Applied Geomatics最新文献

{"title":"Remote sensing-based spatio-temporal rainfall variability analysis: the case of Addis Ababa City, Ethiopia","authors":"Esubalew Nebebe Mekonnen,&nbsp;Ephrem Gebremariam,&nbsp;Aramde Fetene,&nbsp;Shimeles Damene","doi":"10.1007/s12518-024-00554-x","DOIUrl":"10.1007/s12518-024-00554-x","url":null,"abstract":"<div><p>Climate variability is a highly debated and unavoidable global environmental challenge that has adverse effects on Ethiopia, a developing country. Hence, the objective of this research is to examine the changes in rainfall patterns in Addis Ababa City, Ethiopia, from 1981 to 2018, considering both spatial and temporal aspects. The study utilized a time-series dataset of climate information, which had a spatial resolution of 4 × 4 km, obtained from the National Meteorological Agency of Ethiopia. Supplementary data was also acquired from the Ethiopian Space Science and Geospatial Institute. To examine the rainfall variability, statistical measures such as the coefficient of variation (CV) and standardized anomaly index (SAI) were employed. Geospatial technologies and “R” programming were also used to perform a non-parametric Mann-Kendall (MK) test and Sen’s slope estimator for the investigation of both the trend and magnitude of changes. The annual, <i>Kiremt</i> (main rainy), and <i>Belg</i> (spring) seasons rainfall exhibited low to moderate variability with CV &lt; 20% and CV &lt; 30%, respectively, and very high variability for the <i>Belg</i> season (CV &gt; 30%). The <i>Bega</i> season’s variability was extreme (CV &gt; 70%). In contrast, decadal rainfall variability was generally very low (CV &lt; 10%). The months from October to March showed higher inter-monthly variability, with CV exceeding 100%. In contrast, the <i>Kiremt</i> season, July, and August, experienced lower inter-monthly variability (CV &lt; 30%). The western, north-east, and southern parts of Addis Ababa demonstrated relatively higher rainfall variability, and the trends decreased in all seasons and months, except the <i>Kiremt</i> season and the months of May, June, and September. However, none of these seasonal and monthly changes were statistically significant (<i>P</i> &gt; 0.05). The study identified 6 years (1982, 1984, 1997, 1999, 2014, and 2015) with varying degrees of drought. Consequently, the spatio-temporal variability of precipitation should be considered in development plans, disaster risk reduction strategies, and policy measures such as flood management.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 2","pages":"365 - 385"},"PeriodicalIF":2.3,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140419209","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 the girth distribution of rubber trees using support and relevance vector machines","authors":"Bambang Hendro Trisasongko,&nbsp;Dyah Retno Panuju,&nbsp;Rizqi I’anatus Sholihah,&nbsp;Nur Etika Karyati","doi":"10.1007/s12518-024-00550-1","DOIUrl":"10.1007/s12518-024-00550-1","url":null,"abstract":"<div><p>Within the context of agricultural planning, spatial data have played a crucial role, replacing conventional tabular-based data. Plantation, one of the key agricultural commodities, has been of interest since they occupy large coverage of landmass. Primary data supplies have been provided by space agencies, allowing detailed, updated satellite data to monitor this resource, with the aid of machine learning. This article discusses the opportunity of implementing support vector machines (SVM) and relevance vector machines (RVM) for estimating tree girth as a predictor of tree maturity and plantation productivity. The current research indicated that baseline SVR models were unable to yield a sufficient outcome. The complexity of the problem suggested that only the radial basis function (RBF) kernel was promising. Tuning SVM on linear and polynomial kernels did not enhance the quality of the models, although it appeared that the phenomenon of diminishing return existed. After parameter tuning, this research yielded a model with root mean squared error (RMSE) around 8.5 cm with <i>R</i>² around 0.69. Although it was recently introduced, RVM with the same RBF kernel did not yield a sufficient model with RMSE about 52 cm. This concludes that the optimal model should be sought through examining a wide range of machine learning approaches.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 2","pages":"337 - 345"},"PeriodicalIF":2.3,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957804","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":"Conceptual design of a nationwide spatial decision support system for forest fire prevention and fighting","authors":"Abdullah Sukkar,&nbsp;Ahmet Ozgur Dogru,&nbsp;Ugur Alganci,&nbsp;Dursun Zafer Seker","doi":"10.1007/s12518-024-00556-9","DOIUrl":"10.1007/s12518-024-00556-9","url":null,"abstract":"<div><p>Wildfires have become a growing global concern due to the environmental and economic damage they cause. Climate change is a primary cause of wildfires as it increases the frequency, extent, and severity of wildfires. In addition to climate change, human activities have become a major cause of wildfires, particularly in the Mediterranean region. Since wildfire is a very complicated environmental problem, effectively responding to and minimising the danger of a wildfire necessitates the integration of all available information into decision-making systems. The complexity of wildfires can have a negative impact on decision-making, particularly when decisions are temporally made under dynamic, uncertain, and contradictory conditions. Since the early 1990s, there has been a rise in the occurrence of “mega-fires” throughout Europe, which are characterised by wildfires that surpass the present firefighting capabilities. Controlling mega-fires exceeds the response capacity of the individual institutions as effective wildfire management requires extensive coordination of the institutions and all available resources at a local, regional, and national level. This cooperation necessitates the integration of advanced technologies with scientific knowledge, as well as the combination of various heterogeneous spatial and non-spatial data. GIS technology provides an efficient, expedited, and economical process of data collection and analysis. In the last decades, GIS-based decision support systems have been used to improve the efficiency of firefighting processes like planning, management, and decision-making. In this study, a conceptual framework of a GIS-based decision support system for wildfire prevention and fighting in Turkey was proposed. The presented conceptual design aims to improve the firefighting capacity by providing decision-oriented spatial information on wildfire risks and dangers timely through integrated functional tools efficiently.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 2","pages":"347 - 363"},"PeriodicalIF":2.3,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140444433","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":"Characterizing land use-land cover changes in N’fis watershed, Western High Atlas, Morocco (1984–2022)","authors":"Wiam Salhi,&nbsp;Ouissal Heddoun,&nbsp;Bouchra Honnit,&nbsp;Mohamed Nabil Saidi,&nbsp;Adil Kabbaj","doi":"10.1007/s12518-024-00549-8","DOIUrl":"10.1007/s12518-024-00549-8","url":null,"abstract":"<div><p>The examination of changes in land use and land cover (LULC) holds a pivotal role in advancing our comprehension of underlying processes and mechanisms. The advancement of sophisticated earth observation programs has opened unprecedented opportunities to meticulously observe geographical areas, courtesy of the vast array of satellite imagery available across time. However, effectively analyzing this wealth of data to process LULC information remains a significant challenge within remote sensing. Recent times have witnessed the introduction of diverse techniques for scrutinizing satellite images, encompassing remote sensing technologies and machine/deep learning (M/DL) methods. This research endeavors to explore the transformation of LULC within the N’fis watershed, situated in the Western High Atlas region of Morocco, covering the timeline from 1984 to 2022. By harnessing remote sensing technologies, we have traced alterations in dams, forests, agriculture, and soil over this duration. Moreover, we have conducted comparisons among multiple machine and deep learning (M/DL) models to simulate and forecast LULC changes specifically for the year 2030. Our study outcomes manifest remarkable accuracy in LULC classification, consistently ranging between 91% and 97% for most years, with the kappa coefficient maintaining a range between 89% and 95%. Regarding predictive analysis, the Random Forest (RF) model emerges as the most precise, displaying an accuracy rate of 91%.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 2","pages":"321 - 335"},"PeriodicalIF":2.3,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959851","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":"Integration of multi-criteria decision analysis and statistical models for landslide susceptibility mapping in the western Algiers Province (Algeria) using GIS techniques and remote sensing data","authors":"Safia Mokadem,&nbsp;Ghani Cheikh Lounis,&nbsp;Djamel Machane,&nbsp;Abdeldjalil Goumrasa","doi":"10.1007/s12518-024-00548-9","DOIUrl":"10.1007/s12518-024-00548-9","url":null,"abstract":"<div><p>Landslide susceptibility assessment and prediction are among the main processing for disaster management and land use planning activities. Therefore, the general purpose of this research was to evaluate GIS-based spatial modeling of landslides in the western Algiers Province using five models, namely, frequency ratio (FR), weights of evidence (WoE), evidential belief function (EBF), logistic regression (LR), and analytical hierarchy process (AHP), and then compare their performances. At first, a landslide inventory map was prepared according to Google Earth satellite images, historical records, and extensive field surveys. The recorded landslides were divided into two groups (70% and 30%) to establish the training and validation models. In the next step, GIS techniques and remote sensing data were used, to prepare a spatial database containing 13 landslide conditioning factors: lithology, distance to lithological boundaries, permeability, slope, exposure, altitude, profile curvature, plan curvature, precipitation, distance to rivers, topographic wetness index, normalized difference vegetation index, and distance to roads. Finally, the landslide susceptibility maps were produced using the five models and validated by the areas under the relative operative characteristic curve (AUC). The AUC results showed a significant improvement in susceptibility map accuracy; the FR model has the best performance in the training and prediction process (90%), followed by LR (88%, 89%), WoE (88%, 87%), EBF (86%,86%), and AHP (76%,75%), respectively. The produced maps in the current study could be useful for land use planning and hazard mitigation purposes in western Algiers Province.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 1","pages":"235 - 280"},"PeriodicalIF":2.3,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775505","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":"Monitoring groundwater quality using principal component analysis","authors":"Manaswinee Patnaik,&nbsp;Chhabirani Tudu,&nbsp;Dilip Kumar Bagal","doi":"10.1007/s12518-024-00552-z","DOIUrl":"10.1007/s12518-024-00552-z","url":null,"abstract":"<div><p>For areas without perennial surface water sources, groundwater might be considered the second-largest source of drinking water after surface water. However, groundwater is highly prone to contamination as the groundwater reservoir is formed by the movement of surface water into the subsoil; in its due course of motion, it may dissolve any probable contaminants such as agrochemicals, landfill leachates, the oil spill from underground pipelines, and sewer waste and further convey the contaminated water to join some groundwater aquifers from where the water is again pumped out for human consumption. Therefore, prior to its potable applicability, the quality of groundwater should be evaluated for the presence of alkalinity, hardness, and undesirable and heavy minerals. The Central Ground Water Board (CGWB), Bhubaneswar, collects data on 61 stations in the Kalahandi District for 15 physiochemical parameters, including pH, bicarbonate, hardness, sulphate, Cl⁻, total dissolved solids, Mg⁺⁺, K⁺, Na⁺, total alkalinity, nitrate, fluoride, carbonate, electrical conductivity, and calcium, to assess the quality of the groundwater. The goals were to pinpoint the major elements influencing water quality and comprehend the groundwater quality measures’ regional distribution. Data from the Central Groundwater Board (CGWB) were collected as part of our research, and PCA was used to identify the major impacting elements. To further minimize the dataset’s multidimensionality, a principal component analysis is used. Together, the first three major components explain 76.64% of the overall variability. The first two principal factors themselves explain about 56.9% of the total variance. The three principal factors indicate salinity, hardness, and relative alkalinity and acidity, respectively, in the groundwater.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 1","pages":"281 - 291"},"PeriodicalIF":2.3,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775541","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":"Mobile mapping system for historic built heritage and GIS integration: a challenging case study","authors":"Daniele Treccani,&nbsp;Andrea Adami,&nbsp;Valerio Brunelli,&nbsp;Luigi Fregonese","doi":"10.1007/s12518-024-00555-w","DOIUrl":"10.1007/s12518-024-00555-w","url":null,"abstract":"<div><p>To manage the historic built heritage, it is of fundamental importance to fully understand the urban area under study, so that all its characteristics and critical issues related to historical conformation, stratification, and transformations can be better understood and described. Geometric surveying allows a deeper investigation of these characteristics through analytical investigation in support of urban planning theories as well. To date, geomatics provides various tools and techniques to meet the above-mentioned needs, and mobile mapping system (MMS) is a technology that can survey large areas in a short time, with good results in terms of density, accuracy, and coverage of the data. In this context, the article aims to verify whether this approach can also be useful in the complex and stratified reality of the historic urban context. The case analyzed—the historical center of Sabbioneta—presents some criticalities found in many urban centers of historical layout. Examples are narrow streets inserted in an urban context with multi-story buildings and consequent difficulty in receiving the GNSS signal and difficulty in following general MMS survey guidelines (trajectories with closed loops, wide radius curves). The analysis presented, relating to a survey carried out with Leica Pegasus:Two instrumentation, in addition to describing the strategies used to properly develop the survey, aims to analyze the resulting datum by discussing its possibilities for use in urban modeling, for cartographic or three-dimensional information modeling purposes. Particular attention is paid to assessing whether the quality of the data (accuracy, density) is suitable for the urban scale. Finally, an analysis of the data obtained from MMS was made with the geographic-topographic database (DBGT), in a GIS (Geographic Information System) environment, to check the possibilities of use and integration between the two models.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 1","pages":"293 - 312"},"PeriodicalIF":2.3,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-024-00555-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139776767","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":"Performance evaluation of state-of-the-art multimodal remote sensing image matching methods in the presence of noise","authors":"Negar Jovhari,&nbsp;Amin Sedaghat,&nbsp;Nazila Mohammadi,&nbsp;Nima Farhadi,&nbsp;Alireza Bahrami Mahtaj","doi":"10.1007/s12518-024-00553-y","DOIUrl":"10.1007/s12518-024-00553-y","url":null,"abstract":"<div><p>To date, various image registration approaches have been conducted to deal with distortions between multimodal image pairs. However, significant existing noise as an unavoidable issue deteriorates many conventional and advanced methods. The critical key is to choose a highly robust local feature detection and description method as the principle for many matching frameworks. However, few studies have concentrated on dealing with the noise issue. For this purpose, this paper evaluates the most well-known and state-of-the-art feature descriptors against artificial sequential noise levels. The employed methods consist of various handcrafted learning-based descriptors. It is further indicated that in addition to the designed structural feature map, multiple criteria, such as spatial arrangement, and the magnitude of the support area, play roles in achieving successful matching, especially in the presence of dramatic noise and complex distortion between multimodal images. Moreover, to filter out the noisy features, the employed local feature detectors are integrated with the uniform competency algorithm. Experimental results demonstrate the overall superiority (20.0% on average) of the MKD (multiple-kernel descriptor) due to advanced designed integrated kernels and polar arrangements.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 1","pages":"215 - 233"},"PeriodicalIF":2.3,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777066","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":"Geometrical evaluation of the UTFPR-DV building area using images of an unmanned aerial vehicle (UAV) with non-metric camera","authors":"Raoni Wainer Duarte Bosquilia,&nbsp;Gabriela Oliveira Silva,&nbsp;Maria Madalena Santos da Silva","doi":"10.1007/s12518-024-00551-0","DOIUrl":"10.1007/s12518-024-00551-0","url":null,"abstract":"<div><p>Nowadays, with the increase in the use of unmanned aerial vehicles (UAVs), small-area aerial photography has become a viable alternative to traditional data surveys, such as topography or satellite imagery analysis, mainly due to its high spatial and temporal resolution. Thus, the objective of this work was to evaluate and compare the survey of the built area of the UTFPR – Dois Vizinhos Campus, Brazil, conducted in the field using total station, with an orthomosaic obtained from a UAV using non-metric camera, with both methods using georeferenced control points in the ground. The analyses showed that there was a high correlation between the areas obtained by these methodologies, with an acceptable error for many purposes, as shown by the Pearson correlation coefficient of 0.9991 and the relative error of 2.23432%, proving to be an effective tool for such surveys. Thus, this work concluded that it is possible to survey the built area from a UAV orthomosaic using a non-metric camera, which required less equipment and allowed to obtain the data in a shorter time when compared to a classical topography survey on the field.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 1","pages":"47 - 55"},"PeriodicalIF":2.3,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139786280","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":"Machine learning-enabled soil classification for precision agriculture: a study on spectral analysis and soil property determination","authors":"Amol D. Vibhute,&nbsp;Karbhari V. Kale,&nbsp;Sandeep V. Gaikwad","doi":"10.1007/s12518-023-00546-3","DOIUrl":"10.1007/s12518-023-00546-3","url":null,"abstract":"<div><p>Surface soil type classification is essential to enhance food production in precision farming. However, soil classification is time-consuming, laborious, and costly through the traditional methods. Recently, artificial intelligence-based methods, especially machine learning, have played a vigorous role in soil classification and its mapping. However, machine learning still makes exterior soil type classification and its mapping difficult due to various features and spatio-temporal inconsistencies. Therefore, the present study has tried to determine soil properties and sort accordingly using hyperspectral datasets and machine learning methods. We used field spectra generated by ASD Field Spec 4 device and satellite image. The proposed approach has identified three prominent soil types, <i>Regur</i> soil, <i>Lateritic</i> soil, and <i>sand dunes</i> according to soil taxonomy, with more than 95% success rate using satellite hyperspectral image and machine learning models. Thus, the outcome of the present study can be effectively utilized in healthy agricultural practices to increase global food production. In addition, the suggested strategy can be used in precision agriculture and environmental management.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 1","pages":"181 - 190"},"PeriodicalIF":2.3,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139388644","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}