Spatio-temporal evolution and future scenario prediction of karst rocky desertification based on CA–Markov model

Abstract. Karst rocky desertification (KRD) is used to characterize the processes that transform a karst area covered by vegetation and soil into a rocky landscape almost devoid of soil and vegetation. This situation seriously affects and threatens the living environment and standards of local people, which results in a series of social problems. In view of the importance and harmfulness of KRD, many scholars have studied the spatial and temporal evolution of KRD and its driving forces. In this paper, the Visual Interpretation Marks of Rocky Desertification in Southwest China in 1960s are constructed by using the DISP image of the United States, combined with DEM data and Hydrogeological data. The area of rocky desertification in Guangnan and Funing counties, where rocky desertification is more serious, is about 2457.729 km2. The area of rocky desertification can be used as the basic data for studying the historical changes in southwestern China by researchers.

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Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification

Geocarto International ◽

10.1080/10106049.2019.1595175 ◽

2019 ◽

pp. 1-19 ◽

Cited By ~ 4

Author(s):

Fei Chen ◽

Shijie Wang ◽

Xiaoyong Bai ◽

Fang Liu ◽

Dequan Zhou ◽

...

Keyword(s):

Temporal Evolution ◽

Driving Forces ◽

Karst Rocky Desertification ◽

Rocky Desertification

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Spatiotemporal Evolution Analysis and Future Scenario Prediction of Rocky Desertification in a Subtropical Karst Region

Remote Sensing ◽

10.3390/rs14020292 ◽

2022 ◽

Vol 14 (2) ◽

pp. 292

Author(s):

Chunhua Qian ◽

Hequn Qiang ◽

Changyou Qin ◽

Zi Wang ◽

Mingyang Li

Keyword(s):

Sustainable Development ◽

Markov Model ◽

Landscape Change ◽

Ecological Environment ◽

Karst Region ◽

Dynamic Feature ◽

Karst Rocky Desertification ◽

Rocky Desertification ◽

Karst Landform ◽

And Function

Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable development. In this paper, remote sensing imagery and machine learning methods are utilized to model and analyze the spatiotemporal variation of rocky desertification in Guizhou. Based on an improved CA-Markov model, rocky desertification scenarios in the next 30 years are predicted, providing data support for exploration of the evolution rule of rocky desertification in subtropical karst areas and for effective management. The specific results are as follows: (1) Based on the dynamic degree, transfer matrix, evolution intensity, and speed, the temporal and spatial evolution of rocky desertification in Guizhou from 2001 to 2020 was analyzed. It was found that the proportion of no rocky desertification (NRD) areas increased from 48.86% to 63.53% over this period. Potential rocky desertification (PRD), light rocky desertification (LRD), middle rocky desertification (MRD), and severe rocky desertification (SRD) continued to improve, with the improvement showing an accelerating trend after 2010. (2) An improved CA-Markov model was used to predict the future rocky desertification scenario; compared to the traditional CA-Markov model, the Lee–Sallee index increased from 0.681 to 0.723, and figure of merit (FOM) increased from 0.459 to 0.530. The conclusions of this paper are as follows: (1) From 2001 to 2020, the evolution speed of PRD was the fastest, while that of SRD was the slowest. Rocky desertification control should not only focus on areas with serious rocky desertification, but also prevent transformation from NRD to PRD. (2) Rocky desertification will continue to improve over the next 30 years. Possible deterioration areas are concentrated in high-altitude areas, such as the south of Bijie and the east of Liupanshui.

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Spatio-temporal evolution of a transversally excited electrical discharge in Nitrogen

Journal de Physique II ◽

10.1051/jp2:1991203 ◽

1991 ◽

Vol 1 (9) ◽

pp. 1021-1031 ◽

Cited By ~ 11

Author(s):

N. Spyrou ◽

Ch. Manassis

Keyword(s):

Electrical Discharge ◽

Temporal Evolution ◽

Spatio Temporal

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Spatio-temporal evolution and dynamic origin of Jurassic-Cretaceous magmatism in the South China Block

Earth-Science Reviews ◽

10.1016/j.earscirev.2021.103605 ◽

2021 ◽

Vol 217 ◽

pp. 103605

Author(s):

Xianzhi Cao ◽

Nicolas Flament ◽

Sanzhong Li ◽

R. Dietmar Müller

Keyword(s):

South China ◽

Temporal Evolution ◽

South China Block ◽

The South ◽

Spatio Temporal ◽

Cretaceous Magmatism

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Spatio-temporal evolution and influencing mechanism of the COVID-19 epidemic in Shandong province, China

Scientific Reports ◽

10.1038/s41598-021-86188-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jinlong Shi ◽

Xing Gao ◽

Shuyan Xue ◽

Fengqing Li ◽

Qifan Nie ◽

...

Keyword(s):

Prevention And Control ◽

Temporal Evolution ◽

Geographical Location ◽

Shandong Province ◽

Control Measures ◽

Middle Aged ◽

Health Crisis ◽

Aged People ◽

Spatio Temporal ◽

And Control

AbstractThe novel coronavirus pneumonia (COVID-19) outbreak that emerged in late 2019 has posed a severe threat to human health and social and economic development, and thus has become a major public health crisis affecting the world. The spread of COVID-19 in population and regions is a typical geographical process, which is worth discussing from the geographical perspective. This paper focuses on Shandong province, which has a high incidence, though the first Chinese confirmed case was reported from Hubei province. Based on the data of reported confirmed cases and the detailed information of cases collected manually, we used text analysis, mathematical statistics and spatial analysis to reveal the demographic characteristics of confirmed cases and the spatio-temporal evolution process of the epidemic, and to explore the comprehensive mechanism of epidemic evolution and prevention and control. The results show that: (1) the incidence rate of COVID-19 in Shandong is 0.76/100,000. The majority of confirmed cases are old and middle-aged people who are infected by the intra-province diffusion, followed by young and middle-aged people who are infected outside the province. (2) Up to February 5, the number of daily confirmed cases shows a trend of “rapid increase before slowing down”, among which, the changes of age and gender are closely related to population migration, epidemic characteristics and intervention measures. (3) Affected by the regional economy and population, the spatial distribution of the confirmed cases is obviously unbalanced, with the cluster pattern of “high–low” and “low–high”. (4) The evolution of the migration pattern, affected by the geographical location of Wuhan and Chinese traditional culture, is dominated by “cross-provincial” and “intra-provincial” direct flow, and generally shows the trend of “southwest → northeast”. Finally, combined with the targeted countermeasures of “source-flow-sink”, the comprehensive mechanism of COVID-19 epidemic evolution and prevention and control in Shandong is revealed. External and internal prevention and control measures are also figured out.

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