A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

Abstract

Over the past few decades, the growth of population and the development of the economy have had a significant impact on the way land is used and covered (LULC) in the Nile Nyabarongo River basin. However, there is limited knowledge about the patterns of land use and the mechanisms that drive changes in these patterns because of human activities. Therefore, it is crucial to examine how land use and cover are shifting in this area, identify the factors responsible for these changes, and forecast future patterns. This study sought (1) to evaluate the changes in LULC from 1990 to 2020 and (2) to predict future fluctuations until 2060. By analyzing the LULC data for the years 1990, 2000, 2010, and 2020, this study simulated the future LULC patterns of the area for the next 30 years using the LULC transition matrix and the Markov chain model. The study classified the LULC into five categories: forestland, grassland, cropland, settlement, and water. The results revealed that there will be significant changes in the LULC of the study area from 2030 to 2060. The forest area is projected to decrease by 801.7 km, 771.6 km, and 508.2 km, while the cropland area will expand by 6307.2 km, 6249.2 km, and 6420.6 km during this period. The grassland area will experience a small increase of 761.1 km, 802.4 km, and 859.1 km, and the settlement area will also grow by 355.2 km, 407.4 km, and 453.2 km. In contrast, the water area will decrease by 55.9 km, 50.5 km, and 40 km. The ongoing pattern of LULCC is expected to persist over the next three decades, with an increase in cropland area and grassland. This study’s findings can provide valuable insights for land use planners and water resource managers in developing fair land use and water resource management policies for the entire region, enabling them to make well-informed decisions.

Identifying the Land Use Land Cover (LULC) Classifications and Change Using Google Earth Engine (GEE): A Case Study of Narayanganj District, Bangladesh

“…The use of the earth's surface has been altering day by day due to the necessity of people. This alteration to the land surface on earth is referred to as "Land Use and Land Cover (LULC) Change" (Gasirabo et al, 2023).…”

“…Over the last few decades, the LULC has been changed due to population growth and economic development (Gasirabo et al, 2023). This population growth and economic improvement tend to expand the number as well as the area of cities.…”

“…Rashid et al LULC change pattern analysis by direct eld visits are very time-consuming, laborious and error-prone (Kafy et al, 2020). Therefore, various geographic models and publicly available remote sensing data are thought to be highly useful tools for managing and keeping track of the state and evolution of LULC (Gasirabo et al, 2023). Now-a-days researchers are using various Geographic Information System (GIS) technologies and machine learning algorithms for the thematic mapping of LULC classi cation and LULC change (Dr. C. Pande, 2022).…”

“…used three famous accuracy measures such as overall accuracy (OA), user accuracy (UA), and producer accuracy (PA) for the classi cation correctness. Accuracy level for LULC classi cation have been recommended above 90 % in the previous study for the excelleny and reliability(Gasirabo et al, 2023). The accuracy has been calculated using GEE with the help of confusion matrix.…”

A Multi-Objective Scenario Study of County Land Use in Loess Hilly Areas: Taking Lintao County as an Example

“…Ecological protection multi-scenario study mainly focuses on the large-scale level, such as water area [24] and provinces [25], and the distribution change and simulation of different types of ecological space [26]. Integrated land use simulation studies mainly carry out a multi-scenario simulation study of land use with different development speeds, development goals and development focuses at different levels of the six major land use types, such as the whole country [27], water area [28,29], urban agglomerations [30], cities [31], as well as a three-life spatial multi-situational simulation study for large regions [32], provinces [33], and municipalities [34] that serves homeland spatial planning. In recent years, land use simulation research has attracted much attention.…”