Dynamic shoreline alterations and their impacts on Olive Ridley Turtle (Lepidochelys olivacea) nesting sites in Gahirmatha Marine Wildlife Sanctuary, Odisha (India).

Currently, sea turtle habitats are being altered by climate change and human activities, with habitat loss posing an urgent threat to Indian sea turtles. Thus, the objective of this study is to analyze the dynamic shoreline alterations and their impacts on Olive Ridley Sea Turtle (ORT) nesting sites in Gahirmatha Marine Wildlife Sanctuary from 1990 to 2022. Landsat satellite images served as input datasets to assess dynamic shoreline changes. This study assessed shoreline alterations and their rates across 929 transects divided into four zones using the Digital Shoreline Analysis System (DSAS) software. The results revealed a significant 14-km northward shift in the nesting site due to substantial coastal erosion, threatening the turtles' Arribada. This study underscores the need for conservation efforts to preserve nesting environments amidst changing coastal landscapes, offering novel insights into the interaction between coastal processes and marine turtle nesting behaviors.

Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation.

Forest fires (FF) in tropical seasonal forests impact ecosystem. Addressing FF in tropical ecosystems has become a priority to mitigate impacts on biodiversity loss and climate change. The escalating frequency and intensity of FF globally have become a mounting concern. Understanding their tendencies, patterns, and vulnerabilities is imperative for conserving ecosystems and facilitating the development of effective prevention and management strategies. This study investigates the trends, patterns, and spatiotemporal distribution of FF for the period of 2001-2022, and delineates the forest fire susceptibility zones in Odisha State, India. The study utilized: (a) MODIS imagery to examine active fire point data; (b) Kernel density tools; (c) FF risk prediction using two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest (RF); (d) Receiver Operating Characteristic and Area Under the Curve, along with various evaluation metrics; and (e) a total of 19 factors, including three topographical, seven climatic, four biophysical, and five anthropogenic, to create a map indicating areas vulnerable to FF. The validation results revealed that the RF model achieved a precision exceeding 94 % on the validation datasets, while the SVM model reached 89 %. The estimated forest fire susceptibility zones using RF and SVM techniques indicated that 20.14 % and 16.72 % of the area, respectively, fall under the "Very High Forest Fire" susceptibility class. Trend analysis reveals a general upward trend in forest fire occurrences (R2 = 0.59), with a notable increase after 2015, peaking in 2021. Notably, Angul district was identified as the most affected area, documenting the highest number of forest fire incidents over the past 22 years. Additionally, forest fire mitigation plans have been developed by drawing insights from forest fire management strategies implemented in various countries worldwide. Overall, this analysis provides valuable insights for policymakers and forest management authorities to develop effective strategies for forest fire prevention and mitigation.

Multi-datasets to monitor and assess meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region.

This study analyzed the meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region from 1994 to 2016. In recent decades, this region has faced prolonged and severe droughts, leading to marked reductions in agricultural productivity and significant challenges to food security and water availability. The datasets employed included a digital elevation model, land use and cover data, soil characteristics, climatic data (temperature, wind speed, solar radiation, humidity, and precipitation), runoff data, images from the MODIS/TERRA and AQUA sensors (MOD09A1 and MODY09A1 products), and soil water content. A variety of methods and products were used to study these droughts: the meteorological drought was analyzed using the Standardized Precipitation Index (SPI) derived from observed precipitation data, while the hydrological drought was assessed using the Standardized Soil Index (SSI), the Nonparametric Multivariate Standardized Drought Index (NMSDI), and the Parametric Multivariate Standardized Drought Index (PMSDI). These indices were determined using water balance components, including streamflow and soil water content, from the Soil Water Assessment Tool (SWAT) model, and evapotranspiration data from the Surface Energy Balance Algorithm for Land (SEBAL). The findings indicate that the methodology effectively identified variations in water dynamics and drought periods in a headwater basin within Brazil's semiarid region, suggesting potential applicability in other semiarid areas. This study provides essential insights for water resource management and resilience building in the face of adverse climatic events, offering a valuable guide for decision-making processes.

A multi-temporal analysis of shoreline dynamics influenced by natural and anthropogenic factors: Erosion and accretion along the Digha Coast, West Bengal, India.

This investigation analyzed shoreline evolution along India's Digha Coast from 1992 to 2022, using multispectral Landsat satellite images and the Digital Shoreline Analysis System (DSAS). Methods included identifying zones and transects, shoreline extraction, and applying spatial statistical techniques. The study area, divided into five zones with 587 transects, enabled both short- and long-term analysis. Key findings indicate that the mean long-term rate of shoreline change is -0.54 m per year, with 70.70 % of transects experiencing erosion and 29.30 % accretion. Notably, Zone V had the highest accretion rate (8.55 m/year), while Zone III faced the most erosion (-7.47 m/year). Short-term analysis from 1997 to 2017 indicated significant erosion, contrasting with accretion during 1992-1997 and 2017-2022. Particularly, Zones II, III, and IV underwent major erosion, especially from 1997 to 2002. The study underscores the need for continuous shoreline management strategies and demonstrates geospatial technology's effectiveness in capturing coastal landscape changes.

Current patterns and trends of microplastic pollution in the marine environment: A bibliometric analysis.

Microplastics are pervasive in the natural environment and pose a growing concern for global health. Plastic waste in marine environments has emerged as a global issue, threatening not only marine biota but also human health due to its implications for the food chain. This study aims to discern the patterns and trends of research, specifically on Marine Microplastic Pollution (MMP), based on a bibliometric analysis of scientific publications from 2011 to 2022. The methodology utilized in this study comprises three stages: (a) creating a bibliographical dataset from Scopus by Elsevier and the Web of Science Core Collection by Clarivate Analytics, (b) analyzing current research (trends and patterns) using bibliometric analysis through Biblioshiny tool, and (c) examining themes and subthemes in MMP research (wastewater treatment, plastic ingestion, the Mediterranean Sea, microplastics pollution, microplastics in freshwater, microplastic ingestion, plastic pollution, and microplastic pollution in the marine environment). The findings reveal that during the studied period, the number of MMP publications amounted to 1377 articles, with an average citation per publication of 59.23 and a total citation count of 81,553. The most cited article was published in 2011, and since then, the number of publications on this topic has been increasing steadily. The author count stood at 5478, with 22 trending topics identified from the 1377 published titles. Between 2019 and 2022, the countries contributing most to the publication of MMP articles were China, the United States of America (USA), and the United Kingdom (UK). However, a noticeable shift in the origin of author countries was observed in the 2019-2022 timeframe, transitioning from a dominance by the USA and the UK to a predominance by China. In 2019, there was a substantial increase in the volume of publications addressing the topic of microplastics. The results show that the most prevalent themes and subthemes pertained to MMP in the Mediterranean Sea. The journals with the highest number of MMP articles published were the Marine Pollution Bulletin (253 articles) and Science of the Total Environment (190 articles). The analysis concludes that research on MMP remains prominent and appears to be increasing each year.

Coastal evolution and future projections in Conde County, Brazil: A multi-decadal assessment via remote sensing and sea-level rise scenarios.

Global sea levels, having risen by approximately 20 cm since the mid-19th century, necessitate a critical examination of their impacts on shoreline dynamics. This research evaluates the historical (1985-2022) and future shoreline changes in Conde County, Paraíba State, Brazil, an area of significant touristic interest. Employing Landsat satellite imagery, the study utilized the Digital Shoreline Analysis System (DSAS) and a Kalman filter algorithm for cloud removal, while also assessing land use and land cover changes using data from the MapBiomas Project for 2000, 2010, and 2020. These analyses informed projections of potential inundation under various sea-level rise (SLR) scenarios: 1, 2, 5, and 10 m. Key findings revealed a negative average coastline change rate of -0.27 m/year from 1985 to 2022, indicative of erosive trends likely accelerated by human activities. Long-term projections for 2032 and 2042 anticipate continued erosion in areas identified as highly vulnerable. The SLR scenario analysis underscores the urgent need for adaptive climate measures; while a 1- or 2-meter SLR presents limited immediate effects, a 5-meter rise could lead to significant inundation across key sectors, including urban and agricultural landscapes. The projected severity of a 10-meter SLR necessitates immediate, comprehensive interventions to safeguard both natural and human systems.

Geo-ecological, shoreline dynamic, and flooding impacts of Cyclonic Storm Mocha: A geospatial analysis.

This research comprehensively assesses the aftermath of Cyclonic Storm Mocha, focusing on the coastal zones of Rakhine State and the Chittagong Division, spanning Myanmar and Bangladesh. The investigation emphasizes the impacts on coastal ecology, shoreline dynamics, flooding patterns, and meteorological variations. Employed were multiple vegetation indices-Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), Disaster Vegetation Damage Index (DVDI), and Fractional Vegetation Cover (FVC)-to evaluate ecological consequences. The Digital Shoreline Assessment System (DSAS) aided in determining shoreline alterations pre- and post-cyclone. Soil exposure and flood extents were scrutinized using the Bare Soil Index (BSI) and Modified Normalized Difference Water Index (MNDWI), respectively. Additionally, the study encompassed an analysis of microclimatic variables, comparing meteorological data across pre- and post-cyclone periods. Findings indicate significant ecological impacts: an estimated 8985.46 km2 of dense vegetation (NDVI >0.6) was adversely affected. Post-cyclone, there was a discernible reduction in EVI values. The mean mVCI shifted negatively from -0.18 to -0.33, and the mean FVC decreased from 0.39 to 0.33. The DVDI underscored considerable vegetation damage in various areas, underscoring the cyclone's extensive impact. Meteorological analysis revealed a 245 % increase in rainfall (20.22 mm on May 14, 2023 compared to the May average of 5.86 mm), and significant increases in relative humidity (14 %) and wind speed (205 %). Erosion was observed along 74.60 % of the studied shoreline. These insights are pivotal for developing comprehensive strategies aimed at the rehabilitation and conservation of critical coastal ecosystems. They provide vital data for emergency response initiatives and offer resources for entities engaged in enhancing coastal resilience and protecting local community livelihoods.

Remote sensing, GIS, and analytic hierarchy process-based delineation and sustainable management of potential groundwater zones: a case study of Jhargram district, West Bengal, India.

The present investigation delineates groundwater potential zones (GPZ) in the Jhargram district through an integrated approach employing analytical hierarchical process (AHP), remote sensing, and geographical information systems (GIS). Twelve parameters were utilized for GPZ analysis based on the Groundwater Potential Index, subsequent to multicollinearity testing. Classification of GPZ yielded five distinct categories: very poor, poor, moderate, good, and very good. Validation through receiver operating characteristics (ROC) and cross-validation with borewell yield data affirmed prediction accuracies of 78.4% and 84%, respectively. Spatial distribution analysis revealed that 30.39%, 30.86%, and 13.19% of the surveyed area fell within the poor, moderate, and good potentiality zones, respectively, whereas 15.86% and 9.69% were categorized as very poor and very good GPZs. Sensitivity analysis highlighted the significance of geology, elevation, geomorphology, slope, and lineament density as influencing parameters; elimination of any single parameter engendered significant alterations in the GPZ classification. The investigation culminated in the formulation of a block-wise sustainable groundwater management blueprint designed to inform policy initiatives.

Analysis of marine diversity and anthropogenic pressures on Seixas coral reef ecosystem (northeastern Brazil).

Coral reefs, vital and ecologically significant ecosystems, are among the most jeopardized marine environments in the Atlantic Ocean, particularly along the northeastern coast of Brazil. The persistent lack of effective management and conservation has led to fragmented information on reef use and pressures, hindering the understanding of these ecosystems' health. Major difficulties and challenges include inadequate data, diverse anthropogenic pressures, and the complex interaction between marine species. This study sought to bridge this knowledge gap by conducting a comprehensive analysis of marine diversity and anthropogenic pressures, specifically focusing on Seixas coral reef near João Pessoa city, an area notably impacted by tourism. Utilizing 25 monitoring transects, subdivided into 1 m2 quadrants, the marine diversity was meticulously evaluated through innovative procedures including (a) sedimentological and geochemical field surveys, (b) application of Shannon-Weaver diversity and Simpson dominance indices, (c) cluster analysis, (d) species identification of macroalgae, coral, and fish, and (e) an examination of anthropogenic interactions and pressures on the coral reef. The assessment encompassed three distinct zones: Back Reef, Reef Top, and Fore Reef, and identified a total of 25 species across 15 genera and 10 fish families. The findings revealed the prevalence of brown macroalgae, fish, and coral, with heightened abundance of red macroalgae in the Fore Reef, which also exhibited the greatest diversity (2.816) and dominance (0.894). Original achievements include the identification of specific spatial variations, recognition of the anthropogenic factors leading to ecological changes, and the formulation of evidence-based recommendations. The study concludes that escalating urbanization and burgeoning daily tourist visits to the reef have exacerbated negative impacts on Seixas coral reef's marine ecosystem. These insights underscore the urgent need for strategic planning and resource management to safeguard the reef's biodiversity and ecological integrity.

Assessment of macrobenthos diversity and a zoning proposal for Seixas coral reefs (northeastern Brazil).

Coral reefs worldwide are under severe threat due to their inherent fragility and urgent need for conservation. The escalating tourism in coral reefs significantly impacts the marine ecosystem's biodiversity and conservation. This study analyzed the diversity and conservation status of macrobenthos in the Seixas coral reef, located in northeastern Brazil, and proposed a zoning plan. We employed monitoring protocols adapted from the Reef Check Program, the Rapid Assessment Protocol for Atlantic and Gulf Reefs, and the Protocol for Monitoring Coastal Benthic Habitats. Species identification was carried out by analyzing 25 transects, each divided into 1 m2 grids, with photos recorded for each grid, totaling 625 photos. Margalef, Shannon-Weaver, Simpson, and Pielou indices were used to analyze species distribution and diversity. The results indicated Dictyotaceae, Sargassaceae, and Corallinaceae as prevalent families. This research offers decision-makers a snapshot of species distribution in the Seixas coral reefs, providing a non-destructive, efficient methodology for assessing environmental impacts on coastal coral reefs.

Assessing coastal vulnerability and land use to sea level rise in Jeddah province, Kingdom of Saudi Arabia.

Sea level rise is one of the most serious outcomes of increasing temperatures, leading to coastal flooding, beach erosion, freshwater contamination, loss of coastal habitats, increased soil salinity, and risk of damage to coastal infrastructures. This study estimates the vulnerability to inundation for 2100 in coastal zones in Jeddah Province, Kingdom of Saudi Arabia, under various sea level rise (SLR) scenarios of 1, 2, 5, and 10 m. The predicted flooding was estimated using a combination of factors, including SLR, the bathtub model, digital elevation model, climate scenarios, and land use and land cover. The climate scenarios used were Representative Concentration Pathway (RCP) scenarios 1.9, 2.6, 4.5, and 8.5. The results of the SLR scenarios of 1, 2, 5, and 10 m revealed that 1.6, 4.7, 14.9, and 30.6% (or 88, 214, 679, 1398 km2) of the study area's coast could be classified as inundated areas. The various SLR scenarios can inundate 3.3 to 34% of the road area/length. The inundated built-up and road areas were estimated to range between 0.31 and 0.79 km2, accounting respectively for 1.18 to 3.01% of the total class areas for 1-meter and 2-meter SLR scenarios. In contrast, the inundated area will be significant in the situation of 5 and 10 m SLR scenarios. Regarding the case of a 10-meter SLR scenario, the inundation will negatively impact the built-up and road infrastructure areas, inundating 8.9 km2, with industrial infrastructures affected by inundation estimated at 0.21 km2, followed by green space infrastructures at 0.013 km2. The spatial information based on various SLR scenario impact mapping for Jeddah Province can be highly valuable for decision-makers to better plan future civil engineering structures within the framework of sustainable development.

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil.

This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.

A bibliometric analysis of sustainable development goals (SDGs): a review of progress, challenges, and opportunities.

The Sustainable Development Goals (SDGs) are a global appeal to protect the environment, combat climate change, eradicate poverty, and ensure access to a high quality of life and prosperity for all. The next decade is crucial for determining the planet's direction in ensuring that populations can adapt to climate change. This study aims to investigate the progress, challenges, opportunities, trends, and prospects of the SDGs through a bibliometric analysis from 2015 to 2022, providing insight into the evolution and maturity of scientific research in the field. The Web of Science core collection citation database was used for the bibliometric analysis, which was conducted using VOSviewer and RStudio. We analyzed 12,176 articles written in English to evaluate the present state of progress, as well as the challenges and opportunities surrounding the SDGs. This study utilized a variety of methods to identify research hotspots, including analysis of keywords, productive researchers, and journals. In addition, we conducted a comprehensive literature review by utilizing the Web of Science database. The results show that 31% of SDG-related research productivity originates from the USA, China, and the UK, with an average citation per article of 15.06. A total of 45,345 authors around the world have contributed to the field of SDGs, and collaboration among authors is also quite high. The core research topics include SDGs, climate change, Agenda 2030, the circular economy, poverty, global health, governance, food security, sub-Saharan Africa, the Millennium Development Goals, universal health coverage, indicators, gender, and inequality. The insights gained from this analysis will be valuable for young researchers, practitioners, policymakers, and public officials as they seek to identify patterns and high-quality articles related to SDGs. By advancing our understanding of the subject, this research has the potential to inform and guide future efforts to promote sustainable development. The findings indicate a concentration of research and development on SDGs in developed countries rather than in developing and underdeveloped countries.

Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin.

Characterized by their high spatiotemporal variability, rainfalls are difficult to predict, especially under climate change. This study proposes a multilayer perceptron (MLP) network optimized by Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), and Teleconnection Pattern Indices - such as North Atlantic Oscillation (NAO), Southern Oscillations (SOI), Western Mediterranean Oscillation (WeMO), and Mediterranean Oscillation (MO) - to model monthly rainfalls at the Sebaou River basin (Northern Algeria). Afterward, we compared the best-optimized MLP to the application of the Extreme Learning Machine optimized by the Bat algorithm (Bat-ELM). Assessment of the various input combinations revealed that the NAO index was the most influential parameter in improving the modeling accuracy. The results indicated that the MLP-FFA model was superior to MLP-GA and MLP-PSO for the testing phase, presenting RMSE values equal to 33.36, 30.50, and 29.92 mm, respectively. The comparison between the best MLP model and Bat-ELM revealed the high performance of Bat-ELM for rainfall modeling at the Sebaou River basin, with RMSE reducing from 29.92 to 11.89 mm and NSE value from 0.902 to 0.985 during the testing phase. This study shows that incorporating the North Atlantic Oscillation (NAO) as a predictor improved the accuracy of artificial intelligence systems optimized by metaheuristic algorithms, specifically Bat-ELM, for rainfall modeling tasks such as filling in missing data of rainfall time series.

A cloud-integrated GIS for forest cover loss and land use change monitoring using statistical methods and geospatial technology over northern Algeria.

Over the last two decades, forest cover has experienced significant impacts from fires and deforestation worldwide due to direct human activities and climate change. This paper assesses trends in forest cover loss and land use and land cover changes in northern Algeria between 2000 and 2020 using datasets extracted from Google Earth Engine (GEE), such as the Hanssen Global Forest Change and MODIS Land Cover Type products (MCD12Q1). Classification was performed using the pixel-based supervised machine-learning algorithm called Random Forest (RF). Trends were analyzed using methods such as Mann-Kendall and Sen. The study area comprises 17 basins with high rainfall variability. The results indicated that the forest area decreased by 64.96%, from 3718 to 1266 km2, during the 2000-2020 period, while the barren area increased by 40%, from 134,777 to 188,748 km2. The findings revealed that the Constantinois-Seybousse-Mellegue hydrographic basin was the most affected by deforestation and cover loss, exceeding 50% (with an area of 1018 km2), while the Seybouse River basin experienced the highest percentage of cover loss at 40%. Nonparametric tests showed that seven river basins (41%) had significantly increasing trends of forest cover loss. According to the obtained results, the forest loss situation in Algeria, especially in the northeastern part, is very alarming and requires an exceptional and urgent plan to protect forests and the ecological system against wildfires and climate change. The study provides a diagnosis that should encourage better protection and management of forest cover in Algeria.

A methodological proposal to analyze urban sprawl, negative environmental impacts, and land degradation in the case of João Pessoa City (Brazil) between 1991 and 2018.

Urbanization and changes in urban spaces have caused severe environmental and social problems in large Brazilian cities. As such, this study presents a methodological proposal to analyze urban sprawl, negative environmental impacts, and land degradation. The methodology employed involves a combination of remote sensing data, environmental modeling techniques, and mixed-method analyses of environmental impacts from 1991 to 2018. Analyzed variables included vegetation, surface temperature, water quality, and soil degradation within the study area. These variables were assessed based on an interaction matrix used to evaluate environmental impacts (low, medium, or high impacts). The obtained results show conflicts of land use and land cover (LULC), a lack of urban sanitation infrastructure, and an absence of environmental monitoring and inspection. A reduction of 24 km2 of arboreal vegetation was observed from 1991 to 2018. High values of fecal coliforms were found in March across nearly all analyzed points, indicating a seasonal discharge of effluents. The interaction matrix presented various negative environmental impacts, including increased land surface temperature, soil degradation, inappropriate solid waste disposal, devastation of remaining vegetation, water pollution by domestic effluents, and the incidence of erosive processes. Ultimately, the impact quantification determined that the study area has a medium degree of significance in terms of environmental impacts. Thus, refining this quantification method will contribute to future research by making the analysis processes more objective and efficient.

Multi-proxy assessment of coral reef formation and biotic-abiotic diversity in an urban coastal reef ecosystem in northeastern Brazil.

Coral reefs are habitats with high animal and mineral diversity and are subject to both climate change and anthropogenic impacts. This article presents novel and relevant data on the Seixas coral reef environment's geological, sedimentological, mineralogical, and biotic aspects in Paraíba State, northeastern Brazil. The aim of this study is to evaluate the processes of reef formation and the diversity of coral reef species in urban coastal environments in northeastern Brazil using a multi-proxy approach. Materials and methods employed to analyze the formation and diversity of biotic and abiotic species include (a) bathymetric survey, (b) collection of sedimentological, mineralogical, and granulometric data, (c) geological and stratigraphic determination, and (d) identification of biotic and abiotic species. Mineralogical slide results reveal that the Seixas Reef is a recent biogenic coral-algal carbonate formation associated with coastline evolution, high coastal sedimentation, and changes that occurred alongside sea-level rise (Holocene-Quaternary period). The diversity results indicate that benthic organism settlement occurred on a consolidated arenite base, with the fauna undergoing continuous succession processes. It can be concluded that this coral reef is highly vulnerable due to the material of its formation and comprises subsectors with high diversity (fore reef) and others with low diversity (reef top), which are affected by both anthropogenic and natural factors. Studies of this nature can contribute to understanding the evolution of coastal reefs, as their proximity to the continent makes them more vulnerable, and they experience direct physical impacts from fishing and tourist activities.

Quantitative assessment of present and the future potential threat of coastal erosion along the Odisha coast using geospatial tools and statistical techniques.

The eastern coast of India is one of the regions where most of the population resides in urban areas in the low-elevation coastal zone, making it vulnerable to frequent extreme weather events. The objectives of this study are to assess the short- to long-term shoreline changes of the Odisha coast, to understand how anthropogenic influences, and particularly extreme natural events, affect these changes, and to predict shoreline changes for 2050. This study utilized multi-temporal/spectral/spatial resolution satellite images and a digital shoreline analysis (DSAS) tool to appraise the short- (at five/six-year intervals) and long-term (1990-2019) shoreline dynamics along the coastal part of Odisha over the past three decades (1990-2019). The long-term shoreline analysis shows that the mean shoreline change is about 0.67 m/year and highlights that 52.47 % (227.4 km), 34.70 % (150.4 km), and 12.83 % (55.6 km) of the total Odisha coastline exhibit erosion, accretion, and stability, respectively. During the short-term analysis, the 2000-2005 period had the highest percentage of erosion (64.27 %), followed by the 2005-2010 period with an erosional trend of 59.06 %. The 1995-2000 period showed an accretion trend, whereas, during the last period, i.e., 2015-2019, the percentage of transects depicting erosion and accretion was almost similar. In 2050, 55.85 % of the transects are expected to show accretion, while 44.15 % would show erosion or a constant trend. The study identified the hotspots of coastal erosion along delineated study zones by synthesizing data from previous studies as well. The regional analysis of shoreline change along the Odisha coast would not only provide coastal managers with critical information on shoreline dynamics but also draw attention to vulnerable areas linked to shoreline dynamicity along the coast.

Deciphering the impact of anthropogenic coastal infrastructure on shoreline dynamicity along Gopalpur coast of Odisha (India): An integrated assessment with geospatial and field-based approaches.

Odisha's coastline supports various development activities that are critical to the state and national economy, such as oil and gas, ports and harbors, power plants, fishing, tourism, and mining that continues to not only detriment the coastal ecology but also affect the overall shoreline morphodynamics. The morphological changes are complicated processes involving both natural and human-induced drivers, but it is critical to understand how recent development activities further impact beach morphodynamics and shoreline dynamicity. The study analyzes the overall shoreline morphodynamics in response to the recent development of port and other related infrastructure for annual and decadal scale using two-dimensional (2-D) shoreline changes along with detailed 3-D beach profile volumetric changes for different studied zones along the Gopalpur coast. The results reveal that nearly all studied zones of the Gopalpur shoreline, Zone-4 (EPR = -05.64 m a-1 and LRR = -04.25 m a-1), Zone-3 (EPR = -04.51 m a-1 and LRR = -07.01 m a-1) and Zone-1 (EPR = -2.85 m a-1 and LRR = -01.46 m a-1), experienced erosion between 2010 and 2020 except Zone-2 (EPR = 24.31 m a-1 and LRR = 25.96 m a-1), which showed overall sign of deposition. The interannual shoreline analysis depicted that Zone-1 (tourist beach area) remained almost stable, Zone-2 (south of the breakwater of Gopalpur Port) showed accretion trends, Zone-4 (north side of the port) dominantly showed an erosion pattern, whereas Zone-3 (port area) showed a high level of uncertainty in the context of erosional or deposition trends. Calculated volumetric loss along the surveyed 3-D beach profiles supports these 2-D changes for all the studied zones. The results showed substantial changes in coastal morphodynamics in different studied zones of the Gopalpur region and severe erosion along its northern segment of the constructed coastal infrastructure. These findings can potentially promote effective coastal zone management and prevent further deterioration along the Gopalpur coast in future.

Deforestation and fires in the Brazilian Amazon from 2001 to 2020: Impacts on rainfall variability and land surface temperature.

Deforestation and fires in the Amazon are serious problems affecting climate, and land use and land cover (LULC) changes. In recent decades, the Amazon biome area has suffered constant fires and deforestation, causing severe environmental problems that considerably impact the land surface temperature (LST) and hydrological cycle. The Amazon biome lost a large forest area during this period. Thus, this study aims to analyze the deforestation and burned areas in the Amazon from 2001 to 2020, considering their impacts on rainfall variability and LST. This study used methods and procedures based on Google Earth Engine for analysis: (a) LULC evolution mapping, (b) vegetation cover change analysis using vegetation indices, (c) mapping of fires, (d) rainfall and LST analyses, and (e) analysis of climate influence and land cover on hydrological processes using the geographically weighted regression method. The results showed significant LULC changes and the main locations where fires occurred from 2001 to 2020. The years 2007 and 2010 had the most significant areas of fires in the Brazilian Amazon (233,401 km2 and 247,562 km2, respectively). The Pará and Mato Grosso states had the region's largest deforested areas (172,314 km2 and 144,128 km2, respectively). Deforestation accumulated in the 2016-2020 period is the greatest in the period analyzed (254,465 km2), 92% higher than in the 2005-2010 period and 82% higher than in the 2001-2005 period. The study also showed that deforested areas have been increasing in recent decades, and the precipitation decreased, while an increase is observed in the LST. It was also concluded that indigenous protection areas have suffered from anthropic actions.