Journal Description
Earth
Earth
is an international, peer-reviewed, open access journal on earth science, published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within ESCI (Web of Science), Scopus, GeoRef, AGRIS, and other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21.7 days after submission; acceptance to publication is undertaken in 1.8 days (median values for papers published in this journal in the first half of 2024).
- Journal Rank: CiteScore - Q2 (Environmental Science (miscellaneous))
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
Impact Factor:
2.1 (2023);
5-Year Impact Factor:
2.1 (2023)
Latest Articles
Quantum Tensor DBMS and Quantum Gantt Charts: Towards Exponentially Faster Earth Data Engineering
Earth 2024, 5(3), 491-547; https://doi.org/10.3390/earth5030027 (registering DOI) - 14 Sep 2024
Abstract
Earth data is essential for global environmental studies. Many Earth data types are naturally modeled by multidimensional arrays (tensors). Array (Tensor) DBMSs strive to be the best systems for tensor-related workloads and can be especially helpful for Earth data engineering, which takes up
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Earth data is essential for global environmental studies. Many Earth data types are naturally modeled by multidimensional arrays (tensors). Array (Tensor) DBMSs strive to be the best systems for tensor-related workloads and can be especially helpful for Earth data engineering, which takes up to 80% of Earth data science. We present a new quantum Array (Tensor) DBMS data model and new quantum approaches that rely on the upcoming quantum memory and demonstrate exponential speedups when applied to many toughest Array (Tensor) DBMS challenges stipulated by classical computing and real-world Earth data use-cases. We also propose new types of charts: Quantum Gantt (QGantt) Charts and Quantum Network Diagrams (QND). QGantt charts clearly illustrate how multiple operations occur simultaneously across different data items and what are the input/output data dependencies between these operations. Unlike traditional Gantt charts, which typically track project timelines and resources, QGantt charts integrate specific data items and operations over time. A Quantum Network Diagram combines several QGantt charts to show dependencies between multistage operations, including their inputs/outputs. By using a static format, QGantt charts and Quantum Network Diagrams allow users to explore complex processes at their own pace, which can be beneficial for educational and R&D purposes.
Full article
Open AccessFeature PaperArticle
A Spatial Econometric Analysis of Weather Effects on Milk Production
by
Xinxin Fan and Jiechao Ma
Earth 2024, 5(3), 477-490; https://doi.org/10.3390/earth5030026 - 11 Sep 2024
Abstract
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Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century, has been considerably affecting short-term weather conditions, such as increasing weather variability and the incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use
[...] Read more.
Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century, has been considerably affecting short-term weather conditions, such as increasing weather variability and the incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, the spatial error model (SEM) and the spatial Durbin model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches, and then over that threshold, milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance.
Full article
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Open AccessArticle
Analysis of the Status of Irrigation Management in North Carolina
by
Anuoluwapo Omolola Adelabu, Blessing Masasi and Olabisi Tolulope Somefun
Earth 2024, 5(3), 463-476; https://doi.org/10.3390/earth5030025 - 7 Sep 2024
Abstract
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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
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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
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<p>The geographical location of North Carolina in the U.S.</p> Full article ">Figure 2
<p>Irrigated acres in North Carolina.</p> Full article ">Figure 3
<p>Reasons for discontinuing irrigation by certain farms in North Carolina.</p> Full article ">Figure 4
<p>Irrigated farms by size.</p> Full article ">Figure 5
<p>Irrigated acres by irrigation method.</p> Full article ">Figure 6
<p>Number of farms per irrigation scheduling method.</p> Full article ">Figure 7
<p>Irrigated acres by sprinkler methods.</p> Full article ">Figure 8
<p>Sources of irrigation information.</p> Full article ">
<p>The geographical location of North Carolina in the U.S.</p> Full article ">Figure 2
<p>Irrigated acres in North Carolina.</p> Full article ">Figure 3
<p>Reasons for discontinuing irrigation by certain farms in North Carolina.</p> Full article ">Figure 4
<p>Irrigated farms by size.</p> Full article ">Figure 5
<p>Irrigated acres by irrigation method.</p> Full article ">Figure 6
<p>Number of farms per irrigation scheduling method.</p> Full article ">Figure 7
<p>Irrigated acres by sprinkler methods.</p> Full article ">Figure 8
<p>Sources of irrigation information.</p> Full article ">
Open AccessArticle
Support Vector Machine Algorithm for Mapping Land Cover Dynamics in Senegal, West Africa, Using Earth Observation Data
by
Polina Lemenkova
Earth 2024, 5(3), 420-462; https://doi.org/10.3390/earth5030024 - 6 Sep 2024
Abstract
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
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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
(This article belongs to the Topic Environmental Footprints Forecasts Using Remote Sensing, Information Technology and Artificial Intelligence Methods)
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<p>Study area with segments of the Landsat images shown on a topographic map of Senegal. Software: GMT. Map source: Author.</p> Full article ">Figure 2
<p>Data capture of Landsat images from the USGS EarthExplorer repository.</p> Full article ">Figure 3
<p>Landsat images in RGB colors covering the Cape Verde Peninsula region and Saloum River Delta, West Senegal, in February: (<b>a</b>) 2015; (<b>b</b>) 2018; (<b>c</b>) 2020; (<b>d</b>) 2021; (<b>e</b>) 2022; (<b>f</b>) 2023.</p> Full article ">Figure 4
<p>Workflow scheme illustrating the data and the main methodological steps. Software: R version 4.3.3, library DiagrammeR version 1.0.11. Diagram source: Author.</p> Full article ">Figure 5
<p>False color composites of the Landsat 8-9 OLI/TIRS images with vegetation colored red, using a combination of spectral bands 5 (Near Infrared (NIR)), 4 (Red), and 3 (Green) of the Landsat OLI sensor covering the study area in the Cape Verde Peninsula region and Saloum River Delta, West Senegal, using February scenes: (<b>a</b>) 2015; (<b>b</b>) 2018; (<b>c</b>) 2020; (<b>d</b>) 2021; (<b>e</b>) 2022; (<b>f</b>) 2023.</p> Full article ">Figure 6
<p>Land cover types in Senegal according to the FAO classification scheme. Software: QGIS v. 3.22. Map source: Author.</p> Full article ">Figure 7
<p>Classification of the Landsat images from 2020 covering the Cape Verde Peninsula region and the Saloum River Delta, West Senegal: (<b>a</b>) 2015; (<b>b</b>) 2018; (<b>c</b>) 2020; (<b>d</b>) 2021; (<b>e</b>) 2022; (<b>f</b>) 2023.</p> Full article ">Figure 8
<p>Results of the Support Vector Machine (SVM)-based classification of the Landsat images covering the Cape Verde Peninsula region and Saloum River Delta, West Senegal: (<b>a</b>) February 2015; (<b>b</b>) February 2018; (<b>c</b>) February 2020; (<b>d</b>) February 2021; (<b>e</b>) February 2022; (<b>f</b>) February 2023.</p> Full article ">Figure 9
<p>Accuracy evaluated based on the pixel confidence levels with rejection probability values for the Landsat images covering the Cape Verde Peninsula region and Saloum River Delta, West Senegal: (<b>a</b>) 2015; (<b>b</b>) 2018; (<b>c</b>) 2020; (<b>d</b>) 2021; (<b>e</b>) 2022;(<b>f</b>) 2023.</p> Full article ">
<p>Study area with segments of the Landsat images shown on a topographic map of Senegal. Software: GMT. Map source: Author.</p> Full article ">Figure 2
<p>Data capture of Landsat images from the USGS EarthExplorer repository.</p> Full article ">Figure 3
<p>Landsat images in RGB colors covering the Cape Verde Peninsula region and Saloum River Delta, West Senegal, in February: (<b>a</b>) 2015; (<b>b</b>) 2018; (<b>c</b>) 2020; (<b>d</b>) 2021; (<b>e</b>) 2022; (<b>f</b>) 2023.</p> Full article ">Figure 4
<p>Workflow scheme illustrating the data and the main methodological steps. Software: R version 4.3.3, library DiagrammeR version 1.0.11. Diagram source: Author.</p> Full article ">Figure 5
<p>False color composites of the Landsat 8-9 OLI/TIRS images with vegetation colored red, using a combination of spectral bands 5 (Near Infrared (NIR)), 4 (Red), and 3 (Green) of the Landsat OLI sensor covering the study area in the Cape Verde Peninsula region and Saloum River Delta, West Senegal, using February scenes: (<b>a</b>) 2015; (<b>b</b>) 2018; (<b>c</b>) 2020; (<b>d</b>) 2021; (<b>e</b>) 2022; (<b>f</b>) 2023.</p> Full article ">Figure 6
<p>Land cover types in Senegal according to the FAO classification scheme. Software: QGIS v. 3.22. Map source: Author.</p> Full article ">Figure 7
<p>Classification of the Landsat images from 2020 covering the Cape Verde Peninsula region and the Saloum River Delta, West Senegal: (<b>a</b>) 2015; (<b>b</b>) 2018; (<b>c</b>) 2020; (<b>d</b>) 2021; (<b>e</b>) 2022; (<b>f</b>) 2023.</p> Full article ">Figure 8
<p>Results of the Support Vector Machine (SVM)-based classification of the Landsat images covering the Cape Verde Peninsula region and Saloum River Delta, West Senegal: (<b>a</b>) February 2015; (<b>b</b>) February 2018; (<b>c</b>) February 2020; (<b>d</b>) February 2021; (<b>e</b>) February 2022; (<b>f</b>) February 2023.</p> Full article ">Figure 9
<p>Accuracy evaluated based on the pixel confidence levels with rejection probability values for the Landsat images covering the Cape Verde Peninsula region and Saloum River Delta, West Senegal: (<b>a</b>) 2015; (<b>b</b>) 2018; (<b>c</b>) 2020; (<b>d</b>) 2021; (<b>e</b>) 2022;(<b>f</b>) 2023.</p> Full article ">
Open AccessArticle
Index-Based Alteration of Long-Term River Flow Regimes Influenced by Land Use Change and Dam Regulation
by
Raoof Mostafazadeh, Mostafa Zabihi Silabi, Javanshir Azizi Mobaser and Bita Moezzipour
Earth 2024, 5(3), 404-419; https://doi.org/10.3390/earth5030023 - 31 Aug 2024
Abstract
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
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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
(This article belongs to the Topic Climate Change and Human Impact on Freshwater Water Resources: Rivers and Lakes)
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<p>The location of the Urmia study area, Band hydrometery station, and Shahrchai Dam in Iran. Red area: Political boundary of Urmia city.</p> Full article ">Figure 2
<p>Monthly mean flow before and after the construction of the Shahrchai Dam.</p> Full article ">Figure 3
<p>Land use/land cover map for the years 1987 (<b>top</b>) and 2017 (<b>bottom</b>) for the Shahrchai–Urmia watershed.</p> Full article ">Figure 3 Cont.
<p>Land use/land cover map for the years 1987 (<b>top</b>) and 2017 (<b>bottom</b>) for the Shahrchai–Urmia watershed.</p> Full article ">Figure 4
<p>The degree of variation in 33 IHA parameters in 3 RVA target classes for the Urmia Band hydrometric station.</p> Full article ">Figure 5
<p>The flow duration curves of the river flow pre- and post-dam construction for the Urmia Band hydrometric station.</p> Full article ">
<p>The location of the Urmia study area, Band hydrometery station, and Shahrchai Dam in Iran. Red area: Political boundary of Urmia city.</p> Full article ">Figure 2
<p>Monthly mean flow before and after the construction of the Shahrchai Dam.</p> Full article ">Figure 3
<p>Land use/land cover map for the years 1987 (<b>top</b>) and 2017 (<b>bottom</b>) for the Shahrchai–Urmia watershed.</p> Full article ">Figure 3 Cont.
<p>Land use/land cover map for the years 1987 (<b>top</b>) and 2017 (<b>bottom</b>) for the Shahrchai–Urmia watershed.</p> Full article ">Figure 4
<p>The degree of variation in 33 IHA parameters in 3 RVA target classes for the Urmia Band hydrometric station.</p> Full article ">Figure 5
<p>The flow duration curves of the river flow pre- and post-dam construction for the Urmia Band hydrometric station.</p> Full article ">
Open AccessArticle
Using the Contrast Boundary Concentration of LST for the Earthquake Approach Assessment in Turkey, 6–8 February 2023
by
Serhii Nikulin, Kateryna Sergieieva, Olga Korobko and Vita Kashtan
Earth 2024, 5(3), 388-403; https://doi.org/10.3390/earth5030022 - 18 Aug 2024
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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
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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
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<p>Study area in the East Anatolian Fault Zone: (<b>a</b>) major tectonic faults in the region. Basemap: Here Wego Terrain (EPSG:4326–WGS 84); (<b>b</b>) Landsat 8 Natural Color image of the study area acquired on 21 January 2023 and epicenters of the earthquakes in different time periods.</p> Full article ">Figure 2
<p>Landsat 8–9 image acquisition dates.</p> Full article ">Figure 3
<p>Constructing and analyzing LST contrast boundary concentration maps.</p> Full article ">Figure 4
<p>LST maps from Landsat 8 data before and after the earthquake 6–8 February 2023: (<b>a</b>) 5 January 2023; (<b>b</b>) 21 January 2023; and (<b>c</b>) 22 February 2023.</p> Full article ">Figure 5
<p>Example of contrast boundaries for Landsat 8 Thermal Infrared band 1 from 5 January 2023: (<b>a</b>) thermal infrared band 1; (<b>b</b>) contrast boundaries.</p> Full article ">Figure 6
<p>Cumulative length of oriented contrast boundaries (km).</p> Full article ">Figure 7
<p>LST contrast boundary concentration maps at 0° and 90° azimuths before the 6–8 February 2023 earthquake: (<b>a</b>) 0°, 2 November 2022; (<b>b</b>) 0°, 5 January 2023; (<b>c</b>) 0°, 21 January 2023; (<b>d</b>) 90°, 2 November 2022; (<b>e</b>) 90°, 5 January 2023; (<b>f</b>) 90°, 21 January 2023; (<b>g</b>) 0° and 90°, 2 November 2022; (<b>h</b>) 0° and 90°, 5 January 2023; and (<b>i</b>) 0° and 90°, 21 January 2023.</p> Full article ">Figure 8
<p>Concentration of contrast boundaries for 2.5 × 2.5 km areas centered on earthquake epicenters (orange line), at distances greater than 5 km from earthquake epicenters (green line), and at random sites uniformly distributed over the study area (blue line), and the number of earthquake epicenters of magnitude greater than 4 Mw that occurred within a month after the corresponding Landsat 8–9 image acquisition date (red bar chart).</p> Full article ">Figure 9
<p>Average concentration of LST contrast boundaries at 0° and 90° azimuth (m).</p> Full article ">Figure 10
<p>SMI maps before the 6–8 February 2023 earthquake: (<b>a</b>) 5 January 2023; (<b>b</b>) 21 January 2023.</p> Full article ">Figure 11
<p>Fragment of concentration maps of contrast LST boundary at 0° and 90° azimuths: (<b>a</b>) 21 January 2023; (<b>b</b>) 21 February 2023.</p> Full article ">Figure 12
<p>LST values for images from (<b>a</b>) 21 January 2023 and (<b>b</b>) 24 January 2024; contrast boundaries concentrations at 0° and 90° azimuth for images from (<b>c</b>) 21 January 2023 and (<b>d</b>) 24 January 2024.</p> Full article ">
<p>Study area in the East Anatolian Fault Zone: (<b>a</b>) major tectonic faults in the region. Basemap: Here Wego Terrain (EPSG:4326–WGS 84); (<b>b</b>) Landsat 8 Natural Color image of the study area acquired on 21 January 2023 and epicenters of the earthquakes in different time periods.</p> Full article ">Figure 2
<p>Landsat 8–9 image acquisition dates.</p> Full article ">Figure 3
<p>Constructing and analyzing LST contrast boundary concentration maps.</p> Full article ">Figure 4
<p>LST maps from Landsat 8 data before and after the earthquake 6–8 February 2023: (<b>a</b>) 5 January 2023; (<b>b</b>) 21 January 2023; and (<b>c</b>) 22 February 2023.</p> Full article ">Figure 5
<p>Example of contrast boundaries for Landsat 8 Thermal Infrared band 1 from 5 January 2023: (<b>a</b>) thermal infrared band 1; (<b>b</b>) contrast boundaries.</p> Full article ">Figure 6
<p>Cumulative length of oriented contrast boundaries (km).</p> Full article ">Figure 7
<p>LST contrast boundary concentration maps at 0° and 90° azimuths before the 6–8 February 2023 earthquake: (<b>a</b>) 0°, 2 November 2022; (<b>b</b>) 0°, 5 January 2023; (<b>c</b>) 0°, 21 January 2023; (<b>d</b>) 90°, 2 November 2022; (<b>e</b>) 90°, 5 January 2023; (<b>f</b>) 90°, 21 January 2023; (<b>g</b>) 0° and 90°, 2 November 2022; (<b>h</b>) 0° and 90°, 5 January 2023; and (<b>i</b>) 0° and 90°, 21 January 2023.</p> Full article ">Figure 8
<p>Concentration of contrast boundaries for 2.5 × 2.5 km areas centered on earthquake epicenters (orange line), at distances greater than 5 km from earthquake epicenters (green line), and at random sites uniformly distributed over the study area (blue line), and the number of earthquake epicenters of magnitude greater than 4 Mw that occurred within a month after the corresponding Landsat 8–9 image acquisition date (red bar chart).</p> Full article ">Figure 9
<p>Average concentration of LST contrast boundaries at 0° and 90° azimuth (m).</p> Full article ">Figure 10
<p>SMI maps before the 6–8 February 2023 earthquake: (<b>a</b>) 5 January 2023; (<b>b</b>) 21 January 2023.</p> Full article ">Figure 11
<p>Fragment of concentration maps of contrast LST boundary at 0° and 90° azimuths: (<b>a</b>) 21 January 2023; (<b>b</b>) 21 February 2023.</p> Full article ">Figure 12
<p>LST values for images from (<b>a</b>) 21 January 2023 and (<b>b</b>) 24 January 2024; contrast boundaries concentrations at 0° and 90° azimuth for images from (<b>c</b>) 21 January 2023 and (<b>d</b>) 24 January 2024.</p> Full article ">
Open AccessArticle
Using Public Participation GIS to Assess Effects of Industrial Zones on Risk and Landscape Perception: A Case Study of Tehran Oil Refinery, Iran
by
Mahdi Gheitasi, David Serrano Giné, Nora Fagerholm and Yolanda Pérez Albert
Earth 2024, 5(3), 371-387; https://doi.org/10.3390/earth5030021 - 16 Aug 2024
Abstract
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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
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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
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<p>Case study location and surroundings.</p> Full article ">Figure 2
<p>A screenshot of the online survey (translation of the texts in picture: please indicate which locations you think are the source of the technological risk caused by the oil refinery in the industrial area).</p> Full article ">Figure 3
<p>Pictures captured at selected points.</p> Full article ">Figure 4
<p>Level of technological risk perception among Tehran-based participants.</p> Full article ">
<p>Case study location and surroundings.</p> Full article ">Figure 2
<p>A screenshot of the online survey (translation of the texts in picture: please indicate which locations you think are the source of the technological risk caused by the oil refinery in the industrial area).</p> Full article ">Figure 3
<p>Pictures captured at selected points.</p> Full article ">Figure 4
<p>Level of technological risk perception among Tehran-based participants.</p> Full article ">
Open AccessArticle
Blockchain Projects in Environmental Sector: Theoretical and Practical Analysis
by
Matteo Vaccargiu and Roberto Tonelli
Earth 2024, 5(3), 354-370; https://doi.org/10.3390/earth5030020 - 14 Aug 2024
Abstract
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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,
[...] Read more.
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
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<p>Number of articles published by year.</p> Full article ">Figure 2
<p>Number of issues opened by year.</p> Full article ">Figure 3
<p>Number of repositories created by year.</p> Full article ">Figure 4
<p>VOSviewer map analysis plots using the full counting method.</p> Full article ">Figure 5
<p>VOSviewer map analysis plots using the binary counting method.</p> Full article ">Figure 6
<p>A barplot of the topics obtained by the two topic extraction processes.</p> Full article ">Figure 7
<p>A plot of the evolution over time of the top 5 topics obtained by the two topic extraction processes.</p> Full article ">Figure 8
<p>Top 5 most cited topics obtained by the two topic extraction processes.</p> Full article ">
<p>Number of articles published by year.</p> Full article ">Figure 2
<p>Number of issues opened by year.</p> Full article ">Figure 3
<p>Number of repositories created by year.</p> Full article ">Figure 4
<p>VOSviewer map analysis plots using the full counting method.</p> Full article ">Figure 5
<p>VOSviewer map analysis plots using the binary counting method.</p> Full article ">Figure 6
<p>A barplot of the topics obtained by the two topic extraction processes.</p> Full article ">Figure 7
<p>A plot of the evolution over time of the top 5 topics obtained by the two topic extraction processes.</p> Full article ">Figure 8
<p>Top 5 most cited topics obtained by the two topic extraction processes.</p> Full article ">
Open AccessArticle
The Impact of Land Cover on Nest Occupancy of the White Stork (Ciconia ciconia (L.)): A Case Study of Kampinos Forest, 2006–2018
by
Joanna Bihałowicz, Axel Schwerk, Izabela Dymitryszyn, Adam Olszewski and Jan Stefan Bihałowicz
Earth 2024, 5(3), 336-353; https://doi.org/10.3390/earth5030019 - 1 Aug 2024
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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
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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
Figure 1
Figure 1
<p>Young storks in a nest; photo: J. Bihałowicz.</p> Full article ">Figure 2
<p>Location of the study area in Poland.</p> Full article ">Figure 3
<p>Summary of data on nest occupancy in study area in Kampinos National Park. Panel (<b>a</b>) presents histogram of average nest occupancy in years 2006–2011; (<b>b</b>) for years 2012–2017; (<b>c</b>) [resents average occupation, year by year.</p> Full article ">Figure 4
<p>Land cover in the study area according to CLC 2018.</p> Full article ">Figure 5
<p>Share of individual land cover categories according to CORINE Land Cover in 2006 in the study area depending on the distance from white stork nests for 122 nests. The legend provides CORINE Land Cover codes: 112—discontinuous urban fabric, 121—industrial or commercial units, 122—road and rail networks and associated land, 123—port areas, 124—airports, 131—mineral extraction sites, 132—dump sites, 133—construction sites, 141—green urban areas, 142—sport and leisure facilities, 211—non-irrigated arable land, 222—fruit trees and berry plantations, 231—pastures, 242—complex cultivation patterns, 243—land principally occupied by agriculture, with significant areas of natural vegetation, 311—broad-leaved forest, 312—coniferous forest, 313—mixed forest, 324—transitional woodland/shrub, 411—inland marshes, 511—water courses, 512—water bodies.</p> Full article ">Figure 6
<p>Share of individual land cover categories according to CORINE Land Cover in 2012 in the study area depending on the distance from white stork nests for 122 nests. The legend provides CORINE Land Cover codes: 112—discontinuous urban fabric, 121—industrial or commercial units, 122—road and rail networks and associated land, 123—port areas, 124—airports, 131—mineral extraction sites, 132—dump sites, 133—construction sites, 141—green urban areas, 142—sport and leisure facilities, 211—non-irrigated arable land, 222—fruit trees and berry plantations, 231—pastures, 242—complex cultivation patterns, 243—land principally occupied by agriculture, with significant areas of natural vegetation, 311—broad-leaved forest, 312—coniferous forest, 313—mixed forest, 324—transitional woodland/shrub, 411—inland marshes, 511—water courses, 512—water bodies.</p> Full article ">Figure 7
<p>Share of individual land cover categories according to CORINE Land Cover in 2018 in the study area depending on the distance from white stork nests for 122 nests. The legend provides CORINE Land Cover codes: 112—discontinuous urban fabric, 121—industrial or commercial units, 122—road and rail networks and associated land, 123—port areas, 124—airports, 131—mineral extraction sites, 132—dump sites, 133—construction sites, 141—green urban areas, 142—sport and leisure facilities, 211—non-irrigated arable land, 222—fruit trees and berry plantations, 231—pastures, 242—complex cultivation patterns, 243—land principally occupied by agriculture, with significant areas of natural vegetation, 311—broad-leaved forest, 312—coniferous forest, 313—mixed forest, 324—transitional woodland/shrub, 411—inland marshes, 511—water courses, 512—water bodies.</p> Full article ">
<p>Young storks in a nest; photo: J. Bihałowicz.</p> Full article ">Figure 2
<p>Location of the study area in Poland.</p> Full article ">Figure 3
<p>Summary of data on nest occupancy in study area in Kampinos National Park. Panel (<b>a</b>) presents histogram of average nest occupancy in years 2006–2011; (<b>b</b>) for years 2012–2017; (<b>c</b>) [resents average occupation, year by year.</p> Full article ">Figure 4
<p>Land cover in the study area according to CLC 2018.</p> Full article ">Figure 5
<p>Share of individual land cover categories according to CORINE Land Cover in 2006 in the study area depending on the distance from white stork nests for 122 nests. The legend provides CORINE Land Cover codes: 112—discontinuous urban fabric, 121—industrial or commercial units, 122—road and rail networks and associated land, 123—port areas, 124—airports, 131—mineral extraction sites, 132—dump sites, 133—construction sites, 141—green urban areas, 142—sport and leisure facilities, 211—non-irrigated arable land, 222—fruit trees and berry plantations, 231—pastures, 242—complex cultivation patterns, 243—land principally occupied by agriculture, with significant areas of natural vegetation, 311—broad-leaved forest, 312—coniferous forest, 313—mixed forest, 324—transitional woodland/shrub, 411—inland marshes, 511—water courses, 512—water bodies.</p> Full article ">Figure 6
<p>Share of individual land cover categories according to CORINE Land Cover in 2012 in the study area depending on the distance from white stork nests for 122 nests. The legend provides CORINE Land Cover codes: 112—discontinuous urban fabric, 121—industrial or commercial units, 122—road and rail networks and associated land, 123—port areas, 124—airports, 131—mineral extraction sites, 132—dump sites, 133—construction sites, 141—green urban areas, 142—sport and leisure facilities, 211—non-irrigated arable land, 222—fruit trees and berry plantations, 231—pastures, 242—complex cultivation patterns, 243—land principally occupied by agriculture, with significant areas of natural vegetation, 311—broad-leaved forest, 312—coniferous forest, 313—mixed forest, 324—transitional woodland/shrub, 411—inland marshes, 511—water courses, 512—water bodies.</p> Full article ">Figure 7
<p>Share of individual land cover categories according to CORINE Land Cover in 2018 in the study area depending on the distance from white stork nests for 122 nests. The legend provides CORINE Land Cover codes: 112—discontinuous urban fabric, 121—industrial or commercial units, 122—road and rail networks and associated land, 123—port areas, 124—airports, 131—mineral extraction sites, 132—dump sites, 133—construction sites, 141—green urban areas, 142—sport and leisure facilities, 211—non-irrigated arable land, 222—fruit trees and berry plantations, 231—pastures, 242—complex cultivation patterns, 243—land principally occupied by agriculture, with significant areas of natural vegetation, 311—broad-leaved forest, 312—coniferous forest, 313—mixed forest, 324—transitional woodland/shrub, 411—inland marshes, 511—water courses, 512—water bodies.</p> Full article ">
Open AccessEditorial
Progress in the Earth Journal
by
Charles Jones
Earth 2024, 5(3), 332-335; https://doi.org/10.3390/earth5030018 - 1 Aug 2024
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The Earth journal (ISSN 2673-4834) is an open-access international high-quality peer review venue that promotes multi-disciplinary research over a broad spectrum of natural, social and applied sciences [...]
Full article
Figure 1
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<p>CiteScore comparison between 2022 and 2023.</p> Full article ">Figure 2
<p>Total submission, publication and rejection rate from 2020 to 2023.</p> Full article ">Figure 3
<p>(<b>a</b>) Total number of papers viewed during 2020–2023. (<b>b</b>) Monthly number of papers viewed during 2023.</p> Full article ">Figure 4
<p>Publication distribution by country during 2023.</p> Full article ">Figure 5
<p>Distribution of days taken for papers to be published once they are accepted. Blue line with dots represent linear trend.</p> Full article ">
<p>CiteScore comparison between 2022 and 2023.</p> Full article ">Figure 2
<p>Total submission, publication and rejection rate from 2020 to 2023.</p> Full article ">Figure 3
<p>(<b>a</b>) Total number of papers viewed during 2020–2023. (<b>b</b>) Monthly number of papers viewed during 2023.</p> Full article ">Figure 4
<p>Publication distribution by country during 2023.</p> Full article ">Figure 5
<p>Distribution of days taken for papers to be published once they are accepted. Blue line with dots represent linear trend.</p> Full article ">
Open AccessArticle
Investigating Seismic Events along the Eurasian Plate between Greece and Turkey: 10 Years of Seismological Analysis and Implications
by
Alexandra Moshou
Earth 2024, 5(3), 311-331; https://doi.org/10.3390/earth5030017 - 26 Jul 2024
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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
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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
Figure 1
Figure 1
<p>General structure map of the North Aegean Sea. Red circles represent prefectures. The green stars indicate strong historical earthquakes. Purple and yellow triangles indicate the permanent stations of the Unified Seismological Network (HUSN), from the Seismological Laboratory of Thessaloniki (HT) and National Observatory of Athens (HL), and finally, red lines indicate the main active faults for the study area, available at <a href="https://land.copernicus.eu/imagery-in-situ/eu-dem" target="_blank">https://land.copernicus.eu/imagery-in-situ/eu-dem</a> [<a href="#B13-earth-05-00017" class="html-bibr">13</a>].</p> Full article ">Figure 2
<p>General structure map of the broader area of the North Aegean Sea; purple squares represent prefectures, while red lines are the main active faults in the study area. Bathymetry was obtained using Emodnet Bathymetry [<a href="#B33-earth-05-00017" class="html-bibr">33</a>] and DEM. Light blue triangles represent the permanent stations of the Unified Seismological Network (HUSN) [<a href="#B11-earth-05-00017" class="html-bibr">11</a>], orange stations represent the corresponding seismological stations from the Seismological Laboratory of the Aristotle University of Thessaloniki [<a href="#B10-earth-05-00017" class="html-bibr">10</a>], and the two red stars indicate strong earthquakes (12 June 2017, ML 6.1, and 30 October 2020, ML 6.7) that occurred in the region of Lesvos Island and Samos Island. The distribution of epicenters for the period 2014–2019 is indicated via the size and color of the points according to magnitude and depth.</p> Full article ">Figure 3
<p>Moment tensor solution of the 30 October 2020 (11:51 UTC) earthquake. The green arrow in the misfit/compensated linear vector dipole (CLVD)-versus-depth graph (center-up) indicates the selected solution. The center-lower portion displays a summary of the answers together with the accompanying beach ball. At the inverted stations for the radial, tangential, and vertical components, the observed and synthetic displacement waveforms are shown on the left as continuous and dotted lines, respectively. A summary of the solution and the fault plane solution as a lower-hemisphere equal-area projection is shown at the left-center-right and upper-middle-lower portions, respectively.</p> Full article ">Figure 4
<p>Moment tensor solution of the 12 June 2017 (15:28 UTC) earthquake. The center-lower portion displays a summary of the answers together with the accompanying beach ball. At the inverted stations for the radial, tangential, and vertical components, the observed and synthetic displacement waveforms are shown on the left as continuous and dotted lines, respectively. The summary of the solution and the fault plane solution as a lower-hemisphere equal-area projection are shown at the left-center-right and upper-middle-lower portions, respectively.</p> Full article ">Figure 5
<p>Visualization of the seismicity rate of the North Aegean region for 2013–2023. The number of seismological recordings of 8.138 manually located events. The green line indicates the normal distribution that follows the number of earthquakes relative to the magnitude.</p> Full article ">Figure 6
<p>For the 8.138 seismological records, the location with time is represented as a function of magnitude. Each occurrence from the events that occurred in the broader area of the North Aegean Sea is represented by its date (years) on the x-axis and its magnitude (ML) on the y-axis.</p> Full article ">
<p>General structure map of the North Aegean Sea. Red circles represent prefectures. The green stars indicate strong historical earthquakes. Purple and yellow triangles indicate the permanent stations of the Unified Seismological Network (HUSN), from the Seismological Laboratory of Thessaloniki (HT) and National Observatory of Athens (HL), and finally, red lines indicate the main active faults for the study area, available at <a href="https://land.copernicus.eu/imagery-in-situ/eu-dem" target="_blank">https://land.copernicus.eu/imagery-in-situ/eu-dem</a> [<a href="#B13-earth-05-00017" class="html-bibr">13</a>].</p> Full article ">Figure 2
<p>General structure map of the broader area of the North Aegean Sea; purple squares represent prefectures, while red lines are the main active faults in the study area. Bathymetry was obtained using Emodnet Bathymetry [<a href="#B33-earth-05-00017" class="html-bibr">33</a>] and DEM. Light blue triangles represent the permanent stations of the Unified Seismological Network (HUSN) [<a href="#B11-earth-05-00017" class="html-bibr">11</a>], orange stations represent the corresponding seismological stations from the Seismological Laboratory of the Aristotle University of Thessaloniki [<a href="#B10-earth-05-00017" class="html-bibr">10</a>], and the two red stars indicate strong earthquakes (12 June 2017, ML 6.1, and 30 October 2020, ML 6.7) that occurred in the region of Lesvos Island and Samos Island. The distribution of epicenters for the period 2014–2019 is indicated via the size and color of the points according to magnitude and depth.</p> Full article ">Figure 3
<p>Moment tensor solution of the 30 October 2020 (11:51 UTC) earthquake. The green arrow in the misfit/compensated linear vector dipole (CLVD)-versus-depth graph (center-up) indicates the selected solution. The center-lower portion displays a summary of the answers together with the accompanying beach ball. At the inverted stations for the radial, tangential, and vertical components, the observed and synthetic displacement waveforms are shown on the left as continuous and dotted lines, respectively. A summary of the solution and the fault plane solution as a lower-hemisphere equal-area projection is shown at the left-center-right and upper-middle-lower portions, respectively.</p> Full article ">Figure 4
<p>Moment tensor solution of the 12 June 2017 (15:28 UTC) earthquake. The center-lower portion displays a summary of the answers together with the accompanying beach ball. At the inverted stations for the radial, tangential, and vertical components, the observed and synthetic displacement waveforms are shown on the left as continuous and dotted lines, respectively. The summary of the solution and the fault plane solution as a lower-hemisphere equal-area projection are shown at the left-center-right and upper-middle-lower portions, respectively.</p> Full article ">Figure 5
<p>Visualization of the seismicity rate of the North Aegean region for 2013–2023. The number of seismological recordings of 8.138 manually located events. The green line indicates the normal distribution that follows the number of earthquakes relative to the magnitude.</p> Full article ">Figure 6
<p>For the 8.138 seismological records, the location with time is represented as a function of magnitude. Each occurrence from the events that occurred in the broader area of the North Aegean Sea is represented by its date (years) on the x-axis and its magnitude (ML) on the y-axis.</p> Full article ">
Open AccessArticle
DNA Takes Over on the Control of the Morphology of the Composite Self-Organized Structures of Barium and Calcium Silica–Carbonate Biomorphs, Implications for Prebiotic Chemistry on Earth
by
Mayra Cuéllar-Cruz, Selene R. Islas and Abel Moreno
Earth 2024, 5(3), 293-310; https://doi.org/10.3390/earth5030016 - 24 Jul 2024
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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
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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
Figure 1
Figure 1
<p>SEM microphotographs of the calcium silica–carbonate biomorphs synthesized in two atmospheric conditions. (<b>A</b>,<b>B</b>) Control, without DNA or kaolinite. (<b>C</b>,<b>D</b>) DNA from <span class="html-italic">E. coli</span>. (<b>E</b>,<b>F</b>) DNA from <span class="html-italic">C. albicans</span>. (<b>G</b>,<b>H</b>) DNA from <span class="html-italic">E. coli</span> with kaolinite. (<b>I</b>,<b>J</b>) DNA from <span class="html-italic">C. albicans</span> with kaolinite. STP: Current temperature and CO<sub>2</sub> atmospheric conditions. Precambrian: 5% CO<sub>2</sub>, 50 °C.</p> Full article ">Figure 2
<p>Identification by Raman spectroscopy of the polymorphs obtained in the calcium silica–carbonate biomorphs synthesized in STP and Precambrian conditions. (<b>a</b>,<b>b</b>) Control. (<b>c</b>,<b>d</b>) DNA from <span class="html-italic">E. coli</span>. (<b>e</b>,<b>f</b>) DNA from <span class="html-italic">E. coli</span> and kaolinite. (<b>i</b>) SEM micrograph; (<b>ii</b>) Raman spectra; (<b>iii</b>) Raman map.</p> Full article ">Figure 3
<p>Characterization by Raman spectroscopy of the polymorphs synthesized in the calcium silica–carbonate biomorphs, in STP and Precambrian conditions. (<b>a</b>,<b>b</b>) Control. (<b>c</b>,<b>d</b>) DNA from <span class="html-italic">C. albicans</span>. (<b>e</b>,<b>f</b>) DNA from <span class="html-italic">C. albicans</span>, kaolinite.</p> Full article ">Figure 4
<p>SEM microphotographs of the barium silica–carbonate biomorphs synthesized in two atmospheric conditions. (<b>A</b>,<b>B</b>) Control, without DNA without kaolinite. (<b>C</b>,<b>D</b>) DNA from <span class="html-italic">E. coli</span>. (<b>E</b>,<b>F</b>) DNA from <span class="html-italic">C. albicans</span>. (<b>G</b>,<b>H</b>) DNA from <span class="html-italic">E. coli</span> with kaolinite. (<b>I</b>,<b>J</b>) DNA from <span class="html-italic">C. albicans</span> with kaolinite. STP: Current atmospheric conditions of temperature and CO<sub>2</sub>. Precambrian: 5% CO<sub>2</sub>, 50 °C.</p> Full article ">Figure 5
<p>Identification of the crystalline phase of the barium silica–carbonate biomorphs by Raman and FTIR spectroscopy. (<b>a</b>) Control biomorphs, STP. (<b>b</b>) Control biomorphs, Precambrian. (<b>c</b>) <span class="html-italic">E. coli</span>, genomic DNA, STP. (<b>d</b>) <span class="html-italic">E. coli</span>, genomic DNA, Precambrian. (<b>e</b>) <span class="html-italic">E. coli</span> genomic DNA and kaolinite, STP. (<b>f</b>) <span class="html-italic">E. coli</span> genomic DNA and kaolinite, Precambrian. (<b>i</b>) Raman spectra. (<b>ii</b>) SEM micrograph. (<b>iii</b>) Confocal image. (<b>iv</b>) Raman map. (<b>v</b>) FTIR spectra.</p> Full article ">Figure 6
<p>Identification of the crystalline phase of the barium silica–carbonate biomorphs by Raman spectroscopy and by FTIR (<b>a</b>) Control biomorphs, STP. (<b>b</b>) Control biomorphs, Precambrian. (<b>c</b>) <span class="html-italic">C. albicans</span> genomic DNA, STP. (<b>d</b>) <span class="html-italic">C. albicans</span> genomic DNA, Precambrian. (<b>e</b>) <span class="html-italic">C. albicans</span> genomic DNA and kaolinite, STP. (<b>f</b>) <span class="html-italic">C. albicans</span> genomic DNA and kaolinite, Precambrian. (<b>i</b>) Raman spectra. (<b>ii</b>) SEM micrograph. (<b>iii</b>) Raman map. (<b>iv</b>) FTIR spectra.</p> Full article ">
<p>SEM microphotographs of the calcium silica–carbonate biomorphs synthesized in two atmospheric conditions. (<b>A</b>,<b>B</b>) Control, without DNA or kaolinite. (<b>C</b>,<b>D</b>) DNA from <span class="html-italic">E. coli</span>. (<b>E</b>,<b>F</b>) DNA from <span class="html-italic">C. albicans</span>. (<b>G</b>,<b>H</b>) DNA from <span class="html-italic">E. coli</span> with kaolinite. (<b>I</b>,<b>J</b>) DNA from <span class="html-italic">C. albicans</span> with kaolinite. STP: Current temperature and CO<sub>2</sub> atmospheric conditions. Precambrian: 5% CO<sub>2</sub>, 50 °C.</p> Full article ">Figure 2
<p>Identification by Raman spectroscopy of the polymorphs obtained in the calcium silica–carbonate biomorphs synthesized in STP and Precambrian conditions. (<b>a</b>,<b>b</b>) Control. (<b>c</b>,<b>d</b>) DNA from <span class="html-italic">E. coli</span>. (<b>e</b>,<b>f</b>) DNA from <span class="html-italic">E. coli</span> and kaolinite. (<b>i</b>) SEM micrograph; (<b>ii</b>) Raman spectra; (<b>iii</b>) Raman map.</p> Full article ">Figure 3
<p>Characterization by Raman spectroscopy of the polymorphs synthesized in the calcium silica–carbonate biomorphs, in STP and Precambrian conditions. (<b>a</b>,<b>b</b>) Control. (<b>c</b>,<b>d</b>) DNA from <span class="html-italic">C. albicans</span>. (<b>e</b>,<b>f</b>) DNA from <span class="html-italic">C. albicans</span>, kaolinite.</p> Full article ">Figure 4
<p>SEM microphotographs of the barium silica–carbonate biomorphs synthesized in two atmospheric conditions. (<b>A</b>,<b>B</b>) Control, without DNA without kaolinite. (<b>C</b>,<b>D</b>) DNA from <span class="html-italic">E. coli</span>. (<b>E</b>,<b>F</b>) DNA from <span class="html-italic">C. albicans</span>. (<b>G</b>,<b>H</b>) DNA from <span class="html-italic">E. coli</span> with kaolinite. (<b>I</b>,<b>J</b>) DNA from <span class="html-italic">C. albicans</span> with kaolinite. STP: Current atmospheric conditions of temperature and CO<sub>2</sub>. Precambrian: 5% CO<sub>2</sub>, 50 °C.</p> Full article ">Figure 5
<p>Identification of the crystalline phase of the barium silica–carbonate biomorphs by Raman and FTIR spectroscopy. (<b>a</b>) Control biomorphs, STP. (<b>b</b>) Control biomorphs, Precambrian. (<b>c</b>) <span class="html-italic">E. coli</span>, genomic DNA, STP. (<b>d</b>) <span class="html-italic">E. coli</span>, genomic DNA, Precambrian. (<b>e</b>) <span class="html-italic">E. coli</span> genomic DNA and kaolinite, STP. (<b>f</b>) <span class="html-italic">E. coli</span> genomic DNA and kaolinite, Precambrian. (<b>i</b>) Raman spectra. (<b>ii</b>) SEM micrograph. (<b>iii</b>) Confocal image. (<b>iv</b>) Raman map. (<b>v</b>) FTIR spectra.</p> Full article ">Figure 6
<p>Identification of the crystalline phase of the barium silica–carbonate biomorphs by Raman spectroscopy and by FTIR (<b>a</b>) Control biomorphs, STP. (<b>b</b>) Control biomorphs, Precambrian. (<b>c</b>) <span class="html-italic">C. albicans</span> genomic DNA, STP. (<b>d</b>) <span class="html-italic">C. albicans</span> genomic DNA, Precambrian. (<b>e</b>) <span class="html-italic">C. albicans</span> genomic DNA and kaolinite, STP. (<b>f</b>) <span class="html-italic">C. albicans</span> genomic DNA and kaolinite, Precambrian. (<b>i</b>) Raman spectra. (<b>ii</b>) SEM micrograph. (<b>iii</b>) Raman map. (<b>iv</b>) FTIR spectra.</p> Full article ">
Open AccessArticle
Integration of UH SUH, HEC-RAS, and GIS in Flood Mitigation with Flood Forecasting and Early Warning System for Gilireng Watershed, Indonesia
by
Muhammad Rifaldi Mustamin, Farouk Maricar, Rita Tahir Lopa and Riswal Karamma
Earth 2024, 5(3), 274-292; https://doi.org/10.3390/earth5030015 - 8 Jul 2024
Abstract
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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
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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
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Figure 1
<p>Research location.</p> Full article ">Figure 2
<p>Flood discharge analysis process.</p> Full article ">Figure 3
<p>The process of building terrain model components in the RAS Mapper tool.</p> Full article ">Figure 4
<p>The process of constructing geometry components and land cover layers in RAS Mapper.</p> Full article ">Figure 5
<p>Components of HEC-RAS 2D simulation model.</p> Full article ">Figure 6
<p>Flood hazard level at monitoring points.</p> Full article ">Figure 7
<p>Average rainfall trend of Gilireng watershed area.</p> Full article ">Figure 8
<p>Effective rainfall distribution of Gilireng watershed.</p> Full article ">Figure 9
<p>Flood discharge analysis with flood (Passeloreng reservoir) attenuation.</p> Full article ">Figure 10
<p>Two-dimensional numerical simulation results of Gilireng River flooding.</p> Full article ">Figure 11
<p>Comparison of historical and simulated flood depths.</p> Full article ">Figure 12
<p>Map of Gilireng River flood-prone areas.</p> Full article ">Figure 13
<p>The extent of the floodplain by district in the Gilireng watershed.</p> Full article ">Figure 14
<p>Flood forecasting and early warning system for Gilireng watershed.</p> Full article ">Figure 15
<p>Flood evacuation route map of Gilireng watershed.</p> Full article ">
<p>Research location.</p> Full article ">Figure 2
<p>Flood discharge analysis process.</p> Full article ">Figure 3
<p>The process of building terrain model components in the RAS Mapper tool.</p> Full article ">Figure 4
<p>The process of constructing geometry components and land cover layers in RAS Mapper.</p> Full article ">Figure 5
<p>Components of HEC-RAS 2D simulation model.</p> Full article ">Figure 6
<p>Flood hazard level at monitoring points.</p> Full article ">Figure 7
<p>Average rainfall trend of Gilireng watershed area.</p> Full article ">Figure 8
<p>Effective rainfall distribution of Gilireng watershed.</p> Full article ">Figure 9
<p>Flood discharge analysis with flood (Passeloreng reservoir) attenuation.</p> Full article ">Figure 10
<p>Two-dimensional numerical simulation results of Gilireng River flooding.</p> Full article ">Figure 11
<p>Comparison of historical and simulated flood depths.</p> Full article ">Figure 12
<p>Map of Gilireng River flood-prone areas.</p> Full article ">Figure 13
<p>The extent of the floodplain by district in the Gilireng watershed.</p> Full article ">Figure 14
<p>Flood forecasting and early warning system for Gilireng watershed.</p> Full article ">Figure 15
<p>Flood evacuation route map of Gilireng watershed.</p> Full article ">
Open AccessArticle
Disaggregating Land Degradation Types for United Nations (UN) Land Degradation Neutrality (LDN) Analysis Using the State of Ohio (USA) as an Example
by
Elena A. Mikhailova, Hamdi A. Zurqani, Lili Lin, Zhenbang Hao, Christopher J. Post, Mark A. Schlautman and Camryn E. Brown
Earth 2024, 5(2), 255-273; https://doi.org/10.3390/earth5020014 - 20 Jun 2024
Abstract
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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,
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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
Graphical abstract
Graphical abstract
Full article ">Figure 1
<p>Anthropogenic land degradation (LD) is the sum of the individual quantities of barren, developed, and agricultural land covers.</p> Full article ">Figure 2
<p>Soil map of the state of Ohio (OH), USA (38°24′ N to 41°59′ N; 80°31′ W to 84°49′ W) acquired from the SSURGO soils database [<a href="#B11-earth-05-00014" class="html-bibr">11</a>] with boundaries for economic development regions [<a href="#B12-earth-05-00014" class="html-bibr">12</a>]. The inherent soil quality (soil suitability) of OH is dominated by agriculturally important soil orders of Mollisols (17.4%) and Alfisols (60.7%).</p> Full article ">Figure 3
<p>Land cover map of the state of Ohio (OH) (USA) for 2016 (38°24′ N to 41°59′ N; 80°31′ W to 84°49′ W) (based on data from MRLC [<a href="#B20-earth-05-00014" class="html-bibr">20</a>]).</p> Full article ">Figure 4
<p>The proportion of anthropogenically degraded land (%) by county in the state of Ohio (OH) (USA) in 2016. Anthropogenically degraded land was calculated as a sum of degraded land from agriculture (hay/pasture, and cultivated crops), from development (developed, open space; developed, low intensity; developed, medium intensity; developed, high intensity), and barren land.</p> Full article ">Figure 5
<p>Relationship between the combined proportion of Alfisols and Mollisols (%) for each county in Ohio and the proportion of land degradation (%) in that county.</p> Full article ">Figure 6
<p>Damages from land degradation because of soil carbon (C) loss with associated emissions from more recent land developments between 2001 and 2016 in Ohio (OH) (USA).</p> Full article ">Figure 7
<p>Damages from land degradation because of loss of land for potential soil carbon (C) sequestration from land developments that occurred between 2001 and 2016 for Ohio (OH) (USA).</p> Full article ">Figure 8
<p>Damages from land degradation emissions, which can be measured as “realized” social costs of soil carbon (C) (SC-CO<sub>2</sub>) from recent land developments in the state of Ohio (OH) (USA) from 2001 to 2016. Note: M = million = 10<sup>6</sup>, B = billion = 10<sup>9</sup>, USD = United States Dollar.</p> Full article ">
Full article ">Figure 1
<p>Anthropogenic land degradation (LD) is the sum of the individual quantities of barren, developed, and agricultural land covers.</p> Full article ">Figure 2
<p>Soil map of the state of Ohio (OH), USA (38°24′ N to 41°59′ N; 80°31′ W to 84°49′ W) acquired from the SSURGO soils database [<a href="#B11-earth-05-00014" class="html-bibr">11</a>] with boundaries for economic development regions [<a href="#B12-earth-05-00014" class="html-bibr">12</a>]. The inherent soil quality (soil suitability) of OH is dominated by agriculturally important soil orders of Mollisols (17.4%) and Alfisols (60.7%).</p> Full article ">Figure 3
<p>Land cover map of the state of Ohio (OH) (USA) for 2016 (38°24′ N to 41°59′ N; 80°31′ W to 84°49′ W) (based on data from MRLC [<a href="#B20-earth-05-00014" class="html-bibr">20</a>]).</p> Full article ">Figure 4
<p>The proportion of anthropogenically degraded land (%) by county in the state of Ohio (OH) (USA) in 2016. Anthropogenically degraded land was calculated as a sum of degraded land from agriculture (hay/pasture, and cultivated crops), from development (developed, open space; developed, low intensity; developed, medium intensity; developed, high intensity), and barren land.</p> Full article ">Figure 5
<p>Relationship between the combined proportion of Alfisols and Mollisols (%) for each county in Ohio and the proportion of land degradation (%) in that county.</p> Full article ">Figure 6
<p>Damages from land degradation because of soil carbon (C) loss with associated emissions from more recent land developments between 2001 and 2016 in Ohio (OH) (USA).</p> Full article ">Figure 7
<p>Damages from land degradation because of loss of land for potential soil carbon (C) sequestration from land developments that occurred between 2001 and 2016 for Ohio (OH) (USA).</p> Full article ">Figure 8
<p>Damages from land degradation emissions, which can be measured as “realized” social costs of soil carbon (C) (SC-CO<sub>2</sub>) from recent land developments in the state of Ohio (OH) (USA) from 2001 to 2016. Note: M = million = 10<sup>6</sup>, B = billion = 10<sup>9</sup>, USD = United States Dollar.</p> Full article ">
Open AccessArticle
Combining Low-Cost UAV Imagery with Machine Learning Classifiers for Accurate Land Use/Land Cover Mapping
by
Spyridon E. Detsikas, George P. Petropoulos, Kleomenis Kalogeropoulos and Ioannis Faraslis
Earth 2024, 5(2), 244-254; https://doi.org/10.3390/earth5020013 - 19 Jun 2024
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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
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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
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Figure 1
<p>Maps illustrating (<b>a</b>) the raw RGB UAV imagery; (<b>b</b>,<b>c</b>) the geographical location of the almond orchard in Greece acting as the experimental site of this study. Red pin depicts the location of this study experimental site in Thessaly region, Greece.</p> Full article ">Figure 2
<p>A graphical illustration of the methodological steps implemented in this study.</p> Full article ">Figure 3
<p>The LULC Classification maps with (<b>a</b>) presenting the RF-derived classification and (<b>b</b>) the SVM classification map.</p> Full article ">
<p>Maps illustrating (<b>a</b>) the raw RGB UAV imagery; (<b>b</b>,<b>c</b>) the geographical location of the almond orchard in Greece acting as the experimental site of this study. Red pin depicts the location of this study experimental site in Thessaly region, Greece.</p> Full article ">Figure 2
<p>A graphical illustration of the methodological steps implemented in this study.</p> Full article ">Figure 3
<p>The LULC Classification maps with (<b>a</b>) presenting the RF-derived classification and (<b>b</b>) the SVM classification map.</p> Full article ">
Open AccessArticle
Using Google Earth Engine to Assess the Current State of Thermokarst Terrain on Arga Island (the Lena Delta)
by
Andrei Kartoziia
Earth 2024, 5(2), 228-243; https://doi.org/10.3390/earth5020012 - 12 Jun 2024
Abstract
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
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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
(This article belongs to the Topic Effects of Climate Change on Geomorphology, Water Geochemistry and Pollution)
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<p>Location of the study area (the black rectangle) in a mosaic of satellite Sentinel-2 images of the Lena Delta. The red square on the map represents a portion of the study area shown in <a href="#earth-05-00012-f002" class="html-fig">Figure 2</a>. The study area covers approximately 2000 km<sup>2</sup>.</p> Full article ">Figure 2
<p>The fragment of the study area (the red square in <a href="#earth-05-00012-f001" class="html-fig">Figure 1</a>): R-G-B composite (<b>a</b>); PCA 1-2-3 composite (<b>b</b>); NDVI (<b>c</b>); EVI (<b>d</b>); NDWI (<b>e</b>); TCW (<b>f</b>); TCG (<b>g</b>). The fragment has a size of approximately 7 by 7 km.</p> Full article ">Figure 3
<p>Identified training polygons for each revealed LC class in R-G-B composite.</p> Full article ">Figure 4
<p>Spectral signatures of revealed LC classes. The y-axis represents normalized reflectance for the Sentinel-2 bands, as well as index values for the indices.</p> Full article ">Figure 5
<p>The correlation matrix for the bands of analyzed composite image. The values of Pearson’s correlation coefficients are presented after the Fisher Z-transformation. The mean values are presented after reverting back to correlation coefficients using the inverse Fisher transform.</p> Full article ">Figure 6
<p>The selection of the bands is based on both criteria. The columns represent the following. Mean values of each LC class (from <a href="#earth-05-00012-f003" class="html-fig">Figure 3</a>): 1—water bodies; 2—stable terrains; 3—thermokarst-affected terrains; 4—slopes; 5—blowouts. MV—the mean values of Pearson’s correlation coefficients of each band (from <a href="#earth-05-00012-f005" class="html-fig">Figure 5</a>); SD—the standard deviation of the average spectral characteristics of each LC class in all bands. The chosen bands are indicated by a yellow color. The bands are sorted by SD column.</p> Full article ">Figure 7
<p>The map of the LC class distribution.</p> Full article ">Figure 8
<p>The confusion matrix. The classifier achieved an overall accuracy of 0.9834 and a Kappa coefficient of 0.9782.</p> Full article ">Figure 9
<p>A visual inspection of the classification results: (<b>a</b>) the R-G-B composite; (<b>b</b>) the map of the LC classes’ distribution; (<b>c</b>) a transparent map of the LC class distribution on the RGB composite; (<b>d</b>) the PCA 1-2-3 composite. The yellow circles indicate certain aspects of the classification results that require further attention. These include the following: 1. Difficulties in distinguishing between pixels belonging to different classes at the boundaries between them. 2. Confusion in the recognition of water bodies and thermokarst-affected terrains. 3. Challenges in distinguishing slopes from stable terrains.</p> Full article ">Figure 10
<p>The spatial distribution of the LC classes.</p> Full article ">
<p>Location of the study area (the black rectangle) in a mosaic of satellite Sentinel-2 images of the Lena Delta. The red square on the map represents a portion of the study area shown in <a href="#earth-05-00012-f002" class="html-fig">Figure 2</a>. The study area covers approximately 2000 km<sup>2</sup>.</p> Full article ">Figure 2
<p>The fragment of the study area (the red square in <a href="#earth-05-00012-f001" class="html-fig">Figure 1</a>): R-G-B composite (<b>a</b>); PCA 1-2-3 composite (<b>b</b>); NDVI (<b>c</b>); EVI (<b>d</b>); NDWI (<b>e</b>); TCW (<b>f</b>); TCG (<b>g</b>). The fragment has a size of approximately 7 by 7 km.</p> Full article ">Figure 3
<p>Identified training polygons for each revealed LC class in R-G-B composite.</p> Full article ">Figure 4
<p>Spectral signatures of revealed LC classes. The y-axis represents normalized reflectance for the Sentinel-2 bands, as well as index values for the indices.</p> Full article ">Figure 5
<p>The correlation matrix for the bands of analyzed composite image. The values of Pearson’s correlation coefficients are presented after the Fisher Z-transformation. The mean values are presented after reverting back to correlation coefficients using the inverse Fisher transform.</p> Full article ">Figure 6
<p>The selection of the bands is based on both criteria. The columns represent the following. Mean values of each LC class (from <a href="#earth-05-00012-f003" class="html-fig">Figure 3</a>): 1—water bodies; 2—stable terrains; 3—thermokarst-affected terrains; 4—slopes; 5—blowouts. MV—the mean values of Pearson’s correlation coefficients of each band (from <a href="#earth-05-00012-f005" class="html-fig">Figure 5</a>); SD—the standard deviation of the average spectral characteristics of each LC class in all bands. The chosen bands are indicated by a yellow color. The bands are sorted by SD column.</p> Full article ">Figure 7
<p>The map of the LC class distribution.</p> Full article ">Figure 8
<p>The confusion matrix. The classifier achieved an overall accuracy of 0.9834 and a Kappa coefficient of 0.9782.</p> Full article ">Figure 9
<p>A visual inspection of the classification results: (<b>a</b>) the R-G-B composite; (<b>b</b>) the map of the LC classes’ distribution; (<b>c</b>) a transparent map of the LC class distribution on the RGB composite; (<b>d</b>) the PCA 1-2-3 composite. The yellow circles indicate certain aspects of the classification results that require further attention. These include the following: 1. Difficulties in distinguishing between pixels belonging to different classes at the boundaries between them. 2. Confusion in the recognition of water bodies and thermokarst-affected terrains. 3. Challenges in distinguishing slopes from stable terrains.</p> Full article ">Figure 10
<p>The spatial distribution of the LC classes.</p> Full article ">
Open AccessArticle
Regional Hydro-Chemistry of Hydrothermal Springs in Northeastern Algeria, Case of Guelma, Souk Ahras, Tebessa and Khenchela Regions
by
Ibtissem Djaafri, Karima Seghir, Vincent Valles and Laurent Barbiero
Earth 2024, 5(2), 214-227; https://doi.org/10.3390/earth5020011 - 8 Jun 2024
Abstract
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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
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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
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Figure 1
<p>Thermal bands in northeastern Algeria (adapted from Verdeil [<a href="#B41-earth-05-00011" class="html-bibr">41</a>]), the location of the study site and sampled thermal sources. The dashed lines represent rainfall isohyetes for 2021.</p> Full article ">Figure 2
<p>Lithological map of northern Algeria (adapted from Fekraoui and Kedaid [<a href="#B49-earth-05-00011" class="html-bibr">49</a>]).</p> Full article ">Figure 3
<p>Some pictures of hot springs and hammams in the study area: (<b>a</b>–<b>c</b>) Hammam Salhine of Tebessa, (<b>d</b>,<b>e</b>) Hammam Sidi Yahia of Tebessa, (<b>f</b>) Hammam Ouled Ali of Guelma, (<b>g</b>) Hammam Guerfa of Guelma, (<b>h</b>) Hammam Meskoutine cascade of Guelma and (<b>i</b>) Hammam Meskoutine Ain Chfa of Guelma.</p> Full article ">Figure 4
<p>Quantile–quantile plots for parameters (<b>a</b>) NO<sub>3</sub><sup>−</sup>, (<b>b</b>) log (NO<sub>3</sub><sup>−</sup>), (<b>c</b>) EC and (<b>d</b>) log (EC). The dotted line is where the normal distributed pairs of quantiles are placed.</p> Full article ">Figure 5
<p>Distribution of parameters (pH, T, log (EC) and log (concentration)) in score plots: (<b>a</b>) PC1-PC2 and (<b>b</b>) PC3-PC4.</p> Full article ">Figure 6
<p>Sample distribution on factorial plots: (<b>a</b>) PC1-PC2 and (<b>b</b>) PC1-PC3 (see text for group correspondence).</p> Full article ">Figure 7
<p>Dendrogram based on the coordinates of the centroids of each region on the first four principal components.</p> Full article ">
<p>Thermal bands in northeastern Algeria (adapted from Verdeil [<a href="#B41-earth-05-00011" class="html-bibr">41</a>]), the location of the study site and sampled thermal sources. The dashed lines represent rainfall isohyetes for 2021.</p> Full article ">Figure 2
<p>Lithological map of northern Algeria (adapted from Fekraoui and Kedaid [<a href="#B49-earth-05-00011" class="html-bibr">49</a>]).</p> Full article ">Figure 3
<p>Some pictures of hot springs and hammams in the study area: (<b>a</b>–<b>c</b>) Hammam Salhine of Tebessa, (<b>d</b>,<b>e</b>) Hammam Sidi Yahia of Tebessa, (<b>f</b>) Hammam Ouled Ali of Guelma, (<b>g</b>) Hammam Guerfa of Guelma, (<b>h</b>) Hammam Meskoutine cascade of Guelma and (<b>i</b>) Hammam Meskoutine Ain Chfa of Guelma.</p> Full article ">Figure 4
<p>Quantile–quantile plots for parameters (<b>a</b>) NO<sub>3</sub><sup>−</sup>, (<b>b</b>) log (NO<sub>3</sub><sup>−</sup>), (<b>c</b>) EC and (<b>d</b>) log (EC). The dotted line is where the normal distributed pairs of quantiles are placed.</p> Full article ">Figure 5
<p>Distribution of parameters (pH, T, log (EC) and log (concentration)) in score plots: (<b>a</b>) PC1-PC2 and (<b>b</b>) PC3-PC4.</p> Full article ">Figure 6
<p>Sample distribution on factorial plots: (<b>a</b>) PC1-PC2 and (<b>b</b>) PC1-PC3 (see text for group correspondence).</p> Full article ">Figure 7
<p>Dendrogram based on the coordinates of the centroids of each region on the first four principal components.</p> Full article ">
Open AccessReview
Biological Carbon Sequestration: From Deep History to the Present Day
by
Denis J. Murphy
Earth 2024, 5(2), 195-213; https://doi.org/10.3390/earth5020010 - 30 May 2024
Cited by 2
Abstract
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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,
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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
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Figure 1
<p>Global carbon budget showing the imbalance between emissions and sinks. The main sinks are terrestrial and oceanic sequestration of CO<sub>2</sub>. Due to this imbalance, atmospheric CO<sub>2</sub> concentrations are now at a 16-million-year high. Graphic from Ref. [<a href="#B17-earth-05-00010" class="html-bibr">17</a>].</p> Full article ">Figure 2
<p>Changes in the atmospheric composition since 4.0 Ga. At the start of the Archaean, about 4.0 Ga of the Earth’s atmosphere was dominated by methane (orange), CO<sub>2</sub> (yellow), and nitrogen (blue). Oxygen (green) was less than one-millionth of current levels and remained low from 4.0 to 2.4 Ga. Graphic from Ref. [<a href="#B25-earth-05-00010" class="html-bibr">25</a>].</p> Full article ">Figure 3
<p>Low concentrations of atmospheric CO<sub>2</sub> were the norm for the past 800,000 years until recent decades. Graphic from Ref. [<a href="#B12-earth-05-00010" class="html-bibr">12</a>].</p> Full article ">Figure 4
<p>The climate over the past 100,000 years was highly erratic until the onset of the Holocene about 11,700 years ago (X), and since then, it has been relatively warm and moist—the so-called Holocene Climatic Optimum (red bar). Despite their small size and duration, the two minor oscillations at ~8200 years (Y) and ~4200 years ago (Z) caused significant, albeit localised, social collapses, emphasising the narrow climatic range required for agriculture-based human life. Graphic from Ref. [<a href="#B77-earth-05-00010" class="html-bibr">77</a>].</p> Full article ">Figure 5
<p>Changes in agricultural land use and atmospheric CO<sub>2</sub> concentrations over the past 8 millennia of the mid-late Holocene. (<b>a</b>) Global area of crop and pasture land (million km<sup>2</sup>). (<b>b</b>) Atmospheric CO<sub>2</sub> concentration (ppm). CE, common era. Graphic from Ref. [<a href="#B85-earth-05-00010" class="html-bibr">85</a>].</p> Full article ">Figure 6
<p>Annual total CO<sub>2</sub> emissions by world region. At about 1950, there was an inflection point (red arrow) as CO<sub>2</sub> emissions entered a period of more rapid and sustained increases. Graphic from the Carbon Dioxide Information Analysis Center (CDIAC); Global Carbon Project (GCP).</p> Full article ">
<p>Global carbon budget showing the imbalance between emissions and sinks. The main sinks are terrestrial and oceanic sequestration of CO<sub>2</sub>. Due to this imbalance, atmospheric CO<sub>2</sub> concentrations are now at a 16-million-year high. Graphic from Ref. [<a href="#B17-earth-05-00010" class="html-bibr">17</a>].</p> Full article ">Figure 2
<p>Changes in the atmospheric composition since 4.0 Ga. At the start of the Archaean, about 4.0 Ga of the Earth’s atmosphere was dominated by methane (orange), CO<sub>2</sub> (yellow), and nitrogen (blue). Oxygen (green) was less than one-millionth of current levels and remained low from 4.0 to 2.4 Ga. Graphic from Ref. [<a href="#B25-earth-05-00010" class="html-bibr">25</a>].</p> Full article ">Figure 3
<p>Low concentrations of atmospheric CO<sub>2</sub> were the norm for the past 800,000 years until recent decades. Graphic from Ref. [<a href="#B12-earth-05-00010" class="html-bibr">12</a>].</p> Full article ">Figure 4
<p>The climate over the past 100,000 years was highly erratic until the onset of the Holocene about 11,700 years ago (X), and since then, it has been relatively warm and moist—the so-called Holocene Climatic Optimum (red bar). Despite their small size and duration, the two minor oscillations at ~8200 years (Y) and ~4200 years ago (Z) caused significant, albeit localised, social collapses, emphasising the narrow climatic range required for agriculture-based human life. Graphic from Ref. [<a href="#B77-earth-05-00010" class="html-bibr">77</a>].</p> Full article ">Figure 5
<p>Changes in agricultural land use and atmospheric CO<sub>2</sub> concentrations over the past 8 millennia of the mid-late Holocene. (<b>a</b>) Global area of crop and pasture land (million km<sup>2</sup>). (<b>b</b>) Atmospheric CO<sub>2</sub> concentration (ppm). CE, common era. Graphic from Ref. [<a href="#B85-earth-05-00010" class="html-bibr">85</a>].</p> Full article ">Figure 6
<p>Annual total CO<sub>2</sub> emissions by world region. At about 1950, there was an inflection point (red arrow) as CO<sub>2</sub> emissions entered a period of more rapid and sustained increases. Graphic from the Carbon Dioxide Information Analysis Center (CDIAC); Global Carbon Project (GCP).</p> Full article ">
Open AccessArticle
Projecting Urban Expansion by Analyzing Growth Patterns and Sustainable Planning Strategies—A Case Study of Kamrup Metropolitan, Assam, North-East India
by
Upasana Choudhury, Shruti Kanga, Suraj Kumar Singh, Anand Kumar, Gowhar Meraj, Pankaj Kumar and Saurabh Singh
Earth 2024, 5(2), 169-194; https://doi.org/10.3390/earth5020009 - 27 May 2024
Abstract
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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
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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
Figure 1
Figure 1
<p>Location map of the study area, Kamrup Metropolitan District.</p> Full article ">Figure 2
<p>The standardized factors and constraints used in AHP: (<b>a</b>) Elevation; (<b>b</b>) proximity to built-up; (<b>c</b>) proximity to point of interest (POI); (<b>d</b>) proximity to roads; (<b>e</b>) slope; (<b>f</b>) water body; (<b>g</b>) reserved forest (protected areas).</p> Full article ">Figure 3
<p>Land use and land cover classification for the years (<b>a</b>) 2000, (<b>b</b>) 2014, and (<b>c</b>) 2022.</p> Full article ">Figure 4
<p>Graphical representation of the changing trend of LULC (2000–2022).</p> Full article ">Figure 5
<p>Built-up distribution of Kamrup Metropolitan among different buffer zones for the years (<b>a</b>) 2000, (<b>b</b>) 2014, and (<b>c</b>) 2022.</p> Full article ">Figure 6
<p>Representing the built-up density in each buffer for the years 2000, 2014, and 2022.</p> Full article ">Figure 7
<p>Diagram illustrating landscape metrics at the class level within the Kamrup Metropolitan District.</p> Full article ">Figure 8
<p>(<b>a</b>) Built-up for 2022, (<b>b</b>) projected Built-up for 2032, and (<b>c</b>) projected Built-up for 2052.</p> Full article ">
<p>Location map of the study area, Kamrup Metropolitan District.</p> Full article ">Figure 2
<p>The standardized factors and constraints used in AHP: (<b>a</b>) Elevation; (<b>b</b>) proximity to built-up; (<b>c</b>) proximity to point of interest (POI); (<b>d</b>) proximity to roads; (<b>e</b>) slope; (<b>f</b>) water body; (<b>g</b>) reserved forest (protected areas).</p> Full article ">Figure 3
<p>Land use and land cover classification for the years (<b>a</b>) 2000, (<b>b</b>) 2014, and (<b>c</b>) 2022.</p> Full article ">Figure 4
<p>Graphical representation of the changing trend of LULC (2000–2022).</p> Full article ">Figure 5
<p>Built-up distribution of Kamrup Metropolitan among different buffer zones for the years (<b>a</b>) 2000, (<b>b</b>) 2014, and (<b>c</b>) 2022.</p> Full article ">Figure 6
<p>Representing the built-up density in each buffer for the years 2000, 2014, and 2022.</p> Full article ">Figure 7
<p>Diagram illustrating landscape metrics at the class level within the Kamrup Metropolitan District.</p> Full article ">Figure 8
<p>(<b>a</b>) Built-up for 2022, (<b>b</b>) projected Built-up for 2032, and (<b>c</b>) projected Built-up for 2052.</p> Full article ">
Open AccessArticle
Relative and Combined Impacts of Climate and Land Use/Cover Change for the Streamflow Variability in the Baro River Basin (BRB)
by
Shimelash Molla Kassaye, Tsegaye Tadesse, Getachew Tegegne, Aster Tesfaye Hordofa and Demelash Ademe Malede
Earth 2024, 5(2), 149-168; https://doi.org/10.3390/earth5020008 - 24 Apr 2024
Abstract
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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
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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
Figure 1
Figure 1
<p>Map indicating the geographical scope of the study area.</p> Full article ">Figure 2
<p>LULC classes for the BRB using GEE.</p> Full article ">Figure 3
<p>LULCC for 1990, 2000, 2020, and 2050 in the BRB.</p> Full article ">Figure 4
<p>Projected land use maps for (<b>a</b>) 1990, (<b>c</b>) 2000, (<b>e</b>) 2020, and (<b>g</b>) 2050, and proportions of each land use/cover types under (<b>b</b>) 1990, (<b>d</b>) 2000, (<b>f</b>) 2020, and (<b>h</b>) 2050.</p> Full article ">Figure 4 Cont.
<p>Projected land use maps for (<b>a</b>) 1990, (<b>c</b>) 2000, (<b>e</b>) 2020, and (<b>g</b>) 2050, and proportions of each land use/cover types under (<b>b</b>) 1990, (<b>d</b>) 2000, (<b>f</b>) 2020, and (<b>h</b>) 2050.</p> Full article ">Figure 5
<p>Distribution of the annual average rainfall in the basin.</p> Full article ">Figure 6
<p>Spatial distribution of (<b>a</b>) maximum temperature and (<b>b</b>) minimum temperature of the BRB.</p> Full article ">Figure 6 Cont.
<p>Spatial distribution of (<b>a</b>) maximum temperature and (<b>b</b>) minimum temperature of the BRB.</p> Full article ">Figure 7
<p>Comparison of observed and simulated daily streamflow for (<b>a</b>) the calibration period (2001–2009) and (<b>b</b>) the validation period (2010–2014).</p> Full article ">Figure 8
<p>Graph showing the variability of mean annual flow under constant land use/cover but varying climate scenarios.</p> Full article ">Figure 9
<p>Boxplot showing the range of annual mean flow under different climate scenarios using (<b>a</b>) the LULC of 1990 and (<b>b</b>) the LULC of 2050.</p> Full article ">Figure 10
<p>Graph showing the variability of mean annual flow under constant climate but different land use/cover scenarios.</p> Full article ">Figure 11
<p>Boxplot showing the range of annual mean flow using baseline climate under different land use/cover change scenarios.</p> Full article ">Figure 12
<p>Seasonal mean flow percentage change between SSP2-4.5 and SSP5-8.5 with baseline.</p> Full article ">
<p>Map indicating the geographical scope of the study area.</p> Full article ">Figure 2
<p>LULC classes for the BRB using GEE.</p> Full article ">Figure 3
<p>LULCC for 1990, 2000, 2020, and 2050 in the BRB.</p> Full article ">Figure 4
<p>Projected land use maps for (<b>a</b>) 1990, (<b>c</b>) 2000, (<b>e</b>) 2020, and (<b>g</b>) 2050, and proportions of each land use/cover types under (<b>b</b>) 1990, (<b>d</b>) 2000, (<b>f</b>) 2020, and (<b>h</b>) 2050.</p> Full article ">Figure 4 Cont.
<p>Projected land use maps for (<b>a</b>) 1990, (<b>c</b>) 2000, (<b>e</b>) 2020, and (<b>g</b>) 2050, and proportions of each land use/cover types under (<b>b</b>) 1990, (<b>d</b>) 2000, (<b>f</b>) 2020, and (<b>h</b>) 2050.</p> Full article ">Figure 5
<p>Distribution of the annual average rainfall in the basin.</p> Full article ">Figure 6
<p>Spatial distribution of (<b>a</b>) maximum temperature and (<b>b</b>) minimum temperature of the BRB.</p> Full article ">Figure 6 Cont.
<p>Spatial distribution of (<b>a</b>) maximum temperature and (<b>b</b>) minimum temperature of the BRB.</p> Full article ">Figure 7
<p>Comparison of observed and simulated daily streamflow for (<b>a</b>) the calibration period (2001–2009) and (<b>b</b>) the validation period (2010–2014).</p> Full article ">Figure 8
<p>Graph showing the variability of mean annual flow under constant land use/cover but varying climate scenarios.</p> Full article ">Figure 9
<p>Boxplot showing the range of annual mean flow under different climate scenarios using (<b>a</b>) the LULC of 1990 and (<b>b</b>) the LULC of 2050.</p> Full article ">Figure 10
<p>Graph showing the variability of mean annual flow under constant climate but different land use/cover scenarios.</p> Full article ">Figure 11
<p>Boxplot showing the range of annual mean flow using baseline climate under different land use/cover change scenarios.</p> Full article ">Figure 12
<p>Seasonal mean flow percentage change between SSP2-4.5 and SSP5-8.5 with baseline.</p> Full article ">
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