Land-Use Change Modelling in the Upper Blue Nile Basin

Past and future land use change dynamics: assessing the impact of urban development on agricultural land in the Pantura Jabar region, Indonesia

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017-2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87-88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29-86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual.

Assessment of spatiotemporal dynamics of land and vegetation cover change detection in Maze National Park, Southwest Ethiopia

Biodiversity conservation areas include National Parks which are integral parts of protected areas. In parks, the dynamics of land and vegetation covers are significantly affected by anthropogenic activities and natural factors. These resources are exposed to challenges due to unwise practices. Assessing and monitoring the status of park's natural resources enables us to understand the extent of land and vegetation resources. Few studies have been conducted in Maze National Park (MzNP) but none of them quantified the land and vegetation cover dynamics. The purpose of this study is, therefore, to quantify the extents spatiotemporal dynamics of land and vegetation cover of MzNP using GIS and RS technology considering three decades Landsat images acquired from USGS. GPS and GCPs were collected for the analysis of land cover classification and accuracy assessment, respectively. The trend analysis result revealed that scattered tree and grass land cover area have increased from 19.8% in 1987 to 51.6% in 2019. The area of sparse vegetation cover increased from 19.7% in 2000 to 78.4% in 2019. Highly dense and dense vegetation cover decreased remarkably. Accuracy assessment was 98% and computed Kappa coefficient was 0.97. The results suggested that the extent of MzNP's vegetation cover change was basically due to anthropogenic pressure. This study could be used to alarm responsible bodies to restructure and implement better environmental protection strategies for restoration of the rapidly deteriorating vegetation resource of MzNP.

Modeling and Prediction of Land Use Land Cover Change Dynamics Based on Land Change Modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin, Ethiopia

Change of land use land cover (LULC) has been known globally as an essential driver of environmental change. Assessment of LULC change is the most precise method to comprehend the past land use, types of changes to be estimated, the forces and developments behind the changes. The aim of the study was to assess the temporal and spatial LULC dynamics of the past and to predict the future using Landsat images and LCM (Land Change Modeler) by considering the drivers of LULC dynamics. The research was conducted in Nashe watershed (Ethiopia) which is the main tributary of the Upper Blue Nile basin. The total watershed area is 94,578 ha. The Landsat imagery from 2019, 2005, and 1990 was used for evaluating and predicting the spatiotemporal distributions of LULC changes. The future LULC image prediction has been generated depending on the historical trends of LULC changes for the years 2035 and 2050. LCM integrated in TerrSet Geospatial Monitoring and Modeling System assimilated with MLP and CA-Markov chain have been used for monitoring, assessment of change, and future projections. Markov chain was used to generate transition probability matrices between LULC classes and cellular automata were used to predict the LULC map. Validation of the predicted LULC map of 2019 was conducted successfully with the actual LULC map. The validation accuracy was determined using the Kappa statistics and agreement/disagreement marks. The results of the historical LULC depicted that forest land, grass land, and range land are the most affected types of land use. The agricultural land in 1990 was 41,587.21 ha which increased to 57,868.95 ha in 2019 with an average growth rate of 39.15%. The forest land, range land, and grass land declined annually with rates of 48.38%, 19.58%, and 26.23%, respectively. The predicted LULC map shows that the forest cover will further degrade from 16.94% in 2019 to 8.07% in 2050, while agricultural land would be expanded to 69,021.20 ha and 69,264.44 ha in 2035 and 2050 from 57,868.95 ha in 2019. The findings of this investigation indicate an expected rapid change in LULC for the coming years. Converting the forest area, range land, and grass land into other land uses, especially to agricultural land, is the main LULC change in the future. Measures should be implemented to achieve rational use of agricultural land and the forest conversion needs to be well managed.

Land Cover Change in the Blue Nile River Headwaters: Farmers’ Perceptions, Pressures, and Satellite-Based Mapping

The headwaters of the Blue Nile River in Ethiopia contain fragile mountain ecosystems and are highly susceptible to land degradation that impacts water quality and flow dynamics in a major transboundary river system. This study evaluates the status of land use/cover (LULC) change and key drivers of change over the past 31 years through a combination of satellite remote sensing and surveying of the local understanding of LULC patterns and drivers. Seven major LULC types (forest land, plantation forest, grazing land, agriculture land, bush and shrub land, bare land, and water bodies) from Landsat images of 1986, 1994, 2007, and 2017 were mapped. Agriculture and plantation forest land use/cover types increased by 21.4% and 368.8%, respectively, while other land use/cover types showed a decreasing trend: water body by 50.0%, bare land by 7.9%, grassland by 41.7%, forest by 28.9%, and bush and shrubland by 38.4%. Overall, 34.6% of the landscape experienced at least one LULC transition over the past 31 years, with 15.3% representing the net change and 19.3% representing the swap change. The percentage change in plantation forest land increased with an increasing altitude and slope gradient during the study period. The mapped LULC changes are consistent with the pressures reported by local residents. They are also consistent with root causes that include population growth, land tenure and common property rights, persistent poverty, weak enforcement of rules and low levels of extension services, a lack of public awareness, and poor infrastructure. Hence, the drivers for LULC should be controlled, and sustainable resources use is required; otherwise, these resources will soon be lost and will no longer be able to play their role in socioeconomic development and environmental sustainability.

Water Quality Pollution Control and Watershed Management Based on Community Participation in Maros City, South Sulawesi, Indonesia

Increases in the number of urban residents have significant impacts on spatial pressure, affecting the utilization of river basins. The purpose of this study is to analyze (1) the increase in population and changes in spatial use as determinants of the complexity of the watershed ecosystem; (2) the effect of housing development, urban activity systems, and land use changes on the degradation of the environmental quality of the watershed; and (3) the direct and indirect effects of changes in spatial use, land reclamation, and community behavior on water pollution and the sustainability of watershed management in Maros City. The research method used is a sequential explanatory design combining quantitative and qualitative research methods. Data were obtained through observation, in-depth interviews, surveys, and documentation. The study findings show that land use change, complexity of spatial use, and community behavior have a negative impact on the environmental quality of the watershed. Housing development, urban activity systems, and changes in land use had a significant effect on environmental quality degradation, with a coefficient of determination of 73.9%. Furthermore, the influence of spatial use, land reclamation, and community behavior on water pollution in the watershed was 72.82%. This study may serve to assist the decision-making of and policy formation by the Maros Regency Government in the framework of controlling the use of watersheds, working towards their social, economic, and environmental sustainability.

Watershed Hydrological Response to Combined Land Use/Land Cover and Climate Change in Highland Ethiopia: Finchaa Catchment

Land use/land cover (LULC) and climate change affect the availability of water resources by altering the magnitude of surface runoff, aquifer recharge, and river flows. The evaluation helps to identify the level of water resources exposure to the changes that could help to plan for potential adaptive capacity. In this research, Cellular Automata (CA)-Markov in IDRISI software was used to predict the future LULC scenarios and the ensemble mean of four regional climate models (RCMs) in the coordinated regional climate downscaling experiment (CORDEX)-Africa was used for the future climate scenarios. Distribution mapping was used to bias correct the RCMs outputs, with respect to the observed precipitation and temperature. Then, the Soil and Water Assessment Tool (SWAT) model was used to evaluate the watershed hydrological responses of the catchment under separate, and combined, LULC and climate change. The result shows the ensemble mean of the four RCMs reported precipitation decline and increase in future temperature under both representative concentration pathways (RCP4.5 and RCP8.5). The increases in both maximum and minimum temperatures are higher for higher emission scenarios showing that RCP8.5 projection is warmer than RCP4.5. The changes in LULC brings an increase in surface runoff and water yield and a decline in groundwater, while the projected climate change shows a decrease in surface runoff, groundwater and water yield. The combined study of LULC and climate change shows that the effect of the combined scenario is similar to that of climate change only scenario. The overall decline of annual flow is due to the decline in the seasonal flows under combined scenarios. This could bring the reduced availability of water for crop production, which will be a chronic issue of subsistence agriculture. The possibility of surface water and groundwater reduction could also affect the availability of water resources in the catchment and further aggravate water stress in the downstream. The highly rising demands of water, owing to socio-economic progress, population growth and high demand for irrigation water downstream, in addition to the variability temperature and evaporation demands, amplify prolonged water scarcity. Consequently, strong land-use planning and climate-resilient water management policies will be indispensable to manage the risks.