Spatiotemporal assessment of land use land cover change, driving forces, and consequences using geospatial techniques: The case of Naqamte city and hinterland, western Ethiopia

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...

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Bibliographic Details
Main Authors: Birhanu Tadesa Edosa, Milkessa Dangia Nagasa
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:Environmental Challenges
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2667010023001531
Description
Summary: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.
ISSN:2667-0100