Environmental Phenomena Research Articles

Loop-Mediated Isothermal Amplification-Integrated CRISPR Methods for Infectious Disease Diagnosis at Point of Care.

Infectious diseases continue to pose an imminent threat to global public health, leading to high numbers of deaths every year and disproportionately impacting developing countries where access to healthcare is limited. Biological, environmental, and social phenomena, including climate change, globalization, increased population density, and social inequity, contribute to the emergence of novel communicable diseases. Rapid and accurate diagnoses of infectious diseases are essential to preventing the transmission of infectious diseases. Although some commonly used diagnostic technologies provide highly sensitive and specific measurements, limitations including the requirement for complex equipment/infrastructure and refrigeration, the need for trained personnel, long sample processing times, and high cost remain unresolved. To ensure global access to affordable diagnostic methods, loop-mediated isothermal amplification (LAMP) integrated clustered regularly interspaced short palindromic repeat (CRISPR) based pathogen detection has emerged as a promising technology. Here, LAMP-integrated CRISPR-based nucleic acid detection methods are discussed in point-of-care (PoC) pathogen detection platforms, and current limitations and future directions are also identified.

The wrapped XLindley distribution

In the study of many environmental phenomena, circular statistical analysis and applications to directional data are crucial. In this paper, we introduce the wrapped XLindley distribution, a novel circular distribution with a single parameter. We derive expressions for the distribution’s characteristic function, trigonometric moments, and other associated descriptive measures. The unknown distributional parameter is estimated using the maximum likelihood, least squares, and weighted least squares approach, and the accuracy of these estimates is tested using a simulation study. Finally, to clarify the suggested distribution’s modeling potential, we fit the proposed model to two circular real-world data sets and evaluate the goodness of fit of this distribution in comparison to the wrapped Lindley, wrapped modified Lindley, Von Mises, Jones–Pewsey, and Kato–Jones distributions.

Effects of stratification and mixing on spatiotemporal dynamics and functional potential of microbial community in a subtropical large-deep reservoir driven by nutrients and ecological niche

Seasonal thermal stratification is a common environmental phenomenon in reservoir water bodies and an important research topic for deep water reservoirs. A thermally stratified reservoir is an ideal system for investigating the interaction between factors affecting the environment and microorganism communities under spatial–temporal variation. However, few studies have focused on the effects of water stratification and mixing periods in microbial communities and nitrogen conversion. Therefore, using metagenomic sequencing and quantitative polymerase chain reaction, we investigated the microbial diversity and function in the Xin'anjiang Reservoir during water stratification and mixing period. Principal component analysis indicated that the bacterial communities were mostly affected by water stratification, mainly at 10-m depths, whereas the archaea community was influenced by the entire water profile. The predominant archaeal phyla changed from Candidatus_Woesearchaeota and Euryarchaeota to Thaumarchaeota with the increasing water depth during water stratification. The stratified variations of the dominant archaeal phyla strongly reflected the seasonal changes and vertical gradient in water temperature, dissolved oxygen, and pH. Linear regression analysis indicated that variations in microbial composition altered the overall functional attributes of the communities, especially in the archaeal community. Regarding nitrogen metabolism across the whole reservoir, the number of nitrification and denitrification genes dropped in the same direction as the river flow in spring and summer, while the opposite trend was observed in autumn and winter. The functional genes of the water profiles in the transitional and lacustrine zones varied considerably except in winter. Additionally, the copies of nitrification-related genes were more during the stratification stage than during the mixing stage, whereas copies of denitrification-related genes showed the opposite trend. Overall, this study provides unique insights regarding the effects of seasonal stratification on the distribution and function of microorganisms in large-deep reservoirs, which should be considered when establishing guidelines for reservoirs management.

A Comprehensive Review of the Recent Advancement in Integrating Deep Learning with Geographic Information Systems

The integration of deep learning (DL) techniques with geographical information system (GIS) offers a promising avenue for gaining novel insights into environmental phenomena by using the capabilities of spatial, temporal, and spectral resolutions, as well as data integration. The integration of these two technologies can result in the development of a highly efficient system for assessing environmental conditions by analyzing the interplay between texture, size, pattern, and process. This viewpoint has gained appeal across various academic disciplines. GIS heavily relies on processors, especially for tasks such as 3D computations, map rendering, and route calculation. In contrast, DL has the capability to efficiently analyze vast quantities of data. DL has garnered significant attention in recent times due to its potential for delivering a wide range of promising outcomes. Moreover, there is clear evidence of the increasing utilization of deep learning techniques across various fields, including GIS. The objective of this study is to provide an overview of the application of DL techniques in the field of GIS. This paper presents a concise review of the fundamental DL ideas that are pertinent to GIS, with a focus on the most current research findings. The present study investigates the various uses and technology of remote sensing in diverse domains, including mapping, hydrological modeling, disaster management, and transportation route planning. This study offers insights into contemporary framework approaches and proposes avenues for further research.

Anarchiving the Anthropocene: Waste and relationality

The archive produces a linear time that reaches towards ‘what could be’ by asserting ‘what has been’, providing us reassurance of our existence through the assertion of a reliably past past. But the Anthropocene is an era of uncontained material ramifications, where the past juts into the future and temporality warps as change accelerates unexpectedly. As an ecological and geologic epoch, documentation of the Anthropocene inherently has a relationship to natural history museums and archives. These institutions, however, troublingly rest on what Elaine Graham calls ‘ontological hygiene’ – the separateness of the human subject from ‘nature’. The Anthropocene however, challenges the western post-Enlightenment binary of man vs. nature, as it (supposedly) naturalizes humans into an earth systems force. As such, documentation of this epoch might resist ontological sanitation. This article outlines an artistic research practice to create artefacts of the Anthropocene – one that invites material idiosyncrasies, objects-in-becoming, ephemerality, and decay. I suggest that artistic ‘anarchices’ prioritize process over objects, and resist linear and static representation in lieu of material, embodied and sensory artefacts. These include relational encounters with waste materials, reworked forms of ‘geology’, edible artefacts and multimedia works at landfills and feral sites. The anarchives are interpolated by a rejection of hegemonic logic, positivism and ‘objective’ truths about environmental phenomena; Situated, ambiguous and material knowledges are inwrought into the so-called Anthropocene (and any alternatives to this term). By focusing on waste as an analytical category, I explore some of the ontological breakdowns present in the Anthropocene and question how to unorder systems and disturb materiality.

Blending of Landsat and Sentinel images using Multi-sensor fusion

Global surveillance based on remote sensing always has a trade-off between spectral, temporal, and spatial resolutions of the sensor data. Since unique land cover information could be estimated at a good spatial scale than the temporal resolution, the Sentinel sensor, for example, has a revisit of 5 days and gathers images at a 10-20m spatial resolution. The Landsat sensor can only be revisited every 16 days with a spatial scale of 30 m. Fine temporal and spatial resolution data are essential for seasonable observation of dynamic agricultural, environmental, or ecological phenomena. This trade-off problem can be solved by using fusion methods based on the linear differences between images with fine (Sentinel) and coarse (Landsat) spatial resolution. As a result, an image with high spatial resolution can be predicted than acquired from a single Landsat-like sensor. The proposed work will combine Landsat and Sentinel data to bridge the gap between the Landsat image (spatial resolution,30m) and the Sentinel image (temporal resolution,10m or 20m). As a result, detailed investigations of the Earth’s surface and dynamics will be possible. Evaluation metrics – RMSE, SRE, and SSIM are used to compare the predicted image with the Sentinel image of the predicted date. The metrics analysis found that the predicted image is like the reference image with optimal values for all three-evaluation metrics.

Data-driven polynomial chaos-interval metamodel for dynamics and reliability analysis under hybrid uncertainty

As the complexity of engineering systems increases, the probability distribution information of model parameters becomes limited owing to the influence of environmental or natural phenomena. To solve this problem, a data-driven polynomial chaos-interval metamodel (DDPCIM) is proposed herein to quantify random and interval uncertainties in an engineering system. The orthogonal polynomial basis corresponding to each dimensional random variable was based on the statistical moments of the random variable. The multidimensional orthogonal polynomial basis was obtained by performing a tensor-product operation on a one-dimensional orthogonal polynomial basis. Legendre (or Chebyshev) polynomials can be selected as the polynomial basis functions corresponding to the interval variables. In addition, the interval sampling technique was improved to make the bounds of the output response closer to realistic results. The proposed DDPCIM has higher computational accuracy and efficiency than traditional methods, such as the polynomial Chaos–Legendre metamodel (PCLM) and the method combining Monte Carlo and scanning tests (CMCST). Finally, the DDPCIM was applied to achieve the desired level of reliability for engineering systems with hybrid uncertainty. Numerical examples show that the DDPCIM is a robust and efficient method for analysing the reliability of engineering structures with hybrid uncertainty.

Global warming and Ice Nucleation in Central Himalayas: A Case Study of Skardu District

Water is an essential need for life and demand for water supply is exponentially increasing with elevating human population. Ice nucleation plays a fundamental role in shaping precipitation patterns and influencing the Earth's climate system. Skardu's unique location amidst the Karakorum. As temperatures decrease, there is a noticeable increase in stream water and ice melt water, potentially impacting biological activity and ecological processes. Following Vali’s equation and finding number of frozen drops from -1 to -10 degree Celsius after 5 mints incubation at each degree. The correlation between temperature and frozen drops adds complexity, highlighting the heightened sensitivity of environmental phenomena to temperature variations, with frozen drops peaking at extremely low temperatures. Ice nucleation results at Skardu showed that stream water can maximize IN 17.5 times from its starting point of freezing points while ice melt water increased 9 times, sediments 5 times. Biotic factors of plant 1 and plant 2 showed 25 times and 24 times increase in IN from starting point of freezing. Result of IN at Shigari showed, stream water increased 9.5 times, ICM increased 9 times, sediment 4 times, plant 21 times and plant 2 showed 23.5 times from their first freezing happened. Cold desert, IN results showed that stream water increased 11 times, ICM 19 times, sediments 4 times, plant-1 25 times and plant-2 24.5 times increased in IN from their first freezing point.

Ground Truth Validation of Sentinel-2 Data Using Mobile Wireless Ad Hoc Sensor Networks (MWSN) in Vegetation Stands

Satellite-based remote sensing (RS) data are increasingly used to map and monitor local, regional, and global environmental phenomena and processes. Although the availability of RS data has improved significantly, especially in recent years, operational applications to derive value-added information products are still limited by close-range validation and verification deficits. This is mainly due to the gap between standardized and sufficiently available close-range and RS data in type, quality, and quantity. However, to ensure the best possible linkage of close-range and RS data, it makes sense to simultaneously record close-range data in addition to the availability of environmental models. This critical gap is filled by the presented mobile wireless ad hoc sensor network (MWSN) concept, which records sufficient close-range data automatically and in a standardized way, even at local and regional levels. This paper presents a field study conducted as part of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN), focusing on the information gained with respect to estimating the vegetation state with the help of multispectral data by simultaneous observation of an MWSN during a Sentinel-2A (S2A) overflight. Based on a cross-calibration of the two systems, a comparable spectral characteristic of the data sets could be achieved. Building upon this, an analysis of the data regarding the influence of solar altitude, test side topography and land cover, and sub-pixel heterogeneity was accomplished. In particular, variations due to spatial heterogeneity and dynamics in the diurnal cycle show to what extent such complementary measurement systems can improve the data from RS products concerning the vegetation type and atmospheric conditions.

Predicting quantity of cannabis smoked in daily life: An exploratory study using machine learning

BackgroundCannabis use is prevalent in the United States and is associated with a host of negative consequences. Importantly, a robust indicator of negative consequences is the amount of cannabis consumed. MethodsData were obtained from fifty-two adult, regular cannabis flower users (3+ times per week) recruited from the community; participants completed multiple ecological momentary assessment (EMA) surveys each day for 14 days. In this exploratory study, we used various machine learning algorithms to build models to predict the amount of cannabis smoked since participants’ last report including forty-three EMA measures of mood, impulsivity, pain, alcohol use, cigarette use, craving, cannabis potency, cannabis use motivation, subjective effects of cannabis, social context, and location in daily life. ResultsOur best-fitting model (Gradient Boosted Trees; 71.15% accuracy, 72.46% precision) found that affects, subjective effects of cannabis, and cannabis use motives were among the best predictors of cannabis use amount in daily life. The social context of being with others, and particularly with a partner or friend, was moderately weighted in the final prediction model, but contextual items reflecting location were not strongly weighted in the final prediction model, the one exception being not at work. ConclusionsMachine learning approaches can help identify additional environmental and psychological phenomena that may be clinically-relevant to cannabis use.

Dolphin Conservation in Pengelana Laut Short Story: Greg Garrard's Ecocriticism Study

The short story "Pengelana Laut" by Linda Christanty focuses on the theme of preserving dolphins. Therefore, this study aims to investigate the environmental phenomena and the importance of environmental education in the story. This qualitative design presents descriptive data using the Grag Garrard ecocritical approach. The primary data source is the short story "Pengelana Laut," published in Kompas newspaper and ruangsastra.com, containing words, phrases, and sentences related to marine ecological phenomena, collected using the literature study techniques. The data obtained were analyzed using interactive interpretive models involving data condensation, data display, and conclusion drawing/verifying. The NVivo software program was used to code the data. The result showed that the short story contains various environmental phenomena, such as pollution, wilderness, apocalypse, dwelling, animals, and earth, with a focus on the exploitation of dolphins by humans. In conclusion, environmental education is crucial for dolphin conservation, hence it is imperative to incorporate such material in literacy skills for students to cultivate a sense of concern for and environmental preservation of marine life.

Consideration of the impact of the transition from a cash crop economy on the carbon footprint

The object of the study is the carbon footprint (CO2), which is skyrocketing despite augmented awareness of this issue and a growing willingness to act. The effects of climate change have recently become more severe and have garnered international attention. Recent discussion has focused on carbon footprint as one of the most urgent global issues facing all nations. The tradeoff between carbon footprint and economic growth for credible climate change measures is still understudied in terms of rigorous economic causal analysis. To comprehend the magnitude and speed of the transition away from an agricultural-based economy, it is necessary to quantify and compare the levels of carbon footprint associated with the agricultural, industrial, and service sectors of the country. In order to understand each economic sector's individual contributions to the overall carbon footprint and to assess the relationship between level of economic diversification and the levels of emissions, first identify the main factors and forces that have an impact on each sector's carbon footprint and then consider how the country's transition away from an agricultural-based economy has affected emissions in other economic sectors. This study investigates the impact of the economy's transition away from cash crops on carbon footprint, analyzes the conversion-affecting variables, and quantifies the significance applying the environmental Kuznets curve (EKC), and then regress the model. It is found that there is an inverted U-shaped pattern in the association between carbon footprint and each of industry, service, and manufacturing value added; agriculture, however, shows insignificant inverted U-shaped pattern. In addition, we discovered that every dependent variable – aside from the GDP contribution of agriculture – has a positive correlation with carbon footprint. Analysis revealed that improving agriculture results in lower carbon dioxide emissions. While the economic contributions of agriculture are more environmentally friendly, those of industry, services, and manufacturing leave carbon footprints behind to achieve sustainability, agricultural policy subsidies and deregulation may function as driving factors for the expansion of the cash crop economy. On the one hand, tax policy may be an effective instrument for boosting low-carbon energy consumption in the sector. It is presumptive that environmental phenomena, such as earthquakes, tsunamis, and flues, have not had a significant impact on the economy. This article is pertinent to the nations now dealing with significant environmental problems.

Using Learning Managemant System in Fundamental of Analytical Chemsitry Course: Effect on Students’ Critical Thinking through The Authentic Assessment

Critical thinking is the main skill for connecting relationships between chemical science concepts in a coherent environment. Therefore, a learning and evaluation method is needed to help students master the meaning of chemistry to environmental phenomena. The Fundamental of Analytical Chemistry course invites students to study the technique of determining the concentration of a compound. This is relevant to improve their critical thinking skills. This study aims to analyze the effect of the implementation of Technology-based Authentic Assessment (TbAA) on students' critical thinking skills in Fundamental of Analytical Chemistry course. Designed as a pre-experimental, one group pretest-posttest study was applied. A total of 26 undergraduate Chemistry students were included as samples through purposive sampling. TbAA was held for 7 meetings, then critical thinking skills were assessed through essay instruments covering aspects of interpretation, analysis, evaluation, inference. The validity test was carried out theoretically by 2 learning chemists on 4 critical thinking questions that had been developed. After the data was obtained, descriptive statistics and the Wilcoxon test were carried out to answer the research objectives. Based on the results of the analysis, there is a significant difference in students' critical thinking skills after the application of TbAA.

Converging Directions of Organic Architecture and City Planning: A Theoretical Exploration

The 'organic’ direction and energy saving are key postulates of sustainable development today. The term "organic architecture" was first formulated in the 1890s in the works of Louis Sullivan, who used this phrase to distinguish himself from prevailing eclecticism. Later, its principles were defined in the works of Frank Lloyd Wright, developed in the works of Alvar Aalto, Richard Neutra, Luigi Fiumara, and others. This paper examines this issue of organic architecture and city planning and its relevance to the modern world. It employs complex and systematic approach as a research methodology. Systematic approach was manifested in the analysis and synthesis of the main directions of organic architecture, their types and genres, which correlate with the evolution of city planning, Historical and urban planning analysis has been utilized to understand the impact of organic principles on cityscapes, and a scientifically based complex approach explores the relationships between organic architecture and a range of social, economic and environmental phenomena at the current stage. The research methodology employed in this study revolves around a comprehensive examination of the formation and evolution of organic architecture and city planning, with a focus on the integration of energy-saving and Nature-saving technologies. The paper identifies four main directions of organic architecture. The first is based on a harmonious combination of Nature, Architecture, and Man. Here, ‘organic’ means ‘subordinate to the landscape and climatic conditions of the environment.’ The founder of this idea is Frank Lloyd Wright. The period of emergence and flourishing was the 20s in the 20th century. The second stage, biomorphism, was formed in the 1950s and 1960s and was based on imitating natural forms. Oleksandr Lazarev, Santiago Kalatrava and others promoted this aspect of organic architecture. The third which appeared at the end of the 20th century, is aimed at preserving natural resources using energy-saving technologies. The fourth is the reuse of building materials and products and recycling. Receieved Reviewed Revised Published 27.07.2023 17.08.2023 25.08.2023 31.08.2023 ISVS e-journal, Vol. 10, Issue 8 August, 2023 Journal of the International Society for the Study of Vernacular Settlements Scopus Indexed Since 2016 224 The paper shows a gradual convergence of all directions; that is, buildings are increasingly combined with the surrounding spaces, and simultaneously, their forms are becoming more plastic, reminiscent of natural ones. Energy-saving technologies and recycling building materials and waste are becoming integral to organic architecture. Nevertheless, the most important thing is a person's connection with the environment and creating comfortable living conditions.

Long Short-Term Memory and Attention Models for Simulating Urban Densification

This paper introduces a novel cellular automata model that combines Long Short-Term Memory, Attention, and Neural Network models to capture spatio-temporal Land Use Change (LUC) behaviors while addressing the challenge of imbalanced datasets. The proposed method is developed and validated using data from Belgium, defined as three (100x100) m raster-based built-up maps for 2000, 2010, and 2020. The model is trained and validated using data from 2000 to 2010, and its effectiveness is tested using data from 2010 to 2020. The key contribution of our approach lies in its ability to tackle long-term temporal dependency and class imbalance problems in LUC science. Our proposed method significantly enhances the performance of spatio-temporal LUC simulation. Additionally, we adopt a data splitting strategy that takes into account the different transitions between classes, improving the accuracy of the model predictions of minority class. The obtained results demonstrate the efficiency of the proposed model in capturing complex spatio-temporal dynamics and reducing the impact of imbalanced datasets surpassing existing methods. The implications of our study extend beyond LUC modeling, as the proposed approach can be applied to a wide range of applications where machine learning is used to model complex environmental and geographical phenomena.

Evaluating the spatial coherence of composite indexes of land degradation at small administrative units

In light of the ‘zero-net land degradation strategy’, the United Nations Convention to Combat Desertification (UNCCD) guidelines for policy-relevant indicators of land quality, have stressed the importance of comparable indexes' informative value across spatial scales. However, the proliferation of composite indexes of land sensitivity, vulnerability, or exposure to desertification risk, has been occasionally grounded on a systematization of theoretical concepts – both in advanced economies and in emerging countries. Rationalization of the operational approaches to land degradation assessment implies a (spatially explicit) comparison and verification of their outcomes at different (policy-relevant) spatial scales. To answer this issue, we implemented a statistical approach evaluating the coherence and reliability of four composite indexes of land degradation (ES3, ES4, LVI, DSI), with a practical application for Italy, a Mediterranean country classified as ‘affected’ by UNCCD. Using (municipal) administrative units as the elementary analysis' domain, sequential econometric techniques were used to test the internal (spatially explicit) coherence of the four composite indexes, and the role of information redundancy, for calibration of local-scale land degradation strategies. The outcomes of spatial regressions documented the potential of the four indexes in a short-term assessment of land vulnerability, confirming that such indexes provided a coherent ranking of Italian municipalities based on their intrinsic degree of land degradation. Principal Component Analysis documented the superior informative power of the ES4 index and confirms the appropriateness of an extensive use of the Environmentally Sensitive Area Index (ESAI) at small spatial scales. The proposed framework supports the creation of an index dashboard to monitoring complex environmental phenomena.

Modelling the spatiotemporal dynamics of land susceptibility to desertification in Algeria

Desertification is a significant environmental and socioeconomic issue that requires monitoring and early detection. In this context, holistic studies are more useful than localized ones in addressing this issue. In this study, we used the desertification sensitivity index (DSI) to pinpoint regions of Algeria that are at risk of desertification and to examine significant shifts in the sensitivity of land to desertification between 2000 and 2020, as well as the primary drivers of these shifts. Unlike the standard Mediterranean Desertification and Land Use (MEDALUS) approach, which uses equal weighting of parameters and quality indicators to construct the environmental sensitivity areas, the DSI applies statistically generated weights. Our results showed that approximately 106,000 km2 of the non-desert area was extremely susceptible to desertification in 2000, 83,200 km2 in 2010, and 98,200 km2 in 2020, with critical zones mainly located in the High Plains (steppe rangelands). Our analysis revealed that climate and vegetation quality indicators were the main drivers of the DSI, which showed a nonlinear trend during the study period. The analysis of changes in land sensitivity to desertification revealed that most of the changes were negative and occurred in the High Plains, as well as certain areas of the Tell Atlas Mountain ranges and the littoral fringe. However, some positive changes occurred due to the establishment of new forest plantations and croplands. The results provide a comprehensive overview of the risk of desertification in Algeria and its main drivers and can serve as a scientific basis for the development and implementation of necessary measures to interrupt and combat this destructive environmental phenomenon.

Bimodal interferences of Pb(II) induced by parallel deposition in Pb(II)-Cu(II) electrochemical detections: Voltammetric signals analysis combined with numerical simulations on transient interfacial phenomena

The perplexity of double peaks in Pb(II) detections has been a threat to the reliability of Pb(II) electroanalysis results for a long term. For the complexity of electrode interfaces, rare studies were taken on mechanisms of Pb(II) double peaks through interfacial kinetics. In this work, analyses on experimental signals and interfacial simulations were working together to reveal that the generation of Pb(II) double peaks in Pb(II)-Cu(II) systems is the deposition of Pb(II) on Cu deposits occurring in parallel. By applying anode stripping voltammetry and cyclic voltammetry, a parallel deposition reaction was found to influence the shape of Pb(II) peaks, and the existence of the second peak was controlled through the adjustment of experimental conditions. A kinetic model was built to reveal the interference of electroanalysis signals caused by a parallel deposition reaction and simulations based on the model were combined with experiments to illustrate that double peaks of Pb(II) were caused by the parallel deposition on Cu(II) deposits. This work proposes another insight of Pb(II) double peaks from macroscale kinetics and pays more attention on the dynamic procedure of electroanalysis interfaces, which makes the study on environmental electroanalysis interface phenomena more clear and is enlightening to develop efficient electrical methods for pollutant monitoring.

Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery

Remote sensing plays a central role in the assessment of environmental phenomena and has increasingly become a powerful tool for monitoring shorelines, river morphology, flood-wave delineation and flood assessment. Optical-based monitoring and the characterization of river evolution at long time scales is a key tool in fluvial geomorphology. However, the evolution occurring during extreme events is crucial for the understanding of the river dynamics under severe flow conditions and requires the processing of data from active sensors to overcome cloud obstructions. This work proposes a cloud-based unsupervised algorithm for the intra-event monitoring of river dynamics during extreme flow conditions based on the time series of Sentinel-1 SAR data. The method allows the extraction of multi-temporal series of spatially explicit geometric parameters at high temporal and spatial resolutions, linking them to the hydrometric levels acquired by reference gauge stations. The intra-event reconstruction of inundation dynamics has led to (1) the estimation of the relationship between hydrometric level and wet area extension and (2) the assessment of bank erosion phenomena. In the first case, the behavior exhibits a change when the hydrometric level exceeds 1 m. In the second case, the erosion rate and cumulative lateral erosion were evaluated. The maximum erosion velocity was greater than 1 m/h, while the cumulative lateral erosion reached 130 m. Time series of SAR acquisitions, provided by Sentinel-1 satellites, were analyzed to quantify changes in the wet area of a reach of the Tagliamento river under different flow conditions. The algorithm, developed within the Python-API of GEE, can support many types of analyses of river dynamics, including morphological changes, floods monitoring, and bio-physical habitat dynamics. The results encourage future advancements and applications of the algorithm, specifically exploring SAR data from ICEYE and Capella Space constellations, which offer significantly higher spatial and temporal resolutions compared to Sentinel-1 data.

The life of the institutional forest-farming community to reserve the forest properly with the agroforestry concept (Studi in the Balesari Village – Malang Regency)

A classic environmental phenomenon afflicts communities around mountainous forests in Indonesia. Needs institutional management to achieve a prosperous life based on environmental conservation took place in the people as inhabitants hold a potential risk, bearer subject of 'deforestation and degradation or, in contrast, acts as a conservation agent of 'reforestation and revitalization' in the capacity of forestry landfill. This non-positivistic qualitative study revealed the unique actions of "institutional and agroforestry" actors. The approach takes a paradigm that places forest communities as the research subjects. These main subjects feel and then deal directly with all forestry problems regarding deforestation and land degradation. Those forest people who live and depend on forests need attention and social justice. The research process includes tracking history, organization, and interaction between or among the groups of forest farmers in their institutions. The authentic findings of this study are: It needs a real activity, norms and ethics for the business process. So it can foster a synergy of positive externality values in maintaining the traditional forest conservation rules. Furthermore, developing them suits the concept of agroforestry as productive farming in a sustainable environment and adaptive to the character of the forest's natural behaviour.