Relief Degree Of Land Surface Research Articles

Mineral resource dependence and green transformation: the impact and challenges of green finance in Chinese cities

Overcoming the resource curse while balancing environmental protection and economic growth presents significant challenges. We developed an evaluation system to measure the green economic efficiency (GEE) of 262 cities and introduced the concept of the mineral resource curse. This study investigates both linear and non-linear relationships between mineral resource dependence (MRD) and GEE, as well as the role of green finance in this context. To address the endogeneity issue arising from the bidirectional causality between MRD and GEE, we used the interaction of relief degree of land surface and time as instrumental variables (IV). The empirical results indicate that a city’s MRD significantly inhibits GEE. Specifically, an increase in MRD hinders GEE by reducing green investment and crowding out green credit. By categorizing cities according to resource cycle theory, we found that non-resource-based and regenerative cities effectively avoided the mineral resource curse. However, resource-based, growing, mature, and declining cities did not benefit from their mineral resources. This conclusion remains robust even after changing IV and using various estimation methods such as IVTOBIT. Furthermore, this study discusses the practical effects of green finance. We found that green investment has a limited impact, failing to offset the negative influence of MRD on GEE. Under the influence of green credit, the relationship between MRD and GEE exhibits an inverted U-shape. This finding highlights the limitations of green finance policies at the municipal level in China. Overall, this study provides new evidence for improving China’s green finance system and promoting green economic development. It offers a fresh perspective for China and other developing countries in their green transformation efforts and avoidance of the mineral resource curse.

Geospatial Analysis of Relief Degree of Land Surface in the Forest-Steppe Ecotone in Northern China

Geospatial Analysis of Relief Degree of Land Surface in the Forest-Steppe Ecotone in Northern China

Changes in human settlement environments and their drivers in valley cities located in arid and semi-arid regions: A case study of Lanzhou in Western China

Development of urban human settlement environments (HSEs) is an integral part of promoting high-quality and sustainable regional development and constructing a beautiful China. The city of Lanzhou, located at the geometric center of China, is the only provincial capital traversed by the Yellow River. Given the constraints posed by the valley topography and the need for economic development, the development of this HSE, which is located within an arid region, poses considerable challenges. Evidently, an understanding of the evolution of HSEs and drivers of changes in them contributes to high-quality, sustainable urban development in arid and semi-arid regions. An analytical model was developed using the parameters of relief degree of land surface, human comfort days, the land cover index, nighttime light index, and precipitation. This model was used in combination with population density and the gross domestic product to analyze the spatial distribution of Lanzhou's HSE and its drivers. The results showed that landscapes in Lanzhou underwent significant changes between 2000 and 2022, with an increase in building-up land (+0.946%), cultivated land (+0.134%), and forest land (+0.018%) and a decrease in grassland (−1.10%). There was significant outward expansion of the main urban zone of Lanzhou and of various county towns, with the increase in building-up land being most prominent. During this period, there were significant changes in the periphery of the core urban area and county towns in Lanzhou, with decreases moving from the urban center (the highest value) to the surrounding areas (Yongdeng County had the lowest value). The correlation between the HSE and population density grew stronger in Anning and Chengguan Districts but became weaker in Xigu and Qilihe Districts. Spatiotemporal variations in the HSE were primarily caused by climate change, followed by human activities, and were also influenced by the valley topography. Overall, the spatial distribution of population density and the HSE in Lanzhou demonstrated good consistency under the influence of economic development and urbanization.

Spatio-temporal distribution and evolution of the Tujia traditional settlements in China.

The rapid modernization in China has aggravated the reduction of the traditional settlements and aroused concern about the protection and research. This study aims to examine the spatial-temporal variations Tujia traditional settlements in China and to delineate the driving mechanism of the settlement distribution. Previous studies have focused on the characteristics of settlements in provincial or smaller areas, providing lacked information regarding spatial distribution heterogeneity of Tujia traditional settlements in China. In this study, the spatial heterogeneity and influence factors of the distribution of traditional Tujia settlements were examined using the GIS platform and statistical methods. The results reveal that the spatial distribution of settlements exhibits clustering with the pattern of "scattered distribution in a large region, while concentrated in small areas". The settlements were generally built in low hills, gentle slopes, sunny slopes and low-relief terrain areas, with elevation, relief degree of land surface (RDLS), slope and aspect were the key factors affecting the distribution. In Song, Yuan, Ming and Qing dynasties, settlements showed significant clustering all through, though the location and number of clustering center kept changing. In this process, the history of the Tujia chieftain and the transportation and marketing lines of Sichuan salt had a profound influence on the historical evolution of the settlement.

Spatial pattern of urban-rural integration in China and the impact of geography

Urban-rural integration (URI) is a global challenge that is highly related to inequalities, poverty, economic growth, and other Sustainable Development Goals (SDGs). Existing research has evaluated the extent of URI and explored its influencing factors, but urban-rural linkages are seldom incorporated in evaluation systems, and geographical factors are rarely recognized as the influencing factors. We construct a URI framework including regional economy, rural development, urban-rural linkage, and urban-rural gap. Based on a dataset consisting of 1,669 counties in China in 2020, we reveal the spatial pattern of URI and find a high correlation between the spatial pattern of URI and the relief degree of land surface (RDLS). Using structural equation modeling, we discover that topography has direct (−0.18, p < 0.001) and indirect (−0.17, p < 0.001) effects on URI. The indirect negative effects are mediated through the infrastructure, and the combination of localized advantages and modern technical conditions could mitigate the negative impact of topography. Finally, we identify 742 counties as lagging regions in URI, which can be clustered into eight types. Our findings could facilitate policy designing for those countries striving for integrated and sustainable development of urban and rural areas.

Identifying the essential influencing factors of landslide susceptibility models based on hybrid-optimized machine learning with different grid resolutions: a case of Sino-Pakistani Karakorum Highway.

This study attempts to explore the essential influencing factors of landslides and explores the effects of different datasets on landslide susceptibility mapping (LSM) at six grid resolutions (i.e., 10 m, 30 m, 300 m, 1000 m, 2000 m, and 3000 m). Firstly, the geospatial dataset of 21 influencing factors was extracted from 1847 historical landslide InSAR (Interferometric Synthetic Aperture Radar) points, which were taken as a sample for the Sino-Pakistani Karakorum Highway. Secondly, Spearman correlation coefficient (SCC), random forest feature selection (RFFS), and their combinations (SCC-RFFS) were selected at different grid resolutions to identify the essential influencing factors from the 21 original factors. A random division into training set (70%) and test set (30%) was performed. Then, the LSM models for the original influencing factors and the selected influencing factors were constructed separately using machine learning models. Finally, the reasonableness of the essential influencing factors was verified by comparing the accuracy of the models under different grid resolutions. The results show that (1) relief degree of land surface (RDLS), SPI, and rainfall have significant effects on landslide occurrence. (2) The primary elements (i.e., RDLS, slop, rainfall) are less affected by the grid resolution, while the secondary elements (TWI) are more affected by the grid resolution. (3) At 30 m, the SCC-RFFS-RF model can get the highest landslide susceptibility model accuracy. The prediction will also provide scientific guidance for the allocation of land resources on a regional and global scale, and minimize the human and economic costs along the highway, while ensuring safe highway operations.

Exploring the Correlations Between SNPP-VIIRS Nighttime Light Data and Population From a Multiple Scale Perspective

Remote sensing nighttime light (NTL) data are widely used for fine-grained estimation of population due to their effectiveness in monitoring anthropic lights at night. However, there is a lack of research exploring the correlations between population and NTL data at various administrative and grid scales, as well as their related impact factors. Thus, to analyze the spatial effect of NTL on NTL-based population estimation, five models (linear, quadratic, exponential, logarithmic, and power function) were used in this letter to examine the correlations between NTL and population at multiscale by using the Visible Infrared Imaging Radiometer Suite data from the National Polar-orbiting Partnership. Results show that the correlations between NTL and population were significantly related to geographic scales. The power function model had the highest fitting accuracy between NTL and population when the grid scales were less than 5 km. The quadratic model had the highest precision on grid scales larger than 5 km. It showed a sharp increase in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values between 0.5 and 10 km as the scale increase. At different scales, the correlations between NTL and population was negatively impacted by temperature, Normalized Difference Vegetation Index (NDVI), and Road Network Density (RND), but they were positively impacted by the Relief Degree of Land Surface (RDLS). We also found that RDLS had the greatest impact on model accuracy, followed by NDVI and temperature. In addition, RND had a low impact on the accuracy of population estimation on rough grids.

Debris Flow Susceptibility Assessment of Sichuan-Tibet Railway Based on GIS and CF-LR Coupling Model

Sichuan-Tibet Railway is an important transportation system in China, which is of great significance to promote the communication between Tibet and the mainland. This paper takes the lin-chang section of the Sichuan-Tibet railway with a width of 2km on both sides as the research area. The occurrence of debris flow must have appropriate solid source, water source and topographic conditions. In this paper, ten disaster-causing factors are selected, including relief degree of land surface, profile curvature, silty soil, distance to the rive, distance to the fault, elevation, slope aspect, NDVI, average annual rainfall and land utilization rate. The information of disaster-causing factors was extracted from Arcgis and evaluated by the certainty factor model. The probability of debris flow in the study area was calculated by the coupling model of binary Logistic regression model combined with certainty factor model. ROC curve analysis of the CF-LR model shows that AUC is 0.89, indicating that the model has a good effect. The distribution map of debris flow susceptibility in the study area was obtained by using grid calculator in GIS. The results show that under the CF-LR model, extremely low, low, medium and high prone areas cover 2547km₂, 235km₂, 246km₂ and 1099km₂ respectively, accounting for 61.72%, 5.69%, 5.96% and 26.63% of the total area of the study area, respectively. The high risk areas are mainly in Milin, Nyingchi and Bomi counties. This study provides an important reference for assessing the risk of debris flow geological disaster on Sichuan-Tibet Railway.

Site Suitability Analysis for Urban Settlements along River Jhelum, Pakistan using GIS and Remote Sensing Techniques

Infrastructure development is critical to the success of economic growth policies. Remote sensing and GIS tools have an important role to play in the development of various urban infrastructures. Due to the rapid growth of urban population and urbanization, it is necessary to find out the site's suitability for sustainable urban development. The main aim of the study is to study the growth and trend of urbanization, as well as to find out the suitable sites for further urban development in northern Punjab, along the river Jhelum, Pakistan. The study illustrates the use of geographic information system (GIS) and Remote Sensing based techniques i.e. Human Natural Environment Index (HNEI) applied with a foundation of Relief Degree of Land Surface (RDLS), Temperature Humidity Index (THI), Water Resource Index (WRI), and Land Cover Index (LCI) for selection of the suitable site for urban settlements along river Jhelum, Pakistan. For this purpose, Toposheet and Landsat satellite data were used to generate various thematic layers using ArcGIS software. The results were generated in form of five categories i.e., highly suitable, moderately suitable, relatively low suitable, low suitable, and non-suitable. The final results indicated that district Sargodha is most suitable for long-term sustainable urban settlements favored by relief, climate, water availability, land cover scenario, and flood hazard-free area. Eventually, a site suitability map is prepared for further urban development. The present study allows the local people as well as urban planners for the appropriate plans of land use planning in sustainable urban development.

Land surface temperature downscaling in the karst mountain urban area considering the topographic characteristics

To obtain high-spatial-resolution land surface temperature (LST) in karst areas, it is necessary to select a downscale regression model with a better simulation effect and the scale factors that can best represent the topographic characteristics of karst mountainous areas. In Guiyang, a typical karst mountain city, two areas are selected as the study area, which is dominated by natural surface and construction land. Based on the data of Landsat-8 Thermal Infrared Sensor (TIRS), Sentinel-2, Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM), and meteorological stations, the scale factors representing bare land: bare soil index and topographic relief: mountain shadow (hillshade), relief degree of land surface (RDLS), solar incident angle, and sky view factor (SVF) are added on the basis of the conventional factors. At the same time, random forest (RF) and extreme gradient boosting (XGBoost) models are used to construct an LST downscaling method that is more suitable for karst mountain cities. After the above steps, the LST product with a spatial resolution of 10 m is finally estimated. The results show that, due to the characteristics of large elevation variation, fragmentation, and high heterogeneity of surface landscape in karst areas, digital elevation model (DEM), RDLS, and SVF factors need to be considered in the downscaling of surface temperature, and the contribution rates of these factors are all more than 6% in the model. In terms of accuracy evaluation of ground temperature, XGBoost model has the highest accuracy with an average absolute error of 1.67K, RF model has an average error of 1.90K, and thermal image sharpening has the worst accuracy with an average error of 2.41K. In terms of accuracy evaluation of ascending scale, the XGBoost model also shows higher accuracy and richer texture details. The research results can provide basic data for the acquisition of high-resolution LST and its intermediate parameters in this area and also provide a method reference for the reduction of high-resolution LST in similar areas.

The Natural Suitability of Human Settlements and Their Spatial Differentiation in the Nenjiang River Basin, China

The natural suitability of human settlements (NSHS) of the Nenjiang River Basin was carried out by evaluation indices such as terrain characteristics, vegetation, hydrology, and climate of the region. The NSHS model is built using a GIS spatial analysis platform to reveal the suitability zoning and spatial differentiation (SD) characteristics of human settlements in the study area. The results show that the NSHS index of the study area ranges from 0.31 to 0.92, with an average of 0.54, indicating that the values show a gradual increase from northwest to southeast and from mountainous and hilly areas to plains. The most suitable and generally suitable areas constitute nearly 41.81% of the basin, and the population of these terrain accounts for about 84.25% of the total population of the basin. The suitable area category covers the largest area among all other categories accounting for about 25.16%, and the population of these regions accounts for about 12.46%. The critical suitable area category accounts for 21.70% of the basin, whereas unsuitable areas account for 11.32%, constituting the smallest areas compared to other categories. It is also evident that diverse limiting factors of NSHS characterize distinct areas in the study area. For example, high NSHS index, the normalized difference vegetation index (NDVI), and water resource index (WRI) are the main limiting factors in plane areas, low NSHS index, the relief degree of land surface (RDLS) index, and temperature-humidity index (THI) are the major limiting factors in the mountainous, hilly and plateau areas.

Identification and Correlation Analysis of Engineering Environmental Risk Factors along the Qinghai–Tibet Engineering Corridor

Global warming has increased the security risk of permafrost environment in the Tibetan Plateau, which has been threatening infrastructures along the Qinghai–Tibet Engineering Corridor (QTEC). Combined with the traditional risk identification and the causal feedback relationship of system dynamics, the authors present a novel engineering environment risk identification model including five risk subsystems, i.e., regional geomorphology, climate change, ecological environment, permafrost environment and water environment. Our model could successfully identify the interaction relationships and transmission path among risk factors of the environment of the QTEC. The basic data calculation, interaction degree analysis and regional distribution characteristic analysis of the identified risk factors were carried out by using a geographic information system (GIS), a partial correlation analysis and a zoning analysis. The results show that the static factors (i.e., elevation, slope, aspect, relief degree of land surface and volume ice content) mainly affected the spatial distribution of environmental risk factors, while the climate change factors (i.e., mean annual air temperature, mean annual precipitation and surface solar radiation), among the dynamic factors, were the root factors of the dynamic changes in environmental risks. The model identified five types of parallel risk paths in the QTEC. This novel method and proposed model can be used to identify and assess multi-scale engineering environmental risks in the cryosphere.

Correction: Yang et al. Influence of Relief Degree of Land Surface on Street Network Complexity in China. ISPRS Int. J. Geo-Inf. 2021, 10, 705

In the original publication [...]

Influence of Relief Degree of Land Surface on Street Network Complexity in China

The relief degree of land surface (RDLS) was often calculated to describe the topographic features of a region. It is a significant factor in designing urban street networks. However, existing studies do not clarify how RDLS affects the distribution of urban street networks. We used a Python package named OSMnx to extract the street networks of different cities in China. The street complexity metrics information (i.e., street grain, connectedness, circuity, and street network orientation entropy) were obtained and analyzed statistically. The results indicate that street network exhibits more complexity in regions with high RDLS. Further analysis of the correlation between RDLS and street network complexity metrics indicates that RDLS presents the highest correlation with street network circuity; that is, when RDLS is higher, the routes of an urban street network is more tortuous, and the additional travel costs for urban residents is higher. This study enriches and expands research on street networks in China, providing a reference value for urban street network planning.

Digital inclusive finance and rural consumption structure – evidence from Peking University digital inclusive financial index and China household finance survey

PurposeThis paper examines the impact and mechanism of China's digital inclusive finance on rural consumption upgrade. First, the impact of the development of digital inclusive finance on the upgrading of rural household consumption structure is to be theoretically analyzed and empirically tested. Secondly, in terms of heterogeneity analysis, it pays attention to the age heterogeneity of users that digital inclusive finance influencing rural residents' developmental consumption upgrade, which is related to the issue of intergenerational “digital gap”. Thirdly, the mechanism of digital inclusive finance in promoting rural consumption upgrade is to be investigated. Finally, how to promote the role of digital inclusive finance in upgrading the structure of rural consumption to a developmental demand level will be showed.Design/methodology/approachFrom the perspective of the micro-household, this study is conducted by using the instrumental variable (IV) method, with 2SLS model and IV-Tobit model, based on the matched city-level data of Digital Inclusive Financial Index (DIFI) with the Chinese Household Financial Survey (CHFS). “The relief degree of land surface” is an ideal instrumental variable of digital inclusive finance, for including regional altitude difference and terrain factors of regional area, has theoretical influence on the development of digital inclusive finance, and is not affected by other economic variables.FindingsThe conclusions show that the digital inclusive finance plays a significant role in promoting the rural households' developmental consumption, but has no significant effect on the rural households' survival-type consumption and hedonistic consumption. Furthermore, this paper examines the impact and mechanism of China's digital inclusive finance on rural consumption upgrade. First, the impact of the development of digital inclusive finance on the upgrading of rural household consumption structure is to be theoretically analyzed and empirically tested. Secondly, it is discovered that digital inclusive finance is age heterogeneous in promoting the upgrade of consumption structure of rural household, and its effect on the elderly is weaker than that on the young for the intergenerational “digital gap”. Thirdly, these conclusions reveal that the digital inclusive finance does affect the consumption of rural residents through three mechanisms: increasing income and wealth, easing liquidity constraints and facilitating payment methods. Finally, how to promote the role of digital inclusive finance in upgrading the structure of rural consumption to a developmental demand level will be showed.Originality/valueThe current research on the relationship between digital inclusive finance and rural consumption only stays at the level of total rural consumption and has not stressed the structural problems of rural consumption. Can digital inclusive finance promote the upgrade of rural consumption structure? To what level can digital inclusive finance promote the upgrading of rural consumption structure? Therefore, it is of great theoretical value to study the upgrading of rural consumption structure from the micro level. Can the current digital inclusive finance benefit the elderly and help break the vulnerability of the elderly to enjoy finance? In this regard, evidence of heterogeneity remains to be provided.

Ecosystem service pattern of Fuzhou City based on ecosystem service bundles.

Based on InVEST model, recreational opportunity spectrum, factor analysis, cold and hot spot analysis, SOFM neural network, relief degree of land surface (RDLS) methods, we evalua-ted six kinds of ecosystem service in Fuzhou City and analyzed the trade-offs and synergies among them, with the aim to clarify the dominant services and the spatial pattern of service bundles in different topographical regions. The results showed substantial spatial heterogeneity in different ecosystem services of Fuzhou City. Water yield, carbon sequestration, habitat quality, and recreational opportunities were totally high, whereas the supply of agricultural products and soil and water conservation were low and substantial differences. The interaction between water yield and cultural servi-ces were synergies, but trade-offs occurred among regulating service, supporting service and agricultural products service. There were spatial aggregations for both trade-offs and synergies. Ecosystem service bundles could be divided into suburban recreation bundle, water conservation bundle, agricultural products bundle, urban life bundle, and forest ecological conservation bundle. The composition of service bundles clusters was different under different RDLS, with obvious spatial heterogeneity.

The coupling mechanism between the suitable space and rural settlements considering the effect of mountain hazards in the upper Minjiang River basin

Some settlements were located in unsuitable regions due to limited land resources in mountainous areas, some settlements were even even constructed in areas prone to geological hazards in Southwest China. Therefore, it was important to evaluate the spatial appropriateness of a region and determine the areas that were unsuitable for settlements, and then find out the settlements located in unsuitable regions. It will assist in decision making associated with the relocation of settlements. Furthermore, it will be the key to ensure the safety of inhabitants and promoting sustainable development in mountainous areas. This study explored the coupling mechanism between suitable space and rural settlements in the upper Minjiang River basin, which is an ecologically fragile area with high-frequency of natural hazards. Firstly, we identified relief degree of land surface (RDLS), elevation, and disaster risk as the limiting factors. Then, by determining the thresholds of these limiting factors, we recognized the suitable areas for inhabitation in the upper Minjiang River basin with GIS. Finally, using the distribution map of rural settlements and that of suitable space, the distribution of rural settlements located at unsuitable area was obtained by coupling relationship analysis. Consequently, an in-depth understanding of this relationship was achieved as follows: (1) The suitable space of the upper Minjiang River basin is 13.7 thousand km2, accounting for 54.9% of the total land space; (2) There were 196 settlements located in the unsuitable area, the total area of these settlements was 125.27 km2, and there were 68000 people in these settlements, accounting for 17.65% of the total population; (3) There were 65 settlements located near mountain hazard areas, accounting for 4.9% of the total. These findings suggest that it was necessary to carefully investigate settlements with risks and develop targeted relocation policies to help find the most effective way of using land safely and to good effect. The details are as follows: (1) Fully consider the safety of residents: For the 196 settlements distributed in the unsuitable region, the government should undertake a point-by-point survey and classify these settlements into different categories for relocation; (2) For the 65 settlements closely related with mountain hazards, professional geological prospecting teams should be organized to conduct a field survey at each point; (3) Besides considering the safety of residents during the relocation process, it is necessary to pay more attention to the cultural customs of inhabitants and livelihood sustainability

Responses of Stream Geomorphic Indices to Piedmont Fault Activity in the Daqingshan Area of China

A recent correlation of stream geomorphic indices to fault activity has revealed that stream geomorphologies in bedrock mountain areas are good records of local fault movements. The Daqingshan piedmont fault is one of the main active faults in the fault system on the northern margin of the Hetao Basin and has produced frequent large-scale earthquakes since the Late Pleistocene. In the present study, following the segmentation regime of previous studies, we divide the fault zone into five segments, namely, the Baotou, Tuyouqi West, Tuzuoqi West, Bikeqi, and Hohhot segments, and we discuss the relationship between the drainage basin geomorphology and the piedmont fault activity in the Daqingshan area using 30 m spatial resolution Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data. We use a range of geomorphic indices to examine the drainage basins in the Daqingshan area, including the channel steepness index (ksn), slope, hypsometric integral (HI), relief degree of land surface (RDLS), and stream lengthgradient index (SL), extracted with ArcGIS and MATLAB, and we also consider local lithologic and climate aspects. Furthermore, we compare the geomorphic indices with the slip rates of individual segments of the Daqingshan piedmont fault and paleoseismic data. The results show that the geomorphic indices of drainage basins in the Daqingshan area are primarily affected by the piedmont fault activity in the Daqingshan area. The geomorphic indices also demonstrate that piedmont fault activity has been the most intense in the middle segment of this fault system since the Late Quaternary and decreases towards the two sides.

Spatiotemporal Characteristics and Driving Force of Ecosystem Health in an Important Ecological Function Region in China.

Quantitative assessment can scientifically determine the health status of a regional ecosystem, identify regional eco-environmental problems, and assist in promoting regional sustainable development and environmental management. Taking China’s important ecological function region, the Yellow River affected area as an example, this study constructed an extended evaluation index system based on the pressure-state-response framework, and remote sensing and GIS techniques were used to dynamically evaluate the spatial and temporal characteristics of ecosystem health in the study area. Furthermore, influencing factors on ecosystem health in the study area were extensively analyzed using the GeoDetector model. The results show that the ecosystem health level in the study area shows significant spatial heterogeneity from 1995–2015, and showed a fluctuating change process. Areas with large fluctuations in health level were mainly distributed in extreme climate areas, ecologically fragile areas, on plains and in hilly areas. Spatial differences of ecosystem health were well explained by using the biological abundance index, relief degree of land surface, soil type, annual average precipitation, elevation, annual average temperature, and population density. Influencing factors have significant interactive effects on ecosystem health.

Regionalization of Agricultural Nonpoint Source Pollution over China with a Combination of Qualitative and Quantitative Method

Agricultural nonpoint source pollution has been a serious problem in China; however, currently a lack of basic data and quantitative analysis hinders control and reduction of agricultural nonpoint source pollution. Therefore, it is necessary to explore a regionalization method in the study of nationwide agricultural nonpoint source pollution over China. This paper proposes a method of combining both quantitative calculation and qualitative analysis. Based on agricultural nonpoint source pollution mechanism, we first proposed the natural environment index, which was calculated from relief degree of land surface, thermal humidity index, water resources quantity and precipitation index, and land cover index. Second, we proposed basic agricultural environment index, which was calculated based on the area of cultivated land use and the quality of integrated soil fertility. Third, we simplified the spatial distribution of natural environment and basic agricultural environment with the method of choropleth map classification, thematic map series, and gravity centers curve. Fourth, we conducted a qualitative analysis for both the natural environment and basic agricultural environment by overlaying the classification and existing regionalization maps to reveal the intra-region homogeneity and inter-region heterogeneity with a high reliability. The regionalization method used in this study resulted in a nationwide regional zoning of agricultural nonpoint source pollution over China, and China can be divided into 10 regions, which can be a trustworthy reference for agricultural nonpoint source pollution study and management.