Detection and Prediction of Sundarban Reserve Forest using the CA-Markov Chain Model and Remote Sensing Data

Kundu, Krishan; Halder, Prasun; Mandal, Jyotsna Kumar

doi:10.1007/s12145-021-00648-9

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…Drought prediction as one of the strategies to deal with this natural phenomenon and reduce its destructive effects is something that has attracted the attention of meteorological and agricultural experts in recent years, and various activities have been carried out to monitor and predict this phenomenon by related organizations (Kundu et al 2021)and (Javadinejad et al 2022) .…”

Section: )mentioning

confidence: 99%

Analysis and forecasting drought characterisation with using Markov chain model

Javadinejad¹,

Dara²,

Krause

et al. 2023

Saf. Extreme Environ.

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Drought is one of the inseparable parts of climate uctuations that cause a lot of damage every year. Considering the effects of drought on different parts of the environment, agriculture, natural resources, wildlife, etc., its prediction can be useful for managing the crisis and reducing the damages caused by it. In the current research, monthly drought was calculated based on the standard precipitation index in several stations in the south of Iran during the years 1980-2020; Then, using the Markov chain, monthly drought was predicted for the years 2020 to 2040. According to the results, most of the stations have normal, moderate and severe drought conditions.The transition probability matrix showed that in all stations, the probability of passing from a certain state to the same state and the probability of passing from wet to dry state is high; But the probability of transition from dry to wet is low. Also, the predictive results were measured at different stations with different levels of accuracy.In addition, the results showed that the highest probability of drought in the years 2020-2040 is related to normal, moderate and severe classes, and at the level of the studied area, from class one to seven, the 13.4, 26.81, 27,74, 37.11, 4.76, 2.88, and 0.70% of the predicted months drought will happen respectively.

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Section: )mentioning

confidence: 99%

Analysis and forecasting drought characterisation with using Markov chain model

Javadinejad¹,

Dara²,

Krause

et al. 2023

Saf. Extreme Environ.

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show abstract

“…The CA-Markov model was used for future LUCCs, where the kappa coefficient values were determined to be within −1 to 1. The value descriptions kappa ≤ 0.5, 0.5 ≤ kappa ≤ 0.75, and 0.75 ≤ kappa < 1 indicate a low exception, a medium exception, and a very high exception [51,52]. These values are represented by Equations ( 7)- (9).…”

Section: Driver Variable Determination and Model Validation For Lucc ...mentioning

confidence: 99%

Geospatial Analysis of Land Use/Cover Change and Land Surface Temperature for Landscape Risk Pattern Change Evaluation of Baghdad City, Iraq, Using CA–Markov and ANN Models

et al. 2022

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Understanding future landscape risk pattern change (FLRPC) scenarios will help people manage and utilize natural resources. In this study, we have selected a variety of landscape and anthropogenic factors as risk parameters for FLRPC assessment. Land use/cover change (LUCC) and land surface temperature (LST) are regarded as significant factors that have resulted in large-scale environmental changes. Result analysis of the previous LUCC from 1985 to 2020 showed that construction land and water body (WB) increased by 669.09 and 183.16 km2, respectively. The study continues to predict future LUCC from 2030 to 2050, in which the result has shown that a large land use conversion occurred during the future prediction period. In addition, the LST forecasting analysis illustrated that the previous LST maximum and minimum are 38 °C and 15 °C, which will be increased to 40.83 °C and 26.25 °C in the future, respectively. Finally, the study used the weighted overlay method for the FLRPC analysis, which applies analytic hierarchy process techniques for risk evaluation. The FLRPC result demonstrated that Baghdad City is in the low-risk and medium-risk to high-risk categories from 2020 to 2050, while AL and BL are in the very-high-risk categories. Meanwhile, WB and NG have always been safe, falling into the very-low-risk and low-risk categories from 2020 to 2050. Therefore, this study has successfully assessed the Baghdad metropolitan area and made recommendations for future urban development for a more safe, resilient, and sustainable development.

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“…(1) Among them: U represents the area of a certain forest use type; a, b is the time node of the start and end of the study; T is the total time. The CA simulation system is composed of states, fields, cells and transformation rules [10]. Generally, a 5x5 Moore field is used as a filter.…”

Section: Establishment Of a Dynamic Prediction Model For Forest Land ...mentioning

confidence: 99%

Study on forest land change dynamics based on CA-Markov and FORECAST models

Wang¹,

Li²,

Yang³

et al. 2022

BCPBM

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In order to study the dynamics of forest land change in Sichuan province, this paper developed a CA-Markov model to estimate the total amount of CO2 stored in the region in the future 100 years. Then, the visualization results of forest land change are combined with forest comprehensive value to provide suggestions for forest managers in the region. Finally, this prediction model was used to study the impact of different rotation periods on forest carbon sinks of specific tree species in the study area.

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Detection and Prediction of Sundarban Reserve Forest using the CA-Markov Chain Model and Remote Sensing Data

Cited by 15 publications

References 26 publications

Analysis and forecasting drought characterisation with using Markov chain model

Analysis and forecasting drought characterisation with using Markov chain model

Geospatial Analysis of Land Use/Cover Change and Land Surface Temperature for Landscape Risk Pattern Change Evaluation of Baghdad City, Iraq, Using CA–Markov and ANN Models

Study on forest land change dynamics based on CA-Markov and FORECAST models

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