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

Forecasting shoreline dynamics and land use/land cover changes in Balukhand-Konark Wildlife Sanctuary (India) using geospatial techniques and machine learning

Coastal ecosystems play a critical role in biodiversity conservation, climate regulation, and supporting human livelihoods. This study investigates future shoreline and land use/land cover change (LULCC) projections in the Balukhand-Konark Wildlife Sanctuary (BKWS). Shoreline and LULCC dynamics were quantified using satellite imagery, geospatial tools, and predictive modeling. An ensemble machine learning approach, combining Cellular Automata-Artificial Neural Networks (CA-ANN) and Random Forest techniques, was employed to enhance prediction accuracy. The shoreline dynamics analysis indicated persistent erosion in the northern zones and accretion in the southern zones, trends projected to continue through 2033 and 2043. LULC assessments showed a high reduction in dense vegetation, from 41.8 % in 1993 to 37.1 % in 2023, alongside an increase in sparse vegetation to 50.3 %. Predictions suggest continued degradation in the northern sections of BKWS, stabilization of sparse vegetation, and expansion of sandy areas by 2043. The results highlight high shoreline erosion and LULC changes. This study shows the need to integrate shoreline dynamics and LULC analyses into coastal management practices and provides actionable recommendations for habitat restoration, sustainable land-use planning, and climate adaptation strategies.

Forest fire probability zonation using dNBR and machine learning models: a case study at the Similipal Biosphere Reserve (SBR), Odisha, India

Forests play a vital role in environmental balance, supporting biodiversity and contributing to atmospheric purification. However, forest fires threaten this balance, making the identification of forest fire probability (FFP) areas crucial for effective mitigation. This study assesses forest fire trends and susceptibility in the Similipal Biosphere Reserve (SBR) from 2012 to 2023 using four machine learning models-extreme gradient boosting tree (XGBTree), AdaBag, random forest (RF), and gradient boosting machine (GBM). A forest fire inventory was created using the delta normalized burn ratio (dNBR) index, and 19 conditioning factors were incorporated after rigorous collinearity testing. FFP maps were generated and evaluated using ROC-AUC, MAE, MSE, and RMSE metrics. The frequency ratio (FR) model was also applied to assess the importance of variables. The results show that approximately 40.85% of the study area is high to very high susceptible to forest fires, with the RF model achieving the highest accuracy (AUC = 0.965). An average analysis across all models revealed that high susceptibility areas accounted for 23.08% of the study area, the largest among all classes. Moderate susceptibility zones covered 16.19%, while very high susceptibility areas comprised 18.23%. Interestingly, very low and low susceptibility zones together represented 42.50%, indicating a large portion of the area is at relatively low fire risk. Temporal analysis identified 2021 as the peak year for fire incidents, with 94.72% of the fires occurring during March and April. The buffer zone experienced the highest number of incidents, with a significant anthropogenic influence. Using the FR model, variable importance analysis showed that land use and land cover (LULC), NDVI, and NDMI were the most influential factors in fire susceptibility. This study contributes to forest fire management by integrating the dNBR index with machine learning models and FR analysis to generate precise FFP maps. These findings provide valuable insights for policymakers and conservationists, enabling targeted interventions in high-risk zones and enhancing fire management strategies to reduce the impact of forest fires.

Rapid impact assessment of severe cyclone storm Michaung along coastal zones of Andhra and Tamil Nadu, India: A geospatial analysis

The coastal regions of India, particularly the Bay of Bengal, are highly vulnerable to the severe weather conditions induced by tropical cyclones. This study presents a comprehensive analysis of the changes in vegetation cover, shoreline dynamics, and meteorological variations resulting from Cyclone Michaung and subsequent post-monsoon events along the coastal zones of Andhra Pradesh and Tamil Nadu, India. A suite of vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), and Disaster Vegetation Damage Index (DVDI), were employed to assess changes in vegetation cover. The Digital Shoreline Assessment System (DSAS) was utilized to evaluate shoreline changes, and a range of meteorological variables were analyzed to assess the impacts of Cyclone Michaung and post-monsoon events. The findings reveal significant ecological impacts, with a notable decrease in Very Healthy Vegetation from 5.71% to 1.30%. The mean value of mVCI shifted from -0.2 to -0.16, indicating vegetation stress. DVDI analysis showed that 56.49% of the area experienced moderate damage, while 40.24% suffered severe vegetation damage. Additionally, erosion was observed along 79.46% of the shoreline transects in the study area. These insights are critical for assisting coastal managers in developing resilient coastal systems. Remarkably, a significant change in rainfall was recorded between the pre-cyclone period and the landfall day, with maximum rainfall intensifying from 13.93 mm/h on December 3rd to 164.26 mm/h on December 4th, and subsequently decreasing to 144.39 mm/h on December 5th.

Optimal allocation of distributed energy resources to cater the stochastic E-vehicle loading and natural disruption in low voltage distribution grid

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact.

An advanced TSMK-FVC approach combined with Landsat 5/8 imagery for assessing the long-term effects of terrain and climate on vegetation growth

Introduction

As an exceptional geographical entity, the vegetation of the Qinghai-Tibetan Plateau (QTP) exhibits high sensitivity to climate change. The Baima Snow Mountain National Nature Reserve (BNNR) is located in the south-eastern sector of the QTP, serving as a transition area from sub-tropical evergreen broadleaf forest to high-mountain vegetation. However, there has been limited exploration into predicting the temporal and spatial variability of vegetation cover using anti-interference methods to address outliers in long-term historical data. Additionally, the correlation between these variables and environmental factors in natural forests with complex terrain has rarely been analyzed.

Methods

This study has developed an advanced approach based on TS (Theil-Sen slope estimator) MK (Mann-Kendall test)-FVC (fractional vegetation cover) to accurately evaluate and predict the time and spatial shifts in FVC within the BNNR, utilizing the GEE (Google Earth Engine). The satellite data utilized in this paper consisted of Landsat images spanning from 1986 to2020. By integrating TS and MK methodologies to monitor and assess the FVC trend, the Hurst index was employed to forecast FVC. Furthermore, the association between FVC and topographic factors was evaluated, the partial correlation between FVC and climatic influences was analyzed at the pixel level (30×30m).

Results and discussion

Here are the results of this research: (1) Overall, the FVC of the BNNR exhibits a growth trend, with the mean FVC value increasing from 59.40% in 1986 to 68.67% in 2020. (2) The results based on the TS-MK algorithm showed that the percentage of the area of the study area with an increasing and decreasing trend was 59.03% (significant increase of 28.04%) and 22.13% (significant decrease of 6.42%), respectively. The coupling of the Hurst exponent with the Theil-Sen slope estimator suggests that the majority of regions within the BNNR are projected to sustain an upward trend in FVC in the future. (3) Overlaying the outcomes of TS-MK with the terrain factors revealed that the FVC changes were notably influenced by elevation. The partial correlation analysis between climate factors and vegetation changes indicated that temperature exerts a significant influence on vegetation cover, demonstrating a high spatial correlation.

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.