Applied Geomatics最新文献_第7页

GIS, remote sensing, and analytical hierarchy process (AHP) approach for rainwater harvesting site selection in arid regions: Feija Plain case study, Zagora (Morocco) 干旱地区雨水收集选址的地理信息系统、遥感和层次分析法（AHP）：扎戈拉（摩洛哥）费加平原案例研究

IF 2.3

Applied Geomatics Pub Date : 2024-09-20 DOI: 10.1007/s12518-024-00585-4

Adil Moumane, Abdelhaq Ait Enajar, Fatima Ezzahra El Ghazali, Abdellah Khouz, Ahmed Karmaoui, Jamal Al Karkouri, Mouhcine Batchi

{"title":"GIS, remote sensing, and analytical hierarchy process (AHP) approach for rainwater harvesting site selection in arid regions: Feija Plain case study, Zagora (Morocco)","authors":"Adil Moumane, Abdelhaq Ait Enajar, Fatima Ezzahra El Ghazali, Abdellah Khouz, Ahmed Karmaoui, Jamal Al Karkouri, Mouhcine Batchi","doi":"10.1007/s12518-024-00585-4","DOIUrl":"10.1007/s12518-024-00585-4","url":null,"abstract":"<div><p>The watermelon cultivation industry in Morocco's arid desert regions has experienced swift expansion due to increasing demand both nationally and globally. Nevertheless, this growth has led to the depletion of the already scarce groundwater resources, necessitating a paradigm shift in water resource management. This study adopts an integrated approach, leveraging field measurements, laser diffraction for soil particle size analysis, GIS mapping, and remote sensing, to pinpoint optimal sites for rainwater harvesting (RWH). A comprehensive methodology involving Soil Conservation Service Curve Number (SCS CN), and various conditioning criteria layers (Rainfall, Land Use and Land Cover, Geomorphology, Slope, Topographic Wetness Index, Infiltration number, and Aspect) was applied. The Analytic Hierarchy Process (AHP) assigned weights to criteria, and a Weighted Linear Combination (WLC) approach in GIS produced an RWH suitability map. The map, classified into four zones (unsuitable, low, moderate, and high cover), showed promising potential for 5.24% of the study area. Field data validation after significant rain events confirmed an 86 percent overall map accuracy. Eight recommended RWH sites, including GPS coordinates, are proposed for decision-makers to facilitate strategic implementation, ensuring sustainable water availability for both drinking and irrigation in this arid region.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"861 - 880"},"PeriodicalIF":2.3,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598937","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}

引用次数: 0

GIS-Based optimum path analysis for tourist places in Haridwar City 基于地理信息系统的哈里德瓦尔市旅游景点最佳路径分析

IF 2.3

Applied Geomatics Pub Date : 2024-09-19 DOI: 10.1007/s12518-024-00587-2

Pankaj P. Tasgaonkar, Rahul Dev Garg, Pradeep Kumar Garg

引用次数: 0

Glacier lakes detection utilizing remote sensing integration with satellite imagery and advanced deep learning method 利用卫星图像遥感集成和先进的深度学习方法探测冰川湖泊

IF 2.3

Applied Geomatics Pub Date : 2024-09-19 DOI: 10.1007/s12518-024-00594-3

Anita Sharma, Chander Prakash, Divyansh Thakur

{"title":"Glacier lakes detection utilizing remote sensing integration with satellite imagery and advanced deep learning method","authors":"Anita Sharma, Chander Prakash, Divyansh Thakur","doi":"10.1007/s12518-024-00594-3","DOIUrl":"10.1007/s12518-024-00594-3","url":null,"abstract":"<div><p>The Himalayan glaciers are extremely susceptible to global climate change, leading to substantial glacial retreat, the creation and expansion of glacial lakes, and a rise in GLOFs. These alterations have changed river flow patterns and moved glaciers' borders, resulting in significant socioeconomic damage. Accurately monitoring glacial lakes is essential for managing GLOF events and evaluating the effects of climate change on the cryosphere. This study utilizes a Deep Learning-based U-net technique to extract glacial lakes from Landsat-8 satellite imagery by propagating characteristics and minimizing information loss. The method improves the importance given to glacial lakes, reduces the influence of low contrast, and handles different pixel categories. We applied this methodology to the Chandra-Bhaga basin, Himachal Pradesh, located in NW Indian Himalaya, and successfully extracted 107 glacial lakes. The U-net model attains an accuracy of 97.32%, precision of 95.98%, recall of 95.23%, MSE 0.0043, Kappa Coefficient 97.43% and an IoU of 97.45% during validation with high-resolution photos from Google Earth and a digital elevation model. The suggested approach could be beneficial for precise and effective monitoring of glacial lakes in different areas, assisting in the management of natural disasters and offering vital information on the effects of climate change on the cryosphere.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"829 - 850"},"PeriodicalIF":2.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598968","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}

引用次数: 0

The impact of climate change on Al-wala basin based on geomatics, hydrology and climate models 基于地学、水文学和气候模型的气候变化对 Al-wala 盆地的影响

IF 2.3

Applied Geomatics Pub Date : 2024-08-25 DOI: 10.1007/s12518-024-00578-3

Farah Kloub, Samih B. Al Rawashdeh, Ghayda Al Rawashdeh

{"title":"The impact of climate change on Al-wala basin based on geomatics, hydrology and climate models","authors":"Farah Kloub, Samih B. Al Rawashdeh, Ghayda Al Rawashdeh","doi":"10.1007/s12518-024-00578-3","DOIUrl":"10.1007/s12518-024-00578-3","url":null,"abstract":"<div><p>Jordan is severely affected by climate change, it suffers from significance fluctuation and decrease in the amounts of the annual precipitation basically during the last decade which had dire consequences for farmers and the provision of fresh water. In this study, the impact of climate change on the Al-Wala basin was analyzed during the period 2013 to 2024 using Geomatics techniques, Google Earth Engine (GEE) and machine learning codes. Soil and Water Assessment Tool (SWAT) model was used to simulate the hydrological process up to year 2064. Moreover, the Meteorological Research Institute Earth System Model (MRI-ESM2-0) was used to predict the change of water surface area of the Al-Wala dam lake in the future. Annual satellite images: Lanadsat and sentinel, covering the period of the study area were downloaded and enhanced. They permit to provide the necessary information to carry out this study. As result, an important fluctuation of the amount of annual rainfall quantity was observed as well as, the amounts of annual rainfall expected to increase and decrease wobbly for several years in the future. Overall the average annual runoff will increase by 10% compared to the baseline scenario. The minimum temperature is expected to be higher than their rates throughout the year by 0.09°- 0.11<sup>o</sup> C, this will increase the evaporation rates with about 0.03%. The analysis of the sensitivity using the SWAT model was identified by 6 parameters out of 17. The regression coefficient (R<sup>2</sup>), Nash and Sutcliffe efficiency (NSE), on monthly basis, were above 0.60 for both of them which indicates satisfactory model results.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 4","pages":"813 - 827"},"PeriodicalIF":2.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598858","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}

引用次数: 0

Correction: The performance of landslides frequency-area distribution analyses using a newly developed fully automatic tool 更正：使用新开发的全自动工具进行滑坡频率-面积分布分析的性能

IF 2.3

Applied Geomatics Pub Date : 2024-08-05 DOI: 10.1007/s12518-024-00583-6

Ali Bounab, Younes El Kharim, Mohamed El Kharrim, Abderrahman El Kharrim, Reda Sahrane

引用次数: 0

The effect of spatial lag on modeling geomatic covariates using analysis of variance 空间滞后对利用方差分析建立地理共变量模型的影响

IF 2.3

Applied Geomatics Pub Date : 2024-07-22 DOI: 10.1007/s12518-024-00579-2

Darghan C. Aquiles E., Taborda L. Darlley S., González S. Nair J., Rivera M. Carlos A., Ospina N. Jesús E.

{"title":"The effect of spatial lag on modeling geomatic covariates using analysis of variance","authors":"Darghan C. Aquiles E., Taborda L. Darlley S., González S. Nair J., Rivera M. Carlos A., Ospina N. Jesús E.","doi":"10.1007/s12518-024-00579-2","DOIUrl":"10.1007/s12518-024-00579-2","url":null,"abstract":"<div><p>In recent years, statistical methods have been developed that include spatial considerations, for example, those that incorporate data with georeferencing. The descriptive part of geographical information systems currently provides many visualization and analysis tools; however, in terms of analysis, these systems are still quite limited, therefore, ignorance of these limitations may result in data with spatial effects being treated with conventional statistical methods for non-spatial use, which can certainly invalidate the excellent work of data capture with advanced tools such as those that are used daily in the geomatic context. This prompted the current document, drawing attention to how geomatic information analyzed with statistical methods that imply independence in modeled observations can be invalid. The Moran index is compared with a proposal for a spatial lag coefficient in the context of experimental design so that users of variance analysis do not apply this well-known procedure in a ritualistic way, perhaps revising some assumptions and perhaps ignoring more important ones. The distortion of the p value generated from the analysis of variance is clear in the presence of spatial dependence. In this case, it is associated with the lag or spatial overlap. The methodology is easy to apply in other designs with the development of the design matrix, its reparameterization and the choice of the respective weight matrix. This may cause users to reconsider the traditional method of analysis and incorporate some appropriate analysis methodology to address spatial effects present in data or in outputs from the modeling process.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 3","pages":"779 - 788"},"PeriodicalIF":2.3,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-024-00579-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816240","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}

引用次数: 0

Flood susceptibility mapping using machine learning and remote sensing data in the Southern Karun Basin, Iran 利用机器学习和遥感数据绘制伊朗南卡伦盆地洪水易发性地图

IF 2.3

Applied Geomatics Pub Date : 2024-07-19 DOI: 10.1007/s12518-024-00582-7

Mohamad Kazemi, Fariborz Mohammadi, Mohammad Hassanzadeh Nafooti, Keyvan Behvar, Narges Kariminejad

{"title":"Flood susceptibility mapping using machine learning and remote sensing data in the Southern Karun Basin, Iran","authors":"Mohamad Kazemi, Fariborz Mohammadi, Mohammad Hassanzadeh Nafooti, Keyvan Behvar, Narges Kariminejad","doi":"10.1007/s12518-024-00582-7","DOIUrl":"10.1007/s12518-024-00582-7","url":null,"abstract":"<div><p>Floods in Iran lead to significant human and financial losses annually. Identifying flood-prone regions is imperative to minimize these damages. This study aims to pinpoint flood-susceptible areas in the Great Karun Plain using remote sensing data, Google Earth Engine (GEE), and machine learning techniques. For the analysis, Landsat 8 data from April 8, 2019, and multiple variables including actual evapotranspiration, aspect, soil bulk density, clay content, climate water deficit, elevation, NDVI, land cover, Palmer Drought Severity Index, reference evapotranspiration, precipitation accumulation, sand content, soil moisture, minimum temperature, and maximum temperature were employed. These variables were utilized in the machine learning process to establish flood susceptibility zones. During the machine learning process, the base flow data of the Karun River was extracted from the Landsat image. A total of 19,335 samples were incorporated into the machine learning procedure using techniques such as MARS, CART, TreeNet, and RF. The model assessment criteria encompassed ROC, sensitivity, specificity, overall accuracy, F<sub>1</sub>score and mean sensitivity. Results indicated that the TreeNet technique yielded the most promising outcomes among the machine learning algorithms with ROC values of 0.965 for test data. The characteristic criterion reached 91.2%, while the overall accuracy criterion stood at 91.12%. The model’s average sensitivity was 90.81%, and F1score was 63.51% for this technique. Additionally, analysis of the relative importance of independent variables highlighted that factors like vegetation cover (0.37), cumulative precipitation (0.23), soil water deficit (0.12), drought intensity (0.12), and landscapes (0.1) exerted a more pronounced influence on flooded areas compared to other variables.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 3","pages":"731 - 750"},"PeriodicalIF":2.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141820980","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}

引用次数: 0

Spatial assessment of groundwater potential zones using remote sensing, GIS and analytical hierarchy process: A case study of Siliguri subdivision, West Bengal 利用遥感、地理信息系统和层次分析法对地下水潜力区进行空间评估：西孟加拉邦西里古里分区案例研究

IF 2.3

Applied Geomatics Pub Date : 2024-07-19 DOI: 10.1007/s12518-024-00577-4

Pritam Saha, Saumyajit Ghosh, Shasanka Kumar Gayen

{"title":"Spatial assessment of groundwater potential zones using remote sensing, GIS and analytical hierarchy process: A case study of Siliguri subdivision, West Bengal","authors":"Pritam Saha, Saumyajit Ghosh, Shasanka Kumar Gayen","doi":"10.1007/s12518-024-00577-4","DOIUrl":"10.1007/s12518-024-00577-4","url":null,"abstract":"<div><p>One of the most significant natural resources, groundwater is essential to providing a long-term, reliable and sustainable global water supply. Therefore, delineating Groundwater potential zones (GWPZs) is crucial in effectively managing groundwater reserves. The present study attempts to delineate GWPZs in the Siliguri subdivision of West Bengal using integrated Remote Sensing (RS), Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) in the light of a considerable shift in the patterns of groundwater usage, especially considering the ongoing rise in demand for groundwater owing to a variety of causes. Raster layers of fourteen causative factors Viz. geomorphology, lithology, lineament density, soil texture, elevation, slope, land use and land cover (LULC), river density, rainfall, pre-monsoon groundwater depth, post-monsoon groundwater depth, groundwater fluctuation, topographic wetness index (TWI<i>)</i> and topographic roughness index (TRI) are used to delineate GWPZs using AHP in GIS software. The final GWPZs map was categorised into five classes: very high (25.67%), High (31.77%), moderate (20.73%), low (17.67%) and very low (4.15%). The results are further validated by evaluating the receiver operating characteristic (ROC) curve with the groundwater level depth from 39 dug wells. The ROC curve shows that the AUC value is 0.818, representing a prediction accuracy of 81.80%. The comprehensive map of GWPZs will enhance managing natural assets to guarantee the continued preservation of water resources and the development of agriculture. The method utilised in this research may be used in other natural contexts with a comparable environment.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 3","pages":"751 - 778"},"PeriodicalIF":2.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823449","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}

引用次数: 0

Sequential Gaussian simulation for mapping the spatial variability of saturated soil hydraulic conductivity at watershed scale 绘制流域尺度饱和土壤导水性空间变异图的序列高斯模拟法

IF 2.3

Applied Geomatics Pub Date : 2024-07-18 DOI: 10.1007/s12518-024-00580-9

Rodrigo César de Vasconcelos dos Santos, Tirzah Moreira Siqueira, Mauricio Fornalski Soares, Rômulo Félix Nunes, Luís Carlos Timm

{"title":"Sequential Gaussian simulation for mapping the spatial variability of saturated soil hydraulic conductivity at watershed scale","authors":"Rodrigo César de Vasconcelos dos Santos, Tirzah Moreira Siqueira, Mauricio Fornalski Soares, Rômulo Félix Nunes, Luís Carlos Timm","doi":"10.1007/s12518-024-00580-9","DOIUrl":"10.1007/s12518-024-00580-9","url":null,"abstract":"<div><p>The saturated soil hydraulic conductivity (K<sub>sat</sub>) exhibits high spatial variability due to the various physical, chemical, and biological processes that act simultaneously with different intensities in soil formation. The use of geostatistics as a tool to study soil heterogeneity facilitates the understanding of the spatial variability of K<sub>sat</sub>. This study aimed to simulate the spatial variability of K<sub>sat</sub> and evaluate its uncertainties using sequential Gaussian simulation (SSG) in a tropical watershed located in southern Brazil. Soil sampling was conducted in an experimental watershed-scale grid with a sample spacing of 300 m, and K<sub>sat</sub> was analyzed. Descriptive statistics were applied to assess the behavior of K<sub>sat</sub> spatial variability, followed by geostatistical analysis, specifically SSG. Variogram parameters were defined, and SSG was used to generate 100 equiprobable random fields. The results showed that K<sub>sat</sub> in the Santa Rita watershed (SRW) is heterogeneous, and uncertainties among the hundred fields ranged from 58.70 to 81.10 mm h-1 for the 5th and 95th percentiles, respectively, possibly influenced by soil type, land use, density, and texture. The criteria for validating SSG simulation were met and successfully described the spatial continuity of K<sub>sat</sub> in the SRW. Thus, SSG proved to be an effective tool for understanding the magnitude and structure of K<sub>sat</sub> spatial variability at the watershed scale, contributing to effective soil and water management in the SRW.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 3","pages":"719 - 730"},"PeriodicalIF":2.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826246","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}

引用次数: 0

Geoinformatics and Analytic Hierarchy Process (AHP) in modelling groundwater potential in Obudu Plateau, Southeastern Nigeria Bamenda Massif 地理信息学和层次分析法（AHP）在尼日利亚东南部巴门达高原奥布杜高原地下水潜力建模中的应用

IF 2.3

Applied Geomatics Pub Date : 2024-07-13 DOI: 10.1007/s12518-024-00565-8

Gregory Udie Sikakwe, Andrew Uzondu Onwusulu, Samuel Adebayo Ojo, Henry Ibe Agunanna

{"title":"Geoinformatics and Analytic Hierarchy Process (AHP) in modelling groundwater potential in Obudu Plateau, Southeastern Nigeria Bamenda Massif","authors":"Gregory Udie Sikakwe, Andrew Uzondu Onwusulu, Samuel Adebayo Ojo, Henry Ibe Agunanna","doi":"10.1007/s12518-024-00565-8","DOIUrl":"10.1007/s12518-024-00565-8","url":null,"abstract":"<div><p>Water is a vital resource used in effective sanitation, hygiene, drinking and agricultural uses. This study was in Obudu Plateau covering an area of 3,053.08km<sup>2</sup>. Analysis of remote sensing, geographic information system (GIS), and global positioning system (GPS) data obtained from satellite imageries and digital elevation model (DEM) assessed groundwater potential. The model was validated using borehole data in the area. Thematic layers of geology, lineament density, slope, geomorphology, land use and land cover and drainage density were integrated using GIS software. Multicriteria evaluation of the layers was by analytic hierarchy process (AHP). Pairwise comparison matrix shows consistency the consistency ratio is 0.07 or 7%. This shows the comparison of groundwater controlling factors is within acceptable limit of consistency. Overlay analysis produced groundwater potential map classified into five zones of very high 2.66% (81.31km<sup>2</sup>), high 6.92% (211.38km<sup>2</sup>) very low 9.60% (292.69km<sup>2</sup>), moderate 46.95% (1,433.46 km<sup>2</sup>) and low 33.87% (1,034.23km<sup>2</sup>). Structural geological setting determines largely the suitability of an area to groundwater occurrence. Overlaying each thematic layer with lineament density map produced a more credible groundwater potential model compared to preceding related works. This method is suitable for both local and regional groundwater development.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"16 3","pages":"695 - 718"},"PeriodicalIF":2.3,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141651543","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}

引用次数: 0