The research, led by Remilekun Olaide Olaiya of Tai Solarin Federal University of Education and Olayinka Akinsumbo Ajala of Obafemi Awolowo University, challenges the way scientists and planners usually think about urban growth. Most land-change studies treat each conversion as an independent event: a forest becomes a suburb, a farm becomes a road. But the Ibadan evidence points to something more structured, a path-dependent process in which the state of the land today shapes what it becomes tomorrow. In this framing, bare land is not a dead end or a stable category. It is a waiting room.

To uncover that sequence, the team assembled a remarkable satellite record. Using cloud-free dry-season imagery from four different Landsat sensors, Landsat 7 in 2005, Landsat 5 in 2010, Landsat 8 in 2015 and Landsat 9 in 2025, all at 30-metre resolution, they classified the landscape into four categories: built-up area, bare land, vegetation and water. The images were carefully pre-processed, converted to top-of-atmosphere reflectance and atmospherically corrected with the Dark Object Subtraction method, so that differences between sensors and years could not masquerade as genuine change. Ground-truthing combined a 2025 field survey of 180 points recorded with handheld GNSS receivers and historical high-resolution Google Earth imagery for the earlier epochs.

The numbers are striking. In 2005, vegetation covered 688 square kilometres, more than two-thirds of the corridor, while built-up land occupied just 87.3 square kilometres, or 8.39 percent. By 2015, the picture had flipped dramatically: bare land had surged to 484 square kilometres, becoming the dominant class, while vegetation had lost nearly half its extent. Built-up land had more than doubled to 199.5 square kilometres. Then, between 2015 and 2025, the pattern reversed again. Bare land shrank by 215.9 square kilometres as built-up land climbed to 373 square kilometres, 35.83 percent of the corridor. Land that had been cleared in the first decade was being paved over in the second.

The sequencing becomes unmistakable when the transition matrices are visualised as Sankey flow diagrams, where the width of each link is proportional to the area converted. The single largest flow was vegetation to bare land, 333.5 square kilometres, followed by bare land to built-up land at 173.5 square kilometres. Direct vegetation-to-built-up conversion, the pathway most studies implicitly assume, accounted for only 112.2 square kilometres. Meanwhile, built-up land showed complete persistence: every square kilometre urban in 2005 was still urban in 2025. Cities, once established, do not give land back.

The spatial pattern reinforced the temporal one. Kernel density hotspot analysis, which converts newly built-up pixels into a continuous surface of growth intensity, showed that between 2005 and 2015 the hottest expansion zones were confined to Lagelu and southern Egbeda, while most of Akinyele remained quiet. In the following decade, very-high-intensity hotspots spread across Lagelu and Egbeda and pushed into Akinyele, reflecting infilling and densification of previously cleared ground. The result is a pronounced south-to-north urbanisation gradient, with growth radiating from the existing core along major transport corridors, a pattern familiar from other rapidly expanding African metropolitan fringes.

The ecological cost is measured not only in lost hectares but in shattered habitat structure. Using morphological spatial pattern analysis, the researchers found that the number of core vegetation patches, large intact habitat areas, collapsed from 82 to 21 over the study period. Total core area fell from 482.8 to 132.6 square kilometres, mean patch size dropped from 6.42 to 1.58 square kilometres, and the Landscape Shape Index, a measure of edge complexity, nearly doubled from 14.62 to 27.34. Patch density more than doubled. In plain terms, what remains of the corridor’s vegetation is increasingly chopped into small, isolated, edge-exposed fragments, a condition that ecologists consider a more sensitive indicator of stress than raw area loss alone.

What happens next depends on whether recent history repeats itself, and the team built a model to test exactly that. They coupled a first-order Markov chain, which estimates the probability of each land class converting into every other class, with a cellular automata component that allocates change spatially using a 5-by-5 contiguity filter, so that new growth clusters next to existing development. Consolidated built-up cells were treated as absorbing states, physically barred from reverting. Before projecting forward, the model was hindcast: calibrated on 2005 to 2015 data and asked to simulate 2025, then compared cell by cell against the independently classified 2025 map. It scored a standard Kappa of 0.89 and a Figure of Merit of 0.36, figures consistent with the best-performing land-change models in the literature.

Run forward to 2050, the validated model delivers a sobering scenario. Built-up land is projected to reach approximately 580 square kilometres, up from 373 in 2025, while vegetation declines to 198.3 square kilometres, less than a third of its 2005 extent. Bare land continues its role as the conveyor belt, progressively converted to urban uses as the vegetation reservoir shrinks. The Vegetation-to-Bare-Land-to-Built-up sequence persists through the projection rather than giving way to direct conversion, echoing findings from Greater Cairo and other rapidly urbanising Global South landscapes, and aligning with United Nations projections that Africa will experience the fastest urban growth on Earth through mid-century.

The policy implications may be the study’s most valuable contribution. Because land clearing reliably precedes construction, the bare-land stage represents a critical and identifiable intervention window. During the vegetation stage, the authors argue, planners should prioritise conservation zoning, urban growth boundaries and enforcement of statutory plans. Once land is cleared, development control, land banking and time-bound permits can regulate speculation before concrete sets. After build-out, the focus shifts to infill, densification and transit-oriented development. The authors caution that their projection is a conditional continuation scenario, not a deterministic forecast, and that the model does not capture future policy shifts, economic shocks or the fine-scale informal development that 30-metre pixels cannot resolve. But the core message stands: peri-urban transformation is not a roll of the dice. It is a sequence with recognisable stages, and the moment to steer it is while the ground is still bare.

Subject of Research: Path-dependent peri-urban land transformation and simulated urban expansion to 2050 in the Ibadan corridor, Nigeria

Article Title: Path dependent peri urban land transformation and urban expansion in Ibadan Nigeria

Article References: Olaiya, R. O., & Ajala, O. A. (2026). Path dependent peri urban land transformation and urban expansion in Ibadan Nigeria. Discover Geoscience, 4(1), Article 381. https://doi.org/10.1007/s44288-026-00749-2

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00749-2

Keywords: peri-urban transformation, urban expansion, Ibadan, Nigeria, Landsat, land-use change, CA-Markov model, Sankey diagram, landscape fragmentation, remote sensing, path dependency, Sub-Saharan Africa

The research team, led by Anaelle Brunda Djiaha and Marlène Ngansop Tounkam of the University of Douala together with Philippes Mbevo Fendoung of the National Advanced School of Public Works in Yaoundé, built their analysis on Landsat satellite scenes captured in the dry-season months of February 2000, March 2010, December 2014, and January 2022. Choosing dry-season imagery was a deliberate technical decision: cloud cover in Cameroon’s wet season can obscure the ground and corrupt classification algorithms, so the researchers favored dates when visibility was near optimal. Each scene was assembled into multispectral composites, radiometrically corrected to convert raw digital numbers into surface reflectance, and classified using the Maximum Likelihood algorithm, a supervised method that assigns each pixel to the land cover class it statistically most resembles. Five classes emerged: mature highland forest, medium-aged secondary forest, shrub savannah, agrosystems, and built-up areas.

The rigor of this classification was validated on two fronts. Confusion matrices built from thousands of reference points, ranging from 3,215 samples in 2000 to more than 31,000 in 2022, yielded Kappa coefficients between roughly 0.93 and 0.99 across all four dates, well above the 0.85 threshold generally accepted as indicating strong agreement between classified and reference data. On the ground, the team surveyed more than 110 GPS-truth points over 20 days of fieldwork, correcting misclassified pixel blocks and confirming which land cover categories were genuinely present. This dual validation matters because the entire downstream analysis, from change-detection matrices to carbon accounting, rests on the accuracy of those classifications.

The temporal story that emerges is dramatic. In 2000, high-altitude forest covered 66 percent of the reserve, roughly 6,199 hectares, with secondary forest at 13 percent, savannah at 15 percent, and agriculture and settlement each at 3 percent. By 2010 the mature forest share had slipped to 63 percent, but the steepest decline came in just four years: between 2010 and 2014, highland forest plummeted from 5,932 to 3,693 hectares, an annual loss of more than 570 hectares, while medium-aged secondary forest nearly doubled as degraded and regenerating land replaced old-growth canopy. By 2022, mature forest covered only 38 percent of the reserve, agrosystems had surged from 6 to 14 percent, and built-up areas had climbed to 5 percent, more than doubling from 136 to 456 hectares in just eight years. Transition matrices confirm the scale of conversion: over the full 22-year period, more than 6,000 hectares of land shifted out of secondary forest trajectories and over 1,250 hectares of mature forest were converted to other uses.

To understand what is driving this transformation, the researchers combined remote sensing with field observation and interviews. The verdict was unambiguous: agriculture accounts for 46 percent of degradation, human occupation for 34 percent, illegal logging for 14 percent, and bushfires for the remaining 6 percent. The reserve is surrounded by villages inhabited primarily by Mbo’o and Bamiléké communities whose livelihoods depend on cash crops such as cocoa, coffee, and oil palm as well as food crops like maize and cassava. A 20-kilometer secondary road linking Foumban to Bale has intensified access and anthropization, while selective artisanal sawmilling and slash-and-burn clearing accelerate the fragmentation. Elephants and panthers have already vanished from the reserve; the remaining wildlife, including primates, pangolins, aulacodes, and monitor lizards, now survives in an increasingly perforated habitat.

Beyond mapping change, the study quantified what this forest still stores in climate terms. Using established allometric equations for Cameroon’s tropical moist forests, the team measured every tree in 30 square plots of 30 by 30 meters distributed across the vegetation types, converting diameter and wood density into aboveground biomass, then into carbon using the standard 0.47 conversion fraction. The reserve’s overall carbon rate came to 0.874 tonnes of carbon per hectare, with medium-aged secondary forests standing out as the strongest carbon sinks at 0.534 tC/ha, followed by highland forest at 0.280 tC/ha. Agrosystems stored a negligible 0.002 tC/ha. The authors note candidly that these values are well below the 30 to 50 tC/ha typically reported for tropical secondary forests, attributing the gap to advanced degradation, plot representativeness, and extrapolation uncertainty, and they cross-validated their spatial carbon maps with an R-squared of 0.78 and a margin of error of roughly plus or minus 15 percent.

Perhaps the most consequential result is spatial: by overlaying the 2000 and 2022 forest classifications and subtracting what remained, the team identified 3,703 hectares, 53 percent of the entire reserve, as suitable for reforestation. These are zones where forest existed at the start of the century and no longer does, prime candidates for planting native species or assisted natural regeneration. If restored, the researchers estimate, this area could hold a carbon storage capacity of about 0.83 tC/ha, a meaningful contribution to both biodiversity recovery and climate mitigation in a region where protected areas are increasingly recognized as critical carbon reservoirs.

Looking forward, the team ran a CA-Markov cellular automata model in Idrisi Selva software, using transition probabilities derived from the historical record to simulate land cover in 2050. The projections, which the authors carefully frame as exploratory scenarios rather than predictions, suggest agricultural land expanding by 30 percent, built-up areas by 40 percent, and agroecosystems by 25 percent, primarily at the expense of remaining primary forest and savannah. The model rests on a stationarity assumption, that past trends will persist, which the authors acknowledge is vulnerable to disruption by policy shifts, land reform, or economic shocks, and they call for sensitivity analyses and hindcasting validation in future work. Even with those caveats, the trajectory is clear: without intervention, demographic and economic pressure will continue converting the reserve into farmland and settlements.

The policy implications extend well beyond one Cameroonian reserve. The authors argue that reversing these trends requires a comprehensive package: promoting sustainable agricultural practices, launching reforestation in the 3,703 hectares of priority zones, and, critically, involving local communities through training and participatory natural resource management. They point to evidence that REDD+ initiatives succeed best when payments for ecosystem services are coupled with local development programs such as agroforestry, and they stress the need for stricter land use regulations and the integration of conservation objectives into public policy. For a reserve whose very existence as a protected area is being tested by the communities that surround it, the path forward depends on aligning economic incentives with ecological survival, ensuring that Santchou’s remaining forests, and the carbon and biodiversity they harbor, are still standing in 2050.

Subject of Research: Forest cover dynamics, carbon stock assessment, and 2050 land use projections for the Santchou Wildlife Reserve in western Cameroon

Article Title: Forest dynamics and carbon stocks in the Santchou wildlife reserve from 2000 to 2022 and projections for 2050

Article References: Djiaha, A. B., Tounkam, M. N., & Fendoung, P. M. (2026). Forest dynamics and carbon stocks in the Santchou wildlife reserve from 2000 to 2022 and projections for 2050. Discover Conservation, 3(1), Article 28. https://doi.org/10.1007/s44353-026-00097-9

Image Credits: AI Generated

DOI: 10.1007/s44353-026-00097-9

Keywords: deforestation, forest degradation, carbon stocks, remote sensing, Landsat, Cameroon, protected areas, CA-Markov model, reforestation, REDD+, biodiversity conservation, land use change

The research, led by Hai Sun of the Ocean University of China together with colleagues at Qingdao University of Technology, Clemson University, and Western Sydney University, tackles one of the most persistent blind spots in flood management: the pathways linking land-use patterns to hydrological responses. Urbanization reshapes basin hydrology by replacing soils and vegetation with impervious surfaces such as roads, rooftops, and parking lots. These surfaces prevent infiltration, accelerate runoff generation, and amplify flood peaks, sending more water into rivers faster and overwhelming channels and drainage systems. Green infrastructure — permeable pavements, rain gardens, wetlands, and vegetated storage areas — counteracts this by improving infiltration, storage, and surface roughness. But until now, planners have lacked a rigorous, basin-scale framework for measuring how these benefits vary across space and under different storm conditions.

To close that gap, the team constructed three urban expansion scenarios for the year 2044 in the Poyang Lake Basin: a no-green-infrastructure baseline, a storage-oriented green infrastructure scenario, and a permeability-oriented scenario. The Poyang Lake Basin, China’s largest freshwater lake system and a critical node in the Yangtze River’s hydrology, is a natural laboratory for this question. Its low-lying floodplains, dense tributary network, and rapidly expanding urban platforms make it acutely sensitive to changes in land cover.

Land-use changes under each scenario were simulated using a cellular automaton–Markov (CA–Markov) model, a technique that combines transition probabilities derived from historical land-change data with spatial neighborhood constraints to project how urban footprints evolve. This allowed the researchers to generate realistic maps of where impervious surfaces would spread by 2044 and where green infrastructure would be deployed under each strategy. The hydrological consequences were then evaluated through a coupled one-dimensional and two-dimensional modeling framework, chaining HEC-HMS, a rainfall-runoff model, to HEC-RAS, a river hydraulics and flood inundation model. The coupling is significant: it enables a continuous simulation chain from land-use evolution to runoff generation to flood dynamics, rather than treating each link in isolation.

The baseline results are sobering. Under unconstrained urban expansion, impervious surface coverage in the basin rises from 3.93 percent to 7.37 percent — nearly a doubling of sealed ground. Correspondingly, the basin-averaged curve number, a standard parameter in the Soil Conservation Service runoff method that encapsulates how readily a landscape converts rainfall into runoff, increases from 68 to 72. That seemingly modest shift carries heavy consequences: the researchers calculate an approximately 18 percent decrease in potential maximum retention, the landscape’s capacity to absorb and store rainfall before it becomes floodwater. In plain terms, by mid-century the basin could surrender nearly a fifth of its natural buffering capacity to concrete and asphalt.

Green infrastructure partially blunts this trajectory, but the two strategies do so very differently. Permeability-oriented green infrastructure — designed to restore infiltration across the urban surface — achieves the strongest reduction in impervious coverage, limiting the rise to 6.68 percent, and effectively reverses the degradation of infiltration and storage capacity captured by the curve number. Storage-oriented green infrastructure, which concentrates water retention in discrete facilities, shows only limited improvement in these landscape-scale parameters. The reason is structural: storage works locally, behind berms and inside basins, while permeability works everywhere, beneath every street and rooftop it touches.

Those parameter-level differences propagate directly into flood behavior. Under small to moderate rainfall events, permeability-oriented green infrastructure reduces peak discharge by 6.2 percent and total runoff volume by 3.54 percent, and — critically — it maintains its effectiveness even under extreme conditions, because infiltration capacity does not fill up the way a storage basin does. Storage-oriented measures, by contrast, remain constrained by finite capacity: once a retention facility fills, additional rainfall passes through unattenuated. Yet the picture is not one-sided. The analysis reveals clear spatial heterogeneity: in areas with sufficient storage capacity, storage-based strategies actually outperform infiltration-based measures during large rainfall events, when rainfall intensity outpaces the soil’s ability to absorb water and detention volume becomes the deciding factor. The catch is that this advantage is limited by spatial and capacity constraints at the basin scale — there is simply not enough suitable land and storage volume to deploy it everywhere.

The two-dimensional hydraulic component of the framework sharpens this spatial story further. Permeability-oriented green infrastructure reduces the extent of high-depth and high-velocity flood hotspots, with the strongest benefits concentrated along river corridors and urban platforms — precisely the locations where people and assets cluster. This leads the authors to a practical siting logic built around the curve number itself: boost permeability and roughness on the slopes, maintain them through the channels, and add storage capacity at confluences where flows converge and backwater effects amplify. In other words, read the landscape’s hydrological fingerprints and match the intervention to the terrain.

The study’s most consequential recommendation is that no single strategy suffices. Because green infrastructure performance varies with location, terrain, and storm magnitude, the authors argue for differentiated strategies aimed at residual high-risk areas: infiltration-dominated measures for moderate rainfall, enhanced storage and conveyance capacity for extreme events, and spatially targeted deployment that combines infiltration and storage synergies in low-lying zones, storage-control combinations along flow corridors, and strict land-use regulation in high-risk areas. This is a departure from the one-size-fits-all deployments that have characterized much green infrastructure planning, including China’s high-profile sponge city program, and it provides theoretical support and decision-making guidance for coordinated planning and refined flood risk management at the basin scale.

The timing could hardly be more urgent. Recent global research has documented rapid urban growth inside flood zones since 1985 and projected substantial increases in future fluvial flood risk across China’s major urban agglomerations, with the Global South bearing disproportionately higher exposure. As climate change intensifies extreme rainfall and cities continue to seal their surfaces, the Poyang Lake findings offer a template that travels: simulate the land, couple it to the water, and let the spatial heterogeneity of performance — not generic best practice — dictate where every permeable meter and every storage basin goes. The difference, this research suggests, may be measured not just in percentage points of peak discharge, but in neighborhoods that stay dry.

Subject of Research: Spatial variability in the flood mitigation performance of green infrastructure under future urban expansion in the Poyang Lake Basin

Article Title: Spatial heterogeneity of green infrastructure performance in flood mitigation under urban expansion

Article References: Sun, H., Wang, H., Chu, Y., Yao, W., Fan, C., Chu, Z., & Liang, B. (2026). Spatial heterogeneity of green infrastructure performance in flood mitigation under urban expansion. Natural Hazards, 122(19), Article 637. https://doi.org/10.1007/s11069-026-08312-5

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08312-5

Keywords: green infrastructure, flood mitigation, urban expansion, Poyang Lake Basin, CA-Markov model, HEC-HMS, HEC-RAS, curve number, impervious surfaces, runoff reduction, spatial heterogeneity, flood risk management