Quantitatively Assessing the Future Land-Use/Land-Cover Changes and Their Driving Factors in the Upper Stream of the Awash River Based on the CA–Markov Model and Their Implications for Water Resources Management

Abstract

Despite the rapid economic and population growth, the risks related to the current dynamics of land use and land cover (LULC) have attracted a lot of attention in Ethiopia. Therefore, a complete investigation of past and future LULC changes is essential for sustainable water resources and land-use planning and management. Since the 1980s, LULC change has been detected in the upper stream of the Awash River basin. The main purpose of this research was to investigate the current dynamics of LULC and use the combined application of the cellular automata and the Markov chain (CA–Markov) model to simulate the year 2038 LULC in the future; key informant interviews, household surveys, focus group discussions, and field observations were used to assess the consequences and drivers of LULC changes in the upstream Awash basin (USAB). This research highlighted the importance of remote sensing (RS) and geographic information system (GIS) techniques for analyzing the LULC changes in the USAB. Multi-temporal cloud-free Landsat images of three sequential data sets for the periods (1984, 2000, and 2019) were employed to classify based on supervised classification and map LULC changes. Satellite imagery enhancement techniques were performed to improve and visualize the image for interpretation. ArcGIS10.4 and IDRISI software was used for LULC classification, data processing, and analyses. Based on Landsat 5 TM-GLS 1984, Landsat 7 ETM-GLS 2000, and Landsat 8 2019 OLI-TIRS, the supervised maximum likelihood image classification method was used to map the LULC dynamics. Landsat images from 1984, 2000, and 2019 were classified to simulate possible LULC in 2019 and 2038. The result reveals that the maximum area is covered by agricultural land and shrubland. It showed, to the areal extent, a substantial increase in agricultural land and urbanization and a decrease in shrubland, forest, grassland, and water. The LULC dynamics showed that those larger change rates were observed from forest and shrubland to agricultural areas. The results of the study show the radical changes in LULC during 1984–2019; the main reasons for this were agricultural expansion and urbanization. From 1984 to 2019, agriculture increased by 62%, urban area increased by 570.5%, and forest decreased by 88.7%. In the same year, the area of shrubland decreased by 68.6%, the area of water decreased by 65.5%, and the area of grassland decreased by 57.7%. In view of the greater increase in agricultural land and urbanization, as well as the decrease in shrubland, it means that the LULC of the region has changed. This research provides valuable information for water resources managers and land-use planners to make changes in the improvement of future LULC policies and development of sub-basin management strategies in the context of sustainable water resources and land-use planning and management.

Anthropogenic Land Use and Land Cover Change as Potential Drivers of Sediment Sources in the Upper Crocodile River, North West Province, South Africa

“…This study used geospatial and remote sensing data sources, since these provide first-hand information that is useful for identifying and understanding the dynamics and drivers of the LULCC of any landscape [ 15 , 46 ]. Remote-sensed data from 1996, 2009 and 2022 were selected for this study.…”

“…The satellite imagery was geo-referenced using ground control points (GCPs) and projected using the Universal Transverse Mercator (UTM) system projection. Subsequently, the geo-referenced terrain was used to verify that the land use and land cover class map passed the test of the ground-truth method, as prescribed by Daba and You [ 15 ]. A combination of ground-truth validation, Google Earth ® imagery and a visually interpreted detailed topographic map with a scale of 1:50,000 produced in 1996 were used to delineate and identify sample training locations.…”

“…According to Aneseyee et al [ 13 ], LULCC are major causal drivers of soil loss in upland watersheds, indicating the transformative interaction between land use changes and land degradation. Different studies [ 9 , 14 , 15 , 16 , 17 ] have investigated the association between LULCC, soil erosion and river contamination. However, the analysis of LULCC as causal drivers of land degradation alone cannot quantitatively distinguish the relative contributions of each type of sediment source to the overall sediment load being delivered into rivers.…”

“…These techniques have been accepted as global research tools and have been widely used to generate valuable quantifiable data, enabling researchers to visualise present and future changes in different environments [ 34 , 35 , 36 ]. Different scholars have used the maximum likelihood classifier (MLC) [ 37 , 38 ], artificial neural network (ANN) [ 39 ], CA–Markov model and built-in GIS software and algorithms [ 15 , 40 ] to quantitatively and qualitatively analyse change detection and predict future scenarios using maps. However, there are limited studies which have applied the fingerprinting technique by incorporating LULCC in assessing environmental changes.…”

Does the recent afforestation program in Ethiopia influenced vegetation cover and hydrology? A case study in the upper awash basin, Ethiopia

“…The mean annual precipitation of the upper Awash basin is about 832 mm where the area receives 80% of the precipitation during the main rainy season (June to September) ( Gebremichael et al., 2022 ). The major land use land cover types of the study area include agricultural land, forest, bush and shrub land, and built up where agriculture is the dominant ( Daba and You, 2022 ).…”