Map Of Area Research Articles

Enhancing Burned Area Mapping Accuracy: Integrating Multi-temporal PCA with NDVI Analysis

Forested lands in the west coast of Turkey, with their similarity to Mediterranean forests, are often found to be highly susceptible to wildfires, necessitating the development of a forest management program to refine and quantify forest fires and their impacts on the environment. In light of this fact, a multi-temporal approach combining Principal Component Analysis (PCA) and Normalized Difference Vegetation Index (NDVI) analysis derived from Sentinel-2 imagery is suggested in the current study. Through PCA of carefully selected bands of Sentinel-2, both recent and historic fire impacts are attempted to be captured. It was found that the first two principal components (PC1 and PC2) predominantly describe landscape characteristics, while the third and fourth components (PC3 and PC4) have high abilities in detecting burn scars. It is worth noting that an increase in the ability to detect burn scars was observed with the inclusion of NDVI and its difference in time (dNDVI) within the PCA process. A high effectiveness level in distinguishing burnt areas from unburnt landscapes was presented by the multi-temporal PCA approach, particularly with dNDVI integration. PC2 and PC3, especially with dNDVI integration, are found to be strong indicative factors of burnt areas. In the classification result, accuracies of different years of fire events differed, and a high accuracy of 98.76% was found in the last fire event year of 2019. However, slight underestimation and overestimation were also observed in older fire scars. Mean accuracy, on average, for the PCA-dNDVI method was found to be higher than that of the MLC method. Furthermore, significant vegetation losses by fire, particularly by the 2019 fire incident, were realized through NDVI assessment. Although it worked well in recent fire scars, overestimating the extent in the case of burned areas from previous years was observed. The potential of multi-temporal PCA integration with NDVI for analysis in mapping burned areas at different scales in fire-prone ecosystems in western Turkey is underlined by the results of this work. Much more successful forest management and assessment strategies after fires have occurred in these ecosystems are helped to be created by this approach. Moreover, the approach is suggested to be one of the strong tools for monitoring fire induced damages across many time scales toward better understanding and management of long-term impacts caused by forest fires in the region.

Spatial multi-criteria approaches for estimating geogenic radon hazard index

The geogenic radon hazard index (GRHI) map plays a crucial role in evaluating radon exposure risks. The construction of this map requires a comprehensive analysis of radon levels in soil gas and some critical factors, such as uranium content in bedrock, soil permeability, and geological inhomogeneities. In this context, the spatial multi-criteria decision analysis is proposed to integrate the GRHI-based criteria for identifying the high-potential radon areas. In particular, the multivariate integration involves the fuzzy gamma operator and a hybrid multi-criteria decision-making technique, namely AHP-TOPSIS, which represents a novel approach in GRHI mapping. Thus, a comparison is provided through the definition of the GRHI map of an unexplored study area, that is the Apulia region, located in Southern Italy. In order to evaluate the output maps, high radon potential areas are identified based on some available indoor radon measurement data. The success-rate curve, as a valid evaluation metric, is employed for the performance assessment and comparison of these two methods. The results demonstrate that although both generated GRHI maps are closely correlated with high-potential radon zones in Apulia, the hybrid AHP-TOPSIS method is preferable in identifying areas with elevated radon potential.

An Improved LandTrendr Algorithm for Forest Disturbance Detection Using Optimized Temporal Trajectories of the Spectrum: A Case Study in Yunnan Province, China

Forest disturbance mapping plays an important role in furthering our understanding of forest dynamics. The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used in forest disturbance mapping. However, it neglects the quality of the temporal trajectory and its change trends for forest disturbance mapping. Therefore, the aim of this paper is to improve LandTrendr (iLandTrendr) for forest disturbance mapping by optimizing its temporal trajectories and the post-processing of detection results. Specifically, the temporal trajectory of complex forest disturbance types was optimized using the Savitzky–Golay (SG) filter with constraints. That is, the smooth value generated from the SG filter for the disturbance year was replaced by the satellite observations when the nonlinear abrupt signals were included in the multi-temporal data. The forest disturbance detected by LandTrendr was further modified using the consistency of spectral variation trends. A case study using iLandTrendr to detect forest disturbance in Yunnan Province was conducted. Compared to the LandTrendr method, which has an overall accuracy (OA) of 35.88%, iLandTrendr generated forest disturbance mapping with an OA of 89.32%, which was significantly higher. The total mapped area of disturbance was 1,985,820.9 km2, accounting for 49.69% of the total area. The disturbances were predominately caused by natural factors, such as wildfires, pests and diseases, and forest degradation, accounting for 85.31% of the total disturbed area. iLandTrendr can quickly and accurately detect the occurrence year of complex forest disturbance types and can be extended for the forest disturbance mapping of a large area.

Landslide susceptibility prediction and mapping in Taihang mountainous area based on optimized machine learning model with genetic algorithm

Landslide susceptibility prediction and mapping in Taihang mountainous area based on optimized machine learning model with genetic algorithm

The reliability and validity of rapid transcranial magnetic stimulation mapping for muscles under active contraction

Rapid mapping is a transcranial magnetic stimulation (TMS) mapping method which can significantly reduce data collection time compared to traditional approaches. However, its validity and reliability has only been established for upper-limb muscles during resting-state activity. Here, we determined the validity and reliability of rapid mapping for non-upper limb muscles that require active contraction during TMS: the masseter and quadriceps muscles. Eleven healthy participants attended two sessions, spaced two hours apart, each involving rapid and ‘traditional’ mapping of the masseter muscle and three quadriceps muscles (rectus femoris, vastus medialis, vastus lateralis). Map parameters included map volume, map area and centre of gravity (CoG) in the medial-lateral and anterior-posterior directions. Low to moderate measurement errors (%SEMeas = 10–32) were observed across muscles. Relative reliability varied from good-to-excellent (ICC = 0.63–0.99) for map volume, poor-to-excellent (ICC = 0.11–0.86) for map area, and fair-to-excellent for CoG (ICC = 0.25–0.8) across muscles. There was Bayesian evidence of equivalence (BF’s > 3) in most map outcomes between rapid and traditional maps across all muscles, supporting the validity of the rapid mapping method. Overall, rapid TMS mapping produced similar estimates of map parameters to the traditional method, however the reliability results were mixed. As mapping of non-upper limb muscles is relatively challenging, rapid mapping is a promising substitute for traditional mapping, however further work is required to refine this method.

Determining epidemiological patterns in disease identification using mathematical models on machine learning based multilayer structures

The object of the study is epidemiological grouping using the SEIR mathematical model on a machine learning-based multilayer network. The problems in this research are related to managing epidemiological data on a large scale to determine disease patterns and identification such as the number of recovered cases, number of infected cases and number of deaths and demographic factors. In the process, traditional methods make it difficult to carry out processes such as determining patterns and identifying diseases. So, it is necessary to use machine learning and the SEIR (Susceptible-Exposed-Infectious-Recovered) mathematical model which can be integrated with multilayer networks to increase accuracy and effectiveness in identifying diseases and determining patterns. The results obtained from this research are a model that can identify and determine patterns of disease spread in large-scale epidemiological data. In its application, the SEIR mathematical model combined into a social layer and an environmental layer in a multilayer network. This research is research with a level of novelty in the application of the SEIR mathematical model to multilayer networks and machine learning so that the model formed can be used to view the distribution of epidemiological data for disease-related information. Machine learning aims to process large-scale data in minimal time resulting in clustering and identification of diseases such as flu, Covid-19 and pneumonia. This research has an accuracy of 94 % using the MAPE evaluation technique. It is hoped that the resulting model can be used in the world of health for disease mapping in certain areas as a reference for mitigating the spread of disease

High-resolution population maps derived from Sentinel-1 and Sentinel-2

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in data-scarce regions. To address it we have developed Popcorn, a population mapping method whose only inputs are free, globally available satellite images from Sentinel-1 and Sentinel-2; and a small number of aggregate population counts over coarse census districts for calibration. Despite the minimal data requirements our approach surpasses the mapping accuracy of existing schemes, including several that rely on building footprints derived from high-resolution imagery. E.g., we were able to produce population maps for Rwanda with 100m GSD based on less than 400 regional census counts. In Kigali, those maps reach an R2 score of 66% w.r.t. a ground truth reference map, with an average error of only ±10 inhabitants/ha. Conveniently, Popcorn retrieves explicit maps of built-up areas and local building occupancy rates, making the mapping process interpretable and offering additional insights, for instance about the distribution of built-up, but unpopulated areas, e.g., industrial warehouses. With our work we aim to democratize access to up-to-date and high-resolution population maps, recognizing that some regions faced with particularly strong population dynamics may lack the resources for costly micro-census campaigns. Project page: https://popcorn-population.github.io/.

Evaluation of soil erosion rate using geospatial techniques for enhancing soil conservation efforts

According to reports, Ethiopia is one of the countries in sub-Saharan Africa with the worst affected by soil erosion. It has both on-site and off-site consequences on biophysical and socioeconomic settings in an area. The study area is heavily affected by soil erosion forming diverse erosion structures, particularly in the upper course of the watershed. Hence, this work seeks to estimate the geographically distributed annual soil loss rate and mapping of soil erosion hazard hotspot areas in the watershed using the Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions. The RUSLE parameters, such as rainfall erosion factor (R-factor), soil erodibility factor (K-factor), slope steepness and slope length factor (LS-factor), land cover factor (C-factor), and conservation practice factor (P-factor) were considered as data input for the analysis to quantify the soil loss rate in the study area. A digital elevation model (DEM) with a 12.5 × 12.5-meter resolution was employed for catchment delineation and determination of the LS factor.The mean yearly rainfall data from the surrounding rain gauge stations was used to analyze the R-factor. The results of the current conditions showed that the average typical soil loss rate from the entire watershed is 23.8 t ha-1 yr-1, and the quantity of soil loss from the study area ranged from 0 to 776.71tan /ha-1 yr-1. Nonetheless, Tiro Afeta experiences mean soil erosion at a rate of roughly 50.2 t ha-1 yr-1, exceeding the acceptable threshold of 11 t ha-1 yr-1. Determining the sustainability of soil production requires assessment, particularly in cases where significant yearly soil erosion occurs. Due to intensive agricultural activities in the Xiro Afeta watershed, significant soil erosion is predominantly occurring in thissteep upper region. Consequently, this area urgently requires appropriate soil protection measures.

Mapping of K-Means Clustering Crime Prone Areas in Brebes Regency

Mapping of K-Means Clustering Crime Prone Areas in Brebes Regency

Tracking paddy rice acreage, flooding impacts, and mitigations during El Niño flooding events using Sentinel-1/2 imagery and cloud computing

The frequent occurrence of El Niño events, in the context of climate change, brings heavy precipitation and extreme heat, severely disrupting agricultural production. Previous efforts have focused on monitoring crop planting areas and evaluating affected crops during disasters. Nevertheless, a comprehensive analysis, including crop planting area mapping, crop damage assessment, and mitigation effectiveness throughout the entire course of a disaster, has been seldom addressed. In this study, we built a comprehensive framework to rapidly investigate the areas of early rice, the extent of flooding impacts, and the post-flood mitigations of early rice during the El Niño flooding event in a typical rice production region – Jiangxi Province in 2023. Early rice planting areas were first mapped by integrating 15-day time series gap-filled Sentinel-1/2 datasets using the Google Earth Engine (GEE) platform, based on a random forest classifier built with the 55 optimized training features. Then the flood-affected early rice map was produced by integrating the early rice planting areas and the Sentinel-1 images-based flood map. Finally, the post-flood newly planted rice fields were identified using the random forest algorithm and classification features from the Sentinel-1/2 images composited during four phenology phases of newly planted rice. The results showed the early rice planting area map, the flooding map, and the newly planted early rice map have overall accuracies of over 90 %. The early rice planting areas reached 120 × 104 ha, and an area of 3.60 × 104 ha (3 %) was flooded due to the heavy rain, and 3.43 × 104 ha flooded areas were newly planted, eventually mitigating the flooding impacts on the production of early rice. This study showcases the potential of all the available Sentinel-1/2 data, cloud computing, and well-established mapping algorithms for tracking rice areas, flooding impacts, and mitigations (i.e., after-flooding replanting) during extreme climate events. The established framework is expected to serve as an early warning system for agricultural adaptation to extreme climate events.

Interpretation of Microtremor Data Through Analysis Horizontal to Vertical Spectral Ratio (HVSR) for Microzonation of Seismic Earthquake Hazards in Baubau City

Research has been carried out on seismic hazard microzonation in the city of Bau – bau using microtremor data. The aim of this research is to determine the seismic vulnerability index based on horizontal to vertical spectral ratio (HVSR) analysis and create a dominant frequency microzonation map (F0), dominant period map (T0), amplification factor map (A0), and seismic vulnerability index map (Kg) from microtremor data which can describe the level of vulnerability of an area to seismic hazards. Microtremor measurements were carried out using a portable three-component seismograph (TDS. 303), the data was analyzed with gepsy software via the HVSR method. The research results show that the distribution of dominant frequency values ​​(F0), ranges from 0.4795 Hz to 16.6383 Hz, dominant period values ​​(T0), ranges from 0.0601 seconds to 2.0854 seconds, amplification factor values ​​(A0), ranges from 1.2285 to 4.9043, and the seismic vulnerability index (Kg) value ranges from 0.1043 to 11.0276. The values ​​(F0), (T0), (A0), and (Kg) are interpreted by referring to literature data and surface geological maps of the research area. The results show that the study area has a small portion of sediment layers that are vulnerable to seismic hazards.

Eigen-6c4 Gravity and Alos Palsar Radar Data Integration for Delineating Geological Lineaments in North Ghadames Basin, NW Libya

Introduction The ambiguity regarding the geological interpretation has the potential to be significantly decreased with the use of remote sensing, geophysical data, and the history of geology. Aims The objective of this work is to delineate geological lineaments and faults using EIGEN-6C4 satellite gravity and ALOS PALSAR radar data in the north Ghadames basin, of northwest Libya. Methods The satellite gravity dataset of the study region was used to perform a complete Bouguer anomaly map of the study area to start the gravity interpretation. Then different filters were performed on the gravity dataset, such as the total horizontal gradient (THG), CET grid analysis, 3-dimensional Euler solution (ED), and a tilt derivative (TDR) using the commercial Oasis Montaj programme. The techniques of edge identification (THG, TDR, and also CET grid analysis) are utilised for locating and identifying the boundaries or edges of geological structures that contribute to gravity anomalies. The 3-dimensional Euler solution, in conjunction with the TDR method, is employed to precisely figure out the positions and estimated depths associated with subsurface sources. Radiometric calibration, speckle filtering, and geometric correction were applied to preprocess the ALOS PALSAR L 1.1 image via the Sentinel Application Platform (SNAP) software. For automatic extraction, the PCI Geomatica software's LINE module was applied. Results The gravity data results indicate that the main trends of the identified geological lineaments are oriented in the North-South, East-West, Northwest-Southeast, and North-Northwest to South-Southeast directions. Furthermore, the depths of the sources observed underneath the study region differ from 250 m to 2750 m. The orientation of extracted lineaments from the ALOS PALSAR L1.1 images, specifically the horizontal-horizontal as well as horizontal-vertical polarisation images, predominantly have orientations in the north-south, north-northeast to south-southwest, east-west, north-northwest to south-southeast, and northeast-southwest directions within the study area. Conclusion All these findings of lineaments are associated with the tectonic features of the area. Consequently, identifying these lineaments/faults is important to reduce the ambiguity of geological interpretation and provide more information on the dominant trends for future exploration activities in the study region.

Mapping in healthy subjects different body areas for dermal thickness and skin hardness by high frequency ultrasound and durometry.

Body mapping of normal values of skin thickness and hardness may be a useful aid in daily practice. By employing non-invasive techniques, our pilot study provides these values in healthy individuals using high frequency ultrasound (HFUS) and durometry in areas used to evaluate the modified Rodnan skin score (mRSS). One-hundred-fifty-two healthy volunteers from Ghent and Genova University Hospitals (mean ages 31.2, 35.5, and 64.9 years), were evaluated to exclude rheumatologic diseases. HFUS and durometry were used to assess the dermal status in mRSS areas. Exploratory analyses were performed to assess the impact of demographic and anthropometric characteristics on intra-subject skin measurements. Statistical analysis was performed with Datatab®. The upper and lower arms exhibited significantly higher durometry values and lower dermal thickness compared to the trunk regions, underscoring distinct variations across these areas (all p<0.05). The hardest skin was found on the finger, while the thickest dermal measurements were at the abdomen and thighs. Dermal thickness was higher in men in multiple areas in the three cohorts, albeit with relatively modest effect sizes (r coefficients ranging between 0.02 and 0.6). Despite the presence of significant inter-group differences in dermal thickness, HFUS mapping showed similar topographical distributions in both centres. Our study offers a comprehensive skin mapping status in healthy individuals. Key findings indicate lower dermal thickness in the upper arms, legs, and feet, and higher skin hardness in peripheral areas like fingers, compared to truncal regions.This skin mapping pilot study might provide the normal distribution values in outpatient clinics for physicians to be used when comparing the same areas in pathological conditions like systemic sclerosis-related fibrotic skin.

Hydrothermal alteration processes in monzogranite: a case study from the Eastern Desert of Egypt: implications from remote sensing, geochemistry and mineralogy

The South Eastern Desert (SED) of Egypt is one of the most promising areas in Egypt; it is widely explored for exploring the rare earth elements (REEs) and uranium-bearing ores. It is a main part of the Arabian-Nubian Shield (ANS). Therefore, the present study concerns with Sikait-Nugrus area as one of the most prolific sites in this region. The study provides a detailed geological, structural, and mineralogical investigation of the monzogranites to describe and characterize the various alteration types and sequence. For this purpose, remote sensing, geochemical and petrographical techniques were applied. The remote sensing technique helped in constructing a detailed geologic map of the study area to follow up strictly the alteration zone of the Sikait-Nugrus area. Petrographically, the granites predominates in the study area, they are described as slightly and highly altered monzogranites. The slightly altered one is composed mainly of quartz (~ 20–35%), alkali feldspar (~ 25–30%), plagioclase (~ 25–30%), and mica (~ 5–15%), while accessory minerals are represented by zircon and monazite. On the other hand, the portion of this granite close to the shearing zone is intensively altered and characterized by sericitization as the main alteration processes. This sheared portion is characterized by accessory minerals as, uranothorite, allanite, fluorite and Nb-minerals (ishikawaite). Minerlogically, the altered monzogranites are predominated by the following mineral groups: (1) radioactive minerals as uranyl silicates (soddyite, uranophane and kasolite), and thorium minerals (thorite and uranothorite), (2) Nb–Ta minerals (betafite, plumbobetafite, columbite, fergusonite, and aeschynite), (3) REE minerals (monazite, cheralite and xenotime), and (4) zircon and fluorite as accessory minerals. Geochemically, the recorded pattern of the REEs tetrad effect (M-type) for the highly altered samples indicate that these granites are highly evolved and affected by late stage of hydrothermal alteration and the effective water-rich alteration processes that connected to intensive physico-chemical changes. The total REE concentrations equal 241.8 and 249.75 ppm for the highly and slightly altered samples. A significant mass change (MC) was analyzed by the isocon technique (22.95 & 11.11) and volume change (VC) (1.8 &-7.99) for the highly and slightly altered samples, respectively. The mass balance calculations and the isocon diagrams revealed that some major oxides were removed from the slightly altered monzogranites and transformed later into highly altered monzogranites with increasing the alteration intensity due to the impacts of hydrothermal alteration processes. The studied area is virgin, where no detailed studies have been applied to this region. It is extendable to other parts of the Arabian-Nubian Shield in around the Red Sea in Egypt, Sudan, Saudi Arabia and Yemen. The applied technical workflow is also extendible to other surface analogues everywhere.

Mapping of Kharif Sown Area Using Temporal RISAT-1A SAR and Optical Data

Mapping of Kharif Sown Area Using Temporal RISAT-1A SAR and Optical Data

Combining statistical analyses and GIS-based approach for modeling the sanitary boundary of drinking water wells in Yaoundé, Cameroon

In slums and urban areas with unplanned housing such as in the city of Yaounde, Cameroon, poor water quality and inadequate sanitation pose significant health risks. The absence of locally-defined sanitary boundaries, tailored to hydrogeological conditions, hinders effective zoning and land use planning, exacerbating environmental degradation and health hazards. In this study, the sanitary boundary between drinking water wells and sources of pollution in the city of Yaoundé was defined using statistical analysis techniques and Geographic Information Systems (GIS). The Groundwater Quality Index (GWQI) and certain significant parameters affecting water quality notably the transmissivity of the aquifer and well depth were used to establish the sanitary boundary equation which was then interpolated in a GIS environment to obtain the sanitary boundary map of the study area. The linear equation deduced from the significant factors was defined to map the health boundaries between wells and pollution sources for a nominal value of the GWQI (GWQI = 25). Of the 112 wells analysed, 37 % had an excellent GWQI, 16 % were good while the remaining 47 % were poor. Statistical analysis showed a strong correlation between the GWQI and significant factors of groundwater pollution, such as the distance between well and pit latrines (r = −0.753), the aquifer transmissivity of the formation (r = 0.671) and the depth of the wells (r = - 0.855) but no correlation with elevation (r = 0.017) and well age (r = 0.090). Linear regression analysis confirmed the association of the GWQI with the main factors of pollution (p ≤ 0.05). A coefficient of determination of R2 = 0.85 was obtained when validating the linear regression plot based on independent data between measured and predicted GWQI. The sanitary boundary map shows that the wells in our study area should be located between 39 m and 370 m, with an average value of 215 m. New regulations on the distance between well and pit latrines are essential to prevent groundwater pollution.

Integration of bare soil and crop growth remote sensing data to improve the accuracy of soil organic matter mapping in black soil areas

Accurately mapping the spatial distribution of soil organic matter (SOM) content is critical for informed land management decisions and comprehensive climate change analyses. Remote sensing-based SOM mapping models during periods of bare soil exposure have demonstrated efficacy in various regional studies. However, integrating bare soil imagery with growing season imagery for SOM content mapping remains a complex process. We conducted a study in Youyi Farm, a representative area of black soil in Northeast China. We collected 574 soil samples (0–20 cm) with SOM content through field sampling and laboratory analysis. Additionally, cloud-free Sentinel-2 images were obtained from the Google Earth Engine (GEE) platform for both the bare soil period (April-June, October) and crop growth period (July-September) from 2019 to 2021. To assess the influence of crop growth information on SOM mapping, we incorporated remote sensing imagery during the crop growth period, considering different crop type zones (maize (Zea mays L.), soybean (Glycine max L.), and rice (Oryza sativa L.)). We conducted overall and zonal regressions using the random forest (RF) model to validate the prediction results through cross-validation. Our findings indicate that: (1) adding crop growth period images to the bare soil period images in different years can improve the accuracy of SOM mapping. For example, in the overall regression model of 2020, the highest accuracy was achieved by using the combination of May-July images, with an R2 value of 0.70 and an RMSE value of 0.71 %; (2) zonal regression by differentiating crop types can effectively improve the SOM mapping accuracy. In 2019, using zonal regression, the R2 of SOM mapping accuracy was improved by 0.02 and the RMSE was reduced by 0.03 % compared with the overall regression; (3) precipitation is an important factor affecting the accuracy of SOM prediction, and the lower the precipitation, the higher the accuracy of SOM prediction. In summary, the results of this study show that in the SOM remote sensing mapping of the black soil area, the growing period remote sensing information of different crop types should be comprehensively considered and combined with the image data of the years of lower precipitation, the accuracy of the SOM mapping can be effectively improved, which provides a new technological path and an application basis for the enhancement of the accuracy in remote sensing mapping with soil attributes.

Mapping of irrigated vineyard areas through the use of machine learning techniques and remote sensing

Effective and sustainable management of aquifers in regions with intensive groundwater use for irrigation requirements accurate mapping or irrigated areas to control water resource exploitation and plan rational water usage. This study proposes a cost-effective methodology based on satellite images to identify irrigated areas utilizing surface water and groundwater resources. The methodology integrates soil moisture estimations, environmental variables, and variables that affect to retention of water soil, that join a ground truth dataset, to estimate irrigated surface through a machine learning method during the irrigation period of 2021. Spectral data derived parameters and crop morphology, along with official data on agricultural parcels, were utilized to define vineyard irrigation areas at the plot scale within the Requena-Utiel aquifer in Eastern Spain. A machine learning classification technique was applied,yielding a remarkable precision of 91.8 % when compared to ground truth data.Discrepancies between the remote sensing-based irrigated area estimation and official data are highlighted. This study represents the most accurate plot-scale irrigation mapping of woody crops in the region to date.

In Vivo Tracking and 3D Mapping of Cell Death in Regeneration and Cancer Using Trypan Blue.

Tracking cell death in vivo can enable a better understanding of the biological mechanisms underlying tissue homeostasis and disease. Unfortunately, existing cell death labeling methods lack compatibility with in vivo applications or suffer from low sensitivity, poor tissue penetration, and limited temporal resolution. Here, we fluorescently labeled dead cells in vivo with Trypan Blue (TBlue) to detect single scattered dead cells or to generate whole-mount three-dimensional maps of large areas of necrotic tissue during organ regeneration. TBlue effectively marked different types of cell death, including necrosis induced by CCl4 intoxication in the liver, necrosis caused by ischemia-reperfusion in the skin, and apoptosis triggered by BAX overexpression in hepatocytes. Moreover, due to its short circulating lifespan in blood, TBlue labeling allowed in vivo "pulse and chase" tracking of two temporally spaced populations of dying hepatocytes in regenerating mouse livers. Additionally, upon treatment with cisplatin, TBlue labeled dead cancer cells in livers with cholangiocarcinoma and dead thymocytes due to chemotherapy-induced toxicity, showcasing its utility in assessing anticancer therapies in preclinical models. Thus, TBlue is a sensitive and selective cell death marker for in vivo applications, facilitating the understanding of the fundamental role of cell death in normal biological processes and its implications in disease.

Mapping pedestrian network level outdoor heat hazard distributions in Philadelphia

With the rise of global temperature, many cities are suffering from more and more frequent extreme heat in hot summers. Quantitative information on the spatial distributions of urban heat has become more and more important for extreme heat mitigation and adaptation in cities. This study first investigated the fine-level heat hazard distributions at the sidewalk and building block level from the pedestrian perspective in Philadelphia, Pennsylvania. The urban microclimate modeling based on a high-resolution urban geometrical model was used to generate the 1m resolution outdoor heat hazard map in the study area. The sidewalk map was overlaid on the generated high-resolution heat hazard map to estimate the sidewalk level heat hazard. Based on the sidewalk level heat hazard map, this study further calculated the heat hazard level in the 400m walkshed along sidewalks for each building block. The building level hazard data were then aggregated at the census tract level to compare with the socioeconomic and racial/ethnic variables. The result shows that neighborhoods with higher proportion of African Americans have a higher heat hazard level in Philadelphia. This study would provide new insights for developing more thermally comfortable and pedestrian-friendly neighborhoods in the context of climate change.