Using RS Data-Based CA–Markov Model for Dynamic Simulation of Historical and Future LUCC in Vientiane, Laos

Abstract

: Land use/cover change (LUCC) is one of the causes of global climate and environmental change. Understanding rapid LUCC in urbanized areas is vital for natural resources management for sustainable development. This study primarily considered Vientiane, the capital of Laos, which experienced rapid LUCC due to both natural and anthropogenic factors. The study used geographical information system (GIS) combined with ERDAS and TerrSet technologies to objectively process the ground surveyed and remotely obtained data in order to investigate the historical LUCC as well as predict future LUCC in the study area during the periods of 1995–2018 and 2030–2050, respectively. A comprehensive list of assessment factors comprised of both natural and anthropogenic factors was used for analysis using the cellular automata–Markov (CA–Markov) model. The results show a historical loss of intact forest of 24.36% and of bare land of 1.01%. There were also tremendous increases in degraded forest (11.36%), agricultural land (8.91%), built-up areas (4.49%) and water bodies (1.16%). Finally, the LUCC prediction results indicate the conversion of land use from one type to another, particularly from natural to anthropogenic use, in the near future. These changes demonstrate that the losses associated with ecosystem services will destructively impact human wellbeing in the city and other areas of the country. The study results provide the basic scientific knowledge for LUCC planners, urban designers and natural resources managers. They serve as a decision-making support tool for the establishment of sustainable land resource utilization policies in Vientiane and other cities of similar conditions.

Identification and Prediction of Wetland Ecological Risk in Key Cities of the Yangtze River Economic Belt: From the Perspective of Land Development

“…The Kappa coefficient is between −1 and 1. CA-Markov model can reconstruct the spatial pattern of land use in the future based on the quantitative prediction of Markov model, so as to improve the efficiency of land use model [58][59][60]. The specific steps are as follows: [61,62] and the characteristics of the study area, this study took wetland as the limiting factor.…”

Spatio-Temporal Classification and Prediction of Land Use and Land Cover Change for the Vembanad Lake System, Kerala – a Machine Learning Approach

“…To summarise, it was found that GEE aids in the study of LULC shift in a cost-effective and timeconsuming manner, and it is commonly used in the literature (Agarwal and Nagendra 2019;Gomes et al 2020;Noi Phan et al 2020;Sidhu et al 2018;Tamiminia et al 2020;Tassi and Vizzari 2020;Xing et al 2021). CA -Markov chain model helps in determining the future land cover change and their land-use patterns for decision-makers to provide sustainable development (Aburas et al 2018;Ansari and Golabi 2019;Bose and Chowdhury 2020;Faichia et al 2020;Fu et al 2018;116 Ghosh et al 2017;Gidey et al 2017;Halmy et al 2015;Hamad et al 2018;Ozturk 2015). 117 In this work, LULC was classified in the GEE platform using two standard machine learning 118 approaches, the Random Forest (RF) classifier and the Support Vector Machines (SVM).…”

Monitoring the changes in land use and landscape pattern in recent 20 years: A case study in Wuhan, China

“…Land use and cover change (LUCC) depicts the spatial pattern of the earth's surface, and reflects the interaction between natural processes and human society, as well as serves as an important part of global environmental change and sustainable development [1][2][3]. Previous researches suggest that with the in-depth development of the urbanization process, significant changes have taken place in land use and landscape pattern in many places around the world [4,5].…”

“…Our main objectives in this study are: (1) to examine the changes of land use using time-series images taken in 2000, 2010, and 2020; (2) to investigate the variations of landscape pattern by calculating landscape metrics; (3) to provide scientific suggestions for optimizing land use and landscape pattern.…”