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Monitoring carbon emissions is crucial for assessing and addressing economic development and climate change, particularly in regions like the nine provinces along the Yellow River in China, which experiences significant urbanization and development. However, to the best of our knowledge, existing studies mainly focus on national and provincial scales, with fewer studies on municipal and county scales. To address this issue, we established a carbon emission assessment model based on the "NPP-VIIRS-like" nighttime light data, aiming to analyze the spatiotemporal variation of carbon emissions in three different levels of nine provinces along the Yellow River since the 21st century. Further, the spatial correlation of carbon emissions at the county level was explored using the Moran's I spatial analysis method. Results show that, from 2000 to 2021, carbon emissions in this region continued to rise, but the growth rate declined, showing an overall convergence trend. Per capita carbon emission intensity showed an overall upward trend, while carbon emission intensity per unit of GDP showed an overall downward trend. Its spatial distribution generally showed high carbon emissions in the eastern region and low carbon emissions in the western region. The carbon emissions of each city mainly showed a trend of "several"; that is, the urban area around the Yellow River has higher carbon emissions. Meanwhile, there is a trend of higher carbon emissions in provincial capitals. Moran's I showed a trend of decreasing first and then increasing and gradually tended to a stable state in the later stage, and the pattern of spatial agglomeration was relatively fixed. "High–High" and "Low–Low" were the main types of local spatial autocorrelation, and the number of counties with "High–High" agglomeration increased significantly, while the number of counties with "Low–Low" agglomeration gradually decreased. The findings of this study provide valuable insights into the carbon emission trends of the study area, as well as the references that help to achieve carbon peaking and carbon neutrality goals proposed by China. [ABSTRACT FROM AUTHOR]

Over the past two decades, the location and morphology of the coastline, as well as the land use/land cover (LULC) in the Circum-Bohai region in China, have undergone significant changes due to rapid industrialization and urbanization. Analyzing the temporal and spatial variation in coastal lines and LULC can provide a meaningful basis for the rational allocation of land resources. Using Landsat TM/OLI series dates from the Google Earth Engine (GEE) platform, this study applied the Linear Superposition Water Index (LSWI) and the Otsu threshold method (OTSU) algorithm to extract and analyze the coastline of the Circum-Bohai region. Additionally, the Random Forests (RF) method was employed to extract LULC information in the coastal zone. Using the geographical detector, we further explored the influence of social and economic factors, as well as natural factors, on spatial differentiation mechanisms of LULC change in the Circum-Bohai. Our results show that between 2000 and 2020, the Circum-Bohai coastline generally expanded towards the ocean by a total of 1062.99 km. The highest rate of change occurred during 2010 to 2015, and human activities were the primary cause of most of the changes, with the exception of the Yellow River Delta, where natural factors were dominant. The main types of LULC in the study area from 2000 to 2020 were farmland and construction land. The area of farmland proportion decreased by 1.75%, while the area of construction land proportion increased from 16.73% to 29.54%. Our findings indicate that the degree of land use in the Circum-Bohai is deepening. Based on our factor detection analysis, the added value of the secondary industry was the most critical influencing factor on LULC. Furthermore, the combined effect of the added value of the secondary industry and gross domestic product (GDP) has a significant driving impact on LULC. These findings can provide reference and data support for the sustainable development and comprehensive management of land resources. The relevant departments can use these results to prompt corresponding policies for the rational allocation of land resources. [ABSTRACT FROM AUTHOR]

Dynamic monitoring of building environments is essential for observing rural land changes and socio-economic development, especially in agricultural countries, such as China. Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the "beautiful countryside" construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 × 105 m2. The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide. [ABSTRACT FROM AUTHOR]

Automatic building extraction based on high-resolution aerial imagery is an important challenge with a wide range of practical applications. One of the mainstream methods for extracting buildings from high-resolution images is deep learning because of its excellent deep feature extraction capability. However, existing models suffer from the problems of hollow interiors of some buildings and blurred boundaries. Furthermore, the increase in remote sensing image resolution has also led to rough segmentation results. To address these issues, we propose a generative adversarial segmentation network (ASGASN) for pixel-level extraction of buildings. The segmentation network of this framework adopts an asymmetric encoder–decoder structure. It captures and aggregates multiscale contextual information using the ASPP module and improves the classification and localization accuracy of the network using the global convolutional block. The discriminator network is an adversarial network that correctly discriminates the output of the generator and ground truth maps and computes multiscale L 1 loss by fusing multiscale feature mappings. The segmentation network and the discriminator network are trained alternately on the WHU building dataset and the China typical cities building dataset. Experimental results show that the proposed ASGASN can accurately identify different types of buildings and achieve pixel-level high accuracy extraction of buildings. Additionally, compared to available deep learning models, ASGASN also achieved the highest accuracy performance (89.4% and 83.6% IoU on these two datasets, respectively). [ABSTRACT FROM AUTHOR]

• Evaluate the urban resilience from a multidimensional perspective. • Validate and analyze by using NPP-VIIRS nighttime light data. • The average urban resilience index increased from 0.027 to 0.058 from 2011 to 2021. • The coefficient of variation decreased from 0.823 in 2011 to 0.751 in 2021. • The moran's i of urban resilience increased from 0.2017 in 2011 to 0.4476 in 2021. Modern cities are facing increasingly complex challenges, and assessing urban resilience is crucial to improve their ability to withstand various types of shocks and disasters, especially in the China's North-South Seismic Belt with frequent tectonic activities and natural disasters. To address this issue, this study determined the weights of the assessment system based on the entropy weight method, and evaluated the urban resilience of four provinces in China's North-South Seismic Belt in multiple dimensions from the perspectives of society, economy, infrastructure, and ecology. Furthermore, we systematically explored the spatiotemporal trends of urban resilience and its spatial correlation from 2011 to 2021. Lastly, the assessment results were validated and analyzed by four historical earthquake cases using national polar-orbiting partnership - visible infrared imaging radiometer suite (NPP-VIIRS) nighttime light data. The findings reveal that the average urban resilience index increased from 0.027 to 0.058 from 2011 to 2021, signifying a remarkable surge of 115.42%. Sichuan emerged as a consistent frontrunner in terms of urban resilience. Overall, the study area shows a spatial distribution pattern of higher urban resilience in the east and lower resilience in the west, due to the larger population and more developed economy in the east. However, the south has a higher average annual growth rate, while the north has a lower average annual growth rate. A notable observation is the gradual reduction in the coefficient of variation of urban resilience from 0.823 in 2011 to 0.751 in 2021, indicating a decline in the disparity of resilience levels. Meanwhile, Moran's I gradually increased from 0.2017 in 2011 to 0.4476 in 2021, signifying a progressive intensification in the spatial correlation of urban resilience and an evident propensity toward aggregation. By selecting shifts in the total nighttime light index (TNLI) during representative earthquake incidents as an indirect gauge of urban resilience levels, this study finds congruence with the assessment outcomes, thereby further substantiating the precision of the urban resilience evaluations. Policymakers should prioritize the allocation of resources to less resilient cities to improve their ability to withstand and recover from disaster events. The urban resilience assessment system in this study, despite its multidimensionality, may still not be able to fully cover all the factors affecting urban resilience. This study provides a significant decision-making basis for policymakers in urban development and disaster response, as well as a useful reference for building a more robust and sustainable urban future. [ABSTRACT FROM AUTHOR]

Seismic vulnerability assessments play a significant role in comprehensive risk mitigation efforts and seismic emergency planning, especially for urban areas with a high population density and a complex construction environment. Traditional approaches such as in situ fieldwork are accurate for conducting seismic vulnerability assessments of buildings; however, they are too much time and cost-consuming, especially in moderate to low seismic hazard regions. To address this issue, an integrated approach for a macroseismic vulnerability assessment composed of data mining methods and GIScience technology was presented and applied to Urumqi, China. First, vulnerability proxies were established via in situ data of buildings in the Tianshan District with an EMS-98 vulnerability classification scheme and two data mining methods, namely, support vector machine and association rule learning methods. Then, vulnerability proxies were applied to the Urumqi database, and the accuracy was validated. Finally, seismic risk maps were constructed through data consisting of direct damage to buildings and human casualties. The results indicated that the two data mining methods could achieve desirable accuracies and stabilities when estimating the seismic vulnerability. The seismic risk of Urumqi was estimated as Slight with a predicted number of 61,380 homeless people for a seismic intensity scenario of VIII. [ABSTRACT FROM AUTHOR]

On the basis of data obtained from China Earthquake Administration and China National Statistics Bureau, this paper focuses on the earthquake disaster loss characteristics in 31 provinces or municipalities of China from 1993 to 2016. The trends of direct economic losses, casualties and affected population are also analyzed by constructing two relative earthquake loss indexes (losses/GDP and losses/E) and employing gray correlation theory. Results show that during the 24 years, without taking Wenchuan Ms8.0 earthquake into account, the direct economic losses exhibit a significant increasing trend while losses/GDP also increases but is not significant. Meanwhile, the injuries and affected population both witness a decline trend but the fatalities show the opposite. According to the correlational degree of each year, 8 years (1995, 1996, 1998, 2003, 2008, 2010, 2013 and 2014) suffered losses of equal or above moderate grade. Regionally, the western China suffered the most accumulative direct economic losses and losses/GDP. However, the ranking results of losses/E demonstrate that the central China is the most vulnerable when confronting with the same earthquake. Specifically, Sichuan and Yunnan suffered losses of extra-large grade, Qinghai of large grade and Tibet, Xinjiang and Guangdong of moderate grade. These discoveries are helpful for earthquake mitigation and preparedness as well as earthquake loss evaluation in China. [ABSTRACT FROM AUTHOR]

Economic development has rapidly progressed since the implementation of reform and opening up policies, posing significant challenges to sustainable development, especially to vegetation, which plays a crucial role in maintaining ecosystem service functions and promoting green low-carbon transformations. In this study, we estimated the fractional vegetation cover (FVC) in Shandong Province from 2000 to 2020 using the Google Earth Engine (GEE) platform. The spatial and temporal changes in FVC were analyzed using gravity center migration analysis, trend analysis, and geographic detector, and the vegetation changes of different land use types were analyzed to reveal the internal driving mechanism of FVC changes. Our results indicate that vegetation cover in Shandong Province was in good condition during the period 2000 to 2020. The high vegetation cover classes dominated, and overall changes were relatively small, with the center of gravity of vegetation cover generally shifting towards the southwest. Land use type, soil type, population density, and GDP factors had the most significant impact on vegetation cover change in Shandong Province. The interaction of these factors enhanced the effect on vegetation cover change, with land use type and soil type having the highest degree of influence. The observational results of this study can provide data support for the policy makers to formulate new ecological restoration strategies, and the findings would help facilitate the sustainability management of regional ecosystem and natural resource planning., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)