Search Results (5,040)

Urban carbon emissions significantly contribute to climate change, exacerbating environmental issues such as global warming. Understanding carbon metabolism is vital for identifying key emission sources and implementing targeted mitigation strategies. This study presents an innovative carbon metabolism analysis framework that integrates an ecological network analysis (ENA) with land use dynamics, enriching the theoretical system and providing policy recommendations for sustainable urban development. We investigated carbon metabolism in the Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA) from 2000 to 2020 using land use and statistical data. The ENA method quantified the ecological relationships between land use compartments. Our findings revealed that industrial and transportation land exhibited the highest carbon emission density, while forest land demonstrated the highest carbon sequestration density. Notably, the negative net horizontal carbon flow indicated that land use changes exacerbated the disorder of carbon metabolism. The increasing mutualism index suggested a reduction in the negative impacts of land use changes on carbon metabolism. This study highlights the importance of spatial planning in transforming ecological relationships and provides a comprehensive understanding of carbon metabolism dynamics influenced by land use changes. The insights gained can inform effective mitigation strategies in the BTHUA and similar urban agglomerations, ultimately contributing to sustainable urban development. Full article

Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis is applied to identification indicators for unlicensed taxis. Secondly, the mathematical model for identifying unlicensed taxis is established. The model is validated using the Hosmer–Lemeshow test, confusion matrix and ROC curve analysis. Finally, by applying methods such as geographic information matching, the spatiotemporal distribution characteristics of suspected unlicensed taxis in a city in Anhui Province are identified. The results show that the model effectively identifies suspected unlicensed taxis (ACC = 99.10%). The daily average mileage, daily average operating time, and number of operating days for suspected unlicensed taxis are significantly higher than those for private cars. Additionally, the suspected unlicensed taxis exhibit regular patterns in their travel origin–destination points and temporal distribution, enabling traffic management authorities to implement targeted regulatory measures. Full article

Methane (CH₄), the second-largest greenhouse gas contributing to global warming, has a high warming potential despite its short atmospheric lifespan. Inner Mongolia, due to its high carbon and energy consumption industries, faces significant methane emission challenges. This study uses TROPOMI satellite data (February 2019 to December 2022) to analyze the long-term trends and spatial distribution of methane in Inner Mongolia. The results indicate significant spatial heterogeneity in the methane concentration distribution in Inner Mongolia, China. Higher methane concentrations are observed in the southeastern regions, whereas the central regions exhibit relatively lower concentrations. Temporally, the methane concentrations show an increasing trend with seasonal peaks from late August to early September. Using multiple stepwise regression and geographically weighted regression (GWR) methods, the study identifies the key factors influencing methane concentrations. Increased precipitation and soil temperature, along with intensified human activity, contribute to higher methane levels, while rising surface temperatures and increased vegetation suppress methane concentrations. The GWR model provides a better fit compared to the traditional methods, especially in regions with higher methane levels. This research offers insights for developing strategies to mitigate methane emissions and supports China’s emission control targets. Full article

Digital disparities remain a significant challenge in Indonesia, particularly across its diverse regions, with uneven access to digital infrastructure, skills, and economic opportunities. This study aims to map these digital disparities at the district level, analyze the spatial distribution and clustering of digital transformation using the Digital Society Index of Indonesia (IMDI), and investigate the key drivers of digital inequality across four core pillars: Infrastructure and Ecosystem, Digital Skills, Empowerment, and Jobs. To measure the IMDI, primary data were collected from the industrial sector and the general population over three years (2022–2024), combined with secondary data on internet usage and service standards. A multistage random sampling approach ensured representativeness, considering demographic variations and industrial segments. The analysis employed spatiotemporal methods to capture temporal trends and spatial clustering. The results revealed a significant IMDI increase from 37.80 in 2022 to 43.18 in 2023, followed by stability at 43.34 in 2024. The hotspots of digital transformation remain concentrated on Java Island, while low spots persist in eastern Indonesia. This study provides critical insights into Indonesia’s digital readiness, identifying priority areas for targeted interventions to bridge the digital divide and foster equitable digital development. Full article

Understanding the current water quality dynamics is necessary to ensure that ecological and sociocultural services are provided to the population and the natural environment. Water quality monitoring of lakes is usually performed with in situ measurements; however, these are costly, time consuming, laborious, and can have limited spatial coverage. Nowadays, remote sensing offers an alternative source of data to be used in water quality monitoring; by applying appropriate algorithms to satellite imagery, it is possible to retrieve water quality parameters. The use of global remote sensing water quality products increased in the last decade, and there are a multitude of products available from various databases. However, in Latin America, studies on the inter-comparison of the applicability of these products for water quality monitoring is rather scarce. Therefore, in this study, global remote sensing products estimating various water quality parameters were explored on Lake Titicaca and compared with each other and sources of data. Two products, the Copernicus Global Land Service (CGLS) and the European Space Agency Lakes Climate Change Initiative (ESA-CCI), were evaluated through a comparison with in situ measurements and with each other for analysis of the spatiotemporal variability of lake surface water temperature (LSWT), turbidity, and chlorophyll-a. The results of this study showed that the two products had limited accuracy when compared to in situ data; however, remarkable performance was observed in terms of exhibiting spatiotemporal variability of the WQ parameters. The ESA-CCI LSWT product performed better than the CGLS product in estimating LSWT, while the two products were on par with each other in terms of demonstrating the spatiotemporal patterns of the WQ parameters. Overall, these two global remote sensing water quality products can be used to monitor Lake Titicaca, currently with limited accuracy, but they can be improved with precise pixel identification, accurate optical water type definition, and better algorithms for atmospheric correction and retrieval. This highlights the need for the improvement of global WQ products to fit local conditions and make the products more useful for decision-making at the appropriate scale. Full article

Current marine research that leverages remote sensing data urgently requires gridded data of high spatial and temporal resolution. However, such high-quality data is often lacking due to the inherent physical and technical constraints of sensors. A necessary trade-off therefore exists between spatial, temporal, and spectral resolution in satellite remote sensing technology: increasing spatial resolution often reduces the coverage area, thereby diminishing temporal resolution. This manuscript introduces an innovative remote sensing image fusion algorithm that combines Sentinel-2 (high spatial resolution) and Sentinel-3 (relatively high spectral and temporal resolution) satellite data. The algorithm, based on a cross-attention mechanism and referred to as the Cross-Attention Spatio-Temporal Spectral Fusion (CASTSF) model, accounts for variations in spectral channels, spatial resolution, and temporal phase among different sensor images. The proposed method enables the fusion of atmospherically corrected ocean remote sensing reflectance products (Level 2 OSR), yielding high-resolution spatial data at 10 m resolution with a temporal frequency of 1–2 days. Subsequently, the algorithm generates chlorophyll-a concentration remote sensing products characterized by enhanced spatial and temporal fidelity. A comparative analysis against existing chlorophyll-a concentration products demonstrates the robustness and effectiveness of the proposed approach, highlighting its potential for advancing remote sensing applications. Full article

The paper addresses the spatiotemporal variability of anthropogenic film pollution (AFP) in Avacha Gulf near the Kamchatka Peninsula based on satellite synthetic-aperture radar (SAR) imagery. Coastal waters of the study area are subject to significant anthropogenic impacts associated with intensive marine traffic, as well as the flow of household and industrial wastewater from factories located on the coast. A quantitative approach to the registration and quantitative analysis of spatiotemporal AFP distributions was applied. This approach is based on the processing of long-term time series of SAR imagery, taking into account inhomogeneous observation coverage and changing hydrometeorological conditions of different regions of water areas in various time periods. In total, 318 cases of AFP were detected in 2014–2023 in Avacha Gulf, covering 332 km² of the total area (~3% of the water area) based on the 1134 processed radar Sentinel-1A/B scenes. The average value of AFP exposure, e, was about 93 ppm, evidencing the high level of AFP in the studied water area (comparable to areas of the Black Sea with intensive marine traffic, for which e was previously determined to be between ~90 and ~130 ppm). An interannual positive trend was revealed, indicating that over the 10-year period under study, the exposure of the waters of Avacha Bay (the most polluted part of Avacha Gulf) to AFP increased ~3-fold. An analysis of AFP spatial distributions and marine traffic maps indicates that this type of activity is a significant source of anthropogenic film pollution in Avacha Gulf (including Avacha Bay). It was shown that the generated quantitative information products using the introduced AFP exposure concept can be interpreted and used, for example, for making management decisions. Full article

The ecological environment is fundamental to human survival and development, and China has seen a historical shift from localized to widespread improvements in its ecological conditions. Aksu, a typical ecologically sensitive region in Xinjiang, China, is significant for the study of vegetation dynamics and their driving factors, which is crucial for ecological conservation. This study evaluates the spatiotemporal changes in vegetation coverage in Aksu from 2000 to 2020 using long-term Normalized Difference Vegetation Index (NDVI) data and trend analysis. Additionally, this study explores key factors influencing vegetation changes through correlation analysis with temperature, precipitation, and nighttime light data. The results indicate the following: (1) vegetation coverage in Aksu exhibits significant spatial heterogeneity, with annual NDVI increasing at a rate of 0.83% per year (p < 0.05); (2) the influence of temperature and precipitation on NDVI was weakly correlated from 2000 to 2020; and (3) a strong positive correlation was found between nighttime light intensity and NDVI, suggesting that urban development plays a dominant role in vegetation change, while temperature and precipitation have comparatively minor impacts. The findings provide a scientific basis for ecological conservation and sustainable development in the region. Full article

Quantitative analysis performance is considered the Achilles’ heel of laser-induced breakdown spectroscopy. Improving the raw spectral signal is fundamental to achieving accurate quantification. Signal-to-noise ratio enhancement and uncertainty reduction are two targets to improve the raw spectral signal. Most LIBS studies choose the maximum signal-to-noise ratio as the target to optimize the signal. However, there are no precise conclusions about how to optimize signal until now. It has been insisted by our group that the lowest signal uncertainty should be the optimization criterion, which is verified in this article. This study performed quantitative analysis on brass samples at three typical pressures: atmospheric pressure (100 kPa), pressure corresponding to the maximal signal-to-noise ratio (60 kPa), and pressure corresponding to the lowest signal uncertainty (5 kPa) under the optimal spatiotemporal window at each pressure based on a previous study. The results indicate that a pressure of 60 kPa led to a decrease in the accuracy and an increase in the precision of the quantitative analysis; the pressure of 5 kPa led to the highest accuracy and the best precision of the quantitative analysis. Reasons for changes in quantitative analysis are analyzed in detail through matrix effects and signal uncertainty. Therefore, selecting the pressure that corresponds to the lowest signal uncertainty can better improve the LIBS quantitative analysis performance. Signal uncertainty reduction is recommended as a more important direction for the LIBS community. Full article

Groundwater, which is widely used in arid regions due to scarcity of surface sources, has excellent quality and, under certain conditions, can be consumed directly. Human activities have caused climate change, leading to decreased precipitation and increased temperatures, which reduces water recharge and increases underground extraction volume. To estimate the natural recharge of the Chupaderos aquifer, located in the State of Zacatecas, México, a spatiotemporal analysis methodology was used, using a soil moisture balance, which includes satellite information on precipitation and temperature, to obtain infiltration, evapotranspiration, and moisture. Using a Geographic Information System (GIS), a distributed spatial model was created in which the potential recharge areas that can be defined by raster images. The results show that there is a maximum annual recharge of 137 mm in the soil where Fluvisol and Kastanozem predominate, an indicator of a texture of sandy soil and franco-sandy area, which is mainly covered by forest and scrub. This result confirms that these characteristics are indispensable for the use of water in soil. Therefore, the preservation of the ecosystem is essential for aquifer recharge. Full article

The comprehensive protection and restoration of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts is critical for enhancing ecological environmental quality and fulfilling the aspirations of ecological civilization in the modern era. Centered on the key project area of the Mountain-River Project within the Luohe River Basin of the Eastern Qinling Mountains, this study employs the InVEST model to assess spatiotemporal variations in habitat quality (HQ), water yield (WY), carbon sequestration (CS), and soil retention (SR) for the years 2000, 2010, and 2020. This study further examines the trade-offs and synergies among these ecosystem services, integrates the Ordered Weighted Averaging (OWA) and GIS methodology with human activity patterns, determines the optimal management scenario, and offers targeted recommendations for optimization. The findings reveal that areas of high habitat quality, carbon sequestration, and soil retention are predominantly concentrated in the western and southwestern regions of the basin, whereas high-value zones of water yield are primarily situated in the southern and southwestern sectors. Habitat quality demonstrates significant synergies with other ecosystem services, whereas water yield presents a notable trade-off with soil retention. By conducting a comparative analysis of protection efficiency, we identified priority conservation areas predominantly located in the southern and southwestern regions of the basin. Moreover, through overlaying the priority conservation zones with the Human Footprint Index (HFI), the priority conservation area was precisely delineated to encompass 5.41 × 10⁵ hectares. This methodology provides critical guidance for the implementation of the Mountain-River Project and offers substantial value in scientifically advancing ecological restoration initiatives. Full article

Soil salinity is considered one of the biggest constraints to crop production, particularly in arid and semi-arid regions affected by recurrent and long periods of drought, where high salinity levels severely impact plant stress and consequently agricultural production. Climate change accelerates soil salinization, driven by factors such as soil conditions, land use/land cover changes, and water deficits, over extensive spatial and temporal scales. Continuous monitoring of areas at risk of salinization plays a critical role in supporting effective land management and enhancing agricultural production. For these purposes, this work aims to propose a spatiotemporal method for monitoring soil salinization using spectral indices derived from Earth observation data. The proposed approach was tested in the Zaghouan Region in northeastern Tunisia, a region where soils are characterized by alarming levels of salinization. To address this concern, remote sensing techniques were applied for the analysis of satellite imagery generated from Landsat 5, Landsat 8, and Landsat 9 missions. A comprehensive field survey complemented this approach, involving the collection of 229 geo-referenced soil samples. These samples were representative of distinct soil salinity classes, including non-saline, slightly saline, moderately saline, strongly saline, and very strongly saline soils. Soil salinity modeling using Landsat-8 OLI data revealed that the SI-5 index provided the most accurate predictions, with an R² of 0.67 and an RMSE of 0.12 dS/m. By 2023, 42.3% of the study area was classified as strongly or very strongly saline, indicating a significant increase in salinity over time. This rise in salinity corresponds to notable land use and land cover (LULC) changes, as 55.9% of the study area experienced LULC shifts between 2000 and 2023. A decline in vegetation cover coincided with increasing salinity, showing an inverse relationship between these factors. Additionally, the results highlight the complex interplay among these variables demonstrating that soil salinity levels are significantly impacted by climate change indicators, with a negative correlation between precipitation and salinity (r = −0.85, p < 0.001). Recognizing the interconnections between soil salinity, LULC changes, and climate variables is essential for developing comprehensive strategies, such as targeted irrigation practices and land suitability assessments. Earth observation and remote sensing play a critical role in enabling more sustainable and effective soil management in response to both human activities and climate-induced changes. Full article

In recent decades, global ecosystems have increasingly faced impacts from heightened precipitation variability. Specifically, water availability is an essential factor in wetland dynamics and has ecological importance in the high-Andean wetlands in both mountains and downstream ecosystems, particularly in semi-arid regions. This study focused on a chain of twelve high-Andean wetlands within the “Estero Derecho” nature sanctuary at the headwaters of the Elqui River in north-central Chile. The analysis of the spatiotemporal dynamics of precipitation and vegetation cover used the Landsat 5 and 8 Satellite imagery-derived normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) time series during the austral summer (December–March). We employed time series, boxplots, and least-squares regression analyses to explore vegetation cover behavior in relation to precipitation, water quality, and vegetation indices. Precipitation had a marked influence on vegetation behavior, particularly during the Chilean “megadrought” phenomenon. For both the NDVI and NDMI indices and precipitation, negative trends in the time series were observed, along with a highly significant correlation with a one-year lag between both indices and precipitation. The analysis of the individual wetlands showed different vegetation cover behaviors, which were attributable to the altitude, terrain slope, and additional water inputs from streams that have also given rise to alluvial fans that exert a shaping influence on the wetlands. In addition, significant correlations between both indices and water quality parameters (CE, Cl, Mg, Na, and Fe) were identified. The findings of this study can be incorporated into the Sanctuary’s management plan and concretely assist communities involved with wetland conservation. Full article

Traffic flow prediction, crucial for traffic management, relies on spatial and temporal data to achieve high accuracy. However, standard performance metrics only measure the average prediction errors and overlook the spatiotemporal aspects. To address this gap, this study introduces three simple spatial key performance indicators (KPIs): Global Moran’s I, Getis-Ord General G, and Adapted PageRank Algorithm Modified (APAM). We evaluated the traffic prediction results for synthetic clustering scenarios and four different prediction methods applied to the PeMSD8 dataset using spatial KPIs. Spatial KPIs are calculated based on traffic prediction errors and the adjacency matrix of the traffic network. Our results demonstrate that spatial KPIs can effectively differentiate between synthetic clustering scenarios. Global Moran’s I measures the spatial autocorrelation, Getis-Ord General G measures the spatial clustering of high/low values, and the univariate analysis of APAM deduces the distribution of node importance by considering node centrality and node values. Getis-Ord General G showed the highest sensitivity, being capable of distinguishing between methods with similar average RMSE, whereas Global Moran’s I and APAM univariate analysis revealed subtle differences between methods. Spatial KPIs serve as important complementary metrics for performance evaluation in the design of robust traffic management systems. Full article

The accurate measurement of spatiotemporal parameters, such as step length and step frequency, is crucial for analyzing running and sprinting performance. Traditional methods like video analysis and force platforms are either time consuming or limited in scope, prompting the need for more efficient technologies. This study evaluates the effectiveness of a commercial Global Positioning System (GPS) unit integrated with an Inertial Measurement Unit (IMU) in capturing these parameters during sprints at varying velocities. Five experienced male runners performed six 40 m sprints at three velocity conditions (S: Slow, M: Medium, F: Fast) while equipped with a GPS-IMU system and an optical system as the gold standard reference. A total of 398 steps were analyzed for this study. Step frequency, step length and step velocity were extracted and compared using statistical methods, including the coefficient of determination (r²) and root mean square error (RMSE). Results indicated a very large agreement between the embedded system and the reference system, for the step frequency (r² = 0.92, RMSE = 0.14 Hz), for the step length (r² = 0.91, RMSE = 0.07 m) and the step velocity (r² = 0.99, RMSE = 0.17 m/s). The GPS-IMU system accurately measured spatiotemporal parameters across different running velocities, demonstrating low relative errors and high precision. This study demonstrates that GPS-IMU systems can provide comprehensive spatiotemporal data, making them valuable for both training and competition. The integration of these technologies offers practical benefits, helping coaches better understand and enhance running performance. Future improvements in sample rate acquisition GPS-IMU technology could further increase measurement accuracy and expand its application in elite sports. Full article