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Multi resolution appraisal of Cork Harbour estuary: An object based image analysis approach

GEOMORPHOLOGY 2023年第1期439卷

作者： Summers, Gerard Lim, Aaron Wheeler, Andrew J. Univ Coll Cork Environm Res Inst Sch Biol Earth & Environm Sci Distillery FieldsNorth Mall Cork T23 TK30 Ireland Univ Coll Cork UCC Dept Geog Cork T12 K8AF Ireland Univ Coll Cork Environm Res Inst Cork Ireland Univ Coll Cork Irish Ctr Appl Geosci ICRAG Marine & Renewable Energy Inst MaREI Cork P43 C573 Ireland

Lithic sedimentary benthic habitats are often organised by the overlying hydrodynamic regime, generating seabed sedimentary bedforms (SSBs) whose morphology and sediment composition are controlled by the current flow properties including speed, direction, and duration. Furthermore, SSBs can manifest at a variety of spatial scales reflecting different aspects of the hydrodynamic regime that operate at distinct intensities and temporal scales. Acoustic seabed mapping of SSBs permits the quantification of the complex seafloor hydrodynamics without in situ measurements. As such, seabed mapping efforts must determine the minimum spatial resolution required to effectively capture the complete seabed hydrodynamic picture. Here, we provide a semi-automated, object-based image analysis (OBIA) of SSBs occurring over 0.5, 1.0, 1.5, 2.0, and 2.5 m spatial resolutions of multibeam echosounder (MBES) data acquired in Cork Harbour, Ireland, to ascertain the optimum resolution at which to derive current velocity attributes. Estimations of seabed sediment grain size were completed through the angular range analysis (ARA) of co-located 400 kHz multibeam backscatter data. Results show that current flow estimations derived at 0.5, 1.0 and 1.5 m resolutions of MBES bathymetry agree with previous in situ current speed measurements. Moreover, sediment grain size estimations produced during the ARA of the 400 kHz backscatter data displayed a clear pattern of sediment resuspension at each section of the harbour, and this was effectively depicted at 0.5, 1.0, and 1.5 m resolution of MBES bathymetry data. However, current flow estimates derived from 2.0 and 2.5 m resolution failed to portray the short-term current variation induced by the ebb phase of the tide. Therefore, it is recommended that MBES data with a pixel size of >50 % of the wavelength of the SSB observed is insufficient for inclusion in an OBIA workflow.

关键词： object-based image analysis Seabed sedimentary bedforms Geomorphology Spatial resolution Current velocities

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Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes

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GEOCARTO INTERNATIONAL 2019年第10期34卷 1144-1161页

作者： Zhang, Meng Zeng, Yongnian Huang, Wei Li, Songnian Cent South Univ Sch Geosci & Infophys Changsha Hunan Peoples R China Cent South Univ Spatial Informat Technol & Sustainable Dev Res Ct Changsha Hunan Peoples R China Heidelberg Univ Inst Geog Heidelberg Germany Ryerson Univ Dept Civil Engn Toronto ON Canada

Remote sensing has been proven promising in wetland mapping. However, conventional methods in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM + images, which have great uncertainty due to the spectral similarity of different land covers, and pixel-based classifications may not meet the accuracy requirement. This paper proposes an approach that combines spatiotemporal fusion and object-based image analysis, using the spatial and temporal adaptive reflectance fusion model to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice, and the time series of MODIS NDVI to calculate phenological parameters for identifying wetlands with an object-based method. The results of a case study indicate that different types of wetlands can be successfully identified, with 92.38%. The overall accuracy and 0.85 Kappa coefficient, and 85% and 90% for the user's accuracies of sedge swamp and paddy respectively.

关键词： Wetland mapping spatiotemporal fusion object-based image analysis complex heterogeneity urban landscape

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image Segmentation and object-based image analysis for Environmental Monitoring: Recent Areas of Interest, Researchers' Views on the Future Priorities

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REMOTE SENSING 2020年第11期12卷 1772-1772页

作者： Johnson, Brian Alan Ma, Lei Inst Global Environm Strategies Nat Resources & Ecosyst Serv 2108-11 Kamiyamaguchi Hayama Kanagawa 2400115 Japan Nanjing Univ Sch Geog & Ocean Sci Nanjing 210023 Peoples R China Tech Univ Munich Signal Proc Earth Observat D-80333 Munich Germany

image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018-2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018-2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

关键词： GEOBIA object-based image analysis high-spatial-resolution image segmentation parameter optimization

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Automated mound detection using lidar and object-based image analysis in Beaufort County, South Carolina

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Southeastern Archaeology 2019年第1期38卷 23-37页

作者： Davis, Dylan S. Sanger, Matthew C. Lipo, Carl P. Department of Anthropology Binghamton University Binghamton NY United States

The study of precontact anthropogenic mounded features—earthen mounds, shell heaps, and shell rings—in the American Southeast is stymied by the spotty distribution of systematic surveys across the region. Many extant, yet unidentified, archaeological mound features continue to evade detection due to the heavily forested canopies that occupy large areas of the region, making pedestrian surveys difficult and preventing aerial observation. object-based image analysis (OBIA) is a tool for analyzing light and radar (lidar) data and offers an inexpensive opportunity to address this challenge. Using publicly available lidar data from Beaufort County, South Carolina, and an OBIA approach that incorporates morphometric classification and statistical template matching, we systematically identify over 160 previously undetected mound features. This result improves our overall knowledge of settlement patterns by providing systematic knowledge about past landscapes. © 2018, © Southeastern Archaeological Conference 2018.

关键词： Lidar object-based image analysis remote sensing semi-automatic survey South Carolina

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Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data

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ENGINEERING GEOLOGY 2019年 260卷 105264-000页

作者： Comert, Resul Avdan, Ugur Gorum, Tolga Nefeslioglu, Hakan A. Gumushane Univ Dept Geomat Engn Gumushane Turkey Eskisehir Tech Univ Inst Earth & Space Sci Tepebasi Turkey Istanbul Tech Univ Eurasia Inst Earth Sci Istanbul Turkey Hacettepe Univ Dept Geol Engn Ankara Turkey

The Black Sea Region of Turkey is one of the most landslide prone areas due to its high slope topography, heavy rainfall, and highly weathered hillslope material conditions. Preparation of landslide inventory maps is the first step in producing landslide susceptibility maps. Ground-based methods for mapping landslide occurrences are time-consuming and expensive. Additionally, landslide mapping based on satellite imageries and aerial photographs has some limitations, including climatic conditions, cost, and limited repetitive measurement capacity. Visual interpretation-based landslide mapping, which is based on satellite imageries and aerial photographs, is a time-consuming procedure that requires an experience-based expert opinion. Therefore, the data acquisition based on unmanned aerial vehicle (UAV) and landslide event inventory maps using an object-based classification approach can be superior to other methods in terms of speed and cost. In this study, we developed a semiautomatic model using object-based image analyses for rapid mapping of shallow landslides from the data obtained from UAVs after major landslide events in the Black Sea Region of Turkey. For this purpose, two test sites-Kurucasile (Bartin) and Cayeli (Rize)-were selected. Landslide mapping models were developed in the investigation sites, and the performance of the models was evaluated. The landslides' data obtained with the developed models were compared to the landslides' data produced by the experts. The comparison process revealed that landslides mapped by using UAV data have an accuracy rate higher than 86% according to the number of landslides and 83% according to the landslide area.

关键词： object-based image analysis UAV Landslide Kurucasile (Bartin) Cayeli (Rite)

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Quantifying and characterizing the dynamics of urban greenspace at the patch level: A new approach using object-based image analysis

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REMOTE SENSING OF ENVIRONMENT 2018年 204卷 94-108页

作者： Wang, Jing Zhou, Weiqi Qian, Yuguo Li, Weifeng Han, Lijian Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Accurate and quantitative description of spatial pattern of urban greenspace and its change over time is crucial for understanding ecosystem service provision and urban sustainability. Patches are the basic management and planning units in urban landscapes, and urban designers and planners and natural resource managers frequently seek tools to understand changes at the patch level. High spatial resolution imagery has been increasingly used to measure fine-scale changes of percent cover of urban greenspace. However, few studies have investigated the dynamics of urban greenspace at the patch level, to quantify the process of growth, shrinking, or disappearance of patches. Here, we developed a new approach to quantify and characterize urban greenspace change at the patch level, by implementing object-based image analysis. We used2.5 m resolution ALOS and SPOT image data captured in 2005 and 2009, and tested the new approach in Beijing, China. With this approach, we identify different types of change in patches of urban greenspace, such as expansion and shrinking, newly created, and totally lost, in addition to gain and loss information. The results show: 1) 9.25% of the total area (6179 ha) was generated from expansion of extant patches, while 5.36% (3582 ha) was lost due to shrinking of patches;2) 0.91% (607 ha) of urban greenspace was totally removed, and 1.23% (821 ha) was newly created;and 3) Urban greenspace with different ranges of patch size, from 0.01 to 1000 ha, all experienced large changes, with a tendency for small-sized patches (< 1 ha) to be totally removed. This new approach provides an effective tool for better understanding changes of urban greenspace, and results from this study provide important insights for planners and managers to devise target-specific measures and plans to more effectively protect urban green space.

关键词： Urban greenspace Patch dynamic object-based image analysis Ecosystem services Beijing

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Adjusting Emergent Herbaceous Wetland Elevation with object-based image analysis, Random Forest and the 2016 NLCD

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REMOTE SENSING 2019年第20期11卷 2346-2346页

作者： Munoz, David E. Cissell, Jordan R. Moftakhari, Hamed Univ Alabama Dept Civil Construct & Environm Engn Tuscaloosa AL 35487 USA Univ Alabama Dept Geog Tuscaloosa AL 35487 USA Univ Alabama Ctr Complex Hydrosyst Res Tuscaloosa AL 35487 USA

Emergent herbaceous wetlands are characterized by complex salt marsh ecosystems that play a key role in diverse coastal processes including carbon storage, nutrient cycling, flood attenuation and shoreline protection. Surface elevation characterization and spatiotemporal distribution of these ecosystems are commonly obtained from LiDAR measurements as this low-cost airborne technique has a wide range of applicability and usefulness in coastal environments. LiDAR techniques, despite significant advantages, show poor performance in generation of digital elevation models (DEMs) in tidal salt marshes due to large vertical errors. In this study, we present a methodology to (i) update emergent herbaceous wetlands (i.e., the ones delineated in the 2016 National Land Cover Database) to present-day conditions;and (ii) automate salt marsh elevation correction in estuarine systems. We integrate object-based image analysis and random forest technique with surface reflectance Landsat imagery to map three emergent U.S. wetlands in Weeks Bay, Alabama, Savannah Estuary, Georgia and Fire Island, New York. Conducting a hyperparameter tuning of random forest and following a hierarchical approach with three nomenclature levels for land cover classification, we are able to better map wetlands and improve overall accuracies in Weeks Bay (0.91), Savannah Estuary (0.97) and Fire Island (0.95). We then develop a tool in ArcGIS to automate salt marsh elevation correction. We use this 'DEM-correction' tool to modify an existing DEM (model input) with the calculated elevation correction over salt marsh regions. Our method and tool are validated with real-time kinematic elevation data and helps correct overestimated salt marsh elevation up to 0.50 m in the studied estuaries. The proposed tool can be easily adapted to different vegetation species in wetlands, and thus help provide accurate DEMs for flood inundation mapping in estuarine systems.

关键词： National Land Cover Database object-based image analysis random forest salt marsh Savannah Estuary Weeks Bay Fire Island

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An object based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran

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CATENA 2021年 198卷 105073-105073页

作者： Feizizadeh, Bakhtiar Garajeh, Mohammad Kazemi Blaschke, Thomas Lakes, Tobia Univ Tabriz Dept Remote Sensing & GIS Tabriz Iran Univ Tabriz Inst Environm Tabriz Iran Univ Salzburg Dept Geoinformat Z GIS Salzburg Austria Humboldt Univ Dept Geog Lab Geoinformat Sci Berlin Germany

Landform mapping has increasingly become part of the digital domain. While the majority of approaches evaluates Digital Elevation Models (DEM) on a per-pixel basis, some examples exist were object-based image analysis (OBIA) has been applied to terrain data to identify a variety of landforms, including glacial landforms. The main objective of this study is to develop a semi-automated object-based rule set for detecting and delineating volcanic and glacier landforms in the area of the Sahand Mountain, Northern Iran. First, we applied a multi-resolution segmentation algorithm on a freely available Sentinel-2 optical satellite image and then selected the relevant features to define appropriate segmentation scales for each landform category. object-based rule sets were then developed using spatial (DEM and its derivatives, e.g. slope, aspect, curvature and flow accumulation) and spectral information. Volcanic and glacial landforms were detected and classified into eight classes: caldera, volcanic cone, tuff formation, andesite lava, dacite lava, glacier valley, suspension valley, glacier cirque. An accuracy assessment was applied based on the fuzzy synthetic evaluation technique, together with the error matrix and kappa coefficient, using field data and geomorphological units derived from geological maps and very high resolution aerial photographs. The resulting overall accuracies for each class were 96.2%, 93.3%, 92.4%, 94.2%, 93.01, 95.1, 90.1 and 90.5, respectively. Our research demonstrated that spatial (e.g. density, shape index, length/width) and spectral (e.g. mean, brightness and standard deviation) algorithms together with a grey-level co-occurrence matrix (GLCMs) were efficient features for detecting and mapping volcanic and glacial landforms. We conclude that the OBIA-based algorithms and features provide a high potential for detecting and classifying landforms. Results of this study are of great importance for geomorphology and geology as well as geo-tourism

关键词： object-based image analysis Semi-automated approach Volcanic and glacier landform Sahand Mountain Iran

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Identifying Vegetation in Arid Regions Using object-based image analysis with RGB-Only Aerial imagery

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REMOTE SENSING 2019年第19期11卷 2308-2308页

作者： Silver, Micha Tiwari, Arti Karnieli, Arnon Ben Gurion Univ Negev Jacob Blaustein Inst Desert Res Remote Sensing Lab IL-84105 Beer Sheva Israel

Vegetation state is usually assessed by calculating vegetation indices (VIs) derived from remote sensing systems where the near infrared (NIR) band is used to enhance the vegetation signal. However VIs are pixel-based and require both visible and NIR bands. Yet, most archived photographs were obtained with cameras that record only the three visible bands. Attempts to construct VIs with the visible bands alone have shown only limited success, especially in drylands. The current study identifies vegetation patches in the hyperarid Israeli desert using only the visible bands from aerial photographs by adapting an alternative geospatial object-based image analysis (GEOBIA) routine, together with recent improvements in preprocessing. The preprocessing step selects a balanced threshold value for image segmentation using unsupervised parameter optimization. Then the images undergo two processes: segmentation and classification. After tallying modeled vegetation patches that overlap true tree locations, both true positive and false positive rates are obtained from the classification and receiver operating characteristic (ROC) curves are plotted. The results show successful identification of vegetation patches in multiple zones from each study area, with area under the ROC curve values between 0.72 and 0.83.

关键词： segmentation classification vegetation arid regions gray-level co-occurrence matrix texture object-based image analysis threshold optimization

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Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

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GEOCARTO INTERNATIONAL 2018年第10期33卷 1064-1083页

作者： Stefanidou, A. Dragozi, E. Stavrakoudis, D. Gitas, I. Z. Aristotle Univ Thessaloniki Dept Forestry & Nat Environm Fac Agr Forestry & Nat Environm Thessaloniki Greece

Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land imager), combined with object-based image analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis.

关键词： Remote sensing fuel-type mapping DMC Landsat-8 OLI object-based image analysis

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