Ground Photographs Research Articles

Clarifying the information of the «Public Cadastral Map» based on user ground photography

The necessity of clarifying the public cadastral maps using satellite images as a background is considered in this article. The reasons for mismatch between different layers and the measures to improve the quality of the original photogrammetric base are substantiated here as well. The authors developed the methodology for the information base of the "public cadastral map" service based on user ground photography in order to improve the quality of photographic bases of public cadastral maps. The authors also made the conclusions and recommendations for the implementation of such measures.

Development of a trash classification system to map potential Aedes aegypti breeding grounds using unmanned aerial vehicle imaging

Aedes aegypti mosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potential Ae. aegypti breeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productive Ae. aegypti breeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promoting Ae. aegypti proliferation, generating opportunities for further research on trash associations with disease and trash interventions.

Assessing the impact of hurricane Fiona on the coast of PEI National Park and implications for the effectiveness of beach-dune management policies

The impact of waves, storm surge, and aeolian transport associated with Post-tropical Storm Fiona (offshore significant wave height ∽ 8 m, storm surge up to 2 m) on the sandy beaches and foredunes of the north shore of Prince Edward Island National Park (PEINP), Canada, are assessed. Management policies and practices, as they apply to sandy beach systems within PEINP, are reviewed in the context of the shoreline changes attributed to Fiona. The effectiveness of these policies and practices are evaluated to inform the potential performance of beach-foredune systems as natural protection measures that mitigate the impacts of large-magnitude storms and relative sea-level rise (RSLR) on shoreline change. The analyses utilise survey data, ground photography, and unoccupied aerial vehicle (UAV) imagery collected before (October 2021 to July 2022) and after (October 2022 and May 2023) Fiona. In general, the largest dunes were characterised by erosion of the stoss slope, with landward retreat of the dune toe by < 6 m and minimal impact on crest height and position. Small foredunes (< 5 m in height) generally showed significantly greater erosion in terms of dune profiles, with dune breaching occurring at some locations. Foredunes perched on bedrock and till, which were typically smallest in size, were subject to complete erosion, thereby exposing the hard underlying surface. Overall, the impact of Fiona on sandy beach systems in PEINP was relatively modest in many locations, reflecting the success of existing management policies and practices that protect and maintain the integrity of foredunes by minimizing human impacts and avoiding ‘coastal squeeze’.

‘Tankography’ of the first battle of Bullecourt: archival sources as tools to locate battlefield sites of tank wrecks

ABSTRACT This article uses archival resources to investigate the material remains of tank warfare at Bullecourt. During the Battle of Arras, tanks were used to assist the British 1917 Spring Offensive. The first assault at Bullecourt was a complete disaster and in addition to the high infantry casualties, the majority of the tanks employed were destroyed on the battlefield. The action of the tanks, their routes and final positions in the landscape has often been neglected because accurate sources are limited. This article incorporates tankography viz. combining unit war diaries, contemporary historical aerial and ground photographs, and soldier’s testimonies to determine the spatial distribution and possible sites for locating material remains of the derelict tanks.

A Novel Optimization Approach for Revolutionizing Architectural Design in Chinese Cultural Heritage

Chinese cultural heritage architecture, a blend of traditional and modern construction methods, showcases the country's technological, artistic, and cultural advancements, yet its preservation faces challenges due to structural decay and aesthetic degeneration. Using Microtrans Maryland 4-1000, Dazu Rock Carvings, Nanchan Temple and Foguang Temple smokescreen were captured; surface images and ground photos were captured with smokescreen resolution of 5192 x 4153 pixels. The goal of the research is to use an Ensemble Ant Colony Fused Convolutional Capsule Neural Network (EAC-CCNN) to enhance fault analysis in Chinese cultural heritage structures images, then the combination of Augmented Reality (AR), and Building Information Modeling (BIM) to enhance the designing model for the safety management and decision making. The process entails gathering and annotating a variety of information, creating a hybrid EAC-CCNN model to investigate the architectural building's problem, training it, integrating it with BIM, doing on-site inspections, and utilizing AR-enhanced BIM models to analyze the flaws found. The findings demonstrate how this integrated method improves the precision of flaw diagnosis, fosters teamwork, and aids in the upkeep and preservation of cultural heritage assets. Metrics including accuracy and F1 score are used to evaluate the machine learning model recognizes and categorizes flaws in Chinese cultural heritage architecture. The defect identifying and safety management model of architectural designing outcome of the accuracy is 93.29% and F1 Score is 95.47%. During the training, validation, and testing phases, performance is evaluated by project goals. With the help of this method, machine learning models can be trained to identify patterns, identify flaws, and generate well-informed predictions in a variety of circumstances.

Biennial analysis of probable permafrost distribution for Kullu district, North‐west Himalaya using Landsat 8 satellite data

AbstractClimate change and increased anthropogenic activity affect permafrost distribution, threatening ecosystem stability, water resources, and greenhouse gas emissions. In this study, we present updated probable permafrost maps at 30 m resolution for Kullu district of Himachal Pradesh derived from the predicted mean annual air temperature (MAAT) (2013 to 2020) based on Landsat 8 land surface temperature (LST). We also analysed the mean annual snow cover extent of the study area for this time period. Our probable permafrost distribution map shows the areas falling under permafrost regions in snow‐covered areas; permafrost regions in non‐snow‐covered areas; non permafrost regions along with glacierised regions. The temporal variation of permafrost over 7 years from 2013 to 2020 shows that permafrost distribution follows a cyclic variation ranging between 4.42% and 7.51% of the total area of the district. The permafrost maps were validated with the presence of rock glaciers as seen on high resolution satellite images and the ground photographs. The maps display the expected spatial pattern of near‐surface air temperatures that match with the known permafrost boundary. The findings of this study provide more precise information on permafrost distribution and essential data for future permafrost research in Kullu district.

Mapping lichen abundance in ice-free areas of Larsemann Hills, East Antarctica using remote sensing and lichen spectra

Lichen communities are known to be most resistant and adapted organisms to the extreme environments; however, their abundance is not well mapped. Extensive lichen surveys were conducted as part of the 39th Indian scientific expedition and in-situ spectra (350 nm–2500 nm) of lichens were collected in the Larsemann Hills, East Antarctica during austral summer of 2020. Lichen abundance mapping was carried out with the help of Sentinel-2 MSI L2 data and surveyed records along with in-situ spectra. We generated feature collections for lichen, snow, water, bare surface and trained a random forest (RF) classification algorithms implemented in GEE and generated multi-class outputs. We finally merged all non-lichen classes and produced binary pixels with a confidence value (between 0 and 100) depicting similarity of its spectral response to that of a lichen pixel. Total 92 lichen points, 20 bare rock points, 26 points of water and 74 snow points were used to generate the probabilistic lichen abundance map. Resubstitution accuracy of 97.31% was obtained with 10 number of RF trees. Validation was done with geotagged ground photographs having 232 lichens, 20 bare rocks, 22 water and 69 snow points and achieved test accuracy of 82.44%.

Northeastern coast of Lake Baikal as a habitat of baikal seal &lt;i&gt;Pusa sibirica&lt;/i&gt; in the summer period

Filling of the rookeries of baikal seal Pusa sibirica on the northeastern coast of Lake Baikal is surveyed instrumentally for the first time within the boundaries of protected area. The main coastal haulouts of the species are located in this area, so all places suitable by their lithological parameters for the seals rookeries were examined to assess intensity of their use in modern conditions of anthropogenic impact. In total, ≈ 246 km of the coastline were traced, including 170 km surveyed with unmanned aerial vehicle DJI (in 317 minutes) and visual observations on the rest of the coast with binoculars and ground photo- and video-recording. Only 9 % of the surveyed coastline (&gt; 21 km) is suitable for rookeries, located mainly in the northern part of the area. Unfavorable lithological properties (sands, sandy pebbles, small stones, rocks) prevail on the other part of the coast, though many places (usually big boulders) are available for small rookeries there, too. Currently all these places are almost never used by seals, except of short, sporadic haulouts, mainly in June, immediately after complete disappearance of the floating ice. The main reason why the animals leave their traditional rookeries in this area in July-August is an intensive anthropogenic load, in particular at the places where the largest rookeries existed previously, as the bays Frolikha, Ayaya, Khakusy, capes Khaman-Kit, Pongonye, and some others. The leaving of rookeries contradicts to increasing needs of seals for a solid substrate to complete their molt in conditions of climate warming. Possibly, the threat of losing the northeastern coast as a summer habitat for a significant part of the baikal seal population is underestimated.

3D PHOTOGRAMMETRY MODELING FOR SAFETY MONITORING OF FORTRESS WALLS

Abstract. In the monitoring to prevent collapse or tipping in the structure of linear-shaped fortress walls, the record of shapes like slope or protrusion is very important(Jo and Hong, 2019). As the way to record the shapes, laser scan or photogrammetric technology is employed. A laser scan that has to install several points of measurement has a limitation in recording them if the terrain is undulating, or the structure becomes huge. Photogrammetry has a shortening effect in terms of cost and process (Kim, 2020) compared to that.This study examined a measure for producing a 3D mesh model with aerial and ground photography for fortress walls. In addition, this study tested if it would be possible to monitor the slope and protrusion, etc. of fortress walls. This study tested the accuracy of the model produced with photogrammetry to use in the diagnosis and monitoring of the structural safety of fortress walls.The research subject was Korea’s cultural heritage, Seosan Haemieupseong Fortress, and a model was produced for the section about 100m of the fortress walls.The positional accuracy, elevation/section accuracy, LOD, and texture joint precision of two models (Model A &amp;amp; Model B) produced with photogrammetry and laser scan were compared.This study revealed that the model produced at LOD Level 4 would be effective for monitoring the displacement since it could measure the shape, curvature, and slope of stones of the fortress walls.

Damage assessment for the 2018 lower East Rift Zone lava flows of Kīlauea volcano, Hawaiʻi

Cataloguing damage and its correlation with hazard intensity is one of the key components needed to robustly assess future risk and plan for mitigation as it provides important empirical data. Damage assessments following volcanic eruptions have been conducted for buildings and other structures following hazards such as tephra fall, pyroclastic density currents, and lahars. However, there are relatively limited quantitative descriptions of the damage caused by lava flows, despite the number of communities that have been devastated by lava flows in recent decades (e.g., Cumbre Vieja, La Palma, 2021; Nyiragongo, Democratic Republic of Congo, 2002 and 2021; Fogo, Cape Verde, 2014–2015). The 2018 lower East Rift Zone (LERZ) lava flows of Kīlauea volcano, Hawaiʻi, inundated 32.4 km2 of land in the Puna District, including residential properties, infrastructure, and farmland. During and after the eruption, US Geological Survey scientists and collaborators took over 8000 aerial and ground photographs and videos of the eruption processes, deposits, and impacts. This reconnaissance created one of the largest available impact datasets documenting an effusive eruption and provided a unique opportunity to conduct a comprehensive damage assessment. Drawing on this georeferenced dataset, satellite imagery, and 2019 ground-based damage surveys, we assessed the pre-event typology and post-event condition of structures within and adjacent to the area inundated by lava flows during the 2018 LERZ eruption. We created a database of damage: each structure was assigned a newly developed damage state and data quality category value. We assessed 3165 structures within the Puna District and classified 1839 structures (58%) as destroyed, 90 structures (3%) as damaged, and 1236 (39%) as unaffected. We observed a range of damage states, affected by the structural typology and hazard characteristics. Our study reveals that structures may be damaged or destroyed beyond the lava flow margin, due to thermal effects from the lava flow, fire spread, or from exposure to a range of hazards associated with fissure eruptions, such as steam, volcanic gases, or tephra fall. This study provides a major contribution to the currently limited evidence base required to forecast future lava flow impacts and assess risk.

The influence of contrasting fire management practice on bush encroachment: Lessons from Bwabwata National Park, Namibia

AbstractQuestionsBush encroachment, (i.e. disproportionate woody vegetation increase at the cost of grassland) has negative impacts for biodiversity conservation and tourism by homogenising habitat structure and decreasing grazing and game‐viewing. While herbivory, rainfall, and CO2 all influence changes in woody vegetation cover, fire has the best potential for vegetation management. Changes in fire management can either encourage or suppress bush encroachment and a better understanding of how changes in fire regime affect vegetation structure is needed. Therefore, this study addressed three questions: (a) how has woody cover changed over two decades (1999–2019); (b) what is the role of land use, rainfall, and fire in influencing woody cover change; and (c) what are the management implications?LocationBwabwata National Park (BNP), Namibia.MethodsThe study used a novel combination of repeat ground photography and satellite‐based remote‐sensing products to explore the change in woody vegetation in relation to rainfall, land use, and fire seasonality.ResultsWoody vegetation has increased by 13% since 1999 in BNP. Change in vegetation structure differed in the east and west of the park. Early‐season burns in the east of the park were associated with an increase in trees over 3 m tall consisting primarily of Dialium englerianum, Terminalia sericea and Burkea africana. Repetitive late dry‐season fires in the west of the park were associated with an increase in shrubs under 3 m dominated by Baphia massaiensis and Terminalia sericea.ConclusionsBoth early‐ and late‐season fires are of value in management of bush encroachment. Early dry‐season fires appear to reduce the rate of bush encroachment and contribute to maintaining a heterogeneous vegetation structure. This fire management strategy reduces wildfire risk, conserves biodiversity, and promotes tourism and is, therefore, recommended for the park.

THE GREEK – ROMAN AQUEDUCT: THE THREE-DIMENSIONAL RECONSTRUCTION WITH TERRESTRIAL PHOTOGRAPHY

Abstract. The current paper is a part of the scientific category HBIM, with ground photography and photogrammetric processing. The photogrammetry is an important tool for recording, storing, promoting and documenting buildings. The purpose of the work is to construct a three-dimensional model of the Greek-Roman aqueduct of the city of Kavala, in northern Greece. First of all, the aqueduct has a very important historical and aesthetic value for the city, because it is a tourist attraction. Also, it is an urban physical boundary, which separates part of the old town from the new roads of the coastal front. Moreover, the methodology was based on photographic shots, then on photogrammetric processing with Zephyr software. The methodology of the 3D model had as a purpose the terrestrial photography in a rapid and effective way. The result, of the work is the presentation of the three-dimensional model of the aqueduct, the analysis of the methodological steps of photogrammetric processing and the analysis of the historical transformation of the aqueduct area by a medieval and modern coastal city. The advantages of photogrammetric documentation have a role in the storage of the three-dimensional form of the historical building and in the smart promotion of its historical, touristic, urban aspects.

Parameters and modes of ground photography of crops to determine weed location

Parameters and modes of ground photography of crops to determine weed location

Об опыте разработки ГИС-проекта кампуса университета

The article discusses the results of the work on the design and creation of a GIS project of the university campus. A model and methodology for creating a geographical information system (GIS) of the university campus was developed and tested in the Non-profit limited company “Manash Kozybayev North Kazakhstan university” setting. The GIS was created on the basis of open spatial data of the Remote Sensing of the Earth, materials of ground photography and aerial photography from an unmanned aerial vehicle (UAV). Geoinformation mapping and modeling was carried out using specialized programs: ArcGIS 10.4 (ESRI Inc.), Agisoft PhotoScan Professional Edition. The article presents the GIS structure of the campus, its main elements, and describes the stages of development. The work on the creation of GIS included the following stages: the development of its preliminary model and structure, the collection of the necessary initial information and data about the studied territory, the conduct of ground surveys and aerial photography, field work, processing of collected materials in special software applications, the creation of an electronic map and database, filling and correcting the latter. The campus GIS includes a digital cartographic framework and an associated database. Its structure contains a number of modules that combine thematic layers. Along with this, geo-referenced photorealistic three-dimensional models of university buildings were created during the research. A 3D model of the campus was built on the basis of aerial photography materials from the UAV. Developed GIS, its functionality can be useful to the university and find application in solving practical problems. The research was carried out within the framework of grant funding under the Sustainability Living Lab program.

Understanding Urban Growth through Heat Islands Using Remotely Sensed Data: Yaounde Case Study, Cameroon

Studies in urban dynamics have focused on population growth, urban sprawl and activities expansion to determine, understand and study the phenomenon of urbanization in the world. The methods used in these studies have explore remotely sense data from Global Positioning Systems (GPS), satellite imagery, aerial and ground photography for interpretation, analysis and explanation of urban land use and land cover evolution. This data is often combined with ground observations and other methods in social sciences to understand urban growth. In this article, data is tapped from temperature differences analysis in multi-date satellite images and combined with population statistics, human activities and infrastructure build up to explain the phenomenon of urbanization. The results of the findings will enrich our knowledge in Urban Geography on approaches and methods used in understanding urbanization and its problems. Drawing on case study material from Yaounde in Cameroon, this article examines how remote sensing techniques can help in understanding urban heat island in Yaounde and its negative outcomes on urban population, activity and the environment. As the study shows, urban growth has a direct relation with temperature increase and an inverse relation with vegetation change. Also, Increase in bare surfaces due to deforestation for development and settlement increases surface temperature and vice versa. Vegetation regulates surface temperature by absorbing energy from solar radiation and remission in the form of latent heat through the process of photosynthesis. Analysis of surface temperature increase through remote sensing techniques, urban land use evolution, determinants and implications of Yaounde heat islands constitute the focus of this paper. The implications of rise in surface temperature and relations with urban growth are important for decision making. This knowledge is essential for urban geography research, new methodological approaches to urbanization and policy.

Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets

Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily rely on pixel DN values for classification. However, the limited spectral range of ground photos requires additional characteristics to differentiate lichen from spectrally similar objects, such as bright logs. By applying a neural network to tiles of a UAV orthomosaics, additional characteristics, such as surface texture and spatial patterns, can be used for inferences. Our methodology used a neural network (UAV LiCNN) trained on ground photo mosaics to predict lichen in UAV orthomosaic tiles. The UAV LiCNN achieved mean user and producer accuracies of 85.84% and 92.93%, respectively, in the high lichen class across eight different orthomosaics. We compared the known lichen percentages found in 77 vegetation microplots with the predicted lichen percentage calculated from the UAV LiCNN, resulting in a R2 relationship of 0.6910. This research shows that AI models trained on ground photographs effectively classify lichen in UAV orthomosaics. Limiting factors include the misclassification of spectrally similar objects to lichen in the RGB bands and dark shadows cast by vegetation.

Global Land Cover Assessment Using Spatial Uniformity Validation Dataset

The Degree Confluence Project (DCP) is a volunteer-based validation dataset that comprises useful information for global land cover map validation. However, there is a problem with using DCP points as validation data for the accuracy assessment of land cover maps. While resolutions of typical global land cover maps are several hundred meters to several kilometers, DCP points can only guarantee an area of several tens of meters that can be confirmed by ground photographs. So, the objective of this study is to create a land cover map validation dataset with added spatial uniformity information using satellite images and DCP points. For this, we devised a new method to semiautomatically guarantee the spatial uniformity of DCP validation data points at any resolution. This method can judge the validation data with guaranteed uniformity with a user’s accuracy of 0.954. Furthermore, we conducted the accuracy assessment for the existing global land cover maps by the DCP validation data with guaranteed spatial uniformity and found that the trends differed by class and region.

Recurrent landslides of Lagam escarpment, Kaben Location, Marakwet East, Kenya

The landslides of the Lagam escarpment, Kaben Location, Marakwet, Elgeyo Marakwet County, are not only recurrent but are highly cataclysmic; causing mayhem whenever they occur. The first known landslides on the Lagam escarpment of Kaben location -Marakwet, happened during the 1961 El-Nino, when a major rainstorm hit the area (Kiptungu). These landslides re-fashioned Embosumer River and left huge boulders scattered over many farmlands. In 2010, a catastrophic landslide hit Kaben location and again, Embosumer River was the perpetrator, having broken its banks due to excessive water inflow. Many lives were lost and lots of property was destroyed. In 2020, heavy rains pounded the area causing massive mudslides that involved many rivers and streams; such as Embosumer, Emboreberwo and Karengor. As a result, twenty-six (26) lives were lost, homes, schools and farmlands were washed away and to date, there are several bodies believed to be trapped in the mudslide material. This study is geared towards investigating the causes of the recurrent landslides and proposing possible mitigation measures. Data was collected through secondary data analysis and has been presented in the form of ground photographs, satellite images and discussions made thereon. It is hoped that this preliminary study of the Lagam escarpment landslides will create awareness pertaining to the recurrent landslides and draw positive action from the area residents and all concerned.

ACCOUNTING OF GREEN SPACES BY REMOTE SENSING MATERIALS

The article states the necessity of urban green spaces assessment. The current methods of urban green inventory are described. The necessity of modernization of the methods taking into account the achievements of remote sensing and Geographic Information Systems is stated. The basic outline of using of free-of-charge remote sensing data and ground photography data for green spaces inventory is suggested. A case study of using said data for green space inventory of the selected area in Leninsky district of Novosibirck city, Russia, is described.

Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning

AbstractFaults form dense, complex multi‐scale networks generally featuring a master fault and myriads of smaller‐scale faults and fractures off its trace, often referred to as damage. Quantification of the architecture of these complex networks is critical to understanding fault and earthquake mechanics. Commonly, faults are mapped manually in the field or from optical images and topographic data through the recognition of the specific curvilinear traces they form at the ground surface. However, manual mapping is time‐consuming, which limits our capacity to produce complete representations and measurements of the fault networks. To overcome this problem, we have adopted a machine learning approach, namely a U‐Net Convolutional Neural Network (CNN), to automate the identification and mapping of fractures and faults in optical images and topographic data. Intentionally, we trained the CNN with a moderate amount of manually created fracture and fault maps of low resolution and basic quality, extracted from one type of optical images (standard camera photographs of the ground surface). Based on a number of performance tests, we select the best performing model, MRef, and demonstrate its capacity to predict fractures and faults accurately in image data of various types and resolutions (ground photographs, drone and satellite images and topographic data). MRefexhibits good generalization capacities, making it a viable tool for fast and accurate mapping of fracture and fault networks in image and topographic data. The MRefmodel can thus be used to analyze fault organization, geometry, and statistics at various scales, key information to understand fault and earthquake mechanics.