Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Selangor, Malaysia

Abstract

Land use land cover (LULC) has altered dramatically because of anthropogenic activities, particularly in places where climate change and population growth are severe. The geographic information system (GIS) and remote sensing are widely used techniques for monitoring LULC changes. This study aimed to assess the LULC changes and predict future trends in Selangor, Malaysia. The satellite images from 1991–2021 were classified to develop LULC maps using support vector machine (SVM) classification in ArcGIS. The image classification was based on six different LULC classes, i.e., (i) water, (ii) developed, (iii) barren, (iv) forest, (v) agriculture, and (vi) wetlands. The resulting LULC maps illustrated the area changes from 1991 to 2021 in different classes, where developed, barren, and water lands increased by 15.54%, 1.95%, and 0.53%, respectively. However, agricultural, forest, and wetlands decreased by 3.07%, 14.01%, and 0.94%, respectively. The cellular automata-artificial neural network (CA-ANN) technique was used to predict the LULC changes from 2031–2051. The percentage of correctness for the simulation was 82.43%, and overall kappa value was 0.72. The prediction maps from 2031–2051 illustrated decreasing trends in (i) agricultural by 3.73%, (ii) forest by 1.09%, (iii) barren by 0.21%, (iv) wetlands by 0.06%, and (v) water by 0.04% and increasing trends in (vi) developed by 5.12%. The outcomes of this study provide crucial knowledge that may help in developing future sustainable planning and management, as well as assist authorities in making informed decisions to improve environmental and ecological conditions.

Prioritization of Susceptible Watershed to Sediment Yield and Evaluation of Best Management Practice: A Case Study of Awata River, Southern Ethiopia

“…e nature and characteristics of underlying soils determine how a river basin reacts to rainfall events and the susceptibility of a watershed to erosion. For di erent layers of each soil type, the SWAT model requires di erent soil types and physiochemical parameters such as soil texture, available water content, hydraulic conductivity, bulk density, and organic carbon content [25]. e study area is covered by ve dominant soil types as listed in Table 2.…”

Assessment of Land Cover and Land Use Change Dynamics in Kibwezi Watershed, Kenya

“…Tis growth has led to the clearing of shrubs for built-up area and also for agricultural activities. Te fndings are similar to [12,27], where population growth and GDP plays a big role in LULC conversion.…”

“…Rapid LULC change is occurring largely in the developing world [ 9 , 10 ] with natural vegetation converted to agricultural land [ 11 , 12 ] which has resulted in a decline in water, soil, and vegetation resources [ 13 ]. It affects hydrological processes in the watershed such as evapotranspiration, interception, and infiltration, consequently altering surface and subsurface flows [ 14 , 15 ].…”

Simulation of Land Use Pattern Based on Land Ecological Security: A Case Study of Guangzhou, China

“…The first is quantitative prediction models, including the Markov [ 13 ] and system dynamics (SD) models [ 14 ], which are mainly used to describe the structural and quantitative characteristics of the land use change process and cannot describe changes at spatial scales. The second is spatial models, such as the cellular automata (CA) model [ 15 ]; the agent-based model (ABM) [ 16 ]; and the slope, land use, exclusion, urban extent, transportation, and hill shade (SLEUTH) model [ 17 ]. The CA model focuses on the influence of environmental suitability on land use patterns, but the transformation rules are difficult to determine and are usually combined with other models to improve simulation accuracy; the ABM determines rules according to subject characteristics, which are not universal; and the SLEUTH model is a simulation model developed based on the CA model, with the advantages of simple operation and an open-source environment, but less consideration of the impact of socio-economic factors [ 18 ].…”