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Provincial-level natural resources survey and monitoring play a crucial role in the national natural resources survey and monitoring system. Constructing a provincial-level system is essential for effectively implementing the national survey and monitoring work. However, the construction of such a system in Guangdong Province, a typical tropical and subtropical coastal region with diverse natural resources and distinctive geographical characteristics, presents significant challenges. This study focuses on addressing several problems encountered in Guangdong Province, including the lack of optical image data due to the region's cloudy and rainy climate, fragmented monitoring patches resulting from complex terrain and landforms, rapid changes in the surface caused by high-intensity human activities, and the difficulty in managing a wide range of monitoring objects. By clarifying the goals of natural resources survey and monitoring, this study systematically reviews the status and practical needs of a provincial-level survey and monitoring work and summarizes the key issues in the construction of the survey and monitoring system. This study proposes an overall construction plan for a provincial-level system that includes establishing a unified basic platform, which aids in implementing a fast and coordinated monitoring mode, developing flexible and efficient application services, and strengthening intensive and refined support environments. It also analyzes critical challenges, such as organizational model restructuring, accurate object recognition, and technological integration. Finally, this study presents a path to achieve the provincial-level natural resources survey and monitoring, focusing on object-based businesses, information implementation, technological intelligence, targeted evaluation, and service knowledge. Regarding investigation targets, this study emphasizes adopting a "business-oriented" approach, enhancing the alignment of investigation and monitoring with precise business strategies and facilitating bidirectional feedback. Regarding organizational implementation, this study emphasizes strengthening support through "information technology," establishing provincial-level coordination and a municipal and county-level division of labor model, commonly referred to as the "general-subordinate-general" organizational structure. Regarding monitoring technology, this study emphasizes adopting an "intelligence-led" approach; it is primarily centering on pivotal technological research related to collaborative perception, intelligent processing, integrated management, and intelligent services. Regarding analysis and evaluation, this study emphasizes a "target-oriented" approach, centered around supporting economic and social development and optimizing natural resource management through comprehensive analysis and evaluation. Regarding application services, this study emphasizes delivering "knowledge-based" services and exploring the development of a model that encompasses the knowledge ontology of natural resource investigation and monitoring. This study promotes the orderly construction of a survey and monitoring system in Guangdong Province, provides a scientific basis for provincial-level natural resource surveys and monitoring work and serves as a reference for the construction of survey and monitoring systems in other regions.

Guangdong Province is actively building a national marine ecological civilization construction demonstration area, and it is crucial to conduct research and assessment on the resources and environmental carrying capacity of the coastal zones. Under the guidance of the "double evaluation" of the territorial space planning, this study takes the coastal areas of Guangdong Province, namely the 45 coastal counties and the sea areas within the scope of the marine functional areas of Guangdong Province as the research scope It proceeded from the functional orientation of ecological protection, agricultural production, and urban construction to construct the evaluation index system of the resources and environmental carrying capacity of the coastal zone of Guangdong Province. In 2019, based on the evaluation index system, a comprehensive evaluation of the resources and environmental carrying capacity of the coastal zone of Guangdong Province was performed using integrate and integrate multi-scale spatial data, remote sensing, geographic information system, and other technical means and its resource and environmental carrying status were quantitatively revealed. The results showed that approximately 84% of the land area in the coastal zone and more than 1/2 of the sea areas (cities, districts) have overall good comprehensive bearing status of the resources and environmental carrying capacity, which is mainly characterized by loadability. The scale of human socio-economic activities in most regions is within the range of the resources and environmental carrying capacity; however, in some regions it exceeds the threshold, particularly in regions distributed in the Pearl River Delta. Specifically, under the guidance of the ecological protection function, 60.05% of the land area and 14 counties (cities, districts) of the sea area are in a loadable state, mainly distributed in the east and west wings of the urban agglomeration in the Greater Bay Area of Guangdong, Hong Kong, and Macao. Under the guidance of the agricultural production function, the carrying capacity of land agriculture is relatively severe, mainly critical overload, while the number of counties (cities, districts) that can be carried by sea agricultural production is relatively small. Compared with the functions of ecological protection and agricultural production, the area of the loadable area under the urban construction function is the highest, with 86.93% of the land area and 29 counties (cities, districts) of the sea area in the loadable state, and only 3.01% of the land area and 10 counties (cities, districts) of the sea area in the overloaded state. Guangdong Province is a major province of marine economy. As an important place for economic activities in the coastal zone, the ocean is a strategic place to promote the high-quality development of Guangdong Province.. Under the mode of sustainable development, there is still a large scope for the development of resources and environmental carrying capacity in the coastal zone of Guangdong Province. On the premise of ensuring the stability of resources and environment, the loadable area can be further developed and utilized selectively, the allocation of resource elements can be optimized, and a new management mode of high-level protection and efficient utilization of resources can be built. Thus the research results provide a basis for the compilation of territorial space planning and the optimization of territorial space development and protection pattern in Guangdong Province.

Since the reform and opening up, China has achieved great progress in industrialization and urbanization. However, due to the long-term implementation of the economic development model of "emphasizing cities over township" and the urban-rural dual system, the gap between urban and rural development is constantly increasing, and the problems of land resource allocation and contradiction between man and land in rural areas have gradually emerged. Rural homestead is the main place where farmers live, with unique land use characteristics. The information such as the structure and utilization status of houses on it can clearly reflect the local rural economic situation and homestead problems. Therefore, understanding the characteristics and differences of rural homestead housing is of great significance to understand and grasp the situation and problems of rural development and promote the implementation of rural revitalization strategy. Based on the rural cadastral survey data of Guangdong Province and combined with GIS spatial analysis method, this paper analyzes the characteristics of rural homestead housing. The results show that: (1) The rural housing in the study area is relatively low, mainly with one floor, supplemented by two and three floors, mainly brick and mixed structure, supplemented by concrete structure, and there is a certain correlation between the number of floors and the spatial distribution of the structure. Among them, the proportions of one storey house, brick structure and mixed structure house are 59.42%, 37.71% and 42.60% respectively. (2) There are obvious regional differences in the number of floors and structural spatial distribution of rural homestead housing in the study area. Low-rise, brick and mixed structure houses are mainly distributed in remote areas such as northern and western Guangdong, and high-rise and concrete structure houses are mostly concentrated in the Pearl River Delta. (3) The vacancy and idleness of rural homestead housing in the study area are common. The average vacancy rate and idle rate are 6.75% and 5.66% respectively, and the rate of one househould with multiple houses reaches 23.88%. The phenomenon of rural hollowing is obvious, and 50.87% of the villages in districts and counties are above the moderate hollowing, mainly distributed in the fringe area of the Pearl River Delta and Meizhou in northern Guangdong. At the same time, there is a certain phenomenon that the average household homestead area exceeds the standard. (4) The characteristics of rural housing utilization in Guangdong Province are affected by factors such as policies, economy, transportation, physical geographical condition and local customs. And the influencing factors in different regions are different, such as the special folk customs have a greater impact on rural housing in eastern Guangdong, and the economic situations and urban siphon have a greater impact on the characteristics of rural housing in the edge of the Pearl River Delta. There are obvious spatial differences in the utilization of rural homestead housing in Guangdong Province, and there are many idle homestead resources. There is great potential for the optimization and adjustment of rural construction land for rural revitalization.

Breaking the theoretical limits of external quantum efficiency (EQE) and obtaining quantum dot light‐emitting diodes (QLEDs) with high brightness, high efficiency, and low operating voltage is the basis of commercial applications in display and illumination. Devices with an EQE of over 20% can be realized by carrier equilibrium injection and light out‐coupling enhancement. However, it is difficult to synergistically enhance the performance of QLEDs by combining the light out‐coupling and the hole enhancement injection. In this paper, the injection enhancement of holes and the light out‐coupling are realized simultaneously only by nano‐imprint lithography (NIL). The π─π bonds of the PEDOT: PSS film treated by NIL demonstrate a molecular orientation perpendicular to the substrate, which improves the injection of the hole. The PEDOT: PSS patterned by NIL further presents improved light out‐coupling. Consequently, the EQE of red‐QLED is 30.42% with a synergistic enhancement factor of 61%, accompanying the maximum brightness increased by 8% to 125 200 cd m−2 at 4.6 V (Vturn‐on, 1.74 V). The EQE and brightness of green‐QLED increased by 38.9% and 34.3% to 24.16% and 279 700 cd m−2, respectively. Therefore, NIL will provide new insights into synergistically enhancing light out‐coupling and internal quantum efficiency to break through the performance of electroluminescence (EL). [ABSTRACT FROM AUTHOR]

Digital techniques are critical in the digital age. Research in various disciplines requires the use of digital technology. Extracting and digitizing information from text, particularly historical geographical information, is the most fundamental operation of digital humanities. However, digitizing historical geographical information is a challenging work. This article proposes a framework for constructing a digital system of historical geographic information. The feasibility of the digital system was verified in a case study of Tibetan Buddhist monasteries in Tibetan inhabited areas of China. The results demonstrate that the digital system proposed in this study can be used as a guide for digitizing historical geographic information of Tibetan Buddhist monasteries from the perspective of digital humanities. This study provides a useful reference for the digitalization of historical geographic information in the digital humanities discipline. [ABSTRACT FROM AUTHOR]

Gelugpa is the most influential extant religious sect of Tibetan Buddhism, which is the spiritual prop for Tibetans, with thousands of monasteries and followers in Tibetan areas of China. Studies on the spatial diffusion processes of Gelugpa can not only reveal its historical geographical development but also lay the foundation for anticipating its future development trend. However, existing studies on Gelugpa lack geographical perspective, making it difficult to explore the spatial characteristics. Furthermore, the prevailing macro-perspective overlooks spatiotemporal heterogeneity in diffusion processes. Therefore, taking monastery as the carrier, this study establishes a multi-level diffusion model to reconstruct the diffusion networks of Gelugpa monasteries, as well as a framework to explore the detailed features in the spatial diffusion processes of Gelugpa in Tibetan areas of China based on a geodatabase of Gelugpa monastery. The results show that the multi-level diffusion model has a considerable applicability in the reconstruction of the diffusion networks of Gelugpa monasteries. Gelugpa monasteries in the Three Tibetan Inhabited Areas present disparate spatial diffusion processes with diverse diffusion bases, speeds, stages, as well as diffusion regions and centers. A powerful single-center diffusion-centered Gandan Monastery was rapidly formed in U-Tsang. Kham experienced a slower and more varied spatial diffusion process with multiple diffusion systems far apart from each other. The spatial diffusion process of Amdo was the most complex, with the highest diffusion intensity. Amdo possessed the most influential diffusion centers, with different diffusion shapes and diffusion ranges crossing and overlapping with each other. Multiple natural and human factors may contribute to the formation of Gelugpa monasteries. This study contributes to the understanding of the geography of Gelugpa and provides reference to studies on religion diffusion. [ABSTRACT FROM AUTHOR]

Quantum‐dot light‐emitting diodes (QLEDs) are proposed as one of the most promising candidates for next‐generation displays, but their commercial application is seriously limited due to the poor performance of blue QLEDs (B‐QLEDs). Herein, this work uses Ti3C2Tx nanosheets to tune the work function (WF) of ZnMgO and to engineer the quantum dot (QD)/electron transport layer (ETL) interface. The B‐QLEDs with ZnMgO‐Ti3C2Tx hybrid ETL exhibit a maximum EQE of 15.81% and a remarkable T50 operation lifetime of 3284 h at 100 cd m−2. In addition to that, ultraviolet photoemission spectroscopy and density functional theory calculations both confirm that the addition of Ti3C2Tx to ZnMgO can effectively tune the ZnMgO's work function, further give rise to the reduction of QD/ETL energy barrier, which finally results in the alleviation of charge accumulation at the QD/ETL interface. The findings of interface engineering by Ti3C2Tx not only provide a promising strategy for the application of 2D materials in optoelectronic devices, but also pave the way to construct high‐performance light‐emitting diodes. [ABSTRACT FROM AUTHOR]

Linear spectral mixture analysis (LSMA) and regression analysis are the two most conventionally used methods to estimate impervious surfaces at the subpixel scale in an urban area. However, LSMA lacks the sensitivity to pixel brightness, which leads to inter variability of endmembers and affects the ability to distinguish features with a similar spectral signature. This research aims to develop LSMA aided by a regression analysis model to estimate impervious surfaces with higher accuracy. A spectral angle mapping (SAM) based regression analysis model is introduced to reduce errors. Based on high-resolution images and field survey data, the SAM-based regression analysis can estimate non-impervious surface and high-impervious surface densities with high accuracy, while less accurate in impervious surfaces with low/medium density. In contrast, LSMA is able to estimate low/medium-density impervious surfaces with higher accuracy. We propose an improved approach by integrating the two methods, regression analysis aided LSMA, for impervious surface estimation. The proposed method increases the overall accuracy of the impervious surface estimation to 85.24%, which is significantly greater than that of the conventional methods. [ABSTRACT FROM AUTHOR]

In the past two decades, the Ministry of Agriculture and Rural Affairs of China (MARA) has issued a series of strict cultivated land protection policies to prevent the spread of farmland abandonment and maintain a dynamic balance between the quantity and quality of arable land. However, high-speed economic development, strict arable land protection policies, and ecological security and sustainable development strategies interacting with human activities have brought challenges to quantifying the effectiveness of arable land protection policies. In this study, we proposed a method to quantify the impacts of the arable land protection policies and evaluate the quantitative impacts on farmland abandonment in Guangdong Province after 2014 from the perspective of landscape ecology. The results illustrated that the landscape fragmentation of farmland abandonment in Guangdong Province decreased after the new arable land policies were issued. More annual farmland abandonment (AFA) shifted to seasonal farmland abandonment (SFA), revealing the considerable pronounced effects of farmland abandonment management. The new policies effectively restrained the area increase for AFA in the regions with lower rural population (RPOP) and lower gross domestic product (GDP), and reduced the fragmentation of AFA in the regions with the highest RPOP and lower GDP. Additionally, the new policies effectively restrained the fragmentation increase for SFA in the regions with lower RPOP and lower GDP, and reduced the area increase for SFA in the regions with the highest RPOP and lower GDP. The management effect was not that significant in the regions with higher RPOP and higher GDP. These findings will provide important data references for arable land decision making in southern China. [ABSTRACT FROM AUTHOR]

The classification of urban functional areas plays an important role in urban planning and resource management. Although previous studies have confirmed that different urban functional areas have different morphological structures and Land Surface Temperature (LST) characteristics, these two types of characteristics have rarely been fully integrated and used for functional area classification. In this paper, a new framework for classifying urban functional areas is proposed by combining urban morphological features and LST features. First, metrics are constructed from three levels, namely, building, road and region, which are used to portray urban morphology; LST is retrieved using thermal infrared remote sensing to reflect LST features with four metrics: the average temperature, maximum temperature, temperature difference and standard deviation of temperature. Then, the functional areas are classified into four categories: service/public land, commercial land, residential land and industrial land. A random forest algorithm is used to effectively fuse the features of these two categories and classify the functional areas. The effectiveness of the proposed framework is tested in the study area of Shenzhen City, Guangdong Province. The results show that the combined classification accuracy of the proposed classification method is 0.85, which is 0.26 higher than that of the classification model based on urban morphology and 0.1 higher than that of the classification model based on LST features. The proposed framework verifies that the integration of LST features into urban functional area classification is reliable and effectively combines urban morphology and LST features for functional area classification. [ABSTRACT FROM AUTHOR]

Aims Recent warmer and wetter climate in northern China remains a hot topic in recent years, yet its effect on vegetation growth has not been fully understood. This study investigated the temporal change of vegetation cover and its correlations with climatic variables from 1982 to 2018 for grasslands in northern China. Our aim is to clarify whether the warmer and wetter climate in recent years drives the greening of the vegetation in this region. Methods We investigated the temporal dynamic of vegetation normalized difference vegetation index (NDVI) and its driving forces based on long time-series data. Piecewise regression was used to examine whether there was a turning point of the trend of NDVI and climatic variables. Pearson correlation analyses were conducted to quantify the relationship between NDVI and climatic factors. Stepwise multivariable regression was used to quantify the contributions of climate variables to the temporal variations in NDVI. Important Findings We found a turning point of NDVI trend in 2008, with GIMMS NDVI indicating a slight increase of 0.00022 yr−1 during 1982–2008 to an increase of 0.002 yr−1 for GIMMS NDVI during 2008–2015 and 0.0018 yr−1 for MODIS NDVI during 2008–2018. Precipitation was the predominant driver, and air temperature and vapor pressure deficit exerted a minor impact on the temporal dynamics of NDVI. Overall, our results suggest a turning point of NDVI trend, and that recent warmer and wetter climate has caused vegetation greening, which provides insights for better predicting the vegetation cover in this region under changing climate. [ABSTRACT FROM AUTHOR]

The rapid expansion and unreasonable design of impervious surfaces caused by urbanization have seriously aggravated urban waterlogging. Determining how to optimize the spatial layout of impervious surfaces is the focus of urban waterlogging prevention in urban renewal. The existing urban‐renewal methods include constructing low‐impact development measures by reducing the area of impervious surfaces or implementing a local high‐strength design based on the ant colony algorithm (ACA) from the viewpoint of optimization design. However, these methods have obvious shortcomings in terms of time efficiency, and their optimized design schemes are insufficient at considering the global impervious surface design. Therefore, to address these problems, the study proposes a coupled model of multivariate linear programming and Soil Conservation Service curve number. The model is employed in the central city of Guangzhou, China. The results show, first, that the optimal design of impervious surfaces in urban renewal is to construct a discontinuous connection of high‐low‐high‐density impervious surfaces; second, that, compared with ACA, our method has higher robustness, increases the average optimization rate by 4.48 to 14.00%, and reduces the optimization time over 30 days to 20.8 s; and third, that the optimal results realize the global low‐strength transformation as complements of the existing design scheme of local high‐strength transformation. This study optimizes methods for alleviating urban waterlogging urban renewal at different scales or intensities. [ABSTRACT FROM AUTHOR]

Impervious surface (IS) is a key indicator to measure the urbanization process and ecological environment. Many studies have observed an urbanization process based on IS at the city scale. Understanding the changes in the IS over a period at a regional level offers an alternative and effective approach to characterize and quantify the spatial process of urban agglomeration. This study focuses on the urban agglomeration of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) by utilizing the sensor-based Landsat data during 1987-2017 and investigates the spatiotemporal distribution of IS expansion at both regional and city scales. The modified linear spectral mixture analysis (MLSMA) method is used to extract the IS of the GBA. Then, the IS mapping accuracies were assessed after comparison with high-resolution historical data. The spatiotemporal and directional changes of IS surfaces for GBA are analyzed by using Gravity Center (GC) and Standard Deviational Ellipse (SDE). Finally, Shannon's Diversity Index (SHDI) is used to analyze the overall characteristics of landscape level, and the Patch Density (PD) and Landscape Shape Index (LSI) are used to describe the characteristics of different classes of the IS. The results show that the IS of the whole region experienced rapid and massive expansion during the past 30 years and exhibited a distinct characteristic along the Pearl River Delta (PRD) and the coastline. Furthermore, the IS area increased rapidly in the PRD, while it is relatively stable in Hong Kong and Macao. We believe that the findings of this study can help policy makers to better understand and maintain the sustainable development of the GBA. [ABSTRACT FROM AUTHOR]

Explicit information of tree species composition provides valuable materials for the management of forests and urban greenness. In recent years, scholars have employed multiple features in tree species classification, so as to identify them from different perspectives. Most studies use different features to classify the target tree species in a specific growth environment and evaluate the classification results. However, the data matching problems have not been discussed; besides, the contributions of different features and the performance of different classifiers have not been systematically compared. Remote sensing technology of the integrated sensors helps to realize the purpose with high time efficiency and low cost. Benefiting from an integrated system which simultaneously acquired the hyperspectral images, LiDAR waveform, and point clouds, this study made a systematic research on different features and classifiers in pixel-wised tree species classification. We extracted the crown height model (CHM) from the airborne LiDAR device and multiple features from the hyperspectral images, including Gabor textural features, gray-level co-occurrence matrix (GLCM) textural features, and vegetation indices. Different experimental schemes were tested at two study areas with different numbers and configurations of tree species. The experimental results demonstrated the effectiveness of Gabor textural features in specific tree species classification in both homogeneous and heterogeneous growing environments. The GLCM textural features did not improve the classification accuracy of tree species when being combined with spectral features. The CHM feature made more contributions to discriminating tree species than vegetation indices. Different classifiers exhibited similar performances, and support vector machine (SVM) produced the highest overall accuracy among all the classifiers. [ABSTRACT FROM AUTHOR]

In social‐cognitive research, little attention has been paid to the developmental course of spontaneous trait inferences about the actor (STIs about the actor) and spontaneous trait transferences about the informant (STTs about the informant). Using a false recognition paradigm, Study 1 investigated the developmental course of STIs and Study 2 investigated the developmental course of STTs, comparing 8‐, 9‐, 10‐, 11‐, 12‐ and 13‐year olds. The results of Study 1 showed that 8‐year olds could make STIs about the actor, and the magnitude of STIs increased from ages 8 to 10 years, stabilised at the age of 10, 11, 12 years, and decreased from ages 12 through 13 years. The results of Study 2 showed that 8‐year olds could make STTs about the informant, and the magnitude of STTs did not vary with age. In all age groups, the magnitude of STIs about the actor was greater than that of STTs about the informant. [ABSTRACT FROM AUTHOR]

Prior research has indicated that positive moods increase but negative moods decrease the trait activation of spontaneous trait inferences ( STIs). However, it is unknown whether this difference is also present in STIs about the actor. In Study 1, using a false recognition paradigm, we found that Chinese undergraduates made STIs about the actor. In Study 2, we found that the happy Chinese undergraduates were more likely to make STIs about the actor than the sad Chinese undergraduates. These findings showed that Chinese people made STIs about the actor and moods had an influence on their STIs about the actor. [ABSTRACT FROM AUTHOR]

Understanding the driving factors and assessing the risk of rainstorm waterlogging are crucial in the sustainable development of urban agglomerations. Few studies have focused on rainstorm waterlogging at the scale of urban agglomeration areas. We used the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China as a case study. Kernel density estimation (KDE) and spatial autocorrelation analysis were applied to study the spatial distribution characteristics of rainstorm waterlogging spots during 2013–2017. A geographical detector (GD) and geographically weighted regression (GWR) were used to discuss the driving mechanism of rainstorm waterlogging by considering eight driving factors: impervious surface ratio (ISR), mean shape index of impervious surface (Shape_MN), aggregation index of impervious surface (AI), fractional vegetation cover (FVC), elevation, slope, river density, and river distance. The risk of rainstorm waterlogging was assessed using GWR based on principal component analysis (PCA). The results show that the spatial distribution of rainstorm waterlogging in the GBA has the characteristics of multicenter clustering. Land cover characteristic factors are the most important factors influencing rainstorm waterlogging in the GBA and most of the cities within the GBA. The rainstorm waterlogging density increases when ISR, Shape_MN, and AI increase, while it decreases when FVC, elevation, slope, and river distance increase. There is no obvious change rule between rainstorm waterlogging and river density. All of the driving factors enhance the impacts on rainstorm waterlogging through their interactions. The relationships between rainstorm waterlogging and the driving factors have obvious spatial differences because of the differences in the dominant factors affecting rainstorm waterlogging in different spatial positions. Furthermore, the result of the risk assessment of rainstorm waterlogging indicates that the southwest area of Guangzhou and the central area of Shenzhen have the highest risks of rainstorm waterlogging in GBA. These results may provide references for rainstorm waterlogging mitigation through urban renewal planning in urban agglomeration areas. [ABSTRACT FROM AUTHOR]

Changes in cropping intensity reflect not only changes in land use but also the transformation of land functions. Although both natural conditions and socioeconomic factors can influence the spatial distribution of the cropping intensity and its changes, socioeconomic developments related to human activities can exert great impacts on short term cropping intensity changes. The driving force of this change has a high level of uncertainty; and few researchers have implemented comprehensive studies on the underlying driving forces and mechanisms of these changes. This study produced cropping intensity maps in China from 2001 to 2016 using remote sensing data and analyzed the impacts of socioeconomic drivers on cropping intensity and its changes in nine major agricultural zones in China. We found that the average annual cropping intensity in all nine agricultural zones increased from 2001 to 2016 under rapid socioeconomic development, and the trends in the seven major agricultural zones were significantly increased (p < 0.05), based on a Mann–Kendall test, except for the Northeast China Plain (NE Plain) and Qinghai Tibet Plateau (QT Plateau). Based on the results from the Geo-Detector, a widely used geospatial analysis tool, the dominant factors that affected cropping intensity distribution were related to the arable land output in the plain regions and topography in the mountainous regions. The factors that affected cropping intensity changes were mainly related to the arable land area and crop yields in northern China, and regional economic developments, such as machinery power input and farmers' income in southern China. These findings provide useful cropping intensity data and profound insights for policymaking on how to use cultivated land resources efficiently and sustainably. [ABSTRACT FROM AUTHOR]

In a location-based social network, users socialize with each other by sharing their current location in the form of "check-in," which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called "check-in behavior," can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users' daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term "check-in behavior," which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as "Weibo"), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend. [ABSTRACT FROM AUTHOR]

Urban rainstorm waterlogging has become a typical "city disease" in China. It can result in a huge loss of social economy and personal property, accordingly hindering the sustainable development of a city. Impervious surface expansion, especially the irregular spatial pattern of impervious surfaces, derived from rapid urbanization processes has been proven to be one of the main influential factors behind urban waterlogging. Therefore, optimizing the spatial pattern of impervious surfaces through urban renewal is an effective channel through which to attenuate urban waterlogging risk for developed urban areas. However, the most important step for the optimization of the spatial pattern of impervious surfaces is to understand the mechanism of the impact of urbanization processes, especially the spatiotemporal pattern of impervious surfaces, on urban waterlogging. This research aims to elucidate the mechanism of urbanization's impact on waterlogging by analysing the spatiotemporal characteristics and variance of urban waterlogging affected by urban impervious surfaces in a case study of Guangzhou in China. First, the study area was divided into runoff plots by means of the hydrologic analysis method, based on which the analysis of spatiotemporal variance was carried out. Then, due to the heterogeneity of urban impervious surface effects on waterlogging, a geographically weighted regression (GWR) model was utilized to assess the spatiotemporal variance of the impact of impervious surface expansion on urban rainstorm waterlogging during the period from the 1990s to the 2010s. The results reveal that urban rainstorm waterlogging significantly expanded in a dense and circular layer surrounding the city centre, similar to the impervious surface expansion affected by urbanization policies. Taking the urban runoff plot as the research unit, GWR has achieved a good modelling effect for urban storm waterlogging. The results show that the impervious surfaces in the runoff plots of the southeastern part of Yuexiu, the southern part of Tianhe and the western part of Haizhu, which have experienced major urban engineering construction, have the strongest correlation with urban rainstorm waterlogging. However, for different runoff plots, the impact of impervious surfaces on urban waterlogging is quite different, as there exist other influence factors in the various runoff plots, although the impervious surface is one of the main factors. This result means that urban renewal strategy to optimize the spatial pattern of impervious surfaces for urban rainstorm waterlogging prevention and control should be different for different runoff plots. The results of the GWR model analysis can provide useful information for urban renewal strategy-making. [ABSTRACT FROM AUTHOR]

Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001–2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation. [ABSTRACT FROM AUTHOR]