The team classified land cover for three time points: Landsat imagery from 2000 and 2012, and Sentinel-2 imagery from 2025. Rather than relying on manual interpretation, they applied a random forest algorithm, a machine learning method that builds hundreds of decision trees and merges their outputs to assign each pixel to a land cover class. Random forest classifiers have become a mainstay of remote sensing because they handle complex, non-linear relationships between spectral bands and land cover types while remaining robust to noise in training data. By moving the entire classification workflow into Google Earth Engine, the researchers could process large archives of satellite data in the cloud, avoiding the computational bottlenecks that traditionally limited regional land cover mapping.

The results document a landscape under growing anthropogenic pressure. Forest, grassland, and Afro-alpine ecosystems all declined over the twenty-five-year record, while farmland, built-up areas, and bare land expanded. Each of these conversions matters for different reasons. Forest loss removes the shaded, moisture-retaining habitats that support much of the region’s endemic fauna. Grassland conversion eliminates grazing grounds that sustain both wildlife and pastoral livelihoods. The contraction of Afro-alpine zones, which crown the massif and shelter specialized cold-adapted species, signals that even the most remote high-altitude habitats are not insulated from human expansion.

To assess ecosystem condition beyond simple land cover categories, the researchers turned to vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer, or MODIS, sensor aboard NASA’s Terra and Aqua satellites. Two indices anchored the analysis: the Normalized Difference Vegetation Index, which exploits the contrast between red and near-infrared reflectance to estimate the density and vigor of green vegetation, and the Enhanced Vegetation Index, which corrects for atmospheric effects and soil background reflectance to provide a more stable measure of photosynthetic activity, particularly in areas with high biomass. Tracking these indices seasonally across the study period revealed a troubling trajectory: dry-season greenness declined, and the recovery of vegetation during the wet season weakened, especially after 2012. That timing suggests the ecosystem is losing its capacity to rebound between dry periods, a hallmark of accumulating stress.

The authors interpret these vegetation trends as evidence of combined pressure from land-use expansion and climatic variability. Weakened wet-season recovery is particularly significant because it indicates that rainfall is no longer translating into vegetation regrowth as effectively as it once did. Degraded soils, reduced organic matter, and the loss of deep-rooted perennial vegetation can all blunt the response of a landscape to favorable growing conditions. When that pattern coincides with the direct conversion of natural habitat to farmland and settlement, the result is a landscape that is simultaneously shrinking and functioning less efficiently.

Landscape metrics added a structural dimension to the analysis. Fragmentation increased across the study period, reflected in higher patch density, greater edge effects, and reduced connectivity among remaining habitat blocks. These are not abstract statistics. Patch density measures how many discrete fragments a habitat type has been broken into, and rising values mean continuous forest and grassland are being carved into smaller, more isolated islands. Edge effects describe the altered microclimatic and ecological conditions along fragment boundaries, where increased light, wind exposure, and human access degrade conditions for interior-dwelling species. Reduced connectivity means the stepping stones that once allowed animals and plants to move between habitat patches are disappearing, a process with well-documented consequences for gene flow, dispersal, and long-term population viability.

The biodiversity analysis drew on species occurrence records from the Global Biodiversity Information Facility and iNaturalist, two crowdsourced repositories that aggregate millions of georeferenced observations from scientists and citizen naturalists worldwide. When the researchers overlaid these records on their land cover maps, a clear pattern emerged: biodiversity observations are increasingly concentrated in remnant natural habitats. This concentration indicates habitat contraction, with species being squeezed into ever-smaller refugia as the surrounding matrix converts to agriculture and settlement. For the Bale Mountains, which support iconic endemic species such as the Ethiopian wolf and the mountain nyala, this pattern raises the vulnerability of species already restricted to narrow altitudinal and ecological ranges.

The authors are careful to note an important limitation: while the spatial associations between land cover change, vegetation stress, and biodiversity concentration are strong, causal relationships cannot be established from these data alone. Occurrence records from citizen science platforms are unevenly distributed, reflecting where people observe rather than where species actually occur, and the temporal resolution of biodiversity data does not always align with the satellite record. Nevertheless, the convergence of independent lines of evidence, from land cover classification to vegetation indices to species records, paints a coherent picture of an ecosystem under compounding stress.

What makes this study notable beyond its regional findings is the methodological integration it demonstrates. By combining cloud-based land cover mapping, time-series vegetation indices, landscape fragmentation metrics, and crowdsourced biodiversity data within a single analytical framework, the researchers show how modern geospatial tools can deliver ecosystem monitoring that would once have required years of fieldwork and expensive computation. Google Earth Engine has democratized planetary-scale analysis, allowing research teams with modest resources to process archives that span decades and continents. The Bale Mountains study illustrates how that capability can be directed at conservation questions with real urgency.

The implications for conservation are direct. The authors argue for strategies focused on habitat protection, restoring connectivity between fragmented remnants, and promoting sustainable land management practices that reduce pressure on remaining natural ecosystems. As the Bale Mountains face continued agricultural expansion and climatic variability, the satellite record now provides a baseline against which future change can be measured. Whether that record shows stabilization or continued decline will depend on decisions made in the coming years, both within Ethiopia and in the international conservation community that has long recognized the Bale Mountains as one of Africa’s most important biodiversity hotspots.

Subject of Research: Land use and land cover dynamics and their impacts on ecosystem health and biodiversity in the Bale Mountains, Ethiopia

Article Title: Geospatial and Google Earth Engine-based assessment of land use and land cover dynamics and its impacts on ecosystem health and biodiversity in the Bale Mountains, Ethiopia

Article References: Werati, G. T., Wolteji, B. N., & Sori, M. D. (2026). Geospatial and Google Earth Engine-based assessment of land use and land cover dynamics and its impacts on ecosystem health and biodiversity in the Bale Mountains, Ethiopia. Environmental Monitoring and Assessment, 198(9), Article 995. https://doi.org/10.1007/s10661-026-15847-4

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15847-4

Keywords: Bale Mountains, Ethiopia, land use land cover change, Google Earth Engine, remote sensing, habitat fragmentation, biodiversity, NDVI, random forest classification, ecosystem health, Geospatial, Google

The research team, led by botanists working with the National Herbarium of Uzbekistan, spent three consecutive years documenting the flora of the Yozyovon sands between 2023 and 2025. Their surveys, combined with historical herbarium specimens from collections in Tashkent, Moscow, and St. Petersburg, produced a comprehensive inventory of 296 vascular plant taxa belonging to 175 genera and 44 families. What makes this number remarkable is not just its size but its composition: nearly 45 percent of the recorded species are psammophytes, plants so specialized for life on sand that they cannot survive anywhere else.

Among these sand specialists are species found nowhere else on Earth. The team identified 14 trigger taxa that qualify the site for protection under the Important Plant Area, or IPA, framework, an international standard for identifying sites of exceptional botanical significance. One of them, Calligonum elegans, is listed as Endangered on the global IUCN Red List and in Uzbekistan’s national Red Data Book. Its rediscovery in the Yozyovon sands extends its confirmed range beyond what was documented in a 2007 assessment, which had restricted the species to the Sokh River basin. Three voucher-backed localities, including one inside the proposed IPA boundary, now confirm that this endangered shrub persists in the lowland sands of Central Fergana.

Other trigger species include Astragalus rubellus, another nationally Endangered endemic known from just five historical specimens collected between 1928 and 1952, and three Fergana Valley endemics: Asparagus ferganensis, Haplophyllum ferganicum, and Limonium ferganense. Nine additional taxa qualify under a regional subcriterion that captures species whose conservation importance is tied to habitat specialization rather than formal global threat listings. Many of these plants have broad geographic ranges but tiny, fragmented areas of occupancy, a pattern the researchers describe as especially pronounced in arid landscapes where suitable sandy habitats exist only as isolated remnants.

To quantify how fast the sands are vanishing, the team turned to remote sensing. They applied Random Forest classification, a machine-learning algorithm, to Landsat satellite imagery from four reference years: 1991, 2000, 2015, and 2025. The results are stark. Within a fixed study area of 416.24 square kilometers, mapped sand declined from 125.95 square kilometers in 1991 to just 54.79 square kilometers in 2025, a reduction of 56.50 percent. The largest single loss occurred between 2000 and 2015, when sand extent dropped by nearly 38 square kilometers at an annualized rate of 2.53 square kilometers per year. Classification accuracy was high across all epochs, with overall accuracy ranging from 0.925 to 0.983.

The drivers of this collapse are not mysterious. An adapted threat assessment aligned with IUCN standards identified 18 direct pressures affecting the site, with irrigated agriculture, hydrological modification, and substrate disturbance ranking as the dominant forces. Shifting agriculture, agro-industrial farming, and other ecosystem modifications all scored as High impact, reflecting their broad spatial scope, rapid degradation rates, and ongoing activity. Sand extraction, road construction, and land leveling further physically alter the substrate, while water abstraction for irrigation destabilizes dune systems and accelerates salinization.

What makes the Yozyovon sands particularly significant is their biogeographical position. The Fergana Valley, wedged between the Tian Shan and Pamir-Alay mountain systems, is one of Central Asia’s most distinctive and densely populated landscapes. Three IPAs have already been identified there, but all are associated with foothill or mountain environments. The Yozyovon sands represent something fundamentally different: the last extensive remnant of lowland psammophytic ecosystem in Central Fergana, shaped by aeolian processes, groundwater dynamics, and substrate specialization rather than altitudinal gradients.

The floristic composition reflects this uniqueness. The life-form spectrum is dominated by therophytes, annual plants that complete their life cycles during brief periods of favorable moisture, comprising 58 percent of the flora. This pattern reflects adaptation to seasonal aridity and unstable substrates. Meanwhile, the woody component, including species of Populus, Salix, Tamarix, and Haloxylon, contributes to dune stabilization and habitat structure, though field observations confirm increasing fragmentation from grazing and fuelwood collection. The site also functions as a relict lowland analog of desert-riverine landscapes that once occupied larger portions of the Fergana Depression following the retreat of the Syr Darya.

The proposed IPA covers 54.79 square kilometers of remaining mapped sandy habitat and would become the fourth IPA in the Fergana Valley if formally recognized. The Yazyavan Natural Monument, a small existing protected area, sits within the broader proposed boundary, but the site currently lacks formal protected status at the landscape scale. The researchers emphasize that IPA designation does not impose immediate legal restrictions, making it a pragmatic tool for reconciling conservation with the socioeconomic realities of one of Central Asia’s most intensively cultivated regions.

Beyond its regional significance, the study offers a methodological template for conservation planning in data-poor arid environments worldwide. By integrating floristic surveys, herbarium verification, standardized threat assessment, and validated satellite-based land-cover analysis, the researchers demonstrate how evidence-based prioritization can work even where botanical knowledge is uneven and ecosystems are transforming rapidly. As global commitments under the Kunming-Montreal Biodiversity Framework push nations toward conserving 30 percent of terrestrial areas by 2030, the fate of overlooked lowland sand ecosystems like the Yozyovon may well determine whether that target means anything for the world’s most specialized and vulnerable flora.

Subject of Research: Floristic assessment and habitat loss analysis of the Yozyovon Sand Massif as a proposed Important Plant Area in the Fergana Valley, Uzbekistan

Article Title: The Yozyovon Sand Massif as a proposed Important Plant Area in the Fergana Valley: floristic value, habitat loss, and threats

Article References: Tojibaev, K. S., Hoshimov, H. R., Sharipov, I. K., Akbarov, F. I., Abdullaev, S. S., Batoshov, A. R., Karimov, F. I., Dekhkonov, D. B., Kor, L., Baasanmunkh, S., Gil, H.-Y., Oh, A., & Choi, H. J. (2026). The Yozyovon Sand Massif as a proposed Important Plant Area in the Fergana Valley: floristic value, habitat loss, and threats. Nature Conservation, 64, 229-281. https://doi.org/10.3897/natureconservation.64.200242

Image Credits: AI Generated

DOI: 10.3897/natureconservation.64.200242

Keywords: Important Plant Area, Fergana Valley, Uzbekistan, psammophytic flora, sand desert ecosystems, habitat loss, Random Forest classification, Landsat, IUCN Red List, Calligonum elegans, endemic plants, Central Asia

The research, published in Frontiers in Sustainable Resource Management, was conducted by Oliver Simeon Lihuluku, Deo D. Shirima, and Agnes A. Sirima of Sokoine University of Agriculture in Morogoro. The team focused on three strategically contrasting destinations in Tanzania’s Pwani and Dar es Salaam regions: Pugu Kazimzumbwi Nature Forest Reserve, a protected coastal forest about 25 kilometers southwest of Dar es Salaam; Mbudya Island Marine Reserve, a coral reef and seagrass site within the Dar es Salaam Marine Reserve System; and the Kaole Ruins near Bagamoyo, a thirteenth-to-sixteenth-century archaeological site fringed by mangroves dominated by Avicennia marina and Rhizophora mucronata. Together, the sites span forest, marine, and cultural heritage conservation contexts, allowing the researchers to test whether the ecotourism-restoration link holds across fundamentally different ecosystem types.

The study’s architecture combined three data streams. First, the authors compiled official visitor statistics from the Tanzania Forest Services Agency and marine park records covering 2012 through 2022, cleaning and encoding eleven years of annual visitation data for statistical analysis. Second, they acquired multitemporal Landsat satellite imagery from the USGS archive, selecting scenes from Path 166/Row 065 with less than ten percent cloud cover. Raw digital numbers were converted to top-of-atmosphere reflectance using metadata-derived gain and bias coefficients, and thermal bands were translated into brightness temperature. Land use and land cover classes—vegetation, built-up area, bare land, water bodies, farmland, and rangeland—were then mapped with a Random Forest machine-learning classifier implemented through the Dzetsaka plugin in QGIS, with accuracy assessed via confusion matrices, user’s and producer’s accuracy, overall accuracy, and the Kappa coefficient. Third, the team conducted semi-structured interviews with fifteen key informants drawn from district councils, village elders, agency representatives, and tour operators, recruited through purposive sampling until data saturation was reached.

The visitation data alone tell a story of explosive growth. Over the eleven-year window, the three sites collectively recorded 353,492 visits. Kaole Ruins led with 170,971 visitors despite drawing nobody until 2016, when promotion and infrastructure finally arrived; its numbers then climbed steeply to 36,640 in 2022, the highest single-site annual total in the dataset. Mbudya Island maintained a steady marine tourism presence, totaling 130,000 visits anchored by snorkeling, diving, and beach activities. Pugu Kazimzumbwi was the late bloomer: essentially unvisited until 2017, it recorded an exponential rise after 2019, reaching 22,491 visitors in 2022—a 45 percent growth in visitation over the study period at the site level. Across all activities, worshiping at Kaole Ruins attracted the most participants, followed by hiking in the Pugu forest. Men outnumbered women overall, 179,515 to 173,977.

When the researchers correlated annual visitation with restoration indicators, the associations were remarkably strong. Pearson correlation coefficients between visitation rate and restoration index reached 0.992 at Pugu Kazimzumbwi, 0.971 at Kaole Ruins, and 0.858 at Mbudya Island, all statistically significant at the p < 0.01 level. The authors are careful to stress that these are exploratory correlations based on only eleven annual observations per site, and cannot establish causality. Still, the pattern is consistent with a plausible mechanism: entrance fees and tourism revenues are reinvested into restoration, so more visitors mean more money for conservation. Interview testimony reinforced this reading. As one key informant from Pugu Kazimzumbwi put it, “When we started the reforestation program, more visitors came to see the young trees and participate in planting activities,” while a Mbudya tour guide observed that “tourists are more willing to visit when they can see healthy coral reefs during snorkeling”—a feedback loop the researchers describe as a virtuous cycle.

The restoration portfolio documented at the sites is tangible. At Pugu Kazimzumbwi and Kaole, tree planting initiatives funded substantially by ecotourism revenues are organized by the Tanzania Forest Services Agency, the Tanzania Wildlife Management Authority, local government authorities, and WWF, with the Jane Goodall project supporting tree nurseries. Communities supply labor for nursery establishment and planting, and native species are prioritized for restoring degraded forest. At Mbudya Island, local government authorities coordinate regular beach clean-ups and coral reef restoration with tour operators and volunteers, with reef funding correlating positively with visitor growth. The interviews suggest these efforts are not incidental: tourism income appears to be a genuine financing engine for coastal restoration in the region.

Yet the satellite record complicates the optimistic narrative. Pugu Kazimzumbwi lost ten percent of its vegetation cover between 2012 and 2022, dropping from 49 to 39 percent of its area, while built-up land nearly doubled from 11 to 20 percent, bare land expanded from 25 to 35 percent, and water bodies shrank from 15 to 6 percent. Kaole Ruins saw farmland plummet from 35 to 10 percent of its area as built-up land surged from 5 to 22 percent, reflecting conversion of agricultural land to tourism-related and residential development—including new accommodation such as the Kaole homestay. Only Mbudya Island showed unambiguous ecological gain, with vegetation rising from 25 to 35 percent through mangrove recovery and bare land halving, though its open water declined from 60 to 50 percent, a shift the authors attribute to sedimentation, tidal changes, and managed restoration. Classification reliability improved over time, with Kappa coefficients rising from roughly 0.85 to above 0.91 by 2022, lending confidence to these trends.

The tension between rising restoration funding and degrading land cover points to what the authors frame as a threshold effect in newer destinations. Sites that recently opened to tourism lacked the refined management protocols, visitor impact mitigation strategies, and carrying capacity controls that mature destinations typically develop over time, so rapid growth in visitation can outpace the ability of management systems to absorb the pressure. Institutional history matters here too. Pugu Kazimzumbwi, established in 1936, suffered decades of logging and agricultural encroachment before the Tanzania Forest Services Agency assumed management in 2010 and introduced specialized patrols and community engagement through Village Forest Committees—though the authors note that benefit-sharing remains limited and community relations strained. Mbudya’s dedicated Marine Parks and Reserves Unit, with its wardens, rangers, and Beach Management Units, exemplifies the specialized institutional capacity the study links to better outcomes.

The study also surfaced an unexpected demographic pattern. At Mbudya Island, male visitation correlated strongly with restoration indicators, while female visitation did not—a disparity the researchers link to the physical demands and perceived safety risks of water-based activities such as diving and snorkeling. Field observations showed men more often engaging directly with visible marine conservation, while women tended to prefer beach-based activities less connected to restoration outcomes. At the land-based sites of Pugu Kazimzumbwi and Kaole Ruins, both genders showed similarly strong correlations, suggesting that activity type and safety perceptions, rather than restoration itself, shape who responds to ecosystem recovery.

The authors’ recommendations follow directly from the mixed evidence: strengthen site-specific management strategies, enforce carrying capacity limits before visitor growth overwhelms sensitive habitats, and ensure tourism revenues are systematically reinvested in restoration. They caution that the cross-sectional, correlational design cannot definitively prove that tourism drives restoration, and call for longitudinal and experimental research to clarify causal pathways. What the study does establish is that ecotourism in coastal Tanzania is neither a conservation panacea nor a false promise. Managed well, visitor spending can bankroll tree planting, reef rehabilitation, and habitat protection while drawing communities into stewardship. Managed poorly, the same foot traffic—and the development it attracts—can erode the very ecosystems the business model depends on. The net ecological balance, the findings suggest, is decided not by the tourists themselves but by the institutions charged with managing them.

Subject of Research: The relationship between ecotourism visitation rates and coastal ecosystem restoration outcomes at three protected sites in Tanzania

Article Title: Ecotourists' visitation rates and coastal ecosystem restoration: evidence from Tanzania

Article References: Lihuluku, O. S., Shirima, D. D., & Sirima, A. A. (2026). Ecotourists' visitation rates and coastal ecosystem restoration: evidence from Tanzania. Frontiers in Sustainable Resource Management, 5, Article 1918143. https://doi.org/10.3389/fsrma.2026.1918143

Image Credits: AI Generated

DOI: 10.3389/fsrma.2026.1918143

Keywords: ecotourism, coastal ecosystems, Tanzania, ecosystem restoration, land cover change, satellite imagery, Random Forest classification, conservation financing, mangroves, coral reefs, carrying capacity, Pugu Kazimzumbwi

The research, led by Rajib Pegu of Sankardeva Mahavidyalaya with colleagues from institutions across Assam, Arunachal Pradesh, and New Delhi, harnessed the archival power of the Landsat program, which has been imaging the Earth’s surface since the 1970s. By processing multi-temporal Landsat imagery on the Google Earth Engine platform, a cloud-computing service that has revolutionized large-scale environmental analysis, the team was able to classify land cover and retrieve surface temperatures for multiple epochs spanning 1995 to 2024 without downloading or manually processing petabytes of raw data.

The technical pipeline behind the study reflects the current state of the art in remote sensing. Land use and land cover maps were generated using a Random Forest classifier, a machine learning algorithm that builds hundreds of decision trees from training samples and votes on the most probable class for each pixel. Random forest classification has become a workhorse of land cover mapping because it handles noisy, high-dimensional satellite data robustly and resists overfitting. For temperature retrieval, the researchers turned to the radiative transfer approach, converting the thermal infrared radiation measured by Landsat’s sensors into true land surface temperature, with corrections for atmospheric effects and for the emissivity of different surfaces, which they estimated using the Normalized Difference Vegetation Index, a standard proxy for vegetation density.

Land surface temperature is not the same as the air temperature reported by weather stations. Satellites measure the radiative skin of the ground itself: rooftops, asphalt, bare soil, crop fields, forest canopies, and water. Because these surfaces absorb and release heat very differently, land surface temperature is an extraordinarily sensitive indicator of what a landscape is made of. Dark, impervious materials like asphalt and concrete absorb solar radiation during the day and re-emit it slowly, while vegetated surfaces cool themselves through evapotranspiration, the process by which plants release water vapor and shed heat. Water bodies, with their enormous heat capacity, remain the coolest surfaces of all.

The Guwahati data bore this physics out with striking clarity. Built-up areas recorded the highest mean surface temperature of any land cover class, at 36.11 degrees Celsius, while water bodies registered the lowest at 27.29 degrees Celsius, a gap of nearly nine degrees across the same city on the same days. This thermal stratification is the surface signature of the urban heat island effect, the phenomenon first described by Luke Howard in London in the early nineteenth century and quantified for modern cities by climatologist T. R. Oke in the 1970s. What makes the Guwahati study notable is that it documents this effect unfolding in a mid-sized city of the Global South, where urban growth is fastest and adaptive capacity is often thinnest.

To establish that the warming was real and not an artifact of a few unusually hot years, the team applied the Mann-Kendall trend test, a non-parametric statistical method that detects monotonic trends in time series without assuming any particular distribution, together with Sen’s slope estimator, which provides a robust measure of the magnitude of change. The result was a statistically significant warming trend at the conventional p-value threshold of 0.05, confirming that Guwahati’s surface has been heating steadily across the three-decade record rather than merely fluctuating.

The statistical core of the study came from a pixel-level analysis of the 2024 dataset. The researchers drew 5000 randomly sampled pixels and examined how vegetation greenness, expressed through the NDVI, related to surface temperature. Pearson’s correlation analysis revealed a significant inverse relationship, with a correlation coefficient of negative 0.492, meaning that greener pixels were systematically cooler. Linear regression showed that vegetation alone explained approximately 24.2 percent of the spatial variability in surface temperature, a substantial fraction given how many other factors, from elevation to soil moisture to building geometry, also shape the thermal landscape. A one-way analysis of variance confirmed that temperature differences among land cover classes were highly significant, with a p-value below 0.001.

These numbers carry a message that extends well beyond one Indian city. Guwahati sits at the gateway to Northeast India, a region of extraordinary biodiversity, and its growth mirrors that of hundreds of secondary cities across Asia and Africa where urban expansion is consuming wetlands, farmland, and forest fringes. Previous work on Guwahati had already flagged declining groundwater levels and shifting heat island patterns, and studies from Pune, Jaipur, Delhi, Mumbai, and the East Kolkata Wetlands have documented similar dynamics. The new study adds a rigorous, three-decade quantitative baseline that links landscape change to thermal change in a single coherent framework.

The implications for planning are direct. Because vegetation explains roughly a quarter of the spatial variation in surface temperature, protecting and expanding urban green cover is not merely an aesthetic preference but a measurable cooling strategy. The authors point to the conservation of urban vegetation, wetlands, and other natural landscapes as priorities, and frame their findings as evidence for climate-responsive urban planning aligned with the United Nations Sustainable Development Goals on sustainable cities, climate action, and life on land. In a warming century, the coolest surfaces a city owns are the ones it has not yet paved.

The study also demonstrates how the tools of planetary observation have been democratized. A team of researchers at regional Indian institutions, using free Landsat archives and the Google Earth Engine platform, produced an analysis that would once have required a supercomputing facility and a large budget. As urbanization accelerates across the Global South, this kind of accessible, reproducible satellite monitoring offers city governments a way to see, in near real time, the thermal consequences of every hectare of green space lost, and every hectare of concrete gained.

Subject of Research: The relationship between land use land cover change and land surface temperature dynamics in Guwahati, India, analyzed with Landsat imagery and Google Earth Engine

Article Title: Urbanization-driven thermal changes: a remote sensing approach to land surface temperature (LST) and land use land cover (LULC) dynamics in Guwahati, Assam, India

Article References: Pegu, R., Mazumder, T., Borah, H., Bora, K., & Hazarika, P. (2026). Urbanization-driven thermal changes: a remote sensing approach to land surface temperature (LST) and land use land cover (LULC) dynamics in Guwahati, Assam, India. Environmental Science and Pollution Research, 33(28), 14266-14281. https://doi.org/10.1007/s11356-026-38169-x

Image Credits: AI Generated

DOI: 10.1007/s11356-026-38169-x

Keywords: land surface temperature, land use land cover change, urban heat island, Guwahati, remote sensing, Landsat, Google Earth Engine, random forest classification, NDVI, urbanization, Mann-Kendall test, Assam

Amchang occupies a precarious position in every sense. Declared a wildlife sanctuary in 2004, it sits on the southern bank of the Brahmaputra River, hemmed in by Guwahati’s sprawl to the west and rural agricultural land to the south and east. It forms part of the Indo-Burma biodiversity hotspot, one of only 34 such regions recognised worldwide, and shelters endangered species including the Asian elephant, the hoolock gibbon and the leopard. Its vegetation ranges from tropical moist deciduous and semi-evergreen forest to bamboo brakes and grassland, dominated by trees such as sal, teak and Terminalia species. Yet the sanctuary also carries a heavy burden of biological invasion: earlier surveys have recorded 57 invasive plant species from 22 families, with more than 47 percent of the total area invaded by the four most common invaders and roughly 27 percent of that invaded zone severely impacted.

To track three decades of change, the research team led by Trishna Changkakoti of The Assam Royal Global University assembled multi-temporal Landsat imagery at 30-metre resolution, drawing on Landsat 5 TM data from 1995, Landsat 7 ETM+ from 2005, and Landsat 8 OLI/TIRS data for 2015 and 2025. Images were selected from the dry season months of February and March to minimise cloud cover and phenological differences between years. All processing, from pre-processing through classification, was carried out in Google Earth Engine and ArcGIS 10.8, with radiometric correction converting raw digital numbers into surface reflectance values so that the multi-temporal datasets could be compared consistently. The team then layered in three spectral indices designed to sharpen the contrast between land cover types: the Normalised Difference Vegetation Index for vegetation greenness, the Normalised Difference Water Index for surface water, and the Normalised Difference Built-up Index for urban surfaces.

The heart of the method was a Random Forest classifier, a machine learning algorithm that builds hundreds of decision trees from randomly sampled training data and assigns each pixel a class by majority vote. Nine predictor variables were used, combining six Landsat spectral bands with the three indices, and the classifier was configured with 100 decision trees. Reference points gathered from field surveys and high-resolution Google Earth imagery were split 70:30 into training and validation subsets. The resulting maps distinguished seven land use and land cover categories: dense forest, moderately dense forest, open forest, agriculture, built-up land, barren land and water bodies. Accuracy assessment using an independent confusion matrix yielded an overall accuracy of 81.57 percent and a Kappa coefficient of 0.78, figures the authors describe as good, though slightly below the conventional threshold of 0.80 that would indicate near-excellent agreement.

The classified maps tell a story of two opposing processes running in parallel. In 1995, open forest dominated the sanctuary at 45.4 percent of the area, with moderately dense forest at 21.8 percent and dense forest at 10.5 percent. By 2025, dense forest had fallen to just 5.1 percent and open forest to 38.9 percent, while built-up land climbed from 9.8 to 13.6 percent. Yet moderately dense forest expanded substantially, rising by 35.5 percent or 6.11 square kilometres, a pattern the researchers attribute to secondary succession, the regrowth of woody vegetation on land where pressure has eased or where former agricultural plots have been abandoned. This regenerative pulse, however, cannot compensate for the losses elsewhere. Water bodies, which covered 0.33 percent of the sanctuary in 1995, had dwindled to a mere 0.08 percent by 2025, a hydrological decline the authors link to the broader disturbance regime squeezing the reserve.

Beyond simple area statistics, the study deployed landscape ecology metrics to capture how the sanctuary’s structure, not just its extent, has changed. Shannon’s Diversity Index, which measures the variety and evenness of land cover classes, fluctuated across the period, dipping to 1.36 in 2005 before rising again to 1.49 by 2025. Patch count remained constant at seven discrete patches, but edge length told a more troubling tale: after falling from 68,958 metres in 1995 to 58,518 metres in 2005, total edge length surged to 70,749 metres by 2025. Longer edges mean more of the forest interior is exposed to external pressures such as grazing, fuelwood extraction, encroachment and microclimatic stress, a phenomenon ecologists call the edge effect. The authors interpret the combination of stable patch count and rising edge length as evidence of a landscape that is structurally fragmented yet ecologically dynamic, alternating between degradation and partial recovery.

To determine whether these changes represented genuine long-term trends or mere fluctuation, the team applied two complementary statistical tools. The Theil-Sen slope estimator, which calculates the median of all pairwise slopes in a time series, provides a robust, outlier-resistant measure of the magnitude of change, while the Mann-Kendall test assesses the statistical significance of monotonic trends using Z-statistics and p-values. The pixel-level analysis was unambiguous: 2,748 pixels showed significant decreasing trends between 1995 and 2025, more than double the 1,267 pixels showing positive trends, with 739 pixels remaining stable. In other words, despite pockets of regeneration, the sanctuary’s overall trajectory is one of degradation, and the authors argue that rapid intervention is required to manage the losses.

The spatial pattern of change points to specific drivers. The decline in dense forest and water bodies aligns with encroachment for settlement and agricultural expansion along the sanctuary’s western boundary, where it adjoins Guwahati, while the concentration of new built-up land near transport corridors implicates infrastructure development as a principal engine of forest loss at the periphery. The magnitude of the losses also stands out when set against comparable research: regional assessments in Northeast India have typically reported broad forest declines of roughly 9 to 10 percent, whereas Amchang’s 51.7 percent dense-forest loss over three decades far exceeds those figures, underscoring the severity of unchecked urban expansion and weak buffer-zone enforcement. The authors frame their findings within the Sustainable Development Goals, linking sustainable urban planning to SDG 11, forest conservation and climate mitigation to SDG 13, and ecosystem restoration to SDG 15.

What makes the study methodologically distinctive is its integration of three analytical strands, Random Forest classification, landscape fragmentation metrics and non-parametric trend statistics, within a single framework applied to a peri-urban protected area over 30 years. Because the entire workflow relies on freely available Landsat imagery and open-access processing in Google Earth Engine, the authors argue it can be readily transferred to other protected areas in data-poor settings facing similar pressures at the urban edge. The study is not without limitations: the 30-metre resolution of Landsat constrains the detection of fine-scale fragmentation, dry-season imagery cannot capture seasonal forest dynamics, and the classification accuracy, while respectable, leaves residual uncertainty that propagates into the fragmentation and trend analyses. The authors recommend future work with higher-resolution satellite data and deep learning classifiers. For now, the message from the satellites is clear: without stronger buffer-zone compliance and better land use planning around Guwahati, one of Northeast India’s most biodiverse sanctuaries will continue to erode at its edges, one pixel at a time.

Subject of Research: Three-decade geospatial assessment of land use and land cover change in a peri-urban protected area of Assam, India

Article Title: Spatio temporal land use and land cover changes in a peri urban protected area of Kamrup Metropolitan District, Assam, India using geospatial techniques

Article References: Changkakoti, T., Saikia, P., Borthakur, P., & Roy, M. (2026). Spatio temporal land use and land cover changes in a peri urban protected area of Kamrup Metropolitan District, Assam, India using geospatial techniques. Discover Forests, 2(1), Article 75. https://doi.org/10.1007/s44415-026-00134-4

Image Credits: AI Generated

DOI: 10.1007/s44415-026-00134-4

Keywords: land use land cover change, remote sensing, Random Forest classification, forest fragmentation, peri-urban protected area, Amchang Wildlife Sanctuary, Guwahati, Landsat, landscape metrics, Mann-Kendall test, biodiversity hotspot, urbanization

Mozambique holds approximately 300,000 hectares of mangroves, the third largest extent in Africa and the thirteenth largest in the world. These salt-tolerant forests, dominated in the south by species such as Avicennia marina, Rhizophora mucronata, Bruguiera gymnirrhiza, Ceriops tagal and Xylocarpus granatum, buffer shorelines against storms, sustain fisheries, cycle nutrients, and store substantial quantities of carbon. Yet the southern provinces have not been spared. Between 2008 and 2015, an estimated 5,898 hectares of mangrove were lost in the region, with the heaviest impacts recorded in Maputo, Inhambane and Gaza provinces. Timber extraction, coastal population growth, agricultural expansion, aquaculture, and intensifying climate impacts have all contributed to the pressure.

In response, Mozambique has woven mangrove conservation into its national policy framework. The country’s commitments under Sustainable Development Goal 14 were formalized through Decree 103/2019, which established eight marine protected areas, all of which contain mangroves. A National Mangrove Restoration Strategy and Action Plan covering 2015 to 2024 remains in effect, and it identifies systematic assessment and monitoring as central pillars of successful restoration. The new study speaks directly to that need, providing exactly the kind of long-term, spatially explicit evidence the strategy calls for.

Technically, the research is a tour de force of open satellite data. The team combined imagery from Landsat 7 ETM+, Landsat 8 OLI/TIRS, and Sentinel-2 MSI, harmonized to a common 30-meter resolution and processed on the Google Earth Engine platform. Cloud screening with a threshold of 20 percent or less ensured radiometric reliability, and Sentinel-2 data were resampled to match the Landsat record. The researchers then deployed a Random Forest classifier, configured with 100 trees, to sort every pixel into seven land-cover classes: water, mangrove, forest, habitation, cultivation, prairie, and exposed soil. Between roughly 209 and 896 training points were collected per class in each region, totaling close to 3,000 samples per site, with validation points collected independently to avoid inflated accuracy estimates.

The classification performed robustly. Overall accuracy exceeded 0.80 in all four regions and reached a maximum of 0.96, while Kappa coefficients ranged from 0.76 to 0.95, indicating strong to excellent agreement with reference data. Water and mangrove classes were the most reliably mapped, with producer’s and user’s accuracy consistently above 0.94 in most regions. Forest also performed well. The greatest confusion arose in the heterogeneous landscapes of Tembe and Limpopo, where settlements, cultivation, and savanna-like vegetation overlap spectrally, a well-known challenge in coastal land-cover mapping.

Beyond the classified maps, the team computed a battery of spectral indices to probe vegetation health and hydrology. These included the Normalized Difference Vegetation Index and Enhanced Vegetation Index as general vigor measures, the Mangrove Vegetation Index as a mangrove-specific probe, and water- and soil-related indices such as NDWI, MNDWI, and NDSI. Temporal trends were tested with the non-parametric Mann-Kendall test, quantified with Kendall’s tau and 95 percent confidence intervals. The results showed statistically significant upward trends in vegetation indices across all regions, while water-related indices consistently indicated hydrological reduction or retraction. The mangrove-specific indices confirmed the same modest improvement, though the confidence intervals included zero, signaling uncertainty in effect size even where the direction of change was clear.

The spatial patterns are where the story becomes genuinely compelling. Tembe recorded a net mangrove gain of 41 hectares relative to its base year, with the water class stable throughout. Limpopo and Belavista also registered slight increases, which the authors link to restoration measures including hydrological rehabilitation and the restoration of tidal dynamics, alongside mild temperatures favorable to mangrove growth. In Belavista, declining cultivation and prairie classes appear to have been colonized by forest and mangrove vegetation, suggesting that abandoned or degraded land is being reclaimed naturally where pressures have eased. Limpopo’s estuary, with its sediment accretion and community-based management, has previously been identified as a zone of natural recovery, and the new analysis is consistent with that picture.

Inhambane stands out as the exception. Mangrove cover there declined by more than 2,000 hectares despite years of relative stability, a trajectory the researchers attribute to the late start of local restoration efforts, which only began in 2020, combined with anthropogenic pressure, forest encroachment into mangrove zones, and reduced rainfall. The contrast with the other three sites underscores a central lesson of restoration ecology: timing matters, and interventions launched after degradation has gained momentum face a steeper uphill battle. It also echoes national assessments that have linked urban expansion and economic activity to mangrove decline in some coastal systems even as others expand under favorable geomorphological and hydrological conditions.

Climate emerges as a silent but powerful actor throughout the study period. Although temperatures remained within the normal range for mangrove growth, roughly between 22 and 35 degrees Celsius, rainfall was persistently scarce, and the period coincided with severe and moderate El Niño events in southern Mozambique. Previous research has shown that precipitation and mangrove cover follow a sigmoid relationship, with a reflection point near 1,368 millimeters, and that rainfall below 1,000 millimeters can induce physiological stress. Comparable El Niño episodes have driven catastrophic mangrove dieback in northern Australia and limited restoration success elsewhere in Mozambique, including massive dieback documented at the Maputo River estuary. The modest gains detected in this study are best understood against that climatic backdrop: restoration can work, but drought and hydrological stress can throttle seedling survival and canopy recovery even where human pressures are controlled.

The study is candid about its limits. It rests entirely on remote sensing, without field or socioeconomic data, and the 30-meter resolution of the imagery means mixed pixels can blur the boundary between mangroves and adjacent vegetation, a recognized source of uncertainty in mangrove classification. Tidal variation can also distort spectral indices when images are not acquired under comparable conditions, though multi-temporal image extraction was used to mitigate this. The authors recommend that future assessments incorporate full hydrological and socioeconomic datasets to strengthen causal interpretation.

Even so, the implications for policy are clear. Restoration alone, the findings suggest, is not a guarantee of recovery; it must be paired with systematic monitoring, coordination between government and scientific institutions, and genuine participation by local communities. The mangroves of southern Mozambique occupy narrow intertidal zones where every hectare counts, and the satellite record now shows precisely where recovery is taking root and where it is slipping away. As climate variability intensifies, that kind of evidence will be indispensable for deciding where to plant, where to protect, and where to let the tide do the work.

Subject of Research: Spatiotemporal monitoring of mangrove cover change in restoration areas of southern Mozambique using satellite remote sensing from 2009 to 2025

Article Title: Mangrove spatiotemporal changes in four areas with restoration projects in Southern Mozambique from 2009 to 2025

Article References: Munguambe, A. J., Domingues, R. B., & Baptista, V. (2026). Mangrove spatiotemporal changes in four areas with restoration projects in Southern Mozambique from 2009 to 2025. Environmental Challenges, 25, Article 101669. https://doi.org/10.1016/j.envc.2026.101669

Image Credits: AI Generated

DOI: 10.1016/j.envc.2026.101669

Keywords: mangroves, Mozambique, remote sensing, restoration, Random Forest classification, Google Earth Engine, spectral indices, El Niño, coastal ecosystems, land cover change, blue carbon, Mann-Kendall trend test

Kakinada is not a household name outside India, but it is a textbook example of the forces reshaping coastal Asia. Once a regional agrarian trading hub, the city has been propelled by major investments in the Kakinada Special Economic Zone, the Kakinada Gateway Port, and offshore energy development in the Krishna-Godavari basin. That economic engine has demanded land, and lots of it. The study found that built-up area within the 427-square-kilometer urban agglomeration grew from just 5.87 percent of the landscape in 1990 to 15.84 percent in 2022, a nearly threefold expansion consumed overwhelmingly at the expense of cropland and natural vegetation.

To track this change, the researchers, led by Chandra Shekhar Dwivedi and Devbart Kumar of Central University of Jharkhand together with colleagues, processed multi-temporal Landsat satellite imagery on the Google Earth Engine cloud platform. Each year of analysis relied on a post-monsoon median composite built from every cloud-free image available between November and February, minimizing seasonal distortion. Land cover was classified into five categories, built-up, wasteland, natural vegetation, waterbody, and cropland, using a Random Forest machine learning algorithm, a choice the authors note is now standard for complex coastal environments because it outperforms traditional classifiers in landscapes mosaicked with aquaculture ponds, mangroves, and paddy fields. Training samples were verified against Survey of India topographic sheets and historical imagery in Google Earth Pro, and each classification was validated against an independent dataset using confusion matrices, overall accuracy, and the Kappa coefficient.

The resulting land-change record reads like an economic history written in pixels. In the first decade, coinciding with India’s post-liberalization boom, built-up area nearly doubled from 25.10 square kilometers to 44.93 square kilometers, drawing most heavily on cropland and natural vegetation. Between 2000 and 2010, urban growth moderated, but the landscape shifted in another revealing way: the waterbody class expanded by more than 12 square kilometers, reflecting a wholesale conversion of paddy fields into shrimp aquaculture, a practice that permanently alters soil salinity and hydrology. Then, in the most recent period from 2010 to 2022, urban expansion accelerated again under the impetus of the special economic zone and port construction, even as something stranger appeared on the map.

That something was wasteland. Between 2010 and 2022, the area classified as wasteland exploded from 6.60 square kilometers to 57.45 square kilometers, with a staggering 52.39 square kilometers of cropland converted in just twelve years. The authors interpret this as a likely signal of speculative land banking: agricultural plots acquired in advance by developers and industrial entities, cleared of vegetation, and left fallow until construction begins. An alternative and more troubling possibility is ecological. Saline water seeping from the booming aquaculture ponds into adjacent fields can salinize soils and force abandonment, a semi-permanent loss of the delta’s food-producing capacity. Either way, these stripped, barren tracts in the peri-urban fringe are vulnerable zones, prone to erosion and offering no resistance to runoff.

To characterize the shape rather than merely the amount of growth, the team applied the Shannon entropy method, dividing the study area into zones radiating from the city center and measuring how evenly built-up land was distributed. Relative entropy values approaching 1.0 indicate dispersed, fragmented development; values near 0 indicate compact growth. Kakinada’s buffer zones scored between 0.94 and 0.98 in every year analyzed, an unambiguous signature of sprawl. Growth is leapfrogging outward along road corridors and the coastline rather than densifying the core. Even within the municipal boundary, entropy dipped to 0.85 by 2010 as the urban core infilled, then rose again to 0.90 by 2022, suggesting that fragmentation is now penetrating the historic city itself.

The second half of the study confronts that sprawl with the ocean’s future. Regional projections for the Bay of Bengal near Visakhapatnam, drawn from the IPCC Sixth Assessment Report via the NASA Sea Level Projection Tool, suggest a likely sea-level rise of roughly 0.56 meters under the intermediate SSP2-4.5 scenario and 0.71 meters under the high-emission SSP5-8.5 scenario by 2100, with low-confidence, high-impact ice-sheet scenarios reaching 1.6 meters. The team modeled inundation using FABDEM, a 30-meter digital elevation model with forests and buildings algorithmically removed to approximate true bare-earth terrain, a critical correction in vegetated deltas where conventional elevation models overestimate ground height and thereby underestimate risk. A modified bathtub approach with a hydrological connectivity algorithm ensured that only cells with a contiguous path to the sea were flagged, eliminating isolated inland depressions that would inflate the hazard maps.

The exposure figures scale non-linearly and make for sobering reading. Under a 1-meter rise scenario, 3.96 square kilometers of the study area lie exposed, including about 0.31 square kilometers of built-up land. In the critical zone below 2 meters in elevation, chosen as a conservative proxy for the convergence of projected rise and the higher tidal range, 5.79 square kilometers of existing built-up land already sits today. Under an extreme 7-meter scenario representing a major storm surge atop long-term rise, exposure balloons to 337.12 square kilometers of total area, sweeping in 148.26 square kilometers of cropland and 43.12 square kilometers of built-up environment. Crucially, the newer peripheral developments account for a disproportionate share of the exposed land, while the historic core generally occupies higher ground, direct evidence that recent growth is migrating toward topographic vulnerability.

Local dynamics could compress these timelines considerably. The IPCC projections capture global and regional ocean trends but not vertical land motion, and interferometric synthetic aperture radar studies in the Godavari delta have recorded subsidence rates of 10 to 20 millimeters per year, driven by sediment compaction and groundwater extraction. Superimposed on a eustatic rise of 6 to 8 millimeters per year under high emissions, the effective rate of relative sea-level rise experienced by Kakinada could approach triple the global average, meaning the year-2100 inundation scenarios likely represent a conservative baseline. The delta’s natural defense, vertical accretion from river-borne sediment, has been crippled by upstream damming that starves the coast of sediment, a sediment-starvation crisis that leaves the land unable to keep pace.

The authors frame the findings as a warning for planners far beyond one Indian city. Kakinada’s trajectory undermines Sustainable Development Goals 11, 13, and 14, on sustainable cities, climate action, and marine ecosystems respectively, and it illustrates a pattern now global: worldwide, the expansion of settlements into flood-prone zones is increasing exposure faster than climate change itself. The study argues that resilience planning must prioritize geomorphological constraints, enforcing no-build zones in the high-entropy, low-lying fringe while incentivizing vertical densification on safer ground. Without such structural shifts, the ports and industries that powered Kakinada’s ascent risk functional obsolescence, not through economic decline, but through the patient, measurable encroachment of the sea that satellite records have now documented decade by decade.

Subject of Research: Coastal urbanization patterns and future sea-level rise inundation exposure in the Krishna-Godavari delta, India

Article Title: Spatiotemporal dynamics of coastal urbanization and future inundation risk in the krishna godavari delta

Article References: Dwivedi, C. S., Kumar, D., Pandey, A. C., & Basheer Ahammed, K. K. (2026). Spatiotemporal dynamics of coastal urbanization and future inundation risk in the krishna godavari delta. Discover Cities, 3(1), Article 182. https://doi.org/10.1007/s44327-026-00365-2

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00365-2

Keywords: urban sprawl, sea-level rise, Krishna-Godavari delta, Kakinada, remote sensing, Google Earth Engine, Random Forest classification, Shannon entropy, FABDEM, IPCC AR6, coastal flooding, land use land cover change