Historical Imagery Research Articles

Historical Imagery and Mnemonic Constitutionalism in Belarus

Abstract The article examines the historical imagery in the Belarusian country study through the lense of mnemonic constitutionalism. The first part of the article explains the concept and relevance of mnemonic constitutionalism, particularly in the context of Belarus. It then further sketches comparative perspectives on mnemonic constitutionalism and memory laws in the region of Central and Eastern Europe, briefly drawing on Hungarian, Polish, Russian and Ukrainian examples. In the third part, the article unpacks the choices for historical imagery made by Belarusian memory politics and apparent in the constitutional referendum of 27 February 2022. The article further explains the rise of Belarusian memory laws and coercive mnemonic constitutionalism in Belarus prior to, and after, Putin’s invasion of Ukraine on 24 February 2022, in the fourth part. The conclusions highlight the distinct features of the Belarusian model of mnemonic constitutionalism in Europe, shaped by dystopian historical imagery and authoritarian memory politics.

Enhancing Visual Analytics systems with guidance: A task-driven methodology

Enhancing Visual Analytics (VA) systems with guidance, such as the automated provision of data-driven suggestions and answers to the user’s task, is becoming increasingly important and common. However, how to design such systems remains a challenging task. We present a methodology to aid and structure the design of guidance for enhancing VA solutions consisting of four steps: (S1) defining the target of analysis, (S2) identifying the user tasks, (S3) describing the guidance tasks, and (S4) placing guidance. In summary, our proposed methodology specifies a space of possible user tasks and maps them to the corresponding space of guidance tasks, using recent abstract task typologies for guidance and visualization. We exemplify this methodology through two case studies from the literature: Overview, a system for exploring and labeling document collections aimed at journalists, and DoRIAH, a system for historical imagery analysis. We show how our methodology enriches existing VA solutions with guidance and provides a structured way to design guidance in complex VA scenarios.

A Small-Scale Landslide in 2023, Leshan, China: Basic Characteristics, Kinematic Process and Cause Analysis

Sudden mountain landslides can pose substantial threats to human lives and property. On 4 June 2023, a landslide occurred in Jinkouhe District, Leshan City, Sichuan Province, resulting in 19 deaths and 5 injuries. This study, drawing on field investigations, geological data, and historical imagery, elucidates the characteristics and causes of the landslide and conducts a reverse analysis of the landslide movement process using Massflow V2.8 numerical simulation software. The results indicate that rainfall and human engineering activities are key factors that triggered this landslide. Numerical simulation shows that the landslide stopped after 60 s of sliding, with a movement distance of approximately 286 m, a maximum sliding speed of 17 m/s, and a maximum accumulation thickness of 7 m, eventually forming a loose landslide debris accumulation of approximately 5.25 × 103 m3. The findings of this study provide significant reference value for research on landslide movement characteristics and disaster prevention and mitigation in mountainous areas.

Fragments of the past: The intersection of AI, historical imagery, and early childhood creativity

AbstractThis paper explores the intersection of early childhood education and AI, with a focus on promoting multimodal play. Drawing from implementation research in preschools, we explore the potential of an AI‐powered painting tool to foster historical imagery, creativity, and self‐expression. The findings demonstrate how technology can be used to democratize access to cultural heritage resources while enabling children's creative play centered on variation, improvisation, and human interaction. The human‐in‐the‐loop approach is crucial in aligning AI integration with early childhood pedagogy, ensuring that technology supports, rather than replaces, teachers' roles in the classroom. The paper highlights the importance of maintaining a human presence in the learning process to create educational experiences that are developmentally appropriate and transformative for young children.

Distribution and Characteristics of Spiny Lobster (Panulirus spp.) Puerulus Collection Sites in Palawan, the Philippines

Identifying the collection sites for the wild seeds of a highly valued species is crucial for the conservation and management of its wild population and habitat. This study is the first to document the distribution and the characteristics of spiny lobster puerulus collection sites in Palawan, Philippines. The data were obtained from various key informants, site visits, and historical imagery from Google Earth Pro and Sentinel 2A of Copernicus between January 2021–December 2023. The data were loaded into the QGIS v3.28 software to produce a map of each locality projecting the distribution and characteristics of spiny lobster puerulus collection sites. The results showed that the collection began in the municipality of Sofronio Española in 2019 and eventually spread southward and northward along the east coast of Palawan. The municipalities on the east coast of Palawan such as Sofronio Española, Narra, and Roxas had the highest number of collection sites, whereas fewer on the west coast such as in Rizal. Most of the collection sites were located generally along the intertidal zones or embayment with sandy- muddy to rubble substrates adjacent to mangrove forests, or within seagrass or seaweed, and coral reef ecosystems. To conserve and manage the spiny lobster puerulus fishery, the revision of the provincial ordinance on spiny lobster puerulus collection in Palawan should include the proper zoning of collection sites, imposing catch limits, and annual catch monitoring. Venturing into spiny lobster aquaculture can help reduce the fishing pressure and enhance the earnings of the people.

Land-Use/Cover Change and Driving Forces in the Pan-Pearl River Basin during the Period 1985–2020

Land use/cover change (LUCC) is a vital aspect representing global change and humans’ impact on Earth’s surface. This study utilized the ESRI Land Cover 2020 and China Land Cover Dataset (CLCD), along with historical imagery from Google Earth, to develop a method for the assessment of land use data quality. Based on the assessment, the CLCD was updated to generate an improved Re-CLCD for the Pan-Pearl River Basin (PPRB) from 1985 to 2020, and to analyze LUCC in the PPRB over the past 35 years. The results indicate the following: (1) Among the seven land uses, built-up land experienced the most dramatic change, followed by cropland, forestland, grassland, shrubland, waterbody, and bare land, with notable increases in built-up land and forestland, and rapid decreases in cropland, grassland, and shrubland. (2) The magnitude of land use changed very widely, with the highest change in the Pearl River Delta, followed by small coastal river basins in southern Guangdong and western Guangxi, the Dongjiang River Basin, the Hanjiang River Basin, the Xijiang River Basin, the Beijiang River Basin, and lastly, Hainan Island. (3) The largest increase happened in built-up land, with a total increase of 12,184 km2, mainly due to the occupation of cropland and forestland, corresponding to the highest decrease in cropland, with a net loss of 10,435 km2, which was primarily converted to forestland and built-up land. The study results are valuable in providing a scientific basis for policy overhaul regarding land resources and management to safeguard ecological balance and promote sustainable development in the Pan-Pearl River Basin.

Combining Google Earth historical imagery and UAV photogrammetry for urban development analysis

Rural-urban migration often triggers additional demand for housing and infrastructural development to cater for the growing population in urban areas. Consequently, town planners and urban development authorities need to understand the urban development trend to make sustainable urban planning decisions. Yet, methods to analyse changes and trends in urban spatial development are often complex and require costly data collection. This article thus presents a simplified method to analyse the urban development trend in an area. The method integrates Google Earth (GE) historical imagery (baseline data) and unmanned aerial vehicle (UAV) photogrammetry (recent data) to quantify the changes over time. This approach can be applied to study the urban development trends in low-income countries with budget constraints. The method is discussed under four main headings: (1) background, (2) method details, (3) limitations, and (4) conclusion.•Google Earth historical image can be extracted with its associated world file.•The population of an area can be estimated by using average household size data and the number of residential buildings in the area.•The building height ratio can be used to ascertain if the land is being used parsimoniously.

Applying a deep learning pipeline to classify land cover from low-quality historical RGB imagery

Land use and land cover (LULC) classification is becoming faster and more accurate thanks to new deep learning algorithms. Moreover, new high spectral- and spatial-resolution datasets offer opportunities to classify land cover with greater accuracy and class specificity. However, deploying deep learning algorithms to characterize present-day, modern land cover based on state-of-the-art data is insufficient for understanding trends in land cover change and identifying changes in and drivers of ecological and social variables of interest. These identifications require characterizing past land cover, for which imagery is often lower-quality. We applied a deep learning pipeline to classify land cover from historical, low-quality RGB aerial imagery, using a case study of Vancouver, Canada. We deployed an atrous convolutional neural network from DeepLabv3+ (which has previously shown to outperform other networks) and trained it on modern Maxar satellite imagery using a modern land cover classification. We fine-tuned the resultant model using a small dataset of manually annotated and augmented historical imagery. This final model accurately predicted historical land cover classification at rates similar to other studies that used high-quality imagery. These predictions indicate that Vancouver has lost vegetative cover from 1995–2021, including a decrease in conifer cover, an increase in pavement cover, and an overall decrease in tree and grass cover. Our workflow may be harnessed to understand historical land cover and identify land cover change in other regions and at other times.

Estimating the extent of urchin barrens and kelp forest loss in northeastern Aotearoa, New Zealand

ABSTRACT On shallow rocky reefs in northeastern Aotearoa, New Zealand, urchin barrens are indicators of the ecosystem effects of overfishing reef predators. Yet, information on their extent and variability is lacking. We use aerial imagery to map the urchin barrens and kelp forests on reefs (<30 m depth) across seven locations, including two long-established marine reserves and a marine protected area that allows recreational fishing. Urchin barrens were present in all locations and were restricted to reefs <10–16 m deep. Urchin barrens covered 30% (7–49%) of shallow reefs in fished areas, with variation among and within locations likely related to the relative extent and topography of reefs and wave exposure. Within the marine reserves, barrens covered <2% of shallow reefs. Long-term comparison of historical imagery at two fished locations demonstrates that current areas of urchin barrens were historically dominated by kelp forests, but barrens have persisted since at least the early 2000s. We estimate ∼30 km2 of barrens exist across the region. This demonstrates the widespread nature of barrens, the potential long-term effectiveness of no-take marine protection in restoring urchin barrens, and the need for a multifaceted management approach to restore kelp forests and prevent further expansion of barrens.

Mangrove mapping and monitoring using remote sensing techniques towards climate change resilience

Mangroves are amongst the richest ecosystems in the world providing valuable goods and services to millions of people while enhancing the resilience of coastal communities against climate change induced hazards, especially island nations. However, these mangroves are severely affected by many anthropogenic activities. Therefore, understanding the spatial variability of mangroves in island nations is highly essential in the events of ongoing climatic change. Thus, this study assessed the use of remote sensing techniques and GIS to map and monitor mangrove cover change at selected sites, namely Le Morne and Ferney, on the tropical island of Mauritius. Freely available 2013 SPOT-5 and 2023 Sentinel 2A images were retrieved and processed using ArcGIS Pro tools and SNAP; mangroves were mapped based on Google Earth Pro historical imagery and ground truthing at the respective sites. Following the application of selected vegetation indices, GLCM and PCA analysis, mosaicked images were classified using the Random Trees algorithm. Kappa values of all the classified images were in the 90 s; Le Morne showed a significant increase in mangrove cover over the decadal scale with main class change from mudflat to mangroves. This study demonstrates how geo-spatial tools are crucial for monitoring mangroves as they provide spatially explicit and time sensitive information. Decision makers, researchers, and relevant stakeholders can utilize this data to bolster tailored mitigation and adaptation strategies at specific sites, thereby enhancing resilience to climate change.

Exploiting the Matched Filter to Improve the Detection of Methane Plumes with Sentinel-2 Data

Existing research indicates that detecting near-surface methane point sources using Sentinel-2 satellite imagery can offer crucial data support for mitigating climate change. However, current retrieval methods necessitate the identification of reference images unaffected by methane, which presents certain limitations. This study introduces the use of a matched filter, developing a novel methane detection algorithm for Sentinel-2 imagery. Compared to existing algorithms, this algorithm does not require selecting methane-free images from historical imagery in methane-sensitive bands, but estimates the background spectral information across the entire scene to extract methane gas signals. We tested the algorithm using simulated Sentinel-2 datasets. The results indicated that the newly proposed algorithm effectively reduced artifacts and noise. It was then validated in a known methane emission point source event and a controlled release experiment for its ability to quantify point source emission rates. The average estimated difference between the new algorithm and other algorithms was about 34%. Compared to the actual measured values in the controlled release experiment, the average estimated values ranged from −48% to 42% of the measurements. These estimates had a detection limit ranging from approximately 1.4 to 1.7 t/h and an average error percentage of 19%, with no instances of false positives reported. Finally, in a real case scenario, we demonstrated the algorithm’s ability to precisely locate the source position and identify, as well as quantify, methane point source emissions.

Historic British Royal Memes

Historic British Royal Memes

Identifying urban built-up areas using CORONA imagery in the pre-Landsat era: a case study of Shanghai in 1965

ABSTRACT The Corona project (1959–1972) created a large number of high-resolution images that provided valuable historical research data before the advent of Landsat (1972) and even compensated for the lack of accuracy in early stages. In this study, we collected eight available Corona images to create a historical remote sensing image of Shanghai in the 1960 and developed an automatic image segmentation and classification methodology. In the experimental part, we verified its accuracy, and discussed the urbanization process in Shanghai. The results showed that 1) In 1965, the urban area of Shanghai was mainly distributed in Puxi, and the urbanization degree of the entire hinterland of Pudong was still very low. Built-up areas were highly concentrated in the main urban areas. 2) The overall accuracy was 91.5% and the kappa coefficient was 0.762. 3) We compared the built-up area in 1965 with the current urban area, which increased from 306.23 to 2,804.25 km2. Besides, historical imagery also captured the state of environmental pollution at that time. Overall, our method could inform the mapping and classification of remotely sensed land surfaces on a global scale in the 1960s, extending pre-Landsat era research to studies of urban history, archaeology and cultural heritage.

Deglaciation patterns in the Upper Zemmgrund, Austria: An exploration of clean-ice disintegration scenarios

The European Alps are rapidly losing glacier mass due to climatic warming and are anticipated to be largely ice-free by the year 2100. Long-term glacier monitoring in the Alps provides a record of anthropogenically-driven climate change since the Little Ice Age maximum in ~1850. Understanding these long-term glacier changes provides a basis for mitigating hazards (e.g., mass movements) associated with a transition to a paraglacial environment and for predicting future scenarios. Here, we present a post-Little Ice Age-maximum record of glacial landscape changes in the Upper Zemmgrund, Austria, utilising a multi-method framework integrating multi-decadal geomorphological mapping, historical imagery and remote sensing of glacier change. This study contributes a high-resolution, quantifiable record of the transition from a glacial to paraglacial landscape. We find that individual glacier response to climatic change varies within the Upper Zemmgrund, attributed to glacier characteristics such as hypsometry and size. Nonetheless, all glaciers show signs of growing instability such as an increase in crevasses and the collapse of circular tension structures, which have dammed meltwater at the terminus of one glacier, posing a substantial hazard to downstream communities. Future glacier disintegration is anticipated to accelerate in the Upper Zemmgrund, which may result in an ice-free landscape within the next ~40–60 years.

Surveying and monitoring submerged archaeological sites in inland waters through a multiproxy strategy: The case of Dolmen de Guadalperal and other sites from Valdecañas reservoir (Spain)

AbstractThe evolving dynamics of climate change and water resource management present unique challenges for the research and conservation of archaeological heritage, particularly within reservoirs. The Dolmen of Guadalperal in Spain and its surrounding archaeological sites exemplify such a scenario. This study is dedicated to the development and application of a comprehensive methodology for archaeological documentation and site detection, integrating an array of approaches including Synthetic Aperture Radar (SAR) from Sentinel‐1, photogrammetric restitution using historical imagery and geometric recording of sites with diverse sensing equipment. A distinctive aspect of this research lies in juxtaposing multibeam sonar data, typically associated with underwater surveys, with standard land‐based documentation methods like photogrammetry and LiDAR. The results demonstrate the effectiveness of integrating data from these diverse techniques in detecting new archaeological sites and monitoring their alterations, which could be potentially harmful.

Forest Height Extraction Using GF-7 Very High-Resolution Stereoscopic Imagery and Google Earth Multi-Temporal Historical Imagery

With the advent of very high-resolution (VHR) imaging satellites, it is possible to measure the heights of forest stands or even individual trees more accurately. However, the accurate geometric processing of VHR images depends on ground control points (GCPs). Collecting GCPs through fieldwork is time-consuming and labor-intensive, which presents great challenges for regional applications in remote or mountainous regions, particularly for international applications. This study proposes a promising approach that leverages GF-7 VHR stereoscopic images and Google Earth’s multi-temporal historical imagery to accurately extract forest heights without the need for fieldworks. Firstly, an algorithm is proposed to collect GCPs using Multi-temporal Averaging of historical imagery provided by Google Earth (GE), known as MAGE. Digital surface model (DSM) is then derived using GF-7 stereoscopic imagery and MAGE GCPs in Switzerland. Forest heights are finally extracted by subtracting ground surface elevations from GF-7 DSM. Results show that absolute coordinate errors of MAGE GCPs are less than 2.0 m. The root mean square error (RMSE) of forest heights extracted from GF-7 DSM, derived using the original geolocation model, is 12.3 m, and the determination coefficient ( R 2 ) of linear estimation model is 0.72. When the geolocation model is optimized using MAGE GCPs, the RMSE is reduced to 1.5 m and the R 2 increases to 0.95. These results not only demonstrate the effectiveness of MAGE GCPs but, more importantly, also reveal the significance of precise geometric processing of VHR stereoscopic imagery in forest height estimations.

Revisiting the Past: A comparative study for semantic segmentation of historical images of Adelaide Island using U-nets

The TriMetrogon Aerial (TMA) archive is an archive of historical images of Antarctica taken by the US Navy between 1940 and 2000 with analogue cameras. The analysis of such historic data can give a view of Antarctica's glaciers predating modern satellite imagery and provide unique insights into the long-term impact of changing climate conditions with essential validation data for climate modelling. However, the lack of semantic information for these images presents a challenge for large-scale computer-driven analysis.Such information can be added to the data using semantic segmentation, but traditional algorithms fail on these scanned historical grayscale images, due to varying image quality, lack of colour information and artefacts in the images. To address this, we present a deep-learning-based U-net workflow. Our approach includes creating training data by pre-processing and labelling the raw images. Furthermore, different versions of the U-net are trained to optimize its hyperparameters and augmentation methods. With the optimal hyper-parameters and augmentation methods, a final model has been trained for a use-case to segment 118 images covering Adelaide Island.We tested our approach by segmenting challenging historical images using a U-net model with just 80 training images, achieving an accuracy of 73% for 20 validation images. While no test data is available for our use case, a visual examination of the segmented images shows that our method performs effectively.The comparison of the hyper-parameters and augmentation methods provides directions for training other U-net-based models so that the presented workflow can be used to segment other archives with historical imagery. Additionally, the labelled training data and the segmented images of the test are publicly available at https://github.com/fdahle/antarctic_segmentation.

Discovery of an Active Forearc Fault in an Urban Region: Holocene Rupture on the XEOLXELEK‐Elk Lake Fault, Victoria, British Columbia, Canada

AbstractSubduction forearcs are subject to seismic hazard from upper plate faults that are often invisible to instrumental monitoring networks. Identifying active faults in forearcs therefore requires integration of geomorphic, geologic, and paleoseismic data. We demonstrate the utility of a combined approach in a densely populated region of Vancouver Island, Canada, by combining remote sensing, historical imagery, field investigations, and shallow geophysical surveys to identify a previously unrecognized active fault, the XEOLXELEK‐Elk Lake fault, in the northern Cascadia forearc, ∼10 km north of the city of Victoria. Lidar‐derived digital terrain models and historical air photos show a ∼2.5‐m‐high scarp along the surface of a Quaternary drumlinoid ridge. Paleoseismic trenching and electrical resistivity tomography surveys across the scarp reveal a single reverse‐slip earthquake produced a fault‐propagation fold above a blind southwest‐dipping fault. Five geologically plausible chronological models of radiocarbon dated charcoal constrain the likely earthquake age to between 4.7 and 2.3 ka. Fault‐propagation fold modeling indicates ∼3.2 m of reverse slip on a blind, 50° southwest‐dipping fault can reproduce the observed deformation. Fault scaling relations suggest a M 6.1–7.6 earthquake with a 13 to 73‐km‐long surface rupture and 2.3–3.2 m of dip slip may be responsible for the deformation observed in the paleoseismic trench. An earthquake near this magnitude in Greater Victoria could result in major damage, and our results highlight the importance of augmenting instrumental monitoring networks with remote sensing and field studies to identify and characterize active faults in similarily challenging environments.

Geographic variability in headward erosion of marsh tidal creeks: Ecological and physical causes

AbstractExpansion of drainage networks through the headward erosion of tidal creeks is an eco‐geomorphologic response of salt marshes to accelerated sea‐level rise (SLR). This response can counter the negative impacts of an elevation deficit by increasing drainage and encouraging plant health, thereby reducing potential for submergence and marsh platform loss. In the wetlands of Cape Romain, SC, intense bioturbation near creek heads by the common marsh crab Sesarma reticulatum has been found to facilitate sediment erosion and rapid creek growth. This keystone grazer has been recently observed to have increasing influence on landscape evolution throughout the southeast US coast. Here, we compare measurements taken at Sapelo Island, GA, with those previously collected at Cape Romain, to confirm that eco‐geomorphic feedbacks facilitating creek growth at each location are similar, and to compare these processes under differing background conditions. We use sediment cores, precise elevation measurements and historical imagery to compare substrate properties, elevation within the tidal frame, creek growth rates and drainage morphology at both sites. Our results show identical processes; however, the higher elevation of the marsh at Sapelo Island leads to shallower and shorter periods of tidal inundation, explaining the greater soil strength and lower belowground biomass compared with the marsh at Cape Romain. The smaller tidal range at the site in Cape Romain compared with Sapelo Island translates to a proportionally shallower depth of tidal creeks, which therefore requires less erosion to produce headward creek extension. These effects are likely to have contributed to slower growth rates of tidal creeks at Sapelo Island during the past several decades of SLR. Our findings highlight the similarities in process but differences in rates in how marshes are responding to climate‐related stress.

GRADUAL CHANGES IN SNOW PEAKS IN UPPER INDUS BASIN, PAKISTAN: A GOOGLE EARTH BASED REVIEW

The hydrology and climate of mid to high-latitude mountainous areas are significantly impacted by snow cover. Since adding or removing snow cover significantly impacts the snowpack’s capacity to operate as a reservoir for water storage, the snowfall-dominated basins of mid- to higher latitudes are anticipated to see the largest shifts in the hydrological cycle because of global warming. By moving the time slider in the historical imagery feature of Google Earth Pro, the Upper Indus Basin study area was examined from the years 1984 to 2020 to track changes in the snow cover. All observations were made with longitude and latitude at 35o, 34', 51.79" N and 74o, 34', 24.21" E, and the eye altitude at 344.46 miles. Google Earth captured pictures of all the observations on December 31st of every year. The data from 1984 to 2020 was examined keenly, and it was observed that as time goes on, global warming is showing its effects and producing climate changes, which has a negative impact on the region's snow and glacier availability. The Landsat images make it abundantly evident that the lower areas of the upper Indus Basin's snow cover are more negatively impacted than the downstream side areas due to the variation in altitude. The authors also referred to the research work by other researchers in the study to compare with their work. The study observed that some areas were utterly showing no snow in 2020 as compared to 1984 as time moved on with an increase in global warming in 36 years.