Environmental Change Detection Research Articles

Monitoring and Control System for Temperature, Humidity, and Air Quality in LVMDP Panel Rooms to Improve the Reliability of LVMDP Panel Components Using Fuzzy Logic

Power Distribution System plays a crucial role in modern life. Low Voltage Distribution Panel (LVMDP) is a critical component within this system, responsible for distributing low-voltage electricity to various loads. Optimal reliability and performance of LVMDP are essential to ensure continuity and stability of power supply. Environmental factors such as temperature, humidity, and air quality can significantly impact the reliability and lifespan of electrical equipment in LVMDP. An Internet of Things (IoT) and Fuzzy Logic-based Monitoring and Control System offers an innovative solution to address these issues. The IoT Monitoring System enables real-time monitoring of temperature, humidity, and air quality, allowing for early detection of environmental changes that could potentially damage equipment. The Fuzzy Control System then responds to these changes automatically and adaptively, regulating cooling, heating, circulation, dehumidifier, or humidifier devices to maintain optimal environmental conditions. Implementing this Monitoring and Control System not only enhances equipment reliability and lifespan but also reduces the risk of power supply disruptions. Therefore, the development and implementation of this system serve as an innovative and essential solution in maintaining the reliability of the power system. Advancements in IoT and Fuzzy Logic technologies open up opportunities to improve the overall reliability and efficiency of the Power Distribution System. This Monitoring and Control System presents a strategic step that can yield significant positive impacts on the reliability and performance of the Power Distribution System as a whole.

AGROTECH: A Smart Agricultural System Using IOT and ML

Agriculture remains one of the most important sectors that support human survival which supplies basic needs, a source of income for many dependants' community. Despite these advantages, resource scarcity, adverse environmental conditions and pest infestations remain serious threats to crop products. To mitigate these concerns, we present a smart agriculture system which uses the most modern technologies like IoT (Internet of Things) machine learning (ML) and sensors with automation. The system consists of an intelligent irrigation mechanism for efficient utilization of water, an animal detection system used to trace cattle, and a Light Dependent Resistor (LDR) Sensor joined with buzzer for early detection of any global environmental changes. Additionally, the integration of plant disease detection capabilities further enhances the system's effectiveness. By employing various sensors to collect data on environmental factors, including moisture and temperature, the proposed framework enables timely decision-making for crop management. The aim is to provide farmers with actionable insights, ensuring food security while minimizing resource consumption and economic losses. This review paper discusses the potential benefits and implementation strategies of such an intelligent agricultural system, emphasizing the need for innovation in agricultural practices to meet the growing demands of an increasing population

Monitoring changes in walkability over time: An environmental exposure change detection framework with implications for equity and social justice

Evidence suggests that walkability and greenspace impact travel related activity patterns and vehicle emissions which affect sustainability, public health, and equity. Resulting levels of physical activity, active, or sedentary travel time impact obesity, diabetes, and heart disease which impact COVID-19 mortality. It is now possible to track changes in locally controlled land use characteristics known to impact sustainability and health. This information can provide decisionmakers with feedback required to spatially prioritize and better link state and nationally funded transportation investments with locally sanctioned land use actions. Linking the achievement of established benchmarks of health equity-based indicators with funding establishes a more performance-based approach connecting land use with transportation investment. This study longitudinally tracks neighborhood-level walkability features at the census tract level for 2013 and 2020 for the entire USA. Longitudinal volatility-based change detection models are developed to examine how changes in walkability over time correlate with racialization and social justice. Walkability tends to increase over time with significant variations across metro regions and the urban-rural continuum. Largest and smallest increases in walkability were observed in Western Pacific and Northwest states, respectively. Increased racial and social justice disparities were observed in access to more walkable infrastructure by marginalized populations (such as less-educated, older, unemployed, and black individuals). Significant heterogeneity in the spatial distribution of walkability was observed, over the variation captured by observed sociodemographic, regional, and urban/rural factors. The findings highlight the potential for an “environmental surveillance” system to support a “performance-based” approach to transportation funding that prioritizes resource allocation consistent with Justice40 and United Nation's Sustainable Development Goals.

Robust chemical analysis with graphene chemosensors and machine learning.

Ion-sensitive field-effect transistors (ISFETs) have emerged as indispensable tools in chemosensing applications1-4. ISFETs operate by converting changes in the composition of chemical solutions into electrical signals, making them ideal for environmental monitoring5,6, healthcare diagnostics7 and industrial process control8. Recent advancements in ISFET technology, including functionalized multiplexed arrays and advanced data analytics, have improved their performance9,10. Here we illustrate the advantages of incorporating machine learning algorithms to construct predictive models using the extensive datasets generated by ISFET sensors for both classification and quantification tasks. This integration also sheds new light on the working of ISFETs beyond what can be derived solely from human expertise. Furthermore, it mitigates practical challenges associated with cycle-to-cycle, sensor-to-sensor and chip-to-chip variations, paving the way for the broader adoption of ISFETs in commercial applications. Specifically, we use data generated bynon-functionalized graphene-based ISFET arrays to train artificial neural networks that possess a remarkable ability to discern instances of food fraud, food spoilage and food safety concerns. We anticipate that the fusion of compact, energy-efficient and reusable graphene-based ISFET technology with robust machine learning algorithms holds the potential to revolutionize the detection of subtle chemical and environmental changes, offering swift, data-driven insights applicable across a wide spectrum of applications.

Sirtuin insights: bridging the gap between cellular processes and therapeutic applications.

The greatest challenges that organisms face today are effective responses or detection of life-threatening environmental changes due to an obvious semblance of stress and metabolic fluctuations. These are associated with different pathological conditions among which cancer is most important. Sirtuins (SIRTs; NAD+-dependent enzymes) are versatile enzymes with diverse substrate preferences, cellular locations, crucial for cellular processes and pathological conditions. This article describes in detail the distinct roles of SIRT isoforms, unveiling their potential as either cancer promoters or suppressors and also explores how both natural and synthetic compounds influence the SIRT function, indicating promise for therapeutic applications. We also discussed the inhibitors/activators tailored to specific SIRTs, holding potential for diseases lacking effective treatments. It may uncover the lesser-studied SIRT isoforms (e.g., SIRT6, SIRT7) and their unique functions. This article also offers a comprehensive overview of SIRTs, linking them to a spectrum of diseases and highlighting their potential for targeted therapies, combination approaches, disease management, and personalized medicine. We aim to contribute to a transformative era in healthcare and innovative treatments by unraveling the intricate functions of SIRTs.

Towards the humane slaughter of decapod crustaceans: identifying the most effective indicators of insensibility following electrical stunning

Decapod crustaceans provide a valuable food source worldwide, both through wild capture and captive rearing. They are also used extensively in scientific research, both as subjects for the investigation of basic biological processes and as model organisms for the detection of environmental changes. There is now an increasing acceptance that decapod crustaceans are sentient, and legislation is being introduced in numerous countries to ensure crustacean welfare when they are harvested or held captive. Moreover, methods for the humane slaughter of these animals are being developed, and of these electrical stunning is a prime candidate. Optimisation of electrical stunning is underway for a range of commercially-exploited or scientifically-important species, and the present study contributes to that process by examining further crab (Carcinus maenas) and lobster (Homarus gammarus) species using a rigorous neurophysiological approach. By recording nerve activity in both the central and peripheral nervous systems, we have found that electrical stunning with a standard commercial instrument arrests nerve activity in both these species at all levels: sensory, motor and central, rendering the animals neurologically insensible. This methodology is the most direct and effective way to establish if insensibility is successfully achieved. However, ultimately the routine monitoring of effective stunning in commercial and laboratory settings will have to depend on simple, yet reliable physiological or behavioural indicators, following their calibration against neurological methods. Monitoring heartbeat is used widely to establish metabolic activity, locomotory performance, agonistic interactions and responses to environmental conditions. We have therefore assessed the potential to use cardiac activity as an indicator for the state of sensibility. We recorded the heartbeat in both C. maenas and H. gammarus before and after electrical stunning, but find that in many cases even though nerve activity has ceased both centrally and peripherally, cardiac activity actually continues (though at a reduced rate) for a substantial time (>1h). The heartbeat is therefore not a reliable indicator of the state of sensibility, making it an unsuitable indicator of effective stunning. Possible reasons for these findings, and ways to validate behavioural measures that may be more appropriate for routinely establishing effective electrical stunning, are discussed.

NRhFluors: Quantitative Revealing the Interaction between Protein Homeostasis and Mitochondria Dysfunction via Fluorescence Lifetime Imaging.

Degenerative diseases are closely related to the changes of protein conformation beyond the steady state. The development of feasible tools for quantitative detection of changes in the cellular environment is crucial for investigating the process of protein conformational variations. Here, we have developed a near-infrared AIE probe based on the rhodamine fluorophore, which exhibits dual responses of fluorescence intensity and lifetime to local viscosity changes. Notably, computational analysis reveals that NRhFluors fluorescence activation is due to inhibition of the RACI mechanism in viscous environment. In the chemical regulation of rhodamine fluorophores, we found that variations of electron density distribution can effectively regulate CI states and achieve fluorescence sensitivity of NRhFluors. In addition, combined with the AggTag method, the lifetime of probe A9-Halo exhibits a positive correlation with viscosity changes. This analytical capacity allows us to quantitatively monitor protein conformational changes using fluorescence lifetime imaging (FLIM) and demonstrate that mitochondrial dysfunction leads to reduced protein expression in HEK293 cells. In summary, this work developed a set of near-infrared AIE probes activated by the RACI mechanism, which can quantitatively detect cell viscosity and protein aggregation formation, providing a versatile tool for exploring disease-related biological processes and therapeutic approaches.

A novel pH-sensitive hemi-cyanine containing tetrahydropyridine ring near-infrared fluorescence probe with lysosome-targeting ability

In recent years, hemi-cyanine dyes have been widely used as biological probes due to their red-light emission characteristics and high fluorescence quantum yield. In this study, we synthesized a novel hemi-cyanine dye containing a tetrahydropyridine ring. A lysosomal target was introduced into its structure to create a new pH-sensitive near-infrared fluorescent probe that successfully targeted lysosomes. The results showed that when the probe solution was excited at the absorption wavelength of 650 nm, its fluorescence emission wavelength was about 700 nm, and the peak intensity changed with different pH values in a wide range. Therefore, this probe enabled non-invasive detection of changes in the acidic environment of lysosomes in living organisms and showed good imaging capabilities. Moreover, the probe displays high sensitivity and good stability. The theoretical calculation of a probe structure has also been completed to discuss the relationship between structure and property.

Soundscape for urban ecological security evaluation

The security of the Earth system has been extensively discussed through the concept of planetary boundaries, which hypothesizes the Anthropocene as the crisis for pushing environmental variables beyond safe limits. Cities, as burgeoning population centers, warrant heightened attention to issues surrounding planetary boundaries and ecological security. While groundwork has been laid for environmental change detection, the acoustic or soundscape perspective is rarely considered. This paper presents abundant empirical evidence supporting the feasibility of leveraging the soundscape as a valuable lens for exploring ecosystem structures, functions, and their contribution to human well-being. Particularly, it proposes spatialized soundscape maps as practical tools to implement this innovative perspective. We elaborate on two key aspects: (i) soundscape as a reflection of ecosystem evolution, enabling evaluation of ecosystem structures, interactions, and the ecosystem's functions; (ii) soundscape has the attribution of providing cultural services, allowing assessment of its impact on human health. Consequently, we propose two paradigms: (i) “security in soundscape” and (ii) “security of soundscape”, thereby initiated the concept of “soundscape for security”. Furthermore, we outline two generalized pathways: (i) soundscape monitoring, encompassing long-term strategies for real-time tracking of ecosystem evolution, and (ii) soundscape perception, involving detailed surveys to investigate perception and public participatory sensing for large-scale evaluation of ecosystem cultural services. We argue that integrating soundscape considerations holds promise in urban ecological security initiatives and the pursuit of sustainable cities. Moving forward, collective efforts among academics are crucial to establish widely accepted protocols to maximize the value of soundscape for urban ecological security.

Double-space environmental change detection and response strategy for dynamic multi-objective optimize problem

Dynamic multi-objective optimization problems (DMOPs) which contain various Pareto-optimal front (PF) and Pareto-optimal set (PS) have gained much attention. Accurate environmental change detection reveals the change degree of DMOPs and contributes the algorithm to quickly respond to the environment changes. In order to fully detect environmental changes and efficiently track front, a double-space environmental change detection and response strategy (DSDRS) is proposed. It could detect whether the environment has changed while explore the change intensity of PF and PS, respectively. Moreover, different response strategies are implemented for PF and PS. For PF environmental changes, a multiple knee points-guided evolutionary strategy (MKGES) is proposed, which is driven by front shape information and adaptively responds to different PF change intensities. For PS environmental changes, a knowledge guided memory strategy (KGMS) is proposed, which guides population evolution based on environmental information. The effectiveness of DSDRS is confirmed by comparison with five evolutionary algorithms on 20 dynamic multiobjective benchmark functions. Simulation results demonstrate that the performance of proposed algorithm is outstanding on test functions with complex changing PF and PS.

Space-temporal detection of environmental changes in the Brazilian semiarid through Google Earth Engine and GIS

Land Use and Land Cover Changes (LULCC) are the leading cause of worldwide land degradation. However, efficient means of detecting and monitoring these environmental changes improve the understanding of this process, especially in developing more effective policies to combat land degradation. This study evaluated the space-temporal trends of ecological changes in the Cabrobó’s Desertification Nucleus (CDN), Pernambuco, Brazilian semiarid region, between 2001 and 2021. For this, we manipulated CHIRPS data, MODIS products (MOD13A1, MOD11A2, MCD43A3, and MOD16A2), and MapBiomas data in the Google Earth Engine (GEE) digital platform and the QGIS software (version 3.10.9). First, we calculated surface albedo and Vegetation Health Index (VHI). After that, we performed a space-temporal trend assessment for all biophysical parameters via non-parametric tests Mann-Kendall (MK) and Sen's Slope Estimator (SSE). In addition, we analyzed LULCC over the last 21 years using MapBiomas. The results obtained showed the effectiveness of using these orbital products for long-term environmental analysis, as well as highlighting the municipalities of Belém do São Francisco, Itacuruba, and Floresta with significant trends at the 1% probability level of the biophysical indices (from decrease to potential evapotranspiration, NDVI and VHI, and addition to albedo and surface temperature) due to a more change in land use and cover. This methodology proved effective for studies on the discrimination of space-temporal trends in the CDN region. It will apply in other Caatinga areas after the appropriate methodological adjustments. All scripts generated in this work can be accessed and manipulated through the LDA Tool repository in the GEE platform.

Understanding the Susceptibility of the Tropical Proglacial Environment in Peru Using Optical Imagery and Radon Measurements

The tropical glaciers of the Cordillera Blanca have played host to some of the most significant mass movements ever recorded in the world and Peru; many proglacial lakes formed in this mountain range have natural dikes made of moraine material, which, if they collapse, would present a risk for the cities located downstream of a proglacial lake, where the proglacial lake Palcacocha has a remarkable background regarding floods. The Sentinel-2 MSI (Multi-Spectral Instrument, Level-2A) has a specific band for snow probability mapping that indicates glaciers and snow cover; this is effective for recognizing proglacial lakes by calculating the NDWIice. It is also helpful for lithology with SWIR for granite moraine deposits and slate moraines in the proglacial environment Palcacocha; these deposits surround the proglacial lake, with NDWIice determining the perimeter where sediment interacts with the rocks and meltwater. In addition, there are high radon concentrations made by ice avalanche impacts on the proglacial lake. Unstable glacier blocks cause ice avalanches into this proglacial lake, and the radon responds to flow variations from these high-impact avalanches. We used the device RadonEye PLus2, which allows real-time detection of radon flux changes in the proglacial environment. Our results indicated that ice avalanches making a high impact in the proglacial lake cause turbulent flow and generate radon concentration marks with a rising magnitude, while the absence of ice avalanches in the lake will cause the values to go down. The relationships of radon concentrations in the atmosphere for a tropical proglacial environment are radon and temperature (R2 = 0.364), radon and humidity (R2 = 0.469). In a passive proglacial environment with prolonged rainfall, radon concentrations tend to decrease, with an inversely proportional relationship between humidity and radon in the tropical proglacial environment. Proglacial lakes in the tropical zone often have large volumes of freshwater with high slopes from tropical glaciers, and climate change effects are an imminent danger for nearby cities.

Rapid identification of early renal damage in asymptomatic hyperuricemia patients based on urine Raman spectroscopy and bioinformatics analysis.

Objective: The issue of when to start treatment in patients with hyperuricemia (HUA) without gout and chronic kidney disease (CKD) is both important and controversial. In this study, Raman spectroscopy (RS) was used to analyze urine samples, and key genes expressed differentially CKD were identified using bioinformatics. The biological functions and regulatory pathways of these key genes were preliminarily analyzed, and the relationship between them as well as the heterogeneity of the urine components of HUA was evaluated. This study provides new ideas for the rapid evaluation of renal function in patients with HUA and CKD, while providing an important reference for the new treatment strategy of HUA disease. Methods: A physically examined population in 2021 was recruited as the research subjects. There were 10 cases with normal blood uric acid level and 31 cases with asymptomatic HUA diagnosis. The general clinical data were collected and the urine samples were analyzed by Raman spectroscopy. An identification model was also established by using the multidimensional multivariate method of orthogonal partial least squares discriminant analysis (OPLS-DA) model for statistical analysis of the data, key genes associated with CKD were identified using the Gene Expression Omnibus (GEO) database, and key biological pathways associated with renal function damage in CKD patients with HUA were analyzed. Results: The Raman spectra showed significant differences in the levels of uric acid (640cm-1), urea, creatinine (1,608, 1,706cm-1), proteins/amino acids (642, 828, 1,556, 1,585, 1,587, 1,596, 1,603, 1,615cm-1), and ketone body (1,643cm-1) (p < 0.05). The top 10 differentially expressed genes (DEGs) associated with CKD (ALB, MYC, IL10, FOS, TOP2A, PLG, REN, FGA, CCNA2, and BUB1) were identified. Compared with the differential peak positions analyzed by the OPLS-DA model, it was found that the peak positions of glutathione, tryptophan and tyrosine may be important markers for the diagnosis and progression of CKD. Conclusion: The progression of CKD was related to the expression of the ALB, MYC, IL10, PLG, REN, and FGA genes. Patients with HUA may have abnormalities in glutathione, tryptophan, tyrosine, and energy metabolism. The application of Raman spectroscopy to analyze urine samples and interpret the heterogeneity of the internal environment of asymptomatic HUA patients can be combined with the OPLS-DA model to mine the massive clinical and biochemical examination information on HUA patients. The results can also provide a reference for identifying the right time for intervention for uric acid as well as assist the early detection of changes in the internal environment of the body. Finally, this approach provides a useful technical supplement for exploring a low-cost, rapid evaluation and improving the timeliness of screening. Precise intervention of abnormal signal levels of internal environment and energy metabolism may be a potential way to delay renal injury in patients with HUA.

Neural network model for detection of changes in forest environment using multispectral images

The article presents the task of detecting changes in the forest environment using multispectral images for balanced forest management and monitoring of the regional forest ecosystem. The method of semantic segmentation based on assigning each pixel a corresponding class label is used for recognition, object classification on the image, and analysis of multispectral images. By studying the spectral characteristics of pixels, the neural network automatically extracts and memorizes features from the data, using them for classification and change detection. Experimental results based on the developed software prototype confirm the reliability of the theoretical aspects of the model for change detection in the forest environment. Moreover, the neural network model for detecting changes in the forest environment using multispectral images has practical significance and can be applied to solve real tasks of the regional forest ecosystem of the Republic of Buryatia.

Use of Spatial Technology for Land Cover Change Assessment in Protected Area Management

Land cover assessment and monitoring of land cover dynamics are important to understand social and ecological processes in protected areas and it is helpful in monitoring, modeling and detection of environmental changes. Land cover is the characteristics of the physical material on the ground surface. Change in land cover is among the most important human alteration affecting the surface of earth that directly impacts on biological diversity. Such changes affect and increase the vulnerability of population, environment and ecosystem to global phenomena of climate change. This study has assessed the changes in land cover in Shivapuri Nagarjun National Park (SNNP) along with its Buffer Zone during twenty-seven years. The main objective of this study is to identify the changed scenario on land cover condition of the study area based on available topographic maps (1994), Google Earth images (2021) and Key Informant Interview (KII). The study has adopted Remote Sensing (RS), Geographic Information System (GIS) and Global Positioning System (GPS) approach supported by secondary data and focus group discussion for classification and mapping of land cover. Among the different land cover classes, forest land, sandy area, water bodies and others land particularly built up area have been increased whereas, the bush/shrub land, cultivated land and grass land have been found decreased in the park area. On the other hand, the cultivated land, grass land and water bodies have been found decreased, while the bush/ shrub land, forest land, sandy area and others land particularly built up areas have been found significantly increased during 1994 to 2021 in the BZ area. It will provide a baseline for planners and policy makers to make proper plans to sustainably manage the protected area’s land cover.

Environmental threat and change detection in saline lakes from 1960 to 2021: background, present, and future.

Saline lakes are internationally known for unique chemical compositions, incorporate assorted foliage, and give a few biological system benefits and have high environmental, financial, and social qualities are additionally assigned as the Ramsar site for giving wetlands to migratory birds. The anthropogenic and regular unsettling influences have influenced the water nature of these lakes by immediate or circuitous tainting of various contaminations in releases from refining, mining, and modern locales. The impacts of such exercises bring about changes to the typical attributes of saline lakes, principal limnological changes, and loss of biodiversity that debase the upsides of saline lakes. Saline water bodies are not expected and are perceived as significant inland sea-going environments. To review the present circumstance, there is a need to bring issues to light of the idea of dangers and their effects on the environment. The article remembers the situation with water contamination for some saline lakes and its unfavorable impact on biodiversity. The audit plans to cause notice the broad harm of saline lakes that have now gone through because of contamination from last years.

Spatial indices and SDG indicator-based urban environmental change detection of the major cities in Bangladesh

Understanding a city's urban growth trend and associated environmental change scenarios is necessary to ensure the quality of life of urban people by sustaining ecosystem health. We have performed urban environmental change detection for the major cities in Bangladesh based on the analysis of urban indices using remote sensing techniques. We have further correlated these indices-derived outcomes with Sustainable Development Goal (SDG) number 11 (sustainable cities and communities) by estimating the SDG indicator 11.3.1. This indicator deals with the urban land consumption rate ratio to the population growth rate. We calculated the Impervious Surface Area (ISA), Vegetation Fraction (VF), and Biotope Area Ratio (BAR) for Dhaka, Chittagong, Sylhet, and Khulna-the four major cities in Bangladesh. We have also estimated the Normalized Urban Area Composite Index (NAUCI) as NAUCI utilizes further information from other pertinent pre-established indices like NDVI, NDWI, and NDBI. These widely used indices are also essential to consider because these indicators are also directly related to land consumption and population growth. Here we find that each of the considered cities is expanding significantly faster. Therefore, the ecology of the urban environment is degrading as well as the cities' conditions are worsening in terms of SDG 11.3.1. Dhaka, Chittagong, and Sylhet exhibit a lowering trend of the SDG indicator, whereas Khulna city show the opposite; however, we found Khulna's urban growth like the other cities under investigation is harming the ecology too.

Multi-factor biotopes as a method for detailed site characterization in diverse benthic megafaunal communities and habitats in deep-water off Morro Bay, California

Here we describe the methods and results for biological characterization of the benthos on a previously unexplored area of central California, USA seafloor. We conducted 40 remotely operated vehicle dives from 371 to 1173 m water depth. Seafloor habitats and megafauna (fish and invertebrates) were documented from 46.8 km of seafloor video footage. Our expanded development and analysis of biotopes from quantitative data allowed us to describe detailed biological communities, along with the physical characteristics and habitat associations within the study area. This method provides a framework for potential monitoring, detection of future environmental change (natural and anthropogenic) and comprehensive comparison to other deep water regions. From 185 h of observational video at 25 sites, nearly 120,000 annotations of organisms, habitat characters, biological detritus and anthropogenic debris were recorded and analyzed. We identified a total of 228 taxa, with 173 of them present on linear quantitative transects. Species richness for transects ranged from 0.04 to 0.28 m-2 (8–55 taxa), with densities ranging from 0.07 to 5.20 ind. m−2. Both were highest on hard substrate with greatest surface area. Densities decreased with depth. Within soft substratum zones was a large field of pockmarks, which are seafloor depressions averaging 175 m in diameter and 5 m in depth. Pockmarks have sometimes been associated with seafloor gas seepage, but here we found no biological evidence of chemosynthetic organisms. No significant differences were found in either density nor species richness at pockmark sites vs. non-pockmark sites. Mud draped greenish-black coarse sand occurred only in low oxygen areas, while hummocky, rugose mud supported somewhat different species than flat mud plains. Seventy percent of the transects occurred inside the oxygen minimum zone. We conclude that high rugosity, slope, and the presence of hard substratum were better predictors of species richness and density than oxygen concentration in this specific study. Abundant biological detritus, in the form of dead and dying pelagic pyrosomes and salps, created a large, presumably ephemeral flux of carbon to the seafloor during the study period.

Impact of Al-Ahdeb Oil Field on the Surrounding Environment Using Remote Sensing and GIS Techniques, Wasit Governorate, Iraq

Remote sensing and Geographical Information System (GIS) are used in this study to detect environmental changes in the vicinity of Al-Ahdeb oil field, southwest of Kut City, Wasit Governorate. Different image indices, such as Normalized Differences Vegetation Index (NDVI), Normalized Differences Soil Index (NDSoI), and Normalized Differences Salinity Index (NDSI) are used. Two LANDSAT images with acquired data of September 2007 and 2016 are used to detect the environmental changes and to detect the effect of Al-Ahdeb oil field before and after construction and industrial operation. The results of change detection show there is a high decrease in the vegetation cover during the year 2016 compared with 2007, where the area of vegetation cover has decreased from (165.85 Km 2 ) in 2007 to (119.62 Km 2 ) in 2016. The change detection results from NDSI show that the saline soil in (2016) is higher than those in (2007). The NDSI derived from Landsat TM (2007) image confirms that there is significant increase of salinity in the study area, where the calculated area of the salinity in 2007 is (5.65 Km 2 ) while in 2016 it is (21.951 Km 2 ). Change detection, using NDSoI, show that the land in the study area is going toward desertification and soil degradation. The decrease in the vegetation cover, which in turn led to soil erosion in addition to water shortage and the pollution by the waste of the oil field, could be the main reasons of the desertification in the area. Keywords: Environmental change detection; Image Indices; NDVI; NDSI; NDSoI; Al Ahdeb oil field. DOI: 10.7176/JEES/12-8-03 Publication date: August 31 st 2022

First Report of Culturable Skin Bacteria in Melanophryniscus admirabilis (Admirable Redbelly Toad).

Melanophryniscus admirabilis is a small toad, critically endangered with a microendemic distribution in the Atlantic Forest in southern Brazil. The amphibian skin microbiome is considered one of the first lines of defense against pathogenic infections, such as Batrachochytrium dendrobatidis (Bd). The knowledge of skin amphibian microbiomes is important to numerous fields, including species conservation, detection, and quantification of environmental changes and stressors. In the present study, we investigated, for the first time, cultivable bacteria in the skin of wild M. admirabilis, and detected Bd fungus by nested polymerase chain reaction (PCR) technique. Skin swab samples were collected from 15 wild M. admirabilis, and the isolation of bacteria was performed by means of different culture strategies. A total of 62 bacterial isolates being Bacillus (n = 22; 34.48%), Citrobacter (n = 10; 16.13%), and Serratia (n = 12; 19.35%) were more frequently isolated genera. Interestingly, all skin samples tested were Bd negative. Some bacterial genera identified in our study might be acting in a synergic relationship and protecting them against the Bd fungus. In addition, these bacteria may play an essential role in maintaining this species in an environment modulated by anthropic actions. This first report of skin cultivable bacteria from M. admirabilis natural population improves our knowledge of skin amphibian microbiomes, contributing to a better understanding of their ecology and how this species has survived in an environment modulated by anthropic action.