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There has been controversy regarding the air pollutants emitted from sources closely related to the activities of daily life in China, such as cooking, setting off fireworks, sacrificial incense and joss paper burning, and barbecue (which have been named the five missing sources – FMSs), and the extent to which they impact the outdoor air quality. To date, due to the lack of both an activity dataset and emission factors, there have been no estimations of the emission of air pollutants from FMSs. In this work, we have attempted to combine questionnaire data, various statistical data, and data on points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Emission inventories of carbonaceous aerosols with a high spatial–temporal resolution for FMSs were then established, and the spatial variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) emissions were in the range of 4268–4919 t. OC emissions from FMSs were between 1.5 ‰ and 2.2 ‰ of the total emissions in China. Black carbon (BC), elemental carbon (EC), and brown carbon (BrC) absorption cross-section (ACS BrC) emissions from FMSs were in the ranges of 22.6–43.9 t, 213–324 t, and 14.7–35.6 Gm 2 , respectively. Their emissions tended to be concentrated in certain periods and areas. The OC emission intensities in central urban areas were 3.85–50.5 times those in rural areas (due to the high density of human activity), while the ACS BrC emissions in rural regions accounted for 63.0–79.5 % of the total emissions (resulting from uncontrolled fireworks). A mass of fireworks led to much higher ACS BrC and EC emissions on Chinese New Year's Eve, with respective values that were 1444 and 262 times their corresponding yearly averages. Significant (p<0.01) correlations between the population's income and pollutant emissions were also found: these correlations were positive (r=0.94) and negative (r=-0.94) for urban and rural regions, respectively, indicating the necessity to regulate residents' lifestyles and increase residents' income in respective urban and rural regions. This study provides firsthand data to identify emissions, variation trends, and impacting factors from the FMSs. This is helpful for modeling works on air quality, the climate effect, and human health risks during specific periods or in specific regions as well as for modifying emission control policies. The data compiled in this work can found at 10.6084/m9.figshare.19999991.v2 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

There has been controversy about the air pollutants emitted from sources closely related to people's daily life (such as cooking, fireworks burning, sacrificial incenses and joss paper burning, and barbecue, named as five missing sources, FMS) impacting the outdoor air quality to what extent. Till now, there is no emission estimation of air pollutants from FMS, as the missing of both activity dataset and emission factors. We attempted to combine the questionnaire data, various statistical data, and data of points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Then, the emission inventories of carbonaceous aerosol with the high spatial-temporal resolution for FMS were established firstly, and the spatial-variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) increased from 4268 t to 4919 t. The OC emission from FMS was 1.48-2.18 - of its total emission in China. The emissions of black carbon, element carbon (EC), and brown carbon absorption cross-section (ACSBrC) emissions from FMS were in the ranges of 22.6-43.9 t, 213-324 t, and 14.7-35.6 Gm2, respectively. Their emissions tended to concentrate in special periods and areas. The OC emission intensities in central urban areas were 3.85-50.5 times that of rural areas due to the high density of human activities. While the ACSBrC emissions in rural regions accounted for 63.0-79.5% of the total emission result from uncontrolled fireworks burning. A mass of fireworks burning led to extremely higher ACSBrC and EC emissions on Chinese New Year's eve, as 1444 and 262 times their corresponding yearly average values. Interestingly, significant (p<0.01) correlations between human incomes and pollutant emissions were found, but they were positive (r = 0.94) and negative (r = -0.94) for urban and rural regions, indicating the necessity of regulating human lifestyle and increasing income for urban and rural peoples, respectively. This study provided the first-hand data for identifying the emissions, variation trends and impacting factors of FMS, which is helpful for modeling works on air quality, climate effect, and human health risks at specific periods or regions and for modifying their emission control policies. The data in this work could be found at https://doi.org/10.6084/m9.figshare.19999991.v1 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

The total amount of sugarcane planting in China ranks third in the world, and Guangxi is the largest sugarcane planting base in China. In order to help the Guangxi government and industry better understand the annual planting distribution and scale of sugarcane in the whole region, it is convenient to optimize the planting structure and improve the efficiency of resource utilization, so as to ensure the stable development of Guangxi sugar industry. Based on the satellite-based hyperspectral image data of ' Zhuhai No.1 ', this paper carries out sugarcane recognition and area extraction, which fills the application gap of satellite-based hyperspectral image data in extracting sugarcane area in China. A globally optimized extended random walk model was used to identify and extract sugarcane by combining the spatial-spectral information of hyperspectral imagery. Firstly, the Initial probability estimation is performed using the LOR classifier, and then use the data after MNF dimensionality reduction to construct a weighted graph, and finally the probability optimization and recognition are carried out based on the extended random walk model. The results show that the spatial-spectral joint method based on extended random walk can effectively identify sugarcane, and the sugarcane recognition accuracy of 2021 year image can reach 91.4%, the misrecognition rate is 2.8%; the sugarcane recognition accuracy of 2020 year image can reach 90.5 %, the misrecognition rate is 3.1%. [ABSTRACT FROM AUTHOR]

Using the peak ground acceleration (PGA) under four exceedance probabilities calculated via a probabilistic seismic hazard analysis method for China, the probability density function of PGA was obtained by fitting with the Cornell seismic hazard exceedance probability PGA function model. Combined with the seismic fragility function of the water supply system calculated on the basis of the empirical matrix of actual earthquake damage and the exposure of fixed assets, the expected seismic loss and expected loss rate models of the water supply system were established, and the classification standard with the expected seismic loss rate of the water supply system as the index was proposed. The seismic fragility of the water supply system was classified, and the exposure of the water supply system was analyzed. The total fixed assets in the Statistical Yearbook of Urban Water Supply, henceforth called Water Supply Yearbook, were taken as the exposure to earthquake in the region. The accuracy of the fragility model in this paper was verified through the actual earthquake damage losses in Deyang. Taking the water supply system of 720 cities in mainland China as an example, the distribution maps of expected seismic loss and expected loss rate were calculated and drawn. The expected loss rate model was verified by the key earthquake prevention areas in mainland China. The assessment model based on the expected loss and expected loss rate was taken as the seismic risk assessment model of water supply systems in mainland China. [ABSTRACT FROM AUTHOR]

This paper focuses on VR/AR applications in immovable tangible cultural heritage (ITCH) tourism by considering both academic research and cases in the XR industry. The goal is to understand how VR/AR can be used to present heritage information to the public. A framework is developed, including the purpose of using the technology, hardware, software, and user experience to have an overall understanding of VR/AR applications. Cases collected based on the framework are divided into four heritage types to answer the following questions: under a certain heritage context, what is the commonly used technology, is there a correlation between the purpose of using VR/AR, the enabling technology, and the user experience, and is there a difference in the concentration point between academia and the XR industry in China. [ABSTRACT FROM AUTHOR]

As a carrier of history, culture, religion and art, grotto temples are an important part of China's splendid cultural heritage. Among them, there are many grotto statues with similar shapes and scattered on the grotto temples, which have high artistic value. It is of great significance to classify them.The existing classification methods of grotto temple statues are mainly based on traditional manual classification and machine learning classification, which often consumes a lot of labor costs and time costs. To solve these problems, this paper improves the PointNet network and applies it to the classification of statues in grottoes, which greatly improves the automation of the classification of statues. And the point cloud data set of the grotto temple is made for experiment. The results show that the overall accuracy of the method in this paper reaches 89.73%, the average intersection and combination ratio reaches 68.9%, and the accuracy is increased by 5.47% and 4.3% respectively compared with the random forest classification method. It is of great significance to the value research, status assessment and virtual restoration of the subsequent grotto temples. [ABSTRACT FROM AUTHOR]

Photovoltaic power stations utilizing solar energy, have grown in scale, resulting in an increase in operational maintenance requirements. Efficient inspection of components within these stations is crucial. However, the large area of photovoltaic power generation, coupled with a substantial number of photovoltaic panels and complex geographical environments, renders manual inspection methods highly inefficient and inadequate for modern photovoltaic power stations. To address this issue, this paper proposes a method and system for hot spot detection on photovoltaic panels using unmanned aerial vehicles (UAVs) equipped with multispectral cameras. The UAVs capture visible and infrared images of the photovoltaic power plant, which are then processed for photogrammetry to determine imaging position and attitude. The infrared images are stitched together using this information, forming a geographically referenced overall image. Hot spot detection is performed on the infrared images, enabling the identification of faulty photovoltaic panels and facilitating efficient inspection and maintenance. Experimental trials were conducted at a photovoltaic power station in Qingyuan, Guangdong Province China. The results demonstrate the effectiveness of the proposed method in accurately detecting panels with hot spot faults. [ABSTRACT FROM AUTHOR]

Storm surges are a common natural hazard in China's southern coastal area which usually cause a great loss of human life and financial damages. With the economic development and population concentration of coastal cities, storm surges may result in more impacts and damage in the future. Therefore, it is of vital importance to conduct risk assessment to identify high-risk areas and evaluate economic losses. However, quantitative study of storm surge risk assessment in undeveloped areas of China is difficult, since there is a lack of building character and damage assessment data. Aiming at the problem of data missing in undeveloped areas of China, this paper proposes a methodology for conducting storm surge risk assessment quantitatively based on deep learning and geographic information system (GIS) techniques. Five defined storm surge inundation scenarios with different typhoon return periods are simulated by the coupled FVCOM–SWAN (Finite Volume Coastal Ocean Model–Simulating WAves Nearshore) model, the reliability of which is validated using official measurements. Building footprints of the study area are extracted through the TransUNet deep learning model and remote sensing images, while building heights are obtained through unoccupied aerial vehicle (UAV) measurements. Subsequently, economic losses are quantitatively calculated by combining the adjusted depth–damage functions and overlaying an analysis of the buildings exposed to storm surge inundation. Zoning maps of the study area are provided to illustrate the risk levels according to economic losses. The quantitative risk assessment and zoning maps can help the government to provide storm surge disaster prevention measures and to optimize land use planning and thus to reduce potential economic losses in the coastal area. [ABSTRACT FROM AUTHOR]

China's first sub-metre stereo satellite, GaoFen-7, was launched on 7 November 2019. One of the main criteria for a stereo mapping satellite is the geometric accuracy of the images. In this paper, we present a systematic evaluation of the geometry accuracy of Gaofen-7 on two scenes over the centre of Munich, Germany. The geometry accuracy is evaluated in a three-step workflow: 1) direct georeferencing accuracy; 2) image orientation using bundle adjustment with ground control points; 3) height accuracy of the generated digital surface model (DSM). In addition to dense LiDAR point clouds, ground control points were measured in the field. These were used as references. The results show that RPC bundle adjustment with 0 order bias correction is sufficient to achieve sub-metre absolute accuracy. The height accuracy of the generated digital surface models varies with land cover type, ranging from 0.9m (NMAD) in open areas to 4.5m in urban areas. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau stands as one of China's largest middle and low latitude permafrost regions. However, the effects of global warming and human activities have led to permafrost thawing, inducing surface instability and posing significant threats to infrastructure and indigenous communities. The deployment of Lu Tan-1 (LT-1), China's premier L-band synthetic aperture radar (SAR) satellite constellation, offers a novel opportunity to assess these changes. This paper evaluates the deformation of critical engineering corridors, such as the Qinghai-Tibet Railway (QTR) and the Qinghai-Tibet Highway (QTH), utilizing time-series InSAR techniques with LT-1 SAR constellation data. We introduce both Stacking InSAR and a multi-baseline persistent scatterer multitemporal (MT-InSAR) method to enhance permafrost and engineering corridor deformation detection capabilities. Results obtained through the MT-InSAR approach reveal line-of-sight (LOS) deformation velocities of permafrost in the Beiluhe region ranging from -90 mm/y to approximately 70 mm/y, with an average velocity amplitude of 0.06 m/y. Differential displacement between alpine meadows and alpine deserts across the Beiluhe region is successfully discerned using LT-1 SAR data. Deformation velocities of QTR, QTH were found to be lower than that of permafrost, with average velocities of 0.027 m/y. These findings underscore the LT-1 SAR constellation's potential to serve as a valuable SAR data source for monitoring engineering corridor deformation within the Tibetan Plateau permafrost region. [ABSTRACT FROM AUTHOR]

There is large distribution of sugarcane growth in south China which is play an important role of sugar industry. Remote sensing technology is used in sugarcane monitoring for large areas. However, the optical satellite data coverage is influenced by the rainy weather especially in the grand growth period of sugarcane. GF-1 WFV has widely swath 800km and short revisit time which is ideal data for this study area. In this paper, the random forest model was chosen to get a precise classification result of sugarcane based on time-series band value and 5 spectral indexes image is 89.73% and the Kappa coefficient is 0.65 which is satisfied the overall extraction of sugarcane for large area is the southern China. Furthermore, the decision tree classification was chosen as a comparative experience research. [ABSTRACT FROM AUTHOR]

As important green energy, wind power and photovoltaic power have great development prospects. The suitability evaluation of wind and solar power plants is a popular research field, which is related to the sustainable and healthy development of wind and solar power generation. In this paper, based on multiple dimensions such as land types, climatic conditions, topographic features and policy environment, we selected 10 indicators and combined (analytical hierarchy process) AHP method to build a suitability assessment model for evaluating the suitability of solar and wind power in Inner Mongolia, China. The findings revealed that, Inner Mongolia has a great potential to generate wind and solar electricity, for wind power, the category of 'excellent' regions covers 83855 km2 and represents 7.10% of the total surface area; for solar power, 7.66% (nearly 90420 km2) are classified as 'excellent'. The suitability of both solar and wind energy in the western region is considered to have the most suitable development region, parts of Alxa League and Bayannur City have great potential for combined wind and solar power generation. The research results could provide important technical and data support for capacity evaluation and power station location decision. [ABSTRACT FROM AUTHOR]

Sugarcane is an annual or perennial persistent rooted tropical and subtropical herb that grows in tropical and subtropical regions. As China's production ranks among the world's leading, sugarcane industry is an important part of agricultural economy in China. As the largest sugarcane production center in China, Guangxi is one of the most suitable areas for sugarcane cultivation in China and even in the world. Sugarcane industry, as an agricultural advantageous industry in Guangxi, not only has a significant image to the national economy of the region, but also is closely related to the issue of security of national sugar supply. Continuous cropping of sugarcane is very common in Guangxi, which is very helpful for the concentration selection of sugarcane samples. The wide application of satellite remote sensing monitoring technology has become an indispensable means of natural resources monitoring. Using optical satellite remote sensing image to identify and extract sugarcane planting areas is of great significance to quickly and conveniently grasp the information of sugarcane distribution and yield. In this paper, the precision of sugarcane extraction from GF1 and GF2 satellite images is analyzed by using deeplab V3 + model, the effect of optical remote sensing images with different resolution on sugarcane extraction accuracy was studied to provide better data support for dynamic monitoring of sugarcane planting. [ABSTRACT FROM AUTHOR]

Storm surge inundation hazards annually cause billions in economic losses globally, and millions of coastal residents live in danger. Properly understanding and assessing storm surge inundation are essential measures to guarantee the sustainable construction of coastal cities. In this paper, a differentiated urban risk semi-quantitative assessment method for storm surge inundation is proposed to evaluate the risk of storm surge hazard causing inundation to the coastal city. The Finite Volume Community Ocean Model (FVCOM) and the Jelesnianski model restore the historical storm surge cases to reveal hazards. The point of interest data and the urban land use and land cover data are utilized to assess the vulnerability of the coastal city, and a differentiated risk assessment method is proposed to evaluate the risks for urban facilities in the hazard. As an illustration, the method is utilized to assess storm surge Mangkhut in 2018 in Shenzhen, Guangdong Province, China. The vulnerability of the Shenzhen downtown area is assessed and designed as a map to visualize the strategic area. According to numerical simulation and inundation region mapping, the danger and the risk assessment map are made to intuitively present the distribution of the hazard-affected region. [ABSTRACT FROM AUTHOR]

With the development of society, the protection of historical buildings has been paid more and more attention. In recent years, China has increased the surveying and mapping of historical buildings buildings. This paper takes the historical building protection project in Zhejiang Province, China as an example. This paper introduces the method of combining UAV aerial photography, 3D laser scanning, close close-range photogrammetry and traditional surveying and mapping techniques. Mapping and information collection of historical buildings. And on this basis, the content and methods of quality inspection of historical architectural achievements are introduced. Guarantee the reliability of historical building archives and data quality quality. This will play a supporting role in the future repair and maintenance of historical buildings buildings. [ABSTRACT FROM AUTHOR]

The New Baseline Surface Radiation (NBSR) system was established at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station in 2013 to observe nine broadband radiation components, i.e. the global, direct, diffuse, and upwelling shortwave irradiance (GSWI, DSWI, DifSWI, and UpSWI); the photosynthetically active radiation (PAR); the ultraviolet irradiance (UVAI and UVBI); and the down- and upwelling longwave irradiance (DnLWI and UpLWI). To test the 1 min raw radiometric data, a Hybrid Algorithm for Radiation Data Quality Control (HARDQC) is presented in this study based on well-established methods, together with the solar irradiance dataset and the spectral features of the instrument bands. Subsequently, a NBSR dataset, which consists of radiation data at multiple timescales (i.e. 1 min, hourly, daily, monthly, monthly average hourly, and monthly average daily) over 2013–2022, is established and evaluated. Results show that more than 98.7 % of all radiation components passed the physical possibility test. The percentages of those that passed the extremely rare test are greater than 98.6 % for all radiation components except for the DnLWI (97.1 %). The percentages of those that passed the comparison test are greater than 83.3 % (GSWI), 78.3 % (DSWI), 81.7 % (DifSWI), 93.1 % (UpSWI), 88.9 % (PAR), 95.6 % (UVAI), 96.3 % (UVBI), 99.8 % (DnLWI), and 99.7 % (UpLWI), respectively. Due to data logger faults, removal of the instruments for calibration, and lightning strikes, some apparent data gaps in the upwelling radiation components (January 2015–August 2017) and all radiation components (December 2018; July to September 2021) were detected. Despite the existence of a few imperfections in the NBSR dataset, it is still reliable to apply it in many fields such as the validation of satellite products and numerical models, the investigation of relationships between radiation and atmospheric composition, and the detection of changes in the surface fluxes. The dataset described in this paper is available at 10.1594/PANGAEA.963330 (Quan et al., 2023b). [ABSTRACT FROM AUTHOR]

A massive eruption of Okmok volcano in Alaska has been recently discovered and precisely dated to have occurred in 43 BCE. Some Chinese climate records of 43–33 BCE in historical documents have been found that provide descriptions of observed environmental abnormities that appear to be consistent with the anticipated changes due to volcanic climate forcing. We provide full translation with discussions of the Chinese climate records that may be related to the Okmok eruption in this paper. We have converted ancient Chinese calendar dates to modern Gregorian dates and provided the latitudes and longitudes of the geographical locations mentioned in the records. We believe the detailed information contained in these records will be useful for further research on the climate impact of volcanic eruptions. [ABSTRACT FROM AUTHOR]

The atmospheric composition forecasting system used to produce the CAMS forecasts of global aerosol and trace gases distributions, IFS-COMPO, undergoes periodic upgrades. In this paper we describe the development of the future operational cycle 49R1, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12 for describing gas-aerosol partitioning processes for nitrate and ammonium and for providing diagnostic aerosol, cloud and precipitation pH values at global scale. This information on aerosol acidity influences tropospheric chemistry processes associated with aqueous phase chemistry and wet deposition. The other updates to cycle 49R1 include modifications to the description of Desert Dust, Sea-salt aerosols, Carbonaceous aerosols and the size description for the calculation of aerosol optics. The implementation of EQSAM4Clim significantly improves the partitioning of reactive nitrogen compounds decreasing surface concentrations of both nitrate and ammonium, which reduces PM2.5 biases for Europe, U.S. and China, especially during summertime. For aerosol optical depth there is generally a decrease in the simulated biases for wintertime, and for some regions an increase in the bias for summertime. Improvements in the simulated Ångström exponent is noted for almost all regions, resulting in generally a good agreement with observations. The diagnostic aerosol and precipitation pH calculated by EQSAM4Clim have been compared against results from previous simulations (for aerosol pH) and against ground observations (for precipitation pH), with the temporal distribution in the annual mean values showing good agreement against the regional observational datasets. The use of aerosol acidity only has a relatively smaller impact on the aqueous-phase production of sulphate when compared to the changes in gas-to-particle partitioning brought by the use of EQSAM4Clim. [ABSTRACT FROM AUTHOR]

Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface–subsurface hydrological model that employs a finite-volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://ghdc.ac.cn , last access: 1 September 2023), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis. [ABSTRACT FROM AUTHOR]

GaoFen-7 (GF-7) satellite mission is further expanding the very high resolution 3D mapping application. Carrying the first civilian Chinese sub-meter resolution stereo satellite sensors, GF-7 satellite was launched on November 7, 2019. With 0.65 meter resolution on backward view and 0.8 meter resolution forward view, GF-7 has been designed to meet the demand of natural resource monitoring, land surveying, and other mapping applications in China. The use of GF-7 for 3D city reconstruction is unfortunately restricted by the fixed large stereo view angle of forward and backward cameras with +26 and −5 degrees respectively which is not optimal for dense stereo matching in urban regions. In this paper we intensively evaluate the quality of the GF-7 datasets by performing a series of urban monitoring applications, including road detection, building extraction and 3D reconstruction. In addition, we propose a 3D reconstruction workflow which uses the land cover classification result to refine the stereo matching result. Six sub-urban regions are selected from the available datasets in the middle of Germany. The results show that basic elements in urban scenes like buildings and roads could be detected from GF-7 datasets with high accuracy. With the proposed workflow, a 3D city model with a visually observed good quality can be delivered. [ABSTRACT FROM AUTHOR]

At present, China is building a historic heritage conservation and management system from upper to lower level. Therefore, as the middle level, provincial level is becoming increasingly important in the management of famous towns and villages. However, due to the excessive quantities of resources, the lagging management skills and other influential factors, the provincial competent authorities still face a series of difficulties in implementing the conservation and management of historic cities, towns and villages. Focusing on the regulatory issues faced by provincial competent authorities, this study combined geospatial information technology with deep learning technology to explore the intelligent auxiliary identification of traditional texture for historic settlements and the provincial planning guidance method based on GIS evaluation. This paper put forward technical optimization suggestions for the stages of declaration and identification, conservation planning, supervision and inspection in provincial conservation and management. The intervention of new technologies provides a reference for provincial competent authorities in enhance conservation and regulatory capacity, and dealing with the situation of "having laws but lacking technology, and difficult to implement". [ABSTRACT FROM AUTHOR]

Since the conservation of living heritage sites such as historic towns and traditional villages often involves the coordination of multiple interests, the modern socio-economic development of heritage sites and the dominant participatory nature of local heritage communities become the main conflicts in practice. Using a combination of literature research and field cases, this paper firstly compares the history of the theoretical conservation approach to living heritage and explains its core concepts and basic methods. Secondly, it analyses the application and effectiveness of the community co-creation model in living heritage sites using the case of the ancient city of Pingyao in Shanxi Province, China. It is hoped that this will provide a more comprehensive understanding of the current development and future direction of heritage conservation methods, and further consider how to reconcile the historical and daily values of heritage in the conservation process. [ABSTRACT FROM AUTHOR]

Innovation leads the development of technology and society, so the position of innovation and entrepreneurship training to nurture innovative talents is very important. The paper explores the 'trinity' innovation and entrepreneurship talent training model of 'promoting learning through competition, education through competition, and innovation through competition', focus on high level innovation and entrepreneurship competitions, enhance the stickiness among colleges, educators, students, and social enterprises, and enable students to participate in extracurricular academic activities that are highly consistent with the needs of the industrial market. To achieve this goal, School of Remote Sensing and Information Engineering of Wuhan University puts forward a 'remote sensing +' training model. Through the two-year practice of this model, 136 students from different schools and majors participated in the training of the 'Remote Sensing +' Innovation and Entrepreneurship Center in Wuhan University, and won three gold awards in the China's largest innovation and entrepreneurship competition. Due to the characteristics of cross-integration of remote sensing science and technology itself, it has played a supporting role in innovation education, integrating other multi-specialized knowledge, and providing more entrepreneurial and employment opportunities for students of related majors. [ABSTRACT FROM AUTHOR]

To enhance the estimation accuracy of economic loss and casualty in seismic risk assessment, a high-resolution building exposure model is necessary. Previous studies in developing global and regional building exposure models usually use coarse administrative-level (e.g. country or sub-country level) census data as model inputs, which cannot fully reflect the spatial heterogeneity of buildings in large countries like China. To develop a high-resolution residential building stock model for mainland China, this paper uses finer urbanity-level population and building-related statistics extracted from the records in the tabulation of the 2010 population census of the People's Republic of China (hereafter abbreviated as the "2010 census"). In the 2010 census records, for each province, the building-related statistics are categorized into three urbanity levels (urban, township, and rural). To disaggregate these statistics into high-resolution grid level, we need to determine the urbanity attributes of grids within each province. For this purpose, the geo-coded population density profile (with 1 km × 1 km resolution) developed in the 2015 Global Human Settlement Layer (GSHL) project is selected. Then for each province, the grids are assigned with urban, township, or rural attributes according to the population density in the 2015 GHSL profile. Next, the urbanity-level building-related statistics can be disaggregated into grids, and the 2015 GHSL population in each grid is used as the disaggregation weight. Based on the four structure types (steel and reinforced concrete, mixed, brick and wood, other) and five storey classes (1, 2–3, 4–6, 7–9, ≥10) of residential buildings classified in the 2010 census records, we reclassify the residential buildings into 17 building subtypes attached with both structure type and storey class and estimate their unit construction prices. Finally, we develop a geo-coded 1 km × 1 km resolution residential building exposure model for 31 provinces of mainland China. In each 1 km × 1 km grid, the floor areas of the 17 residential building subtypes and their replacement values are estimated. The model performance is evaluated to be satisfactory, and its practicability in seismic risk assessment is also confirmed. Limitations of the proposed model and directions for future improvement are discussed. The whole modelling process presented in this paper is fully reproducible, and all the modelled results are publicly accessible. [ABSTRACT FROM AUTHOR]

The optimal choice of the planetary boundary layer (PBL) parameterization scheme is of particular interest and urgency to a wide range of scholars, especially for many works involving models. At present, there have been many works to evaluate the PBL schemes. However, little research has been conducted into a more comprehensive and systematic assessment of the performance capability of schemes in key regions of China, especially when it comes to the differences in the mechanisms of the schemes themselves, primarily because there is scarcely sufficient observational data, computer resources, and storage support to complete the work. In this companion paper (i.e., Part 1), four typical schemes (i.e., YSU, ACM2, BL, and MYJ) are selected to systematically analyze and evaluate near-surface meteorological parameters, PBL vertical structure, PBL height (PBLH), and turbulent diffusion coefficient (TDC) in five key regions of China (i.e., North China Plain, NCP; Yangtze River Delta, YRD; Sichuan Basin, SB; Pearl River Delta, PRD and Northwest Semi-arid region, NS) in different seasons (i.e., January, April, July, and October). The differences in the simulated 2 m temperatures between the nonlocal closure schemes are mainly affected by the downward shortwave radiation, but to compare the nonlocal closure schemes with the local closure schemes, the effect of sensible heat flux needs to be further considered. The 10 m wind speed is under the influence of factors like the momentum transfer coefficient and the integrated similarity functions at night. The wind speeds are more significantly overestimated in the plains and basin, while less overestimated or even underestimated in the mountains, as a result of the effect on topographic smoothing in the model. Moreover, the overestimation of small wind speeds at night is attributable to the inapplicability of the Monin–Obukhov similarity theory (MOST) at night. The model captures the vertical structure of temperature well, while the wind speed is outstandingly overestimated below 1000 m, largely because of the TDC. The difference between the MOST and the mixing length theory, PBLH, and Prandtl number is cited as the reason for the difference between the TDC of the YSU and ACM2 schemes. The TDCs of the BL and MYJ schemes are affected by the mixing length scale, which of BL is calculated on the basis of the effect of buoyancy, while MYJ calculates it with the consideration of the effect of the total turbulent kinetic energy. The PBLH of the BL scheme is better than the other schemes because of the better simulation results of temperature. In general, to select the optimal scheme, it is necessary to offer different options for different regions with different focuses (heat or momentum). The first focus is on the temperature field. The BL scheme is recommended for January in the NCP region, especially for Beijing, and the MYJ scheme is better for the other 3 months. The ACM2 scheme would be a good match for the YRD region, where the simulation differences between the four schemes are small. The topography of the SB region is more complex, but for most of the areas in the basin, the MYJ scheme is proposed, and if more stations outside the basin are involved, the BL scheme is recommended. The MYJ scheme is applied to the PRD region in January and April, and the BL scheme in July and October. The MYJ scheme is counseled for the NS region. The second focus is the wind field. The YSU scheme is recommended if the main concern is the near-surface layer, and the BL scheme is suggested if focusing on the variation in the vertical direction. The final evaluation of the parameterization scheme and uncertainties will lay the foundation for the improvement of the modules and forecasting of the GRAPES_CUACE regional model developed independently in China. [ABSTRACT FROM AUTHOR]

As earth system modeling develops ever finer grid resolutions, the inputting and outputting (I/O) of the increasingly large data fields becomes a processing bottleneck. Many models developed in China, as well as the community coupler (C-Coupler), do not fully benefit from existing parallel I/O supports. This paper reports the design and implementation of a common parallel input/output framework (CIOFC1.0) based on C-Coupler2.0. The CIOFC1.0 framework can accelerate the I/O of large data fields by parallelizing data read/write operations among processes. The framework also allows convenient specification by users of the I/O settings, e.g., the data fields for I/O, the time series of the data files for I/O, and the data grids in the files. The framework can also adaptively input data fields from a time series dataset during model integration, automatically interpolate data when necessary, and output fields either periodically or irregularly. CIOFC1.0 demonstrates the cooperative development of an I/O framework and coupler, and thus enables convenient and simultaneous use of a coupler and an I/O framework. [ABSTRACT FROM AUTHOR]

In recent years, urban floods occur frequently in China, and urban flood disaster has become one of the important reasons for the sustainable development of cities. Photogrammetry and remote sensing technology has the advantages of large observation range, large amount of information, fast speed, good real-time performance and strong dynamic. It plays an increasingly important role in flood control and disaster reduction, and has become an important support means for flood control and flood reduction in China. This paper mainly reviews the research status and development trend of photogrammetry and remote sensing technology in urban floods, summarizes the monitoring and evaluation of flood disasters, numerical simulation of flood disasters, and acquisition of hydrological characteristics, and prospects the future development of photogrammetry and remote sensing technology in flood disaster management in China. [ABSTRACT FROM AUTHOR]

The Sheshan Geomagnetic Observatory (International Association of Geomagnetism and Aeronomy (IAGA) code SSH), China was built in Xujiahui, Shanghai in 1874 and moved to Sheshan, Shanghai at the end of 1932. So far, the SSH has a history of nearly 150 years. It is one of the earliest geomagnetic observatories in China and one of the geomagnetic observatories with the longest history in the world. In this paper, we present the rescue and quality control (QC) of the historical data at the SSH from 1933 to 2019. The rescued data are the absolute hourly mean values (AHMVs) of declination (D), horizontal (H), and vertical (Z) components. Some of these data are paper-based records and some are stored in a floppy disk in BAS, DBF, MDB, and other file storage formats. After digitization and format transformation, we imported the data into the Toad database to achieve the unified data management. We performed statistics of completeness, visual analysis, outliers detects, and data correction on the stored data. We then conducted the consistency test of daily variation and secular variation (SV) by comparing the corrected data with the data of the reference observatory, and the computational data of the COV-OBS model, respectively. The consistency test reveals good agreement. However, the individual data should be used with caution because these data are suspicious values, but there is not any explanation or change registered in the available metadata and logbooks. Finally, we present examples of the datasets in discriminating geomagnetic jerks and study of storms. The digitized and quality-controlled AHMVs data are available at: 10.5281/zenodo.7005471 (Zhang et al., 2022). [ABSTRACT FROM AUTHOR]

With the process of urbanization in China, the urban waterlogging caused by rainstorms occurs frequently and often leads to serious damage to the natural environment, human life, and the city economy. Rapid detection of rainstorm and urban waterlogging disasters is an essential step to minimize these losses. Weibo, a popular microblog service in China, can provide many real-time Weibo posts for rapid detection. In this paper, we propose a method to identify microblogs with rainstorm and waterlogging information and apply them to waterlogging risk assessment. After pre-processing the microblog texts, we evaluate the performance of clustering (k -means) and classification (support vector machine, SVM) algorithms in the classification task. Apart from word vector features, we also introduce sentiment and publisher features for more real-time and accurate results. Furthermore, we build a waterlogging intensity dictionary to assess the waterlogging risk from the Weibo texts and produce a risk map with ArcGIS. To examine the efficacy of this approach, we collect Weibo data from two rainstorms and waterlogging disasters in Beijing city as examples. The results indicate that the SVM algorithm can be applied for a real-time rainstorm and waterlogging information detection. Compared to official-authentication and personal-certification users, the microblogs posted by general users can better indicate the intensity and timing of rainstorms. The location of waterlogging points is consistent with the risk assessment results, which proves our proposed risk assessment method can be used as a reference for timely emergency response. [ABSTRACT FROM AUTHOR]

Aviation emission accounting is the key to establishing market measures to control aviation pollutant emissions. Based on the fuel percentage method (FPM), this paper applies the improved BFFM2-FOA-FPM (Boeing Fuel Flow Method 2–First Order Approximation FPM) to calculate the emissions of six pollutants (CO 2 , CO, HC, NO x , SO 2 , and PM 2.5) between Chinese and foreign cities from 2014 to 2019, including CCD (climbing, cruising, and descending) emissions and LTO (landing and take-off) emissions. The error rate between the calculated results and the official data is about 2.75 %. The results show that the emissions of six pollutants changed before and after the proposal of the "Carbon Neutral Growth 2020" strategy (CNG2020 strategy). Although the total amount has increased, the average emission per tonne-kilometer of CO 2 , CO, HC, NO x , SO 2 , and PM 2.5 has decreased by 17.77 %, 17.26 %, 25.15 %, 14.32 %, 17.77 %, and 16.35 %, respectively. The results of this paper can provide a data basis and method reference for implementing the CNG2020 strategy and realizing global carbon emission reduction goals. The dataset is available from https://doi.org/10.6084/m9.figshare.20071751.v1 (Cui., 2022). [ABSTRACT FROM AUTHOR]

This paper reveals the dynamic movement characteristics of thunderstorms using total lightning data obtained from very low- and low-frequency (VLF/LF) location measurements. Eight thunderstorms, which were evenly distributed in the morning, midday, afternoon and evening, are selected to compare the different kinematic features over the Pearl River Delta (PRD) region in the south of China from 17 to 23 May 2014. The connected-neighbourhood labelling method is used to identify lightning clusters and obtain the centroids. Significant characterisation parameters are put forward as metrics to reveal the kinematic features of thunderstorms, including the duration time, valid area (VA), movement velocity, movement direction and farthest distance in longitude and latitude during the life cycle of the storm. A common trend is that the storms initiate in the west of the PRD region, moving to the east and disappearing after the thunderstorm travels around 106.5 km in longitude. There are two kinds of distributions to depict the property of the valid area, which are one-peak distribution with the maximum in the mature stage and two-peak distribution with a relatively smaller peak in the early time of the storm. The velocity does not show the same trend as the variation in VA which shows a steady increase or decrease during the lifetime of thunderstorms. The biggest VA and highest velocity are 891 km 2 occurring on the evening of 17 May and 204.8 km h -1 occurring on the morning of 20 May. The 19 May evening storm was the weakest, with the maximum VA and velocity being 253 km 2 and 115.3 km h -1 , respectively. The motion of eight storms shows a distinct pattern as the spread of direction distributes tightly in the range of 0–180 ∘. The movement characteristics of thunderstorms and the associated parameters may help to improve the nowcasting and forecasting system of thunderstorms in the warm season. [ABSTRACT FROM AUTHOR]

Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, it is still challenging to produce a large-scale BRA due to the rather tiny sizes of individual buildings. From the viewpoint of classification methods, conventional approaches utilize high-resolution aerial images (metric or submetric resolution) to map BRA; unfortunately, high-resolution imagery is both infrequently captured and expensive to purchase, making the BRA mapping costly and inadequate over a consistent spatiotemporal scale. From the viewpoint of learning strategies, there is a nontrivial gap that persists between the limited training references and the applications over geospatial variations. Despite the difficulties, existing large-scale BRA datasets, such as those from Microsoft or Google, do not include China, and hence there are no full-coverage maps of BRA in China yet. In this paper, we first propose a deep-learning method, named the Spatio-Temporal aware Super-Resolution Segmentation framework (STSR-Seg), to achieve robust super-resolution BRA extraction from relatively low-resolution imagery over a large geographic space. Then, we produce the multi-annual China Building Rooftop Area (CBRA) dataset with 2.5 m resolution from 2016–2021 Sentinel-2 images. CBRA is the first full-coverage and multi-annual BRA dataset in China. With the designed training-sample-generation algorithms and the spatiotemporally aware learning strategies, CBRA achieves good performance with a F1 score of 62.55 % (+10.61 % compared with the previous BRA data in China) based on 250 000 testing samples in urban areas and a recall of 78.94 % based on 30 000 testing samples in rural areas. Temporal analysis shows good performance consistency over years and good agreement with other multi-annual impervious surface area datasets. STSR-Seg will enable low-cost, dynamic, and large-scale BRA mapping (https://github.com/zpl99/STSR-Seg , last access: 12 July 2023). CBRA will foster the development of BRA mapping and therefore provide basic data for sustainable research (Liu et al., 2023; 10.5281/zenodo.7500612). [ABSTRACT FROM AUTHOR]

Data assimilation techniques are one of the most important ways to reduce the uncertainty in atmospheric chemistry model input and improve the model forecast accuracy. In this paper, an ensemble optimal interpolation assimilation (EnOI) system for a regional online chemical weather numerical forecasting system (GRAPES_Meso5.1/CUACE) is developed for operational use and efficient updating of the initial fields of chemical components. A heavy haze episode in eastern China was selected, and the key factors affecting EnOI, such as localization length scale, ensemble size, and assimilation moment, were calibrated by sensitivity experiments. The impacts of assimilating ground-based PM 2.5 observations on the model chemical initial field PM 2.5 and visibility forecasts were investigated. The results show that assimilation of PM 2.5 reduces the uncertainty in the initial PM 2.5 field considerably. Using only 50 % of observations in the assimilation, the root mean square error (RMSE) of initial PM 2.5 for independent verification sites in mainland China decreases from 73.7 to 46.4 µ g m -3 , and the correlation coefficient increases from 0.58 to 0.84. An even larger improvement appears in northern China. For the forecast fields, assimilation of PM 2.5 improves PM 2.5 and visibility forecasts throughout the time window of 24 h. The PM 2.5 RMSE can be reduced by 10 %–21 % within 24 h, and the assimilation effect is the most remarkable in the first 12 h. Within the same assimilation time, the assimilation efficiency varies with the discrepancy between model forecasts and observations at the moment of assimilation, and the larger the deviation, the higher the efficiency. The assimilation of PM 2.5 further contributes to the improvement of the visibility forecast. When the PM 2.5 increment is negative, it corresponds to an increase in visibility, and when the PM 2.5 analysis increment is positive, visibility decreases. It is worth noting that the improvement of visibility forecasting by assimilating PM 2.5 is more obvious in the light-pollution period than in the heavy-pollution period. The results of this study show that EnOI may provide a practical and cost-effective alternative to the ensemble Kalman filter (EnKF) for the applications where computational cost is the main limiting factor, especially for real-time operational forecast. [ABSTRACT FROM AUTHOR]

The South China Sea Operational Oceanography Forecasting System (SCSOFS), constructed and operated by the National Marine Environmental Forecasting Center of China, has been providing daily updated hydrodynamic forecasting in the South China Sea (SCS) for the next 5 d since 2013. This paper presents recent comprehensive updates to the configurations of the physical model and data assimilation scheme in order to improve the forecasting skill of the SCSOFS. This paper highlights three of the most sensitive updates: the sea surface atmospheric forcing method, the discrete tracer advection scheme, and a modification of the data assimilation scheme. Intercomparison and accuracy assessment among the five sub-versions were performed during the entire upgrading process using the OceanPredict Intercomparison and Validation Task Team Class 4 metrics. The results indicate that remarkable improvements have been made to the SCSOFSv2 with respect to the original version (known as SCSOFSv1). The domain-averaged monthly mean root-mean-square errors of the sea surface temperature and sea level anomaly have decreased from 1.21 to 0.52 ∘ C and from 21.6 to 8.5 cm, respectively. [ABSTRACT FROM AUTHOR]

Forecasting the occurrence of hydrological drought according to a forecasting system is an important disaster reduction strategy. In this paper, a new drought prediction model adapted to changing environments was constructed. Taking the Luanhe River basin in China as an example, first, nonstationarity analysis of hydrological sequences in the basin was carried out. Then, conditional distribution models with the human activity factor as an exogenous variable were constructed to forecast hydrological drought based on meteorological drought, and the results were compared with the traditional normal distribution model and conditional distribution model. Finally, a scoring mechanism was applied to evaluate the performance of the three drought forecasting models. The results showed that the runoff series of the Luanhe River basin from 1961 to 2010 were nonstationary; moreover, when human activities were not considered, the hydrological drought class tended to be the same as the meteorological drought class. The calculation results of the models involving HI as an exogenous variable were significantly different from the models that did not consider human activities. When the current drought class tended towards less severe or normal, the meteorological drought tended to turn into more severe hydrological drought with the increase in human index values. According to the scores of the three drought forecasting models, the conditional distribution models involving the human index can further improve the forecasting accuracy of drought in the Luanhe River basin. [ABSTRACT FROM AUTHOR]

Vernacular architecture is constructed spontaneously and used by ordinary people, and they have been subject to constant modifications by changes in ownership, functions, and constructions according to people's living demands. In the last two decades, many cases which were constructed in the traditional way and survived China's overwhelming urbanization process have been listed in the preservation list with the increasing interest in cultural heritage. Regarding this type of heritage that is a result of multiple modifications, many restorations aim to recover the changed architecture to traditional formal integrity. Due to the rarity of detailed written or image references, some restorations become a kind of "creation" and fall into the cliché of stylistic restoration, which is posing an artificial risk to their historical values. Facing this difficulty, the authors turn to an important historical record - the DISP (declassified intelligence satellite photographs): since 1995, the US has declassified three batches of satellite photographs that were taken from the 1960s to the 1980s, before China's great change in Reform and Opening Up, almost covering all of China. These photographs are an important historical photographic record of the territory as well as the vernacular architecture scattered on it. This paper uses two cases to illustrate the important role of these photographs in China's built vernacular heritage: both as a reference to restoration and as a valuable source for historical research. The authors also discuss the prospect and limitations of this source. [ABSTRACT FROM AUTHOR]

Vernacular architecture has always captured professionals' attention for its unique approaches to construction, its typical way of integrating man and nature and the way it is used. Being a combination of traditional courtyard houses in China with townhouses in the West, Lilong houses or Shikumen houses are representatives of Shanghai vernacular housing. In terms of applying UAV oblique photogrammetry to preserving and documenting historic architectures, some studies have been done in China, but scant literatures are found on Lilong houses. This research is an attempt to preserve and document Lilong houses by adopting UAV in 3D modelling, to explore a rational approach for the application of UAV oblique photogrammetry to narrow lanes and to establish a collection of models to document the ongoing changes as well as nuances of each building more carefully. In this research, Meilan Lane is chosen as an example. Aided by AutoCAD drawings, flight routes and angles are designed. parameters for each flight are pre-set in GoodStation to send quantified instructions to the drone. A total of 4,531 photos are gathered and 4 models are built by ContextCapture Master. Two models with flight height at 40m and 115m are generated for macro-level study and two models with flight height from 20m to 32m are for micro-level study. Through this research, this paper elaborates the pros and cons of using UAV techniques in documenting Lilong residences and tries to find an applicable approach to building other Lilong complex models for more comprehensive studies in the long run. [ABSTRACT FROM AUTHOR]

Cold surge (CS) is considered a favorable weather process to improve air quality and is widely recognized. However, there is no detailed study on the differences in the dispersion ability of different types of CSs in relation to haze days in eastern China (HD EC). This paper uses the hierarchical clustering algorithm to classify the cool-season (November to February of the following year) CSs across eastern China into blocking CSs and wave-train CSs and compares their influences on the number of HD EC from 1980 to 2017. Results show that the wave-train CSs can significantly improve the visibility in eastern China and generally improve air quality for about 2 d longer than the blocking CSs, which indicates that the blocking CSs have a weaker ability to dissipate HD EC compared with the wave-train CSs. The CSs affect the HD EC by changing meteorological elements like thermal inversion potential, horizontal surface wind, sea level pressure (SLP), and surface air temperature (SAT). A period of 4 d after the outbreak of CSs, the variations of thermal inversion potential and horizontal surface wind of two types of CSs tend to be consistent. However, the negative SAT anomalies and the positive SLP anomalies caused by the blocking CSs lasted shorter than those caused by the wave-train CSs, forming favorable conditions for the rapid growth of HD EC. Furthermore, results show that in recent years, especially after the 1990s, the frequency of wave-train CSs has decreased significantly, while the frequency of blocking CSs has slightly increased, indicating that the overall ability of CSs to dissipate HD EC has weakened in general. This work may provide reference for the future formulation of haze control policies in East Asia. [ABSTRACT FROM AUTHOR]

Nitrate is an increasingly important component of fine particulate matter (PM2.5) in Chinese cities. The production of nitrate is not only related to the abundance of its precursor, but it is also supported by the atmospheric photochemical oxidants, raising a new challenge for the current emission control actions in China. This paper uses comprehensive measurements and a regional meteorology–chemistry model with optimized mechanisms to establish the nonlinear responses between particulate nitrate and the emission controls of nitrogen oxides (NOx) in the megalopolises of China. Nitrate is an essential component of PM2.5 in eastern China, accounting for 9.4 %–15.5 % and 11.5 %–32.1 % of the PM2.5 mass for the warm and cold seasons. The hypothetical NOx emission reduction scenarios (- 10 % to - 80 %) during summer–autumn result in almost linearly lower PM2.5 by - 2.2 % in Beijing–Tianjin–Hebei (BTH) and - 2.9 % in Yangtze River Delta (YRD) per 10 % reduction of NOx emissions, whereas they lead to a rather complicated response of PM components in winter. Wintertime nitrate is found to increase by + 4.1 % in BTH and + 5.1 % in YRD per 10 % reduction of NOx emissions, with nearly unchanged nitric acid (HNO3) and higher dinitrogen pentoxide (N 2 O 5) intermediate products produced from the increased atmospheric oxidant levels. An inflexion point appears at 30 %–50 % NOx emission reduction, and a further reduction in NOx emissions is predicted to cause - 10.5 % reduction of nitrate for BTH and - 7.7 % for YRD per 10 % reduction of NOx emissions. In addition, the 2012–2016 NOx control strategy actually leads to no changes or even increases of nitrate in some areas (8.8 % in BTH and 14.4 % in YRD) during winter. Our results also emphasize that ammonia (NH3) and volatile organic compounds (VOCs) are effective in controlling nitrate pollution, whereas decreasing the sulfur dioxide (SO2) and NOx emissions may have counterintuitive effects on nitrate aerosols. This paper helps understand the nonlinear aerosol and photochemistry feedbacks and defines the effectiveness of proposed mitigations for the increasingly serious nitrate pollution in China. [ABSTRACT FROM AUTHOR]

Co-hosted by Tsinghua University, ICOMOS China, Tsinghua Heritage Institute for Digitization, and Beijing University of Civil Engineering and Architecture, the CIPA2021 symposium (28th biennial symposium of the international scientific committee for documentation of cultural heritage) was held online successfully at Tsinghua University during August 28th to September 1st, 2021. This is the first time CIPA held a biennial symposium in mainland of China and in online format since its establishment in 1968. The theme of the symposium is "Great Learning & Digital Emotion". During the 5-day online symposium, opening ceremony & keynote-speech session-1, education session, outstanding youth forum, 12 sub-theme forums, and keynote-speech session-2 & closing ceremony were organized, which included 130 more presentations and attracted more than 1,000 online participants from more than 30 countries on five continents to conduct academic exchanges and seminars.Among all the presentations, four of them are keynote speeches. These include: 1) "deep learning for cultural heritage" by Professor Christian Heipke (President of ISPRS), 2) "space technology in support of world heritage" by Professor Huadong Guo (Academician of Chinese Academy of Sciences), 3) "digital culture heritage and territorial spatial planning — China's practice and perspective" by Professor Jun Chen (Academician of Chinese Academy of Engineering), and 4) "documentation — foundation of any heritage policy" by Professor Teresa Patrício (President of ICOMOS). Besides these keynotes, all other 120 more presentations are selected from the publication papers of ISPRS Annals and Archives.The progress and prospects of research and practice of digital cultural heritage all over the world can be summarized as theory, technology, and method of CHIM that will be described as follows. Basically, CHIM refers of Cultural Heritage Information Modelling. However, based on CIPA2021 symposium and the paper publications, CHIM is not just Modelling and Model, but the current progress and future prospects of digital cultural heritage research and practice, such as "C" represents Collection, Collaboration, Coordination, and Conference; "H" represents High-Speed, High-Quality, High-Tech. and High-Education; "I" represents Integration, Interpret, International, and Intelligent; and "M" represents Multi-Classes, Methodology, Multidisciplinary, and Management. And all of the aspects are included into the following table (see Table 1) and it is clear that CHIM is not only a Model for documentation but also a platform for future intelligent conservation. [ABSTRACT FROM AUTHOR]

Engineering education accreditation is a new engineering education model promoted by the Ministry of Education in China, which aims to cultivate the ability of students to solve complex engineering problems. A course named "integrated practicum of Geomatics" (IPG) was innovatively designed for the undergraduate specialty of Geomatics. This course involves in the current advanced technologies to lay a solid foundation for students to engage in Geomatics through aerial photography technology design, unmanned aerial vehicle photography, control point selection, GNSS-RTK control point survey, photo interpretation and sketch, 3D (DEM, DOM, and DLG) product production, inputting 3D products into the Geodatabase, and GIS application software development. This paper expounds on the objectives, contents, evaluation methods, and continuous improvement of IPG. [ABSTRACT FROM AUTHOR]

In the era of digital twins, high-definition 3D point clouds of cultural relics, such as the bronze drums of ancient Southeast Asia and China, are increasingly available as digital heritage. This study applies an automatic hierarchical clustering method to compare and cluster 14 unstructured 3D models of frogs on drums based on the dissimilarity metric of the minimum error from 2,000 iterations of global registration. Furthermore, this study compares two forms of 3D presentation: surface points and 3D shape skeletons. The experimental results on 14 high-definition frogs showed that four groups – three-legged with baby, four-legged with baby, three-legged without baby, and four-legged without baby – were consistently (TPR = 0.857) detected, regardless of the 3D presentation using point clouds or shape skeletons. Both basic surface points and advanced shape skeleton effectively clustered 3D heritage details for heritage digital twins and advanced heritage documentation. The findings also imply that geospatial analytics using either surface 3D point clouds or skeleton can shed light on unsupervised learning and quantitative understanding of unstructured point clouds of numerous cultural heritages. [ABSTRACT FROM AUTHOR]

The alpine grassland ecosystem accounts for 53 % of the Qinghai–Tibet Plateau (QTP) area and is an important ecological protection barrier, but it is fragile and vulnerable to climate change. Therefore, continuous monitoring of grassland aboveground biomass (AGB) is necessary. Although many studies have mapped the spatial distribution of AGB for the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to estimate the grassland AGB on the QTP from 2000 to 2019. The innovations were as follows: (1) in terms of ground data acquisition, spatial-scale matching among the traditional ground samples, UAV photos, and MODIS pixels was considered. A total of 906 pairs between field-harvested AGB and UAV sub-photos and 2602 sets of MODIS pixel-scale UAV data (over 37 000 UAV photos) were collected during 2015–2019. Therefore, the ground validation samples were sufficient and scale-matched. (2) In terms of model construction, the traditional quadrat scale (0.25 m 2) was successfully upscaled to the MODIS pixel scale (62 500 m 2) based on the random forest and stepwise upscaling methods. Compared with previous studies, the scale matching of independent and dependent variables was achieved, effectively reducing the impact of spatial-scale mismatch. The results showed that the correlation between the AGB values estimated by UAV and MODIS vegetation indices was higher than that between field-measured AGB and MODIS vegetation indices at the MODIS pixel scale. The multi-year validation results showed that the constructed MODIS pixel-scale AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m -2. Our dataset provides an important input parameter for a comprehensive understanding of the role of the QTP under global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Terre.tpdc.272587; H. Zhang et al., 2022). [ABSTRACT FROM AUTHOR]

The dynamic change mode of land cover not only reflects the change characteristics of the land cover itself, but also reflects the influence exerted by external factors. Empirical mode decomposition is an effective time-frequency analysis method for nonlinear and nonstationary signals. In this paper, empirical mode decomposition firstly uses to each pixel time series to get its intrinsic mode functions, each intrinsic mode function relates to change mode with some time scale, clustering those intrinsic mode functions over the image, and different land cover change modes are obtained. The experimental dataset used is MODIS subset from the Jan 2011 to Dec 2020, located in the Ningxia Hui Autonomous Region, China. The clustering results of half-annual cycle and one-year cycle of intrinsic mode functions show the influence of local climate and policy factors on land cover change. [ABSTRACT FROM AUTHOR]

In the era of earth observation big data, a new paradigm for the unified management of natural resource conservation and utilization has been established in China. The current focus of natural resources monitoring is on fully leveraging the value of big data in geospatial and temporal dimensions to support new work on natural resources management. An overall framework for a spatial-temporal big data analysis platform is proposed in this paper, which explores innovative technologies such as heterogeneous cloud collaborative services, business capabilities, data resources, and open sharing. Utilizing private cloud computing resources, the platform establishes collaborative services for storage cloud, computing cloud, and database cloud, demonstrating its ability to perform large-scale online computation of spatial-temporal data. Mainstream spatial data analysis computing methods such as vector computing, grid computing, and other grid computing methods are also established, and an open service model for interdisciplinary applications is explored. The feasibility of the proposed platform is verified using nationwide land cover data of 260 million surface area classifications, indicating its great potential for application. [ABSTRACT FROM AUTHOR]

Since the 1950s, heavy plowing of Mollisols, combined with a lack of organic matter intake, has resulted in severe soil degradation in northeast China. The use of biochar in combination with fertilizer is a sustainable method of improving soil quality. In this paper, we conducted field experiments to explore the response of the stability mechanism of the soil aggregate, the dynamic properties of organic carbon, and changes in the microbial community structure to biochar. The biochar input levels were C1, C2, and C3 (9.8, 19.6, and 29.4 Mg C ha -1 , respectively), while the nitrogen (N) fertilizer rates were N1/2 (300 kg N ha -1) and N (600 kg N ha -1). Results indicated that biochar combined with N fertilizer effectively increases soil carbon storage and aggregates stability (P<0.05). And C2N treatment increased the aggregate contents of the >2 mm and 0.25–2 mm fractions by 56.59 % and 23.41 %, respectively. The phospholipid fatty acid (PLFA) analysis revealed that microbial community structure was effectively improved with biochar combined with N fertilizer application (P<0.05). The F/B ratio increased by 25.22 % and the gram-positive (Gm +) to gram-negative (Gm -) ratio by 4.65 % under the C2N1/2 treatment. This study concluded that the response of Mollisols to biochar is primarily determined by the interplay of aggregate, organic carbon, and microorganisms. Therefore, the use of biochar combined with N fertilizer might mitigate soil degradation of Mollisols under an optimal application ratio, but the underlying mechanism still requires further exploration. This study will provide a scientific basis for the conservation and sustainable utilization of Mollisols resources. [ABSTRACT FROM AUTHOR]

With the rapid development of remote sensing, geographic information (GI), artificial intelligence (AI) and other technologies, China is continuously strengthening emergency rescue innovation and numerous breakthroughs related to emergency rescue technologies and equipment which have been made through the National Key Research and Development Program. This paper first proposed and illustrated the concept, connotation and characteristics of integrated space-air-ground emergency rescue communication command from the perspective of mapping and geographic information, and then analysed the challenges faced by the integrated space-air-ground emergency rescue communication command in China. Subsequently, a space-air-ground emergency rescue command theoretical system is introduced by combining broadband emergency communication, accurate acquisition of real-time disaster information, disaster situation and scenario projection, emergency communication command services, and resilient emergency rescue command. The proposed integrated space-air-ground emergency communication and command design concept will be applied for China's disaster emergency rescue and command in the future to comprehensively improve the emergency rescue communication and command capability in China. [ABSTRACT FROM AUTHOR]

Storm surge is a common nature disaster in China southern coastal area, which usually causes heavy human life and economic losses. With the economic development and population concentration of coastal cities, the storm surges may result in more impacts and damage in the future. Therefore, it is of vital importance to conduct risk assessment to identify high-risk areas and evaluate economic losses. However, quantitative study of storm surge risk assessment in undeveloped areas of China is difficult, since there is a lack of building characters and damage assessment data. Aiming at the problem of data missing in undeveloped areas of China, this paper proposes a methodology for conducting storm surge risk assessment quantitatively based on deep learning and geographic information system (GIS) techniques. Five defined storm surge inundation scenarios with different typhoon return periods are simulated by coupled FVCOM-SWAN model, the reliability of which is validated using official measurements. Building footprints of the study area are extracted through TransUNet deep learning model and Remote Sensing Image (RSI), while building heights are obtained through Unmanned Aerial Vehicle (UAV) measurement. Subsequently, economic losses are quantitatively calculated by combing the adjusted depth-damage functions and overlay analysis of the buildings exposed to storm surge inundation. Zonation maps of the study area are illustrated to display the risk levels according to the economic losses. The quantitative risk assessment and zonation maps can help the government to make storm surge disaster prevention measures and optimize land use planning, and thus to reduce the potential economic losses of the coastal area. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) technology is an important method to generate digital surface model (DSM). The past studies on space-borne derived DSM most focused on the elevation correction, due to the relative low resolution of DSM product. As a large number of high-resolution satellite data emerge, the horizontal discrepancies are needed to be considered. This paper proposes a DSM block adjustment method with horizontal constraints, aimed at eliminating the horizontal errors that exist between multiple DSM scenes with overlaps, achieving high precision and consistency in both the horizontal and vertical dimensions. Using ICESat-2 ATL08 point clouds as absolute elevation control and a reference DSM for horizontal control, the adjustment equations are constructed based on the constraint of tie points and controls. The experiment selects 7 image pairs of China TH2-01 SAR satellite, corresponding ICESat-2 ATL08 point and AW3D30 as reference DEM. The block adjustment results show that the proposed method improves the absolute vertical accuracy from 3.78 m to 2.56 m and reduces the average horizontal standard deviation between the InSAR derived DSMs and the reference AW3D30 from 15.31 m to 9.08 m. [ABSTRACT FROM AUTHOR]

With the advancement of China's satellite technology, the HuanJingJianZai-2 A/B (HJ-2 A/B) satellites, equipped with whisk-broom infrared sensors, represent a significant leap forward in environmental monitoring and Earth observation capabilities. This technological leap, however, introduces new challenges in calibration. The unique structure of the HJ-2 A/B infrared spectroradiometer (IRS) necessitates innovative calibration techniques, as traditional methods primarily focused on exterior orientation parameters (EOPs) and often overlooked the importance of interior orientation accuracy, which is essential for accurate multispectral band registration and color rendering. Addressing this gap, we introduce an innovative multi-focal-plane-array joint calibration method specifically designed for whisk-broom cameras. Our method involves selecting a master band from each focal plane array for accurate focal length calibration and deriving ground control points from image matching and altitude interpolation for comprehensive bundle adjustment. This adjustment refines EOPs and interior orientation parameters (IOPs), ensuring globally optimal EOPs and enhanced IOPs calibration stability. The application of our method to the HJ-2 A/B IRS yielded substantial improvements in georeferencing and band registration accuracies, surpassing traditional methods. This paper details the multi-focal-plane-array joint calibration method, describes the IRS and experimental setup, presents the experimental results, and concludes with the implications and potential applications of our findings. [ABSTRACT FROM AUTHOR]