Search Results (22)

As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these methodologies fall short in adequately addressing multi-spatial coupling effects and spatiotemporal weighting, particularly in scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. Experimental results indicate that STA-ConvLSTM provides precise predictions of sound speed. Specifically, relative to the measured data, it achieved a root mean square error (RMSE) of approximately 0.57 m/s and a mean absolute error (MAE) of about 0.29 m/s. Additionally, when compared to single-dimensional spatial analysis, incorporating multi-spatial scale considerations yielded superior predictive performance. Full article

Relevant studies have shown that methane gas has a close relationship with seismic activity. The concentration of methane released within a tectonic zone can reflect the intensity status of tectonic activities, which is important for seismic monitoring. In this study, the January 2020 Xinjiang Jiashi earthquake was taken as the research object, and the mature Robust Satellite Technique (RST) algorithm was used to characterize the L3-level methane product data from the hyperspectral sensor, Atmospheric Infrared Sounder (AIRS), installed on the Earth Observing System (EOS) AQUA satellite at the monthly scale, 8-day scale and daily scale. An analysis of the spatial and temporal distribution of methane was carried out for before and after the earthquake based on the 3D structural condition of the gas, and the 3D structural conditions of the 8-day scale were introduced. An 8-day scale 3D structural condition was introduced and migration validation was performed, and the results showed that (1) the seismic methane anomaly-extraction process proposed in this study is feasible; (2) the 3D contour features indicated that the methane anomalies that occurred before the Jiashi earthquake were caused by geogenic emissions; (3) the anomaly-extraction algorithm from this study did not extract the corresponding anomalies in the non-seismic year, which indicated that the anomaly-extraction algorithm of this study has some degree of feasibility; and (4) the migrated validation of the Wenchuan earthquake of May 2008 further suggested that methane anomalies at the time of the Wenchuan earthquake were caused by the earthquake. Full article

The effectiveness indicator system of remote sensing satellites includes various satellites capabilities. Effectiveness evaluation is the process of calculating these indicators in the digital world, involving many different physical parameters of multiple subsystems. Model-based simulation statistics method is the mainstream approach of effectiveness evaluation, and digital twin is currently the most advanced modeling method for simulation. The satellite digital twin model has the characteristics of multi-dynamic, multi-spatial scale and multi-physics field coupling, which gives rise to challenges related to the stiff problem of ordinary differential equations and multi-scale problem of partial differential equations to the calculation process of indicators. It is difficult to solve these problems by breakthroughs in numerical solution methods. This paper uses the sparsity of the satellite system to group each indicator of the effectiveness evaluation indicator system according to the change period. The satellite system model is decomposed into multiple modules according to the composition and structure, and a series of models with different simulation fidelity are established for each module. The optimization schemes for selecting model granularity when calculating indicators by group is given. Simulation results show that this approach considers the coupling between systems, grasps the main contradiction of indicator calculation and overcomes the loss of indicator accuracy caused by the separate calculation of each subsystem under the neglect of coupling in the traditional method. Additionally, it avoids the difficulty in numerical calculation caused by coupling, while simultaneously balancing the accuracy and efficiency of the model simulations. Full article

With the strengthening of regional and urban–rural interactions, farmers’ livelihood activities are becoming increasingly complex, and environmental factors that influence farmers’ livelihoods have multi-spatial effects. Consequently, comprehending farmers’ livelihoods from a multi-spatial perspective is imperative. Based on surveys conducted in 65 villages and 451 households in Jia County on the Loess Plateau, China, rural locations were deconstructed into natural, traffic, and positional advantages to explore the relationships and mechanisms between the rural environment and farmers’ livelihood stability from local, urban–rural, and interconnected multi-spatial perspectives. We found that 77% of the villages achieved a moderate or high Rural Location Advantage Index (RLAI) rating; 45% still lack natural advantages and are mainly located in hilly and sandy areas because of the fragile ecological environment of the Loess Plateau. Additionally, the Livelihood Stability Index (LSI) was moderate overall, but with significant spatial heterogeneity, and 72% of farmers possess strong transition capacity and have shifted away from relying on monoculture as their primary livelihood strategy. While a certain coupling correspondence exists between the LSI and RLAI, the interaction is intricate rather than a simple linear agglomeration process. The spatial variation in the LSI results from the superposition or interaction of multi-spatial location factors. The rural–urban spatial location factors are the key control element of the LSI and the interaction between rural–urban and local spatial location factors has the greatest influence on the LSI. It is simple for interconnected spatial location factors to produce a scale correlation effect, and have non-negligible effects on farmers’ livelihoods when they interact with other spatial location factors. Understanding the impact of rural location on farmers’ livelihood from a multi-spatial perspective is of great practical significance for identifying the causes of spatial heterogeneity in livelihoods and enhancing multi-level policy coordination on rural revitalization and livelihood security. Full article

Rapid acquisition of chlorophyll content in maize leaves is of great significance for timely monitoring of maize plant health and guiding field management. In order to accurately detect the relative chlorophyll content of summer maize and study the responsiveness of vegetation indices to SPAD (soil and plant analyzer development) values of summer maize at different spatial vertical scales, this paper established a prediction model for SPAD values of summer maize leaves at different spatial scales based on UAV multispectral images. The experiment collected multispectral image data from summer maize at the jointing stage and selected eight vegetation indices. By using the sparrow search optimized kernel limit learning machine (SSA-KELM), the prediction models for canopy leaf (CL) SPAD_CL and ear leaf (EL) SPAD_EL were established, and a linear fitting analysis was conducted combining the measured SPAD_CL values and SPAD_EL values on the ground. The results showed that for SPAD_CL, the R² of the linear fitting between the predicted values and measured values was 0.899, and the RMSE was 1.068. For SPAD_EL, the R² of linear fitting between the predicted values and the measured values was 0.837, and the RMSE was 0.89. Compared with the model established by the partial least squares method (PLSR), it is found that the sparrow search optimized kernel limit learning machine (SSA-KELM) has more precise prediction results with better stability and adaptability for small sample prediction. The research results can provide technical support for remote sensing monitoring of the chlorophyll content of summer maize at different spatial scales. Full article

Residential segregation (RS) is a global phenomenon that has become an enduring and important topic in international academic research. In this review, using RS as the search term, 2520 articles from the period 1928–2022 were retrieved from the Scopus database and were visually analyzed using CiteSpace software. The results revealed the following: (1) The United States and its institutions have made outstanding contributions to RS research, while various scholars (e.g., Johnston, Massey, Forrest, Poulsen, and Iceland) have laid the foundation for RS research. (2) Mainstream RS research originates from three fields—psychology, education, and social sciences—while the trend of multidisciplinary integration is constantly increasing. (3) The research hotspots of RS include racial difference, sociospatial behavior, income inequality, mixed income communities, guest worker minorities, typical district segregation, occupational segregation, health inequalities, metropolitan ghetto, and migrant–native differential mobility. Furthermore, (4) gentrification, spatial analysis, school segregation, health disparity, immigrant, and COVID-19 have become new themes and directions of RS research. Future research should pay more attention to the impact of multi-spatial scale changes on RS as well as propose theoretical explanations rooted in local contexts by integrating multidisciplinary theoretical knowledge. Full article

Although some methods have identified the physical urban area to a certain extent, the driving factors for the identification threshold have not been studied deeply. In this paper, vector building data and road intersection data are used for comparative validation based on the urban expansion curve method to identify the physical urban area using the meso-city scale. The geographical detector technique is used to detect how and to what extent the urban spatial structure factors, geographical environment factors and social economic factors affect the optimal distance threshold of 22 administrative districts in the Chongqing municipality. The results based on the vector buildings are more precise and show the characteristics of the physical urban area of core-periphery distribution and the distribution along the water corridor. From the results of quantitative attribution, it was found that the road network density, building density, urbanization rate and urban population density, and their interaction with regional GDP, play a critical role in the optimal distance threshold, with the index value of influence degree ≥0.79. Under the influence of different factors, the optimal distance thresholds of 22 administrative districts show adaptive characteristics. Looking forward to the future, this study provides ideas for further research on the morphological characteristics and distribution laws of multi-spatial scale cities. Full article

After recovering from the Rwanda Genocide in the last century, Rwanda is experiencing rapid economic growth and urban expansion. With increasing demand for electricity and a strong desire to achieve the Sustainable Development Goals (SDGs), it is significant to further investigate the electrification progress in Rwanda. This study analyzes the characteristics of electrification in Rwanda from 2012 to 2020 using VIIRS nighttime light imagery. Firstly, by analysis of the nighttime light change patterns on a national scale, we find that the electrification in Rwanda is seriously unbalanced, as electrification progress in Kigali is much faster than that in the rest of the country. Secondly, there is a common phenomenon where power grid expansion in Rwanda fails to keep pace with rapid urbanization, especially in areas with an inadequate electricity infrastructure foundation. Quantitatively, original electricity infrastructure level shows a positive impact on the grid access of new settlements, with an R² value of 0.695 in the linear regression. In addition, new settlements inside the urban boundary tend to achieve more extensive grid access compared to those outside the boundary. Finally, the grid access rates are calculated on multi-spatial scales. By comparing the calculated results with the official electricity access rate data, we analyze the development of off-grid access in Rwanda. The results imply that, since 2016, off-grid access has rapidly developed in Rwanda, especially in the rural areas, playing an important role in achieving the SDGs. Full article

The flow–sediment relationship is important to understand soil erosion and sediment transport in severely eroded areas, such as Loess Plateau. Previous research focused on the variation and driving forces of runoff and sediment at the different scales in a watershed. However, the variations of the flow–sediment relationship on multispatial scales (slope, subgully, gully, and watershed scales) and multitemporal scales (annual, flood events, and flood process) were less focused. Taking the Peijiamao watershed, which includes whole slope runoff plot (0.25 ha, slope scale), branch gully (6.9 ha, subgully scale), gully (45 ha, gully scale), and watershed (3930 ha, watershed scale), four different geomorphic units located at the Chinese Loess Plateau, as the research site, a total of 31 flood events from 1986 to 2008 were investigated, and two flood process data were recorded across all the four geomorphic units. The results showed that on the annual timescale, the average sediment transport modulus and runoff depth at four scales exhibited a linear relationship, with determination coefficients of 0.81, 0.72, 0.74, and 0.77, respectively. At the flood event timescale, the relationships between sediment transport modulus and runoff depth at the gully and watershed scales could also be fitted with a linear relationship with high determination coefficients (from 0.77 to 0.99), but the determination coefficient at the slope scale was only 0.37 at the event scale. On the single rainfall event timescale, the flow–sediment relationship at the slope scale showed a figure-eight hysteretic pattern while those relationships at larger scales showed an anticlockwise loop hysteretic pattern. Under the same flow condition, the suspended sediment concentrations during the falling stage were significantly higher than those during the rising stage. Moreover, the difference was bigger as the spatial scale increased due to the wash loads in the downstream gullies, which favored the occurrence of hyper-concentration flow. The results of the study could provide useful insights into the temporal–spatial scale effects of sediment transport and their internal driving mechanisms at the watershed scale. Full article

United Nations Sustainable Development Goal SDG11.3.1—the ratio of land consumption rate (LCR) to population growth rate (PGR) (LCRPGR)—aims to measure the efficiency and sustainability of urban land use. In recent years, SDG11.3.1 has been widely used in sustainable urban development research. However, previous studies have focused on the urban core area, while the sustainable development status of the urban peripheral areas (suburban and rural areas) that contribute significantly to the ecological environment has been neglected. To this end, relying on land use/cover change (LUCC) data obtained from high-resolution remote sensing satellite images rather than the single impervious surface data used in traditional research, according to the multiple functions of the land use type, the city is divided into three types of space: production, living, and ecological spaces. Research from the perspective of multi-scale coordination is of great significance for gaining a comprehensive understanding of the sustainable development status of urban space. Taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China as an example, in this paper, LUCC remote sensing data and comprehensive population and gross domestic product (GDP) data are used. From the multi-functional production-living-ecological space perspective, based on the original land use efficiency indicator, the ratio of land consumption rate (LCR) to economic growth rate (EGR) (LCREGR) is introduced and the analytic hierarchy process (AHP) is used to comprehensively evaluate the sustainable development level (SDL) of the space between 2000–2010 and 2010–2020 on the urban agglomeration and prefecture-level city scales. The results show that (1) the level of and changes in the spatial sustainable development are significantly different at different scales; (2) the division of the production-living-ecological spaces can guide cities to optimize different types of spaces in the future. This paper proposes a new evaluation method for spatial sustainable development, which provides a useful reference for any country or region in the world. Full article

Large-scale 3D point clouds are rich in geometric shape and scale information but they are also scattered, disordered and unevenly distributed. These characteristics lead to difficulties in learning point cloud semantic segmentations. Although many works have performed well in this task, most of them lack research on spatial information, which limits the ability to learn and understand the complex geometric structure of point cloud scenes. To this end, we propose the multispatial information and dual adaptive (MSIDA) module, which consists of a multispatial information encoding (MSI) block and dual adaptive (DA) blocks. The MSI block transforms the information of the relative position of each centre point and its neighbouring points into a cylindrical coordinate system and spherical coordinate system. Then the spatial information among the points can be re-represented and encoded. The DA blocks include a Coordinate System Attention Pooling Fusion (CSAPF) block and a Local Aggregated Feature Attention (LAFA) block. The CSAPF block weights and fuses the local features in the three coordinate systems to further learn local features, while the LAFA block weights the local aggregated features in the three coordinate systems to better understand the scene in the local region. To test the performance of the proposed method, we conducted experiments on the S3DIS, Semantic3D and SemanticKITTI datasets and compared the proposed method with other networks. The proposed method achieved 73%, 77.8% and 59.8% mean Intersection over Union (mIoU) on the S3DIS, Semantic3D and SemanticKITTI datasets, respectively. Full article

Surface albedo, as a key parameter determining the partition of solar radiation at the Earth’s surface, has been developed into a satellite-based product from various Earth observation systems to serve numerous global or regional applications. Studies point out that apparent uncertainty can be introduced into albedo retrieval without consideration of surface anisotropy, which is a challenge to albedo estimation especially from observations with fewer angular samplings. Researchers have begun to introduce smoothed anisotropy prior knowledge into albedo estimation to improve the inversion efficiency, or for the scenario of observations with signal or poor angular sampling. Thus, it is necessary to further understand the potential influence of smoothed anisotropy features adopted in albedo estimation. We investigated the albedo variation induced by BRDF smoothing at both temporal and spatial scales over six typical landscapes in North America using MODIS standard anisotropy products with high quality BRDF inversed from multi-angle observations in 500 m and 5.6 km spatial resolutions. Components of selected typical landscapes were assessed with the confidence of the MCD12 land cover product and 30 m CDL (cropland data layer) classification maps followed by an evaluation of spatial heterogeneity in 30 m scale through the semi-variogram model. High quality BRDF of MODIS standard anisotropy products were smoothed in multi-temporal scales of 8 days, 16 days, and 32 days, and in multi-spatial scales from 500 m to 5.6 km. The induced relative and absolute albedo differences were estimated using the RossThick-LiSparseR model and BRDFs smoothed before and after spatiotemporal smoothing. Our results show that albedo estimated using BRDFs smoothed temporally from daily to monthly over each scenario exhibits relative differences of 11.3%, 12.5%, and 27.2% and detectable absolute differences of 0.025, 0.012, and 0.013, respectively, in MODIS near-infrared (0.7–5.0 µm), short-wave (0.3–5.0 µm), and visible (0.3–0.7 µm) broad bands. When BRDFs of investigated landscapes are smoothed from 500 m to 5.6 km, variations of estimated albedo can achieve up to 36.5%, 37.1%, and 94.7% on relative difference and absolute difference of 0.037, 0.024, and 0.018, respectively, in near-infrared (0.7–5.0 µm), short wave (0.3–5.0 µm), and visible (0.3–0.7 µm) broad bands. In addition, albedo differences caused by temporal smoothing show apparent seasonal characteristic that the differences are significantly higher in spring and summer than those in autumn and winter, while albedo differences induced by spatial smoothing exhibit a noticeable relationship with sill values of a fitted semi-variogram marked by a correlation coefficient of 0.8876. Both relative and absolute albedo differences induced by BRDF smoothing are demonstrated to be captured, thus, it is necessary to avoid the smoothing process in quantitative remote sensing communities, especially when immediate anisotropy retrievals are available at the required spatiotemporal scale. Full article

Evapotranspiration (ET) is essential for connecting ecosystems and directly affects the water consumption of forests, grasslands, and farmlands. Eight global remote sensing-based ET (RS_ET) datasets generated using satellite imagery and ground-based observations were comprehensively assessed using monthly ET time series simulated by the water balance (WB) method at the catchment scale in the Hengduan Mountain (HDM) region, including the Nu River, Lancang River, and Jinsha River basins. The complementary relationship (CR) model, which derives ET from meteorological data, was also evaluated against WB-based ET (WB_ET). In addition, WB_ET, RS_ET, and CR-based ET (CR_ET) data were used to investigate ET spatial and temporal variations at the catchment, grid, and site scale, respectively. Most RS_ET datasets accurately simulated monthly ET with an average index of agreement ranging from 0.71–0.91. The Operational Simplified Surface Energy Balance dataset outperformed other RS_ET datasets, with Nash–Sutcliffe efficiency coefficient (NSE) and Kling–Gupta efficiency values of 0.80 and 0.90, respectively. RS_ET datasets generally performed better in northern semiarid areas than in humid southern areas. The monthly ET simulation by the CR model was consistent with that of the WB_ET in the HDM region, with mean values of correlation coefficient (cc) and NSE at each site of 0.89 and 0.68, respectively. The model showed better performance in simulating monthly ET in the Lancang River Basin than in the Nu River and Lancang River basins, with mean cc and NSE of 0.92 and 0.83, respectively. Generally, annual ET trends were consistent at the catchment, grid, and site scale, as estimated by the WB method, RS_ET datasets, and CR model. It showed a significant decreasing trend in the northern semiarid region of the HDM while exhibiting an increasing trend in the humid southern region. Full article

The Human Development Index (HDI) is a prevailing indicator to present the status and trend of sustainability of nations, hereby offers a valuable measurement on the Sustainable Development Goals (SDGs). Revealing the dynamics of the HDI of the Eastern Hemisphere countries is vital for measurement and evaluation of the human development process and revealing the spatial disparities and evolutionary characteristics of human development. However, the statistical data-based HDI, which is currently widely applied, has defects in terms of data availability and inconsistent statistical caliber. To tackle such an existing gap, we applied nighttime lights (NTL) data to reconstruct new HDI indicators named HDI_NTL and quantify the HDI_NTL at multispatial scales of Eastern Hemisphere countries during 1992–2013. Results showed that South Central Asia countries had the smallest discrepancies in HDI_NTL, while the largest was found in North Africa. The national-level HDI_NTL values in the Eastern Hemisphere ranged between 0.138 and 0.947 during 1992–2013. At the subnational scale, the distribution pattern of HDI_NTL was spatially clustered based on the results of spatial autocorrelation analysis. The evolutionary trajectory of subnational level HDI_NTL exhibited a decreasing and then increasing trend along the northwest to the southeast direction of Eastern Hemisphere. At the pixel scale, 93.52% of the grids showed an increasing trend in HDI_NTL, especially in the urban agglomerations of China and India. These results are essential for the ever-improvement of policy making to reduce HDI’s regional disparity and promote the continuous development of humankind’s living qualities. This study offers an improved HDI accounting method. It expects to extend the channel of HDI application, e.g., potential integration with environmental, physical, and socioeconomic data where the NTL data could present as well. Full article

Background and Objectives: As urbanisation is a significant global trend, there is a profound need for biodiversity protection in urban ecosystems. Moreover, the potential of urban green space to support urban biodiversity should be appreciated. Butterflies are environmental indicators that are sensitive to urbanisation. Therefore, it is important to identify butterfly distribution patterns and the factors influencing butterfly diversity and species composition in urban parks within cities. Research Highlights: To our knowledge, ours is the first study evaluating the effects of both land cover and local habitat features on butterfly species composition in urban parks of Beijing, China. Materials and Methods: In this study, we surveyed butterfly richness and abundance in 28 urban parks in Beijing, China. The parks differed in age and location in the urban area. Meanwhile, we investigated the green space in the surroundings of the parks at multi-spatial scales at the landscape level. We also investigated local park characteristics including the age of the park (Age), perimeter/area ratio of the park (SQPRA), area of the park (ha) (Area), green space cover within the park (Greenp), nectar plant species richness (Necpl), abundance of flowering nectar plants (Necabu) and overall plant species richness (Pl). Generalised linear models (GLMs) and redundancy discriminant analysis (RDA) were applied to relate butterfly diversity and butterfly species composition to environmental variables, respectively. Results: We recorded 3617 individuals belonging to 26 species from July to September in 2019. Parks on the city fringe had significantly higher butterfly diversity. Butterfly species richness was mostly related to total plant richness. The abundance of flowering nectar plants was closely linked to butterfly abundance. Land cover had little impact on butterfly diversity and community structure in urban parks. Conclusions: Once a park has sufficient plants and nectar resources, it becomes a useful haven for urban butterflies, regardless of the surrounding land cover. Well-planned urban parks focused on local habitat quality support butterfly conservation. Full article