The study used Landsat Collection 2 Level-2 surface-reflectance imagery at 30-metre resolution, with scenes from 3 March 1990 and 25 October 2006 acquired by Landsat 5 TM and a 30 November 2022 scene acquired by Landsat 9 OLI-2. Cloud and shadow flags were used to mask affected pixels, and all images were projected into the WGS 84/UTM Zone 45 N coordinate system and aligned to the same grid. A supervised maximum-likelihood classification separated built-up land, water, vegetation, agricultural land, and fallow or barren land. The researchers selected Landsat 5 for 2006 because Landsat 7 ETM+ had systematic scan-line-corrector gaps, which could have produced unequal omissions among small wards, rings and sectors.

Absolute and relative area changes identified the scale of Cooch Behar’s physical expansion. Mapped built-up area rose to 4.55 square kilometres in 2006, an increase of 2.31 square kilometres or 4.53% annually relative to 1990. It then reached 6.28 square kilometres in 2022, adding 1.73 square kilometres during the following interval. Although the absolute gain was smaller in 2006–2022 than in 1990–2006, the built-up total remained almost three times its 1990 level. The median ward gained 0.191 square kilometres across the full period, with an interquartile range of 0.115 to 0.272 square kilometres, indicating that expansion was not confined to a small number of administrative units.

Repeated-measures analysis supported the consistency of the mapped changes. A Friedman test comparing built-up area in the same 20 wards across the three dates returned a chi-square statistic of 40.0 with p below 0.001, while Kendall’s W was 1.00. Exact two-sided Wilcoxon signed-rank comparisons for all three date pairs produced Holm-adjusted p values below 0.001. These tests are within-study diagnostics because the wards are spatial units rather than a random sample, but they show that the temporal increase was consistent across the municipal area. They do not reveal whether new development took the form of infilling, outward edge expansion or disconnected leapfrog development, because the study did not apply patch-adjacency or landscape-expansion metrics.

The study’s central technical distinction is between measuring how much urban land exists and measuring how that land is distributed. Normalized Shannon entropy for built-up land was calculated from each zone’s share of the city’s total built-up area across 20 wards, six concentric 500-metre rings and eight 45-degree sectors. A value near zero indicates concentration, while a value near one indicates an even distribution. Separately, five-class land-use and land-cover entropy was used to describe the mixture of built-up, water, vegetation, agricultural and barren land within each unit. Neither form of entropy can, by itself, demonstrate urban growth: that conclusion requires an independent temporal change in total area.

Ward-level results showed that built-up land became more evenly distributed among administrative zones. Normalized built-up entropy increased from 0.9183 in 1990 to 0.9746 in 2006 and remained nearly stable at 0.9733 in 2022. The effective number of wards represented by the distribution rose from 15.66 to about 18.5, while both the Herfindahl–Hirschman Index and Gini coefficient declined. In 1990, the Gini coefficient was 0.380; it fell to 0.217 in 2006 and edged up to 0.227 in 2022. These complementary measures show less concentration across wards, not a transition to a more compact urban form. The principal 1990-to-later contrast also survived sensitivity tests in which ward areas were independently perturbed by as much as 5% and 10% across 10,000 fixed-seed simulations.

Radial analysis nevertheless showed a shift in the location of development after 2006. Normalized entropy across the six rings was 0.9434 in 1990, 0.9407 in 2006 and 0.9575 in 2022. The two outer rings, covering distances of 2,000 to 3,000 metres from the fixed city centre, together held 25.84% of built-up area in 1990 and 23.62% in 2006, but their share rose to 32.44% in 2022. Over the same period, the two innermost 500-metre rings fell from 38.05% to 22.26% of the total. These results support outward redistribution of built-up land after 2006, although they cannot determine whether the new peripheral land formed through contiguous edge growth or scattered patches. The 3,000-metre radius was an analytical boundary rather than a statutory or functional limit of the city.

Built-up land also remained broadly distributed by direction. Normalized entropy across the eight sectors stayed close to its maximum, measuring 0.9885 in 1990, 0.9848 in 2006 and 0.9892 in 2022. That near-even pattern coexisted with unequal absolute gains. The south-east recorded the largest proportional increase from 1990 to 2022, at 259.89%, followed by east at 237.36% and north at 204.47%; north contained the largest 2022 share at 17.82%. Slower south-western growth is consistent with the Torsa River bordering the city on its southern, south-western and south-eastern sides, but the analysis did not establish the river as a causal barrier. Road access, land availability, public facilities, property values and flood-prone land were also plausible influences, yet none was tested using parcel, transport, pricing or planning-permission data.

The 2022 land-cover map achieved 92.91% overall accuracy, with a Wilson 95% confidence interval of 87.44–96.10% and a kappa coefficient of 0.869. Its assessment used 141 independent reference allocations, of which 131 were correct. Built-up land had producer’s and user’s accuracies of 97.78% and 92.63%, respectively. Quantity disagreement was 4.26%, compared with allocation disagreement of 2.84%, meaning that errors affecting class totals contributed more than errors in spatial assignment. Independent validation was unavailable for the 1990 and 2006 maps, so historical change estimates remain provisional. March imagery for 1990 also differed seasonally from the October–November scenes used in the other years, creating uncertainty in transitions involving vegetation, agriculture and fallow land even though built-up land was the principal change variable.

Together, the results provide a multiscale baseline for a historically planned, river-constrained city but do not amount to a forecast or an audit of urban sustainability. Mapped built-up area increased, its distribution among wards became more even, and the outer-ring share rose after 2006, while directional dispersion remained high. The study also demonstrates why a single entropy number can be misleading: low land-cover compositional entropy can mean that one mapped class dominates a unit, not that urban growth has occurred. A formal land-consumption-to-population-growth assessment was not possible because satellite dates and census years did not align, and only three mapped epochs were available. The authors therefore call for additional validation, patch-based landscape metrics and driver data before assigning firm morphological labels or causal explanations to Cooch Behar’s changing development pattern.

Subject of Research: Remote-sensing analysis of urban growth and built-up land distribution in Cooch Behar, India, from 1990 to 2022.

Article Title: Assessing urban growth dynamics in Cooch Behar city using the Shannon entropy model

Article References: Hossain, S., Alam, M. K., & Islam, N. (2026). Assessing urban growth dynamics in Cooch Behar city using the Shannon entropy model. Discover Cities, 3(1), Article 205. https://doi.org/10.1007/s44327-026-00383-0

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00383-0

Keywords: urban growth, Cooch Behar, Landsat imagery, Shannon entropy, built-up land, remote sensing, land-cover change, spatial distribution, India, urban planning, Assessing, urban

The headline finding is sobering. The average WQI for the entire wetland complex came out at 73.34, a value that falls squarely in the ‘poor’ category, meaning the water is unsuitable for human consumption without treatment. But the average conceals extraordinary variability. At one sampling site in Chengtimari Beel, the WQI soared to 215.03, the worst reading in the study and firmly in the ‘unsuitable’ class. At the opposite extreme, Atiamochar and Noldoba Beel recorded values of just 3.39 and 5.79, indicating excellent water quality. Bochamari and Raichangmari Beel registered a very poor score of 94.63, while the main Rasik Beel itself scored 47.85, placing it in the good category. Such a wide spread within a single wetland complex of roughly 175 hectares is unusual and points to sharply differing local conditions.

The researchers attribute the degradation of Chengtimari Beel to a combination of hydrological isolation and biological choking. Unlike the better-flushed basins in the complex, this waterbody lacks meaningful inflow and outflow, and floating plants blanket its surface for most of the year. Stagnant, shaded water cannot dilute or process incoming pollutants, so organic loads accumulate. By contrast, Atiamochar and Noldoba Beel sit at higher elevations and closer to the sources of their feeder streams, meaning their catchments deliver cleaner water and their basins experience more effective renewal. The study suggests that this combination of elevation, proximity to relatively unpolluted headwaters and regular flushing explains why those two beels remain in near-pristine condition despite the pressures bearing down on the wider complex.

The laboratory results reveal which specific parameters are driving the poor scores. Chemical oxygen demand, a measure of the total oxidisable material in water, ranged from 10.8 to 435.6 milligrams per litre with a mean of 276.88, an extraordinarily high figure that signals substantial organic pollution. Biochemical oxygen demand showed similarly wide swings, from 0.3 to 11.5 milligrams per litre, with a mean of 5.94. Together, these oxygen-demand metrics indicate that decomposing organic matter, likely from domestic wastewater and agricultural runoff, is consuming oxygen in parts of the wetland. Iron concentrations also exceeded the permissible limit of 1 milligram per litre set by the Bureau of Indian Standards, ranging from 0.77 to 3.4 milligrams per litre, which the authors attribute to lithogenic inputs or leaching from iron-rich soils in the catchment.

Not every parameter told a worrying story. Dissolved oxygen averaged a healthy 10.68 milligrams per litre, suggesting good aeration and photosynthetic activity across much of the complex. The pH ranged from 5.9 to 7.4 with a mean of 6.94, slightly acidic to near neutral and within the permissible range for aquatic life. Total dissolved solids averaged 440 milligrams per litre, chloride stayed well below desirable limits, and total hardness of 48.8 milligrams per litre indicated moderately soft water. Trace metals were largely reassuring: zinc, copper and lead concentrations remained within acceptable bounds, and cadmium was below detectable levels in every sample. The wetland, in other words, is not uniformly contaminated; rather, it is a mosaic of clean and stressed zones, and the study’s spatial approach is what makes that mosaic visible.

To untangle the relationships among the fourteen parameters, the team turned to statistical tools. Pearson correlation analysis revealed striking interconnections: alkalinity and hardness correlated at r = 0.972, pointing to a shared origin in the dissolution of carbonate and bicarbonate minerals in the catchment geology. Chloride correlated strongly with iron (r = 0.992) and zinc (r = 0.912), suggesting that chloride-bearing compounds may facilitate metal mobility, possibly through agricultural runoff or domestic effluents. Most tellingly for the index itself, the WQI correlated perfectly with lead (r = 1.000) and strongly with hardness, alkalinity, zinc, copper and chloride, indicating that heavy metals and carbonate chemistry are the key contributors to the overall deterioration of water quality in the complex.

Principal component analysis reinforced this interpretation. Four components with eigenvalues greater than one were extracted, together explaining 100 percent of the variance in the dataset. The first component, accounting for 36.1 percent of variance, loaded heavily on hardness, alkalinity, lead, zinc, chloride and iron, a signature of mineral enrichment from lithological weathering combined with anthropogenic discharges. The second component captured pH, temperature and electrical conductivity, reflecting physicochemical interactions and ionic strength. The third, at 22.6 percent of variance, paired high total dissolved solids with negative loadings on dissolved oxygen and biochemical oxygen demand, an unmistakable organic pollution gradient marking eutrophication and oxygen depletion. The fourth component loaded on chemical oxygen demand and copper, implicating household effluents, fishing activity and agricultural chemicals. Together, the components show that both natural geogenic processes and human activity govern the hydrochemistry of Rasik Beel.

Regression analysis added further nuance. A model using dissolved oxygen, biochemical oxygen demand and chemical oxygen demand as predictors explained 81.9 percent of the variance in the WQI, confirming that biological oxygen dynamics are the dominant controls on overall water quality. Another model using zinc, copper and lead achieved a perfect fit, with lead emerging as the most influential predictor of the index. Physical parameters alone, by contrast, explained only about 36 percent of WQI variation, underscoring that chemical and biological factors, not simple measures of dissolved content, are what determine whether water in the complex is fit for use. The authors caution, however, that with only five sampling sites, the statistical power of these models is limited, and the results should be treated as indicative rather than definitive.

The findings place Rasik Beel in a familiar regional context. Comparable WQI-based assessments at Deepor Beel in Assam, a Ramsar-listed wetland, the East Kolkata Wetlands and Loktak Lake in Manipur have all reported moderate to poor water quality driven by anthropogenic pressures, suggesting that the pattern observed in Cooch Behar is part of a broader story of wetland degradation across northern and eastern India. Wetlands cover more than 4.6 percent of India’s landmass and provide critical services, from flood control and groundwater recharge to fisheries habitat and refuge for migratory birds, yet they face mounting threats from urbanisation, industrial effluents and agricultural intensification. Rasik Beel, a recognised bird sanctuary and part of the Government of India’s Wetland Development Project, exemplifies both the ecological value and the vulnerability of these systems.

The study has clear limitations that the authors acknowledge. Sampling was confined to a single pre-monsoon date, so seasonal and monsoon-driven fluctuations remain unmeasured, and five sites cannot fully capture the heterogeneity of a multi-beel complex. Biological indicators such as plankton diversity and microbial contamination were not assessed, and land-use change in the surrounding catchment was not incorporated. Nevertheless, the work provides a valuable baseline for a wetland that had previously lacked integrated water quality assessment, and it demonstrates how a single numerical index, combined with multivariate statistics, can translate a complex chemical dataset into actionable information. The authors recommend continuous monitoring, seasonal sampling, control of organic and metal inputs, and policy interventions to preserve the ecological integrity of the complex. For a wetland that supports endemic fish, migratory birds and local livelihoods, the message is unambiguous: parts of Rasik Beel are still thriving, but without sustained management, the poor-quality zones will continue to spread.

Subject of Research: Physico-chemical water quality assessment and Water Quality Index evaluation of the Rasik Beel Wetland Complex, West Bengal, India

Article Title: Evaluating physio-chemical characteristics and water quality in the Rasik Beel Wetland complex in Cooch Behar district, West Bengal, India

Article References: Barman, N. R., & Cajee, L. (2026). Evaluating physio-chemical characteristics and water quality in the Rasik Beel Wetland complex in Cooch Behar district, West Bengal, India. Discover Geoscience, 4(1), Article 329. https://doi.org/10.1007/s44288-026-00697-x

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00697-x

Keywords: water quality index, Rasik Beel, wetland, West Bengal, physico-chemical parameters, heavy metals, principal component analysis, dissolved oxygen, BOD, COD, water pollution, Cooch Behar