Environmental Data Research Articles

Textural indices of aerial images reveal urban landscapes temporal dynamics and their effects on plants and birds

Landscape temporal dynamics are a crucial concern in ecology to understand current biodiversity. But historical environmental data are often not easily available. This study assesses for the first time the potential of historical orthophotographs to describe past landscape conditions and determine the temporal lag between landscape changes and biotic communities responses.This method utilizes Gray level Co-occurrence Matrix (GLCM) texture indices computed from black and white orthophotographs to construct continuous metrics of landscape composition. Metrics were first developed using present-day data, i.e. 2015, and calibrated with categorical land cover maps of the Strasbourg Eurometropolis, France. Subsequently, these metrics were applied to historical orthophotographs from 1966, 1976, 1986, and 2000. Plant and bird data from research and citizen science programs were used to estimate the time delay in which these communities respond to evolutions in built areas and high vegetation.Obtained texture-based models reveal that built areas exhibit high contrast and homogeneity, depicted through a linear relation, and that high vegetation display low pixel brightness and high brightness diversity, better described via a nonlinear model. We successfully applied those findings to historical orthophotographs, and revealed dependencies on landscape composition up to 50 years ago for plants and up to 30 years ago for birds, with the time lag and the influence of built and high vegetation areas depending on the selected biodiversity indices.These results demonstrate the utility of archive black and white orthophotographs' texture indices for describing urban landscapes over the past five decades, making them valuable tools for ecological research. These indices are more accessible than categorical data like land cover maps for past years. They have the potential to greatly benefit future studies investigating time lags in landscape ecology, simplifying access to historical landscape features and contributing to sustainable urban planning efforts.

Environmental Monitoring in Internet of Things (IOT)

Abstract: The Internet of Things (IoT) connects physical objects to the internet, enabling autonomous data collection and analysis. In environmental monitoring, IoT plays a crucial role in gathering real-time data on parameters like air and water quality, weather, wildlife behavior, and urban heat islands. This abstract explores how IoT is used in environmental monitoring, covering standards, device types, and applications. IoT comprises interconnected devices that collect environmental data and transmit it for analysis, facilitating continuous observation and better resource management. Standards like MQTT and communication technologies like Wi-Fi govern IoT implementation. Environmental monitoring involves assessing air and water quality, weather conditions, wildlife behavior, and urban heat islands. Air quality monitoring measures pollutant levels in the atmosphere using IoT-based sensor networks. Water quality monitoring assesses chemical and biological characteristics of water bodies. Weather and climate monitoring observe atmospheric conditions using IoT-based sensor networks. Wildlife monitoring tracks animal movements and behavior using IoT-enabled devices. Urban heat island monitoring assesses temperature distribution in urban areas using IoT-based sensors. IoT revolutionizes environmental monitoring by enabling real-time, remote observation across various domains, contributing to better resource management and sustainability efforts.

Sex-specific effects of environmental temperature during gestation on fluctuating asymmetry in deciduous teeth.

External environmental heat exposure during gestation impacts the physiology of human development in utero, but evidence for these impacts has not yet been explored in dentition. We examined deciduous teeth for fluctuating asymmetry (FA), a measure of developmental instability, together with gestational environmental temperature data drawn from historical weather statistics. We measured dental casts from the longitudinal Burlington Growth Study, representing 172 participants (ages 3-6 years) with health records. FA was calculated from crown dimensions and intercuspal distances that develop during gestation. Multiple regression separated by sex (nfemale = 81) examined the effects of mean temperatures in each trimester, controlling for birth year. In females, increased temperatures during the first trimester are significantly associated with an increase in FA (p = 0.03), specifically during the second and third prenatal months (p = 0.03). There is no relationship between temperature and FA for either sex in the second or third trimesters, when enamel is formed. Dental instability may be sensitive to temperature in the first trimester in females during the scaffolding of crown shape and size in the earliest stages of tooth formation. Sexual dimorphism in growth investment strategies may explain the differences in results between males and females. Using enduring dental characteristics, these results advance our understanding of the effects of temperature on fetal physiology within a discrete period.

Online Blood Bank Management System

Blood donation plays a significant role in saving the lives of many people worldwide. The blood bank management system is a crucial tool for maintaining the inventory of blood donations and blood Management The System will help in the efficient management of blood donations and Blood Samples. The system allows blood banks to store and manage their data in a secure environment, providing better accessibility and scalability The web application is also designed to improve the communication between the blood bank and donors. In this System, we propose the design and implementation of a blood bank management system using a visual Studio Code. The system will also allow for tracking of donor information, blood types, and inventory records

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

This research introduces the PEnsemble 4 model, a weighted ensemble prediction model that integrates multiple individual machine learning models to achieve accurate maize yield forecasting. The model incorporates unmanned aerial vehicle (UAV) imagery and Internet of Things (IoT)-based environmental data, providing a comprehensive and data-driven approach to yield prediction in maize cultivation. Considering the projected growth in global maize demand and the vulnerability of maize crops to weather conditions, improved prediction capabilities are of paramount importance. The PEnsemble 4 model addresses this need by leveraging comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the model effectively analyzes temporal patterns in vegetation indices, in particular CIre and NDRE, which serve as reliable indicators of canopy density and plant height. Notably, the PEnsemble 4 model demonstrates a remarkable accuracy rate of 91%. It advances the timeline for yield prediction from the conventional reproductive stage (R6) to the blister stage (R2), enabling earlier estimation and enhancing decision-making processes in farming operations. Moreover, the model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. By synergistically integrating IoT and machine learning technologies, the PEnsemble 4 model presents a novel and promising solution for maize yield prediction. Its application holds the potential to revolutionize crop management and protection, contributing to efficient and sustainable farming practices.

Immersive Emotion Analysis in VR Environments: A Sensor-Based Approach to Prevent Distortion

As virtual reality (VR) technology advances, research has focused on enhancing VR content for a more realistic user experience. Traditional emotion analysis relies on surveys, but they suffer from delayed responses and decreased immersion, leading to distorted results. To overcome these limitations, we propose an emotion analysis method using sensor data in the VR environment. Our approach can take advantage of the user’s immediate response and not reduce immersion. Linear regression, classification analysis, and tree-based methods were applied to electrocardiogram and galvanic skin response (GSR) sensor data to measure valence and arousal values. We introduced a novel emotional dimension model by analyzing correlations between emotions and the valence and arousal values. Experimental results demonstrated the highest accuracy of 77% and 92.3% for valence and arousal prediction, respectively, using GSR sensor data. Furthermore, an accuracy of 80.25% was achieved in predicting valence and arousal using nine emotions. Our proposed model improves VR content through more accurate emotion analysis in a VR environment, which can be useful for targeting customers in various industries, such as marketing, gaming, education, and healthcare.

The potential impact fraction of population weight reduction scenarios on non-communicable diseases in Belgium: application of the g-computation approach

BackgroundOverweight is a major risk factor for non-communicable diseases (NCDs) in Europe, affecting almost 60% of all adults. Tackling obesity is therefore a key long-term health challenge and is vital to reduce premature mortality from NCDs. Methodological challenges remain however, to provide actionable evidence on the potential health benefits of population weight reduction interventions. This study aims to use a g-computation approach to assess the impact of hypothetical weight reduction scenarios on NCDs in Belgium in a multi-exposure context.MethodsBelgian health interview survey data (2008/2013/2018, n = 27 536) were linked to environmental data at the residential address. A g-computation approach was used to evaluate the potential impact fraction (PIF) of population weight reduction scenarios on four NCDs: diabetes, hypertension, cardiovascular disease (CVD), and musculoskeletal (MSK) disease. Four scenarios were considered: 1) a distribution shift where, for each individual with overweight, a counterfactual weight was drawn from the distribution of individuals with a “normal” BMI 2) a one-unit reduction of the BMI of individuals with overweight, 3) a modification of the BMI of individuals with overweight based on a weight loss of 10%, 4) a reduction of the waist circumference (WC) to half of the height among all people with a WC:height ratio greater than 0.5. Regression models were adjusted for socio-demographic, lifestyle, and environmental factors.ResultsThe first scenario resulted in preventing a proportion of cases ranging from 32.3% for diabetes to 6% for MSK diseases. The second scenario prevented a proportion of cases ranging from 4.5% for diabetes to 0.8% for MSK diseases. The third scenario prevented a proportion of cases, ranging from 13.6% for diabetes to 2.4% for MSK diseases and the fourth scenario prevented a proportion of cases ranging from 36.4% for diabetes to 7.1% for MSK diseases.ConclusionImplementing weight reduction scenarios among individuals with excess weight could lead to a substantial and statistically significant decrease in the prevalence of diabetes, hypertension, cardiovascular disease (CVD), and musculoskeletal (MSK) diseases in Belgium. The g-computation approach to assess PIF of interventions represents a straightforward approach for drawing causal inferences from observational data while providing useful information for policy makers.

Comprehensive Assessment of Health Impacts from Exposure to Nonionizing Radiation for Healthcare Practitioners Working with MRI and Ultrasound

Background. It is necessary to monitor the health disorders of healthcare practitioners exposed to a complex of occupational hazards to optimize their activities and substantiation of preventive health measures. The purpose of the study was the hygienic assessment of occupational hazards, analysis of data on self-assessment of the state of health (SASH questionnaire) among healthcare practitioners (MRI diagnostics specialists, ultrasound diagnostics specialists, and ophthalmologists, as a control group) exposed to nonionizing radiation, ultrasound, and noise. Materials and Methods. An analysis of hygienic parameters of the working environment (workplace) and questionnaire data of healthcare practitioners were carried out. The number of illnesses and absences from work, the level of quality of life, the level of depression, and the self-assessment of the state of health were evaluated according to the questionnaire. Results. An analysis of workloads made it possible to rank the severity and intensity of labor. Health disorders (according to the self-assessment of the state of health), the number of absences from work due to illness, an increase in complaints and level of depression, and a decrease in the level of quality of life were corresponded to the severity of labor. It was revealed that healthcare practitioners were exposed to a number of hazards during their work (above the threshold limit value (TLV)), which caused an increase in health disorders. Conclusions. During the organization of workplace ergonomics and implementation of preventive measures directed to maintaining health and early diagnosis of diseases, one should take into account the hygienic indicators of the working environment that exceed TLV, the results of self-assessment of the state of health (complaints) and morbidity of healthcare practitioners, the level of quality of life and the level of depression.

The potential of urban trees to reduce heat-related mortality in London

Increasing temperatures and more frequent heatwave events pose threats to population health, particularly in urban environments due to the urban heat island (UHI) effect. Greening, in particular planting trees, is widely discussed as a means of reducing heat exposure and associated mortality in cities. This study aims to use data from personal weather stations (PWS) across the Greater London Authority to understand how urban temperatures vary according to tree canopy coverage and estimate the heat-health impacts of London’s urban trees. Data from Netatmo PWS from 2015–2022 were cleaned, combined with official Met Office temperatures, and spatially linked to tree canopy coverage and built environment data. A generalized additive model was used to predict daily average urban temperatures under different tree canopy coverage scenarios for historical and projected future summers, and subsequent health impacts estimated. Results show areas of London with higher canopy coverage have lower urban temperatures, with average maximum daytime temperatures 0.8 °C and minimum temperatures 2.0 °C lower in the top decile versus bottom decile canopy coverage during the 2022 heatwaves. We estimate that London’s urban forest helped avoid 153 heat attributable deaths from 2015–2022 (including 16 excess deaths during the 2022 heatwaves), representing around 16% of UHI-related mortality. Increasing tree coverage 10% in-line with the London strategy would have reduced UHI-related mortality by a further 10%, while a maximal tree coverage would have reduced it 55%. By 2061–2080, under RCP8.5, we estimate that London’s current tree planting strategy can help avoid an additional 23 heat-attributable deaths a year, with maximal coverage increasing this to 131. Substantial benefits would also be seen for carbon storage and sequestration. Results of this study support increasing urban tree coverage as part of a wider public health effort to mitigate high urban temperatures.

The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format.

Balancing Protection and Quality in Big Data Analytics Pipelines.

Existing data engine implementations do not properly manage the conflict between the need of protecting and sharing data, which is hampering the spread of big data applications and limiting their impact. These two requirements have often been studied and defined independently, leading to a conceptual and technological misalignment. This article presents the architecture and technical implementation of a data engine addressing this conflict by integrating a new governance solution based on access control within a big data analytics pipeline. Our data engine enriches traditional components for data governance with an access control system that enforces access to data in a big data environment based on data transformations. Data are then used along the pipeline only after sanitization, protecting sensitive attributes before their usage, in an effort to facilitate the balance between protection and quality. The solution was tested in a real-world smart city scenario using the data of the Oslo city transportation system. Specifically, we compared the different predictive models trained with the data views obtained by applying the secure transformations required by different user roles to the same data set. The results show that the predictive models, built on data manipulated according to access control policies, are still effective.

Predicting the odds of chronic wasting disease with Habitat Risk software

Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in captive cervids in Colorado, United States (US) in 1967, but has since spread into free-ranging white-tailed deer (Odocoileus virginianus) across the US and Canada as well as to Scandinavia and South Korea. In some areas, the disease is considered endemic in wild deer populations, and governmental wildlife agencies have employed epidemiological models to understand long-term environmental risk. However, continued rapid spread of CWD into new regions of the continent has underscored the need for extension of these models into broader tools applicable for wide use by wildlife agencies. Additionally, efforts to semi-automate models will facilitate access of technical scientific methods to broader users. We introduce software (Habitat Risk) designed to link a previously published epidemiological model with spatially referenced environmental and disease testing data to enable agency personnel to make up-to-date, localized, data-driven predictions regarding the odds of CWD detection in surrounding areas after an outbreak is discovered. Habitat Risk requires pre-processing publicly available environmental datasets and standardization of disease testing (surveillance) data, after which an autonomous computational workflow terminates in a user interface that displays an interactive map of disease risk. We demonstrated the use of the Habitat Risk software with surveillance data of white-tailed deer from Tennessee, USA.

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment’s findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4–5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

Towards Sustainability: A Framework for Evaluating Portuguese Small-Scale Fisheries

Portugal has a historical tradition in fisheries, and its small-scale fishery (SSF) communities are the backbone of the national industry. There have been increased concerns about the state of fisheries and fish resources, particularly the sustainable performance of fishing sectors. In light of this aspect, understanding economic, social, environmental, and governmental aspects is a key element in assessing sustainability. Given the relevance of the fishing industry in Portugal, it is paramount to understand if the Portuguese SSFs are sustainable. This study aims to develop a contextualized indicator framework for SSFs’ sustainability with economic, social, environmental, and governance dimensions. Further, we developed a systematic literature review to evaluate Portuguese SSFs using such a framework. Of the list of suggested 31 indicators (8 economy, 9 social, 12 environmental, and 8 governance), 1394 published references were recovered from the systematic review for Portuguese SSFs. Results suggests that Portugal must improve socioeconomic and environmental data and strengthen governmental support for a sustainable fishing industry. In particular, understanding fishers’ financial stability and ecological knowledge and promoting further studies on fisheries’ impact on the marine environment is essential. Overall, the qualitative list of indicators provided represents an innovative and unique framework for evaluating fisheries’ sustainability in different worldwide settings.

Wetland vegetation composition and ecology of Lake Abaya in southern Ethiopia.

Wetland vegetation and ecology of Lake Abaya in the southern Ethiopia was studied to determine floristic composition, plant community type and vegetation ecology. A total of 102 plots were laid along transects that were set up preferentially across areas where there were rapid changes in vegetation or marked environmental gradients to collect data on estimate of percentage aerial cover of plant species and environmental variables. Vegetation data was analyzed by agglomerative hierarchical cluster analysis using similarity ratio as a resemblance index and Ward's linkage method. Multivariate data analysis was performed using appropriate packages in R version 2.14.0. Canonical Correspondence Analysis (CCA) was used to explore the relationship between the species composition and environmental variables. The environmental data included in the CCA were determined using stepwise backward and forward selection of variables by ANOVA test. Statistical measurement regarding species diversity, richness and evenness of the plant community types was carried out by using Shannon-Wiener diversity indices. A total of 92 plant species belonging to 66 genera and 34 families were identified. Families Poaceae, Asteraceae, Fabaceae, Cyperaceae, Solanaceae, Euphorbiaceae and Amaranthaceae account for about 56.99% of the total proportion. Based on the cluster analysis, five plant community types were identified. The most important factors influencing the plant species composition and pattern of wetland plant communities were water drainage, water depth, land use, slope, altitude, and hydrogeomorphology. Therefore, these factors should be considered in future management and protection under the circumstance of climate change and human activities.

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low altitudes where microscale turbulent wind flow conditions are prevalent. This introduces flight testing, certification, and operational complexities. To tackle these issues, the UAM industry, aviation authorities, and research communities across the world have provided prescriptive ways, such as the implementation of dynamic weather corridors for safe operation, classification of atmospheric disturbance levels for certification, etc., within the proposed concepts of operation (ConOps), certification standards, and guidelines. However, a notable hindrance to the efficacy of these solutions lies in the scarcity of operational UAM and observational wind data in urban environments. One way to address this deficiency in data is via microscale wind modelling, which has been long established in the context of studying atmospheric dynamics, weather forecasting, turbine blade load estimation, etc. Thus, this paper aims to provide a critical literature review of a variety of wind flow estimation and forecasting techniques that can be and have been utilized by the UAM community. Furthermore, a compare-and-contrast study of the commonly used wind flow models employed within the wind engineering and atmospheric science domain is furnished along with an overview of the urban wind flow conditions.

Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield

One of the crucial research areas in agricultural decision-making processes is crop yield prediction. This study leverages the advantages of hybrid models to address the complex interplay of genetic, environmental, and management factors to achieve more accurate crop yield forecasts. Therefore, this study used the data of wheat growth environment, crop management, and historical yield in experimental fields in Anding District, Dingxi City, Gansu Province from 1984 to 2021 to construct eight machine learning models and ensemble models. Furthermore, Agricultural Production Systems sIMulator (APSIM), machine learning (ML), and APSIM combined with machine learning (APSIM-ML) were employed to predict wheat yields in 2012, 2016, and 2021. The results show that the APSIM-ML weighted ensemble prediction model, optimized to minimize the MSE, performed the best. Compared to the optimized ML and APSIM models, the average improvements in the RMSE, RRMSE, and MBE for the test years were 43.54 kg/ha, 3.55%, and 15.54 kg/ha, and 93.96 kg/ha, 7.55%, and 104.21 kg/ha, respectively. At the same time, we found that the dynamic flow of water and nitrogen between the soil and crops had the greatest impact on wheat yield prediction. This study improved the accuracy of dryland wheat yield prediction in Gansu Province and provides technical support for the intelligent production of dryland wheat in the loess hilly area.

PREDICTIVE MODELING FOR DISEASE OUTBREAKS: A REVIEW OF DATA SOURCES AND ACCURACY

This review explores the dynamic field of predictive modelling for disease outbreaks, focusing on the data sources, modelling techniques, accuracy, challenges, and future directions integral to its advancement. It underscores the significance of diverse data sources, including epidemiological, environmental, social media, and mobility data. It also discusses various modelling approaches, from statistical models to advanced machine learning algorithms and network analysis. The review highlights the critical role of accuracy and validation in predictive models, alongside the challenges posed by data quality, model complexity, and the ethical use of personal data. It outlines promising research avenues, such as improving data collection methods, integrating novel data sources like genomic data, and leveraging emerging technologies such as AI and IoT to enhance predictive capabilities. This comprehensive overview emphasizes the importance of predictive modelling in informing public health decisions and the continuous need for innovation and collaboration to address the complex dynamics of disease outbreaks.
 Keywords:  Predictive Modeling, Disease Outbreaks, Data Sources, Machine Learning, Public Health, Emerging Technologies.

Research Optimizing Building Ventilation Performance through the Application of Trombe Walls in Regions with Hot Summers and Cold Winters: A Case Study in China

The hot-summer and cold-winter climate zone spans a large latitude, where the passive means of retrofitting the Trombe wall (TW) can be utilized to effectively improve the indoor wind environment and thermal comfort. In this study, a public building in Wenzhou, China, was selected as the object, and CFD numerical simulations and the wind environment data in spring, summer, and fall were collected through field experimental measurements. Comparative analyses were carried out to explore the adaptive strategy and effectiveness of the application of the Trombe wall on the local wind environment and climate and to quantify the improvement effect of the application of the Trombe wall on the Indoor environment. The results showed that the application of the Trombe wall in spring and fall in hot-summer and cold-winter regions could obviously increase the average indoor wind speed and the wind speed at the building outlet by 0.2–0.8 m/s and 0.9–3.6 m/s, respectively. This further effectively prolongs the indoor thermal comfort hours in spring and fall, which is a relatively applicable season, while the ventilation performance in summer is poor compared to spring and fall seasons.

The Impacts of Sunlight on the Lichen Scots Pine Forest Community

The habitat of the lichen Scots pine forest is currently one of the communities that requires attention and active protection due to its rapid disappearance. In our study, we identified factors that can be modified to preserve this habitat. The primary focus of our research was on the significance of tree height and sunlight in fostering the development of a lichen-rich layer within the lichen Scots pine forest. Additionally, we investigated the environmental factors influencing the presence of specific species of lichens and bryophytes, including those that form communities and those that displace species characteristic of the lichen Scots pine forest community. Our study was conducted in Bory Tucholskie National Park (N-W Poland) using 20 experimental sites and 248 point-frame plots from spring 2021 to winter 2022. Fieldwork involved species surveys, measurements of photosynthetic activity in lichens and bryophytes, and collection of environmental data. Through parametric testing, modeling, and mapping, our main findings confirmed that light availability, influenced by tree height, is a critical factor in maintaining a well-preserved lichen-rich layer and facilitating habitat reintroduction.