a strong correlation between ANN and RF findings.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 3","pages":"499 - 513"},"PeriodicalIF":2.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141684027","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":"Estimating future bathymetric surface of Kainji Reservoir using Markov Chains and Cellular Automata algorithms","authors":"Pius Onoja Ibrahim,&nbsp;Harald Sternberg,&nbsp;Lazarus Mustapha Ojigi","doi":"10.1007/s12518-024-00564-9","DOIUrl":"10.1007/s12518-024-00564-9","url":null,"abstract":"<div><p>The menace of sedimentation to reservoirs has a significant implication for water quality, storage capacity and reservoir lifetime. Rainfall patterns and other anthropogenic and environmental impacts alter the erosion rate and, by extension, directly affect sedimentation rates if left unchecked. This research focused on using the integration of Markov Chains and Cellular Automata (MC – CA) models to estimate and forecast the future bathymetric surface of the Kainji reservoir in Nigeria for the year 2050. The bathymetric datasets used for this research comprise two different epochs (1990 and 2020). The datasets were acquired using a Single Beam Echosounder at Low and High frequencies of 20 kHz and 200 kHz. The preliminary investigation revealed that sedimentation is exacerbating a greater danger to the reservoir functionality. The results show that the maximum observed depth is 71.2 m, indicating a 7.53% loss in depth from the 1990 archived data and a 16.24% depth loss to sedimentation from 1968 to 2020 and 22.35% depth loss in the year 2050 as shown on the projected surface. Consequently, the integrated model (MC and CA) efficiently predicted the future bathymetric surface of the Kainji reservoir for the year 2050 based on the data characteristics. However, the proven techniques for analysing spatial data, such as the Markov Chain and Cellular Automata, best suited for analysing categorical transition data, show some artefacts (black spots) on the projected generated map which is subject to further investigation.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 3","pages":"515 - 528"},"PeriodicalIF":2.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-024-00564-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141682878","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":"Spatial assessment of produced hailstorm maps in severely affected areas in Northern Thailand based on dual-polarimetric radar using the cloud computing platform Google Earth Engine","authors":"Nattapon Mahavik,&nbsp;Sarintip Tantanee,&nbsp;Fatah Masthawee","doi":"10.1007/s12518-024-00569-4","DOIUrl":"10.1007/s12518-024-00569-4","url":null,"abstract":"<div><p>The objective of this study was to use dual-polarimetric radar data to create an hourly hail product, which would then be analyzed using Google Earth Engine (GEE), a cloud computing platform. We used ground-based weather radar from the Thailand Meteorological Department’s Chiang Rai station in the north of Thailand. Dual-polarimetric weather radar data were analyzed at 15-minute intervals with Python-based open-source radar libraries such as PyArt and Wradlib. Hydrometeor classification was conducted using simulated atmospheric sounding data obtained from ERA5 reanalysis data, which had been classified into ten classes between 17.00 and 20.00 Local Time. At a 2-kilometer altitude grid, similar hydrometeor types with comparable solid-state characteristics were collected and presented in CAPPI format. Furthermore, we used JavaScript programming to conduct a localized impact study of the hailstorm in GEE in order to prove the preliminary damage assessment concept by comprising sophisticated spatial overlays with land use data, urban regions, farmland, population data, and counts of roofed homes. The analysis results in GEE reveal the potential damaging area prone to hailstorm passage. This is the first attempt in Thailand to create an hourly hailstorm product and integrate it into the Geographic Information System (GIS) using GEE’s cloud-based platform. This invention can annually support local organizations in disaster monitoring, impact assessment, and adaptation to hail-related events.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 3","pages":"485 - 497"},"PeriodicalIF":2.3,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142414924","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":"A conversion of the geoid to the quasigeoid at the Hong Kong territories","authors":"Albertini Nsiah Ababio,&nbsp;Ismael Foroughi,&nbsp;Robert Tenzer,&nbsp;Mohammad Bagherbandi","doi":"10.1007/s12518-024-00562-x","DOIUrl":"10.1007/s12518-024-00562-x","url":null,"abstract":"<div><p>A levelling network was readjusted and a new geoid model compiled within the framework of geodetic vertical datum modernization at the Hong Kong territories. To accomplish all project objectives, the quasigeoid model has to be determined too. A quasigeoid model can be obtained from existing geoid model by applying the geoid-to-quasigeoid separation. The geoid-to-quasigeoid separation was traditionally computed as a function of the simple planar Bouguer gravity anomaly, while disregarding terrain geometry, topographic density variations, and vertical gravity changes due to mass density heterogeneities below the geoid surface. We applied this approximate method because orthometric heights of levelling benchmarks in Hong Kong were determined only approximately according to Helmert’s theory of orthometric heights. Considering a further improvement of the accuracy of orthometric heights by applying advanced numerical procedures, we determined the geoid-to-quasigeoid separation by applying an accurate method. The comparison of the accurately and approximately computed values of the geoid-to-quasigeoid separation revealed significant differences between them. The approximate values are all negative and reach -2.8 cm, whereas values from the accurate method vary between -4.1 and + 0.2 cm. In addition, we assessed the effect of anomalous topographic density on the geoid-to-quasigeoid separation by employing a newly developed digital rock density model. According to our estimates the effect of anomalous topographic density reaches a maximum value of 1.6 cm, reflecting a predominant presence of light volcanic rocks and sedimentary deposits at the Hong Kong territories. Our numerical findings indicate that the conversion between geoid and quasigeoid models should be done accurately, even in regions with a moderately elevated topography.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 3","pages":"471 - 483"},"PeriodicalIF":2.3,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-024-00562-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142412622","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":"Landslide susceptibility assessment using GIS-based multicriteria decision analysis (MCDA) along a part of National Highway-1, Kashmir- Himalayas, India","authors":"Iftikhar Hussain Beigh,&nbsp;Syed Kaiser Bukhari","doi":"10.1007/s12518-024-00559-6","DOIUrl":"10.1007/s12518-024-00559-6","url":null,"abstract":"<div><p>The current study aims at GIS-based multicriteria decision analysis to generate a landslide-susceptible map from Baramulla to Uri Road segment along NH-1, Kashmir Himalaya, India. The landslide causative factors examined to generate our AHP matrix are slope gradient, elevation, slope aspect, curvature, distance to drainage, distance to roads, distance to lineaments, geology, land use/land cover, and Rainfall. The study mapped and identified the active landslides along NH-1 through extensive field investigations and other secondary data sources. The landslide events were dominated by rockfall and debris slides. Based on their importance in landslide occurrences, the thematic layers were given relative relevance scores using Saaty's scale. Besides, the Analytic Hierarchy Process was employed to normalize the relative weights and attributes of the various thematic layers. In addition, all thematic data layers were combined using a weighted linear approach to generate the landslide susceptibility map. Furthermore, the resultant landslide susceptibility map was classed into five categories viz., very high (24.18%), high (30.24%), medium (28.61%), low (15.28%), and very low (1.69%). The study reveals that 54.42% of the area falls under the high and very high susceptible zones. Likewise, 78.9% of overall model accuracy of final landslide susceptible zonation map was computed using the area under curve method. Moreover, this study would aid infrastructural, geo-environmental, and landslide hazard planning in the studied region.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 2","pages":"451 - 469"},"PeriodicalIF":2.3,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140379093","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":"Development of an emergency notification system to analyze the access route for emergency medical services using Geo-IoT and pgRouting","authors":"Rhutairat Hataitara,&nbsp;Kampanart Piyathamrongchai,&nbsp;Sittichai Choosumrong","doi":"10.1007/s12518-024-00557-8","DOIUrl":"10.1007/s12518-024-00557-8","url":null,"abstract":"<div><p>The implementation of a Location Service for Emergency Medical Services system (LS4EMSs) is the goal of this study. by integration of pgRouting algorithm, Web Map Application, and Geo-IoT devices. The study is divided into 2 parts: (1) design of a security emergency incident location alarm system that can be used to track security emergencies in real time using Geo-IoT and (2) development of Emergency Routing Service (ERS) system based on web map application. NodeMCU ESP8266 and U-blox Neo-6 m GPS module were used for implementing Geo-IoT which can connect to Wi-Fi and give information including the location of the push button triggered by an individual in an emergency. ERS can determine the best route to take from the hospital or closest ambulance to the location where the Geo-IoT device is located. Free and Open-Source Software for Geospatial (FOSS4G) stack was used in the system’s development, since it is easily adaptable to cover different purposes, including fire, flood, or other transport movements. The Integration of Geo-IoT and Web Routing Service for LS4EMSs improves utility as it is a near real-time ERS system.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 2","pages":"441 - 449"},"PeriodicalIF":2.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140222645","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":"Using resampled nSight-2 hyperspectral data and various machine learning classifiers for discriminating wetland plant species in a Ramsar Wetland site, South Africa","authors":"Mchasisi Gasela,&nbsp;Mahlatse Kganyago,&nbsp;Gerhard De Jager","doi":"10.1007/s12518-024-00560-z","DOIUrl":"10.1007/s12518-024-00560-z","url":null,"abstract":"<div><p>Mapping wetland ecosystems at the species level provides critical information for understanding the nutrient cycle, carbon sequestration, retention and purification of water, waste treatment and pollution control. However, wetland ecosystems are threatened by climate variability and change and anthropogenic activities; thus, their assessment and monitoring have become critical to inform proper management interventions. Contemporary studies show that satellite-based Earth observation (EO) has significant potential for achieving this task. While many multispectral EO data are freely and readily available, its broad spectral bands limit its utility in differentiating subtle differences among similar plant species. In contrast, hyperspectral data has a high spectral resolution, which is superior in discerning minute differences in similar plant species. However, this data is associated with high dimensionality and multicollinearity, which negatively affect the performance of traditional, parametric classification algorithms. To this end, machine algorithms are often preferred to classify hyperspectral data due to their robustness to various data distributions and noise. The current study compared the performance of three advanced machine learning classifiers, i.e., Support Vector Machine (SVM), Random Forest (RF), and Partial Least Squares Discriminant Analysis (PLS-DA), in discriminating four dominant wetland plant species, i.e., Crocosmia sp., Grasses, Agapanthus sp. and Cyperus sp. using simulated hyperspectral data from an upcoming sensor, i.e., nSight-2. The results revealed that SVM is superior, with an overall accuracy of 93.18% (and class-wise accuracies &gt; 85%). In comparison, there were minor differences in the performances of RF and PLS-DA, i.e., 84.09% and 83.63%, respectively. Overall, the results demonstrated that all the evaluated classifiers could achieve acceptable mapping accuracies. However, SVM is more robust, providing exceptional accuracies, and should be considered for operational mapping once the sensor is in space.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 2","pages":"429 - 440"},"PeriodicalIF":2.3,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-024-00560-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242883","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":"Using structure from motion for analyzing change detection and flood events in the context of flood preparedness: a case study for the Laufer Muehle area at the Aisch river in Germany for conducting near real-time analyses","authors":"Michael Kögel,&nbsp;Dirk Carstensen","doi":"10.1007/s12518-024-00561-y","DOIUrl":"10.1007/s12518-024-00561-y","url":null,"abstract":"<div><p>Recent flood events (FE) in Germany have shown that the extent and impact of extreme flood events cannot be estimated solely based on numerical models. For analyzing the development of such an event and to develop and implement safety measures more efficiently, additional data must be collected during the event. Within the scope of this research, the possibilities of near real-time recording using an unmanned aerial vehicle (UAV) and data processing with the Structure from Motion (SfM) method were tested in a case study. Different recording parameter combinations were tested in the Laufer Muehle area on the Aisch river in Germany. The focus of the investigations was the identification of a parameter combination that allows a short recording interval for aerial imagery. Based on these findings, the identification of changes in the study area by comparing multitemporal photography (flood prevention), as well as the recording of flooded areas during a FE should be possible. The accuracy analysis of the different parameter combinations between two point clouds as well as the process of change detection was done by a Multiscale Model to Model Cloud Comparison (M3C2) and including ground control points. As a result, a parameter combination was identified which led to the desired results in the study area. The processes were transformed into fully automated and scripted workflows. The results serve as a basis for establishing a workflow for near real-time analyses in future studies.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 2","pages":"409 - 427"},"PeriodicalIF":2.3,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-024-00561-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140246868","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":"Orthophoto improvement using urban-SnowflakeNet","authors":"Mojdeh Ebrahimikia,&nbsp;Ali Hosseininaveh,&nbsp;Mahdi Modiri","doi":"10.1007/s12518-024-00558-7","DOIUrl":"10.1007/s12518-024-00558-7","url":null,"abstract":"<div><p>With the increasing use of drones for capturing images in urban areas, correcting for distortion and sawtooth effects on orthophotos generated with these images has become a critical issue. This is particularly challenging due to the larger displacements generated by high objects and lower flight altitude of drones compared to crewed aircraft. In addition, image-based point cloud generation methods often fail to produce complete point clouds due to occluded areas and radiometric changes between overlapping images, especially near the borders of high objects. To address these issues, a novel method is proposed in this article for improving the generated point clouds with image-based methods using a deep learning network, called urban-SnowflakeNet, which comprises the following steps: 1) preparing and normalizing the roof's point cloud; 2) completing the point clouds of the building using the proposed deep learning network; 3) restoring the completed point clouds of the buildings to the real coordinates and combining them with the background point cloud; and, 4) correcting the DSM and generating the final true orthophotos. On two different image datasets, our method reduced distortions at the building's edges by 40% on average when compared to the most recent orthophoto enhancement method. However, by maintaining this success on more datasets, the approach has the potential to improve the accuracy and completeness of point clouds in urban regions, as well as other applications such as 3D model improvement, which require further testing in future works.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 2","pages":"387 - 407"},"PeriodicalIF":2.3,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250232","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}