Land Use Cover Change Research Articles

Resilience to climate change by improving air circulation efficiency and pollutant dispersion in cities: A 3D-UFO approach to urban block design

Urbanization presents significant challenges to air quality and climate resilience, necessitating pioneering urban design solutions to enhance air circulation and mitigate pollutants. This urgency intensifies in densely populated and rapidly evolving regions like Wuhan, China, where effective strategies are crucial for sustainable development. This study introduces an innovative 3D Urban Form Optimization (3D-UFO) methodology aimed at advancing urban block design configurations to improve urbanization quality. The 3D-UFO approach systematically addresses the multifaceted challenges of climate change and air quality degradation in rapidly urbanizing areas. Integrating GIS-based analysis for comprehensive Land-Use and Land-Cover Change (LULCC) evaluation with Computational Fluid Dynamics (CFD), our approach employs systematic exploration guided by established urban airflow study protocols. Robust metrics—Airspeed-Ratio (ASR) and Average-Age-of-Local-Air (ALA)—quantify the impact of diverse urban block design strategies on air-circulation efficiency and pollutant dispersion. Analysis across various urban scenarios, yielded by the proposed 3D-UFO approach, reveal significant variations in air-circulation efficiency at street and building levels (SBLs). Optimal urban air circulation achieves efficiency levels of 50–70 % when airflow aligns orthogonally across and parallel to streets. Adjusting street-level building heights, especially incorporating taller structures, boosts ventilation efficiency by 20–30 %, which is crucial for improving airflow dynamics in urban settings. Higher Height-to-Width (H/W) ratios (>5.5) yield a 218.5 % increase in ventilation in specific urban layouts. Notably, the synergy of street-aspect-ratio and building-height-ratio adjustments significantly enhance ASR and ALA, providing a quantitative foundation for sustainable urban development. This 3D-UFO methodology, fusing LULCC analysis, CFD simulations, and systematic exploration, emerge as a valuable framework for urban planners and designers. The study offers informed insights into urban sustainability challenges, demonstrating advancements in addressing environmental concerns and improving living conditions within densely populated environments.

Spatiotemporal changes in LULC and associated impact on urban Heat Islands over Pakistan using geospatial techniques

Urban Heat Islands arise when the natural land cover changes with dense concentrations of pavement, buildings, roads, and other surfaces that absorb and preserve heat. The current study aimed to assess Land Use Land Cover(LULC) changes and associated impact on Urban Heat Island (UHI) from 2000 to 2020 using Geospatial technologies for four metropolitan cities (Islamabad, Lahore, Multan and Muzaffarabad) of Pakistan. A Supervised Classification algorithm was performed on satellite imageries using ArcGIS software for analyzing LULC changes. The study areas were classified into 4 classes such as built-up, barren land, vegetation, and water bodies. Thermal bands of Landsat 4–5 and 8 were used for retrieved Land Surface Temperature (LST) and UHI. The findings of this study indicate that the urban area in all four districts i.e. Islamabad, Lahore, Multan and Muzaffarabad has increased by 21.3 %, 20.9 %, 6.1 % and 15 % respectively during 2000–2020. The built-up and barren land revealed the highest recorded LST, while vegetation and water bodies showed the lowest measured LST values. From 2000 to 2020, the annual maximum LST in Islamabad rose by 1.1 °C, Lahore by 2.1 °C, and Multan and Muzaffarabad by 1.9 °C and 1.5 °C. The expansion of built-up areas and the reduction in vegetation contribute to a favorable contribution to UHI. The study's findings showed that appropriate action plans are required to manage urban heat and promote sustainable city development.

Potential ecological risk assessment based on loss of ecosystem services due to land use and land cover change: A case study of Beijing-Tianjin-Hebei region

Ecosystem services (ESs) bridge the gap between human wellbeing and ecosystem processes. Land use and land cover change (LUCC) affect ecosystem services by altering the structure and function of ecosystems. Beijing-Tianjin-Hebei (BTH), one of the most developed urban agglomerations in China, is subject to intense LUCC that affects ESs while increasing the regional ecological risk (ER). Therefore, to achieve the coordinated development of socioeconomic and ecological quality, there is an urgent need to establish a regional ecological risk assessment (ERA). ESs were introduced into the ERA to clarify their ecological significance and to compensate for the deficiencies of the traditional ERA. In this study, the ERA utilizes key ESs as endpoints to assess potential ER through a new framework by multiplying the probability of LUCC by the resultant loss of key ESs. We found that cultivated land was the most dominant outflow type and built-up land was the predominant inflow type. From 2020 to 2030, there is a greater possibility of changes in cultivated land, forest, grassland, and built-up land as well as in relatively stable water bodies and unused land. Overall, ESs show a positive trend. The overall potential ER was relatively minimal, with a significant northwestern spatially concentrated characteristic of high values. This study helps understand the potential development process of ERs and provides a scientific basis for risk management.

Spatiotemporal Assessment of Land Use System in Kathmandu Valley of Nepal Using Geospatial Techniques

The Kathmandu Valley is one of the growing urbanization trend spreading capital city of Nepal. This metropolitan valley is experiencing a significant transformation of its landscapes in the last few decades resulting in large land use and land cover change. A systematic study of the urbanization trend and LULC has been felt need on this valley. Considering the importance of LULC change this study is carried out using Arc GIS to assess land use system and change dynamics in the study area based on Landsat imagery of the years from 2000 to 2019 at yearly interval. From the study of LULC of Kathmandu valley over the period selected a remarkable change scenario was observed in Agricultural land, Forest area, Vegetation land, Built up and River Bed area. Built up area is found significantly increased by 16% during the period in Kathmandu Valley. Forest area is found increased by 3%, whereas because of concrete structures built in the city caused decrease in water body in the valley by 3% up to the year 2008 and found increased later years with the increase in forest area. The effects in rivers system are observed significantly. River bed area is found decreased over this time period by 9%.This study showed the fast expansion of urbanization area and rapid conversions of agricultural land in Kathmandu Valley (study area).

Land use land cover change in the African Great Lakes Region: a spatial–temporal analysis and future predictions

The African Great Lakes Region has experienced substantial land use land cover change (LULCC) over the last decades, driven by a complex interplay of various factors. However, a comprehensive analysis exploring the relationships between LULCC, and its explanatory variables remains unexplored. This study focused on the Lake Kivu catchment in Rwanda, analysing LULCC from 1990 to 2020, identifying major variables, and predicting future LULC scenarios under different development trajectories. Image classification was conducted in Google Earth Engine using random forest classifier, by incorporating seasonal composites Landsat images, spectral indices, and topographic features, to enhance discrimination and capture seasonal variations. The results demonstrated an overall accuracy exceeding 83%. Historical analysis revealed significant changes, including forest loss (26.6 to 18.7%) and agricultural land expansion (27.7 to 43%) in the 1990–2000 decade, attributed to political conflicts and population movements. Forest recovery (24.8% by 2020) was observed in subsequent decades, driven by Rwanda’s sustainable development initiatives. A Multi-Layer Perceptron neural network from Land Change Modeler predicted distinct 2030 and 2050 LULC scenarios based on natural, socio-economic variables, and historical transitions. Analysis of explanatory variables highlighted the significant role of proximity to urban centers, population density, and terrain in LULCC. Predictions indicate distinct trajectories influenced by demographic and socio-economic trends. The study recommends adopting the Green Growth Economy scenario aligned with ongoing conservation measures. The findings contribute to identifying opportunities for land restoration and conservation efforts, promoting the preservation of Lake Kivu catchment’s ecological integrity, in alignment with national and global goals.Graphical

Land use and cover change accelerated China’s land carbon sinks limits soil carbon

Land use and cover change (LUCC) significantly impacts global carbon cycles and land surface properties, accounting for 25% of the historical atmospheric CO2 increase. We explore a previously overlooked role of LUCC in driving the land carbon cycle by using a three-level meta-analysis and Land Use Harmonization data to drive an ecosystem model. Our findings reveal that a loss of 39.2% of soil organic carbon (SOC) change in China due to LUCC, mitigated by afforestation, doubles gross primary productivity at 0.02 Pg C yr−1, countering central China’s urbanization decline. Indirect climate effects, especially soil bulk density, significantly impact SOC compared to direct climate effects. LUCC has significantly increased the Chinese terrestrial carbon sink, with net ecosystem productivity reaching 0.02 ± 0.12 Pg C yr−1. Our study underscores the importance of reforestation and afforestation in addressing climate change and enhancing carbon sinks in future carbon management.

Changes in Land Use and Land Cover, Normalized Difference Vegetation Index and Land Surface Temperature in the Narsingdi District During 2001 to 2021

Rapid urbanization and industrialization cause land use changes, reduce green spaces, and increase the land surface temperature. Green spaces of a city area maintain environmental quality by absorbing air pollutants and reducing land surface temperature. The present study aimed to detect the changes in land use and land cover (LULC), normalized difference vegetation index (NDVI), and land surface temperature (LST) in the Narsingdi district including six Upazilas from 2001 to 2021. The Landsat 7- Enhanced Thematic Mapper Plus (ETM+), the Landsat 8- Operational Land Imager (OLI) imageries and the Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to analyze the LULC, NDVI, and LST by using Google Earth Engine (GEE) and ArcGIS 10.8.2. The images were analyzed into four land use classifications – agricultural land, built-up area, forest vegetation, and water body. Among all the Upazilas, Narsingdi Sadar was the vulnerable area, where agricultural land, forest coverage, and water body decreased significantly by 6.67%, 2.2%, and 4.66%, respectively. The built-up area increased by a considerable amount of 13.53% during the last twenty years. The forest coverage of Narsingdi Sadar Upazila was calculated at only 8.91% in 2021 and decreased from 11.11% in 2001. The NDVI values surprisingly increased in all the Upazilas, but in Narsingdi Sadar, it is comparatively low (0.08) than in other Upazilas. The increasing trend in LST in the Narsingdi Sadar Upazila is alarming, 1.14oC from 2001 to 2021 (0.57oC/decade). The significant changes in LULC, NDVI, and LST made the Narsingdi Sadar a more critical area in the Narsingdi district. The findings of the study will be helpful to policymakers in making appropriate decisions in future city development. J. of Sci. and Tech. Res. 5(1): 93-118, 2023

Evaluating ecological conservation effectiveness of security patterns under multiple scenarios: A case study of Hubei Province

With the development of the social economy, ecological environment damage has become increasingly serious, and how to better protect ecological security has gradually attracted people’s attention. This paper takes Hubei Province in China as the research object, using the PLUS model version 1.4 to design four development scenarios to predict land use and land cover changes (LUCC) in 2035: the natural development scenario (S1), the cultivated land protection scenario (S2), the ecological protection area restriction scenario (S3), and the ecological security pattern (ESP) restriction scenario (S4). The study evaluates the ecological effects under these four different scenarios using Conefor Sensinode 2.6 software, Fragstats v4.2.1 software, and the InVEST model. The conclusions are as follows: (1) ESP has better ecological landscape connectivity. (2) Comparing the ecological security indices of the four scenarios, they are 0.5378, 0.5288, 0.5318, and 0.5405, respectively, with the S4 scenario showing the best protection effect. (3) Comparing the habitat quality of the four scenarios, high-grade habitats degrade under S1 and S2 scenarios; homogenization occurs under S3 and S4 scenarios, but the retention rate of high-grade habitat areas is the highest under the S4 scenario. In conclusion, compared to natural progression and prioritizing cultivated land protection, implementing ecological protection policies yields better ecological effects, and a planned ESP provides more targeted policy recommendations.

A study on the response of waterbird diversity to habitat changes caused by ecological engineering construction

Wetland degradation is a major factor in the decline of biodiversity. Ecological engineering construction efforts, such as water diversion, have been the main method of wetland restoration. To reveal the effects of biodiversity conservation and ecological engineering construction in the semiarid regions of China, waterbird surveys were carried out 2 years before and 8 years after the construction of the ecological engineering project discussed in this study. The relationships among waterbirds and habitat landscape patterns, hydrology and vegetation conditions were analyzed. The impacts of ecological engineering on habitats and waterbird diversity were revealed. The results showed that (1) there were significant changes in land use, hydrological distributions and vegetation cover in the study area before and after the construction of the ecological project. The quality of waterbird habitats after the construction project was better than that before construction. However, there were fluctuating conditions. (2) The areas of water bodies and marshes were positively correlated with the number and diversity of waterbirds. However, increases in arable land, forestland, grassland and saline land can stress rare waterbirds. Changes in water levels, water body areas and vegetation cover in the conservation area all directly affect the availability of waterbird habitats, which can affect waterbird diversity. The wetland water level is the most important impact factor. (3) The annual water diversion volume in the study area is 5.00 × 107 m3. When the water level is maintained at approximately 1.5-2.5m, the number of waterbirds remains stable. This study can provide scientific guidance and a methodological basis for the construction of ecological projects with waterbirds as protected targets.

Urban Spatial Dynamics and Geo-informatics Prediction of Karachi from 1990–2050 Using Remote Sensing and CA-ANN Simulation

Rapid urbanization significantly impacts land use and land cover (LULC), leading to various socioeconomic and environmental challenges. Effective monitoring and detection of spatial discrepancies are crucial for urban planners and authorities to manage these changes. This study aims to analyze the spatial dynamics of LULC changes and predict future land use patterns. The specific objectives are to classify historical land use from 1990 to 2020, simulate future land use from 2020 to 2050, and interpret the spatial and temporal results. The study utilized remotely sensed images with the semi-automatic classification plugin (SCP) approach for land use classification from 1990 to 2020. Future land use patterns were simulated using the Modules of Land Use Change Evaluation (MOLUSCE)-based Cellular Automata-Artificial Neural Network (CA-ANN) model. The results were then interpreted to comprehend the dynamics of urban expansion. The conclusions direct a significant increase in built-up and grasslands, with a consistent decline in other land use types. From 1990 to 2020, approximately 423.75 km² and 856.97 km² of land were converted into built-up areas and grasslands, respectively. This was accompanied by a decline in rocky bare and bare soil areas, while the proportions of water bodies and mangroves remained steady. Predictions for 2020 to 2050 suggest an additional increase of 561.93 km² in built-up areas, with a progressive decline in other land use classes. The study emphasizes the critical need for spatial planning policies to address challenges arising from rapid urbanization. By analyzing historical land use changes and predicting future patterns this research offers a comprehensive view of urban growth dynamics. The novel application of these techniques provides valuable insights for urban planners to develop informed strategies for managing expansion and mitigating associated socioeconomic and environmental impacts.

Dynamics of land use and land cover change in the distal Okavango Delta, Botswana

ABSTRACT Population increase and socio-economic progress exert pressure on land use and land cover (LULC) in any geophysical environment. If not minimized, the pressure has the potential to unfold as unplanned and uncontrolled alterations in LULC. Remote sensing (RS) and geographical information systems (GIS) prove to be powerful tools for obtaining accurate and real-time information on the spatial distribution of land use and land cover change (LULCC) both spatially and temporally over a wide area. This research paper uses remotely sensed spatial data and a literature review to examine the extent of LULCC over three decades in the distal Okavango Delta with specific attention on Maun, Botswana. The study assesses changes in population growth, built-up areas, road/pavements, water fluctuations, hydrophytes and shrubs/trees dynamics from April 1990 to April 2020. Findings reveal that while the population of Maun increased from 26,768 to 85,293 between 1990–2020 built-up areas increased from 0.68 km2 to 9.61 km2 and shrubs/trees decreased from 13.53 km2 to 7.06 km2, respectively, within the same period. Grasslands increased from 8.57 km2 in 1990 to 13.44 km2 by 2020. The paper thus recommends the establishment of a two-kilometre-wide green belt around the settlement to stem any progressive encroachment into agricultural reserved land.

Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model

ABSTRACT Modelling land-use/landcover (LULC) change is vital for addressing global environmental and sustainability issues and evaluating various land system scenarios. However, existing geosimulation methodologies for global LULC change fail to account for spatial distortions caused by the Earth’s curvature and do not consider multiple LULC change processes. Thus, this research study proposes an enhanced spherical geosimulation modelling approach that integrates deep learning (DL) to simulate change of multiple classes of LULC process under the shared socioeconomic pathways (SSP) at the global level. Based on the simulation results, the frontiers of urbanization, cropland expansion, and deforestation are indicated to be in developing countries particularly in Asia and Africa. The simulation outputs also reveal 42.5%–63.2% of new urban development would occur on croplands. The proposed modelling approach can serve as a valuable tool for spatial decision-making and environmental policy formulation at the global level.

Decadal hydrological impact assessment of evolving land use and land cover in an Indian river basin: a multi-model approach

ABSTRACT All river basins have ever-evolving land use and land cover (LULC) attributes. The impact of these changes may not be significant on short time scales (i.e., monthly, seasonal, yearly), but over a decadal scale, they can substantially alter the hydrological processes of the basin. This study comprehensively quantifies the impacts of LULC changes over Cauvery basin in India using LULC maps from four decades spanning from 1980 to 2020. Simulations were performed using the Soil and Water Assessment Tool (SWAT) with various datasets. To isolate the effects of LULC changes, two sets of SWAT models were developed: A-set models for calibration and validation to establish basin parameters and B-set models to examine LULC change impacts while isolating other factors such as terrain and climate changes. Key findings include a significant increase in urban areas (0.87% in 1985 to 5.54% in 2015), a decline in vegetation cover (25.34% in 1985 to 21.32% in 2015), and an increase in the Curve Number and average annual surface runoff, highlighting the impact of LULC changes on hydrological processes. The A-set models achieved R-squared values of 0.831, 0.728, 0.715, and 0.757, while the B-set models showcased significant changes in hydrological parameters due to LULC changes.

Spatial spillover effects of ecosystem service values in northeast China tiger and leopard national park based on spatial Durbin model

National parks are public green spaces planned and managed by the state, with the primary purpose of protecting natural ecosystems and cultural heritage. Evaluating the Ecosystem Service Value (ESV) of national parks and clarifying their ecological benefits can provide important scientific basis for the ecological protection and restoration of national parks. Therefore, taking the Northeast China Tiger and Leopard National Park (NCTLNP) as the study area, this research constructs an ESV assessment model based on Land Use and Land Cover Change (LUCC), using vegetation net primary productivity（NPP）as a biomass correction factor to modify the Equivalent Factor Approach (EFA). It aims to explore the spatial–temporal pattern changes of the ESV in the NCTLNP from 2000 to 2022. Additionally, the Future Land Use Simulation (FLUS) model is utilized to simulate the future LUCC pattern and ESV evolution trend under ecological protection. Finally, the spatial Durbin model (SDM)is employed, combining socio-economic and environmental factors such as population, GDP, and PM10 and so on, to reveal the driving mechanism of spatial spillover effects of ESV in the NCTLNP. The results indicate that:①The accuracy of the FLUS model is 93.12% with a Kappa coefficient of 0.87. The main LUCC of the NCTLNP is forest. Before 2020, the ecological land had a shrinking trend, while impervious is in a significant increasing trend. After 2020, there is an opposite trend.The most significant changes in LUCC were found in Wangqing County and Hunchun City. ②The ESV of the NCTLNP shows a fluctuating and increasing trend from 2000 to 2022. It increased from 69.315 billion yuan to 69.809 billion yuan. The main service function of the ecosystem in the study area is the regulatory services, which provide 58.03% of the ESV to the NCTLNP. The Hunchun City and Wangqing County have the fastest increasing rate. ③The ESV of the NCTLNP had a significant negative spillover effect, with spatial spillover coefficients of −0.135 and −0.236, respectively, which led to a siphoning phenomenon in the NCTLNP. Overall, by assessing the ESV of the NCTLNP and revealing the spatial spillover effects in the study area, it can provide a vital scientific foundation for the conservation and management of the national park, which will help to fulfill the sustainable development of the ecosystem and the conservation of biodiversity.

Trends in habitat quality and habitat degradation in terrestrial protected areas.

Protected areas are typically considered a cornerstone of conservation programs and play a fundamental role in protecting natural areas and biodiversity. Human-driven land-use and land-cover (LULC) changes lead to habitat loss and biodiversity loss inside protected areas, impairing their effectiveness. However, the global dynamics of habitat quality and habitat degradation in protected areas remain unclear. We used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model based on global annual remotely sensed data to examine the spatial and temporal trends in habitat quality and degradation in global terrestrial protected areas. Habitat quality represented the ability of habitats to provide suitable conditions for the persistence of individuals and populations, and habitat degradation represented the impacts on habitats from human-driven LULC changes in the surrounding landscape. Based on a linear mixed-effects modeling method, we also explored the relationship between habitat degradation trends and protected area characteristics, biophysical factors, and socioeconomic factors. Habitat quality declined by 0.005 (0.6%) and habitat degradation increased by 0.002 (11%) from 1992 to 2020 globally, and similar trends occurred even in remote or restrictively managed protected areas. Habitat degradation was attributed primarily to nonirrigated cropland (62%) and urbanization (27%) in 2020. Increases in elevation, gross domestic production per capita, and human population density and decreases in agricultural suitability were associated with accelerated habitat degradation. Our results suggest that human-induced LULC changes have expanded from already-exploited areas into relatively undisturbed areas, and that in wealthy countries in particular, degradation is related to rapid urbanization and increasing demand for agricultural products.

Trade‐offs and synergistic relationships on soil‐related ecosystem services in Central Asia under land use and land cover change

AbstractDue to climate change and human activities, land desertification, a major environmental issue in arid regions, has exerted an impact on ecosystem services (ES). However, previous ES assessments in arid regions lacked the integrated assessment and trade‐off analysis of soil‐related ecosystem services (S‐ES). To address this, we developed a S‐ES‐Bayesian network (S‐ES‐BN) model to analyze the trade‐offs and synergies between the complex relationships between S‐ES and their multiple influences in Central Asia (CA). The results showed that (1) there is a large spatial and temporal heterogeneity in S‐ES, with the most significant changes in water yield service (WY, −30%) and soil conservation service (SC, −26%) in the past 30 years (1990–2020); (2) land use and land cover change (LUCC) area shifts can have a gaining or weakening effect on the S‐ES. Among them, water to wetland had the most obvious gain effect on SC (+5.1 t·km2·yr−1) and water to forestland had the most pronounced effect on carbon fixation service (CF, +251.22 t); and (3) based on the S‐ES‐BN, an optimal configuration of S‐ES in CA demonstrated that the intensification of a future “warming and wetting” scenario in CA might lead to significant decreases in WY (−37%) and sand fixation (−7%). In addition, the clarification of the land allocation structure (cropland, forestland, and grassland) in LUCC will enhance the spatial demand for CF.

The past and future dynamics of ecological resilience and its spatial response analysis to natural and anthropogenic factors in Southwest China with typical Karst

With the global land use/land cover (LULC) and climate change, the ecological resilience (ER) in typical Karst areas has become the focus of attention. Its future development trend and its spatial response to natural and anthropogenic factors are crucial for understanding the changes of ecologically fragile areas to human behavior. However, there is still a lack of relevant quantitative research. The study systematically analyzed the characteristics of LULC changes in Southwest China with typical Karst over the past 20 years. Drawing on the landscape ecology research paradigm, a potential-elasticity-stability ER assessment model was constructed. Revealing the characteristics and heterogeneity of the spatial distribution, annual evolution, and development trend of ER in the past and under different scenarios of shared socioeconomic pathways and representative concentration pathways (SSP-RCP) in the future. In addition, the spatial econometric model was utilized to reveal the spatial effect response mechanism of ER, and adaptive development strategies were proposed to promote the sustainable development of Southwest China. The study found that : (1) In the past 20 years, the LULC in Southwest China showed an accelerated change trend, the ER decreased declined in general, and there was significant spatial heterogeneity, showing the spatial distribution pattern of “west is larger than east, south is larger than north, and reduction in the west was slower than that in the east.” (2) Under the same SSP scenario, with the increase of RCP emission concentration, the area of the lowest-resilience increased significantly, and the area of the highest-resilience decreased. (3) The woodland was the largest contributor to ER per unit area in the Southwest China, and grassland was the main LULC type, which had a prominent impact on the ER of the study area. (4) The average precipitation and the normalized difference vegetation index (NDVI) were significant natural drivers of ER in the study area, and the economic growth, innovation, and optimization of industrial structure contributed to the ER of Southwest China. Overall, the integration of quantitative assessment and multi-scenario-based modeling not only provides new perspectives for understanding the pattern of change and response mechanisms, but also provides valuable references for other typical Karst regions around the world to achieve sustainable development.

LUCC-induced dust aerosol change increase surface and reduce atmospheric direct radiative forcing in Northern China

Land use and land cover change (LUCC) can alter surface properties, such as albedo, roughness, and vegetation coverage, directly affecting dust emissions and aerosol concentrations, leading to variations in direct radiative forcing (DRF) of dust aerosols and consequently impacting the climate. This study utilized the Weather Research and Forecasting model with Chemistry (WRF-Chem) to quantify the impact of LUCC in northern China from 2000 to 2020 on dust aerosol DRF. Results indicated that LUCC's influence on shortwave radiative forcing of dust was significantly greater than its influence on longwave radiative forcing and exhibited obvious seasonal variations. Overall, LUCC can cause net direct radiative forcing to increase by 5.3 W m−2 at the surface and decrease by 7.8 W m−2 in the atmosphere. Different types of LUCC transformation showed distinct impacts on dust aerosol DRF, with the conversion from sparse vegetation to barren land had the most significant effect on net radiative intensity, resulting in a decrease of 8.1 W m−2 at the surface, an increase of 12.2 W m−2 in the atmosphere, and an increase of 4.1 W m−2 at the top of the atmosphere. Conversely, the conversion from barren land to sparse vegetation led to surface cooling and atmospheric warming. These findings are of great significance for enhancing our knowledge of the effects of LUCC on the radiative balance of dust aerosols.

Assessing the dynamics of land use and land cover change in semi‐arid savannah: A focus on woody plant encroachment utilising historical satellite data

AbstractThe encroachment of woody plants into grassland and the conversion of grasslands to woodlands is a worldwide phenomenon and has been regarded as a major global problem for decades. The rate of woody plant encroachment (WPE) varies across biomes and can be influenced by land use activities and climate conditions. As a result, the current study assessed the spatial distribution of woody plants and land use and land cover (LULC) change within the Letaba River catchment in the Limpopo province of South Africa's subtropical region. Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) satellite data sets were used to map and quantify WPE and other LULC changes in the Letaba River catchment over a 30‐year period (1989–2019). Random forest classifier was used to determine of the rate of change of WPE and LULC within the study area. The results indicated that the Letaba River catchment has undergone a significant change with an increase in woody plant species. The woody plant cover had increased from 36,014 ha in the year 1989 to approximately 561,493 ha by 2019. Meanwhile, grassland has declined by 486,322 ha (33.7%) from 1989 to 2019. The overall classification accuracy (OA) ranged between 91.7% and 95.5%. The study findings will provide critical insights and baseline information about the state of WPE in semi‐arid environments, as well as provide catchment managers with the information they need to take the necessary actions to manage the rapid increase in woody plants. However, fire and herbivory are important factors that influences the WPE, and this might have also played an important role in the findings. The study suggests that WPE is an ongoing process and management strategies are required to mitigate and maintain the intensity of woody plants.

Modeling of land use and land cover changes using google earth engine and machine learning approach: implications for landscape management

A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.