Modelling land use/cover change in Lake Mogan and surroundings using CA-Markov Chain Analysis

Durmusoglu, Z. O.; Tanriover, A. Akin

doi:10.22438/jeb/38/5(si)/gm-15

Cited by 11 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Markov model is a kind of special random motion without after effect (Culik, Hurd, & Yu, 1990), which is applied in the simulation and prediction of many geographical phenomena (Chuang, Lin, Chien, & Chou, 2011;Durmusoglu & Tanriover, 2017). The basic principle of Markov model is to use the empirical transfer probability of discrete states in the observing system to determine the state trend in the system and predict the future state.…”

Section: Ca-markov Modelmentioning

confidence: 99%

Study on NDVI changes in Weihe Watershed based on CA–Markov model

Wang

Liu

et al. 2018

Geological Journal

View full text Add to dashboard Cite

The simulation and prediction of the NDVI are conducive to regional ecological restoration. In this research, the Weihe Watershed is selected as the study area, which is located on the eastern edge of the Tianshan tectonic belt. Firstly, according to NDVI characters, we divide the values into five grades, including very low (<0.3), lower (0.3–0.4), medium (0.4–0.5), higher (0.5–0.6), and very high (>0.6), and analyse the NDVI tempo‐spatial variations in Weihe Watershed from 2006 to 2015. Then, we use CA–Markov model with parameters adjustment to simulate and predict the NDVI distributions in 2015 and 2020, respectively. The results show that (a) at temporal scales, the NDVI in Weihe Watershed increase year by year from 2006 to 2015, indicating a better vegetation recovery. At spatial scales, the NDVI increase from north‐west to south‐east gradually, which is related with the main climate factors such as temperature and precipitation. (b) By adjusting the parameters of state transition suitability atlas in the Markov model, we get the model with higher accuracy to simulate NDVI changes. And the Kappa coefficient between the simulation results and the true values is 0.6123. (c) According to the predictions in 2020, the regions with very high NDVI and higher NDVI mainly locate in Qinling Mountains, Guanzhong Plain, Ziwu Mountains, Jinghe Valley, and Luohe Valley. And these regional area percentages occupying the whole watershed increased by 13.62% and 2.64%, respectively, compared with the area percentages in 2015. The regions with medium NDVI, lower NDVI, and very low NDVI mainly distribute at the north Loess Plateau of the watershed. And these regional area percentages occupying the whole watershed decreased by 7.40%, 6.59%, and 2.28%, respectively, compared with the area percentages in 2015. The above results illustrate that the ecological environment of Weihe Watershed is improving, which can provide scientific reference for assessing the regional ecological risk and formulating ecological protection measures.

show abstract

Section: Ca-markov Modelmentioning

confidence: 99%

Study on NDVI changes in Weihe Watershed based on CA–Markov model

Wang

Liu

et al. 2018

Geological Journal

View full text Add to dashboard Cite

show abstract

“…Recently, dictionary learning has been employed, and it focuses on learning internal feature representations from datasets [141,176,224,225], just like AEs. The cellular automata (CA), a spatially and temporally discrete model inspired by cellular behavior, can help to model future changes in LULC [226] and predict urban spatial expansion [227]. The development of these AI techniques has significantly promoted research on change detection, which helps to develop more automatic, intelligent and accurate methods to meet the needs of various applications.…”

Section: Other Artificial Neural Network and Ai Methodsmentioning

confidence: 99%

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

View full text Add to dashboard Cite

Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

show abstract

“…The Markov model, i.e., the Markov chain (Oliveira Barros et al, 2018), based on probability theory, was used to simulate future land use changes in a stochastic state (that is, a shift with a certain probability from one period to another), and this state was related only to the present and not related to the past and future. This model is fit for predicting long-term trends and has been widely used to simulate future land use change (Iacono et al, 2015;Durmusoglu and Akın Tanrıöver, 2017). The probability transfer matrix is the key to this model.…”

Section: Markov Modelmentioning

confidence: 99%

Path of carbon emission reduction through land use pattern optimization under future scenario of multi-objective coordination

Dong

Huang

Zhang

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

Land use change is one of the crucial factors affecting carbon emissions. The continuously increasing CO2 and global warming have raised concerns about carbon emission reduction in the process of urbanization. In this research, the Markov and multi-objective optimization models were conducted to predict the demands for land use in Nanjing in 2030 and 2060 under the natural growth (NG) and minimum carbon emission (MCE) scenarios to coordinate the needs of economic development, ecological protection and food security as well as the target of carbon emissions reduction in the future. The spatial distribution of land use simulated by the FLUS (Future Land Use Simulation) model was used to evaluate the effects of future land use on carbon emissions. The results showed that 1) The demands for each type of land use in the NG scenario were significantly different from those in the MCE scenario. Considering the goals of food security and ecological protection in the future, the total amount of cultivated land would not decrease in the MCE scenario, and the area of construction land was significantly smaller than that in the NG scenario. 2) The carbon emissions of Nanjing under the MCE scenario would decrease by 3.94 and 11.80 million tons in 2030 and 2060, respectively, accounting for 9.97% and 27.17% of the total carbon emissions. The optimization of land use patterns can effectively reduce carbon emissions in the process of urbanization.

show abstract

Modelling land use/cover change in Lake Mogan and surroundings using CA-Markov Chain Analysis

Cited by 11 publications

References 28 publications

Study on NDVI changes in Weihe Watershed based on CA–Markov model

Study on NDVI changes in Weihe Watershed based on CA–Markov model

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Path of carbon emission reduction through land use pattern optimization under future scenario of multi-objective coordination

Contact Info

Product

Resources

About