Environmental degradation linked to land occupation and use, such as climate change and anthropogenic activities, has led to the modification of the landscape units of the Kadzel sub-watershed. The objective of this s...Environmental degradation linked to land occupation and use, such as climate change and anthropogenic activities, has led to the modification of the landscape units of the Kadzel sub-watershed. The objective of this study is to analyze the dynamics of land use units in the Kadzel area in Diffa between 1992 and 2022 and to propose a future scenario for sustainable environmental management. The approach used relies on remote sensing and geographic information systems to analyze the dynamics of land use units. Additionally, the Markov Cellular Automata (CA) model was used to predict future land use. The land cover maps were produced from a supervised classification by maximum likelihood based on the true and false color compositions of bands 4/3/2 (TM5), 3/2/1 (ETM+) and 7/5/4 (8 OLI). Ten occupation classes were discriminated. Between 1992 and 2022, there was a decrease in the areas of irrigated crops (4.91% and 2.88%), of shrubby tree steppes (14.31% and 9.48%), field-fallow complexes (22.23% and 10.52%), and degraded areas. Grassy steppes (25.76% and 13.32%). However, this reduction has been beneficial for wastelands, urban areas and bodies of water. Based on predictive modeling, it is predicted that by 2052, urban areas, fallow field complexes and bare soils will constitute the main types of housing units. The regressive trend in natural resources appears to continue into the future with current land use practices.展开更多
文摘Environmental degradation linked to land occupation and use, such as climate change and anthropogenic activities, has led to the modification of the landscape units of the Kadzel sub-watershed. The objective of this study is to analyze the dynamics of land use units in the Kadzel area in Diffa between 1992 and 2022 and to propose a future scenario for sustainable environmental management. The approach used relies on remote sensing and geographic information systems to analyze the dynamics of land use units. Additionally, the Markov Cellular Automata (CA) model was used to predict future land use. The land cover maps were produced from a supervised classification by maximum likelihood based on the true and false color compositions of bands 4/3/2 (TM5), 3/2/1 (ETM+) and 7/5/4 (8 OLI). Ten occupation classes were discriminated. Between 1992 and 2022, there was a decrease in the areas of irrigated crops (4.91% and 2.88%), of shrubby tree steppes (14.31% and 9.48%), field-fallow complexes (22.23% and 10.52%), and degraded areas. Grassy steppes (25.76% and 13.32%). However, this reduction has been beneficial for wastelands, urban areas and bodies of water. Based on predictive modeling, it is predicted that by 2052, urban areas, fallow field complexes and bare soils will constitute the main types of housing units. The regressive trend in natural resources appears to continue into the future with current land use practices.
