Earth

Farmers in North Carolina are turning to irrigation to reduce the impacts of droughts and rainfall variability on agricultural production. Droughts, rainfall variability, and the increasing demand for food, feed, fiber, and fuel necessitate the urgent need to provide North Carolina farmers with tools to improve irrigation management and maximize water productivity. This is only possible by understanding the current status of irrigated agriculture in the state and investigating its potential weaknesses and opportunities. Thus, the objective of this study was to perform a comprehensive analysis of the current state of irrigation management in North Carolina based on 15-year data from the Irrigation and Water Management Survey by the United States Department of Agriculture–National Agricultural Statistics Service (USDA-NASS). The results indicated a reduction in irrigation acres in the state. Also, most farms in the state have shifted to efficient sprinkler irrigation systems from gravity-fed surface irrigation systems. However, many farms in North Carolina still rely on traditional irrigation scheduling methods, such as examining crop conditions and the feel of soil in deciding when to irrigate. Hence, there are opportunities for enhancing the adoption of advanced technologies like soil moisture sensors and weather data to optimize irrigation schedules for improving water efficiency and crop production. Precision techniques and data-based solutions empower farmers to make informed, real-time decisions, optimizing water use and resource allocation to match the changing environmental conditions. The insights from this study provide valuable information for policymakers, extension services, and farmers to make informed decisions to optimize agricultural productivity and conserve water resources. Full article

This paper addresses the problem of mapping land cover types in Senegal and recognition of vegetation systems in the Saloum River Delta on the satellite images. Multi-seasonal landscape dynamics were analyzed using Landsat 8-9 OLI/TIRS images from 2015 to 2023. Two image classification methods were compared, and their performance was evaluated in the GRASS GIS software (version 8.4.0, creator: GRASS Development Team, original location: Champaign, Illinois, USA, currently multinational project) by means of unsupervised classification using the k-means clustering algorithm and supervised classification using the Support Vector Machine (SVM) algorithm. The land cover types were identified using machine learning (ML)-based analysis of the spectral reflectance of the multispectral images. The results based on the processed multispectral images indicated a decrease in savannas, an increase in croplands and agricultural lands, a decline in forests, and changes to coastal wetlands, including mangroves with high biodiversity. The practical aim is to describe a novel method of creating land cover maps using RS data for each class and to improve accuracy. We accomplish this by calculating the areas occupied by 10 land cover classes within the target area for six consecutive years. Our results indicate that, in comparing the performance of the algorithms, the SVM classification approach increased the accuracy, with 98% of pixels being stable, which shows qualitative improvements in image classification. This paper contributes to the natural resource management and environmental monitoring of Senegal, West Africa, through advanced cartographic methods applied to remote sensing of Earth observation data. Full article

The growing population and expansion of rural activities, along with changing climatic patterns and the need for water during drought periods, have led to a rise in the water demand worldwide. As a result, the construction of water storage structures such as dams has increased in recent years to meet the water needs. However, dam construction can bring significant alterations to the natural flow regime of rivers, and it is therefore essential to understand the potential effects of human structures on the hydrological regime of rivers to reduce their destructive impacts. This study analyzes the hydrological changes in the Shahrchai River in response to the Shahrchai Dam construction in Urmia, Iran. The study period was from 1950 to 2017 at the Urmia Band station. The Indicators of Hydrological Alteration (IHA) were used to analyze the hydrological changes before and after regulating, accounting for land use changes and climatic factors. The results revealed the adverse effects of the Shahrchai Dam on the hydrological indices. The analysis showed an increase in the average flow rate during the summer season and a decrease in other seasons. However, the combined effects of water transferring for drinking purposes, a decrease in permanent snow cover upstream of the dam, and an increase in water use for irrigation and agricultural purposes resulted in a decrease in the released river flow. Furthermore, the minimum and maximum daily flow rates decreased by approximately 85% and 65%, respectively, after the construction of the Shahrchai Dam. Additionally, the number of days with maximum flow rates increased from 117 days in the pre-dam period to 181 days in the post-dam period. As a concluding remark, the construction of the Shahrchai Dam, land use/cover changes, and a decrease in permanent snow cover had unfavorable effects on the hydrological regime of the river. Therefore, the hydrological indicators should be adjusted to an acceptable level compared to the natural state to preserve the river ecosystem. The findings of this study are expected to guide water resource managers in regulating the sustainable flow regime of permanent rivers. Full article

Land surface temperature (LST) variations and anomalies associated with tectonic plate movements have been documented before large earthquakes. In this work, we propose that spatially extended and dynamic linear zones of high temperature anomalies at the Earth’s surface coinciding with faults in the Earth’s crust may be used as a predictor of an approaching earthquake. LST contrast boundary concentration maps are suggested to be a possible indicator for analyzing temperature changes before and after seismic sequences. Here, we analyze the concentration of LST contrast boundaries estimated from Landsat 8–9 data for the East Anatolian Fault Zone in the vicinity of epicenters of the destructive earthquakes with magnitudes up to 7.8 Mw that occurred in February 2023. A spatial relationship between earthquake epicenters and the maximum concentration of LST boundaries at azimuths of 0° and 90° was found to strengthen as the earthquake approaches and weaken after it. It was found that 92% of epicenters are located at up to 5 km distance from zones of maximum LST boundary concentration. The evidence presented in this work supports the idea that LST may provide valuable information for seismic hazard assessment before large earthquakes. Full article

Petrochemical clusters are forms of industrialization that use compounds and polymers derived directly or indirectly from gas or crude oil for chemical applications. They pose a variety of short- and long-term risks to the environment and the people who live nearby. The aim of this study is to determine whether there is a correlation between the degree of perceived technological risk and the emotional value generated by the contemplation of the petrochemical industry landscape in order to try to establish strategic lines of action to mitigate the perception of risk and improve the emotional well-being of the population. This study uses manipulated pictures and a Public Participation Geographic Information System (PPGIS) survey to assess changes in perception and emotional response in residents in Teheran (Iran). Key findings show an insignificant relationship between technological risk and landscape value perception in both original and manipulated pictures. However, taking into account that, in general, in manipulated pictures, there is a more significant relationship, designing the landscape could help to mitigate the technological risk perception. This study contributes to the broader discussion about industrialization and its environmental and social consequences. It emphasizes the importance of considering public perception when planning and developing industrial areas, so as to balance industrial functionality and environmental and aesthetic considerations for long-term urban development. Full article

The growing interest in environmental sustainability issues and, at the same time, the advantages offered by blockchain technology have strong connections to each other. This study explores the application of blockchain technology across various environmental domains, such as air quality, climate change impacts, and resource management. The research utilised a dual approach, combining a bibliometric analysis with VOSviewer and a topic analysis using BERT models to assess the discourse within both the scientific literature extracted from Scopus and practical blockchain projects obtained from GitHub. The findings reveal that food security, energy, and sustainable agriculture are predominant topics in academic discussions, with a noticeable increase in focus from 2017 onwards. Practical projects are focused on transparent tracking and decentralised management. The overlap between academic and practical spheres is evident in the shared focus on energy and environmental management, demonstrating blockchain’s growing role in addressing global environmental challenges. This study underscores the importance of integrating theoretical research with practical implementations to harness blockchain’s full potential in promoting sustainable environmental practices. Full article

Land cover is one of the spatial factors influencing the ecological niche of animal populations. Some types of land cover predetermine a particular site as a habitat for certain species. One of the flagship species of agrocenosis is the white stork (Ciconia ciconia (L.)). This study focuses on the occupancy of 122 nests in the vicinity of Kampinos National Park in Poland. This area is a mixture of traditional agricultural settlements, forests, the Vistula valley, and the suburbs of Warsaw, Poland. This mix allows for the identification of land cover disturbances that affect the white stork’s nest occupancy. The current state of development and the efficiency of remote sensing-based land cover databases allows us to easily identify spatial factors affecting nest occupancy and to analyse them in a longer timeframe. The study analyses land cover in buffers of 1 to 5 km around white stork nests based on CORINE Land Cover (CLC) for the years 2006, 2012, and 2018. Although the white stork’s habitat is well studied, the CLC-based results provide significant new insights. The results show that nest occupancy increases with an increasing proportion of agricultural land, especially with significant natural vegetation, while the proportion of wetlands and water is not significant. This work provides a description of the ideal habitat for the white stork in terms of nest occupancy. Full article

The North Aegean Sea region in Greece is located at the convergence of the Eurasian, African, and Anatolian tectonic plates. The region experiences frequent seismicity ranging from moderate to large-magnitude earthquakes. Tectonic interactions and seismic events in this area have far-reaching implications for understanding the broader geological processes in the eastern Mediterranean region. This study aims to conduct a comprehensive investigation of the seismic activity of the North Aegean Sea region by employing advanced seismological techniques and data analyses. Data from onshore seismological networks were collected and analyzed to assess the characteristics of the earthquakes in the region. Seismicity patterns, focal mechanisms, and seismic moment calculations were performed to assess current seismic activity. The present study combined spatiotemporal analysis with the analysis of genesis mechanisms, and this resulted in more results than those of previous studies. Detailed analysis of the seismic data showed patterns in the occurrence of earthquakes over time, with periodic episodes of increased seismic activity compared to activities followed by quieter periods. Finally, this study proves that recent earthquakes in the study area (2017, 2020) highlight the complexity of seismicity as well as the consequences of strong earthquakes on people and buildings. Overall, these findings suggest that the North Aegean Sea is becoming increasingly seismically active and is a potential risk zone for adjacent regions. Full article

The origin of life is associated with the existing environmental factors of the Precambrian Era of the Earth. The minerals rich in sodium silicates, in aluminum and in other chemical elements, such as kaolinite, were among the factors present at that time. Kaolinite is an abundant mineral on our planet, which indicates that it possibly had an essential role in the origin of the first blocks that constructed life on Earth. Evidence of this is the cherts, which are rocks with a high concentration of silica that retain the vestiges of the most ancient life on our planet. There are also inorganic structures called biomorphs that are like the cherts of the Precambrian, which take on a morphology and crystalline structure depending on the chemical molecules that make up the reaction mixture. To evaluate the interaction of kaolinite with DNA, the objective of this work is to synthesize biomorphs in the presence of kaolinite and genomic DNA that comes from a prokaryote and a eukaryote microorganism. Our results show that the difference between the prokaryote DNA and the eukaryote DNA favors the morphology and the crystalline phase of the calcium silica–carbonate biomorphs, while in the case of the barium silica–carbonate biomorphs, the environmental factors participate directly in the morphology but not in the crystalline phase. Results show that when a mineral such as kaolinite is present in genomic DNA, it is precisely the DNA that controls both the morphology and the crystalline phase as well as the chemical composition of the structure. This fact is relevant as it shows that, independently of the morphology or the of size of the organism, it is the genomic DNA that controls all the chemical elements toward the most stable structure, therefore allowing the perpetuation, conservation and maintenance of life on our planet (since the origin of the genomic DNA in the Precambrian Era to the present day). Full article

A flood forecasting and early warning system is critical for rivers that have a large flood potential, one of which is the Gilireng watershed, which floods every year and causes many losses in Wajo Regency, Indonesia. This research also introduces an integration model between UH SUH and HEC-RAS in flood impact analysis, as a reference for flood forecasting and early warning systems in anticipating the timing and occurrence of floods, as well as GIS in the spatial modeling of flood-prone areas. Broadly speaking, this research is divided into four stages, namely, a flood hydrological analysis using UH SUH, flood hydraulic tracing using a 2D HEC-RAS numerical model, the spatial modeling of flood-prone areas using GIS, and the preparation of flood forecasting and early warning systems. The results of the analysis of the flood forecasting and early warning systems obtained the flood travel time and critical time at the observation point, the total time required from the upstream observation point to level 3 at Gilireng Dam for 1 h 35 min, Mamminasae Bridge for 4 h 35 min, and Akkotengeng Bridge for 8 h 40 min. This is enough time for people living in flood-prone areas to evacuate to the 15 recommended evacuation centers. Full article

The United Nations (UN) Land Degradation Neutrality (LDN) evaluation stresses the need to account for different types of land degradation (LD) as part of the UN Sustainable Development Goal (SDG 15: Life on Land) and UN Convention to Combat Desertification (UNCCD). For example, one of the indicators, 15.3.1 Proportion of land that is degraded over total land area, can be differentiated between different types of LD (e.g., urban development, agriculture, barren) when considering land use and land cover (LULC) change analysis. This study demonstrates that it is important to consider not only the overall anthropogenic LD status and trend over time, but also the type of LD to confirm LDN. This study’s innovation is that it leverages remote-sensing-based LULC change analysis to evaluate LDN by different types of LD using the state of Ohio (OH) as a case study. Almost 67% of land in OH experienced anthropogenic LD primarily due to agriculture (81%). All six soil orders were subject to various degrees of anthropogenic LD: Mollisols (88%), Alfisols (70%), Histosols (58%), Entisols (55%), Inceptisols (43%), and Ultisols (22%). All land developments in OH can be linked to damages from LD, with 10,116.3 km² developed, resulting in midpoint losses of 1.4 × 10¹¹ kg of total soil carbon (TSC) and a midpoint social cost of carbon dioxide emissions (SC-CO₂) of $24B (where B = billion = 10⁹, USD). Overall, the anthropogenic LD trend between 2001 and 2016 indicated LDN, however, during the same time, there was a six percent increase in developed area (577.6 km²), which represents a consumptive land conversion that likely caused the midpoint loss of 8.4 × 10⁹ kg of TSC and a corresponding midpoint of $1.4B in SC-CO₂. New developments occurred adjacent to current urban areas, near the capital city of Columbus, and other cities (e.g., Dayton, Cleveland). Developments negated OH’s overall LDN because of multiple types of damages: soil C loss, associated “realized” soil C social costs (SC-CO₂), and loss of soil C sequestration potential. The state of OH has very limited potential land (1.2% of the total state area) for nature-based solutions (NBS) to compensate for the damages, which extend beyond the state’s boundaries because of the greenhouse gas emissions (GHG). Full article

Land use/land cover (LULC) is a fundamental concept of the Earth’s system intimately connected to many phases of the human and physical environment. LULC mappings has been recently revolutionized by the use of high-resolution imagery from unmanned aerial vehicles (UAVs). The present study proposes an innovative approach for obtaining LULC maps using consumer-grade UAV imagery combined with two machine learning classification techniques, namely RF and SVM. The methodology presented herein is tested at a Mediterranean agricultural site located in Greece. The emphasis has been placed on the use of a commercially available, low-cost RGB camera which is a typical consumer’s option available today almost worldwide. The results evidenced the capability of the SVM when combined with low-cost UAV data in obtaining LULC maps at very high spatial resolution. Such information can be of practical value to both farmers and decision-makers in reaching the most appropriate decisions in this regard. Full article

The mapping of thermokarst landscapes and the assessment of their conditions are becoming increasingly important in light of a rising global temperature. Land cover maps provide a basis for quantifying changes in landscapes and identifying areas that are vulnerable to permafrost degradation. The study is devoted to assessing the current state of thermokarst terrain on Arga Island. We applied a random forests algorithm using the capabilities of the Google Earth Engine cloud platform for the supervised classification of the composite image. The analyzed composite consists of a Sentinel-2 image and a set of calculated indices. The study found that thermokarst-affected terrains occupy 35% of the total area, and stable terrains cover 29% at the time of image acquisition. The classifier has also mapped water bodies, slopes, and blowouts. The accuracy assessment revealed that the overall accuracy for all the different land cover classes was 98.34%. A set of other accuracy metrics also demonstrated a high level of performance. This study presents significant findings for assessing landscape changes in a region with unique environmental features. It also provides a potential basis for future interdisciplinary research and for predicting future thermokarst landscape changes in the Lena Delta area. Full article

Hydrothermal units are characterized by the emergence of several large-flow thermo-mineral springs (griffons), each with varying temperature and physico-chemical characteristics depending on the point of emergence. It seems, however, that there is variability between the different systems, although it is not easy to characterize it because the variability within each system is high. The regional dimension of the chemical composition of thermal waters is, therefore, an aspect that has received very little attention in the literature due to the lack of access to the deep reservoir. In this study, we investigated the spatial variability, on a regional scale, in the characteristics of thermal waters in northeastern Algeria, and more specifically the hydrothermal systems of Guelma, Souk Ahras, Khenchela and Tébessa. Thirty-two hot water samples were taken between December 2018 and October 2019, including five samples of low-temperature mineral spring water. Standard physico-chemical parameters, major anions and cations and lithium were analyzed. The data were log-transformed data and processed via principal component analysis, discriminant analysis and unsupervised classification. The results show that thermal waters are the result of a mixture of hot waters, whose chemical profile has a certain local character, and contaminated by cold surface waters. These surface waters may also have several chemical profiles depending on the location. In addition to the internal variability in each resource, there are differences in water quality between these different hydrothermal systems. The Guelma region differs the most from the other thermal regions studied, with a specific calcic sulfate chemical profile. This question is essential for the rational development of these regional resources in any field whatsoever. Full article

In the global carbon cycle, atmospheric carbon emissions, both ‘natural’ and anthropogenic, are balanced by carbon uptake (i.e., sequestration) that mostly occurs via photosynthesis, plus a much smaller proportion via geological processes. Since the formation of the Earth about 4.54 billion years ago, the ratio between emitted and sequestered carbon has varied considerably, with atmospheric CO₂ levels ranging from 100,000 ppm to a mere 100 ppm. Over this time, a huge amount of carbon has been sequestered due to photosynthesis and essentially removed from the cycle, being buried as fossil deposits of coal, oil, and gas. Relatively low atmospheric CO₂ levels were the norm for the past 10 million years, and during the past million years, they averaged about 220 ppm. More recently, the Holocene epoch, starting ~11,700 years ago, has been a period of unusual climatic stability with relatively warm, moist conditions and low atmospheric CO₂ levels of between 260 and 280 ppm. During the Holocene, stable conditions facilitated a social revolution with the domestication of crops and livestock, leading to urbanisation and the development of complex technologies. As part of the latter process, immense quantities of sequestered fossil carbon have recently been used as energy sources, resulting in a particularly rapid increase in CO₂ emissions after 1950 CE to the current value of 424 ppm, with further rises to >800 ppm predicted by 2100. This is already perturbing the previously stable Holocene climate and threatening future food production and social stability. Today, the global carbon cycle has been shifted such that carbon sequestration is no longer keeping up with recent anthropogenic emissions. In order to address this imbalance, it is important to understand the roles of potential biological carbon sequestration systems and to devise strategies to facilitate net CO₂ uptake; for example, via changes in the patterns of land use, such as afforestation, preventing deforestation, and facilitating agriculture–agroforestry transitions. Full article

This research focuses on the urban expansion occurring in the Kamrup Metropolitan District—an area experiencing significant urbanization—with the aim of understanding its patterns and projecting future growth. The research covers the period from 2000 to 2022 and projects growth up to 2052, providing insights for sustainable urban planning. The study utilizes the maximum likelihood method for land use/land cover (LULC) delineation and the Shannon entropy technique for assessing urban sprawl. Additionally, it integrates the cellular automata (CA)-Markov model and the analytical hierarchy process (AHP) for future projections. The results indicate a considerable shift from non-built-up to built-up areas, with the proportion of built-up areas expected to reach 36.2% by 2032 and 40.54% by 2052. These findings emphasize the importance of strategic urban management and sustainable planning. The study recommends adaptive urban planning strategies and highlights the value of integrating the CA Markov model and AHP for policymakers and urban planners. This can contribute to the discourse on sustainable urban development and informed decision-making. Full article

The interplay between climate and land use/cover significantly shapes streamflow characteristics within watersheds, with dominance varying based on geography and watershed attributes. This study quantifies the relative and combined impacts of land use/cover change (LULCC) and climate change (CC) on streamflow variability in the Baro River Basin (BRB) using the Soil and Water Assessment Tool Plus (SWAT+). The model was calibrated and validated with observed streamflow data from 1985 to 2014 and projected the future streamflow from 2041 to 2070 under two Shared Socio-Economic Pathway (i.e., SSP2-4.5 and SSP5-8.5) scenarios, based on the ensemble of four Coupled Model Intercomparison Project (CMIP6) models. The LULCC was analyzed through Google Earth Engine (GEE) and predicted for the future using the Land Change Modeler (LCM), revealing reductions in forest and wetlands, and increases in agriculture, grassland, and shrubland. Simulations show that the decrease in streamflow is attributed to LULCC, whereas an increase in flow is attributed to the impact of CC. The combined impact of LULCC and CC results in a net increase in streamflow by 9.6% and 19.9% under SSP2-4.5 and SSP5-8.5 scenarios, respectively, compared to the baseline period. Our findings indicate that climate change outweighs the impact of land use/cover (LULC) in the basin, emphasizing the importance of incorporating comprehensive water resources management and adaptation approaches to address the changing hydrological conditions. Full article

Water footprints have been widely used to illustrate the consumption of water in many situations, for instance, in products, processes, or regions of interest. In this work, we analyzed—using a sensitivity analysis approach—the effect of some variables in the calculation of the water footprint in the viticulture in the Brazilian Serra Gaúcha (the major producing region of Brazilian wine). The classical Penman–Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering a fraction of the vineyard, maximum temperatures for some months, the altitudes and latitudes of the site). A sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to evapotranspiration is more sensitive to the values of the mulch-covered fraction and the altitude of the site in comparison with the latitude and the maximum temperatures. Full article