scholarly journals Information Extraction and Spatial Distribution of Research Hot Regions on Rocky Desertification in China

2018 ◽  
Vol 8 (11) ◽  
pp. 2075 ◽  
Author(s):  
Yunfeng Hu ◽  
Yueqi Han ◽  
Yunzhi Zhang
Keyword(s):  
Spatial Distribution ◽  
Population Distribution ◽  
High Sensitivity ◽  
Guizhou Province ◽  
Vegetation Coverage ◽  
Web Crawler ◽  
Rocky Desertification ◽  
Mountain Area ◽  
Time Space ◽  
Western Yunnan

Rocky desertification is an important type of ecological degradation in southwest of China. The author uses the web crawler technology and obtained 9345 journal papers related to rocky desertification from 1950s to 2016 in China. The authors also constructed a technological process to extract research hot regions on rocky desertification and then a spatial distribution map of research hot regions on rocky desertification was formed. Finally, the authors compared the spatial distribution using the sensitivity map of rocky desertification to find the differences between two maps. The study shows that: (1) rocky desertification research hot regions in China are mainly distributed in Guizhou, Yunnan and Guangxi, especially in Bijie, Liupanshui, Guiyang, Anshun, Qianxinan Autonomous Prefecture, QianNan Autonomous Prefecture, Qiandongnan Autonomous Prefecture in Guizhou Province, Hechi, Baise, Nanning, Guilin in Guangxi Zhuang Autonomous Region and Zhaotong in Yunnan Province. (2) The research hot regions on rocky desertification have good spatial consistency with the sensitivity regions of rocky desertification. At the prefecture level, the overlap rate of the two regions reached 85%. Because of the influence of topography, vegetation coverage, population distribution, traffic accessibility and other factors, there were regions that consisted of combinations of high sensitivity but low research popularity regarding rocky desertification; these sites included Qionglai Mountain-Liangshan Area of Sichuan, Wushan-Shennongjia Area of Hubei, Hengduan Mountain Area of western Yunnan and Dupangling Area of southern Hunan. (3) The research hot regions and sensitive regions cannot be matched completely in time, space and concept. Therefore, we can use their spatial distribution differences to improve the pertinence of planning, governance and study of rocky desertification.

scholarly journals Population distribution characteristics and its relationship with natural factors in karst mountainous areas of Northwest Guangxi

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Shana Shi ◽  
Bingkang Xie ◽  
Baoqing Hu ◽  
Chuanyong Tang ◽  
Yan Yan ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Population Density ◽  
Population Distribution ◽  
Basic Unit ◽  
Distribution Characteristics ◽  
Mountainous Areas ◽  
Mountain Area ◽  
Natural Factors ◽  
The Impact ◽  
Population Spatial Distribution

The smallest administrative unit of the sixth national census-township (town) is selected as the basic unit, the population spatial distribution characteristics at the township (town) level in karst mountainous areas of northwest Guangxi are analyzed by using Lorenz curve and spatial correlation analysis method, and the influence intensity of natural factors on regional population spatial distribution is detected by using geographic detector method. The results show that: 1. the spatial distribution of population at the township (town) level has the characteristics of imbalance, showing generally significant positive correlation and certain aggregation; 2. there are significant differences in the impact of the spatial distribution of various natural factors on the population distribution. For the towns without karst distribution in the northwest and central south of the study area, the population density increases with the increase of factors conducive to human residence, but the average population density is only 79 people / km2. In the towns with karst distribution in the East and south, the spatial distribution of population density and natural factors is not a simple increase or decrease relationship, but fluctuates with the change of karst distribution area. 3. The factor detection results of the geographic detector show that the altitude has the greatest impact on the spatial distribution of population. The interactive detection results show that the impact intensity of any two natural factors after superposition and interaction presents nonlinear enhancement and two factor enhancement. It can be seen that the karst mountain area in northwest Guangxi is similar to other areas. Altitude is one of the main factors affecting the spatial distribution of population, but the river network density and unique geological landform of karst mountain area have a strong catalytic effect on the spatial distribution of population. The superposition and interaction with other factors can further strengthen the impact on population distribution.

scholarly journals Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

2021 ◽  
Vol 13 (2) ◽  
pp. 284
Author(s):  
Dan Lu ◽  
Yahui Wang ◽  
Qingyuan Yang ◽  
Kangchuan Su ◽  
Haozhe Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Relative Error ◽  
Urban Areas ◽  
Large Scale ◽  
Population Distribution ◽  
Spatial Optimization ◽  
Distribution Data ◽  
Mountainous Areas ◽  
Mean Relative Error ◽  
Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

scholarly journals Optimization of Rocky Desertification Classification Model Based on Vegetation Type and Seasonal Characteristic

2021 ◽  
Vol 13 (15) ◽  
pp. 2935
Author(s):  
Chunhua Qian ◽  
Hequn Qiang ◽  
Feng Wang ◽  
Mingyang Li
Keyword(s):  
Learning Algorithm ◽  
Vegetation Type ◽  
Research Area ◽  
Classification Model ◽  
Guizhou Province ◽  
Model Accuracy ◽  
Spatiotemporal Evolution ◽  
Rocky Desertification ◽  
The North ◽  
Svm Model

Building a high-precision, stable, and universal automatic extraction model of the rocky desertification information is the premise for exploring the spatiotemporal evolution of rocky desertification. Taking Guizhou province as the research area and based on MODIS and continuous forest inventory data in China, we used a machine learning algorithm to build a rocky desertification model with bedrock exposure rate, temperature difference, humidity, and other characteristic factors and considered improving the model accuracy from the spatial and temporal dimensions. The results showed the following: (1) The supervised classification method was used to build a rocky desertification model, and the logical model, RF model, and SVM model were constructed separately. The accuracies of the models were 73.8%, 78.2%, and 80.6%, respectively, and the kappa coefficients were 0.61, 0.672, and 0.707, respectively. SVM performed the best. (2) Vegetation types and vegetation seasonal phases are closely related to rocky desertification. After combining them, the model accuracy and kappa coefficient improved to 91.1% and 0.861. (3) The spatial distribution characteristics of rocky desertification in Guizhou are obvious, showing a pattern of being heavy in the west, light in the east, heavy in the south, and light in the north. Rocky desertification has continuously increased from 2001 to 2019. In conclusion, combining the vertical spatial structure of vegetation and the differences in seasonal phase is an effective method to improve the modeling accuracy of rocky desertification, and the SVM model has the highest rocky desertification classification accuracy. The research results provide data support for exploring the spatiotemporal evolution pattern of rocky desertification in Guizhou.

scholarly journals Development and Assessment of a New Framework for Agricultural Nonpoint Source Pollution Control

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3156
Author(s):  
Lili Zhou ◽  
Runzhe Geng
Keyword(s):  
Spatial Distribution ◽  
Large Scale ◽  
Factor Model ◽  
Five Factor Model ◽  
Pollution Source ◽  
Nonpoint Source ◽  
Vegetation Coverage ◽  
Buffer Strip ◽  
Rural Living ◽  
Agricultural Nonpoint Source

The transport of agricultural nonpoint source (NPS) pollutants in water pathways is affected by various factors such as precipitation, terrain, soil erosion, surface and subsurface flows, soil texture, land management, and vegetation coverage. In this study, based on the transmission mechanism of NPS pollutants, we constructed a five-factor model for predicting the path-through rate of NPS pollutants. The five indices of the hydrological processes, namely the precipitation index (α), terrain index (β), runoff index (TI), subsurface runoff index (LI), and buffer strip retention index (RI), are integrated with the pollution source data, including the rural living, livestock and farmland data, obtained from the national pollution source census. The proposed model was applied to the headwater of the Miyun Reservoir watershed for identifying the areas with high path-through rates of agricultural NPS pollutants. The results demonstrated the following. (1) The simulation accuracy of the model is acceptable in mesoscale watersheds. The total nitrogen (TN) and total phosphorus (TP) agriculture loads were determined as 705.11 t and 3.16 t in 2014, with the relative errors of the simulations being 19.62% and 24.45%, respectively. (2) From the spatial distribution of the agricultural NPS, the TN and TP resource loads were mainly distributed among the upstream of Dage and downstream of Taishitun, as well as the towns of Bakshiying and Gaoling. The major source of TN was found to be farmland, accounting for 47.6%, followed by livestock, accounting for 37.4%. However, the path-through rates of TP were different from those of TN; rural living was the main TP source (65%). (3) The path-through rates of agricultural NPS were the highest for the towns of Wudaoying, Dage, Tuchengzi, Anchungoumen, and Huodoushan, where the path-through rate of TN ranged from 0.17 to 0.26. As for TP, it was highest in Wudaoying, Kulongshan, Dage, and Tuchengzi, with values ranging from 0.012 to 0.019. (4) A comprehensive analysis of the distribution of the NPS pollution load and the path-through rate revealed the towns of Dage, Wudaoying, and Tuchengzi as the critical source areas of agricultural NPS pollutants. Therefore, these towns should be seriously considered for effective watershed management. In addition, compared with field monitoring, the export coefficient model, and the physical-based model, the proposed five-factor model, which is based on the path-through rate and the mechanism of agricultural NPS pollutant transfer, cannot only obtain the spatial distribution characteristics of the path-through rate on a field scale but also be applicable to large-scale watersheds for estimating the path-through rates of NPS pollutants.

scholarly journals Spatial distribution, temporal stability, and yield loss estimates for annual grasses and common ragweed (Ambrosia artimisiifolia) in a corn/soybean production field over nine years

Weed Science ◽  
2006 ◽  
Vol 54 (02) ◽  
pp. 380-390 ◽  
Author(s):  
Sharon A. Clay ◽  
Bruce Kreutner ◽  
David E. Clay ◽  
Cheryl Reese ◽  
Jonathan Kleinjan ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Weed Management ◽  
Yield Loss ◽  
Temporal Stability ◽  
Grid Cell ◽  
Specific Information ◽  
Common Ragweed ◽  
Soybean Production ◽  
Time Space ◽  
Annual Grasses

Weeds generally occur in patches in production fields. Are these patches spatially and temporally stable? Do management recommendations change on the basis of these data? The population density and location of annual grass weeds and common ragweed were examined in a 65-ha corn/soybean production field from 1995 to 2004. Yearly treatment recommendations were developed from field means, medians, and kriging grid cell densities, using the hyperbolic yield loss (YL) equation and published incremental YL values (I), maximum YL values (A), and YL limits of 5, 10, or 15%. Mean plant densities ranged from 12 to 131 annual grasses m−2and < 1 to 37 common ragweed m−2. Median weed densities ranged from 0 to 40 annual grasses m−2and were 0 for common ragweed. The grassIvalues used to estimate corn YL were 0.1 and 2% and treatment was recommended in only 1 yr when the highIvalue and either the mean or median density was used. The grassIvalues used for soybean were 0.7 and 10% and estimated YL was over 10% all years, regardless ofIvalue. The common ragweedIvalues were 4.5 and 6% for corn and 5.1 and 15.6% for soybean. On the basis of mean densities, fieldwide treatment would have been recommended in 6 of 9 yr but in no years when the median density was used. Recommendations on the basis of grid cell weed density and kriging ranged from > 80% of the field treated for grass weeds in 3 of 4 yr in soybean to < 20% of the field treated for common ragweed in 2002 and 2004 (corn). Grass patches were more stable in time, space, and density than common ragweed patches. Population densities and spatial distribution generally were variable enough so that site-specific information within this field would improve weed management decisions.

scholarly journals Discussing the Genesis of Karst Rocky Desertification-research Based on the Correlations between Crop land and Settlement in Typical Peak-cluster Depressions

2016 ◽  
Author(s):  
Yang Bing Li ◽  
Qiong Yao Li ◽  
Guang Jie Luo ◽  
Xiao Yong Bai ◽  
Yong Yan Wang ◽  
...  
Keyword(s):  
Population Pressure ◽  
Guizhou Province ◽  
Residential Areas ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Peak Cluster ◽  
Natural Mechanism ◽  
The Relationship ◽  
Local Farmers

Abstract. This paper attempts to explain the theoretical reasons why the local farmers took irrational activities such as steep slope land cultivations in order to reveal the mechanism of Karst Rocky Desertification (KRD) through those typical case studies. Firstly, this paper assumes that the low land capacity is the genesis cause of KRD in peak cluster-depression areas. Furthermore, the ecological quality of the peak cluster-depression zone is influenced by the relationship between the area of depressions and the population of residential areas. The results show that, six typical peak cluster-depression areas in Guizhou Province were selected to compare the distribution circumstances of croplands, the characteristics of settlements and the formation of KRD. Also, the results show that there is a negative correlation between the percentage of the cultivated land and the percentage of KRD (including light KRD, moderate KRD and severe KRD at peak cluster-depressions. The relationship could be concluded as three situations of the process of KRD, which are low, middle and upper carrying capacity of land. The severe KRD is only distributed in peak-cluster depression areas with less flatland, low land capacity and high population. The harmonization between population pressure and bearing capacity of land will influence the ecological qualities in the peak cluster depressions. Therefore, the hypothesis suggested by this paper is correct, and this result will contribute to understanding the natural mechanism of KRD and guide the ecological restoration of KRD land.

Spatial distribution of marine chemicals along a transect from Zhongshan Station to the Grove Mountain area, Eastern Antarctica

2014 ◽  
Vol 57 (10) ◽  
pp. 2366-2373
Author(s):  
Xiang Qin ◽  
ChuanJin Li ◽  
CunDe Xiao ◽  
ShuGui Hou ◽  
MingHu Ding ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Mountain Area ◽  
Zhongshan Station ◽  
Marine Chemicals

scholarly journals Population Distribution in the Wake of a Sphere

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1498 ◽  
Author(s):  
Taraprasad Bhowmick ◽  
Yong Wang ◽  
Michele Iovieno ◽  
Gholamhossein Bagheri ◽  
Eberhard Bodenschatz
Keyword(s):  
Reynolds Number ◽  
Spatial Distribution ◽  
Population Density ◽  
Density Distribution ◽  
Population Distribution ◽  
Fluid Velocity ◽  
Three Dimensional ◽  
Wake Flow ◽  
Sample Number ◽  
Scalar Fluxes

The physics of heat and mass transfer from an object in its wake has significant importance in natural phenomena as well as across many engineering applications. Here, we report numerical results on the population density of the spatial distribution of fluid velocity, pressure, scalar concentration, and scalar fluxes of a wake flow past a sphere in the steady wake regime (Reynolds number 25 to 285). Our findings show that the spatial population distributions of the fluid and the transported scalar quantities in the wake follow a Cauchy-Lorentz or Lorentzian trend, indicating a variation in its sample number density inversely proportional to the squared of its magnitude. We observe this universal form of population distribution both in the symmetric wake regime and in the more complex three dimensional wake structure of the steady oblique regime with Reynolds number larger than 225. The population density distribution identifies the increase in dimensionless kinetic energy and scalar fluxes with the increase in Reynolds number, whereas the dimensionless scalar population density shows negligible variation with the Reynolds number. Descriptive statistics in the form of population density distribution of the spatial distribution of the fluid velocity and the transported scalar quantities is important for understanding the transport and local reaction processes in specific regions of the wake, which can be used e.g., for understanding the microphysics of cloud droplets and aerosol interactions, or in the technical flows where droplets interact physically or chemically with the environment.

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