Analysis Of Imagery Research Articles

Geostatistical Analysis of Lineament Domains: The Study Case of the Apennine Seismic Province of Italy

Regional-scale swarms of subparallel linear topographic features, known as lineament domains, are a common feature of planetary surfaces. Lineament domains are superficial manifestations of the crustal stress field trajectory. Notably, one of the effects of active tectonics is seismicity. Italy is one of the most seismically active regions in the Mediterranean, with many destructive earthquakes that have occurred in past centuries. Here, we assess the seismic meaning of the main lineament domain in the tectonically active region of Central Italy. We describe the use of an automated analysis of satellite imagery coupled with spatial grid analysis to identify three lineament domains of the Central Apennines. Spatial and azimuthal comparisons of the main lineament domain (i.e., the Apennine Domain), with the known locations of earthquakes (moment magnitude of Mw > 5.5) that occurred during the past century, revealed the most seismically active tectonic areas and their spatial distributions. Further, we present a conceptual seismo-geodynamic model for the Central Apennines, which is characterized by regional arching and explains the presence of an extensional tectonic regime in the upper crustal layer of the active Apennines fold-and-thrust belt.

Integrated approach to soil salinity assessment using SEM in Sirdarya province, Uzbekistan

The majority of irrigated land in the Sirdarya area in Uzbekistan is susceptible to salinization. Yet, due to the benefits of its geographical and temporal data, satellite imaging has the ability to efficiently monitor territorial resources. The objectives of this paper are (1) to find how strong surface water mineralization used for irrigation correlated with soil salinity, and (2) to introduce a simplified approach for the soil salinity assessment using SEM based on satellite imagery analysis. The main focus of this research is to explore the potential of using SEM methodology for soil salinity assessment in the study area by streamlining spatial image analysis processes. Throughout data collection of 261 surface water samples and 206 soil samples in Sirdaria irrigation district, while satellite images and other official datasets were our secondary material. We used the Inverse Distance Weighting interpolation method to map soil salinity in ArcGIS, achieving an interpolation accuracy of 72% and a Kappa value of 81%. Statistical packages in R software served as analyst tools to validate our findings. Our findings imply that while MODIS satellite imagery has a lesser connection with EC values, however, the EC values in terms of soil salinization in Sirdarya Province can be predicted using several Landsat bands without using remote sensing indices with correlation coefficients of R ≥ 0.8. We proved that the wavelength ranges of 0.64–0.67 μm and 2.11–2.29 μm are acceptable for mapping soil salinity. Nevertheless, continuous research is needed to understand the mechanisms behind this relationship and the potential applications of these findings in other drylands, land management, and environmental monitoring.

Farmland quality assessment using deep learning and UAVs

The transformation of agricultural landscapes due to the intensification of farming practices has placed a major threat on habitat and species diversity. Stopping the loss of biodiversity is addressed in various international treaties and adequate strategies are required to assess the initial status of biodiversity and to measure the success of policies. While traditional methods of field-based biodiversity monitoring are time consuming, costly, and highly surveyor-dependent, recent advances in remote sensing and machine learning enable a systematic, more general and cost-effective monitoring. Accordingly, this study investigated whether large-scale biodiversity monitoring campaigns such as the European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL) could be supported by the use of Unmanned Aerial Vehicle (UAV) based remote sensing and Convolutional Neural Networks (CNNs). For this purpose, the Structural Nature Value (SNV) indicator was proposed, which allows an intuitive estimation of the species richness of landscape sections. Subsequently, a CNN was trained to directly predict the SNV. For the accuracy assessment, a survey was conducted in which participants were asked to assess the SNV for various landscape patches, both to obtain an independent test set, and to evaluate the intuitiveness of the concept. The CNN achieved a mean weighted F1-score of 0.81 with an overall accuracy of 81 %. It was shown that the CNN can provide promising results to not only aid large-scale biodiversity monitorings, but to enhance the quality of their results through the automated analysis of fine-resolution UAV imagery.

Artificial Intelligence in Orthodontics: Critical Review.

With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist's task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.

Accuracy Assessment of Geometric-Distortion Identification Methods for Sentinel-1 Synthetic Aperture Radar Imagery in Highland Mountainous Regions.

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods-LSM, R-Index, and P-NG-against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings.

Remote Sensing and Machine Learning for Accurate Fire Severity Mapping in Northern Algeria

Forest fires pose a significant threat worldwide, with Algeria being no exception. In 2020 alone, Algeria witnessed devastating forest fires, affecting over 16,000 hectares of land, a phenomenon largely attributed to the impacts of climate change. Understanding the severity of these fires is crucial for effective management and mitigation efforts. This study focuses on the Akfadou forest and its surrounding areas in Algeria, aiming to develop a robust method for mapping fire severity. We employed a comprehensive approach that integrates satellite imagery analysis, machine learning techniques, and geographic information systems (GIS) to assess fire severity. By evaluating various remote sensing attributes from the Sentinel-2 and Planetscope satellites, we compared different methodologies for fire severity classification. Specifically, we examined the effectiveness of reflectance indices-based metrics such as Relative Burn Ratio (RBR) and Difference Burned Area Index for Sentinel-2 (dBIAS2), alongside machine learning algorithms including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), implemented in ArcGIS Pro 3.1.0. Our analysis revealed promising results, particularly in identifying high-severity fire areas. By comparing the output of our methods with ground truth data, we demonstrated the robust performance of our approach, with both SVM and CNN achieving accuracy scores exceeding 0.84. An innovative aspect of our study involved semi-automating the process of training sample labeling using spectral indices rasters and masks. This approach optimizes raster selection for distinct fire severity classes, ensuring accuracy and efficiency in classification. This research contributes to the broader understanding of forest fire dynamics and provides valuable insights for fire management and environmental monitoring efforts in Algeria and similar regions. By accurately mapping fire severity, we can better assess the impacts of climate change and land use changes, facilitating proactive measures to mitigate future fire incidents.

Investigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan

Monitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km2) and vegetation (− 68.50 km2) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions.

Kurgan Phenomenon in the Southern Caucasus: Results of an Interdisciplinary Multi‐Method Remote Sensing Survey Along the Kurekçay Valley (Goranboy Province, Western Azerbaijan)

ABSTRACTThis paper presents the results of a pilot study that combines findings from satellite remote sensing analyses and an intensive interdisciplinary survey conducted in November 2021 on kurgans along the Kurekçay Valley, in western Azerbaijan. Through the integration of various methods, including historical and contemporary satellite and aerial imagery analysis, topographic mapping and geophysical prospections, this research remotely maps and quantifies the density, size, morphology and distribution of the burial mounds in the area. Unlike previous studies focusing solely on individual burial mounds, this investigation adopts a broader approach by contextualizing the kurgans within their respective landscapes. The results contribute to a deeper understanding of the physical and symbolic aspects and relationships of these structures, providing valuable insights into the enduring burial practices that shaped the funerary traditions of the Southern Caucasus during the 4th and 1st millennium BCE. Furthermore, this approach supports an assessment of the condition of the individual kurgans, thereby opening new perspectives to develop innovative plans to protect these burial mounds and their settings.

The Role of X-ray Images for Early Detection of Covid-19 using Machine Learning

The principal aim of the Covid Detection Using X-ray project is to develop a robust platform for accurate and efficient detection of COVID-19 through the analysis of X-ray scans. The proposed system aims to enhance the diagnostic process by leveraging artificial intelligence (AI) algorithms to identify specific patterns associated with COVID-19 in X-ray images. The platform will facilitate timely and precise diagnosis, enabling healthcare professionals to make informed decisions and optimize patient care. This project integrates advanced image processing techniques and machine learning algorithms to discern subtle patterns indicative of COVID-19 in X-ray scans. The system will provide a user-friendly interface for healthcare professionals, allowing seamless uploading and analysis of X-ray images. Additionally, the platform incorporates a comprehensive database of COVID-19-related patterns for accurate comparison and classification. The primary objective of the project is to contribute to the ongoing efforts in combating the COVID-19 pandemic by providing a reliable and efficient tool for early detection. The utilization of cutting-edge technologies, including deep learning and image analysis, ensures a high level of accuracy in identifying potential cases. The project aims to be a valuable resource for healthcare practitioners, aiding in the prompt and accurate diagnosis of COVID-19 through the analysis of X-ray imagery.

Probability of Erosion Utilizing Google Earth Engine and the RUSLE Method in the Tuntang Watershed

The Tuntang Watershed is an important watershed in Central Java. Management of watersheds in the Tuntang stream is a priority for various parties to carry out. One of the things that threatens the sustainability of the Tuntang watershed is erosion. The erosion rate can lead to sediment accumulation and siltation in the Tuntang River reservoir, which can cause catastrophic flooding. Flood disaster mitigation caused by erosion needs to be done, one of which is by calculating the erosion rate per year that occurs in the Tuntang watershed. This study calcultated the predicted erosion rate (per year in the Tuntang watershed) using the Revised Universal Soil Loss Equation (RUSLE) method, processed using the Google Earth Engine (GEE). Google offers a cloud-storage technology called GEE. Programming in JavaScript is required to operate GEE. GEE is a petabyte-scale data-based tool that can be used to analyze and archive geospatial data that is open source. The computing environment is designed for the processing of geospatial data, including the depiction of spatial analysis of satellite imagery. Data for RUSLE is obtained from the database in GEE, and the results can be imaged on a map. According to the study’s findings, the degree of soil erosion throughout the Tuntang Watershed was essentially constant, with Moderate erosion predominating in the majority of locations. Senjoyo Sub Watershed, Rowopening Sub Watershed, and Tuntang Hilir Sub Watershed are the primary locations with severe erosion. Rowopening Sub Watershed is the region that is the worst.

Social-ecological predictors of spotted hyena navigation through a shared landscape.

Human-wildlife interactions are increasing in severity due to climate change and proliferating urbanization. Regions where human infrastructure and activity are rapidly densifying or newly appearing constitute novel environments in which wildlife must learn to coexist with people, thereby serving as ideal case studies with which to infer future human-wildlife interactions in shared landscapes. As a widely reviled and behaviorally plastic apex predator, the spotted hyena (Crocuta crocuta) is a model species for understanding how large carnivores navigate these human-caused 'landscapes of fear' in a changing world. Using high-resolution GPS collar data, we applied resource selection functions and step selection functions to assess spotted hyena landscape navigation and fine-scale movement decisions in relation to social-ecological features in a rapidly developing region comprising two protected areas: Lake Nakuru National Park and Soysambu Conservancy, Kenya. We then used camera trap imagery and Barrier Behavior Analysis (BaBA) to further examine hyena interactions with barriers. Our results show that environmental factors, linear infrastructure, human-carnivore conflict hotspots, and human tolerance were all important predictors for landscape-scale resource selection by hyenas, while human experience elements were less important for fine-scale hyena movement decisions. Hyena selection for these characteristics also changed seasonally and across land management types. Camera traps documented an exceptionally high number of individual spotted hyenas (234) approaching the national park fence at 16 sites during the study period, and BaBA results suggested that hyenas perceive protected area boundaries' semi-permeable electric fences as risky but may cross them out of necessity. Our findings highlight that the ability of carnivores to flexibly respond within human-caused landscapes of fear may be expressed differently depending on context, scale, and climatic factors. These results also point to the need to incorporate societal factors into multiscale analyses of wildlife movement to effectively plan for human-wildlife coexistence.

The Wild Woman Archetype: A Comparative Study of Motif Correspondence Between “Bluebeard” and the Turkish Fairy Tale “İğci̇ Baba”

Given that myths and tales are living and memory areas of archetypes, in this study the French tale “Bluebeard” and the Turkish fairy tale “İğci Baba” are compared both in terms of the wild woman archetype and the motif correspondence based on this archetype. In this respect, two tales are analysed based on archetypal criticism. Moreover, archetypal criticism paves the way for imagery analysis by making it possible to see the collective, universal, and archetypal image of women to be seen through the motif correspondence associated with the wild woman archetype. The correspondence of motifs based on the wild woman archetype makes it possible to compare “Bluebeard” and “İğci Baba”, which are texts from different geographical regions, cultures, and eras. The wild woman archetype and the motifs in the two tales, such as initiation, the forbidden secret room, the irresistible curiosity and desire to know, and the key, are strikingly similar. The encounter with the wild woman archetype in two texts can be explained by the suprapersonal, supracultural and universal character of the archetypes, and the strong correspondence between the two texts based on similar motifs can be explained by the universality of the fairy tales and supracultural motifs.

Damage to medical complexes in the Gaza Strip during the Israel–Hamas war: a geospatial analysis

IntroductionMedical facilities are civilian objects specially protected during armed conflict by international humanitarian law (IHL). These protections are customarily applied regardless of the conflict, parties or contexts involved. Attacks on...

Detecting canopy openings in logged-over forests: a multi-classifier analysis of PlanetScope imagery

This study focused on the detection of forest canopy openings resulting from harvesting activities in hill tropical forests. Canopy openings, whether natural or human-induced, can have detrimental effects on forest ecosystems. Traditional ground surveys to assess the extent of canopy opening can be challenging and time-consuming. Therefore the study aimed to utilise satellite imagery, specifically PlanetScope data, to detect, map and measure canopy openings in logged-over forests. Three different classification algorithms, namely maximum likelihood classifier (MLC), support vector machine (SVM) and object-based image analysis (OBIA) were used and compared to identify canopy opening areas. The assessment was conducted in two stages: a preliminary assessment with three classes (forest, canopy opening and shadow) and a final assessment with two classes (forest and canopy opening). The overall accuracies of the classification algorithms were 82% for MLC, 91% for SVM and 90% for OBIA. Both SVM and OBIA surpassed the accuracy threshold, with SVM being the most effective in detecting and extracting canopy openings in dense forests. Results demonstrated the potential of PlanetSope imagery and advanced classification algorithms to detect canopy openings in logged-over forests. The findings highlighted the importance of regular updates on canopy opening extent, particularly concerning sustainable forest assessment and minimising the negative impacts on forest ecosystems.

Correction to: A content analysis of alcohol imagery in season 2 of The Kardashians

Correction to: A content analysis of alcohol imagery in season 2 of The Kardashians

Understanding the transition of community land use from shifting cultivation to cash cropping in southern Tanintharyi, Myanmar

Abstract Many tropical landscapes have experienced the loss of traditional cultivation practices as they have transitioned to other land use systems. The Tanintharyi Region of southern Myanmar is a landscape experiencing a rapid land use regime shift from traditional subsistence farming to permanent cash crop agriculture. Despite previous research in this region on the expansion of large‐scale agribusiness, such as oil palm and rubber plantations, little is known about how the small‐scale shifting cultivation system practiced by the local Karen ethnic people in Tanintharyi has changed over recent decades or the underlying reasons for this transition. Our study explores this transition process and its drivers from a social and land system change perspective in four villages of Bokpyin Township in Tanintharyi. We investigated the drivers of land cover and land use change by collecting information through focus groups and individual interviews with community members who previously practised shifting cultivation. We also quantified the overall change in land cover and land use through remote sensing analysis of Landsat 8 (2020) and declassified KH‐9 satellite imagery (1976). Prior to a period of civil war in the 1980s, cultivators used a traditional ‘slash‐and‐burn’ agricultural system for subsistence use. The present land use system is dominated by permanent betel nut cultivation, which represents the primary income source for farming households. The reported drivers of the transition to cash crop farming are greater income potential and livelihood security, risk of losing fallow land due to the needs for new cultivation areas for in‐migration and new centralized land use policies and administration after the national ceasefire agreement in 2012. The documented change in land use systems has been paired with a concurrent net loss of forest that, given the conflicted governance within the landscape, seems likely to continue without additional land use planning efforts. Our study contributes to an increased understanding of changes in shifting cultivation systems that are being documented globally. Furthermore, our example of combining qualitative interviews with analysis of historical satellite imagery to detect and explain land use regime shifts can serve as a model for future work in different landscapes. Read the free Plain Language Summary for this article on the Journal blog.

Megafires and koala occurrence: a comparative analysis of field data and satellite imagery

Megafires can have a devastating effect on koala populations. With climate change increasing habitat vulnerability to wildfires, understanding how to efficiently measure the impact of these events on koalas is essential. We analysed the relationship between the 2019-2020 megafires and the probability of koala occurrence in Mid North Coast NSW. We found that two on-field and one satellite-derived variables measuring fire severity equally explained koala occurrence. The probability of koala occurrence decreased with increasing fire severity. This supports the use of remote sensing imagery to efficiently monitor the impact of future fire events on koala populations in the region.

The pace of global river meandering influenced by fluvial sediment supply

Meandering rivers move gradually across the floodplains, and this river movement presents socioeconomic risks along river corridors and regulates terrestrial biogeochemical cycles. Experimental and field studies suggest that fluvial sediment supply can exert a primary control on lateral migration rates of rivers. However, we lack an understanding of the relative importance of environmental boundary conditions, such as floodplain vegetation and sediment supply, in setting the pace of river meandering across different environmental settings. Here, we combine the analysis of satellite imagery and global-in-scale sediment and water discharge models to evaluate the controls on lateral migration rates of 139 meandering rivers that span a wide range in size, climate, and bank vegetation. We show that migration rates normalized by the channel width monotonically increase with the volumetric sediment flux normalized by the characteristic size of the river. This relation is consistent across rivers in vegetated and unvegetated catchments, indicating that enhanced lateral migration rates in unvegetated basins is likely not only facilitated by lower bank mechanical strength, but also by higher normalized sediment supply in ephemeral rivers. Using three case examples, we also demonstrate that width-normalized meander migration rates respond to spatial gradients in sediment supply caused by river impoundments, highlighting the prominent role of sediment supply in setting the pace of meander migration. Our results suggest that sediment-supply variations caused by climate, land-cover and land-use changes can lead to predictable changes in meandering river evolution and ultimately drive architectural changes in sedimentary stratigraphy.

Temporal analysis of settlement areas and city footprints on construction and demolition waste quantification using Landsat satellite imagery

Municipal solid waste management has seen a surge in the use of satellite imagery in decision-making processes, yet its application to analyze quantitative variations in construction and demolition (C&D) waste remains under-investigated. This study employs satellite imagery and multivariate analysis to comprehensively assess and predict C&D waste generation in four diverse urban jurisdictions of Canada (Regina) and the USA (Seattle, Buffalo, and Philadelphia). Factors such as settlement area expansion, economic activities, and population growth significantly influence C&D waste rates. Stepwise multivariate regression models tailored to different city types, such as moderately populated (Group 1) and highly populated (Group 2), showcase acceptable predictive capabilities. For moderately populated cities, settlement area, average humidity, and GDP are identified as key predictors, while for highly populated cities, settlement area, unemployment rate, and building permit value prove effective indicators. These models, characterized by R² values from 0.70 to 0.94, provide tailored insights for distinct demographic conditions, aiding waste management planning. This research underscores the importance of satellite imagery and multivariate analysis in understanding C&D waste dynamics and empowers policymakers and waste management agencies with evidence-based strategies for effective waste management in urban centers.

Assessing lake shoreline change and prediction for 2030 by physical drivers: A Case Study from Lake Batur, Batur UNESCO Global Geopark, Bali

Over ten years, the water level of Lake Batur has increased. Agriculture area and settlements around Lake Batur are threatened by rising water level. This study aims to analyze the shoreline change of Lake Batur, located in the Batur UNESCO Global Geopark (BUGG), during the period of 2007 – 2018 and design a prediction for 2030. Understanding the shoreline change is very important for lake management and planning. Shoreline changes were analyzed in Geographic Information System (GIS) application. The data were obtained based on Remote Sensing (RS) data, Landsat ETM+ imagery on September 24, 2007, and Landsat OLI imagery on October 24th, 2018. Predictions of lake shoreline in 2030 result from modelling by integrating ASTER-GDEM V2 data, lake water level data for 2007–2018, annual average rainfall data for 2007–2018, and bathymetry data for 2013 and 2015. The results of the satellite imagery analysis show that there has been a change in the length of the shoreline, which has increased from 20.47 km in 2007 to 21.28 km (3.96%) in 2018. The lake surface area changed from 15.34 km2 in 2007 to 16.16 km2 in 2018 (5.35%). The prediction of lake shoreline changes in 2030 showed that Lake Batur will increase to 26.90 km (26.41%), and lake surface area is predicted to increase by 17.67 km2 (9.34%) from 2018. This is because of the morphological change of Lake Bottom.