Government College, Chittur

Let G = (V, E) be a graph with non-empty set of vertices V and set of edges E. The eccentric connectivity index of the graph G is defined as ξ^C(G) = ∑_u ∈ V d_u ecc(u) where d_u is the degree and ecc(u) is the eccentricity of the vertex u ∈ V. This article is an attempt to find the eccentric connectivity index of strongly connected digraph D with respect to the metric, maximum distance defined by md(u,v)=max{d⃗(u,v),d⃗(v,u)}. An attempt is also made to find the extremal values for strongly connected digraphs.

As economic development increase without adequate planning, climate change and variabilities will have adverse impact on agro-ecosystems. Extreme climatic events are putting pressure on the Bharathapuzha basin in Palakkad, Kerala which is referred as the state’s rice bowl. Over the past two decades, the Kerala had witnessed numerous floods of catastrophic severity, including the deadly floods of 2018 and 2019, affecting crop health, productivity, agricultural investments, farmer’s livelihood, and well-being. River flow and rice fields are largely affected by the past droughts in 2016 and dry spells in 2023. With the help of ICT tools and social media, farmer groups are generated and agromet advisories are disseminated to the farmers on a biweekly basis. The impact-based weather early warnings are found to be helpful for climate risk management. Automatic Rain gauge is fixed at various locations to know real-time monitoring. Awareness is created among the farmers to follow ICT-based advisories that can leverage better productivity levels, sustainably. Advisories on nature-based solutions such as biological pest and weed management strategies and organic fertilizer and pest management strategies have helped the farmers to choose sustainable options. Awareness was also created as large-scale conversion of arable lands may affect the ecosystem services and local food security. The sustenance of rice farming would depend largely on the willingness of the farmers to cultivate and adapt in a climate smart way. Timely interventions and incentives from government can bring in path breaking changes in the resilience of the farmers and rice farm sector.

This study investigates urban sprawl patterns and their relationship with land use and land cover (LULC) transformations in Palakkad Municipality, a fast-growing medium-sized urban centre in Kerala, India, through the application of geospatial techniques. Such towns are increasingly experiencing significant land transformation due to growing population pressure and infrastructure expansion, yet remain underrepresented in urban sprawl studies. Multi-temporal Landsat imagery for the years 2001, 2011, and 2021 was analysed to perform change detection, and a supervised classification approach based on the maximum likelihood algorithm was employed to delineate five major land use categories. The extent of built-up land increased from 16.17% in 2001 to 50.80% in 2021, representing a net increase of 34.63%. The spatial pattern shows a transition from monocentric growth to linear expansion along major transportation corridors, with mixed tree vegetation and net sown areas being progressively converted to built-up land. These changes, supported by population and workforce growth trends, indicate increasing pressure on land resources and emerging ecological imbalance. The study provides a data-driven basis for urban planners and policymakers to promote sustainable land-use strategies and manage urban expansion in similar emerging urban centres.

Rapid urbanization in coastal and metropolitan regions of South Asia has emerged as a critical challenge for sustainable land-use planning and environmental management. This study examines the urban expansion patterns of Kochi and Ernakulam districts in Kerala, India, over a period spanning from 2000 to 2026, employing Geographical Information Technology (GIT) and Remote Sensing (RS) techniques integrated with the Landscape Expansion Index (LEI). Multi-temporal Landsat satellite imagery obtained from the United States Geological Survey (USGS) archive serves as the primary data source for land use and land cover (LULC) change detection. The LEI framework categorizes urban growth into three distinct modes: infill, edge expansion, and outlying growth, enabling a nuanced understanding of the spatial dynamics driving urban morphology in the study region. Results reveal that edge expansion constitutes the dominant mode of urban growth throughout the study period, with significant transformation of agricultural, wetland, and vegetated landscapes into built-up areas. The study period (2000–2026) captures both historical patterns and near-future projected trajectories, providing a comprehensive temporal baseline for urban planning authorities. Findings contribute to the broader discourse on sustainable urban development, coastal vulnerability, and land resource management in rapidly urbanizing Indian cities. The methodology and outcomes offer replicable frameworks applicable to similar urban agglomerations across South and Southeast Asia.

The Palakkad Gap, the lowest-altitude mountain pass in the Western Ghats, serves as an important bioclimatic corridor that facilitates for biodiversity exchange. However, it is currently experiencing intensifying and unsustainable land-use transformations. Using multi-temporal Landsat imagery (Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM +), and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS)), this study analyses the spatiotemporal dynamics of land use/land cover (LULC), land surface temperature (LST), and land-specific carbon emissions (LCEs) across the Palakkad Gap from 1994 to 2024. Supervised classification (Maximum Likelihood) and the InVEST carbon model were employed to analyze LULC transitions, thermal landscape evolution, and carbon storage changes. Built-up areas expanded significantly by 9,101.86 ha, primarily at the expense of 7,216.54 ha of agricultural land. This land-use shift corresponded with significant thermal alterations—Medium and High LST zones increased by 57,049.18 ha and 12,927.98 ha, respectively. Carbon-rich ecosystems suffered extensive degradation with the Critical carbon category (25 tC/ha) expanding fourfold while Normal and Salubrious zones declined by > 7,000 ha. Total carbon emissions reached 555,619 tons over three decades, demonstrating urbanization’s compounded impact on regional microclimates and carbon sequestration. Land-Specific Carbon Emissions (LCEs) peaked in 2004 (1,906 × 103 tons/year) before declining marginally to 1,853 × 103 tons/year by 2024, with built-up and agricultural lands as dominant emission sources. High-emission sectors (e.g., WSW) exhibited elevated LST, while carbon sinks (e.g., NNW) showed significant LST increases (+ 4.54 °C) due to urbanization legacy effects. Regression analyses indicated a weak inverse relationship between LCEs and LST (R2 = 0.11–0.26), suggesting emissions reductions alone may not fully mitigate warming. This study presents one of the first integrated assessments of LULC, LST, and carbon emissions in the Palakkad Gap using InVEST model. It highlights the urgency of integrated land-use planning, heat mitigation strategies, and ecological zoning to protect this biodiversity hotspot. The findings provide a scientific foundation for policy making in ecologically vulnerable regions, advocating for targeted emissions control and green infrastructure to enhance climate resilience and sustainability across the Palakkad Gap and broader Western Ghats landscape. The 2034 projection highlights a strong forward trajectory of land transformation, with built-up area increasing to 15,663 ha (an additional 3,954 ha from 2024) and agricultural land declining to 95,380 ha (a reduction of 4,127 ha). Carbon storage in high-density categories contracts further, while critical low-carbon zones expand to 16,707 ha. Net land-based emissions rise to 56.6 × 103 t C per year, and mean LST reaches 31.33 °C, dominated by medium (80,786 ha) and high (17,504 ha) temperature zones. These future estimates underscore the likelihood of sustained warming and reduced carbon resilience under continued land-use pressures. This visual summary serves as a pivotal entry point into the research, offering a concise overview of the study’s core findings and methodologies in the Palakkad Gap, the lowest pass in the Western Ghats. The graphical abstract captures the integration of land use/land cover (LULC) transitions, carbon emissions, and land surface temperature (LST) dynamics from 1994 to 2024 using Landsat satellite data and the InVEST carbon model. The diagram showcases urban expansion, agricultural decline, and increasing surface temperature zones. It also highlights spatial patterns of land-specific carbon emissions, peaking in high urbanization areas. The study identifies a weak inverse correlation between carbon emissions and LST, suggesting complex climatic interactions. The visual conveys a clear message: despite emissions control, surface warming persists due to land conversion legacy effects. Through arrows, maps, and charts, the abstract efficiently guides the viewer through the research process and outcomes. It emphasizes the urgent need for integrated land-use policy, climate-resilient zoning and ecological restoration. By providing a rapid visual comprehension of the paper’s essence, the graphical abstract enhances visibility and impact, making it an effective tool for broader scientific dissemination. A simplified topographic map outlines the study area’s location and boundaries, establishing the geographical context. Sequential panels illustrate the timeline of land use/land cover (LULC) transitions from 1994 to 2024, highlighting significant landscape changes—shrinking agricultural land, expanding built-up areas, and degrading vegetation and water bodies—driven by anthropogenic activities. Carbon dynamics are depicted using red arrows for emissions from built-up and agricultural areas and green arrows for carbon sinks like vegetation and water bodies, with quantitative labels showing agricultural emissions. Despite ongoing sequestration, net carbon emissions have risen, disrupting the regional carbon balance. A bar chart shows the land surface temperature (LST) gradient shifting from Very Low (< 26 °C) to Very High (> 38 °C), underscoring increased thermal stress due to land conversion and urban heat island effects. The summary panel visually encapsulates the findings—built-up ↑, agriculture ↓, LST ↑, and carbon storage ↓—emphasizing that land-use change is intensifying climate impacts in the Palakkad Gap. The 2034 projection panel shows built-up land increasing to 15,663 ha and agricultural land declining to 95,380 ha. Critical low-carbon zones expand to 16,707 ha as higher-density carbon classes continue to shrink. Net land-based emissions rise to 56.6 × 103 t C per year. Mean land surface temperature reaches 31.33 °C, with medium and high temperature zones covering 80,786 ha and 17,504 ha. This future scenario highlights continued warming and declining carbon resilience if present land-use trends persist. Together, these elements follow a logical flow from spatial context to transformation, thermal response, emissions, and conclusions, offering a clear and impactful overview of the study’s core findings and implications. Built-up land in the Palakkad Gap expanded by 9,101.86 ha, replacing 7,216.54 ha of agriculture from 1994–2024. Medium and High LST zones rose by over 70,000 ha, signalling intensified surface warming and urban heat effects. Carbon-rich zones declined by > 7,000 ha, while low-carbon Critical zones grew fourfold, reflecting ecosystem degradation. Peak land-specific carbon emissions reached 1,906 × 103 tons/year in 2004, driven by urban and agricultural expansion. A weak inverse LCE-LST correlation (R2 = 0.11–0.26) highlights complex thermal responses to emission changes. The 2034 projection shows built-up land increasing to 15,663 ha, agriculture declining to 95,380 ha, Critical low-carbon zones expanding to 16,707 ha and mean LST rising to 31.33 °C, indicating continued warming and reduced carbon resilience.