Analyzing urban sprawl in response to land use land cover change dynamics in Areka town and surrounding area: Wolaita Zone, Ethiopia

Abstract. Urbanization and climate change are rapidly transforming metropolitan environments, posing serious challenges to sustainable development, climate resilience, and disaster risk management, Among these challenges, concern, where built up areas experiences higher temperatures than surrounding rural regions due to increased impervious surfaces, vegetation loss and altered urban morphology. As UHI intensifies heatwave impacts and public health risks, advanced tools are required to monitor, simulate and mitigate urban heat dynamics. This Study analyses the spatio-temporal evolution of Land Surface temperature (LST) and UHI intensity in Bengaluru for 2004 to 2024 using multi-temporal Landsat data. Key surface indicators include, NDVI, NDBI, NDWI, albedo ad LULC were derived to assess thermal behaviour. Results indicate rapid built-up expansion and declining vegetation, leading to the regression predicts 1-2 °C increases in LST by 2023 in highly urbanized areas.To support urban climate decision making, a 3D Digital Twin platform was developed using CesiumJS, integrating geospatial analysis, remote sensing, and predictive simulations. The framework demonstrates the potential of Digital Twins as an effective decision-supportive tool for climate-adaptive and sustainable urban planning.

ABSTRACT This study proposes a geospatial procedure that leverages Landsat 8/9, TERRACLIMATE, and SRTM-DEM data, to spatialize drought patterns and dynamics, from a confrontation–confirmation–completion perspective of a reference (empirical) and observed (records) warmest period. First, Landsat 8/9 bi-month median image is used to develop the Improved Land Surface General Drought Index – version 3 (LSGDI3) with the Euclidean function. Next, a multi-regression computation supports the downscaling-adjustment of climate (climatic drought) and topography (topographic drought) to the previous step output. Further, the Weighted Residuals Aggregation Polynomial (WRAP) model is proposed, to produce the Land Surface-Climate-Topography Combined Drought Model (LSCTDMComb). As overall finding, the dual data reference/observed alternative is highly efficient to model drought spatial patterns at a regional scale and reflecting the global warming dynamics. For the 2014–2023 studied decade, the average decade cross-receiver operating characteristic/area under curve (cross-ROC/AUC) results are satisfying up to 0.818, sign of a balanced and robust model. Further support is provided by lower values of the root mean square error (RMSE), mean absolute error (MAE), and relative absolute error (RAE) metrics, respectively [0.0368–0.246], [0.0031–0.0233], and [0.005–0.0393]. Whereas the coefficient of determination values obtained between reference and observed warmest bi-months are, R2: [0.64–0.96], regardless of the temporal difference. Spatially, all LSCTDMComb agree on increasing risk of drought hazards with the latitudes at the Sahel–Sahara interface. These trends are similar to the patterns depicted by compared popular models, despite some caveats raised, especially the resolution discrepancies.

Land use land cover (LULC) changes are key indicators of environmental transformation, directly influencing hydrological balance, ecosystem services and sustainable land management. Sher River basin, an agro-ecological diverse sub-basin of the Upper Narmada River system in India, is the primary hydrological and socio-economic lifeline for local communities, but no comprehensive study on Sher River basin related to long term LULC dynamics could be tracked in the literature. To fulfil this research gap, long term LULC spatiotemporal change detection and transition patterns analysis with a dual-seasonal focus on the Rabi and Kharif cropping periods over a 23-year period (2001-2023) in Sher River basin are carried out. LULC maps are created using multi-sensor Landsat data (TM, ETM+ , OLI/TIRS) for 2001, 2006, 2011, 2016 and 2023 at an interval of 5years for both Rabi and Kharif seasons using a supervised classification technique with the maximum likelihood classifier. Five LULC classes, namely, built-up, agricultural, forest, water body and barren land are delineated. All classified maps achieved overall accuracies exceeding 85% with kappa coefficients greater than 0.80. The agricultural land increased significantly in both seasons, more sharply during Rabi (5.86% to 10%) while built-up areas expanded more than fivefold (0.18% to 0.85%) reflecting rapid urbanization during 23years. Barren land declined noticeably, transitioning mainly into agricultural and urban land uses. Forest cover, after an initial decline, showed recovery post 2016 with a modest increase in the later years due to afforestation initiatives. Water bodies remained relatively stable with minor seasonal variations. This study provides critical insights into seasonal land dynamics, highlighting clear seasonal contrasts in land use behavior between Rabi and Kharif periods. The findings emphasize the need for integrated land use planning to balance agricultural growth, urban expansion and ecological sustainability in sub-humid watersheds.

Urban green spaces are increasingly threatened by rapid urban expansion, making their continuous monitoring and prediction essential for sustainable urban management. This study investigates the spatiotemporal dynamics of urban garden landscapes in Shiraz, Iran, by integrating multi-temporal Landsat imagery, GIS analysis, and CA–Markov modeling. Landsat data from 2003, 2013, and 2023 were processed to derive the Normalized Difference Vegetation Index (NDVI), which was classified into four vegetation-density categories to quantify land-cover transitions. A CA–Markov framework implemented in IDRISI TerrSet (Version 20.0) was then employed to simulate spatial dynamics and predict vegetation changes for 2033. Results reveal a significant expansion of non-vegetated areas from 711.93 ha in 2003 to 976.66 ha in 2023, accompanied by a decline in dense vegetation from 403.68 ha to 382.64 ha. Model projections indicate a further reduction in dense vegetation to 239.35 ha by 2033, suggesting ongoing fragmentation of urban green infrastructure driven by development pressures. By combining time-series remote sensing, GIS-based spatial analysis, and predictive modeling, this study provides an integrative framework for detecting, interpreting, and forecasting urban land-cover change. The findings offer evidence-based insights to support sustainable urban planning, green infrastructure protection, and climate-resilient city management in rapidly growing urban environments.

India’s diverse agricultural landscapes demand a single Land Use and Land Cover (LULC) product integrating both intra-annually static (built-up, tree cover, barren land) and dynamic (water seasonality, cropping intensity) classes for sustainable Natural Resource Management (NRM). Existing LULC products suffer from limited thematic coverage of dynamic processes, imprecise delineation of fragmented smallholder features, limited reproducibility, and poor performance of monolithic classifiers on spectrally similar categories. We introduce a hierarchical decision-tree framework that breaks complex classification tasks into targeted sub-tasks, offering methodological improvements in detecting monsoon water, delineating tree-croplands, and classifying cropping intensity. Class-wise evaluations demonstrate superior performance: SAR water detection achieves an NRMSE of 0.33 (vs. baselines 0.53–0.75), tree-cropland macro-average of 0.94, and cropping intensity macro-average of 0.88 (vs. baseline 0.77). Crucially, this study ensures transparency and reproducibility in LULC mapping. We publicly release four curated datasets alongside the entire classification pipeline implemented on the Google Earth Engine commodity platform. The resulting pan-India output maps at 10m resolution are hosted on CoRE-Stack (digital public good) for easy accessibility and analysis [24], already powering real-world applications in water-security planning [61] and agricultural studies [50].

The East and South Eastern Coastal Plain Zone (ESECPZ) of Odisha, situated along the eastern coast of India, present a mosaic of urban and rural regions. The present study aims to estimate the land use land cover change and extent of urbanization in ESECPZ over the span of two decades, from 2000 to 2020. Multi-year Landsat data underwent supervised maximum likelihood classification to estimate LULC change. The ESECPZ of Odisha underwent substantial transformations during the study period. Forest cover decreased from 33.95% to 18.98%, agricultural land decreased from 24.98% to 21.84%, as against the settlements which surged from 21.52% to 39.67%. The maximum, minimum and average Land Surface Temperature (LST) showed steady increase during the period of study. About 5°C increase in the minimum LST range and 3°C increase in maximum LST were recorded. The increase in LST was attributed to the expansion of settlement areas and reduction in the vegetation cover due to population growth and urbanization. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) showed gradual decreasing and increasing trends, respectively. Strong positive correlation was found between LST and NDBI while a strong negative correlation was found between LST and NDVI during all the years under study. This research provides valuable insights into the often-overlooked impacts of urbanization on (LST) and vegetation cover; the findings offer a positive outlook for the future, providing a foundation for forward-thinking initiatives aimed at addressing these environmental challenges.

Enhancing forest aboveground biomass carbon estimation using improved phenological features retrieved by pixel-specific fusion of Landsat and Sentinel-2 time series

A framework for long-term vegetation latent heat estimation and forecasting combining ERA5-land and Landsat data

Rapid urbanization in sub-Saharan Africa poses significant challenges to local food security through the conversion of productive agricultural lands to built-up areas. Kinondoni District in Dar es Salaam has experienced substantial urban growth over the past three decades, yet the implications for food security remain poorly understood.This study analyzed land use and land cover (LULC) changes in Kinondoni District between 1993 and 2023 to assess the impact of urbanization on agricultural land availability and its implications for local food security. Multi-temporal satellite imagery analysis was conducted using Landsat data from four epochs (1993, 2003, 2013, and 2023). Land use was classified into seven classes: water bodies, mangrove forests, bare areas, built-up areas, shrublands, and cultivated lands. Change detection analysis quantified the magnitude and direction of land use transitions over the 30-year period using supervised Maximum Likelihood Classification with overall accuracy ranging from 85-92% and Kappa coefficients of 0.82-0.89. The analysis revealed dramatic urbanization, with built-up areas expanding from 124.20 ha (2.23%) in 1993 to 1,905.14 ha (34.3%) in 2023 a 1,433% increase. Concurrently, cultivated land decreased by 96.6%, from 172.90 ha (3.11%) to 5.88 ha (0.1%), representing a loss of 167.02 ha. Mangrove forests declined by 40.1% (737.62 ha), shrublands decreased by 73.9% (546.41 ha), and bare areas reduced by 77.9% (402.11 ha). Water bodies remained relatively stable, increasing slightly by 3.3% (72.22 ha). The majority of agricultural land (85.2%) was directly converted to built-up areas. The near-complete elimination of cultivated land in Kinondoni District indicates a critical threat to local food production capacity and urban food security. The rapid conversion of agricultural and natural lands to urban infrastructure necessitates urgent policy interventions, including peri-urban agricultural planning, vertical farming initiatives, and regional food supply chain development to ensure food security for Kinondoni's growing population.

ABSTRACT Wetlands provide various goods and services, including habitat, medicine, food, flood mitigation, and other local and global environmental benefits. However, the ecosystem functions of wetlands have been at risk due to human interference and climate change. This review article highlights the application of remote sensing techniques in wetland monitoring and mapping. It emphasizes the use of satellite imagery and the advancement of classification methods to delineate wetland boundaries and assess land‐use dynamics and vegetation health. We observed a significant study on wetland ecosystem mapping with Landsat and Sentinel satellite data. Despite advancements in various remote sensing tools and techniques for wetland monitoring, challenges persist, including cloud cover interference (primarily during rainy seasons), limited access to open data, the need for high‐resolution data and the requirement for more accurate classification methods. Moreover, this review highlights the gaps in current remote sensing applications and suggests future research directions to improve wetland ecosystem management and conservation plans.

Tracking a semi-arid Eastern Mediterranean ecotone through integration of terrestrial and atmospheric earth observation data (2000-2024).

ABSTRACT This research presents an updated glacier inventory of Uttarakhand using Landsat-8 data (2020–2021), addressing limitations of earlier inventories. A total of 1279 glaciers covering about 2311.36 km2 were manually delineated, considering topographical and debris-related complexities. Among them, 16 glaciers exceed 20 km2, 26 range from 10–20 km2, 358 are between 1–10 km2, and 879 are under 1 km2 area. Most glaciers (277) face south, while the fewest (60) face west. Basin-wise, Alaknanda has the most glaciers (539), followed by Sharda (345), Bhagirathi (289), and Yamuna (106). Results were compared with previous inventories, showing significant differences in glacier count and area.

The ecological environment plays a crucial role in maintaining the balance and sustainability of ecosystems, particularly in the context of rapid urbanization. This study investigates the impact of urban growth on ecological environment quality in the Jalpaiguri Planning Area, a rapidly urbanizing area that has been overlooked in previous research. Using remote sensing and geographic information system, this study employed the Remote Sensing Ecological Index (RSEI) to quantitatively assess ecological environment quality by integrating key biophysical properties such as greenness, wetness, dryness, and surface temperature using multi-temporal Landsat data from 1991 to 2021. The results revealed a significant deterioration in eco-environment quality with a decline in mean RSEI values from 0.70 to 0.45. Areas with moderate to excellent ecological quality declined over time, while poor and fair quality zones increased, especially in the urban core and surrounding areas. Moran's I increased from 0.332 to 0.389, suggesting an increasing spatial dependence and clustering of ecological conditions, indicative of growing environmental polarization. Local indicators of spatial association highlighted a decreasing trend in High-High clusters and an expansion of Low-Low clusters, indicating degradation in greenness and wetness due to intensified built-up development. The outcomes of regression analysis revealed a strong and consistent negative correlation between growing built-up areas and RSEI, with correlation coefficients ranging from -0.76 to -0.86 over the study period. The results support targeted planning interventions, including protection of green and blue spaces, control of unplanned built-up expansion, and integration of RSEI-based ecological monitoring into urban planning for informed decision-making.

River channel migration is a dynamic geomorphological process influenced by hydrological, geographic, and anthropogenic factors. Understanding river shifting patterns is important for sustainable catchment management. This study investigates the spatial and temporal changes of the Arpa River channel over 49 years (1972–2021) using remote sensing and GIS techniques. Landsat images and Data Elevation Models (DEM) were used to measure river morphology and channel migration. The study quantified both spatial and temporal variations by integrating cross-sectional analysis of sinuosity ratio estimation and river channel shifting rate calculations. The Autoregressive Integrated Moving Average (ARIMA) model also predicts future river migration trends. The results show that the Arpa River has undergone significant morphological changes, a reduction in channel width, and lateral migration due to hydrological variations, sediment transport, and anthropogenic pressure. Its findings highlight changes to the river due to urbanization, infrastructure development, and deforestation. Integrating remote sensing and GIS with time-series modeling enhances the predictive capabilities of river migration studies and provides valuable information to policymakers and environmental planners. This study underlines the need for sustainable landscape strategies and sediment management policies to mitigate the adverse impacts of riverbank erosion and shifting.

Accurate mapping of near-surface air temperature (Tair) at the fine spatial resolution is required for city-scale monitoring and remains a critical challenge in Earth Observation (EO). Reliance on ground-based measurements is constrained by their sparse spatial coverage and high operational costs. We present a novel, scalable machine learning framework designed to overcome this limitation. Our method utilizes interpretable Convolutional Neural Networks (CNNs) to fuse high-resolution Landsat data, integrating both thermal and reflective spectral bands, with contextual spatiotemporal metadata. This approach allows for inference, at 30 m resolution, of Tair fields without relying on dense, localized ground monitoring networks. Our hybrid CNN architecture is optimized for spatial generalization, maintaining strong and transferable performance (station-wise R2≈0.88) across diverse environments from humid coasts (R2≈0.89) to arid interiors (R2≈0.84). Although focused on a specific geographical region, our results suggest a robust and reproducible pathway for generating spatially consistent temperature fields from globally available EO archives, directly supporting urban heat island mitigation, climate policy development, and high-resolution public health assessment worldwide.

In response to land-use and land-cover (LULC) changes in South Africa, which have varied effects on biodiversity, several studies have characterized LULC changes using remote sensing data due to its cost-effectiveness, repetitiveness, spatial coverage and flexibility. However, the geotemporal and methodological characteristics of these studies remain relatively unknown. In this regard, we review remote sensing-based studies conducted in South Africa using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From the 343 articles retrieved from Web of Science, Google Scholar, and Scopus databases, 103 studies were eligible for analysis. The analysis showed that (a) various remote sensing datasets were increasingly and effectively used to characterize LULC in South Africa over the period 2001–2024, primarily Landsat data with integration of various advanced classification algorithms; (b) most studies were conducted in the eastern seaboard, particularly in the Maputaland–Pondoland–Albany hotspot and highveld to the north, and (c) much research dealt with issues pertaining to “pristine class” conversion to urban area and other human-induced activities, mainly in biodiversity-rich landscapes. Overall, LULC studies achieved consistently reliable accuracies, largely using publicly available geospatial datasets, thereby creating an accessible foundation for all researchers. LULC research is expected to increase as conservation efforts strengthen amid ongoing developments in South Africa.

Bangka’s mangroves contribute to Indonesia’s species-rich coastal ecosystems, yet they have experienced substantial degradation, largely driven by human activities such as tin mining. Establishing long-term records of mangrove extent is essential for understanding distribution dynamics, assessing impacts, and guiding conservation strategies. In this study, we applied change detection techniques, a random forest classifier, and the LandTrendr algorithm to analyze Landsat time-series data from 1994 to 2023 across Bangka Island. We quantified multi-decadal changes in mangrove extent, periods of disturbance and recovery, and discrepancies between local and global datasets. Mangrove dynamics were spatially heterogeneous, with both expansion and loss observed across regions in landward and seaward settings. Over the 30-year period, total gains reached 4956.39 ha (10.30% of the baseline), yet the net change indicated an overall loss of 1055.85 ha. LandTrendr analysis further revealed sustained mangrove expansion since 1989. Observed changes reflect the combined influence of natural processes, including accretion and erosion, and human pressures, particularly tin mining. Although net area loss aligns with national trends, the drivers in this mining-dominated region differ from those elsewhere, and some mangrove areas remain absent from global datasets. These findings emphasize the need to better capture local gain–loss dynamics to support effective management and conservation.

Land surface temperature is a crucial physical parameter in the examination of the natural ecological environment. The study utilized Landsat data to investigate land use indices and thermal environment change in Guangzhou, employing the radiative transfer equation, concentric circles, Pearson correlation coefficient, and other geospatial methods. Overall, as the distance from the city center increased, NDVI values tended to rise, while land surface temperature showed a gradual decreasing trend. Additionally, land surface temperature exhibited a negative correlation with NDVI and a positive correlation with NDBI. Barren had the highest LST, followed by impervious, while the water and the forest were cooler. The high-temperature area took on a V-shape, primarily situated in the west and southern areas, whereas the cooler temperature zone was mainly found in the northeast. The results can offer a scientific foundation for further exploration of the urban heat island formation mechanism, development of rational planning policies, and assessment of urbanization’s impact on local climate.

The Bosten Lake, as an ecological key hub in the arid region of Northwest China, has a relatively unique ecological environment, making it challenging to maintain ecological balance. Studying the dynamic changes of the normalized difference vegetation index (NDVI) in the Bosten Lake Basin and its driving factors is of great significance for maintaining the stability and sustainable development of the basin's ecosystem. Based on Landsat data from 2001 to 2023, the NDVI values of the Bosten Lake Basin were calculated. The Mann-Kendall trend significance test, Sen's slope estimation method, and Hurst index were used to analyze the spatiotemporal dynamic changes of NDVI in the Bosten Lake Basin, and the relationship between climatic factors and NDVI was explored. The results showed that: ① The annual maximum NDVI in the Bosten Lake Basin generally showed an increasing trend, with a growth rate of 0.003 3 a-1. The spatial distribution characteristics of NDVI were relatively obvious, mainly dominated by high vegetation coverage, with 52.18% of the area showing an increasing trend. ② Seasonally, the NDVI during the growing season showed an increasing trend, with the highest NDVI in summer and the lowest in spring, and the trend of summer NDVI changes was consistent with the annual maximum NDVI changes. ③ The Hurst index predicted that 34.72% of the area in the Bosten Lake Basin would show a degradation trend in NDVI, while 65.28% would show an improvement trend. ④ The annual maximum NDVI in the Bosten Lake Basin from 2001 to 2020 was positively correlated with rainfall, temperature, sunshine hours, and evaporation and significantly correlated with sunshine hours and total evaporation, with correlation coefficients of 0.374 and 0.494, respectively. Therefore, the NDVI in the Bosten Lake Basin has shown an improving trend over the past 23 years, positively correlated with climatic factors. This study provides a scientific basis for the ecological environment construction, ecosystem management, and ecological balance maintenance in the Bosten Lake Basin.