Assessment of land use-land cover changes using GIS, remote sensing, and CA-Markov model: a case study of Algiers, Algeria

This paper analyzes the spatio-temporal change of land use and the regression of agricultural and forest areas in the capital of Algeria Algiers. A methodology based on a Geographic Information System (GIS) environment and remote sensing to process and analyze a series of satellite images from 1987 to 2018 was used to categorize the soil into urban, agricultural, forestry, and water areas. In addition, a spatio-temporal simulation was performed to predict and assess changes in each land use class by 2040. These approaches were supported by a field survey of the entire study area to determine the price and availability of buildable land. Results indicated an increase in urban areas at the expense of agricultural and forest areas. This expansion is randomly driven by poor management and the need to meet increasing housing demand. Also, land prices are lower on the periphery than in the center, while the population is increasing remarkably from year to year and requires both public (roads, schools, hospitals) and private (housing, investments) facilities. The prediction model revealed a 69% urbanization of the study area by 2040 and an abusive consumption of agricultural and forest areas in urban zones. In short, this rate threatens the remaining agricultural and forest areas in the study area. These predicted results are only valid if the studied variables used in this research (demographic evolution, development of infrastructures, and socio-economic facilities) remain unchanged over the next 20 years, since the scenario considered in this research is based on a linear logic linked to past trends.

Automated summary

This study uses GIS and remote sensing to analyze the spatio-temporal change of land use in Algiers, the capital of Algeria. The results show that the increase in urban areas at the expense of agricultural and forest areas, driven by poor management and housing demand, poses a threat to the remaining agricultural and forest areas in the study area.