Asian Journal of Geographical Research

Changing Landscapes of Rewa District: A Geospatial Analysis of Land Use and Land Cover Dynamics

2026-05-25T11:02:09+00:00

Land use and land cover (LULC) change is a fundamental indicator of environmental transformation, resource consumption and anthropogenic pressure on natural systems. This study presents a temporal analysis of LULC dynamics in Rewa District, Madhya Pradesh for the years 2020 and 2025, using Sentinel-2 multispectral imagery processed through the Google Earth Engine (GEE) cloud platform. A supervised Random Forest classification approach was adopted, integrating a comprehensive feature set comprising spectral bands, vegetation indices (NDVI, EVI, SAVI) water and built-up indices (MNDWI, NDBI, BSI) Grey Level Co-occurrence Matrix (GLCM) texture metrics and terrain derivatives (DEM, slope, aspect) from the SRTM dataset. A year (January–December) cloud-free median composites were used to minimise seasonal bias. Five LULC classes were mapped: Agriculture Land, Vegetation Land, Water Body, Built-up Land, and Waste Land. The classification achieved an Overall Accuracy of 94.59% and a Kappa coefficient of 0.9308 for 2020, and 95.45% and 0.9387 respectively for 2025. Post-classification change analysis reveals a landscape undergoing active transformation driven by agricultural intensification, rapid built-up expansion and large-scale wasteland reclamation. Agriculture Land remained dominant, expanding from 3,441 km² (54.5%) in 2020 to 3,548 km² (56.2%) in 2025. The Built-up Land class recorded the most dramatic proportional change, expanding by 113 km² (+44.66%), driven by highway corridor development along NH-30 and NH-27 and the creation of Mauganj as a new administrative district. Waste land declined most substantially by 239 km² (-13.87%), as ravines and degraded surfaces were reclaimed for cultivation and solar energy infrastructure. Forest cover recorded a marginal increase of 17 km² (+2.16%) attributable to open forest regeneration and social forestry on the Kaimur Range escarpment. The study demonstrates the utility of cloud-based geospatial analysis for regional LULC monitoring and provides a spatial evidence base for land management and development planning in Rewa District.

Floriculture Practices, Land Use Pattern and Socio-economic Status of Flower Cultivators in Paschim Kalya, Purba Medinipur, West Bengal, India

2026-06-05T11:11:45+00:00

The floriculture has become one of the significant commercial agricultural activities at different parts of West Bengal. The current investigation deals with floriculture condition of Paschim Kalya village, under Panskura-I Block of Purba Medinipur district at West Bengal. The study is primarily centered on given floriculture pattern at national and state level, land use and land cover (LULC) characteristics of the area under study, socio-economic status of flower growers, marketing pattern followed by them along with their labour participation in farming activities as well as SWOT analysis of flowers growing. The study used both primary and secondary data. Household survey were conducted using a structured questionnaire and analysed on Likert scale basis (primary data); other secondary data derived from National Horticulture Board, government reports & satellite imagery. A supervised image classification method was used to prepare the LULC map of the study area. The results indicate that floriculture has a share of land use dominance and an important role in livelihood generation, and rural economic development. Though floriculture is practiced by the marginal farmers, yet they not only benefit phenomenally in terms of income but also this sector remains the due icon of international business. It also highlights a number of potential constraints including flower price instability, low profit margins, middlemen debt, pest problems, lack of storage infrastructure and poor market information and limited institutional support. In contrast to this, the respondents had a more optimistic view on floriculture market growth opportunities and increasing production for future. The study states that Paschim Kalya has enormous development potential for floriculture because of favorable agro-climatic conditions, the availability of fertile alluvial soil, irrigation facilities and transportation connectivity. The study concludes on the note of building enduring market infrastructure, arranging cooperative marketing systems, creating cold storage facilities, ensuring access to scientific training programmers and government support for sustainable floriculture development and livelihood capitalization in the region.

Assessment of Rainfall Data based on the Precipitation Concentration Index of the Darna River Basin, India

2026-06-06T08:15:02+00:00

Precipitation is a significant climatic variable as it changes in intensity and amount, affecting the occurrence of hydrological risks such as floods and drought. The rainfall variability assessment plays a vital role by applying the Precipitation Concentration Index (PCI), which accurately forecasts rainfall patterns, floods, and droughts across the regions. Therefore, the objective set for the current work is to assess the concentration of rainfall by employing the Precipitation Concentration Index (PCI) and the Standardized Rainfall Anomaly Index (SRAI). The monthly rainfall data of 76 years (1950-2025) were utilized for the Igatpuri, Aswali, and Wasali rain gauge stations over the Darna River Basin. Similarly, the Mann–Kendall (MK) trend test and Sen’s Slope Estimator (SSE) were developed to assess the trend in the precipitation concentration based on PCI. The PCI was computed at the annual and seasonal levels using annual and seasonal equations for the evaluation of the concentration of precipitation; similarly, SRAI was used to examine annual precipitation data to determine the number of dry and wet years over time. The spatial distribution of the PCI map was prepared by using the inverse distance weighted (IDW) technique in the GIS environment. The results reveal that all stations' annual average PCI values are >20, indicating that the annual rainfall distribution is strongly irregular. The seasonal breakdown shows that the actual rainy season (Monsoon) has a moderate, regular distribution, although the annual PCI signifying strong irregularity with high concentration. No statistically significant trend observed at Igatpuri throughout the period, whereas a highly significant trend was noted throughout the period at Aswali rain guage station. Annually Aswali station shows the highest (Z=12.03) PCI trend values. The notable severe positive anomaly that peaked at Aswali is 7.71 in 2021, followed by SRAI 3.37 in the year 1995, which interprets the extremely wet (flood) years. The remarkable negative SRAI values at Igatpuri station are -2.24 (1962), -1.97 (1965), and -1.90 (2024 and 2025), that indicates the extremely severe dry years associated with drought. The study is important to show the water potential of the study area. It is also essential to recommend to water managers, farmers, policymakers, and decision-makers to minimize risks and plan future water utilization, in addition to providing a scientific contribution for researchers.

Tidal Estuary Response to Bridge Construction Using DSAS Shoreline Analysis along Vaitarna River, India

2026-06-06T11:40:22+00:00

Estuarine systems are highly sensitive to hydraulic modifications, and bridge construction can significantly alter sediment transport, shoreline dynamics, and geomorphic stability. The present study aims to assess the geomorphic impact of bridge construction on the Vaitarna River estuary and evaluate the influence of bridge-related disturbance on sediment movement; shoreline change and erosion-deposition patterns in the estuary. This study examines the geomorphic impacts of bridge construction on the Vaitarna River estuary. This study employs an integrated design that combines bathymetric survey transects, shoreline analysis (Landsat 1998–2022) using the DSAS model across the Vaitarna estuary. The study area was the Vaitarna River estuary, Palghar District, Maharashtra, India. Shoreline change was analysed for the period 1998 to 2022, while the Vaitarna bridge, constructed in 1886, was considered the key structure influencing geomorphic change. The study used bathymetric cross-profiling, shoreline change analysis, sediment granulometric analysis, hydrodynamic simulation, and DSAS v6.0 shoreline analysis. These methods helped identify scour near bridge piers, bed incision, upstream aggradation, and changes in tidal sediment movement. The Vaitarna estuary showed strong geomorphic instability, including pier-induced scour, downstream bed incision, and upstream aggradation. Bathymetric profiles at Vaitarna revealed pier-induced scour and downstream deepening (>18,000 m³ net erosion, 1998–2022), contrasting with depositional trends. DSAS model resulted in a range of -6.85 to to 7.34 m yr^-1erosion at Vaitarna, confirming tidal control on sediment dynamics. The findings highlighted that bridge construction has significantly altered the geomorphic balance of the Vaitarna River estuary. The study highlights the need for scour protection, regular monitoring, and mangrove restoration to improve estuarine resilience.

Assessment of Urban Air Quality in Dimapur: A Geospatial Analysis of RSPM

2026-06-06T11:46:36+00:00

Air pollution is a major environmental concern in rapidly urbanizing regions, particularly in developing urban centres. Dimapur, the most urbanised district and commercial hub of Nagaland, has experienced increasing deterioration in ambient air quality due to rapid urban expansion, vehicular emissions, construction activities, and commercial growth. This study analyses the spatial distribution and seasonal variation of Respirable Suspended Particulate Matter (RSPM/PM10) across Dimapur during 2023 using Geographical Information System (GIS)-based Inverse Distance Weighted (IDW) interpolation and Exceedance Factor (EF) analysis. The findings reveal that all monitoring locations exceeded the National Ambient Air Quality Standards (NAAQS) annual permissible limit of 60 µg/m³. Dhobinalla recorded the highest annual average RSPM concentration (130 µg/m³), followed by Burma Camp (114 µg/m³), while Viola Colony recorded the lowest concentration (81 µg/m³). Seasonal analysis indicated that winter months experienced the highest pollution levels due to reduced atmospheric dispersion, whereas monsoon months recorded the lowest concentrations because of rainfall-induced pollutant washout. Commercial and traffic-dense areas exhibited significantly higher pollution levels compared to residential and green spaces. The study highlights the urgent need for effective air quality management strategies, including stricter traffic regulation, improved urban planning, expansion of green spaces, and continuous air quality monitoring.

District Level Assessment of Cropping Intensity for Agricultural Intensification in Bihar, India

2026-06-09T11:45:36+00:00

Cropping intensity is an important indicator of agricultural intensification and land-use efficiency, particularly in densely populated agrarian regions where the scope for expansion of cultivable land is limited. In states such as Bihar, increasing cropping intensity has become essential for enhancing agricultural productivity, ensuring food security and improving rural livelihoods under conditions of land fragmentation, population pressure and climatic uncertainty. The present study examines the spatial and temporal patterns of cropping intensity at the district level in Bihar during 2004-05 and 2024-25. The study is based on secondary data collected from agricultural statistics and government publications. Cropping intensity was calculated as the ratio of gross cropped area to net sown area and analysed using statistical and geospatial techniques. Districts were classified into low, medium and high cropping intensity categories using fixed threshold values to ensure temporal comparability. The findings reveal considerable regional variation in cropping intensity across Bihar. The cropping intensity increased from 132.78 % in 2004-05 to 151.65 % in 2024-25. In 2004-05, the highest cropping intensity was observed in district Saharsa (177.41%), while the lowest was in Gaya (101.26%). In 2024-25, the highest cropping intensity was recorded in Rohtas (193.03%) and the lowest in Munger (112.67 %). The highest positive change in cropping intensity was recorded in Rohtas (+58.40%), the largest decline was observed in Gopalganj (-33.37%). However, inter-district disparities continue to persist due to differences in irrigation infrastructure, climatic conditions, technological accessibility and socio-economic factors. The study highlights the importance of sustainable agricultural intensification and region-specific planning for improving land-use efficiency and strengthening food security in Bihar. The findings may support agricultural policy formulation and spatial planning for balanced agricultural development in the state.

Spatio-Temporal Assessment and Prediction of Drought in the Northeast Arid Zone of Nigeria Using Standardized Precipitation Index (SPI) under Two Climate Scenarios

2026-06-12T11:06:23+00:00

Drought is one of the most severe climate-related hazards affecting the semi-arid and Sahelian regions of Nigeria, especially in the North-East arid zone, where rainfall unpredictability and rising temperatures endanger agriculture, water supplies, and socioeconomic livelihoods. This study examined meteorological drought in the northeast arid zone of Nigeria using the Standardised Precipitation Index (SPI) under two climate change scenarios, SSP2-4.5 and SSP5-8. 5. Precipitation outputs from selected CMIP6 Global Climate Models were combined with observed rainfall data from the Nigeria Meteorological Agency (NiMet) and gridded precipitation data from the Global Precipitation Climatology Centre (GPCC) for 1982–2014. Based on their rainfall simulations, MPI-ESM1-2-LR, IPSL-CM6A-LR, NESM3, and CMCC-CM2-SR5 were chosen as CMIP6 models. Droughts were estimated for two periods: 2015–2055 and 2056–2100. Distribution Mapping (DM) bias correction was applied and it significantly improved model dependability. The SPI-3 and SPI-6 indexes measured drought frequency, duration, and severity. A historical drought study showed severe and persistent droughts in 1983–1985, 1987–1988, 1990–1994, and 2013–2014. Both climate scenarios predicted increased drought occurrences, persistence, and severity, with the SSP5-8.5 scenario exhibiting more intense drought conditions, especially in the far future (2056–2100).

Spatio-Temporal Analysis of Land Use Land Cover Change in Nashik City (1991–2021) Using GIS and Remote Sensing

2026-06-13T09:06:26+00:00

Land Use Land Cover change has become a major focus of environmental and urban studies because it reflects the cumulative impact of natural processes and human interventions on the landscape. Accurate assessment of LULC dynamics helps in understanding environmental sustainability, resource utilisation patterns, and the long-term consequences of urban development on ecological systems.

The present study analyses the spatio-temporal changes in Land Use Land Cover (LULC) patterns in Nashik during the period 1991–2021 using Remote Sensing and Geographic Information System (GIS) techniques. The primary objective of the study is to examine the extent of urban expansion and its impact on agricultural land, vegetation cover, fallow land, scrub land, and water bodies. Multi-temporal Landsat satellite imagery, including Landsat TM (1991) and Landsat 8 OLI/TIRS (2021) with 30 m spatial resolution, was utilised for the analysis. The satellite images were processed and analysed using ArcGIS and ERDAS Imagine software. A supervised classification technique based on the Maximum Likelihood Classification algorithm was applied to classify the study area into six major LULC categories: built-up area, agricultural land, vegetation cover, fallow land, scrub land, and water bodies.

To ensure the reliability of the classified outputs, accuracy assessment was carried out using Google Earth imagery, secondary spatial datasets, and ground truth verification methods. The classification results achieved satisfactory accuracy levels, with Overall Accuracy and Kappa Coefficient values indicating reliable classification performance.

The findings reveal substantial transformation in the urban landscape of Nashik over the last three decades. Built-up area increased significantly from 17.67 sq. km (6.82%) in 1991 to 128.45 sq. km (49.59%) in 2021, indicating rapid urbanisation and infrastructural development. In contrast, agricultural land declined from 63.30 sq. km (24.43%) to 41.12 sq. km (15.88%), while fallow land decreased from 85.82 sq. km (33.12%) to 25.30 sq. km (9.77%). Vegetation cover reduced from 15.31 sq. km (5.91%) to 10.55 sq. km (4.07%), and scrub land declined from 73.10 sq. km (28.21%) to 51.08 sq. km (19.71%). Water bodies remained relatively stable, though increasing urban pressure may affect their ecological condition.

The study clearly demonstrates that rapid and largely unplanned urban growth has considerably altered the LULC pattern of Nashik city. The research highlights the effectiveness of GIS and Remote Sensing techniques in monitoring urban expansion and environmental change and provides valuable insights for sustainable urban planning, land resource management, and environmental policy formulation.

Spatio-Temporal Analysis of Tropospheric Atmospheric Pollution Indicators over Kolkata Municipal Corporation (KMC) Using Sentinel-5P Observations

2026-06-13T12:17:37+00:00

Rapid urbanization, industrial expansion, and increasing transportation activities have significantly influenced atmospheric pollution in major metropolitan cities of India, particularly Kolkata. The present study evaluates the spatio-temporal distribution of satellite-derived atmospheric pollution indicators, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), ozone (O₃), and Aerosol Index (AI), over Kolkata Municipal Corporation (KMC) during 2019–2025 using Sentinel-5P observations processed within the Google Earth Engine environment. Annual mean pollutant composites were generated to assess spatial variability, concentration patterns, and long-term atmospheric changes across the metropolitan region. Descriptive statistics, trend analysis, and Pearson correlation analysis were employed to evaluate pollutant dynamics and interrelationships. The results revealed considerable spatial heterogeneity in pollutant distribution throughout the study period. Relatively higher NO₂, SO₂, and CO values were generally observed in the central and northern parts of KMC, whereas O₃ exhibited comparatively higher concentrations in several northern and northeastern sectors. Temporal analysis indicated substantial interannual variability in NO₂ and CO, while SO₂ and O₃ displayed relatively stronger increasing tendencies. The Aerosol Index remained predominantly negative, suggesting limited dominance of UV-absorbing aerosols and highlighting the influence of aerosol type, atmospheric humidity, cloud cover, and retrieval conditions. A noticeable decline in several pollutants was observed during 2020, corresponding to the COVID-19 lockdown period, followed by varying levels of recovery after 2021. Correlation analysis revealed the strongest positive association between O₃ and AI (r = 0.74), followed by SO₂ and O₃ (r = 0.65), indicating potential interactions among atmospheric oxidation processes and aerosol conditions. Overall, the study demonstrates the utility of Sentinel-5P observations and cloud-based geospatial analysis as effective tools for long-term monitoring of atmospheric pollution indicators and provides valuable insights into urban atmospheric variability over Kolkata.

A Hybrid Learning Framework for Bamboo Mapping Using Supervised and Unsupervised Classification

2026-06-19T12:33:50+00:00

Accurate mapping of bamboo resources is important for ecological assessment, forest management and regional resource planning, particularly in landscapes where bamboo occurs with other vegetation types. This study presents a hybrid learning framework for bamboo mapping using Sentinel-2 surface reflectance imagery acquired from January to December 2024 for Karbi Anglong and Dima Hasao districts of Assam, India. The proposed approach integrates unsupervised K-means clustering with supervised Random Forest classification to improve the discrimination of bamboo-related land-cover classes. Spectral bands B2, B3, B4 and B8, together with the Normalised Difference Vegetation Index, were used as input features. K-means clustering was first applied to identify spectrally homogeneous regions and to support the refinement of bamboo training samples. The refined training dataset was then used to classify six land-cover classes: water, land, forest, mixed vegetation, pure bamboo and bamboo-dominated areas. A total of 1,138 training samples was used, and the dataset was divided using a 70:30 train-test split. The proposed Hybrid Random Forest model achieved an overall accuracy of 95.3%, a kappa coefficient of 0.94 and a bamboo F1-score of 0.95. The results indicated improved classification performance compared with the standalone Random Forest and Support Vector Machine models evaluated in the study. Area-wise assessment showed substantial bamboo presence in both study districts, with pure bamboo and bamboo-dominated classes forming major components of the classified landscape. The findings suggest that cluster-assisted training sample refinement can improve bamboo classification in heterogeneous forest landscapes using medium-resolution multispectral satellite data, provided that the training samples and outputs are carefully validated.

Urban Growth at the Edge of Limits: Assessing Land-System Change, Biosphere Integrity, and Freshwater Stress in Greater Accra through the Planetary Boundaries Framework

2026-06-23T06:46:44+00:00

This study examines environmental transformation in Greater Accra between 2000 and 2020 through selected dimensions of the Planetary Boundaries framework, with attention to land-system change, biosphere integrity and freshwater stress. The analysis used Landsat 7 satellite imagery obtained from the United States Geological Survey, together with district shapefiles from the Ghana Open Data Initiative. A descriptive longitudinal design was adopted, and the imagery was processed in ArcMap and ENVI 5.1 using image pre-processing, gap filling, calibration, layer stacking, supervised classification, Normalised Difference Vegetation Index (NDVI) analysis and post-classification change detection. The study was conceptually informed by the Environmental Kuznets Curve, Ecological Modernisation Theory and Planetary Boundaries Theory. Results indicate marked land-use and land-cover transformation over the two decades. Built-up land increased by 102.3%, while water bodies, grassland and dense forest declined by 77.7%, 87.3% and 75.7%, respectively. The reduction in vegetated surfaces suggests pressure on biosphere integrity, while the contraction of surface water bodies indicates growing freshwater-related stress. The combined pattern of urban expansion, vegetation loss and declining water coverage points to increasing pressure on selected planetary boundary dimensions within Greater Accra. The findings suggest that population growth, infrastructure development and land conversion are important drivers of ecological change in the region. By connecting observed land-cover dynamics to threshold-oriented sustainability thinking, the study offers context-specific evidence for urban environmental planning in a rapidly changing metropolitan region. It concludes that urban growth in Greater Accra requires stronger spatial planning, improved enforcement of land-use regulations and greater integration of green infrastructure and nature-based solutions to support development within ecological limits.

Spatio-Temporal Analysis of LULC Change and Its Impacts on Socio-Economic Conditions in Lamka Town, India

2026-06-23T06:57:40+00:00

This study examined the spatio-temporal changes in land use and land cover (LULC) and their perceived socio-economic implications in Lamka Town, Churachandpur District, Manipur, India. The analysis combined geospatial assessment with primary survey data collected from 100 randomly selected respondents through a structured questionnaire. Responses were measured using a five-point Likert scale to assess perceptions across cultural, economic and livelihood, environmental, and social-political institutional domains. The LULC assessment indicated substantial landscape transformation between 1995 and 2025. Vegetation cover declined from 401 km² (67.17%) in 1995 to 118 km² (19.77%) in 2025, while built-up area increased from 48 km² (8.04%) to 170 km² (28.47%). Jhum land also expanded from 93 km² (15.57%) to 279 km² (46.73%), indicating the continued relevance of shifting cultivation practices in the study area. Statistical analysis was conducted using SPSS and included descriptive statistics, Cronbach’s alpha reliability analysis and multiple regression analysis. The reliability coefficients for the four thematic domains ranged from 0.80 to 0.85, indicating acceptable internal consistency. The regression model was statistically significant (F = 8.72, p < 0.001) and explained 46% of the variation in livelihood conditions (R² = 0.460; adjusted R² = 0.438). LULC perception and economic-livelihood factors were the strongest predictors of livelihood conditions, whereas institutional factors showed a weaker and statistically non-significant effect. The findings indicate increasing pressure on natural resources and underline the need for integrated land-use planning, livelihood diversification and strengthened institutional support in Lamka Town.

Landscape Transformation in Jharsuguda District, Odisha: A Review towards an Integrated Analytical Framework for Land Use and Land Cover Change Assessment

2026-06-22T10:44:10+00:00

Landscape transformation in India increasingly reflects the combined influence of industrialisation, mining expansion, urban growth, infrastructure development and changing land-use practices in ecologically sensitive regions. In Eastern India, and particularly in Odisha, these processes are associated with the conversion of forests, agricultural land, wetlands and open landscapes into industrial and built-up areas, with important ecological and socio-economic implications. This review synthesises existing knowledge on landscape transformation in Jharsuguda district, Odisha, and examines the need for an integrated analytical framework for land use and land cover change assessment. A PRISMA-informed review approach was used to identify, screen and synthesise academic literature, policy documents and institutional reports related to land use/land cover change, landscape ecology, geospatial analysis, mining-industrial development and landscape governance. Following the screening process, 107 studies were selected for qualitative synthesis. The review indicates that landscape transformation in Jharsuguda is shaped by the combined effects of coal mining, industrialisation, urbanisation and transport infrastructure expansion, rather than by isolated land-use processes. Existing studies consistently report declining vegetation cover, wetland modification, fragmentation and increasing environmental stress across mining-industrial landscapes. Although advances in remote sensing, geographic information systems and landscape ecological methods have strengthened the monitoring of spatial transformation, many studies remain confined to land-cover mapping or separate ecological assessments. Comparatively limited attention has been given to analyses that connect spatial patterns, ecological processes, governance arrangements and socio-economic change within a unified framework. The review therefore highlights the need for spatially explicit and socio-ecologically integrated district-scale approaches to support sustainable landscape management and regional environmental planning in rapidly transforming mining-industrial regions.