The research examined land use and land cover, abbreviated LULC, and land surface temperature, abbreviated LST, across Bahir Dar City. The team used the Normalized Difference Vegetation Index, Normalized Difference Built-up Index, and Normalized Difference Water Index to characterize surface properties and investigate their relationships with temperature. LULC mapping was performed through supervised object-based image analysis.

The researchers assessed the classification results with a confusion matrix, allowing the accuracy of the mapped land-cover classes to be evaluated. For the 2035 estimates, the study combined Markov Chain, Cellular Automata, and Artificial Neural Network approaches implemented through the MOLUSCE plugin in QGIS. These modeling methods generated projections of future LULC and LST patterns.

The resulting scenario indicated continued urban expansion and a potential increase in surface thermal conditions if recent land-use trends persisted. These are model-based projections rather than measurements from 2035, so they represent a conditional estimate of possible future conditions rather than an observed outcome. The study provides a citywide geospatial view of simultaneous changes in urban development, vegetation, and surface temperature.

Its results show associations between land cover and LST, but they do not isolate how much of the temperature variation was caused by any single LULC class. The supplied findings also do not give the exact area of agricultural land in each analyzed year, and the projections depend on the assumption that recent trends continue.

Subject of Research: Geospatial modeling of land-cover change and land surface temperature in Bahir Dar, Ethiopia

Article Title: Geospatial analysis of LULC change, future prediction, and its impact on LST in Bahir Dar City, Ethiopia

Article References: Fetene, T. A., & Kidanemariam, A. M. (2026). Geospatial analysis of LULC change, future prediction, and its impact on LST in Bahir Dar City, Ethiopia. Discover Sustainability. https://doi.org/10.1007/s43621-026-04719-z

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04719-z

Keywords: Bahir Dar, urban expansion, land surface temperature, land-cover change, Landsat imagery, geospatial analysis, Markov chain, cellular automata, artificial neural networks, Ethiopia, Geospatial, analysis

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 research team, led by Aritra Mandal and colleagues at the Department of Geography, Adamas University in Kolkata, assembled a multi-temporal record from Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, and Landsat 8 Operational Land Imager imagery spanning 1990 to 2020. Before any classification could begin, the satellite scenes were corrected using the Atmospheric and Topographic Correction model, which removes distortions introduced by humidity and aerosol scattering, a critical step in the humid Gangetic delta where atmospheric interference can blur the spectral signatures that distinguish one land cover type from another. Only after this radiometric harmonisation could the team trust that differences between images reflected real changes on the ground rather than artefacts of the sensors or the atmosphere.

For the classification itself, the researchers turned to a Support Vector Machine, a supervised machine learning algorithm prized for its ability to construct optimal separating hyperplanes in high-dimensional feature space. That mathematical property matters enormously in a deltaic landscape, where silted riverbeds, wetlands, and agricultural fields can share deceptively similar spectral profiles. The team implemented the classifier with a Radial Basis Function kernel, tuning the penalty parameter to 100 and the gamma value to 0.1 through grid search and cross-validation, while maintaining a minimum of 100 training samples per class. Accuracy assessment using stratified random sampling and error matrices yielded Kappa coefficients between 0.82 and 0.91 across the four epochs, comfortably exceeding the 0.85 threshold generally accepted for reliable land cover mapping, with the 2020 classification achieving the highest overall accuracy at 95 percent.

The resulting maps tell a striking story of transformation. In 1990, fallow land dominated the basin, covering 199.34 square kilometres, or 52.04 percent of the total area, a signature of extensive shifting cultivation and underutilised ground. Agricultural land accounted for 22.02 percent, homestead vegetation for 14.26 percent, while urban settlement occupied a modest 5.22 percent and aquaculture 5.52 percent. Water bodies were almost negligible at 0.82 percent, underscoring the basin’s dependence on seasonal hydrology. By 2020, that picture had been almost completely inverted: fallow land had collapsed to 11.48 percent of the basin, while agricultural land and homestead vegetation had risen to become co-dominant classes at 36.74 and 36.17 percent respectively, and urban settlement had more than doubled its footprint.

To move beyond static area statistics, the study deployed two indices that capture the velocity and intensity of landscape change. The Land Use Dynamic Degree, expressed as K percent, calculates the annualised rate of change for each land use class, allowing direct comparison of how quickly different categories are expanding or contracting. The Urban Expansion Intensity Index normalises the growth of built-up area against the total basin area and the length of the time interval, enabling comparisons across dissimilar periods. Together, these metrics identified the decade between 2000 and 2010 as the basin’s most turbulent phase. Homestead vegetation expanded at a peak rate of 7.05 percent per year during that interval, urban settlement at 6.48 percent, while fallow land contracted at an accelerating pace that reached minus 6.22 percent per year by the final decade.

The Urban Expansion Intensity Index confirmed that urbanisation peaked between 2000 and 2010 at a low-intensity value of 0.441 percent, framed by a very low 0.146 percent in the 1990s and a near-negligible 0.024 percent between 2010 and 2020. This trajectory suggests the most explosive phase of urban conversion has already passed, with the basin now entering a period of densification rather than outward sprawl. Post-classification change detection added finer texture to the narrative: between 2010 and 2020, 48.77 square kilometres of fallow land and 30.62 square kilometres of agricultural land were converted to vegetated cover, while 4.94 square kilometres of vegetation was lost to settlement, the highest urban encroachment of any decade in the record.

When the researchers divided the river into three reaches, the spatial unevenness of change became apparent. Reach 1 and Reach 2, the segments closest to the expanding municipalities of Barasat and Kalyani, showed the most aggressive conversion, with fallow land in Reach 2 projected to shrink from 93.43 square kilometres in 1990 to just 2.08 square kilometres by 2030, and aquaculture in Reach 1 falling from 8.30 to 0.25 square kilometres over the same span. One-way analysis of variance confirmed that these reach-wise differences were statistically robust, with land use type explaining between 50.7 and 64.7 percent of the variance in class areas, and Duncan’s post-hoc tests cleanly separating extensive classes such as agriculture and homestead vegetation from compact ones such as water bodies and aquaculture.

The forward-looking component of the study rested on a Markov chain model, which treats land cover change as a probabilistic transition system governed by a transition probability matrix derived from observed changes between 2010 and 2020. The matrix revealed, for instance, that only 59.14 percent of agricultural land persisted in its class over that decade, while 34.75 percent reverted to fallow, hinting at declining soil fertility or shifting economic incentives. Urban areas, though only 31.74 percent persistent, sprawled into 31.91 percent of neighbouring farmland. Monte Carlo simulations with 1,000 iterations quantified the uncertainty around the 2030 projections, and validation against observed data produced a Kappa statistic of 0.861 and a chi-square value of 0.052, well below the critical threshold of 11.17, indicating strong agreement between prediction and reality.

The 2030 forecast is sobering. Urban-related categories are projected to dominate the basin, with vegetated settlements covering 143.38 square kilometres, or 37.11 percent of the landscape, and fully urbanised areas covering 54.88 square kilometres, or 14.20 percent, together accounting for more than half of the entire catchment. Agricultural land is expected to slip to 36.55 percent, while water bodies and aquaculture shrink to marginal features of just 0.88 and 2.44 percent respectively. The authors caution that the Markov framework assumes past transition routines will persist, an assumption that future policy shifts, climate-driven changes in rainfall, and socio-economic upheavals could invalidate, and that Landsat’s coarse resolution limits its ability to resolve fragmentation in mixed-use landscapes.

What emerges from this research is a portrait of a river basin in a transient, unstable state, where chronic degradation of the channel through siltation, encroachment, and hydrological obstruction proceeds alongside relentless agricultural intensification and urban expansion. The consequences ripple outward: reduced freshwater flows toward the Sundarban delta, increased salinity intrusion, seasonal flooding in the municipalities that flank the river, and mounting pressure on food security as farmland gives way to concrete. The study’s authors argue that only integrated basin management, ecological restoration, and land use planning that prioritises agricultural zoning and green infrastructure can reconcile the competing demands of a growing population and a fragile deltaic ecosystem. For the Suti, and for the thousands of non-perennial rivers like it that have long escaped scientific scrutiny, the satellite record now offers both a warning and a baseline against which any future recovery will be measured.

Subject of Research: Spatiotemporal land use and land cover change and Markov-based future projection in the Suti River Basin, West Bengal, India

Article Title: Spatiotemporal land use and land cover change and future projection in the Suti River Basin, India

Article References: Mandal, A., Bhadra, T., Das, R., Banerjee, S., Sardar, R., Roy, S., & Sarkar, R. (2026). Spatiotemporal land use and land cover change and future projection in the Suti River Basin, India. Discover Geoscience, 4(1), Article 384. https://doi.org/10.1007/s44288-026-00734-9

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00734-9

Keywords: land use land cover change, Suti River Basin, remote sensing, Support Vector Machine, Markov chain model, urbanisation, Gangetic delta, Landsat imagery, West Bengal, non-perennial river, change detection, sustainable land use planning

The research combined satellite-based land change analysis with extensive fieldwork in three peripheral kebeles, or administrative wards, of Burayu: Burayu Gafarsa, Malka Gafarsa and Gafarsa Guje. On the geospatial side, the authors processed multi-temporal Landsat imagery spanning the years 2000 to 2023, classifying each scene into land cover categories and then applying post-classification comparison, a technique in which independently classified maps are compared across dates to quantify how much land moved from one category to another. Post-classification methods are widely regarded as robust for detecting change because each date is classified separately, allowing analysts to trace the full matrix of conversions between classes rather than merely measuring aggregate shifts in reflectance. The approach was supported by ground-truthing validation, in which field observations were used to confirm that the satellite-derived categories accurately matched conditions on the ground.

The numbers that emerged are stark. Built-up land in the study area expanded by 50.99 hectares every year on average across the 23-year record, a net growth rate of 31.85 percent, as concrete, roofing sheet and graded roadbeds spread outward from the urban core. Cropland absorbed the brunt of this expansion, suffering a net loss of 59.312 hectares per year, equivalent to a 39.14 percent decline over the study period. In other words, for every hectare of new construction, more than a hectare of cultivated land was effectively sacrificed, a relationship that identifies agriculture as the primary spatial donor to the city’s horizontal growth. The authors describe the outcome as the conversion of vital agricultural spaces into fragmented urban zones, with the surrounding landscape broken into a patchwork of residual plots, access roads and half-built structures.

What makes this trajectory consequential is not simply the loss of hectares but the way that loss intersects with livelihoods. The study’s socioeconomic component was built on a defined institutional frame of 4,832 systematically displaced farming households across the three kebeles. From this frame the researchers drew a structured survey of 145 households, supplemented by 12 key informant interviews with local officials, elders and experts, and 6 focus group discussions with affected residents. This mixed-methods design allowed the team to pair the statistical weight of the survey with the explanatory depth of interviews, tracing not only how many households lost land but how they interpreted, negotiated and survived that loss.

The survey results document a livelihood system in free fall. A decisive majority of displaced households, 73.1 percent, reported a total collapse of their agrarian income following displacement, meaning the farming revenues that once anchored family economies simply ceased. Layered atop that loss is heightened tenure insecurity: even after giving up their fields, many families remained uncertain of their legal standing on the land they still occupy, exposed to further expropriation without the protection of formal, documented rights. In Ethiopia’s peri-urban zones, where customary tenure and state land ownership overlap in complicated ways, farmers often hold use rights rather than freehold titles, which weakens their bargaining position when developers and municipal authorities arrive with compensation offers or eviction notices.

Confronted with the collapse of farming income, affected families have assembled survival strategies that the study characterizes as precarious and non-formal. Households have turned to petty trade, daily labor, rental of spare rooms and reliance on informal social networks of kin, neighbors and former farming associations. These coping mechanisms buffer the immediate shock of displacement, but the authors find they fail to fully mitigate the socio-ecological vulnerability that displacement creates. Families that once produced their own food now buy it in volatile urban markets; skills honed over generations in agriculture have limited market value in construction and informal commerce; and the social fabric that once pooled risk at the community level has been thinned by dispersal and the pressures of urban poverty.

Behind the spatial data, the researchers identify a distinctive driver profile. Speculative land markets top the list: as Addis Ababa’s expansion raises land values along the corridor, investors and intermediaries buy and hold farmland in anticipation of appreciation, accelerating conversion regardless of planning designations. Weak institutional planning enforcement compounds the problem, with zoning rules and master plans routinely overridden, ignored or circumvented through informal transactions. Top-down infrastructure development, in which roads and utility corridors are extended into peri-urban areas by central decision-makers with limited local consultation, then anchors and legitimizes the sprawl, effectively redrawing the urban boundary ahead of any participatory planning process. Together these forces create a self-reinforcing cycle in which infrastructure attracts speculation, speculation pressures landholders, and weak governance ensures the conversions proceed without coordination or compensation adequate to rebuild displaced livelihoods.

The governance deficit the study documents extends beyond Ethiopia. Across Sub-Saharan Africa, rapid, uncoordinated horizontal sprawl repeatedly collides with the preservation of peri-urban agricultural systems, producing severe governance failures, landscape fragmentation and socioeconomic marginalization of indigenous farming communities. Peri-urban zones are among the most productive agricultural landscapes on the continent, benefiting from proximity to urban markets and often from richer soils favored by earlier settlement. Their conversion is therefore not a marginal loss but a structural one, undermining urban food security even as cities grow. The Shaggar City corridor, a vast new administrative construction ringing Addis Ababa, has become a laboratory for this dynamic at exceptional scale, and Burayu sits squarely within it.

The authors argue that the remedy requires integrating what has too often been separated: spatial monitoring and social protection. They call for integrated spatial-social monitoring systems that track land conversion and livelihood outcomes together, so that planners can see not only where the city is growing but who is being dispossessed in the process. They urge participatory land governance that gives peri-urban residents a genuine voice in decisions about expropriation, compensation and resettlement, rather than subjecting them to top-down decrees. And they emphasize formal protections for customary tenure, ensuring that families with generations of use rights are not rendered landless and undocumented by the mere arrival of the urban frontier. Ethics approval for the fieldwork was granted by Wollega University’s Research Ethics Approval Committee, and the study was conducted in accordance with the Declaration of Helsinki with informed consent from all participants.

The significance of the Burayu case lies in its warning about trajectory. A growth rate exceeding 31 percent in built-up land over 23 years, purchased at the cost of nearly 40 percent of cropland and the agrarian incomes of roughly three-quarters of surveyed households, describes a form of urbanization that concentrates risk on those least equipped to absorb it. Without deliberate intervention, the study suggests, the corridor’s remaining farmland will continue to convert plot by plot, and its displaced farmers will continue to drift into informal survival strategies that soften but do not solve their vulnerability. Whether the Shaggar City corridor becomes a model of equitable urban transformation or a cautionary tale of dispossession will depend, the authors conclude, on whether Ethiopia’s planners treat satellite maps and household surveys as parts of a single, urgent question: how African cities can grow without consuming the people and fields on their edges.

The temporal window of the analysis is methodologically significant. Landsat sensors, with a spatial resolution of 30 meters and a revisit interval of roughly 16 days, have provided an unbroken archive of the Earth’s surface since the early 1970s, making them the backbone of land change science in data-scarce regions. For a corridor like Shaggar City, where cadastral records are incomplete and informal subdivision outpaces official mapping, satellite archives often constitute the only consistent record of how the urban edge has moved. The 2000–2023 span chosen by the authors captures the period in which Addis Ababa’s metropolitan influence began reshaping its surrounding Oromia Special Zone settlements most intensively.

The study also illustrates a broader shift in displacement research toward household-level evidence. Earlier assessments of African peri-urban change frequently relied on aggregate land statistics, which could document shrinking farmland but not the distributional consequences of that shrinkage. By anchoring their survey in a complete institutional frame of displaced households, the authors could connect each hectare of conversion to identifiable families, an approach that makes the 73.1 percent income collapse figure interpretable as a population-level outcome rather than an anecdote. The triangulation of satellite classification, structured surveys, key informants and focus groups reflects current best practice in land change science, where biophysical measurement and social inquiry are increasingly treated as complementary rather than separate evidence streams.

Subject of Research: Quantifying peri-urban land conversion and its livelihood impacts on displaced farming households in Burayu Town, Ethiopia's Shaggar City corridor

Article Title: Spatial dynamics of per-urban expansion and livelihood impacts in the suburbs of Shaggar City in Ethiopia’s rapidly urbanizing corridor

Article References: Chimdessa, D., & Tolera, H. (2026). Spatial dynamics of per-urban expansion and livelihood impacts in the suburbs of Shaggar City in Ethiopia’s rapidly urbanizing corridor. Discover Sustainability. https://doi.org/10.1007/s43621-026-04564-0

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04564-0

Keywords: Ethiopia, peri-urban expansion, urban sprawl, land use dynamics, livelihood impacts, Shaggar City, Addis Ababa, Landsat imagery, cropland loss, tenure insecurity, displaced households, urban planning

Nigeria has lost more than 90 percent of its original forest cover, making it one of the countries with the highest deforestation rates in Africa. Between 2010 and 2015 alone, the country’s deforestation rate reached roughly five percent, driven by illegal logging, agricultural expansion, and infrastructure development. Ise Forest Reserve, located near the Uso community in Ondo State and spanning parts of Ekiti State, sits squarely within this crisis zone. The reserve is a critical stronghold for the critically endangered Nigeria-Cameroon chimpanzee and also harbors the African forest elephant, the yellow-casqued hornbill, Mona monkeys, and a rich assemblage of plant species including Gmelina arborea, Tectona grandis, and Khaya ivorensis. Despite its protected status, the reserve faces persistent encroachment from loggers and farmers, some of whom clear forestland to cultivate cannabis.

In 2021, the Ekiti State Government took a notable step by gazetting a portion of the reserve to the Southwest/Niger Delta Forest Project for conservation and restoration. The researchers behind the new study seized on this administrative boundary as a natural experiment. They designated the gazetted area as the managed zone and areas outside it as the disturbed zone, then asked a straightforward question: does the managed portion of the forest look ecologically different from the rest? Importantly, the team is careful to note that this division is administrative rather than a measure of true disturbance, and that the managed zone is by no means pristine, having also experienced logging and land-cover change.

To answer the question, the researchers combined painstaking fieldwork with satellite-based remote sensing. In the field, they established eleven sampling plots measuring 25 by 25 meters along transects spaced at least 200 meters apart, with eight plots in the larger managed zone and three in the smaller disturbed zone, allocated proportionally to the mapped area of each zone. Within each plot, they recorded every woody individual, measuring tree height, canopy cover, and diameter at breast height, the standard trunk measurement taken at 1.3 meters above the ground. In total, the survey documented 27 woody species from 17 families. The most frequently recorded species across the reserve was Ricinodendron heudelotii, appearing 55 times, while a single towering Diospyros lotus individual posted the most extreme structural values: a height of 12.20 meters, canopy cover of 18.60 meters, and a trunk diameter of 138.13 centimeters.

The structural comparison between zones was stark. Trees in the managed zone averaged 9.55 meters in height, 4.79 meters of canopy cover, and 21.92 centimeters in trunk diameter, compared with 7.64 meters, 3.35 meters, and 16.77 centimeters respectively in the disturbed zone. Using Welch’s independent t-tests with a Bonferroni-corrected significance threshold to guard against false positives across the three comparisons, the team found these differences were statistically robust: taller trees (p = 0.002), wider canopies (p < 0.001), and thicker trunks (p < 0.001) all favored the managed zone. Diversity metrics told the same story. The Shannon-Wiener index, which blends species richness with evenness; the Simpson concentration index, which reflects the probability that two randomly drawn individuals belong to the same species; and Margalef’s richness index all scored higher in the managed zone, which also contained 241 woody individuals across 19 families compared with 75 individuals across 14 families in the disturbed zone.

The satellite analysis painted a far grimmer picture of the wider landscape. Drawing on Landsat 7 and Landsat 9 imagery at 30-meter resolution from November 2016 and December 2023, the researchers classified land cover into secondary forest, degraded vegetation, and farmland, then computed transition rates between the two dates. In the managed zone, secondary forest collapsed from 31.40 square kilometers, or 97.65 percent of the zone, to just 14.10 square kilometers, or 43.85 percent, a net loss of 17.31 square kilometers. Degraded vegetation in the same zone exploded from a mere 0.14 square kilometers to 17.06 square kilometers. In the disturbed zone, farmland surged from 0.54 square kilometers to 7.24 square kilometers, more than a tenfold expansion, while degraded land shifted dramatically in extent. Across the landscape, conversions from secondary forest to degraded vegetation dominated the change matrix, and transitions back toward secondary forest were negligible, hovering around 0.01 percent in both zones.

The spatial contrasts were meaningful as well as the temporal ones. Farmland expanded more slowly in the managed zone than in the disturbed zone, and the loss of secondary forest proceeded at a lower rate there, suggesting that the conservation presence did dampen the most destructive forms of conversion. The species composition also shifted with disturbance. Ricinodendron heudelotii proved resilient and appeared in both zones, while economically valuable timber species such as Milicia excelsa were underrepresented in disturbed areas, consistent with overexploitation of high-value trees. These patterns echo findings from other Nigerian reserves, including Omo Biosphere Reserve, Ago-Owu, and Doma, where logging, fragmentation, and farming have been shown to strip out species diversity, stem density, and structural integrity.

Yet the authors are refreshingly candid about the limits of what their study can claim. Because no vegetation inventory was conducted before the conservation project began in 2021, the study could not use a before-after-control-impact design. The observed differences between zones represent a spatial snapshot, and pre-existing environmental, historical, or accessibility differences between the zones could partly explain them. The researchers explicitly state that the results cannot be attributed solely to the 2021 project and remain subject to review in a long-term evaluation. The two-date satellite comparison, meanwhile, captures net change over seven years but cannot resolve annual fluctuations, seasonal dynamics, fires, or short disturbance pulses that may have occurred in between. Unequal plot replication, dictated by the zones’ different sizes, also means the species counts should be read as field summaries rather than precise estimates of true richness.

Those caveats do not diminish the study’s central significance. It provides one of the first quantitative baselines for Ise Forest Reserve, coupling ground-truthed structural measurements with remote sensing in a data-scarce landscape, and it demonstrates a replicable template for monitoring restoration efforts across West Africa’s embattled forest reserves. The picture that emerges is of a landscape under siege but not yet lost: management appears to coincide with taller, thicker, more diverse forest and slower farmland encroachment, even as degraded land expands relentlessly across both zones. The researchers call for intensified patrolling, effective buffer-zone management, community livelihood programs, and rehabilitation of degraded patches. Most critically, they recommend permanent monitoring plots and annual dry-season Landsat or Sentinel-2 imagery to track whether the conservation trajectory truly diverges from the region’s grim baseline. For the chimpanzees, elephants, and hornbills that depend on Ise, the answer to that question may determine whether the reserve’s remaining forest endures.

The study’s methodological choices reflect broader trends in tropical forest monitoring. By pairing ground-based plot inventories with freely available Landsat imagery, the researchers worked within the constraints common to conservation science in data-scarce regions, where funding for extensive field campaigns is limited and historical vegetation records are often absent. Satellite indices such as the Normalized Difference Vegetation Index and the Enhanced Vegetation Index, which track photosynthetic activity from orbit, have become standard tools for detecting vegetation condition across large or inaccessible landscapes, and recent Nigerian studies have shown that machine learning classifiers applied to satellite data can produce usable land-cover maps even where ground information is sparse.

The ecological indicators used in the study each capture a different dimension of forest condition. Species richness counts how many distinct woody species occur in a sample, while the Shannon-Wiener index weighs both richness and the evenness with which individuals are distributed among species, and the Simpson concentration index estimates the probability that two individuals drawn at random belong to the same species. Structural measures such as diameter at breast height, height, and canopy cover complement these diversity metrics because they relate directly to carbon storage, habitat complexity, and the capacity of a forest to support wildlife such as primates that depend on layered canopy and mature fruiting trees.

The reserve’s setting also matters for interpreting the results. At roughly 366 meters above sea level, with mean annual rainfall of about 1,380 millimeters concentrated in an eight-month wet season from April to October and temperatures ranging between 19 and 30 degrees Celsius, the forest lies within the humid zone of southwestern Nigeria. Its fragmentation, noted by the authors, is itself ecologically significant, since smaller and more isolated forest patches generally support fewer species than large contiguous tracts. A stream near the eastern boundary and the Ogbese River to the west shape the local landscape, while proximity to the Akure-Benin expressway facilitates both access and encroachment.

Finally, the study underscores a persistent challenge for protected-area governance in Nigeria: legal designation alone has not prevented logging, farming, or hunting within reserve boundaries. The authors’ recommendation for permanent monitoring plots and regular dry-season satellite imagery reflects a growing consensus that restoration success must be measured against quantitative baselines rather than assumed from management status, particularly when interventions begin after degradation is already well advanced.

Subject of Research: Vegetation diversity, forest structure, and land-cover degradation in a managed versus disturbed Nigerian forest reserve

Article Title: Higher vegetation diversity and stronger forest structure occur in managed zones despite widespread land cover degradation in Ise Forest Reserve Nigeria

Article References: Olaniyi, O., Ogunsakin, E., Ogunjemite, B., Rafiu, K., & Anozie, E. (2026). Higher vegetation diversity and stronger forest structure occur in managed zones despite widespread land cover degradation in Ise Forest Reserve Nigeria. Discover Forests, 2(1), Article 67. https://doi.org/10.1007/s44415-026-00129-1

Image Credits: AI Generated

DOI: 10.1007/s44415-026-00129-1

Keywords: forest degradation, vegetation diversity, Ise Forest Reserve, land-cover change, forest structure, remote sensing, conservation management, Nigeria, tropical forest, Landsat imagery, secondary forest, restoration ecology