Actual Map Research Articles

Unsupervised Bushfire Burn Severity Mapping Using Aerial and Satellite Imagery

Abstract. It is critical to assess bushfire impact rapidly and accurately because bushfires play a significant role in forest degradation and present a threat to ecosystems and human lives. Over the past decades, several supervised algorithms of burn severity mapping have been proposed, facing the significant drawback of time-consuming labeling. Moreover, there is no robust framework for burn severity mapping through fusing multi-sensor, multi-resolution, and multi-temporal remote sensing imagery from satellite and aerial platforms. Therefore, this paper presents an unsupervised two-step pipeline: processing 2D data followed by 3D data for burn severity mapping, both of which are acquired from either aircraft or satellites. For the 2D data processing, our proposed unsupervised burned area detection (UsBA detection) model enhances burned area mapping accuracy by integrating Ultra-High Resolution (UHR) aerial imagery with bi-temporal medium-resolution PlanetScope imagery, using a Segment Anything Model (SAM)-assisted UNetFormer (pre-trained on the target-style public dataset – LoveDA Rural) for refinement. The model demonstrates superior burned area segmentation, evidenced by improved evaluation metrics calculated from labeled test sites. For the 3D analysis, the burned areas extracted from 2D processing are further assessed using pre- and post-event airborne laser data. We implement a voxel-based workflow, including necessary steps such as ground filtering through Superpoints in RANSAC Planes (SiRP) method and biomass change analysis. The results indicate that the 3D branch provides a reliable lower bound of the actual damage map, because the vegetation growth between two measurements remains, in essence, undetected. The proposed framework offers a more accurate and robust solution for burn severity mapping utilizing combined 2D and 3D data, evaluated on a multi-source dataset from a real bushfire event that occurred in Bushland Park, South Australia.

Developing GIS-Based Algorithm of Stand Spatial Structure Index and Its Implementation

Accurate information of the fine-scale spatial patterns of trees and their interactions within a stand is critical for explaining the competition, health and vigor status, and future development of a stand. There are a number of indices which can show such patterns, but the stand spatial structure index is the most important. This index can be quantified based on the spatial information of trees (tree positions) within a stand and has paramount importance in identifying candidate trees to be thinned. This study develops a software tool (algorithm), which can conveniently and accurately estimate the stand spatial structure index. Our proposed algorithm considers the spatial relationships between a reference tree and its four adjacent trees, and computes the three most important stand structure indices (neighborhood comparison, uniform angle index and species mingling) using GIS (ArcEngine) and the C# programming language. The implementation of the developed algorithm (stand spatial structure index) shows that, for any reference tree assumed, its four adjacent trees identified by each of the three stand spatial structure indices (uniform angle index—UAI, mingling—M and neighborhood comparison—NC) are the same, indicating the consistency and accuracy between the three-stand spatial structural indices. For the same tree species in a forest stand, the computational results from each of the spatial structure indices used (UAI, M, NC) are also the same. Thus, the results of this algorithm developed in this study are consistent with that of the Winkelmass1.0 software (a type of software used to simulate stand spatial structure). As this article is based on the GIS technique, the computational results can be visually displayed and implemented on actual maps, making it more convenient and intuitive for forest management. The proposed approach will be useful for accurately identifying the trees to be thinned and helpful for maintaining the vigor stand structure. This study also demonstrates the implementation of the algorithm to the real-world data and proves that the computational process is simple and efficient. The application of this algorithm for the identification of trees to be thinned may help the stakeholders to focus their attention towards multi-functional forest management. The algorithm will also provide an important basis for optimizing thinning and maintaining well-structured forest stands.

FaSS-MVS: Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-Borne Monocular Imagery.

With FaSS-MVS, we present a fast, surface-aware semi-global optimization approach for multi-view stereo that allows for rapid depth and normal map estimation from monocular aerial video data captured by unmanned aerial vehicles (UAVs). The data estimated by FaSS-MVS, in turn, facilitate online 3D mapping, meaning that a 3D map of the scene is immediately and incrementally generated as the image data are acquired or being received. FaSS-MVS is composed of a hierarchical processing scheme in which depth and normal data, as well as corresponding confidence scores, are estimated in a coarse-to-fine manner, allowing efficient processing of large scene depths, such as those inherent in oblique images acquired by UAVs flying at low altitudes. The actual depth estimation uses a plane-sweep algorithm for dense multi-image matching to produce depth hypotheses from which the actual depth map is extracted by means of a surface-aware semi-global optimization, reducing the fronto-parallel bias of Semi-Global Matching (SGM). Given the estimated depth map, the pixel-wise surface normal information is then computed by reprojecting the depth map into a point cloud and computing the normal vectors within a confined local neighborhood. In a thorough quantitative and ablative study, we show that the accuracy of the 3D information computed by FaSS-MVS is close to that of state-of-the-art offline multi-view stereo approaches, with the error not even an order of magnitude higher than that of COLMAP. At the same time, however, the average runtime of FaSS-MVS for estimating a single depth and normal map is less than 14% of that of COLMAP, allowing us to perform online and incremental processing of full HD images at 1-2 Hz.

A process mining model to detect adverse patient safety events: Blood products watch

Introduction Patient safety incidents and adverse events are reported voluntarily by healthcare staff. The Global Trigger Tool, developed by the Institute for Healthcare Improvement, is used to capture unreported events. The objective of the new model is to use process mining in the context of adverse event detection to increase reporting efficiency. Methods In the first phase, the process mining model is used to discover the actual process map by querying inpatient processes using the provided log data. Transfusions of blood or blood products within 24 h after surgery, a subset of The Transfusion of Blood or Use of Blood Products trigger of Institute for Healthcare Improvement (GTT-C1) was deployed as a process pattern within the actual process map. Only detected patients were reviewed in the last phase. Results The model ran on a single hospital data set, which included 2870 patients, 1048 of which included surgeries. The model reduced the analyzed data to 57 patients. The expert group analyzed these 57 patients and defined 12 adverse events. The calculated adverse event ratio was 1.1%, comparable with other manual Global Trigger Tool studies. In addition, the reviewers’ time required decreased by 94.6%, from 138.28 to 7.52 h. Two reviewers validated the model by reviewing a sample of patients. Conclusions Process mining can be used in healthcare institutions to conduct routine surveillance and detect unreported safety events. This allows healthcare providers to allocate financial and human resources to more urgent needs and increase efficiency. Further research is needed to evaluate the model with other triggers.

Turbocharger turbine map scaling for virtual engine use

Emissions requirements, performance expectations, and expanding range of fuels for off-road engines are broadening engine air system requirements. As part of reducing engine development cost and risk, virtual engine simulation is used to select architecture and components capable of covering a broad range of requirements through low risk changes to the turbocharger turbine. A method is presented for predicting turbine performance changes due to turbine housing area change, the most effective of these low risk changes, from available turbine map data. The method, developed from a mean line model, learns four simplified geometry parameters, three loss coefficients, and two maximum efficiency rotor inlet incidence coefficients from a training turbine map data set, then predicts a scaled turbine map for changes to a simplified geometry parameter associated with housing area. Reduced mass flow RMS errors less than 4.3% and efficiency RMS errors less than 5.2% are expected when generating scaled turbine maps with this method, which is additional 2% error for flow and 3% error for efficiency over that expected when modeling actual turbine map data.

Predicting urban tomorrow: CA-Markov modeling and district evolution

The global population is experiencing exponential growth, resulting in a substantial increase in urbanization and subsequent urban expansion. This uncontrolled expansion, often termed urban sprawl, poses significant challenges to sustainable urban development. Understanding the importance of this concern, obtaining precise information about changes in Land Use/Land Cover (LULC) becomes crucial. This research examines the transformative processes of LULC and urban expansion over a 20-year period in two distinct study areas. For this purpose, built-up areas are first analysed using satellite-derived land surface temperature data, revealing temperature increase over time attributed to urban expansion. To project future trends using CA-Markov model, the suitability maps for each LULC class aggregated through the Multi Criteria Evaluation (MCE) method. Subsequently, the CA-Markov simulates the LULC maps for 2017 and 2018 for each study area, with a focus on the year 2050. Model calibrated by comparing the simulated maps with the actual maps in both study areas, and the reliability is affirmed by high Kappa coefficients (> 80%). Consequently, the study predicts LULC maps for 2050, revealing that both areas will experience a continued increase in built-up areas, a decrease in forested areas, and a relative stability in agricultural zones over the next 33 years.

Clinical validation of a novel digital twin framework for ventricular functional substrate simulation in post-infarction patients

Abstract Background Catheter ablation is a cornerstone treatment for scar related post-infarction ventricular arrhythmias. Functional substrate driven ablation strategies guided by local activation time (LAT) mapping have been proven as efficient and effective approaches, especially when dealing with large scar areas. In-silico electrophysiological modeling is emerging as a potential tool for procedural planning and guidance. Purpose To clinically validate the feasibility, diagnostic performance, and accuracy of a novel late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) based digital twin framework for simulated LAT map generation in post-infarction patients and their integration with CARTO3 EAM suite. Methods Five ischemic subjects who underwent CMR-aided scar-related ventricular tachycardia ablation at our referral center were retrospectively enrolled. Their CMR derived 3D pixel-signal-intensity (PSI) maps generated with ADAS3D were processed with novel in-house software to obtain patient-specific ventricular geometry, tissue characterization, and to generate a model of Purkinje system and myocyte fiber orientation. According to local normalized pixel intensity (NPI) values, myocardial regions were marked as healthy, scar, or border-zone. Electrophysiological properties of border-zone (BZ) were modulated according to local NPI. Continuous and 8-steps isochronal endo-epicardial LAT maps were generated and imported in CARTO3 system, aligned with electroanatomical mapping (EAM), and compared at an ASE 17-segments level, in terms of sensitivity, specificity, predictive values, and accuracy to actual high-density LAT maps obtained with a multipolar catheter. Deceleration Zones (DZ) were defined as previously described (>3 isochrones within 1 cm radius). Results A total of 85 ASE segments (34 epicardial) were analyzed. 24 EAM segments (28%) held DZs, 40 (47%) segments harbored bipolar scar or BZ. Thirty-seven (44%) CMR segments held scar core/BZ. Thirty (35%) digital model segments harbored DZs. Generated LAT maps were successfully imported and aligned with CARTO3 EAM suite for validation. In terms of DZ localization prediction, our LAT model showed 83% sensitivity (95% CI 63%-95%), 90% specificity (95% CI 80%-96%), 77% positive predictive value (PPV) (95% CI 56%-91%), 93% negative predictive value (NPV) (95% CI 83%-98%), 88% accuracy (95% CI 79%-94%). Conclusion Our newly developed framework for LGE-CMR derived functional substrate simulation in post-infarction patients holds high sensitivity, specificity, predictive values, and accuracy for DZ localization. Further validation, with a larger sample size, is required for evaluating potential implications in clinical practice.

A Multi-Feature Fusion Method for Urban Functional Regions Identification: A Case Study of Xi’an, China

Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field. For this purpose, this paper proposes an urban functional region identification method called ASOE (activity–scene–object–economy), which integrates the features from multi-source data to perceive the spatial differentiation of urban human and geographic elements. First, we utilize VGG16 (Visual Geometry Group 16) to extract high-level semantic features from the remote sensing images with 1.2 m spatial resolution. Then, using scraped building footprints, we extract building object features such as area, perimeter, and structural ratios. Socioeconomic features and population activity features are extracted from Point of Interest (POI) and Weibo data, respectively. Finally, integrating the aforementioned features and using the Random Forest method for classification, the identification results of urban functional regions in the main urban area of Xi’an are obtained. After comparing with the actual land use map, our method achieves an identification accuracy of 91.74%, which is higher than other comparative methods, making it effectively identify four typical urban functional regions in the main urban area of Xi’an (e.g., residential regions, industrial regions, commercial regions, and public regions). The research indicates that the method of fusing multi-source data can fully leverage the advantages of big data, achieving high-precision identification of urban functional regions.

Assessment of land use land cover change and its effects using artificial neural network-based cellular automation

The challenge of urban growth and land use land cover (LULC) change is particularly critical in developing countries. The use of remote sensing and GIS has helped to generate LULC thematic maps, which have proven immensely valuable in resource and land-use management, facilitating sustainable development by balancing developmental interests and conservation measures. The research utilized socio-economic and spatial variables such as slope, elevation, distance from streams, distance from roads, distance from built-up areas, and distance from the center of town to determine their impact on the LULC of 2016 and 2019. The research integrates Artificial Neural Network with Cellular Automta to forecast and establish potential land use changes for the years 2025 and 2040. Comparison between the predicted and actual LULC maps of 2022 indicates high agreement with kappa hat of 0.77 and a percentage of correctness of 86.83%. The study indicates that the built-up area will increase by 8.37 km2 by 2040, resulting in a reduction of 7.08 km2 and 1.16 km2 in protected and agricultural areas, respectively. These findings will assist urban planners and lawmakers to adopt management and conservation strategies that balance urban expansion and conservation of natural resources leading to the sustainable development of the cities.

Hybrid path planning method for USV using bidirectional A* and improved DWA considering the manoeuvrability and COLREGs

This paper proposes a hybrid dynamic path planning algorithm that integrates global path planning (GPP) and local path planning (LPP). The objective is to effectively address the challenges associated with path planning and collision avoidance for unmanned surface vehicles (USV) in complex environments. The bidirectional A* algorithm is used to search the optimized strategy based on the actual geographical information map for the navigation routes of USV. The Dynamic Window Algorithm (DWA) is employed, and a novel cost function is formulated considering the USV's manoeuvring characteristics, dynamic constraints, and environmental information. The primary purpose is to ensure the compliance of the USV with COLREGs during dynamic obstacle avoidance. It accomplishes this by providing positive reinforcement for achieving the correct turning speed when the USV determines the orientation of an approaching ship. Another evaluation function is introduced to mitigate the problem of local optima in complex environments that the LPP may encounter. The actual work map is used to model the application scenario with the obstacles in the presence of incoming ships, the simulation results show that the proposed algorithm is capable of generating adaptive, collision-free routes while adhering to COLREGs rules.

Geospatial Crisis Index

Security geography examines the military geography factors influencing the security system and environment of an area, and its investigation method is rooted in geography. The data set related to this, mainly open source databases, is a rapidly changing data set, so its analysis only shows the current state, but if the data set is large enough and considered mass data, it may be suitable for drawing new connections and conclusions for the future. A geospatial analysis system applicable to security geographic analysis is under development, which is continuously collecting data by taking predefined variables into account for a specific area of interest. The geospatial crisis index can be obtained from these local data following a professional analysis procedure based on predefined algorithms. This new index is a unitless numerical value issued by the geospatial evaluation and analysis system used to analyse crisis zones. It is a benchmark allowing areas to be compared in terms of a fixed risk factor or factors to be ranked in a hierarchy by fixing the area. This helps shape the order of the analysis, speeds up the actual text and map evaluation, reduces the need for human resources, and supports commanders in decision-making.

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches.

This study aimed to identify the most influential soil and environmental factors for predicting wheat yield (WY) in a part of irrigated croplands in southwest Iran, using the FAO-Agro-Climate method and machine learning algorithms (MLAs). A total of 60 soil samples and wheat grain (1m × 1m) in 1200ha of Pasargad plain were collected and analyzed in the laboratory. Attainable WY was assessed using the FAO method for the area. Pearson correlation analysis was used to select the best set of soil properties for modeling. Topographic attributes and vegetation indices were used as proxies of landscape components and cover crop to map actual WY in the study area. Two well-known MLAs, random forest (RF) and artificial neural networks (ANNs), were utilized to prepare an actual continuous WY map. The k-fold method was used to determine the uncertainty of WY prediction and quantify the quality of prediction accuracy. Results showed that soil organic carbon (SOC) and total nitrogen (TN) had a positive and significant correlation with WY. The SOC, TN, normalized different vegetation index (NDVI), and channel network base level (CHN) were recognized as the most important predictors and justifying more than 50% of actual WY. The ANNs outperformed the RF algorithm with an R 2 of 0.75, RMSE of 400 (kg ha-1), and RPD of 2.79, according to statistical indices. The uncertainty analysis showed that the maximum uncertainty of the prediction map [400 (kg ha-1)] was very low compared to the mean value [4937 (kg ha-1)] of WY map. Calculation yield gap using the FAO-agro-climatic model showed that the average yield gap of the region was about 50% of actual yield. The findings of this study demonstrated that integrating simulated attainable crop growth using crop model and a set of soil and environmental covariates with the ANNs algorithm can effectively predict WY gaps in large areas with acceptable and reasonable accuracy. The study emphasizes that the implementation of efficient management practices has the potential to enhance agricultural production in the study area and similar regions. These results represent a significant advancement of sustainable agriculture and provide valuable insights for ensuring global food security.

Impoverished details with preserved gist in remote and recent spatial memory following hippocampal and fornix lesions

IntroductionCognitive Map Theory predicts that the hippocampus (HPC) plays a specialized, time-invariant role in supporting allocentric spatial memory, while Standard Consolidation Theory makes the competing prediction that the HPC plays a time-limited role, with more remote memories gaining independence of HPC function. These theories, however, are largely informed by the results of laboratory-based tests that are unlikely to simulate the demands of representing real-world environments in humans. Validation of these theories is further limited by an overall focus on spatial memory of newly encountered environments and on individuals with extensive lesions to the HPC and to surrounding medial temporal lobe (MTL) regions. The current study incorporates naturalistic tests of spatial memory based on recently and remotely encountered environments navigated by individuals with lesions to the HPC/MTL or that are limited to the HPC's major output, the fornix. MethodsFour participants with bilateral HPC/MTL and/or fornix lesions drew sketch maps of recently and remotely experienced neighbourhoods and houses. Tests of the appearance, distances, and routes between landmarks from the same real-world environments were also administered. Performance on the tasks was compared to that of control participants closely matched in terms of exposure to the same neighbourhoods and home environments as well as to actual maps. ResultsThe performance of individuals with fornix/MTL lesions was found to be largely comparable to that of controls on objective tests of spatial memory, other than one case who was impaired on remote and recent conditions for several tasks. The nature of deficits in recent and remote spatial memory were further revealed on house floorplan drawings, which contained spatial distortions, room/structure transpositions, and omissions, and on neighbourhood sketch maps, which were intact in terms of overall layout but sparse in details such as landmarks. ConclusionLab-based tests of spatial memory of newly learned environments are unlikely to fully capture patterns of spared and impaired representations of real-world environments (e.g., peripheral features, configurations). Naturalistic tasks, including generative drawing tasks, indicate that contrary to Cognitive Map Theory, neither HPC nor MTL are critical for allocentric gross representations of large-scale environments. Conversely, the HPC appears critical for representing detailed spatial information of local naturalistic environments and environmental objects regardless of the age of the memory, contrary to Standard Consolidation Theory.

Soil Erosion Risk Assessment Using Corine Model: A Case Study in Shwan Sub basin, Kirkuk, Iraq

Water erosion is one of the most important problems and challenges facing the optimal management of natural resources, especially soil and water resources, because of its impact on soil degradation. This study aimed to classify the risk of soil water erosion and clarify its distribution in the Shwan Basin in Kirkuk Governorate - Iraq based on the Corine model.In the first stage of the work, the soil erodibilty, erosivity and slope factors were determined, classified and mapped according to the degree of their impact on soil erosion.The potential soil erosion risk map was prepared using Arc GIS 10.4.1.In the second stage, the land cover factor was calculated and classified according to the degree of protection. In the last stage, the actual soil erosion risk map was prepared by multiplying the class of land cover with the map of potential soil erosion risk over the entire study area.The study showed that 28.18% of the studied area is classified under a high risk, 39.57% represents as a medium risk and 32.25% is a low risk .The areas of severe soil erosion risk were concentrated in the southwestern part and some northern and central parts, and the areas of medium risk were concentrated in the northern, central and southern parts, while regarding the low actual risk, they were concentrated in the western parts and in random areas in the center and east of the study area.The results also showed that the Corine model for soil water erosion risk mapping is a highly effective and cost-effective approach.

Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques

Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management.

Assessing future flood inundation in Nandigama through land use, land cover, and rainfall analysis

Abstract Frequent occurrences of high-intensity rainfall have made urban flooding a significant problem. In the present study, flood inundation maps were prepared for the years 2030 and 2035 based on changes in future land use and land cover (LULC) and rainfall patterns in Nandigama, located in Andhra Pradesh, India. The Hydrologic Engineering Center's Hydrologic Modelling System Hydrologic and Hydrologic Engineering Center–River Analysis System hydraulic models were used to assess future runoff and flood inundation areas for the above years. Future LULC and hydro-meteorological data were analysed and incorporated into the models. Predicted LULC maps showed a high level of agreement with actual LULC maps, as indicated by a Kappa index of 88.34%. The average peak runoff flowing out of the basin increased by 1.82%–3.43% for years 2030–2035, respectively. When considering the average inundation percentage area in Nandigama, it was found that 12.73% and 13.83% of the area flooded in both years. The study recommendations give more attention to the Nandigama flood management authority.

Toponymic Landscape of the Kazakhstan-Russia Borderland: Cultural Sovereignty and Renaming Policies

The authors consider the toponymic landscape as a “space of meanings” that reflects the power relations and the dominant ideological positions its time. The concept of “cultural sovereignization” is used to describe the state politics aimed to prevent imaginary or real threats to territorial integrity from irredenta diaspora living in the borderland and in close contact with a neighboring parent country. By the procedure of toponymic re-naming, states not only demonstrate the power of control over their territories, but also project a certain social order to subjected space, and shaping identities. To analyze the changes in the toponymic landscape of the north-Kazakhstani borderland in the post‑Soviet years, authors compare the actual toponymic map of the region with that which existed at the late Soviet era. The dynamics of renaming was considered in the context of the legislative activities of the Kazakhstan government regarding the linguistic and historical policies; toponymic data were compared with ethno-demographic statistics and population dynamics. The authors conclude that the cultural sovereignization policy produced controversial effects on the north-Kazakhstani borderland: erasing “unwanted” toponyms the political actors erase their symbolic meaning and yet eliminate the traces of large-scale historical processes. As a result, the most urbanized and economically developed region looks on the maps like natural area and isolated province. It also causes the social splits in society, alienating a significant part of the local population from historical memory and familiar environment. New environmental context hinders the complementarity between territoriality of the local border communities and Kazakhstni national identity, and mobilizes the ethnic particularism. The article is intended for specialists in the border and frontier studies.

Abstract 16382: Inaccuracy of Non-Invasive Brachial Cuff-Measured Blood Pressure During Surgery

Introduction: Blood pressure (BP) is an essential vital sign to be monitored during surgery. BP is measured only by Non-Invasive brachial Cuff-measured Blood Pressure (NICBP) devices in 90% of surgeries in US. However, previous studies demonstrated possible inaccuracy of NICBP. The inaccuracy of BP during surgery is a significant public concern because it can result in missing therapeutic potentials and consequently increasing cardiovascular risks during and after surgery. However, there is no large study to evaluate the accuracy of NICBP measured during surgery. Method: It has been known that invasive intra-arterial blood pressure (IABP) measured by an arterial line catheter is an optimal reference for validation of NICBP accuracy given its highest level of accuracy. Our study collected 2,814,154 pairs of BP measurements between NICBP and IABP which were measured at the same time during surgery from 38,706 patients who received surgeries at University of California, Los Angeles from 2016 through 2023. Result: We evaluated the accuracy of NICBP compared to IABP using Confusion matrix analysis (Fig 1). We defined predicted BP classification when BP is classified by NICBP. We also defined actual BP classification when BP is classified by IABP. We categorized BP according to Mean Arterial Pressure (MAP; mmHg) into 5 groups; MAP<65, 65≤MAP<75, 75≤MAP<95, 95≤MAP<105, and 105≤MAP. True Positive rates of each classification ranged from 36.7% to 66.7% (Fig 1). It was surprising that 49.9% in actual MAP (aMAP)<65, 56.9% in 65≤aMAP<75, 33.9% in 75≤aMAP<95, 63.4% in 95≤aMAP<105, and 50.6% in 105≤ aMAP was misclassified into the incorrect BP classifications by NICBP. Conclusion: Our study demonstrated noticeable inaccuracy of BP by NICBP compared to IABP during surgery. Given high reliability solely on NICBP in measuring BP during the majority of surgery cases, the inaccuracy of NICBP may cause significant public health concerns due to unoptimized BP management during surgery.

GIS based method for mapping actual LULC by combining seasonal LULCs

One of the most significant applications of remote sensing data is to prepare land use and land cover (LULC) maps. LULC maps are always affected by seasonality and a single LULC map of a particular month is prepared to represent a year in most of the research, especially in change detection research. This does not represent the real view of the landscape because the seasonal variation of different LULC types is always overlooked. Considering the issue, the current method aims to solve the problem by incorporating seasonal LULC using the raster overlay method to remove the seasonality effect on LULC classification. To apply this method, a minimum of two seasonal LULC maps is required for a single study year. The map needs to overlay and then reclassify according to the stable and rotational LULC pattern of the study area. This method will replicate the actual LULC pattern of a study area from satellite images. Summary of the method is as follows:•LULC of each season was classified using image classification technique.•LULC of each seasons are coded and combined using overlay technique.•Combined map is reclassified to prepare the actual LULC map.

Monitoring and Modeling of Spatio-Temporal Urban Expansion and Land Use/Land-Cover Change in Mountain Landscape: A Case Study of Dalat City, Vietnam

The lack of ability to control human activities led to changes of land use/land cover (LULC) in Dalat City where rapid urbanization and the demand to expand agricultural land have resulted in dramatic forest reductions. This study assessed the rate and extent of LULC changes over the past 12 years and simulated future scenarios in Dalat City, Lam Dong Province, Vietnam by using an integrated model of Markov chain and logistics regression. Three land-use maps used to analyze land-use change were extracted from satellite images in 2010, 2016, and 2022 by classification approach. The outcome of this process indicates a significant increase in agricultural and built-up land of 48.22 km2 and 9.36 km2, respectively; a decrease in forest land of 55.61 km2, and a minor change in water bodies and bare land in the 2010-2022 period. Prediction maps of land-use change in 2028 and 2034 are generated after the model is validated by comparing the actual map with the prediction map of LULC in 2022 using Kappa statistics. Transition of forest area to other land use types, especially land for expansion of built-up and agricultural land is the crucial trend of land-use change in the future according to the forecast model. Compared to 2022, forest area in 2034 will decrease by 60.65 km2 while built-up and agricultural land will increase by 14.07 km2 and 43.61 km2, respectively. The research results provide valuable information as a foundation for land-use policy planning and local urban development to ensure sustainable development objectives.