Topographic Data Research Articles

The development of a routable pavement network dataset to support active travel

ABSTRACT Active travel (AT) networks have only recently become a priority for urban planners in many countries, and networks of spatial data and applications that use them are also lacking. Road networks have historically been used as a proxy for pedestrian networks, yet they are a poor one as human movement is much less constrained than that of motorised vehicles. This study presents a novel approach to producing an attribute-rich network for AT, together with a semi-automated method to produce a width-attributed pavement network from topographic mapping agency data (based on a case study of Ordnance Survey (OS) MasterMap data from the UK). A shortest path network routing application using Dijkstra’s algorithm is presented to demonstrate the possibilities of AT routing incorporating increased attribution. The paper concludes by firstly proposing additional steps to further enhance the attribution of the network and finally by describing the relevant policy implications of this work.

Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data

Abstract. Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

Rock glacier inventory and predictive modeling in the Mackenzie Mountains: predicting rock glacier likelihood with a generalized additive model

Rock glaciers have been the subject of extensive research in recent years due to their potential to serve as indicators of past and present climate conditions and their potential impacts on water resources. Location and descriptive rock glacier data within the Mackenzie Mountains were used to build a rock glacier inventory that will serve as a valuable resource for future research and monitoring efforts. Additionally, this study maps the likelihood of rock glacier presence using extracted variables in a generalized additive model (GAM). The model incorporates attribute data, including potential incoming solar radiation (PISR), topographic position index (TPI), slope, elevation, and lithology as controls for rock glacier development. Topographic data were compiled for three study regions of the Mackenzie Mountains from a 30 m digital elevation model (DEM). The analysis of the GAM showed that the most significant explanatory variables were PISR, elevation, slope, and TPI. The GAM model had an accuracy of 0.87 with a sensitivity of 0.92. This study provides important insights into the controls, distribution, and dynamics of rock glaciers in the Mackenzie Mountains, as well as both the limitations and the potential of statistical models in predicting their occurrence.

Modeling the interaction between wildfires and windthrows: A pilot case study for Italian Alps

Wildland fires and windthrows represent relevant disturbances for forest ecosystems worldwide. In this context, especially for Italian catchments, the interaction between windthrows and changes in wildfire behaviour starting from ALS data processing is scarcely investigated. Therefore, this research aims to compute a multi-temporal analysis of the interaction between windthrows and wildfire behaviour in a forested area (Veneto region, northern Italy), recently affected by the renamed Vaia windstorm. The semi-empirical FlamMap model was applied, starting from ALS data processing implemented in R for mapping the spatial distribution of forest attributes and fuels within the catchment. The role of windthrows in altering wildfire behaviour was investigated considering ALS point clouds acquired before and after the occurrence of the storm Vaia. Digital Terrain Models (DTMs), Canopy Height Models (CHMs), topographic data and metrics describing forest structure were extracted from ALS data for both scenarios at 5 m resolution, to compare changes in wildfire behaviour over time. Differences in Rate of Spread (RoS), flame length (FL), midflame windspeed (WS) and arrival time (AT) were assessed, and their correlation with windstorm damages was investigated at the catchment detail. , An increase of RoS, FL, and WS greater than 30 m/min, 3 m and 1.1 m/s were respectively estimated in windthrown areas, as well as a decrease of AT greater than 30 min, attesting the key role of windthrows in altering wildfire behaviour over time. The correlation between windthrows and changes in wildfire attributes was finally modeled by computing regression analysis, with R2 of 0.86, 0.93, 0.62, and 0.91 resulted for RoS, FL, WS and AT. This research represents a pilot case study for better detecting changes in wildfires behaviour due to windthrows occurrence, therefore proposing and carrying out effective planning and management strategies for disturbed forest stands over time.

Numerical Simulations Using iRIC Nays2DH for Sediment Transport Behaviors in Dam Breach Tests

After the breach of a landslide dam, the sediment in the breach opening will be carried downstream by the breach flood. The river channel will also be eroded by the flood, resulting in bed load transport. Three large-scale dam breach tests were conducted to investigate the sediment transport behavior after a dam breach. The topography data of the creek channel were measured before and after the dam breach tests to understand the sediment transport behavior. The sediment transport simulations of the dam breach tests were conducted using the iRIC Nays2DH software. The simulations focused on three types of test setups: the single dam, single dam with a spur dike, and double dam models. The terrain (DEM) for the numerical model input was designated based on the LiDAR results, and a flow hydrograph during the dam breach tests was applied. The accuracy of the simulations was assessed using the “coverage index” and “mean absolute percentage error”. A numerical parametrical study was performed to find the major parameters that influenced the simulations. The results showed that the dynamic behavior of water flow and sediment during the dam breach processes were effectively captured by the iRIC Nays2DH simulation, but with limitations. The average flow velocity of the flood in the single dam case was the fastest among the three types of dam breaches. Due to the contraction of the creek channel caused by the spur dike, severe erosion occurred locally, and the flow rate increased in the narrowed section. Water impoundment between the two dams after the first dam breach and the consequent breach of the second dam were also well-simulated for the double dam breach. The findings and simulations in this study help explain dam breaches better and can guide researchers working on sediment transport during dam-breach floods.

Quantifying Topographic Influence on Irradiance Transfer in Permanently Shadowed Regions

Abstract. In permanently shadowed regions (PSRs), the surface temperature is influenced by secondary illumination, which changes daily and seasonally due to the sunlight reflected by the surrounding terrain. Understanding how topography affects the transfer of radiant energy can help us quickly interpret the thermal behavior using available topographic data. The amount of radiant energy transferred from a sunlit lunar surface to a PSR depends on the distance and orientation of the sunlit surface to the PSR, and is represented by view factors. In this study, we introduce an approach to systematically represent the combined effects of multiple surfaces using statistical analysis applied to view factor maps. We demonstrate that our proposed approach can explain the contrasting temperatures of two PSRs at the lunar south pole. We verify our theoretical findings using PSR images acquired by the ShadowCam instrument aboard the Danuri lunar orbiter.

DISTRIBUSI SEDIMEN PADA WADUK JATIBARANG, KOTA SEMARANG

The main issue in the operation of reservoirs is sedimentation. This can lead to silting and affect the reservoir's storage capacity. The distribution of sediments needs to be understood in order to gain an overview of their spread, so that measures can be taken to address sediment-related issues in the Jatibarang Reservoir. The analysis was conducted using sediment data from previous studies, which reported a total sediment volume of 6.64 million m³ over a period of 50 years. Based on topographic data, it was found that the Jatibarang Reservoir is classified as Type III, with a "hill" classification. The analysis results showed that the dimensionless factor F is 0.22, with a relative depth p of 0.53. Based on the sediment distribution analysis of the Jatibarang Reservoir with Empirical Area Reduction Method, it was found that after 50 years of operation, the new zero elevation of the reservoir would be at an elevation of +126.82 m. In comparison with the low water surface elevation at the existing dead storage of the Jatibarang Reservoir, which is at +136 m, it can be concluded that the Jatibarang reservoir can operate according to the planned lifespan and can still function for more than 50 years

Land-Use-Change-Driven Erosion and Sediment Transport in the Yaqui River Sub-Basin (Mexico): Insights from Satellite Imagery and Hydraulic Simulations

Soil erosion and sediment transport are significant concerns in the Yaqui River sub-basin in northwest Mexico, driven by land use changes and environmental degradation. This study aims to evaluate erosion processes between 2000 and 2020 using a combination of satellite imagery and numerical simulations with Iber software (Version 2.5.2). The primary objective is to assess the impacts of land use changes, particularly the conversion of forest to grassland, on erosion rates and sediment transport. Satellite images from 2000 and 2020 were analyzed to detect land cover changes, while Iber’s sediment transport module was used to simulate erosion patterns based on the Meyer–Peter and Müller equation for bedload transport. Hydrological and topographical data were incorporated to provide accurate simulations of flow velocity, depth, and erosion potential. The results reveal a 35.3% reduction in forest cover, leading to increased erosion and sediment transport in steep areas. Simulation predictions highlighted areas with high future erosion potential, which are at risk of further soil loss if current trends continue. Flow velocity increased, contributing to riverbank destabilization and higher sediment yield, posing a risk to infrastructure such as the Álvaro Obregón Dam. This study underscores the need for targeted erosion control measures and sustainable land management practices to mitigate future risks and protect vital infrastructure in the Yaqui River Basin.

Identifying Landslide Hotspots Using Unsupervised Clustering: A Case Study

Landslides pose significant threats to life, property, and infrastructure. This study explores applying unsupervised learning techniques to identify and understand landslide-prone areas. We analyzed topographic data by employing K-Means, Hierarchical Clustering, Spectral Clustering, Mean Shift Clustering, and DBSCAN to uncover hidden patterns in landslide occurrence. Evaluation metrics, including the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used to assess the performance of these algorithms. Hierarchical Clustering achieved the highest Silhouette Score of 0.635, indicating excellent cluster separation. However, Mean Shift Clustering outperformed the other methods with a superior Davies-Bouldin Index of 0.603 and the highest Calinski-Harabasz Index of 4121.75, demonstrating the best overall clustering performance. DBSCAN also performed well, with a Silhouette Score of 0.610 and 12 noise points identified. These findings contribute to a deeper understanding of landslide spatial distribution and can inform the development of effective early warning systems and mitigation strategies.

MAGUS (Model for the Analysis of Geomorphological Urban Systems): From Conception to Validation on the Historic City Center of Turin (Italy)

This study introduces and tests the MAGUS (Model for the Analysis of Geomorphological Urban Systems), a GIS-based methodological framework designed to analyze the complex interplay between urbanization and natural landscapes. Focusing on the historical evolution of Turin city center (Italy), the research employs an interdisciplinary approach to examine how urban expansion has interacted with pre-existing geomorphological features. The study leverages historical documents, digitized and georeferenced within a cartographic space, to reveal the relationships between natural and anthropogenic landforms from the Roman period through the 20th century. A key innovation of MAGUS is the integration of Multi-Temporal Markers (MTM) and Volumetric Unit of Building (VUB), which enhances the accuracy of historical data analysis. The results demonstrate that urbanization significantly alters the natural landscape in the historic city center of Turin, where the stratification of urban expansion phases partially obscures the original landform. By overlaying topographical data with historical urban expansion maps, the research highlights the concealed geomorphological elements beneath Turin’s urban fabric. This interdisciplinary study not only contributes to the field of urban geomorphology but also provides a foundation for future research into the impacts of urbanization on natural landscapes, which is increasingly critical as urban populations continue to grow.

Examining wildfire dynamics using ECOSTRESS data with machine learning approaches: the case of South‐Eastern Australia's black summer

Wildfires are increasing in risk and prevalence. The most destructive wildfires in decades in Australia occurred in 2019–2020. However, there is still a challenge in developing effective models to understand the likelihood of wildfire spread (susceptibility) and pre‐fire vegetation conditions. The recent launch of NASA's ECOSTRESS presents an opportunity to monitor fire dynamics with a high resolution of 70 m by measuring ecosystem stress and drought conditions preceding wildfires. We incorporated ECOSTRESS data, vegetation indices, rainfall, and topographic data as independent variables and fire events as dependent variables into machine learning algorithms applied to the historic Australian wildfires of 2019–2020. With these data, we predicted over 90% of all wildfire occurrences 1 week ahead of these wildfire events. Our models identified vegetation conditions with a 3‐week time lag before wildfire events in the fourth week and predicted the probability of wildfire occurrences in the subsequent week (fifth week). ECOSTRESS water use efficiency (WUE) consistently emerged as the leading factor in all models predicting wildfires. Results suggest that the pre‐fire vegetation was affected by wildfires in areas with WUE above 2 g C kg−1 H₂O at 95% probability level. Additionally, the ECOSTRESS evaporative stress index and topographic slope were identified as significant contributors in predicting wildfire susceptibility. These results indicate a significant potential for ECOSTRESS data to predict and analyze wildfires and emphasize the crucial role of drought conditions in wildfire events, as evident from ECOSTRESS data. Our approaches developed in this study and outcome can help policymakers, fire managers, and city planners assess, manage, prepare, and mitigate wildfires in the future.

Florida mangrove dieback on a decadal and centennial timescales

Hurricanes are considered some of the most devastating weather phenomena, causing deaths and destruction along the coast. Mangroves are perceived as natural defenses against coastal hazards. However, mangrove gaps, known as mangrove dieback, have occurred in southwestern Florida. This study implemented a spatial-temporal analysis based on satellite, lidar and unmanned aerial vehicle (UAV) images collected along the southwestern Florida coast between Tampa and Naples from 1994 to 2020 CE, and multi-proxy analyses on sediment cores sampled from two mangrove dieback sites to determine the cause(s) of mangrove death and the implications for the sustainability of these forests. The data indicated that out of the 86 identified dieback, 78 % arose between 2004 and 2017 CE, when one tropical storm and three hurricanes made landfall on the studied coast, suggesting hurricanes as the main driver for this mangrove degradation. Multi-proxy analysis indicated mangrove expansion since ~600 cal yr BP due to relative sea-level (RSL) rise. However, hurricanes caused losses in the mangrove area between 1930 and 1950 CE, and during the last two decades in the studied sites. Ground topographic data and aerophotogrammetry revealed circular dieback with permanent ponds (~30 cm depth) inhibiting mangrove recovery. The mangrove death occurred in <3 years, with a gradual dieback expansion due to positive feedback involving physicochemical processes in the sediments. In addition, recent hurricanes increased pre-existing dieback areas. Therefore, dieback sites are vulnerability hotspots for mangroves in the face of projected RSL rise and higher frequency of intense hurricane strikes. Mangrove dieback should be monitored on a temporal (seasonal - annual) and spatial (cm-m) resolution only possible through the combination of satellite and UAV data. Integrating remote sensing and stratigraphic analysis enabled us to identify mangrove diebacks on a decadal to centennial timescale, improving our comprehension of how catastrophic weather events affect these forests.

Enhancing Environmental Sensitivity and Vulnerability Assessments for Oil Spill Responses in the Caspian Sea

Oil spills pose significant threats to marine and coastal ecosystems, necessitating advanced methodologies for environmental sensitivity and vulnerability assessments. This study enhances existing frameworks to better manage oil spill risks in the Caspian Sea, a region characterized by its ecological sensitivity and economic dependence on oil extraction. Utilizing the Environmental Sensitivity Index (ESI), we adapted global standards to the unique conditions of the Caspian Sea and built a sensitivity map of the coastline, which later became one of the components of the integral sensitivity map for the entire Caspian Sea, which includes several biotic and abiotic components. We also developed a comprehensive geodatabase incorporating topographic, infrastructural, and hydrodynamic data. Through the sophisticated modeling of oil spill scenarios using the Oil Spill model of the MIKE 21 software (Release 2016) suite, we simulated spills of varying magnitudes to analyze their potential impacts on the marine and coastal environment. The results enabled the creation of vulnerability maps, pinpointing areas at highest risk and facilitating strategic response planning. Our study demonstrates the critical importance of integrating advanced geospatial analyses and dynamic modeling techniques to improve oil spill preparedness and response strategies. The findings of this study suggest that enhanced monitoring and adaptive management strategies are essential for protecting the Caspian Sea from environmental risks posed by its oil industry.

Analysis of Downstream Sediment Transport Trends Based on In Situ Data and Numerical Simulation

This study conducted an in-depth analysis of the sediment dynamics in the lower reaches of the Changhua River and its estuary on Hainan Island. Through field collection of topographic data and sediment sampling, combined with advanced computational techniques, the study explored the transport pathways and depositional patterns of sediments. The grain size trend analysis (GSTA) method was utilized, in conjunction with the Flemming triangle diagram method, to classify the dynamic environment of the sediments. Furthermore, hydrodynamic modeling results were integrated to further analyze the transport trends of the sediments. The study revealed that the sediment types in the research area are complex, primarily consisting of gravelly sand and sandy gravel, indicating a generally coarse sedimentary environment in the region. The sediments in the lower reaches of the Changhua River generally transport towards the south and southwest (in the direction of Beili Bay). The net sediment transport directions inferred from the GSTA model are largely consistent with the Eulerian residual flow patterns, especially in the offshore area, where discrepancies are observed in the nearshore zone. The nearshore transport is influenced by the combined effects of alongshore currents, residual flows, and river inputs, while the offshore transport exhibits a shift from the northwest to southwest directions, reflecting the regional circulation patterns.

Canopy Height Mapper: a Google Earth Engine application for predicting global canopy heights combining GEDI with multi-source data.

Spatially and temporally discontinuous canopy height footprints collected by NASA’s GEDI (Global Ecosystem Dynamics Investigation) mission are accessible on the Google Earth Engine (GEE) cloud computing platform. This study introduces an open-source, user-friendly, code-free GEE web application called Canopy Height Mapper (CH-GEE), available at https://ee-calvites1990.projects.earthengine.app/view/ch-gee, which automatically generates (10 m) high-resolution canopy height maps for a specific area by integrating GEDI with multi-source remote sensing data: Copernicus and topographical data from the GEE data catalogue. CH-GEE generates local-to-country scale calibrated canopy height maps worldwide using machine learning algorithms and leveraging the GEE platform's big data and cloud computing capabilities. CH-GEE allows customization of geographic area, algorithms and time windows for GEDI and predictors. Canopy heights generated by CH-GEE were validated using the Italian National Forest Inventory across 110,000 km2 at multiple scales (Country-based R-squared = 0.89, RMSE = 17%). CH-GEE's accuracy and scalability make it suitable for forest monitoring.

Integrating Multisource Data and Machine Learning for Supraglacial Lake Detection: Implications for Environmental Management and Sustainable Development Goals in High Mountainous Regions

The accurate detection and monitoring of supraglacial lakes in high mountainous regions are crucial for understanding their dynamic nature and implications for environmental management and sustainable development goals. In this study, we propose a novel approach that integrates multisource data and machine learning techniques for supra-glacial lake detection in the Passu Batura glacier of the Hunza Basin, Pakistan. We extract pertinent features or parameters by leveraging multisource datasets such as radar backscatter intensity VH and VV parameters from Sentinel-1 Ground Range Detected (GRD) data, near-infrared (NIR), NDWI_green, NDWI_blue parameters from Sentinel-2 Multi-spectral Instrument(MSI) data, and surface slope, aspect, and elevation parameters from topographic data. The entire dataset is partitioned into training and testing sets, with machine learning models including the artificial neural network (ANN), the support vector machine (SVM), logistic regression (LR), random forest (RF), and K-nearest neighbour (KNN) trained on the training data (70%). Accuracy assessment employs testing data and involves the evaluation of metrics such as ROC curves and confusion matrices. The best-performing model, ANN, is validated against manually digitized lake polygons derived from Sentinel-2 and Google Earth Pro imagery. Furthermore, the digitized lake polygons are used to analyze glacial lake dynamics from 2016 to 2022. Key findings of this research presented that the NDWI_green, Sigma0_VH, and elevation are the most significant predictors in detecting supra-glacial lakes. Among the various trained and evaluated models, the Artificial Neural Network (ANN) achieved the highest performance (accuracy: 95%, AUC: 0.99) and accurately mapped supra-glacial lakes regardless of their small size. The findings have significant implications for understanding glacial lake behavior in the context of climate change and informing future research and monitoring efforts.

A two-stage deep learning architecture for detection global coastal and offshore submesoscale ocean eddy using SDGSAT-1 multispectral imagery

Submesoscale ocean eddies are essential oceanic phenomenon that control and influence the ocean energy cascade. Most existing eddy detection methods rely on low-resolution satellite altimeter data, which fail to capture submesoscale ocean features and oceanographic phenomena in shallow water. Introducing high-resolution multispectral data can alleviate these problems, yet it has been largely overlooked. A generalized and efficient deep learning architecture that combines developments in deep learning with Sustainable Development Goals Science Satellite 1 (SDGSAT-1) multispectral data from earth observations offers a potential pathway for more fine detection of ocean eddies. Considering that oceanic eddy exhibits spatially sparse characteristics on high-resolution remote sensing scenes, the oceanic eddy detection (OED) model suitable for global coastal and offshore regions is divided into two stages: eddy information judgment and eddy position determination. Correspondingly, SDGSAT-1 multispectral data from November 2021 to December 2022 were carried out to construct two submesoscale eddy datasets for training and testing each stage model. The union validation of multiple metrics demonstrates that the proposed OED model and its stage models achieve state-of-the-art (SOTA) performance, especially in optically complex coastal and offshore waters. We applied the model to real-world scenes captured by SDGSAT-1 in 2023, and found that the detected results were mainly located at the water depth below 200 m. The authenticity of the recognition results is validated using sea surface chlorophyll concentration, temperature, and topography data, indicating that the OED model has achieved remarkable effectiveness under various sea conditions. In addition, the temporal distributions and statistical characteristics of detected submesoscale eddies are analyzed over an extended period (November 2021 to November 2023). Finally, HISEA-2, Landsat-9, and Sentinel-2 served as testing grounds to validate the generalization of the proposed methodology, with experimental results demonstrating that the OED model possesses significant developmental potential for multi-source remote sensing data. This paper presents a comprehensive deep learning framework for the global-scale detection of submesoscale eddies and underscores the pivotal role of high-resolution multispectral imagery as an innovative data source for global coastal and offshore eddy identification.

Corneal Allogenic Intrastromal Ring Segments (CAIRS) Versus Synthetic Segments: A Single Segment Comparative Analysis Using Propensity Score Matching.

To compare and assess the visual, refractive, and tomographic results of patients with corneal ectasia treated with either corneal allogenic intrastromal ring segments (CAIRS) or synthetic intrastromal corneal ring segments (ICRS) without concomitant corneal cross-linking. In this retrospective cohort study, 34 eyes with CAIRS were matched to 34 eyes with ICRS using the propensity score matching technique. Each group was matched on a oneto-one basis using multiple parameters such as central corneal thickness, vertical and horizontal coma, maximum anterior keratometry, steepest keratometry, and age. Visual, refractive, topographic, and aberrometric data were measured at baseline, 1 week, 1 month, 3 months, and 1 year postoperatively. Initial preoperative parameters were similar between the two groups. Both groups showed significant improvement at last follow-up time in corrected distance visual acuity (CDVA) (0.52 ± 0.23 to 0.16 ± 0.18 logarithm of the minimum angle of resolution [logMAR], P < .001; 0.44 ± 0.27 to 0.17 ± 0.21 logMAR, P < .001), topographic astigmatism (4.45 ± 2.75 to 3.14 ± 1.93 diopters [D], P = .001; 3.66 ± 2.22 to 2.36 ± 1.46 D, P = .007), maximum anterior keratometry (55.85 ± 7.53 to 50.69 ± 6.38 D, P < .001; 54.59 ± 6.95 to 50.71 ± 4.51 D, P = .003), and vertical coma (1.49 ± 1.02 to 0.38 ± 0.65 D, P < .001; 1.22 ± 0.75 to 0.52 ± 0.57 D, P < .001) for CAIRS and ICRS, respectively. The improvements observed in both groups at the last follow-up visit were comparable; however, the CAIRS group demonstrated a higher percentage of eyes gaining two or more Snellen lines of CDVA (60% vs 31.58%, P = .04), and a greater magnitude of reduction in vertical coma compared to the ICRS group, although this difference did not reach statistical significance. No major complications were observed with both groups, and one eye lost one CDVA line in the ICRS group. The mean thickness of the CAIRS segments at the last follow-up visit was 401.06 ± 100.12 µm, compared to 435.29 ± 26.19 µm for ICRS. Both CAIRS and ICRS demonstrated significant compression of stromal thickness above the segment (36.19% and 32.00%, respectively). When adequately matched for preoperative disease type and severity, eyes with CAIRS had a similar and notable clinical improvement compared to ICRS, with possibly better improvement in vertical coma and CDVA. [J Refract Surg. 2024;40(11):e863-e876.].

Optimization of territorial ecological space under the constraint of ecosystem service externalities

Ecosystem services are dynamic, following specific paths between regions and generating externalities. However, research on these externalities and the mechanisms within ecological networks remains limited. The quantification, spatial patterns, and evolution of these externalities are still unclear and need further study. Utilizing land use, topography, precipitation, and socio-economic data from 2010 and 2020, the study aims to quantify the externalities of ecosystem services and analyze their spatial distribution and network transmission characteristics within the Territorial Ecological Space (TES) of Jintan District. Using the InVEST model, we assessed the externalities of ecosystem services and integrated these with an ecological network model to reveal the impact of network structure on the flow of externalities. We propose an ecological space optimization plan with various scenarios, offering a reference for regional ecological space optimization. Key findings indicate that ecosystem service externalities exist and display spatial differentiation, with ecological source areas and corridors participating in the flow and dissipation of these services. Based on these findings, an ecological space optimization plan is proposed, consisting of multiple scenarios, which offers a reference for regional ecological space optimization. The study contributes to the sustainable management of ecological spaces by providing new insights and methodological advancements, particularly in the context of optimizing ecological spatial structures and ensuring regional ecological security. By elucidating the spatial dynamics and network transmission of ecosystem service externalities, this research supports informed decision-making in ecological conservation and land use planning.

Perspectives Toward an Integrative Structural Biology Pipeline With Atomic Force Microscopy TopographicImages.

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.