Search Results (16)

The rapid growth of the low-altitude economy and urban air mobility (UAM) is reshaping urban transport and infrastructure systems. However, current planning practices still tend to treat green spaces, stormwater facilities, and drone infrastructure as separate subsystems. This paper proposes a Garden Hydrology UAV collaborative infrastructure framework for resilient urban low-altitude logistics and inspection. Pocket parks and sponge city facilities (rain gardens, detention basins) are redesigned as multi-functional UAV bases that integrate take-off/landing and charging with stormwater retention and recreation. A SWMM-based hydrological model provides time-varying inundation and storage states, which are mapped into dynamic node availability constraints for UAV operations, using EPA SWMM 5.2. A multi-objective optimization model is formulated to minimize logistics operation cost, hydrological risk exposure and noise impact on sensitive receptors, while respecting airspace and battery constraints. A stylized 4 km² high-density district is used to evaluate three scenarios: depot-only operations, garden–UAV integration without hydrological coupling, and the full collaborative framework with SWMM-based node availability and high-precision navigation. Simulation results show that the integrated design reduces makespan by up to 19.7%, energy use by 22.3%, and hydrological risk exposure by 63.4%, while lowering noise exposure by 21.3%, relative to the baseline. The study suggests that garden and sponge city infrastructures can become key physical supports of smart low-altitude networks under the low-altitude economy. Full article

Agriculture in semi-arid regions faces increasing challenges from temperature extremes and moisture stress, necessitating climate-smart and resource-efficient production systems. This study examined maize–mushroom intercropping as a climate-smart strategy for semi-arid regions. Field experiments conducted at Tamil Nadu Agricultural University evaluated four maize planting geometries, with and without mulch, in 2022. Results showed that close-maize spacing (45 × 25 cm) with mulch moderated temperature, increased humidity, and improved mushroom yield and biological efficiency. The treatment achieved a land equivalent ratio above one, indicating superior land use efficiency. Optimal microclimatic conditions (26–33 °C; 80–98% RH) enhanced paddy straw mushroom growth, demonstrating that simple field-level modifications can stabilize microclimate and promote resilient farming in semi-arid ecosystems. Full article

This study evaluated the seasonal variability of soil–atmosphere greenhouse gas (GHG) fluxes—carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—across a land-use gradient in the Andean–Amazon transition zone of Colombia. The gradient included five land-use types incorporating at least one innovative climate-smart practice—improved pasture (IP), cacao agroforestry system (CaAS), copoazu agroforestry system (CoAS), secondary forest with agroforestry enrichment (SFAE), and moriche palm swamp ecosystem (MPSE)—alongside the dominant regional land uses, old-growth forest (OF) and degraded pasture (DP). Soil GHG fluxes varied markedly among land-use types and between seasons. CO₂ fluxes were consistently higher during the dry season, whereas CH₄ and N₂O fluxes peaked in the rainy season. Agroecological and restoration systems exhibited substantially lower CO₂ emissions (7.34–9.74 Mg CO₂-C ha⁻¹ yr⁻¹) compared with DP (18.85 Mg CO₂-C ha⁻¹ yr⁻¹) during the rainy season, and lower N₂O fluxes (0.21–1.04 Mg CO₂-C ha⁻¹ yr⁻¹) during the dry season. In contrast, the MPSE presented high CH₄ emissions in the rainy season (300.45 kg CH₄-C ha⁻¹ yr⁻¹). Across all land uses, CO₂ was the dominant contributor to the total GWP (>95% of emissions). The highest global warming potential (GWP) occurred in DP, whereas CaAS, CoAS and MPSE exhibited the lowest values. Soil temperature, pH, exchangeable acidity, texture, and bulk density play a decisive role in regulating GHG fluxes, whereas climatic factors, such as air temperature and relative humidity, influence fluxes indirectly by modulating soil conditions. These findings underscore the role of diversified agroforestry and restoration systems in mitigating GHG emissions and the need to integrate soil and climate drivers into regional climate models. Full article

Urban growth and environmental pressures in rapidly transforming cities require innovative governance tools that integrate advanced technologies with institutional assessment. This study develops and applies a strategic integration framework that combines spatial analysis, Convolutional Neural Networks (CNNs)-based land-use classification, SHAP-based feature attribution, and stakeholder interviews to evaluate Yerevan, Armenia, as a case of a mid-income city facing accelerated urbanization. The case selection is justified by Yerevan’s rapid built-up expansion, fragmented green areas, and institutional challenges in aligning urban development with sustainability goals. The CNN model achieved 92.4% accuracy in land-use classification, and projections under a business-as-usual scenario indicate a 12.8% increase in built-up areas and a 6.5% decline in green zones by 2030. SHAP analysis identified land surface temperature and NDVI as the most influential predictors, while governance interviews highlighted gaps in regulatory support and technical capacity. The proposed framework advances the literature by integrating AI-driven geospatial analysis with qualitative governance assessment, providing actionable insights for urban policymakers. Findings underscore the potential of combining machine learning, geospatial technologies, and institutional diagnostics to guide smart city planning in transition economies. Full article

This study investigates the spatial urban growth patterns of cities along the Democratic Republic of the Congo (DRC) and Zambia border, a region of significant economic importance characterized by cross-border trade. This activity has led to rapid but unplanned urban growth. The objective is to quantify the spatial expansion of Congolese cities (Kipushi, Kasumbalesa, Mokambo, and Sakania) bordering Zambia and to evaluate associated landscape changes. The methodology of this study includes the supervised classification of Landsat images with a spatial resolution of 30 m for the years 1990, 2000, 2010, and 2023. This classification was validated using field data. Subsequently, landscape metrics such as class area, patch number, Shannon diversity index, disturbance index, urban expansion intensity index, largest patch index, and mean Euclidean distance were calculated for each city and each date. The results reveal substantial landscape transformations in the border cities between 1990 and 2023. These changes are primarily driven by rapid urban expansion, particularly pronounced in Kasumbalesa. Between 1990 and 2023, forest cover declined from 70% to less than 15% in Kipushi, from 80% to 10% in Kasumbalesa, from 90% to 30% in Mokambo, and from 80% to 15% in Sakania. This forest cover loss is accompanied by an increase in landscape element diversity, as indicated by the Shannon diversity index, except in Kipushi, suggesting a transition towards more heterogeneous landscapes. In these border cities, landscape dynamics are also characterized by the expansion of agriculture and savannas, highlighted by an increase in the disturbance index. Analysis of spatial pattern changes shows that built-up areas, agriculture, and savannas exhibit trends of patch creation or aggregation, whereas forests are undergoing processes of dissection and patch attrition. Congolese cities bordering Zambia are undergoing substantial spatial changes propelled by intricate interactions between economic, demographic, and infrastructural factors. Our results underscore the need for sustainable development strategies to address urban sprawl through smart growth policies and mixed-use developments, mitigate deforestation via stricter land use regulations and reforestation projects, and enhance cross-border cooperation through joint environmental management and collaborative research initiatives. Full article

This study provides a detailed analysis of land use and land cover (LULC) changes at the district level within the Delhi–Mumbai Industrial Corridor (DMIC) from 2001 to 2021. Using the Indian Meteorological Department’s sub-divisional framework and MODIS data across seven primary LULC classes, the analysis is instrumental in informing infrastructure planning for existing and future smart cities and industrial clusters within the DMIC. The key findings reveal a yearly increase of 3031.40 sq. km. per year in agricultural land, with decreases in shrubland, grassland, and bareland of −1774.72 sq. km. per year, −1119.62 sq. km. per year, and −203.76 sq. km. per year, respectively. On the other hand, forests grew by a modest 148.14 sq. km. per year, while waterbodies and built-up lands saw minor increases of 55.73 sq. km. and 21.48 sq. km. per year. Ecologically Sensitive Areas (ESAs) were evaluated for LULC changes. The smart cities of Pune and Thane serve as excellent examples of balanced urban development and natural growth management. However, the study also highlights the need for further research to investigate LULC impacts on climatic variables, advocating for a regional planning approach in the DMIC. Full article

Accurately identifying the boundary of urban clusters is a crucial aspect of studying the development of urban agglomerations. This process is essential for comprehending and optimizing smart and compact urban development. Existing studies often rely on a single category of data, which can result in coarse identification boundaries, insufficient detail accuracy, and slight discrepancies between the coverage and the actual conditions. To accurately identify the extent of urban clusters, this study proposes and compares the results of three methods for identifying dense urban areas of three major agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Guangdong–Hong Kong–Macao Greater Bay Area. The study then integrates the results of these methods to obtain a more effective identification approach. The social economic method involved extracting a density threshold based on the fused nuclear density of socio-economic vitality data, including population, GDP, and POI, while the remote sensing method evaluated feature indices based on remote sensing images, including the density index, continuity index, gradient index, and development index. The traffic network method utilizes land transportation networks and travelling speeds to identify the minimum cost path and delineate the boundary by 20–30 min isochronous circles. The results obtained from the three methods were combined, and hotspots were identified using GIS overlay analysis and spatial autocorrelation analysis. This method integrates the multi-layered information from the previous three methods, which more comprehensively reflects the characteristics and morphology of urban clusters. Finally, the accuracy of each identification result is verified and compared. The results reveal that the average overall accuracy (OA) of the three areas delineated by the first three methods are 57.49%, 30.88%, and 33.74%, respectively. Furthermore, the average Kappa coefficients of these areas are 0.4795, 0.2609, and 0.2770, respectively. After performing data fusion, the resulting average overall accuracy (OA) was 85.34%, and the average Kappa coefficient was 0.7394. These findings suggest that the data fusion method can effectively delineate dense urban areas with greater accuracy than the previous three methods. Additionally, this method can accurately reflect the scope of urban clusters by depicting their overall boundary contour and the distribution of internal details in a more scientific manner. The study proposes a feasible method and path for the identification of urban clusters. It can serve as a starting point for formulating spatial planning policies for urban agglomerations, aiding in precise and scientific control of boundary growth. This can promote the rational allocation of resources and optimization of spatial structure by providing a reliable reference for the optimization of urban agglomeration space and the development of regional spatial policies. Full article

Smart management of weather data is an essential step toward implementing sustainability and precision in agriculture. It represents an important input for numerous tasks, such as crop growth, development, yield, and irrigation scheduling, to name a few. Advances in technology allow collecting this weather data from heterogeneous sources with high temporal resolution and at low cost. Generating and using these data in their raw form makes no sense, and therefore implementing adequate infrastructure and tools is necessary. For that purpose, this paper presents a smart weather data management system evaluated using data from a meteorological station installed in our study area covering the period from 2013 to 2020 at a half-hourly scale. The proposed system makes use of state-of-the-art statistical methods, machine learning, and deep learning models to derive actionable insights from these raw data. The general architecture is made up of four layers: data acquisition, data storage, data processing, and application layers. The data sources include real-time sensors, IoT devices, reanalysis data, and raw files. The data are then checked for errors and missing values using a proposed method based on ERA5-Land reanalysis data and deep learning. The resulting coefficient of determination (R²) and Root Mean Squared Error (RMSE) for this method were 0.96 and 0.04, respectively, for the scaled air temperature estimate. The MongoDB NoSQL database is used for storage thanks to its ability to deal with real-world big data. The system offers various services such as (i) weather time series forecasts, (ii) visualization and analysis of meteorological data, and (iii) the use of machine learning to estimate the reference evapotranspiration (ET₀) needed for efficient irrigation. To this, the platform uses the XGBoost model to achieve the precision of the Penman–Monteith method while using a limited number of meteorological variables (air temperature and global solar radiation). Results for this approach give R² = 0.97 and RMSE = 0.07. This system represents the first incremental step toward implementing smart and sustainable agriculture in Morocco. Full article

Climate change significantly impacts global agricultural productivity. Therefore, a more dynamic farming system is needed to enable farmers to better adapt to climate change while contributing to efforts to produce enough food to feed the growing world population. In the context of climate change, this study analyzed the empirical scientific literature on the link between climate-smart agriculture and farm productivity. To evaluate the relevant articles, the authors used the search term “climate-smart agriculture amidst climate change to enhance agricultural production (CSA-CCAP)” to find studies published between 2009 and March 2022 using innovative bibliometric techniques. One hundred and sixteen published papers in BibTeX format were downloaded for further analysis. The most successful selected CSA approaches in Africa, such as in the Congo Basin forest, including sustainable land management practices, water-efficient maize hybrids, and others, aim to counteract climate change with signs of 200 percent output gains. The findings showed an annual growth rate of about 19%, demonstrating that research on CSA-CCAP expanded over time during the study period. Nonetheless, the research output on CSA-CCAP varied, with 2021 accounting for 30%, followed by 2020 with 16% as of March 2022. The study concluded that boosting agricultural productivity in the face of climate change may be accomplished through CSA to end hunger, eradicate poverty, and improve people’s well-being. Full article

From an environmentally conscious and ecological perspective, the sustainability of cities within the effects of climate change are closely related to the wise use of resources and modifications in the ecological status of the environment. In terms of the ecological environment, the sustainability of smart cities entails meeting present and future societal demands for the environment of the water, land, and air, among others. Environmental and the ecological concerns that arise from rapid climate change and monetary developments are shown in the inconsistency between ecological assets, environmental pollution, and the destruction of nature. In this study, the authors aim to develop a strategy to deal with climate change in urban areas using Remote Sensing and the Driver-Pressure-State-Impact-Response (DPSIR) Framework with a case study in Jakarta Smart City. The DPSIR framework, which will be developed and implemented in the city of Jakarta, is a smarter and more sustainable framework that is evaluated through a systematic evaluation of sustainability with quantitative research using the entropy weight method and Partial Least Square-Structural Equation Modeling (PLS-SEM). These methods evaluate 58 representative elements of environments at the urban level, including the shortcomings of earlier research such as data availability, spatial and temporal constraints, and several related ecological indicators, such as soil pH, wind speed, air quality index as well as land changes in the spatial (spatiotemporal) time series. The results of the study show that in the metropolitan city of Jakarta, the Drivers that are related to climate change are the rate of population growth and the rate of industrial growth which, although increases people’s income and GRDP in Jakarta; it also creates Pressures, namely an increase in the amount of water consumption and in the amount of wastewater. Based on these pressures, the environmental conditions (State) of Jakarta city have undergone several environmental changes, such as loss of water supply, changes in wind speed, changes in rainfall, and increasing concentrations of the Air Pollutant Standard Index. The Impact of these three elements resulted in the increase in household and industrial water consumption, an increase in annual electricity consumption, and deteriorating air quality. Hence, the Response to these four interrelated causal variables is that the Jakarta Provincial Government must increase annual funds for the construction of urban community facilities, increase the production capacity of clean water supply, build environment-friendly wastewater treatment facilities, increase the capacity of waste processing infrastructure and transportation fleets, and educate people to use water wisely to reduce the level of water use. Full article

The world population is on the rise, which demands higher food production. The reduction in the amount of land under cultivation due to urbanization makes this more challenging. The solution to this problem lies in the artificial cultivation of crops. IoT and sensors play an important role in optimizing the artificial cultivation of crops. The selection of sensors is important in order to ensure a better quality and yield in an automated artificial environment. There are many challenges involved in selecting sensors due to the highly competitive market. This paper provides a novel approach to sensor selection for saffron cultivation in an IoT-based environment. The crop used in this study is saffron due to the reason that much less research has been conducted on its hydroponic cultivation using sensors and its huge economic impact. A detailed hardware-based framework, the growth cycle of the crop, along with all the sensors, and the block layout used for saffron cultivation in a hydroponic medium are provided. The important parameters for a hydroponic medium, such as the concentration of nutrients and flow rate required, are discussed in detail. This paper is the first of its kind to explain the sensor configurations, performance metrics, and sensor-based saffron cultivation model. The paper discusses different metrics related to the selection, use and role of sensors in different IoT-based saffron cultivation practices. A smart hydroponic setup for saffron cultivation is proposed. The results of the model are evaluated using the AquaCrop simulator. The simulator is used to evaluate the value of performance metrics such as the yield, harvest index, water productivity, and biomass. The values obtained provide better results as compared to natural cultivation. Full article

In each country, rural areas cover the bulk of available land; however, they generally remain outside the mainstream of innovative development processes. Still, they have potential and are vital for implementing the highly valid concept of persistent and sustainable development. The idea of such growth can be implemented in many ways, and the smart development concept is one of these ways. The aim of this paper is to present the smart village concept as a means to achieve the sustainability and resilience of rural areas, relying on the analysis of basic theories of sustainable and persistent growth. The study examines and evaluates the state of the potential for the smart growth of rural areas in all regions of Poland, as well as presents the results of the empirical research on such potential in three regions of Eastern Poland. The smart growth potential of the regions in question was determined by means of 24 variables representing the following fields: management, life quality, economy, society, natural environment and mobility. It was concluded that the smart village concept can be useful in facilitating sustainable development of rural areas. Further research concerning the problem should in particular focus on strengthening the relations between rural communes with cities and towns in their close vicinity. Full article

In the context of rapid urbanization, the spread of cities in the Yangtze River Economic Belt is intensifying, which has an impact on the green and sustainable development of these cities. It is necessary to establish an accurate urban sprawl measurement system. First, the regulation theory of urban sprawl is explained. According to the actual development situation of cities in the Yangtze River Economic Belt, smart growth theory is selected as the basic regulation method of urban sprawl. Second, the back propagation neural network (BPNN) algorithm under deep supervised learning is applied to construct a smart evaluation model of land use growth. Finally, based on the actual development of cities in the Yangtze River Economic Belt, the quantitative growth measurement method is selected to construct a measurement system of urban sprawl in the Yangtze River Economic Belt, and the empirical analysis is carried out. The training results show that the proposed BPNN smart growth evaluation model, based on deep supervised learning, has good evaluation accuracy, and the error is within the preset range. The analysis of the quantitative growth-based measurement system in the increase of urban construction land shows that the increase in urban construction land area of the Yangtze River Economic Belt from 2014 to 2019 was 78.67 km². Meanwhile, the increases in urban construction land area in different years are different. The empirical results show that the population composition of the Yangtze River Economic Belt and the urban construction area between 2005 and 2019 show a trend of increasing annually; at the same time, urban sprawl development shows a staged characteristic. It is of great significance to apply deep learning fusion neural network algorithm in the construction of the urban sprawl measurement system, which provides a quantitative basis for the in-depth analysis and discussion of urban sprawl. Full article

Coastal areas around the world are becoming increasingly urban, which has increased stress to both natural and anthropogenic systems. In the United States, 52% of the population lives along the coast, and North Carolina is in the top 10 fastest growing states. Within North Carolina, the southeastern coast is the fastest growing region in the state. Therefore, this research has developed a methodology that investigates the complex relationship between urbanization, land cover change, and potential flood risk and tested the approach in a rapidly urbanizing region. A variety of data, including satellite (PlanetScope) and airborne imagery (NAIP and Lidar) and vector data (C-CAP, FEMA floodplains, and building permits), were used to assess changes through space and time. The techniques consisted of (1) matrix change analysis, (2) a new approach to analyzing shorelines by computing adjacency statistics for changes in wetland and urban development, and (3) calculating risk using a fishnet, or tessellation, where hexagons of equal size (15 ha) were ranked into high, medium, and low risk and comparing these results with the amount of urbanization. As other research has shown, there was a significant relationship between residential development and wetland loss. Where urban development has yet to occur, most of the remaining area is at risk to flooding. Importantly, the combined methods used in this study have identified at-risk areas and places where wetlands have migrated/transgressed in relationship to urban development. The combination of techniques developed here has resulted in data that local government planners are using to evaluate current development regulations and incorporating into the new long-range plan for the County that will include smart growth and identification of risk. Additionally, results from this study area are being utilized in an application to the Federal Emergency Management Agency’s Community Response System which will provide residents with lower flood insurance costs. Full article

Urbanization has significant impacts on the regional environmental quality through altering natural lands, converting them to urban built-up areas. One common strategy applied by urban planners to manage urbanization and preserve natural resources is to make a comprehensive plan and concentrate future land use in certain areas. However, in practice, planners used to make future land use planning mainly based on their subjective interpretations with limited ecological supporting evidence and analysis. Here, we propose a new approach composed of ecological modelling and land use zoning in the spatial matrix to evaluate the comprehensive plan and identify priority lands for sustainable land use planning. We use the city of Corvallis, OR, as the test bed to demonstrate this new approach. The results indicate that the Corvallis Comprehensive Plan 1998–2020 featured with compact development is not performing efficiently in conserving ecological values, and the land use plan featured with mixed-use spreading development generated by the proposed approach meets the city’s land demands for urban growth, and conserves 103% more ecological value of retaining storm water nitrogen, 270% more ecological value of retaining storm water phosphorus and 19% more ecological value in storing carbon in the whole watershed. This study indicates that if planned with scientific analysis and evidence, spreading urban development does not necessarily result in less sustainable urban environment than the compact development recommended in smart growth. Full article