Applied Geomatics最新文献

{"title":"A Wide-Area Deep Ocean Floor Mapping System: Design and Sea Tests","authors":"Paul Ryu, David Brown, Kevin Arsenault, Byunggu Cho, Andrew I. March, W. Ali, A. Charous, Pierre FJ Lermusiaux","doi":"10.3390/geomatics3010016","DOIUrl":"https://doi.org/10.3390/geomatics3010016","url":null,"abstract":"Mapping the seafloor in the deep ocean is currently performed using sonar systems on surface vessels (low-resolution maps) or undersea vessels (high-resolution maps). Surface-based mapping can cover a much wider search area and is not burdened by the complex logistics required for deploying undersea vessels. However, practical size constraints for a towbody or hull-mounted sonar array result in limits in beamforming and imaging resolution. For cost-effective high-resolution mapping of the deep ocean floor from the surface, a mobile wide-aperture sparse array with subarrays distributed across multiple autonomous surface vessels (ASVs) has been designed. Such a system could enable a surface-based sensor to cover a wide area while achieving high-resolution bathymetry, with resolution cells on the order of 1 m2 at a 6 km depth. For coherent 3D imaging, such a system must dynamically track the precise relative position of each boat’s sonar subarray through ocean-induced motions, estimate water column and bottom reflection properties, and mitigate interference from the array sidelobes. Sea testing of this core sparse acoustic array technology has been conducted, and planning is underway for relative navigation testing with ASVs capable of hosting an acoustic subarray.","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86616634","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 GIS-based landslide susceptibility assessment and mapping around the Aba Libanos area, Northwestern Ethiopia","authors":"Dawit Asmare,&nbsp;Chalachew Tesfa,&nbsp;Mulusew Minuyelet Zewdie","doi":"10.1007/s12518-023-00499-7","DOIUrl":"10.1007/s12518-023-00499-7","url":null,"abstract":"<div><p>The geological hazards caused by natural and manmade activities pose serious property damage, loss of life, and changes in the earth’s features. In this work, GIS-based landslide susceptibility mapping was carried out using the Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) methods for the Chemoga River Sub-Basin (CRSB), in the Aba Libanos area in Northwestern Ethiopia. To produce a susceptibility map, eight influencing factors were selected. They are elevation, slope, aspect, Lithology, land use land cover, curvature, distance to drainage, and distance lineaments. All those influencing factors were statistically analyzed to decide their relationship to past landslides. The relationships between the observed landslide areas and these eight related factors were identified using GIS-based statistical models including AHP and FR. Detailed fieldwork (lithological description and mapping, geological structural measurements, and taking considerations for the impact of each influencing factor on the occurrence of landslides in the area) was conducted to interpret and produce the various maps of the study area. The AHP modeling susceptibility map of the study area was 9.6%, 15.4%, 29.7%, 27.8%, and 17.5% very low, low, moderate, high, and very high respectively. Similarly, based on the value of FR, the study area was classified into five susceptibility zones, 20.7%, 14.6%, 13.0%, 18.6%, and 33.0% very low, low, moderate, high, and very high respectively. Both results showed that steep side slopes and lineaments are very high landslide susceptibility zones. Lastly, the landslide susceptibility maps produced from the two models were validated with detailed fieldwork measurements and observation. Prediction accuracy of these maps that the landslide inventory map was overlaid on the AHP and FR maps. Both susceptibility maps show almost similar results and mainly, introduced some parts of the study areas of the Chemoga river sub-basin (CRSB) as landslide-prone areas.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50039955","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":"Curvature Weighted Decimation: A Novel, Curvature-Based Approach to Improved Lidar Point Decimation of Terrain Surfaces","authors":"P. Schrum, Carter Jameson, Laura G. Tateosian, G. Blank, K. Wegmann, S. A. Nelson","doi":"10.3390/geomatics3010015","DOIUrl":"https://doi.org/10.3390/geomatics3010015","url":null,"abstract":"Increased availability of QL1/QL2 Lidar terrain data has resulted in large datasets, often including large quantities of redundant points. Because of these large memory requirements, practitioners often use decimation to reduce the number of points used to create models. This paper introduces a novel approach to improve decimation, thereby reducing the total count of ground points in a Lidar dataset while retaining more accuracy than Random Decimation. This reduction improves efficiency of downstream processes while maintaining output quality nearer to the undecimated dataset. Points are selected for retention based on their discrete curvature values computed from the mesh geometry of the TIN model of the points. Points with higher curvature values are preferred for retention in the resulting point cloud. We call this technique Curvature Weighted Decimation (CWD). We implement CWD in a new free, open-source software tool, CogoDN, which is also introduced in this paper. We evaluate the effectiveness of CWD against Random Decimation by comparing the resulting introduced error values for the two kinds of decimation over multiple decimation percentages, multiple statistical types, and multiple terrain types. The results show that CWD reduces introduced error values over Random Decimation when 15 to 50% of the points are retained.","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91246898","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":"Semi-automatic extraction of land degradation processes using multi sensor data by applying object based classification technique","authors":"Sudhanshu Raghubanshi,&nbsp;Ritesh Agrawal,&nbsp;A. S. Rajawat,&nbsp;D. Ram Rajak","doi":"10.1007/s12518-023-00503-0","DOIUrl":"10.1007/s12518-023-00503-0","url":null,"abstract":"<div><h2>Abstract\u0000</h2><div><p>A semi-automated method has been developed for the extraction of land degradation processes using multi sensor data by applying an object-based classification. The object-based approach creates homogenous objects, which is the key component of this classification. The study utilized optical satellite (Landsat-8), microwave (RISAT-1, SAR) and Cartosat-1 digital elevation model (DEM) over Kanpur Dehat district, Uttar Pradesh, and Surendranagar district, Gujarat, India. The objects were created using Shepherd segmentation algorithm. Normalized difference vegetation index (NDVI) was used to classify the degraded and no apparent degradation (NAD) objects based on the three seasons (rabi, summer, and kharif) Landsat-8 bands. Degraded objects were further classified into salinity, forest water erosion, and water logging using brightness index based on Landsat-8, proximity analysis near the river channel using RISAT-1, and low-lying area using DEM, respectively. The digitally generated results were validated with manual digitized desertification status maps (DSM) published by Space Applications Centre, Ahmedabad, India. The overall accuracy and kappa coefficient for Kanpur Dehat and Surendranagar districts were found 84.67%, 0.79 and 72.33%, 0.60, respectively. This study was carried out based on integrated analysis of different satellites (optical, microwave, and DEM). The advantage of newly designed framework offers less chance of mixing and narrowing down of the area for further classification with better accuracy. The developed framework is based on analytical approach, which was tested and implemented in the Python environment with efficient computing power. The study illustrates that the developed approach is independent of climatic-topographic conditions and executed over pilot study sites, which could be extended over larger regions of the land use/land cover for land degradation mapping.</p></div></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-023-00503-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50036510","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":"Modeling spatio temporal pattern of urban land use and land cover change by using geospatial technology: a case of Shambu Town, Horo Guduru Wallaga, Ethiopia","authors":"Lachisa Busha Hinkosa,&nbsp;Misgana Lamessa Dinsa,&nbsp;Gamachu Tuge Zalaqa,&nbsp;Mitiku Badasa Moisa","doi":"10.1007/s12518-023-00504-z","DOIUrl":"10.1007/s12518-023-00504-z","url":null,"abstract":"<div><p>\u0000Metropolitan and town planners in Ethiopia are dealing with high racial tensions concerned about the high rate of urban expansion which is posing a challenge to get efficient urban planning. Therefore, this study aimed to evaluate urban land use and land cover (LULC) changes in Shambu Town over the past three decades and to forecast the futurity of urban expansion. The LULC classification was performed by using supervised classification with maximum likelihood from Landsat images of 1990, 2000, 2010, and 2020. The rapid rise of the urban population is a source of urban expansion. According to the study, every LULC type in the study area has been transformed from one LULC to another types. The result shows that agriculture, forest, and grassland land cover declined by 217.2 ha, 39 ha, and 54.8 ha, respectively, in the study area from 1990 to 2020. However, the built-up area increased by 311 ha within the past three decades. However, over the study period, agriculture and grassland both decreased by 474.7 ha and 66.3 ha, respectively. From LULC types of the study area, built-up area and forest land will be expanded by an area of 1064.3 ha and 170.5 ha respectively, in the coming 2050. Based on the finding of this study, we suggested that urban planners, land administration and management offices, environmental protection offices, and other stakeholders can investigate the impacts of LULC change and urban expansion on natural resources and ecological service systems, as well as the impact on people’s livelihoods in the future for natural and land resource management.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50036512","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":"Feature Extraction and Classification of Canopy Gaps Using GLCM- and MLBP-Based Rotation-Invariant Feature Descriptors Derived from WorldView-3 Imagery","authors":"Colbert M. Jackson, Elhadi Adam, Iqra Atif, Muhammad A. Mahboob","doi":"10.3390/geomatics3010014","DOIUrl":"https://doi.org/10.3390/geomatics3010014","url":null,"abstract":"Accurate mapping of selective logging (SL) serves as the foundation for additional research on forest restoration and regeneration, species diversification and distribution, and ecosystem dynamics, among other applications. This study aimed to model canopy gaps created by illegal logging of Ocotea usambarensis in Mt. Kenya Forest Reserve (MKFR). A texture-spectral analysis approach was applied to exploit the potential of WorldView-3 (WV-3) multispectral imagery. First, texture properties were explored in the sub-band images using fused grey-level co-occurrence matrix (GLCM)- and local binary pattern (LBP)-based texture feature extraction. Second, the texture features were fused with colour using the multivariate local binary pattern (MLBP) model. The G-statistic and Euclidean distance similarity measures were applied to increase accuracy. The random forest (RF) and support vector machine (SVM) were used to identify and classify distinctive features in the texture and spectral domains of the WV-3 dataset. The variable importance measurement in RF ranked the relative influence of sets of variables in the classification models. Overall accuracy (OA) scores for the respective MLBP models were in the range of 80–95.1%. The respective user’s accuracy (UA) and producer’s accuracy (PA) for the univariate LBP and MLBP models were in the range of 67–75% and 77–100%, respectively.","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135489448","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":"The green belt of Baqubah city between reality and ambition","authors":"Thikra Adel Mahmoud, Suha Salem Ali","doi":"10.1007/s12518-023-00492-0","DOIUrl":"https://doi.org/10.1007/s12518-023-00492-0","url":null,"abstract":"","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74199940","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":"Object-based classification of hyperspectral images based on weighted genetic algorithm and deep learning model","authors":"Davood Akbari,&nbsp;Vahid Akbari","doi":"10.1007/s12518-023-00500-3","DOIUrl":"10.1007/s12518-023-00500-3","url":null,"abstract":"<div><p>Numerous uses of the hyperspectral remote sensing technology exist for identifying land cover and tracking its evolution. The classification of hyperspectral images must now take into account both spectral and spatial information due to recent advancements and the production of images with high spatial resolution. Convolutional neural networks (CNNs) have much employed in recent years to enhance the classification precision of hyperspectral images. The simultaneous use of spatial feature extraction methods in CNNs has not received significant attention in prior studies. In this study, a novel CNN architecture has been developed for classifying hyperspectral images. The weighted genetic (WG) algorithm is used in the proposed technique to minimize the hyperspectral image’s dimensions. The WG algorithm keeps every band in the image and gives each one weight between zero and one based on how much information it contains. Following the expectation maximization (EM) method to the collected features, the segmented objects are then categorized using the CNN algorithm. Three benchmark hyperspectral images, Pavia, DC Mall, and Indiana Pine, were used to assess the proposed approach. The trials’ findings demonstrate the proposed approach’s superiority over the multilayer perceptron (MLP) algorithm in the Pavia, DC Mall, and Indiana Pine images by 14, 16, and 8% in the overall accuracy parameter, respectively.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-023-00500-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50050130","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":"Automating the Management of 300 Years of Ocean Mapping Effort in Order to Improve the Production of Nautical Cartography and Bathymetric Products: Shom’s Téthys Workflow","authors":"Julian Le Deunf, T. Schmitt, Yann Keramoal, Ronan Jarno, Morvan Fally","doi":"10.3390/geomatics3010013","DOIUrl":"https://doi.org/10.3390/geomatics3010013","url":null,"abstract":"With more than 300 years of existence, Shom is the oldest active hydrographic service in the world. Compiling and deconflicting this much history automatically is a real challenge. This article will present the types of data Shom has to manipulate and the different steps of the workflow that allows Shom to compile over 300 years of bathymetric knowledge. The Téthys project for Shom will be presented in detail. The implementation of this type of process is a scientific, algorithmic, and infrastructure challenge.","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88750308","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":"Integrating random forest and synthetic aperture radar improves the estimation and monitoring of woody cover in indigenous forests of South Africa","authors":"Mcebisi Qabaqaba,&nbsp;Laven Naidoo,&nbsp;Philemon Tsele,&nbsp;Abel Ramoelo,&nbsp;Moses Azong Cho","doi":"10.1007/s12518-023-00497-9","DOIUrl":"10.1007/s12518-023-00497-9","url":null,"abstract":"<div><p>Woody canopy cover (CC) is important for characterising terrestrial ecosystems and understanding vegetation dynamics. The lack of accurate calibration and validation datasets for reliable modelling of CC in the indigenous forests in South Africa contributes to uncertainties in carbon stock estimates and limits our understanding of how they might influence long-term climate change. The aim of this study was to develop a method for monitoring CC in the Dukuduku indigenous forest in South Africa. Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics of 2008, 2015, and 2018, polarimetric features, and Grey Level Co-occurrence Matrix (GLCMs) were used. Machine learning models Random Forest (RF) vs Support Vector Machines (SVM) were developed and calibrated using Collect Earth Online (CEO) data, a free and open-access land monitoring tool developed by the Food and Agriculture Organisation (FAO). The addition of GLCMs produced the highest accuracy in 2008, <i>R</i>² (RMSE) = 0.39 (36.04%), and in 2015, <i>R</i>² (RMSE) = 0.51 (27.82%), and in 2018, only SAR variables gave the highest accuracy <i>R</i>² (RMSE) = 0.55 (29.50). The best-performing models for 2008, 2015, and 2018 were based on RF. During the ten-year study period, shrubland and wooded grassland had the highest transition, at 6% and 13%, respectively. The observed changes in the different canopies provide valuable insights into the vegetation dynamics of the Dukuduku indigenous forest. The modelling results suggest that the CEO calibration data can be improved by integrating airborne LiDAR data.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-023-00497-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50040910","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}