Analysis of the Future Land Use Land Cover Changes in the Gaborone Dam Catchment Using CA-Markov Model: Implications on Water Resources

Dynamics of land cover changes and carbon emissions driven by large dams in China

The recent surge in dam construction has sparked debates regarding their contribution to carbon neutrality and food security, focusing on trade-offs between production benefits and ecological drawbacks. However, how dams affect carbon emissions and land cover changes, including their spatial differentiations, remains unclear. We quantified spatiotemporal variations in carbon emissions and storage of 137 large dams in China from 1992 to 2020, resulting from land cover change in potentially affected areas. We observed a lesser increase in carbon emissions and a more pronounced increase in carbon storage driven by forest conservation and regeneration within dam-affected areas compared to unaffected areas. Additionally, we noticed an increased grain yield in nearby areas potentially due to increased water availability. Our findings highlight the importance of considering land cover change when assessing carbon neutrality or grain yield at regional and national scales. This study provides useful insights into optimizing dam locations to mitigate future carbon emissions effectively.

Modeling the spatiotemporal heterogeneity of land surface temperature and its relationship with land use land cover using geo-statistical techniques and machine learning algorithms

Rapid changes in land use and land cover (LULC) have ecological and environmental effects in metropolitan areas. Since the 1990s, Saudi Arabia's cities have undergone tremendous urban growth, causing urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, etc. This study evaluates the variance and heterogeneity in land surface temperature (LST) because of LULC changes in Abha-Khamis Mushyet, Saudi Arabia, from 1990 to 2020. The research aims to determine the impact of urban biophysical parameters on the High-High (H-H) LST cluster using geospatial, statistical, and machine learning techniques. The support vector machine (SVM) was used to map LULC. The land surface temperature (LST) has been derived using the mono-window algorithm (MWA). The local indicator of spatial associations (LISA) model was implemented on the spatiotemporal LST maps to identify LST clusters. Also, the parallel coordinate plot (PCP) approach was employed to examine the relationship between LST clusters and urban biophysical variables as a proxy of LULC. LULC maps show that urban areas rose by > 330% between 1990 and 2020. Built-up areas had an 83.6% transitional probability between 1990 and 2020. In addition, vegetation and agricultural land have been transformed into built-up areas by 17.9% and 21.8% respectively between 1990 and 2020. Uneven LULC changes in terms of built-up areas lead to increased LST hotspots. High normalized difference built-up index (NDBI) was linked to LST hotspots but not normalized difference water index (NDWI) or normalized difference vegetation index (NDVI). This research could help policymakers develop mitigation strategies for urban heat islands.

Prediction of the spatiotemporal evolution of vegetation cover in the Huainan mining area and quantitative analysis of driving factors

The prediction of the spatiotemporal dynamic evolution of vegetation cover in the Huainan mining area and the quantitative evaluation of its driving factors are of great significance for protecting and restoring the environment in this area. This study uses the Landsat 5 TM and Landsat 8 OLI time-series data to estimate the vegetation cover and uses the transition matrix to analyze the spatiotemporal transfer of vegetation cover from 1989 to 2004, 2004 to 2021, and 2021 to 2030. In addition, a structural equation model (SEM) was established in this study to assess the driving factors of vegetation cover. The quantitative analysis and the cellular automata (CA)-Markov model were performed to predict the future vegetation cover in the Huainan mining area. The results are as follows: (1) In different periods, the vegetation cover types were mainly high cover types transferred to other vegetation cover types; (2) human activities are the key factors affecting the vegetation growth, while topographical factor is the most influential factor promoting the vegetation growth; (3) highly consistent CA-Markov and multi-criteria evaluation (MCE) predicted results of vegetation cover in 2030 compared to that in 2021. The proportion of bare soil and low cover types had increased significantly, mainly concentrated in the internal area of the mines. The prediction of the spatiotemporal evolution of vegetation cover in the Huainan mining area and the quantitative change in driving factors are of significant importance for the restoration of the environment in mining areas.

Predicting future coastal land use/cover change and associated sea-level impact on habitat quality in the Northwestern Coastline of Guinea-Bissau

The assessment of coastal land use/cover (LULC) change is one of the most precise techniques for detecting spatio-temporal change in the coastal system. This study, integrated Land Change Modeler, Habitat Quality Model, and Digital Shoreline Analysis System, to quantify spacio-temporal coastal LULC change and driving forces between 2000 and 2020. Combined the CA-Markov Model with Sea Level Affecting Marshes Model (SLAMM), merged local SLR data with future representative concentration pathway (RCP8.5) scenarios, and predicted future coastal LULC change and associated sea-level rise (SLR) impact on the coastal land use and habitat quality in short-, medium- and long-term. The study area had significant coastal LULC change between 2000 and 2020. The tidal flats, whose change was driven mainly by sea level, registered a total net gain of 57.93 km². We also observed the significant loss of developed land whose change was influenced by tidal flat with a total loss of -75.58 km². The tidal flat will experience a stunning net gain of 80.55 km² between 2020 and 2060, making developed land the most negatively impacted land in the study area. The study led to the conclusion that the uncontrolled conversion of saltmarshes, mixed-forest, and mangroves into agriculture and infrastructures were the main factors affecting the coastal systems, including the faster coastal erosion and accretion observed during a 20-year period. The study also concluded that a low coastal elevation of -1 m and a slope of less than 2° have contributed to coastal change. Unprecedented changes will unavoidably pose a danger to coastal ecological services, socioeconomic growth, and food security. Timely efforts should be made by establishing sustainable mitigation methods to avoid the future impact.

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

In this study, we investigated the accelerating pace of anthropogenic land use and land cover change (LULCC) disturbance, which has generated enormous impacts on the Crocodile River. Spot images from 1996, 2009 and 2022 were used to generate the land use maps and quantify the changes. A supervised classification with the maximum likelihood classifier was used to classify the images. Sediment sources were classified into two sources, revealed by erosional characteristics in the catchment. A gamma spectrometry detector, high-purity germanium (HPGe) "Well" detector by Canberra and inductively coupled plasma mass spectrometry (ICP-MS) were used for the analysis of the samples. The results revealed that from 1996-2022, built-up areas, bare land and water bodies increased by 3.48%, 2.47% and 1.90%, respectively. All the LULCC classes increased annually from 1996-2022, except for grassland, which shrunk. The results of the radionuclides analysis showed that ²¹⁰Pb_ex was found to be a more effective tracer than ¹³⁷Cs. The mass balance model revealed that subsurface sources contributed 60%, while surface sources contributed 40%, of the sediment load in the river. This research provides valuable information necessary for integrated catchment management policies for future LULCC and soil erosion to be adopted.

Impacts of Land-Use Change on the Spatio-Temporal Patterns of Terrestrial Ecosystem Carbon Storage in the Gansu Province, Northwest China

Land-use change is supposed to exert significant effects on the spatio-temporal patterns of ecosystem carbon storage in arid regions, while the relative size of land-use change effect under future environmental change conditions is still less quantified. In this study, we combined a land-use change dataset with a satellite-based high-resolution biomass and soil organic carbon dataset to determine the role of land-use change in affecting ecosystem carbon storage from 1980 to 2050 in the Gansu province of China, using the MCE-CA-Markov and InVEST models. In addition, to quantify the relative size of the land-use change effect in comparison with other environmental drivers, we also considered the effects of climate change, CO2 enrichment, and cropland and forest managements in the models. The results show that the ecosystem carbon storage in the Gansu province increased by 208.9 ± 99.85 Tg C from 1980 to 2020, 12.87% of which was caused by land-use change, and the rest was caused by climate change, CO2 enrichment, and ecosystem managements. The land-use change-induced carbon sequestration was mainly associated with the land-use category conversion from farmland to grassland as well as from saline land and desert to farmland, driven by the grain-for-green projects in the Loess Plateau and oasis cultivation in the Hexi Corridor. Furthermore, it was projected that ecosystem carbon storage in the Gansu province from 2020 to 2050 will change from −14.69 ± 12.28 Tg C to 57.83 ± 53.42 Tg C (from 105.62 ± 51.83 Tg C to 177.03 ± 94.1 Tg C) for the natural development (ecological protection) scenario. By contrast, the land-use change was supposed to individually increase the carbon storage by 56.46 ± 9.82 (165.84 ± 40.06 Tg C) under the natural development (ecological protection) scenario, respectively. Our results highlight the importance of ecological protection and restoration in enhancing ecosystem carbon storage for arid regions, especially under future climate change conditions.

Water Reservoirs as a Driver of Anthropogenic Changes in Landscape and Transport Networks: The Czech Republic Experience

The construction of reservoirs has a major impact on the floodplain landscape, and their existence also affects land use in the hinterland. The aim of this article is to evaluate the influence of artificial lakes on changes in landscape use and transport networks; in this context, an assessment is provided of the significance of this anthropogenic activity as one of the drivers of landscape change. Old topographic maps and archival aerial photographs are used to analyze changes in the use of landscape and road networks, and these materials are complemented with the latest geographic data in digital form. Utilizing geographic information systems, we assessed the landscape changes and processes in the hinterland of those Czech Republic reservoirs that have an area of 100 ha or more. The results of the research show that landscape change processes are more intensive in the hinterland of the lakes than in the surrounding landscape. The predominant utility function of a reservoir emerged as a key factor in landscape use changes and ongoing processes. A different landscape use scenario can be observed in drinking water reservoirs, especially regarding the leisure and irrigation functions that dominate elsewhere. After the completion of reservoirs, the road and railway networks had an impact on, above all, the connection of the nearest villages in the hinterland of the lakes. The information that we found can be employed in projecting future changes in land use and road networks at newly planned dams.

Land Use Dynamic Changes in an Arid Inland River Basin Based on Multi-Scenario Simulation

The Tarim River Basin is the largest inland river basin in China. It is located in an extremely arid region, where agriculture and animal husbandry are the main development industries. The recent rapid rise in population and land demand has intensified the competition for urban land use, making the water body ecosystem increasingly fragile. In light of these issues, it is important to comprehensively grasp regional land structure changes, improve the degree of land use, and reasonably allocate water resources to achieve the sustainable development of both the social economy and the ecological environment. This study uses the CA-Markov model, the PLUS model and the gray prediction model to simulate and validate land use/cover change (LUCC) in the Tarim River Basin, based on remote sensing data. The aim of this research is to discern the dynamic LUCC patterns and predict the evolution of future spatial and temporal patterns of land use. The study results show that grassland and barren land are currently the main land types in the Tarim River Basin. Furthermore, the significant expansion of cropland area and reduction in barren land area are the main characteristics of the changes during the study period (1992–2020), when about 1.60% of grassland and 1.36% of barren land converted to cropland. Over the next 10 years, we anticipate that land-use types in the basin will be dominated by changes in grassland and barren land, with an increasing trend in land area other than for cropland and barren land. Grassland will add 31,241.96 km2, mainly in the Dina River and the lower parts of the Weigan-Kuqu, Kashgar, Kriya, and Qarqan rivers, while barren land will decline 2.77%, with significant decreases in the middle and lower reaches of the Tarim River Basin. The findings of this study will provide a solid scientific basis for future land resource planning.

Assessment of current and future land use/cover changes in soil erosion in the Rio da Prata basin (Brazil)

In recent years, the Cerrado biome in Brazil (Brazilian savannah) has faced severe environmental problems due to abrupt changes in land use/cover (LUC), causing increased soil loss, sediment yield and water turbidity. Thus, this study aimed to evaluate the impacts of soil loss and sediment delivery ratio (SDR) over the last 30 years to simulate future scenarios of soil losses from 2050 to 2100 and to investigate an episode of sediment delivery that occurred in the Rio da Prata Basin (RPB) in 2018. In this study, the following were used: an estimation of soil losses for 1986, 1999, 2007 and 2016 using the Revised Universal Soil Loss Equation (RUSLE), an estimation of SDR, sediment export and sediment deposition using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, an association of RUSLE factor C to LUC data for 2050 and 2100 based on the CA-Markov hybrid model, and an estimation of future soil erosion scenarios for 2050 and 2100. The results show that over the last 30 years (1986-2016), there has been a reduction in the areas of highly intense and severe degrees. Future soil erosion scenarios (2050-2100) showed a 13.84% increase in areas of soil loss >10 Mg ha^-1 year^-1. The results highlighted the importance of assessing the impacts of LUC changes on soil erosion and the export of sediments to agricultural watersheds in the RPB, one of the best ecotourism destinations in Brazil. In addition, the increase in soil loss in the region intensified sediment yield events and increased water turbidity. Furthermore, riparian vegetation, although preserved, was not able to protect the watercourse, showing that it is essential to adopt the best management practices in the agricultural production areas of the basin, especially where ramps are extensive or the slope is greater than 2%, to reduce the runoff velocity and control the movement of sediments on the surface towards the drainage canals. The results of this study are useful for drawing up a soil and water conservation plan for the sustainable production of agriculture and maintenance of ecosystem services in the region.

Impact of Land Cover Changes on the Availability of Water Resources in the Regional Natural Park Serranía de Las Quinchas

Protected areas offer environmental goods and services to local communities, who have based their aptitude on the availability of water resources with practices associated with the legacy of their ancestors. The purpose of this paper is to evaluate the impact of changes in land cover on the availability of water resources in the Serrania de Las Quinchas Regional Natural Park (PNRSQ), located in the department of Boyacá, Colombia. The SWAT tool and the Corine Land Cover methodology were used between 2008 and 2017. In addition, data of hydrometeorological tests were used to determine the water behavior of the basin together with the Digital Elevation Model (DEM). The results show that after the declaration of the area as a protection zone in 2008, there have been changes in the land cover producing a greater availability of water resources and the partial restoration of the study area. Additionally, hydrological modeling allowed knows the behavior of the basin under different conditions. The resulting information allows decision-makers to evaluate the best options to guarantee water resources and generate strategies that allow communities to reinvent their way of production and adapt to ecosystem conditions without affecting their ecological functioning.

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

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.

Analysis of Changes in Land Use/Land Cover and Hydrological Processes Caused by Earthquakes in the Atsuma River Basin in Japan

The 2018 Hokkaido Eastern Iburi earthquake and its landslides threaten the safety and stability of the Atsuma River basin. This study investigates land use and land cover (LULC) change by analyzing the 2015 and 2020 LULC maps of the basin, and its impact on runoff and sediment transport in the basin by using the soil and water assessment tool (SWAT) model to accurately simulate the runoff and sediment transport process. This study finds that the earthquake and landslide transformed nearly 10% of the forest into bare land in the basin. The simulation results showed that the runoff, which was simulated based on the 2020 LULC data, was slightly higher than that based on the 2015 LULC data, and the sediment transport after the earthquake is significantly higher than before. The rate of sediment transportation after the earthquake, adjusted according to the runoff, was about 3.42 times more than before. This shows that as the forest land decreased, the bare land increased. Conversely, the runoff increased slightly, whereas the sediment transport rate increased significantly in the Atsuma River basin after the earthquake. In future, active governance activities performed by humans can reduce the amount of sediment transport in the basin.

Remote Sensing-Based Urban Sprawl Modeling Using Multilayer Perceptron Neural Network Markov Chain in Baghdad, Iraq

The global and regional land use/cover changes (LUCCs) are experiencing widespread changes, particularly in Baghdad City, the oldest city of Iraq, where it lacks ecological restoration and environmental management actions at present. To date, multiple land uses are experiencing urban construction-related land expansion, population increase, and socioeconomic development. Comprehensive evaluation and understanding of the effect of urban sprawl and its rapid LUCC are of great importance to managing land surface resources for sustainable development. The present research applied remote sensing data, such as Landsat-5 Thematic Mapper and Landsat-8 Operation Land Imager, on selected images between July and August from 1985 to 2020 with the use of multiple types of software to explore, classify, and analyze the historical and future LUCCs in Baghdad City. Three historical LUCC maps from 1985, 2000, and 2020 were created and analyzed. The result shows that urban construction land expands quickly, and agricultural land and natural vegetation have had a large loss of coverage during the last 35 years. The change analysis derived from previous land use was used as a change direction for future simulation, where natural and anthropogenic factors were selected as the drivers’ variables in the process of multilayer perceptron neural network Markov chain model. The future land use/cover change (FLUCC) modeling results from 2030 to 2050 show that agriculture is the only land use type with a massive decreasing trend from 1985 to 2050 compared with other categories. The entire change in urban sprawl derived from historical and FLUCC in each period shows that urban construction land increases the fastest between 2020 and 2030. The rapid urbanization along with unplanned urban growth and rising population migration from rural to urban is the main driver of all transformation in land use. These findings facilitate sustainable ecological development in Baghdad City and theoretically support environmental decision making.