Search

Ecological services in metropolitan areas are degrading more quickly due to changes in LULC brought about by urban expansion. To make a sustainable choice about the ideal location, however, merging the existing simulation approach with LULC optimization approaches involves several intricate procedures. Therefore, the goal of this study is to develop a unique technique that can forecast urban expansion over an extended period and to link with optimization of LULC techniques so as to make meaningful decisions on the impacts of urbanization on loss of biodiversity. In this study, three primary procedures were used: (1) an SVM-based supervised classification technique for LULC classification; (2) a Markov chain and cross-tabulation method for the examination of LULC trends in space and time, (3) utilizing the CA-Markov approach to forecast urban growth. By using Landsat imagery of 2008, 2015, and 2023, the study determined how urban cover changed over time, and what kind of LULC-to-urban transition occurred. The study revealed that uncontrolled and haphazard urban expansion was observed in the Sululta sub-city, which could have disastrous repercussions on physical, biological and urban ecosystem. The %age of urban area increased from 9.04% in 2008 to 13.07% in 2015. However, because of the internally displaced people from the Ethio-Somali Region, who have been resettled there since 2017, the ratio of urban areas grew from 13.7% in 2015 to 24.65% in 2023. Furthermore, by 2040, the sub-city will have grown by 27.69 %. The kappa coefficient statistics of the three classified images of the years 2008, 2015, and 2023 were 93.3 %, 97.5%, and 97.5 %, respectively. To identify the areas that would be covered by future city growth, it is advised that this innovative technique be integrated with optimizing land use strategies.

Land use and land cover change (LULCC) is the result of both anthropogenic and natural activities. LULCC has caused biodiversity and climate change in the study area. Land use and land cover (LULC) datasets for the investigation site were created from Landsat images in 1987, 2004, and 2021 using the maximum likelihood method. After LULCC the cross-tabulation and trend-surface analyses were performed to identify dominant land transitions in post-classification maps. The driving forces that caused LULCC and its consequences were examined using qualitative data. Study results show that spatial and temporal LULC classes were significantly changed except for waterbodies. The estimated aerial extent of forest land has decreased dramatically by 14.5 % over the last 34 years (1987–2021), while bare land, built-up land, and agricultural land have increased by 6.83 %, 4.33 %, and 3.21 %, respectively. According to this study, the main causes of LULCC in the district were population increase, growing cropland, the need for firewood, building supplies, and coal, as well as the ineffective use of natural resources and land monitoring systems. Forest deterioration, extinction of plant and animal species, soil degradation, and a lack of animal feed were the primary effects of changes in LULC. The overall accuracy of the three classified images of the years 1987, 2004, and 2021 was 86 %, 94 %, and 97 %, respectively. Lastly, our work used Markov chain analysis to examine LULCC in a spatiotemporal manner. These findings would provide decision support for policymakers to frame future sustainable land use policies.