Land Use and Land Cover Change Monitoring and Prediction of a UNESCO World Heritage Site: Kaziranga Eco-Sensitive Zone Using Cellular Automata-Markov Model

Abstract

The Kaziranga Eco-Sensitive Zone is located on the edge of the Eastern Himalayan biodiversity hotspot region. In 1985, the Kaziranga National Park (KNP) was declared a World Heritage Site by UNESCO. Nowadays, anthropogenic interference has created a significant negative impact on this national park. As a result, the area under natural habitat is gradually decreasing. The current study attempted to analyze the land use land cover (LULC) change in the Kaziranga Eco-Sensitive Zone using remote sensing data with CA-Markov models. Satellite remote sensing and the geographic information system (GIS) are widely used for monitoring, mapping, and change detection of LULC change dynamics. The changing rate was assessed using thirty years (1990–2020) of Landsat data. The study analyses the significant change in LULC, with the decrease in the waterbody, grassland and agricultural land, and the increase of sand or dry river beds, forest, and built-up areas. Between 1990 and 2020, waterbody, grassland, and agricultural land decreased by 18.4, 9.96, and 64.88%, respectively, while sand or dry river beds, forest, and built-up areas increased by 103.72, 6.96, and 89.03%, respectively. The result shows that the area covered with waterbodies, grassland, and agricultural land is mostly converted into built-up areas and sand or dry river bed areas. According to this study, by 2050, waterbodies, sand or dry river beds, and forests will decrease by 3.67, 3.91, and 7.11%, respectively; while grassland and agriculture will increase by up to 16.67% and 0.37%, respectively. The built-up areas are expected to slightly decrease during this period (up to 2.4%). The outcome of this study is expected to be useful for the long-term management of the Kaziranga Eco-Sensitive Zone.

Integrating climate change adaptation policies in spatial development planning in hyperarid regions of Kerman province, Iran

“…Among the methods used, Cellular Automata (CA) [ 30 ], Markov Chain Model (MCM) [ 31 ], Artificial Neural Network (ANN) [ 32 ], Automated Cellular Fusion Methods with Markov Model (ACFM-MM) [ 33 ], Scenario Generator (SG) [ 34 ], SLUCE [ 35 ], GEOMOD [ 36 ] are the best known models for the LCCs evaluation.…”

Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS

“…Meanwhile, the transfer matrix can define the important processes of changes in land cover [107]; we can obtain the temporal and spatial changes in different land cover types and understand the overall status of regional ecosystem service functions from land cover change. In addition, the use of long time series data also provides opportunities for the prediction of land cover in the future, such as the dynamics of land system (DLS) model [65], land change evaluation model [66], CA-Markov model [68,108,109] [111]; however, the above methods also had some limitations. The DLS model required multiple simulations to determine the optimal model parameters.…”

“…The results showed that Landsat 8 had greater advantages over Sentinel 2 in the monitoring of forests, herbaceous vegetation, and water; the former was more accurate [64]. The use of long time series data also provided opportunities for the forecasting of land cover and desert greening in the future, such as a dynamics of land system (DLS) model [65], land change evaluation model [66], CA-Markov model [44,67,68], and GM (1,1) model [69,70]. Among them, the GM (1,1) model can build mathematical models and make forecasts based on a small amount of incomplete information and data by considering the law of the past and present development of objective things [71].…”

Impact of Climate Change and Land Use Land Cover Change on Stream Flow of the Teesta River Basin under CMIP6 Climate Scenarios

“…The CA-Markov model, integrating Cellular Automata (CA) and Markov Chain approaches, predicts spatiotemporal LULC changes. While it may have limitations in capturing the impact of individual cells on the entire space, CA-Markov excels in accurately forecasting transitions between LULC states based on prior conditions (Hossein et al, 2023;Nath et al, 2023). This integrated model offers advantages over traditional methods, providing more reasonable and accurate LULC transition predictions (Long et al, 2021;Matlhodi et al, 2021).…”

“…Acknowledging model limitations related to data and technology support, this study employs the Cellular Automata-Markov (CA-Markov) model for LULC modeling and the Soil Water Assessment Tool (SWAT) for basin-scale flow modeling. CMIP6 datasets contribute to robust simulations, emphasizing the importance of integrating technological progress and data availability in advancing our understanding of complex interactions within river basins (Aksoy & Kaptan, 2022;Girma et al, 2022;Hossein et al, 2023;Nath et al, 2023).…”