Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy.

This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m3/s RMSE (root mean square error) in training to 49.42 m3/s in testing. The RF-XGBoost and models incorporating DWT, like LSTM-DWT and RF-DWT, also perform well, underscoring the significance of feature engineering. Comparisons illustrate enhancements with DWT: LSTM and RF reduce training and testing RMSE substantially when using DWT. Metaheuristic models like MLP-ABC and LSSVR-PSO benefit from DWT as well, with the LSSVR-PSO-DWT model demonstrating excellent predictive accuracy, showing 133.97 m3/s RMSE in training and 47.08 m3/s RMSE in testing. This model synergistically combines LSSVR, PSO, and DWT, emerging as the top performers by effectively capturing intricate reservoir inflow patterns.

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.

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.

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.

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.

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.

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.

Thermal comfort conditions at microclimate scale and surface urban heat island in a tropical city: A study on João Pessoa city, Brazil.

Surface urban heat islands (SUHIs) are one of the most studied phenomena in urban climates because they generate problems for the well-being of the urban population. This study analyzed the thermal comfort conditions at microclimate scale and SUHI for João Pessoa city, Brazil. Micrometeorological data (temperature and air humidity data) collected at 10 stations in 2011 and 2018 were used to calculate Thom's discomfort index (TDI) for João Pessoa city. Satellite images from Landsat 5/TM for 1991, 2006, and 2010 and Landsat 8/OLI for 2018 were used for land use and land cover classification and to identify SUHI. The obtained results highlighted that the SUHI area in João Pessoa city was 26 km2 and that almost half of the heat island area was concentrated in the Geisel, Aeroclube, Valentina, Distrito Industrial, Cristo Redentor, and Mangabeira neighborhoods. Regarding the micrometeorological data, higher values were obtained for 2018 in the dry periods (summer) and during the day. Based on the results, a considerable increase in discomfort during the daytime was observed in urbanized areas of the city from 2010 - 2018 due to the increase in the average temperature in João Pessoa between 1991 and 2018.

Mining impacts on forest cover change in a tropical forest using remote sensing and spatial information from 2001-2019: A case study of Odisha (India).

Worldwide mining activities are one of the major anthropogenic activities that have caused high forest cover loss (FCL). In this study, we have quantified FCL in Odisha State due to mining activities analyzing Hansen Global Forest Change (HGFC) time series data for the period of 2001-2019 in Google Earth Engine platform. Our analysis suggests that Nabarangpur, Puri, Kendrapara, and Kalahandi districts lost more than 20% of their forest cover during this period. Rayagada and Koraput were the top two districts that recorded the highest FCL with mean change rates of 13.81 km2/year and 7.17 km2/year, respectively. The results point out that mining operations have grown in recent years in Odisha State, and the increase in these activities has contributed to the increase in FCL. This study offers a cost-effective methodology to monitor FCL in mining areas which will eventually contribute to the protection of forest biodiversity and forest dwelling tribal population.

An overview of research on natural resources and indigenous communities: a bibliometric analysis based on Scopus database (1979-2020).

Indigenous people constitute an important section of society in many countries. Despite being a numerically smaller section, they are culturally diverse and distributed mostly in valuable natural resources-rich regions worldwide. In the era of globalization, industrialization, and trade liberalization, indigenous communities have become more vulnerable to displacement, land alienation, cultural erosion, and social exclusion. During the last few decades, researchers have tried to evaluate and document their problems and prospects. The present study analyzes the trends and characteristics of research and development conducted about indigenous communities. The research hotspots based on keywords, productive researchers, and journals during 1979-2020 were mapped using the Scopus database. The analysis was carried out using the bibliometrix R-package and VOSviewer software tool. Consistent growth in the number of studies and citations on indigenous communities concerning environmental conservation, natural resources, and economic development was observed during the last four decades. The present findings reveal that research on the indigenous community has attracted the attention of the scientific community in recent years. Qualitative studies with methodological rigor, having potential for social and policy implications, are warranted to understand and respect ingrained cultural and socio-economic diversity among these communities. Graphical abstract.

Drought impacts, social organization, and public policies in northeastern Brazil: a case study of the upper Paraíba River basin.

The upper Paraíba River basin plays a key role in controlling the flow from the Epitácio Pessoa Reservoir, a major reservoir in the semiarid region of Paraíba state. The objective of this study is to analyze historical droughts and public policies and their impacts on the social organization in the upper Paraíba River basin, which is located in the semiarid region of Brazil. In this study, the following methodological procedures were used: (a) historical survey of drought occurrence and dam construction in the semiarid region of Brazil, (b) data processing of hydrologic records (rainfall and streamflow), and (c) field visits to collect and compare data on the changes in the volume of water stored behind dams. The results showed that state intervention in the semiarid region follows a trend based on the characteristics of each historical moment and the interests of the groups that comprise the state sector. It is also observed that the implementation of several public policies, such as social programs, construction of dams, and the transfer of water from the São Francisco River, has resulted in significant changes in the streamflow behavior in this semiarid region. These changes have differed in magnitude depending on the hydrological characteristics of each period (i.e., dry, rainy, or normal). Finally, the use of dams for water management in the semiarid region was identified as the main factor influencing water security and social organization.

Mapping LULC types in the Cerrado-Atlantic Forest ecotone region using a Landsat time series and object-based image approach: A case study of the Prata River Basin, Mato Grosso do Sul, Brazil.

In the last 30 years, the growth of the agriculture and livestock industries in the Cerrado biome has caused severe changes in land use and land cover (LULC), and areas previously occupied by native vegetation are changing to agricultural monocultures (e.g., soybean or corn) and/or pastures. Thus, the objective of this study was to analyze the LULC changes for the years 1986, 1999, 2007, and 2016 based on Landsat time series and object-based image analysis (OBIA) for the Prata River Basin. Twelve LULC classes were mapped: riparian forest, cerrado, swampy grasslands, wetlands, semideciduous forest, pasture, agriculture, fallow agricultural land, barren land, eucalyptus, water bodies, and burnt area. The classifications presented results with an overall accuracy of more than 93% and a kappa coefficient of 0.92. In 2007, the pasture class had the highest increase in area (48.5%), with a total area of 118.32 km2 of Cerrado biome vegetation converted to pasture, and the classes banhado, riparian forest, swampy grasslands, and cerrado had the greatest reductions in area (41.58%, 29.67%, 25.44%, and 21.63%, respectively). More precisely, the wetlands class underwent the greatest decrease under the advancement of pasture in the studied period (- 36.2%). These changes are due to factors favorable to agropastoral practices, such as a flat relief and soil with good agricultural suitability. Graphical abstract.

Integrated spatiotemporal trends using TRMM 3B42 data for the Upper São Francisco River basin, Brazil.

Trend analysis is an important issue for the decision-making processes. Thus, trends of rainfall, consecutive dry days (CDD), and consecutive wet days (CWD) in the Upper São Francisco River basin, Brazil, using daily rainfall data from the Tropical Rainfall Measuring Mission (TRMM) for recent 18 years, were analyzed. Instead of analyzing the trend of one average time series for one specific confidence level, a spatiotemporal analysis over the entire area with 169 continuous time series is done by applying the nonparametric Mann-Kendall and Sen tests for simultaneously 13 confidence levels and a new integrated confidence classification is proposed. The results show that the rainfall has increased during the less rainy periods (from June to October) and has decreased in the rainy periods (from November to May), with the highest and lowest confidence levels, respectively. An analysis of CDD and CWD shows that the number of CDD has decreased, while the number of CWD has increased, which revealed that the dry periods are more frequently interrupted for the period studied.

Drought assessment using a TRMM-derived standardized precipitation index for the upper São Francisco River basin, Brazil.

In this work, the use of Tropical Rainfall Measuring Mission (TRMM) rainfall data and the Standardized Precipitation Index (SPI) for monitoring spatial and temporal drought variabilities in the Upper São Francisco River basin is investigated. Thus, the spatiotemporal behavior of droughts and cluster regions with similar behaviors is identified. As a result, the joint analysis of clusters, dendrograms, and the spatial distribution of SPI values proved to be a powerful tool in identifying homogeneous regions. The results showed that the northeast region of the basin has the lowest rainfall indices and the southwest region has the highest rainfall depths, and that the region has well-defined dry and rainy seasons from June to August and November to January, respectively. An analysis of the drought and rain conditions showed that the studied region was homogeneous and well-distributed; however, the quantity of extreme and severe drought events in short-, medium- and long-term analysis was higher than that expected in regions with high rainfall depths, particularly in the south/southwest and southeast areas. Thus, an alternative classification is proposed to characterize the drought, which spatially categorizes the drought type (short-, medium-, and long-term) according to the analyzed drought event type (extreme, severe, moderate, and mild).

Morphostructural influence and neotectonic activity in the geomorphological configuration of southeast Paraíba and northeast Pernambuco, Brazil.

The current study provides critical insights into the field of geomorphology by examining the impact of lithological structures and tectonic activities on the geomorphological configuration of terrain and drainage networks in the southeast of Paraíba and northeast of Pernambuco, Brazil. This geographical region is characterized by phenomena of uplift and fluvial incision at various sites, yet remains inadequately explored with respect to its active deformation and the broader context of its recent geology. Therefore, the primary aim of this research is to elucidate the morphostructural and neotectonic influences on the geomorphological formations in these sectors of Brazil. The methodology encompasses morphostructural analysis, leveraging data derived from altimetry, slope, and geomorphology maps, categorized into taxonomic terrain units. Additionally, this research incorporates the use of morphometric indices, including the Stream Length-Gradient Index (SL), Valley Floor Width (VF), and Asymmetry Factor (AF) to quantify geomorphological anomalies. The analysis of the SL index indicates that a significant portion (87.5 %) of the drainage network exhibits anomalies. Furthermore, the VF index results reveal that 66 % of the profiles analyzed manifest anomalies of various magnitudes. Within the study region, the AF index elucidates the distribution of sub-basins into categories of low (0-30), medium (31-50), and high (>51) asymmetry, comprising 25 %, 31 %, and 44 % of the sub-basins, respectively. The sub-basins demonstrate channel disequilibrium, as evidenced by the SL index, attributed to numerous transient knickpoints along the drainage profiles. This observation is consistent with previous findings in the Koyna-Warna Shallow Seismic Region. The study's outcomes reveal both qualitatively and quantitatively anomalous patterns in the drainage network and terrain forms, which are likely indicative of recent tectonic events affecting the entire eastern edge of Northeast Brazil. Consequently, the findings highlight the significant role of post-Miocene tectonic events in shaping the relief of the study area.

Climate change and resilience, adaptation, and sustainability of agriculture in India: A bibliometric review.

Climate change (CC) is a global issue, with effects felt across nations, including India. The influences of CC, such as rising temperatures, irregular rainfall, and extreme weather events, have a direct impact on agricultural productivity, thereby affecting food security, income, livelihoods, and overall population health. This study aims to identify trends, patterns, and common themes in research on Climate Change and Resilience, Adaptation, and Sustainability of Agriculture in India (CCRASAI). It also seeks to illuminate potential future research directions to guide subsequent research and policy initiatives. The adverse impacts of CC could push farmers into poverty and undernourishment, underscoring the imperative to focus on the resilience, adaptation, and sustainability of agriculture in India. A bibliometric review was conducted using Biblioshiny and VoSviewer software to analyze 572 articles focused on CCRASAI from the Scopus and Web of Science databases, published between 1994 and 2022. There was an evident upward trend in CCRASAI publications during this period, with steady growth appearing after 2007. Among the States and Union Territories, Delhi, Tamil Nadu, West Bengal, Andhra Pradesh, and Karnataka have the highest number of published research articles. Research on CCRASAI is most concentrated in the southern plateau, the trans-Gangetic and middle Gangetic plains, and the Himalayan regions. The frequently used terms-'climate change impacts,' 'adaptation strategies,' and 'sustainable agriculture'-in CCRASAI research emphasize the focus on analyzing the effects of CC, creating adaptation strategies, and promoting sustainable agricultural practices.

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.

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.

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.