A Land-Use Change Model to Support Land-Use Planning in the Mekong Delta (MEKOLUC)

Abstract

Agricultural land-use changes pose challenges for land managers in terms of ensuring the implementation of local land-use plans. This paper aims to build a land-use change model named MEKOLUC (Mekong Delta land-use change) for simulating land-use changes under the impacts of socioeconomic factors (profitability of land-use types, societal impacts on neighborhoods) and environmental factors (soil, salinity, persistence of salinity). The salinity diffusion map was generated using GAMA software and employed Markov cellular automata to depict the spread of salinity under the influence of dike and sluice gate system operations. The land-use decision-making process was based on multi-criteria selection of the main factors, which were land suitability, land convertibility, density of land use in the neighborhood and profitability of land-use patterns. The input data for the case study were historical land-use maps from 2005, 2010 and 2015 of Soc Trang, a coastal province in the Mekong Delta. The model was calibrated using a land-use map from 2010 (with kappa = 0.86) and was verified with land-use maps from 2015 and 2020 with deviations from 0 to 19%. The simulated results showed that shrimp–rice farming areas have been shrinking, even though these are recommended as sustainable farming systems. Inversely, intensive rice crops tended to change to rice–vegetable crops, vegetable crops or perennial fruit trees, which are projected to be well adapted to climate and salinity intrusion by 2030. This case study shows that the developed model is an essential tool for helping land managers and farmers build land-use plans.

Modelling vegetation land fragmentation in urban areas of Western Province, Sri Lanka using an Artificial Intelligence-based simulation technique

“…recent research has used AI-based modeling tools [1,22,[47][48][49]. However, developing an AIbased modeling framework with multiple factors to explicitly model vegetation land fragmentation is a knowledge gap that needs to be investigated to control vegetation land fragmentation and its consequences [3,27].…”

“…Because vegetation land fragmentation is caused by various variables in a complex environment, the traditional linear regression model is unable to convey the relevance of influencing factors [ 1 , 21 ]. To examine nonlinear interactions in the urban setting, several recent research has used AI-based modeling tools [ 1 , 22 , 47 – 49 ]. However, developing an AI-based modeling framework with multiple factors to explicitly model vegetation land fragmentation is a knowledge gap that needs to be investigated to control vegetation land fragmentation and its consequences [ 3 , 27 ].…”

Urban land use transitions: Examining change over 19 years using sequence analysis. The case of South-East Queensland, Australia

“…There have been attempts to include a spatial component into discrete outcome models (Beron and Vijverberg, 2004) which continues to present ongoing methodological and computational challenges (Billé and Arbia, 2013). Unlike probabilistic models, CA-based techniques are founded on a set of deterministic rules (Clarke and Gaydos, 1998;Truong et al, 2022) and employ remotely sensed data and draw on a range of data from various disparate sources, converting each into raster layers.…”

Agricultural Land Use Planning on Sustained Basis Using Landform and Land Evaluation Approach with Geospatial Analysis (a case study in East Barito District, Central Kalimantan Province)

“…Therefore, the proper land use can determine and indicate areas of lands that suitable for specific kind of land use while at the same time safeguarding balanced environment. In the term of sustainability, in addition to plays an important role in developed areas, it also leads to the protection and enhancement of the environment being used for future utilization with proper guide human activity and intervention [4,5].…”