Applied Geomatics最新文献

{"title":"Quantifying Aboveground Grass Biomass Using Space-Borne Sensors: A Meta-Analysis and Systematic Review","authors":"Reneilwe Maake, Onisimo Mutanga, George Chirima, Mbulisi Sibanda","doi":"10.3390/geomatics3040026","DOIUrl":"https://doi.org/10.3390/geomatics3040026","url":null,"abstract":"Recently, the move from cost-tied to open-access data has led to the mushrooming of research in pursuit of algorithms for estimating the aboveground grass biomass (AGGB). Nevertheless, a comprehensive synthesis or direction on the milestones achieved or an overview of how these models perform is lacking. This study synthesises the research from decades of experiments in order to point researchers in the direction of what was achieved, the challenges faced, as well as how the models perform. A pool of findings from 108 remote sensing-based AGGB studies published from 1972 to 2020 show that about 19% of the remote sensing-based algorithms were tested in the savannah grasslands. An uneven annual publication yield was observed with approximately 36% of the research output from Asia, whereas countries in the global south yielded few publications (<10%). Optical sensors, particularly MODIS, remain a major source of satellite data for AGGB studies, whilst studies in the global south rarely use active sensors such as Sentinel-1. Optical data tend to produce low regression accuracies that are highly inconsistent across the studies compared to radar. The vegetation indices, particularly the Normalised Difference Vegetation Index (NDVI), remain as the most frequently used predictor variable. The predictor variables such as the sward height, red edge position and backscatter coefficients produced consistent accuracies. Deciding on the optimal algorithm for estimating the AGGB is daunting due to the lack of overlap in the grassland type, location, sensor types, and predictor variables, signalling the need for standardised remote sensing techniques, including data collection methods to ensure the transferability of remote sensing-based AGGB models across multiple locations.","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135888568","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":"Applying a Geographic Information System and Other Open-Source Software to Geological Mapping and Modeling: History and Case Studies","authors":"Mauro De Donatis, Giulio Fabrizio Pappafico","doi":"10.3390/geomatics3040025","DOIUrl":"https://doi.org/10.3390/geomatics3040025","url":null,"abstract":"Open-source software applications, especially those useful for GIS, have been used in the field of geology both in research and teaching at the University of Urbino for decades. The experiences described in this article range from land-surveying cases to cartographic processing and 3D printing of geological models. History of their use and development is punctuated by trials, failures, and slowdowns, but the idea of using digital tools in areas where they are traditionally frowned upon, such as in soil geology, is now rooted in and validated by applications in projects of various types. Although the current situation is not definitive, given that the evolution of information technology provides increasingly faster tools that are performance-oriented and easier to use, this article aims to contribute to the development of methodologies through an exchange of information and experiences.","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135853084","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":"Multisensor analysis for environmental targets identification in the region of Funil dam, state of Minas Gerais, Brazil","authors":"Marcelo de Carvalho Alves,&nbsp;Luciana Sanches,&nbsp;Fortunato Silva de Menezes,&nbsp;Lídia Raiza Sousa Lima Chaves Trindade","doi":"10.1007/s12518-023-00523-w","DOIUrl":"10.1007/s12518-023-00523-w","url":null,"abstract":"<div><p>The use of remote sensing to map land cover and changes in land use has proven to be a practical, reliable, and accessible approach. These images provide precise details about the landscape, utilizing image processing techniques, modeling, and classification algorithms. This study aimed to identify different areas, such as coffee plantations, water bodies, urban areas, forests, exposed soil, and pastures in the Funil reservoir region of Minas Gerais, Brazil. Image data from Landsat-8, Sentinel-1, and Sentinel-2 satellites for June 2021 were used. Different supervised classification algorithms such as rf, rpart1SE, and svmLinear2 were applied based on a large volume of remote sensing data. The analyses and maps were performed using the software RStudio, considering a significance level of 5%. The highest accuracy and kappa index values were found for the rf algorithm, followed by svmLinear2 and rpart1SE. The results showed that the rf algorithm achieved the highest accuracy and kappa index values, followed by svmLinear2 and rpart1SE. However, during the validation phase, the svmLinear2 algorithm outperformed based on the statistical results of the confusion matrix. Therefore, it was considered the most suitable for generating the thematic mapping of the landscape. This is because svmLinear2 identified a more significant number of coffee areas and better-distinguished vegetation areas.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135015061","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":"RETRACTED ARTICLE: Automatic geological mapping using remote sensing data: case of the Zgounder deposit (Anti-Atlas, Morocco)","authors":"Driss Elhamdouni,&nbsp;Ismail Karaoui,&nbsp;Abdelkrim Arioua","doi":"10.1007/s12518-023-00524-9","DOIUrl":"10.1007/s12518-023-00524-9","url":null,"abstract":"","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135109907","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":"Land Use and Land Cover Changes in Kabul, Afghanistan Focusing on the Drivers Impacting Urban Dynamics during Five Decades 1973–2020","authors":"Hayatullah Hekmat, Tauseef Ahmad, Suraj Kumar Singh, Shruti Kanga, Gowhar Meraj, Pankaj Kumar","doi":"10.3390/geomatics3030024","DOIUrl":"https://doi.org/10.3390/geomatics3030024","url":null,"abstract":"This study delves into the patterns of urban expansion in Kabul, using Landsat and Sentinel satellite imagery as primary tools for analysis. We classified land use and land cover (LULC) into five distinct categories: water bodies, vegetation, barren land, barren rocky terrain, and buildings. The necessary data processing and analysis was conducted using ERDAS Imagine v.2015 and ArcGIS 10.8 software. Our main objective was to scrutinize changes in LULC across five discrete decades. Additionally, we traced the long-term evolution of built-up areas in Kabul from 1973 to 2020. The classified satellite images revealed significant changes across all categories. For instance, the area of built-up land reduced from 29.91% in 2013 to 23.84% in 2020, while barren land saw a decrease from 33.3% to 28.4% over the same period. Conversely, the proportion of barren rocky terrain exhibited an increase from 22.89% in 2013 to 29.97% in 2020. Minor yet notable shifts were observed in the categories of water bodies and vegetated land use. The percentage of water bodies shrank from 2.51% in 2003 to 1.30% in 2013, and the extent of vegetated land use showed a decline from 13.61% in 2003 to 12.6% in 2013. Our study unveiled evolving land use patterns over time, with specific periods recording an increase in barren land and a slight rise in vegetated areas. These findings underscored the dynamic transformation of Kabul’s urban landscape over the years, with significant implications for urban planning and sustainability.","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136192954","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-based image interpretation and geotagging using artificial intelligence and open-source geospatial technology","authors":"Arati Paul,&nbsp;Sakshi Chauhan,&nbsp;Dibyendu Dutta","doi":"10.1007/s12518-023-00522-x","DOIUrl":"10.1007/s12518-023-00522-x","url":null,"abstract":"<div><p>Image geotagging is a process where geographic coordinates are attached to an image. Mobile-based geotagging application has many advantages, viz. real-time monitoring, ensuring data authenticity etc. Since an ordinary mobile camera cannot interpret the geotagged images, they are manually analysed later for a specific purpose. Therefore, the human interpreters are to put their time and effort to analyse the images. This becomes difficult when the number of images is more. The heterogeneity of captured images, in terms of intensity, viewing angle etc., limits the application of traditional image processing techniques for automatic image interpretation. Hence, artificial intelligence (AI)–based image processing technique needs to be employed that enables machines to learn from instances and provide assistance in field photo interpretation. In the present work, a smartphone-based application, embedded with enhanced capabilities of AI and geospatial technology, has been developed using open-source technology. The application employs AI to detect certain categories’ semantic objects and automatically generates their details. The mean of detection precision, recall and F1 score are estimated as 0.96, 0.91 and 0.93, respectively. The present work successfully demonstrates the use of open-source technology for AI-enabled geotagging and dissemination of ground information through WebGIS application.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89477878","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":"Unraveling the complexities of land transformation and its impact on urban sustainability through land surface temperature analysis","authors":"Saleha Jamal,&nbsp;Mohd Saqib,&nbsp;Wani Suhail Ahmad,&nbsp;Manal Ahmad,&nbsp;Md Ashif Ali,&nbsp;Md Babor Ali","doi":"10.1007/s12518-023-00521-y","DOIUrl":"10.1007/s12518-023-00521-y","url":null,"abstract":"<div><p>Due to the ongoing population increase over the past years, fast and unchecked urbanization has been occurring in the urban centers of developing nations like India. As a result, land transformation is taking place at a fast pace leading to the creation of urban heat island (UHI). Urban heat island (UHI) constitutes a significant human alteration to the Earth system. Hence, this study presents a rigorous and comprehensive analysis of the impact of land use and cover on land surface temperature (LST) in Aligarh City, Uttar Pradesh, India, using multi-dimensional satellite data. The research collected Landsat data for four different phases (1991, 2001, 2011, and 2021) and analyzed it in conjunction with land use and cover (LULC) data to identify trends and variations. The result shows a consistent increase in LST since 1991, with built-up and bare land areas exhibiting the highest temperatures across all phases. Moreover, the study found that impervious land had the most significant effect on LST, followed by water bodies and vegetation cover. The analysis of the proportion of the area with the lowest and highest LST showed interesting trends, with a greater portion of Aligarh City experiencing a temperature range between 15 and 16 °C in 2021 compared to previous years. However, the study also found that 13.55% of the area had a maximum LST of over 17 °C, which is higher than the previous measurement of 9.04%, and has been steadily increasing since 1991. The accuracy of the study was verified by detecting elevated temperatures in non-porous areas and cooler temperatures near green zones and water bodies. This study’s contribution to the research community lies in the data-driven, systematic analysis of the complex relationship between land use and cover and LST in an urban environment. The study’s findings suggest that alterations in land use/cover patterns have a significant impact on LST, which has important implications for urban planning policies. The research provides valuable insights for urban planners, policymakers, and city officials, as it highlights the need for sustainable and efficient urban planning policies to mitigate the effects of urban heat islands and rising temperatures. The study’s results have broader implications beyond Aligarh City and can inform land-use planning and policymaking in other cities facing similar challenges. This research presents a comprehensive analysis that can serve as a framework to inform land-use planning and policymaking, contributing to the development of sustainable and efficient urban environments.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50053335","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":"Temporal Autocorrelation of Sentinel-1 SAR Imagery for Detecting Settlement Expansion","authors":"J. Kapp, J. Kemp","doi":"10.3390/geomatics3030023","DOIUrl":"https://doi.org/10.3390/geomatics3030023","url":null,"abstract":"Urban areas are rapidly expanding globally. The detection of settlement expansion can, however, be challenging due to the rapid rate of expansion, especially for informal settlements. This paper presents a solution in the form of an unsupervised autocorrelation-based approach. Temporal autocorrelation function (ACF) values derived from hyper-temporal Sentinel-1 imagery were calculated for all time lags using VV backscatter values. Various thresholds were applied to these ACF values in order to create urban change maps. Two different orbital combinations were tested over four informal settlement areas in South Africa. Promising results were achieved in the two of the study areas with mean normalized Matthews Correlation Coefficients (MCCn) of 0.79 and 0.78. A lower performance was obtained in the remaining two areas (mean MCCn of 0.61 and 0.65) due to unfavorable building orientations and low building densities. The first results also indicate that the most stable and optimal ACF-based threshold of 95 was achieved when using images from both relative orbits, thereby incorporating more incidence angles. The results demonstrate the capacity of ACF-based methods for detecting settlement expansion. Practically, this ACF-based method could be used to reduce the time and labor costs of detecting and mapping newly built settlements in developing regions.","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77524279","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":"Exploring the capability of high-resolution satellite data in delineating the potential distribution of common invasive alien plant species in the Tshivhase Tea Estate","authors":"Fhulufhedzani Nembambula,&nbsp;Oupa E. Malahlela,&nbsp;Lutendo Mugwedi","doi":"10.1007/s12518-023-00520-z","DOIUrl":"10.1007/s12518-023-00520-z","url":null,"abstract":"<div><p>Invasive alien plants (IAPs) continue to exert significant impacts on agriculture in many countries, resulting in food insecurity. IAPs reduce agricultural production through competition and parasitism with planted crops. More recently, the IAPs continue to extend their plasticity to tea plantations, especially in tropical and subtropical areas. This study thus aimed at exploring the potential of SPOT 7 and Sentinel 2 satellite data in mapping the occurrence and co-occurrence of three common IAPs <i>Solanum mauritianum</i>, <i>Lantana camara</i>, and <i>Chromolaena odorata</i> in the Tshivhase Tea Estate in Limpopo Province, South Africa. The stepwise logistic regression models were generated for <i>Solanum mauritianum</i> and <i>Lantana camara</i> occurrence as well as the observed and conditional co-occurrence probability of <i>S. mauritianum</i> (P1), <i>L. camara</i> (P2) and <i>C. odorata</i> (P3). From the remote sensing indices, the Brightness Index (BI) was significant in most SPOT 7 stepwise logistic regression models at <i>p</i>&lt;0.05 whereas the blue, red, and near infrared (NIR) bands and standard deviation (STDv) variables were significant at <i>p</i>&lt;0.05 in most of the Sentinel 2 models. The SPOT 7 model performed Sentinel-2 models, thus resulting in the area under the curve (AUC) of 0.96 for the conditional co-occurrence of <i>S. mauritianum</i> (P1) and <i>L. camara</i> (P2). The Sentinel 2 model yielded an AUC of 0.83. The SPOT 7 model performed superior in mapping the conditional co-occurrence of <i>S. mauritianum</i> and <i>L. camara</i> than the Sentinel 2 model. These results suggest that high spatial resolution satellite images like SPOT 7 can delineate the potential distribution of IAPs in the tea plantation and thus assisting in management strategies geared towards IAP’s elimination and control.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-023-00520-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50074013","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":"Spatio-temporal analysis of river channel pattern in lower course of River Ravi using GIS and remote sensing","authors":"Noor-ul Huda,&nbsp;Shakeel Mahmood,&nbsp;Rida Sajjid,&nbsp;Muhammad Irfan Ahamad","doi":"10.1007/s12518-023-00519-6","DOIUrl":"10.1007/s12518-023-00519-6","url":null,"abstract":"<div><p>This study aims to detect changes that occurred in Ravi River channel over the period of last three decades (1990 to 2020). This paper spatially and temporally assesses the changes and geo-visualize variation of Ravi River using Landsat imageries. The maximum likelihood image classification technique has been used to process and analyze the spatial data in geographic information system (GIS) environment. It was found from the results that vegetation cover has gradually decreased from 976 km² in 1990 to 905 km² in 2019, whereas the built-up land had increased from 82 to 188 km² in the same temporal extent. Generally, the channel is shifted from east to west and the growth of built-up land to towards river which has pushed the channel. Similarly, the extreme discharge also causes change in channel shifting. Minor floods have been occurred after 2010 but Ravi is not affected much as their discharge was not that much higher to put any abrupt or significant effect on Ravi River’s channel pattern.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50030869","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}