The research, conducted by Godfrey Nkugwa of Wuhan University of Technology, tackles a glaring blind spot in urban climate science. While the Local Climate Zone framework introduced by Stewart and Oke has been widely applied across Europe, Asia and North America through the WUDAPT initiative, standardized LCZ-based analyses in Africa remain strikingly rare. This omission matters because Africa’s urbanization follows a fundamentally different trajectory than that of industrialized regions. Instead of factory-driven growth, East African cities are expanding through what researchers call urbanization without industrialization, fueled by poverty and climate-driven migration, and producing vast informal settlements in the process. More than 70 percent of Dar es Salaam’s inhabitants already live in informal neighborhoods, and Nairobi’s Kibera ranks among the largest slums on the continent.

To capture this distinctive urban fabric, the study introduced a methodological innovation: a validated subclass labeled LCZ 3_(7), which describes compact low-rise morphology built with lightweight informal materials such as corrugated metal, timber and mud. The subclass was defined operationally based on physical surface properties that correlate strongly with known slum boundaries, distinguishing it from the open, single-storey LCZ 7 class by its taller structures and denser layout. Classification accuracy for these informal classes exceeded 0.85 across all three cities and all years, verified through 2,000 randomly sampled points cross-checked against multi-temporal satellite imagery by three independent analysts. The authors caution that this remains a physical proxy rather than a legal designation of informality, but it offers a potentially transferable tool for monitoring vulnerable neighborhoods where tenure data are unavailable.

Using Landsat 8 and 9 imagery acquired in strict dry-season, cloud-free conditions for 2014, 2020 and 2024, the analysis mapped land surface temperature and urban morphology at a uniform 100-meter grid. The results reveal a consistent two-stage trajectory of urban growth across all three cities. Between 2014 and 2020, the metropolises expanded horizontally, converting vegetation and peri-urban land into built fabric at a furious pace. Dar es Salaam was the most aggressive, with its dominant open low-rise class surging from 33.7 percent to 49.0 percent of the built-up area, while its natural tree cover plummeted from 27.2 percent to just 9.0 percent. After 2020, the pattern shifted to spatial consolidation, with class persistence soaring above 85 to 95 percent as cities filled in rather than spread out.

The thermal story is equally dramatic and sharply divergent. Within each city’s seasonal baseline, informal settlement heat island intensity rose over the decade by 1.22 degrees Celsius in Kampala, 2.48 degrees in Dar es Salaam and 3.65 degrees in Nairobi, making Nairobi the most rapidly warming informal environment in the region. By 2024, Nairobi’s LCZ 7 class recorded an absolute heat island intensity of 6.35 degrees Celsius, the highest of any informal class across all three cities. What was previously considered a case of thermal stabilization has now been revealed as a strong warming regime, concentrated precisely in the densifying settlements where the region’s most vulnerable populations live.

Beneath these headline numbers lies a more subtle discovery: a regime shift in the drivers of urban heat. During the expansion phase, two-dimensional impervious cover, measured by the normalized difference impervious surface index, dominated warming across all cities. But after 2020, in the inland capitals of Nairobi and Kampala, three-dimensional building density captured by the normalized difference bareness and building index overtook imperviousness as the primary predictor. This structural transition signals that once the urban footprint is established, the vertical packing of compact low-rise structures becomes the dominant locus of heat retention, effectively locking heat exposure into the morphology of consolidated informal settlements, a legacy effect that is notoriously difficult to retrofit.

Geographically weighted regression, which models how driver relationships vary across space, exposed mechanisms invisible to global statistics. In Kampala, the coefficient for surface moisture reversed sign in dense informal areas between 2020 and 2024, suggesting that water in these neighborhoods no longer provides evaporative cooling but instead marks stagnant moisture trapped within narrow canyons of corrugated metal, creating hyperlocal heat traps. In Nairobi, nighttime lights and building density spatially decoupled after 2020, revealing that the most severe thermal hotspots form precisely where unlit, consolidated low-rise housing blocks intersect with brightly lit commercial corridors, a convergence of poverty and economic activity that defines a specific urban form for targeted intervention. In coastal Dar es Salaam, by contrast, surface albedo delivers measurable cooling only near the shoreline, where sea breezes amplify heat advection, while impervious cover retains primacy inland, suggesting coastal ventilation partially offsets densification-driven warming.

Hotspot analysis using the Getis-Ord Gi* statistic confirmed these shifting geographies of risk. Nairobi’s heat exposure originated in peripheral transitional scrublands during the expansion phase but by 2024 had concentrated firmly within densifying informal settlements. Kampala’s hotspots decayed from intense clusters in compact cores to diffuse patterns, while Dar es Salaam’s persisted in compact low-rise districts with emerging informal heat exposure. The cooling capacity of natural and water-covered zones weakened across the board, from wetlands in Dar es Salaam to vegetated sinks in Kampala, signaling that encroachment on blue and green spaces is eroding the region’s natural thermal defenses.

These divergent trajectories argue forcefully against one-size-fits-all mitigation prescriptions. For Kampala and Dar es Salaam, where the combination of high building density and lightweight materials drives heat amplification, the study points to cool-roof retrofits, transitioning from corrugated metal to high-albedo reflective materials, alongside protection of remaining blue spaces and managed pervious drainage to restore evaporative cooling. Empirical support exists: white roof coatings applied across 11,000 square meters of roofing in Nairobi’s informal settlements and cool-roof paint trials in rural Sub-Saharan Africa have both demonstrated measurable indoor temperature reductions. For Nairobi, the priority is different: surgical, targeted green infrastructure such as pocket parks, green courtyards and strategic street trees at the identified intersection points of dense informal housing and commercial activity, since blanket city-wide greening would be far less effective.

The study’s limitations are candidly acknowledged, including single-date thermal snapshots per year, the absence of in-situ validation, and the fact that biophysical heat exposure rather than socioeconomic vulnerability was assessed. Even so, the validated LCZ framework and its novel informal subclass offer a standardized baseline for tracking thermal equity across data-scarce, rapidly urbanizing regions. As East African cities continue their transition from horizontal expansion to consolidation, the research delivers a clear warning: heat exposure in informal settlements is not homogeneous but is produced by geographically specific intersections of land cover, building density, economic activity and coastal or inland climate. Meeting it will require exactly the kind of context-sensitive, spatially explicit intelligence this study now provides.

Subject of Research: Multi-temporal Local Climate Zone analysis of surface urban heat island drivers in East African informal settlements

Article Title: Socio-spatial drivers of thermal environment evolution in East African informal settlements based on multi-temporal LCZ analysis of major metropolises

Article References: Socio-spatial drivers of thermal environment evolution in East African informal settlements based on multi-temporal LCZ analysis of major metropolises. (n.d.). https://doi.org/10.1007/s44327-026-00366-1

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00366-1

Keywords: local climate zones, surface urban heat island, informal settlements, East Africa, remote sensing, land surface temperature, Nairobi, Kampala, Dar es Salaam, urban heat, climate adaptation, geographically weighted regression

The scale of transformation in Ahmedabad is striking. Multi-decadal analyses of land use and land cover report that the city’s built-up area expanded by more than 130 percent between 1990 and 2019, largely at the expense of farmland, peri-urban vegetation, and open ground. Long-term assessments stretching back to 1976 confirm a pattern of continuous peripheral growth, in which rural and semi-urban zones have been progressively absorbed into the metropolitan fabric. What was once a compact historic core has evolved into an increasingly dispersed and heterogeneous urban system, pushed outward in every direction beyond the Sabarmati River by industrial corridors, major road networks, and large-scale residential development. Manufacturing hubs, commercial centers, and institutional zones have accelerated land conversion across the vast peri-urban belt surrounding the city.

Jaipur’s story is different in shape but similar in substance. Studies of the Rajasthan capital document significant and steady growth in built-up land since the mid-1990s, consumed mainly at the cost of agricultural land, scrubland, and open spaces. Unlike Ahmedabad’s radial spread, Jaipur’s expansion has been strongly directional, concentrated in the western, southern, and northwestern sectors of the city. The review attributes this asymmetry to the development of major roads, industrial estates, institutional nodes, and large residential colonies in those directions, combined with the availability of flat, easily developable land and improving connectivity. Vegetation cover in Jaipur has shown a consistent declining trend across the periods reported in the literature. Although planned green areas such as parks and landscaped localities have increased slightly, these gains are confined to small geographic pockets and do not compensate for the loss of natural and agricultural cover, with negative consequences for the city’s ecological balance.

The thermal consequences of these land conversions are well documented in both cities. Satellite-based studies consistently find a positive correlation between the growth of built-up area and rising land surface temperature, or LST, across multiple temporal scales. The mechanism is straightforward physics. When permeable, vegetated ground is replaced with impervious materials such as concrete and asphalt, the surface absorbs more solar radiation and retains more heat. Urban surfaces typically have lower albedo than natural vegetation, meaning they reflect less sunlight and absorb more energy. At the same time, the loss of vegetation and open land reduces evapotranspiration, the process by which plants release moisture and cool their surroundings. Dense building clusters, narrow streets, and high-rise construction further obstruct airflow and natural ventilation, trapping heat within street canyons and degrading the city’s capacity to shed warmth after dark.

The review distinguishes carefully between related thermal phenomena. The urban heat island, or UHI, describes the situation in which air temperatures in an urban center exceed those of surrounding rural areas because of urbanization, vegetation loss, and waste heat from human activity. The surface urban heat island, or SUHI, is its surface manifestation, measured through satellite-derived estimates of land surface temperature rather than thermometer readings. In Ahmedabad, the interplay between these phenomena proves surprisingly complex. Surface moisture availability and the agricultural cropping cycle strongly influence SUHI intensity. During the pre-monsoon season, when soils are dry and vegetation cover is sparse, rural areas around the city can become extremely hot, sometimes producing lower or even negative daytime SUHI intensity, meaning the city surface is not dramatically hotter than its parched surroundings. Nighttime SUHI intensity, however, remains consistently positive, because urban materials store heat during the day and release it slowly after sunset. Jaipur, by contrast, shows an almost constant rise in land surface temperature driven by the steady expansion of urban surfaces.

Methodologically, the two cities have followed markedly different research trajectories, and this asymmetry is one of the review’s central findings. Ahmedabad has emerged as a methodological benchmark for predictive urban growth modeling in India. Its studies routinely integrate cellular automata–Markov chain models, artificial neural networks, transition probability matrices, and validation statistics such as kappa coefficients, weaving in population data, road proximity, and industrial growth indicators to simulate future expansion scenarios. This modeling sophistication gives planners a genuine capacity to evaluate alternative futures. Jaipur’s literature, in contrast, remains largely retrospective, relying on conventional supervised classification and change detection to document what has already happened, with few predictive modeling studies. Both cities exhibit comparable trends of vegetation loss and rising surface temperature, but the strength of evidence available to support forward-looking urban planning is considerably greater for Ahmedabad.

The review itself was conducted with unusual rigor for a narrative synthesis. The author searched Scopus, Web of Science, Google Scholar, and ScienceDirect from June to December 2025, using combinations of keywords covering land use and land cover, urban and surface heat islands, land surface temperature, remote sensing and GIS, urban growth modeling, and World Heritage City urbanization. The initial search returned 90 articles, which were screened down to 51 full texts and finally to 39 studies included in the qualitative comparative analysis, following a PRISMA-inspired framework to ensure transparency and reproducibility. Because the underlying studies used different satellite sensors, spatial resolutions, classification algorithms, and validation procedures, the review deliberately avoided direct numerical comparison, instead synthesizing evidence across common themes including growth pattern, thermal response, modeling sophistication, and planning relevance.

The stakes extend well beyond thermal maps. Rising urban temperatures elevate the risk of heat illness, dehydration, respiratory disease, and cardiovascular stress, with outdoor laborers, elderly residents, and economically disadvantaged communities facing the greatest exposure. Heat stress is also linked to declining labor productivity in construction, transport, and informal work sectors that dominate rapidly growing semi-arid cities. Climate change compounds these pressures by intensifying heatwaves and droughts and by reducing water availability, while vegetation loss, shrinking water bodies, and groundwater depletion erode urban ecological resilience. Recent Ahmedabad studies have begun integrating land surface temperature, evapotranspiration, vegetation indices, and groundwater parameters into compound analyses of urban growth and climate variation, though comparable integrated research in Jaipur remains scarce. For cities whose historic urban fabric, from Jaipur’s planned eighteenth-century grid to Ahmedabad’s walled old city, is the very asset that earned global recognition, unmanaged heat and sprawl threaten both environmental sustainability and heritage conservation.

The review identifies clear research gaps and a path forward. Most existing work concentrates on biophysical change detected through remote sensing, while socioeconomic, institutional, and governance drivers, including census-based demographics, migration, and land-use policy, remain underexplored in both cities. Peri-urban transition zones, where land conversion is fastest and least controlled, deserve far more detailed spatial assessment. The author calls for integrating land change analysis with urban climate modeling, heat stress mapping, and land-atmosphere interaction studies, and for greater use of machine learning techniques such as random forest, support vector machines, deep learning, and hybrid cellular automata approaches. Heat vulnerability analysis, combining thermal exposure with population density, public health indicators, and access to green space, is flagged as essential for identifying at-risk communities. On the policy side, the prescriptions are concrete: expand green infrastructure, protect urban ventilation corridors, promote permeable surfaces and nature-based solutions, and embed heritage-sensitive climate adaptation into planning. As the review concludes, the future sustainability of India’s semi-arid World Heritage Cities depends on treating land use, urban climate, and cultural heritage as a single, inseparable planning problem rather than three separate bureaucracies.

Subject of Research: Land use transformation and urban heat island dynamics in the UNESCO World Heritage Cities of Jaipur and Ahmedabad, India

Article Title: Land use transformation and urban heat dynamics in India’s world heritage cities Jaipur and Ahmedabad

Article References: Land use transformation and urban heat dynamics in India’s world heritage cities Jaipur and Ahmedabad. (n.d.). https://doi.org/10.1007/s44327-026-00361-6

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00361-6

Keywords: land use land cover, urban heat island, land surface temperature, Jaipur, Ahmedabad, remote sensing, GIS, urbanization, World Heritage Cities, CA-Markov, surface urban heat island, semi-arid cities