Search Results (30,947)

Urban green infrastructure plays a significant role in sustainable development and requires proper land management during planning. This study develops a valuation model for urban green infrastructure in land management, focusing on Zagreb’s 17 city districts. The fuzzy AHP method was used to calculate the weighting coefficients for a suitable set of criteria, and the TOPSIS method was used to select the priority city districts for implementing green infrastructure. The research results are relevant to decision makers, who can utilize them to prioritize areas for the development and implementation of green infrastructure. The green infrastructure index calculated in this study can be compared with other spatial and land data for effective spatial planning. Full article

Shrinking shorelines and the exposed playa of saline lakes can pose public health and air quality risks for local communities. This study combines a community science method with models to forecast future shorelines and PM10 air quality impacts from the exposed playa of the Salton Sea, near the community of North Shore, CA, USA. The community science process assesses the rate of shoreline change from aerial images collected through a balloon mapping method. These images, captured from 2019 to 2021, are combined with additional satellite images of the shoreline dating back to 2002, and analyzed with the DSAS (Digital Shoreline Analysis System) in ArcGIS desktop. The observed rate of change was greatly increased during the period from 2017 to 2020. The average rate of change rose from 12.53 m/year between 2002 and 2017 to an average of 38.44 m/year of shoreline change from 2017 to 2020. The shoreline is projected to retreat 150 m from its current position by 2030 and an additional 172 m by 2041. To assess potential air quality impacts, we use WRF-Chem, a regional chemical transport model, to predict increases in emissive dust from the newly exposed playa land surface. The model output indicates that the forecasted 20-year increase in exposed playa will also lead to a rise in the amount of suspended dust, which can then be transported into the surrounding communities. The combination of these model projections suggests that, without mitigation, the expanding exposed playa around the Salton Sea is expected to worsen pollutant exposure in local communities. Full article

Mobility hubs (MHs), where various transport modes converge, are increasingly being implemented as a key policy strategy to promote sustainable travel behavior. The existing literature is rich with proposals for various types of MH and suitable siting locations for them. However, studies comparing the role of land use patterns on the performance of different types of MH are scarce. This study aims to fill this gap by analyzing transit patronage and active mode share as performance indicators of MHs. It compares the effects of land use patterns on the performance of different types of MH classified by the nature of transport integration (e.g., train-tram-bus, train-tram, and train-bus) in different contexts (e.g., city district and suburb) in the Greater Melbourne Area, Australia. Results show that MHs enhance the use of transit and active transport modes for commuting purposes by up to 279% and 17%, respectively, compared to a unimodal train station, with maximum usage observed in a train-tram-bus hub, followed by train-tram and train-bus hubs. However, the underlying land use patterns significantly affect their performance. Specifically, each additional hectare of commercial land within the catchment of a train-tram-bus MH in the city district, a train-tram-bus MH in a suburban area, a train-tram MH in a suburban area, and a train-bus MH in a suburban area increases transit patronage by 6%, 9%, 5%, and 4%, respectively. These findings suggest that MH typologies should be designed in tandem with supportive land uses to maximize sustainable travel behavior. The findings inform urban and transport planners in designing optimal land use patterns for different types of MH to maximize sustainable travel behavior. They also support the development of tailored land use zoning policies to enhance the effectiveness of MHs. Full article

Field surveys and sample analyses were conducted from January 2018 to December 2019 on Daye Lake, Cihu Lake, Baoan Lake, and Xiandao Lake to understand the water quality characteristics of typical lakes in southeast Hubei. A fuzzy comprehensive evaluation was conducted and the comprehensive trophic level index was applied to evaluate the lakes’ water quality. The results showed differences in the regional, spatial, and temporal distributions of physical and chemical indicators in typical lakes in southeast Hubei. The fuzzy comprehensive evaluation showed that the water quality levels in Daye, Cihu, Baoan, and Xiandao Lakes for 2018 and 2019 were IV, IV, III, and II and V, IV, III, and II, respectively, with seasonal variations in water quality occurring during the year. The trophic level index results showed that Cihu Lake was mildly eutrophic in 2018 and moderately eutrophic in 2019, and Daye, Baoan, and Xiandao Lakes were mildly eutrophic, mildly eutrophic, and mesotrophic, respectively. Lake water quality was influenced by land use types, landscape configuration, inflowing rivers, precipitation, and interactions between land use and seasons. This study helps us to understand the trend and causes of lake pollution in Southeast Hubei, which is conducive to watershed management and the control of water quality deterioration, and also has an important role in regulating the sustainable development of industry and agriculture in the watershed. Full article

Studying historical rural landscapes beyond their archaeological and cultural significance, as has typically been addressed in previous research, is important in the context of current environmental challenges. Some historical rural landscapes, such as Roman land divisions, have persisted for more than 2000 years and may still contribute to sustainability goals. To assess this topic, the hydraulic and vegetation network of the centuriation northeast of Padua were studied, emphasising their multiple benefits. Their length, distribution, and evolution over time (2008–2022) were vectorised and measured using available digital terrain models and orthophotographs in a geographic information system (GIS). The results revealed a significant decline in the length of water ditches and hedgerows across almost all examined areas, despite their preservation being highlighted in regional and local spatial planning documents. These findings indicate the need for a better understanding of the local dynamics driving such trends and highlight the importance of adopting a more tailored approach to their planning. This study discusses the GIS metrics utilised and, in this way, contributes to landscape monitoring and restoration actions. Finally, a multifunctional approach to the sustainable planning of this area is proposed here—one that integrates the cultural archaeological heritage in question with environmental preservation and contemporary climate adaptation and mitigation strategies. Full article

This paper presents a study on the establishment and the capitalization of a remontant red raspberry crop, the Polka variety, on a privately agricultural land area of 0.2 ha in a crop with a support system in V using a geotextile membrane for soil mulching and the method of micro-irrigation by drip. It has been shown that the annual gross profit is advantageous for diversifying the population incomes of rural areas, and the red raspberry is economically profitable regarding cultivation because the recovery of the invested sum is achieved in a maximum of 5 years after the establishment of the culture. The aim of this paper is to explore the growth and commercialization of red raspberry cultivation on privately owned arable land in rural Romania, emphasizing its potential for productivity and sustainability in the context of the circular economy. This initiative not only delivers substantial profits for investors but also fosters rural development and boosts local income levels. The study demonstrates that this cultivation method of red raspberry, aligned with the principles of the circular economy, enhances sustainability by reducing waste, optimizing resource use, and involving local communities in production cycles. Full article

This study assessed catch composition, size selectivity, and fishing efficiency of demersal trawls targeting penaeid shrimp species in the Red Sea. It first compared the currently used diamond mesh codends in two fishing areas, Al Qunfudhah and Jazan, and then compared alternative square mesh codends to diamond mesh codends in Jazan. A total of 33 valid hauls were conducted in 2023, yielding 10,869 kg of total catch. The results showed that the square mesh codend significantly improved size selectivity, particularly for Penaeus semisulcatus and Metapenaeus monoceros, with L₅₀ (50% retention length) values closer to their size at first maturity. The fishing efficiency indicators revealed a reduced retention probability for undersized individuals with square mesh codends. Additionally, bycatch discard rates decreased, indicating potential benefits for ecosystem conservation. This study suggests incorporating square mesh codends into fishery management regulations to enhance size selectivity and reduce bycatch during Red Sea shrimp trawling. Establishing a legal minimum landing size requirement is recommended to complement these efforts and promote sustainable fishing practices. Full article

Quantitative analysis of LULC changes and their effects on carbon stock and sequestration is important for mitigating climate change. Therefore, this study examines carbon stock and sequestration in relation to LULC changes using the Land Change Modeler (LCM) and Ecosystem Services Modeler (ESM) in tropical dry deciduous forests of West Bengal, India. The LULC for 2006, 2014, and 2021 were classified using Google Earth Engine (GEE), while LULC changes and predictions were analyzed using LCM. Carbon stock and sequestration for present and future scenarios were estimated using ESM. The highest carbon was stored in forest land (124.167 Mg/ha), and storage outside the forest declined to 13.541 Mg/ha for agricultural land and 0–8.123 Mg/ha for other lands. Carbon stock and economic value decreased from 2006 to 2021, and are likely to decrease further in the future. Forest land is likely to contribute to 94% of future carbon loss in the study region, primarily due to its conversion into agricultural land. The implementation of multiple-species plantations, securing tenure rights, proper management practices, and the strengthening of forest-related policies can enhance carbon stock and sequestration. These spatial-temporal insights will aid in management strategies, and the methodology can be applied to broader contexts. Full article

For the safe autonomous operations of unmanned aerial vehicles (UAVs) and ground control stations (GCS), including autonomous battery replacement, wireless power transfer, and more, the precise landing of UAVs on GCS is essential. Accurate landing is only possible when the link capacity strength exceeds a certain threshold, but this is often disturbed due to complex terrain. To address this, we developed an extremum seeking (ES)-based radio signal strength optimization (RSSO) algorithm, ES-RSSO, designed to find the optimal positions of the UAV using radio communication signals. This ensures energy-efficient path planning while guaranteeing the minimum received signal strength indication (RSSI) capacity. This algorithm is particularly useful in obstacle-rich environments, where UAVs are limited in power resources. Simulation results demonstrate a 2.37% decrease in the mean, a 62.08% improvement in variance, and a 3.72% decrease in the integration strength of the link capacity when ES-RSSO is applied. These results confirm that the RADIO.rssi maintenance ability remains above a critical boundary level, supporting robust communication links and energy-efficient path planning. Throughout the study, we showed how, in many cases, simply moving the UAV a few meters can significantly improve the communication link. Full article

Coastal land use represents the combination of various land cover forms in a coastal area, which helps us understand the historical events, current conditions, and future progress of a coastal area. Currently, the emergence of high-resolution optical satellite images significantly extends the scope of coastal land cover recognition, and deep learning models provide a significant possibility of extracting high-level abstract features from an optical satellite image to characterize complicated coastal land covers. However, recognition systems for labeling are always defined differently for specific departments, organizations, and institutes. Moreover, considering the complexity of coastal land uses, it is impossible to create a benchmark dataset that fully covers all types of coastal land uses. To improve the transferability of high-level features generated by deep learning to reduce the burden of creating a massive amount of labeled data, this paper proposes an integrated framework to support semantically enriched coastal land use recognition, including foundation model-powered multi-label coastal land cover classification and conversion from coastal land cover mapping into coastal land use semantics with a vector space model (VSM). The experimental results prove that the proposed method outperformed the state-of-the-art deep learning approaches in complex coastal land use recognition. Full article

Vegetation greenness has been one of the most widely utilized indicators to assess the vegetation growth status for the better ecological environment. However, in typical tourist regions, the impact of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes is still a challenge. To address this challenge, we used the Google Earth Engine (GEE) cloud platform combined with a series of Landsat remote sensing images to calculate the fractional vegetation cover (FVC) which can be used as an indicator to characterize the spatiotemporal evolution of vegetation greenness in Hainan Island from 2000 to 2020. Furthermore, we employed geographic detector and structural equation models to quantify the relative importance and explanatory power of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes and to clarify the interaction of mechanisms of various factors in Haikou and Sanya. The results show that the annual growth rate of the FVC in Hainan Island was 0.0025/a. In terms of spatial distribution, the trend of the FVC changes was mainly characterized by non-significant and extremely significant improvement, accounting for 35.34% and 29.38% of the study area. Future vegetation greenness was dominated by weak counter-persistent increase and weak persistent increase. The geographical environmental factors were the main factors affecting vegetation greenness in Haikou, followed by the socioeconomic and the tourism development factors, while the geographical environmental factors also dominate in Sanya, followed by the tourism development factors and finally the socioeconomic factors. Specifically, the spatial distribution of vegetation greenness was primarily influenced by land use types, elevation, slope, and travel services. Geographical environmental factors could indirectly affect changes in socioeconomic and tourism development, thereby indirectly affecting the spatial distribution of vegetation greenness. These findings can provide some significant implications to guide the ecological environmental protection for sustainable development in Hainan Island in China. Full article

This study investigates the cooling potential of vegetation in rural landscapes of the Czech Republic to mitigate heat-related issues. Using remote sensing, the Cooling Capacity Index (CCI) is assessed to measure green spaces’ ability to lower air temperatures using evapotranspiration and shading. Landsat 8/9 and meteorological data are utilised, with CCI calculated based on vegetation cover, albedo, and evapotranspiration. Our results demonstrate significant variations in cooling capacity across different land use types. Forests exhibited the highest cooling potential, while urban areas, characterised by heat-absorbing materials, displayed the least. We analysed temporal and spatial variations in cooling capacity using various visualisation tools and validated the results against the InVEST software (v3.14.0). This study highlights the effectiveness of remote sensing in quantifying ecosystem functions, particularly the cooling services provided by vegetation. Our findings emphasise the crucial role of vegetation in mitigating urban heat islands and addressing climate change. This research provides valuable insights for developing climate change adaptation strategies in rural landscapes. Full article

Solid waste management is a crucial task for municipalities in disposing of city waste. Overcoming socioeconomic obstacles in finding appropriate landfill sites involves a multifunctional team using a process that includes selecting criteria and alternatives. In this study, the FUZZY Analytical Hierarchy Process (AHP) and FUZZY TOPSIS were used to rank five landfill alternatives based on seven criteria. Additionally, Interpretive Structural Modeling (ISM) was employed to establish hierarchical relationships between criteria. MICMAC analysis identified dominant and dependent factors. The study found that Land Capacity carries the highest weight, and the Central Landfill site is the most suitable location. Land Capacity is the dominant factor, while land surface temperature has minimal impact. Roads and communication networks have the highest driving power. The project’s findings can guide the selection of landfill sites and contribute to the development of new sites based on the criteria discussed and their relationships. Full article

Soil erosion is a widespread concern that is indeed considered to be a significant environmental issue, and it has particularly severe consequences in less developed countries like Ethiopia. An effective watershed management procedure for establishing priority is supported by the identification of erosion-susceptible areas. Therefore, the main objective of the study was to assess soil erosion dynamics and its spatial pattern using a novel methodological framework combining the RUSLE and MCDA. The study used data on land use and cover, topography, soil, and climatic data. The analytical hierarchy process (AHP) were used to identify soil erosion-susceptible areas and the factors were weighted using a pairwise comparison matrix, and weights were combined using weighted overlay in GIS. Our results indicated that the mean annual soil loss rate was 27.10 t ha⁻¹ yr⁻¹, while the total soil loss from the entire study area was 3.11 Mt. The highest soil loss was observed in bare land (30.54 t ha⁻¹ yr⁻¹) and farmland (23.65 t ha⁻¹ yr⁻¹), which were considered as the most susceptible land types to erosion. Likewise, 10.3% of the study area is very highly susceptible; 20.2% is highly susceptible, 24.2% of the area is moderately susceptible, 27.1% is low, and 18.2% has very low susceptibility. The district’s most significant erosion-susceptible areas are characterized by steep slopes that are composed of farmland and bare land. This suggests the majority of the area is susceptible to erosion, requiring interventions to reverse the alarming degradation level. The presented framework has a board application to estimate regional soil erosion and to identify spatial patterns of soil erosion. Full article

Climate warming, agricultural intensity, and urban growth are main forces triggering land degradation in advanced economies. Being active over different spatial and temporal scales, they usually reflect—at least indirectly—the impact of additional factors, such as wellbeing, demographic dynamics, and social development, on land quality. Using descriptive statistics and a multiple regression analysis, we analyzed the impact of these three processes comparatively over a decadal scale from 1960 to 2020 at the provincial level (Nuts-3 sensu Eurostat) in Italy. We enriched the investigation with a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic approach. Land degradation was estimated adopting the Environmental Sensitive Area Index (ESAI) measured at the spatio-temporal scale mentioned above. Computing on multiple observations at nearly 300,000 locations all over Italy, provinces were regarded as representative spatial units of the territorial pattern of land degradation. Between 1960 and 1990, the three predictors (climate, agriculture, and urbanization) explained a relatively high proportion of variance, suggesting a modest role for any other (unobserved) factor. All of these factors were found to be highly significant predictors of land degradation intensity across provinces, the most impactful being farming intensity. The highest adjusted-R² coefficient was observed in both 1990 and 2000, and suggests that the three predictors still reflect the most powerful drivers of land degradation in Italy at those times, with a marginal role for additional (unobserved) factors. The impact of farming intensity remained high, with the role of urbanization increasing moderately, and the role of climate aridity declining weakly between 2000 and 2010. In more recent times (2010 and 2020), and in future (2030) scenarios, the adjusted R² diminished moderately, suggesting a non-negligible importance of external (unobserved) factors and the rising role of spatial heterogeneity. The climate factor became progressively insignificant over time, while increasing the role of urbanization systematically. The impact of farming intensity remained high and significant. These results underlie a latent shift in the spatial distribution of the level of land vulnerability in Italy toward a spatially polarized model, influenced primarily by human pressure and socioeconomic drivers and less intensively shaped by biophysical factors. Climate aridity was revealed to be more effective in the explanation of land degradation patterns in the 1960s rather than in recent observation times. Full article