Land Expansion Research Articles

Spatiotemporal dynamics of land use land cover change and its drivers in the western part of Lake Abaya, Ethiopia.

Understanding the dynamics of land use/land cover (LU/LC) changes and what drives these changes is essential for creating effective strategies for sustainable land management. It also helps to monitor the impact on ecosystems and biodiversity, which is crucial for policy-making. This study focused on assessing the trends, rates, and extent of LU/LC change and its causes in the western part of Lake Abaya in Ethiopia. To achieve this, we used a supervised classification method with a maximum-likelihood algorithm to map different land use land cover types. Additionally, we gathered information through field observations, focus group discussions (FGDs), and key informant interviews (KIIs) to identify the factors driving LU/LC change and its consequences between 1990 and 2022. The study findings revealed that vegetation and wetlands significantly decreased over this period, while water bodies, agricultural land, and settlements expanded at the expense of other land uses. The average normalized difference vegetation index (NDVI) values decreased from 0.368 in 1990 to 0.135 in 2022, indicating declining vegetation health. Local communities point to several factors responsible for these changes, including the expansion of agricultural land, increased settlement, firewood collection, and charcoal production (as proximate/immediate drivers), as well as population growth, poverty, unemployment, climate change, and policy-related issues (as underlying causes). Thus, it needs the development and implementation of an integrated and sustainable land management system, and strong land use and restoration policies in order to halt or reduce the rapid expansion of agricultural land and settlement areas at the expense of vegetation and wetlands.

Spatial and Temporal Characteristics of Land Use Changes in the Yellow River Basin from 1990 to 2021 and Future Predictions

Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics, the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model, and a geographic detector to explore the spatial and temporal characteristics of land use changes and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. The most actively changing land use types are cropland, grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution, and the remaining land use types exhibited stable directional distributions. (3) Economic factors had a stronger driving effect on land use changes than topographic and climatic factors. The land use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland, grassland, and forest. However, there will be a significant expansion of impervious surfaces and forest land, with substantial encroachment on cropland and grassland.

Driving the Evolution of Land Use Patterns: The Impact of Urban Agglomeration Construction Land in the Yangtze River Delta, China

The rapid increase in population and economic activities has greatly influenced land use and spatial development. In urban agglomerations where socioeconomic activities are densely concentrated, the clash between ecological protection and economic growth is becoming more evident. Therefore, a thorough quantitative assessment of spatial changes driven by land use dynamics, alongside an examination of temporal and spatial driving factors, is crucial in offering scientific backing for the long-term and sustainable growth of urban agglomerations. This paper focuses on the major urban agglomerations in China’s Yangtze River Delta region, examining the spatiotemporal evolution of land use and landscape patterns from 2000 to 2020. By employing the standard deviation ellipse technique, coupled with multiple linear regression and the geographical detector model, we conduct a quantitative assessment of the directional trends in urban construction land expansion as well as the diverse impacts of temporal and spatial factors on this expansion across various periods and regions. The findings indicate that over the past 20 years, construction land in the Yangtze River Delta Urban Agglomeration expanded in concentrated patches, showing significant scale effects with relatively intact farmland and forest land being increasingly encroached upon. Landscape-type transitions predominantly occurred in cities around Taihu Lake and Hangzhou Bay, with the most significant transition being farmland converted to construction land, resulting in a greater number of patches and more pronounced land fragmentation. Throughout the 20 years, the standard deviation ellipse of construction land in the Yangtze River Delta Urban Agglomeration expanded and shifted, with the predominant expansion trending from the northwest toward the southeast, and the EN orientation being the most intense expansion area, covering 1641.24 km2. The influence of temporal and spatial driving factors on the expansion of urban construction land differed across various periods and regions. This study thoroughly examines the driving factors that affect the evolution of urban construction land in the region, offering valuable scientific evidence and references for future planning and development of the Yangtze River Delta Urban Agglomeration, aiding in the formulation of more precise and efficient urban management and land use strategies.

From center to outlying areas: Navigating built-up land dynamics along urban-rural gradients in Global South megaregions

Understanding the dynamics of built-up land amidst the urban-rural transformation process is crucial for balancing the benefits of urbanization against the challenges of uncontrolled expansion. Yet, how built-up land develops across different types of human settlements, particularly within emerging Global South megaregions (GSMs), is not well documented. Employing an urban-rural gradient (URG) approach, we investigate built-up land dynamics across six representative emerging GSMs spanning 1985–2020, contextualizing our findings within urbanization theory and sustainable development discourse. Our analysis reveals that urban center growth and peri-urbanization drive a substantial proportion of built-up land expansion (36.47% and 27.39%, respectively), in contrast to the minimal increases observed in rural clusters and semi-dense areas (2.48% and 2.44%, respectively). The predominant expansion mode is sprawling growth, notably evident in peripheral areas, while densifying growth is mainly confined to urban centers and mostly uninhabited areas. Sprawling shrinkage is observed in dense and semi-dense urban areas, as well as rural clusters. These nuanced dynamics illustrate the varied ways in which regional territories engage with and are shaped by the urbanization process. Through the lens of the URG analysis, our study enhances understanding of spatial transformations in the GSMs, offering informative insights for fostering sustainable cities and human settlements.

Land use & land cover dynamics and its relationship with nitrate pollution in groundwater around inactive mine areas using geospatial techniques, SW part of Cuddapah basin, Southern India

Geospatial maps can show how the ineffective operations of inactive mines affect water and aquifer quality. As such, the purpose of this study is to assess the impact of mining and irrigation on the aquifer ecosystem through the evaluation of LULC and slope maps through the application of Landsat 8 OLI/TIRS and DEM data. A total of 50 groundwater samples were prepared from villages in the close proximity to inactive mines during pre and post monsoon periods in 2021. The results of the analysis revealed alarming statistics, that 14% of groundwater samples exceeded the WHO nitrate limit in pre & post monsoon season, indicating a high-risk in the study area. According to guidelines (USEPA, 2014), 34% in pre-monsoon and 26% post-monsoon of samples exceeded the THI levels for adults and children respectively, indicating non-carcinogenic health risks. In addition, 80% of the samples in both seasons exhibited high NPI values, indicating nitrate contamination associated with blue baby syndrome. From the Geospatial analysis the findings from the LULC classification indicate that there has been a significant increase in cropland area from 2016 to 2021 due to changes in forest land, fallow land, and water resources. These problems have been exacerbated by the expansion of cultivated land, which has increased from 71.1 square kilometers in 2016 to 118 square kilometers in 2021, accounting for 13.1% of the total area. This expansion, coupled with elevated water body resource availability, has compounded the nitrate pollution including in intensely irrigated regions. The slope map analysis revealed that the inactive mines occur at low slope, high rainfall areas and these are compounded by runoff from other sources such as domestic and agricultural wastes. For these matters, sealing and remediating these inactive mines is essential so as to prevent further nitrate leakage.

Impact of Farm Management on Soil Fertility in Agroforestry Systems in Bali, Indonesia

Expansion and intensification of agricultural land in the tropics increasingly raises environmental concerns and questions about sustainability of production systems. A key parameter to consider when assessing the sustainability of production systems is soil fertility, and of particular interest are macronutrients, pH, electrical conductivity, and microbial communities. To understand which environmental factors influence soil fertility, we studied the abovementioned key parameters in two agroforestry systems (rustic and polyculture) in Bali, Indonesia. Via Generalized Linear Models, we found that agroforestry system, canopy cover, crop richness, tree richness, and yields had differing effects on topsoil (0–5 cm) and subsoil (10–15 cm) properties, including C:N ratios, conductivity, K, organic C, P, and total microbes. We found a higher C:N ratio in topsoil (p = 0.027), higher organic carbon content in topsoil (p = 0.009) and subsoil (p < 0.001), higher total microbes in subsoil (p = 0.001), and lower phosphorus levels in topsoil (p < 0.001) in rustic than in polyculture systems. Rustic systems may foster conditions conducive to soil fertility, and in our study, canopy cover (p < 0.001) and tree richness (p < 0.001) emerge as a key positive drivers of the total number of microbes in topsoil. The positive associations observed between crop and tree richness with electrical conductivity and total microbe counts underscore the importance of biodiversity in enhancing soil fertility, emphasizing the need for diversified agricultural systems to promote soil fertility. With soil fertility declining across the world due to global investments in agricultural intensification, it is vital that food production systems divert to the use of systems such as agroforestry in order to ensure soil sustainability and food security for future generations.

Determinants of multiple maize technology package adoption in Ethiopia: evidence from the Sidama region

The adoption of improved agricultural technology packages is vital in Ethiopia, as the expansion of cultivable land appears nearly exhausted and population size has skyrocketed. However, the country has shown a low adoption rate. Thus, this study aimed to explore the factors that hinder or facilitate the adoption of multiple maize technology packages and intensity of adoption in the northern Sidama zone of Ethiopia. A multistage sampling procedure was applied to gather cross-sectional data from 424 farm households owning 545 maize plots. A multivariate probit and two-limit Tobit models were applied to address the study objectives. The conditional probability results confirmed that maize technology packages are complementary (positive relationship). This infers that agriculture-focused policies that influence the adoption of a single component of technology packages can have a reinforcing advantage over the adoption of other technologies. Furthermore, the results from the models showed that farmers with greater experience, family size, plot size, livestock and oxen ownership, number of maize plots owned, off-farm income, access to credit, extension services and membership in institutions are more likely to adopt at least one of the improved technology packages and achieved a better status of intensity of adoption. Therefore, it is crucial to reinforce and deliver quality extension services, provide credit access, motivate youth to be involved in farming activities, inspire membership and ease the system to access inputs and technologies for broader adoption of technology packages.

Geo-environmental monitoring of coastal and land resources for Coatzacoalcos coastal region

Coatzacoalcos is one of the notable Mexico coastal areas regarding location, activities, and resources. The purpose of the current study is to use geographic techniques to assess the spatial and temporal changes in Land Use/Cover (LULC), Land Surface Temperature (LST), and Shoreline as a result of anthropogenic and natural factors. Four calibrated multi-temporal Landsat images, dated 1993, 2003, 2013, and 2023, were combined and processed to accomplish this goal. The six primary LULC classes forest, built-up land, mining, water bodies, and agriculture were mapped using RF Machine learning algorithms. The last period (2003–2023) shows the highest rate of built-up land expansion, while the years 1993–2013 show the lowest rate of agricultural land reduction. Forest cover in the southeast decreased steadily from 339.25, 308.49, 287.43, and 211.83 km2 in 1993, 2003, 2013, and 2023. There is a slight difference in mean LST between built-up areas that recorded 26, 29, 32, and 33 °C in 1993, 2003, 2013, and 2023, respectively. The following is an order of LULC from lowest to highest LST during 2023 water body 12 °C, Forest 19 °C, Agriculture Land 22 °C, Mining 26 °C, Barren Land 34 °C and Built-up Land 33 °C. In Zone B near Coatzacoalcos showed particularly high accretion as a result of building artificial structures. Maximum rates of accretion and erosion were recorded at 2.0 m/year and −9.63 m/year, respectively. Fortunately, Zone C has minimal urban development and is home to forests and agricultural areas, so the potential negative effects are reduced. This study aids in understanding spatial and temporal LULC changes, LST variations, and shoreline dynamics, crucial for the sustainable management of Coatzacoalcos coastal resources in Mexico.

Estimation of land surface temperature and LULC changes impact on groundwater resources in the semi-arid region of Madhya Pradesh, India

The land surface temperature (LST) changes influence on many factors such as land use and land cover (LULC), vegetation and water resources etc. Now a day’s urban population growth, industrial development and human activity are increased due to human migration and urban expansion. The surface temperature is very harmful to vegetation, groundwater level, and ecosystem health. Urban pollution growth and air pollution factor is also playing important role for increase and decrease the LST with climate change impact. Demand of drinking water are increases particular in the urban city due to the population growth and urban expansion, which all of this direct effect on surface and groundwater sources and resources. Hence we have been chosen the latest technologies such as remote sensing (RS), Geographic information System (GIS) and Google Earth Engine (GEE) are very effective for mapping, monitoring and assessment of spatial maps of LST, LULC, and NDVI (Normalised differential vegetation index) estimation. In this study, main focus on the LULC of 2011 and 2021 were classified using random forest (RF) algorithm, ML and remote sensing datasets. LULC were divided into four categories i. e. agricultural land, built up land, waterbody and waste land. For better understanding about the growth of LST, this study examines the association between LST and NDVI change, what vegetation condition impact on the LST. The main and important results have been found the built-up land and LST positive correlation due to increases both the factors in the urban area. LST increases gradually and its bad effect on the green land, water level and expansion of barren land. The range of LST is 43.60 °C to 45.30 °C. Less vegetation cover showing highest LST and vice versa. All of the LST, NDVI, and LULC direct effect on the groundwater availability in the area. This paper important result found the built-up land and LST both are increased in 2021, NDVI and LST having negative correlation. The results of study can very much useful for understanding the LST, NDVI and LULC, these factors what impact on groundwater availability in the study area.

Determinants of spatiotemporal changes of land use carbon emissions for counties in Shaanxi Province, China.

In China, urban sprawl and developed land expansion challenge the country's "carbon peak" and "carbon neutrality" goals. Counties as the basic governance units arecrucial for effective carbon reduction policies. This study examines land use carbon emissions (LUCE) in Shaanxi Province at the county level, essential for China's low-carbon strategy. Analyzing data from 107 counties between 2000 and 2020, we found that developed land, though increasing, is the primary carbon source with a slowing growth rate. The Conversion of Cropland to Forests and Grasslands national policy mitigated the impact on carbon absorption. Carbon emissions displayed positive autocorrelation and spatial heterogeneity, varying across the region. Using the Spatial Durbin Error Model, we linked county-level emissions to GDP per capita, population, urbanization rate, and research and development expenditure for direct and indirect influence. These factors correlate with fossil fuel use and high-quality industrial development. Promoting public transits and reducing private car use are vital for achieving local and regional low-carbon goals.

Study on Optimization of Land Use Structure in Fujian Province Based on Low-Carbon Perspective

Carbon peaking and carbon neutrality strategies are pivotal in addressing climate change. Optimizing land use structure is a fundamental approach to achieving low-carbon development within a given territory. This study focuses on Fujian Province as the research subject, predicting carbon emissions for the next decade by analyzing the correlation between land use types and carbon emissions using the gray model. This analysis is based on land use panel data spanning from 2007 to 2021. The study applies the FLUS-Markov model to simulate Fujian’s land use in 2030. A multi-objective optimization model is developed from a low-carbon perspective, integrating carbon emissions, economic, and ecological factors. The study explores land use under three scenarios: natural development scenario (NS), low carbon scenario (LCS), and comprehensive scenario (CS). Findings highlight the relationship between land use-related carbon emissions, urbanization, and relevant policies in Fujian. The FLUS-Markov simulations suggest that under the NS scenario, carbon emissions in 2030 will reach 77.829 million tons, an increase of 11.013 million tons from 2020. In contrast, the LCS demonstrates that optimizing land use structures can effectively balance carbon reduction, economic growth, and ecological preservation. Under the CS, 2030 emissions could be reduced by 7.2854 million tons while maintaining economic and ecological benefits. Despite variations in construction land expansion across these scenarios, all follow a “one belt, one core” development pattern. The study concludes with policy recommendations, including industrial layout optimization and clean energy promotion. These findings support the alignment of land use optimization with Fujian’s future development needs, offering guidance for land-use planning and policies focused on low-carbon objectives.

Assessment of Landscape Change and its Relationships with Surface Temperature and Geo-spatial Indices during 1992 - 2022 at Haldia Industrial Region, West Bengal, India

Unplanned local development and alterations in Land Use and Land Cover (LULC) are an early alert for towns and cities around the world. The population pressure has resulted in the conversion of natural land into impermeable areas, which has altered the surface energy budget and created microclimatic differences locally. The main objective of the present study is to estimate LULC changes and its relation to increases Land Surface Temperature (LST) in the Haldia Industrial Region (HIR) of West Bengal using multi-temporal Landsat 5 Thematic Mapper (TM) of 1992 and Landsat 8 Operational Land Imager (OLI) of 2022 with 30-m spatial resolution images were obtained from the USGS website. The supervised classification method was applied by using Maximum Likelihood classification (MLC) to classify the satellite images into various LULC classes such as settlement, waterbody, vegetation, agriculture land, and fallow land, respectively. The result suggested an overall increase of settlement area from 13.49 km2 in 1992 to 44.54 km2 in 2022. On the other hand, the agriculture land decreased from 214.26 km2 to 152.34 km2 due to fast urbanization, decline green lands, and expansion of barren lands. The outcome result from LST was exhibited that the LST value has increased 3.77°C during 1992 to 2022 in this study area. The Normalized Difference Vegetation Index (NDVI) was showed a strong negative relationship with LST with a determination coefficient (R2) values found as 0.491 (1992) and 0.356 (2022). The positive determination coefficient between LST with Normalized Difference Water Index (NDWI) was found as 0.0002 (1992), and 0.0417 (2022). R2 values were estimated as 0.174 (1992), and 0.0347 (2022) between LST with Normalized Difference Built-up Index (NDBI) that indicated a positive determination coefficient. The findings enhanced understanding of the relationship between urban LST and LULC in developing an inclusive climate resilience policy and making the HIR more sustainable to the effects of climate change.

Spatiotemporal characteristics and multi-scenario simulation of land use change and ecological security in the mountainous areas: Implications for supporting sustainable land management and ecological planning

The acceleration of global urbanization has intensified land use activities, which threaten the ecological environment and hinder the achievement of sustainable socioeconomic development goals. With the background of continuous land use expansion, the reasonable management of land use and ecological protection has increasingly become an important issue. This study constructs an ecological security (ES) index to evaluate the ES pattern, and employs the Future Land-Use Simulation (FLUS) model for multi-scenario simulation of land use patterns in mountainous areas. The results showed that from 2000 to 2020, cultivated land and grassland showed a sharp decrease, with areas of −128.64 and −228.18 km² respectively, while forest land, urban land, and other construction land increased, with areas of 116.12, 31.21, and 120.69 km² respectively. Over time, the mountainous areas mainly exhibited low and moderate ES patterns. Furthermore, for 2020–2030, under the natural development scenario, cultivated land, forest land, and grassland will show a decrease, while urban and other construction land will show a sharp increase. In contrast, under the ecological protection scenario, ecological land will show significant increase, and the patch expansion of urban construction land will be smaller. These results confirm the positive contribution of ES protection effects to improving the land use development, and support insightful guidance for formulating policies on ecological management in mountainous areas.

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

Enhancing natural carbon sinks: Simulation of land use change under different carbon market scenarios by developing an ANN-ABM model

The carbon market mechanism presents an innovative approach to achieving carbon neutrality, however, its impact on regional carbon sink enhancement remains uncertain. This paper proposes an analysis framework based on an Artificial Neural Network-Agent-Based Model (ANN-ABM) to simulate the potential contribution of carbon market mechanism in enhancing carbon sinks. Taking the case of Chongming, China, the results demonstrate significant economic potential with forest land expansion from 2010 to 2020, equivalent to 30.69 % of eco-compensation. Under scenarios introducing a carbon market, there is an increased probability of converting land to high carbon sink types. Compared to the baseline scenario, carbon prices at 1.34 US$/t and 6.27 US$/t result in additional funds of 8.94 % and 41.46 %, respectively, and increase the carbon sink by 12.52 % and 30.28 %. The findings contribute to an understanding of how the value of carbon sinks can be realized through market mechanism and offer guidance for innovative eco-compensation approaches.

Assessment of wetland ecosystem health in Rarh Region, India through P-S-R (pressure-state-response) model

The current study attempted to assess wetland ecosystem health (EH) in the Murshidabad district's Rarh tract using the P-S-R (Pressure-State-Response) model and machine learning (ML) algorithms and validated it with a field-based validation approach as well as conventional validation approaches. To assess the ecosystem's health, 27 metrics were used to monitor the wetlands' pressure, state, and response. All of the models found that 46.1 % of wetlands in strong EH zones have transformed to 11.41 % in relatively fragile EH zones during the previous thirty years, demonstrating a progressive loss of EH quality throughout larger wetland areas. All of the applied models were deemed to be acceptable based on the results of the model validation process, however, the Random Forest (RF) model performed exceptionally well. The deterioration of EH in the wetlands happened due to the rapid expansion of settlement areas and agricultural land. So, the findings of the study deepen our knowledge about EH in the Rarh tract's wetlands, assisting decision-makers in creating sustainable wetland management strategies.

Implications of landscape changes for ecosystem services and biodiversity: A national assessment in Ecuador

Ecuadorian ecosystems experience high pressure due to anthropogenic activities and climate change. Despite the need of regular monitoring of biodiversity and ecosystem services (BES), attempts to assess the current and future interdependencies of BES and landscape changes are still lacking. This study suggests a spatial assessment of the capacity of ecosystems/land use types to provide BES as status quo and its future development under scenarios of deforestation and climate change. To address data scarcity and improve legitimacy, spatial modeling was combined with participatory approaches. Specifically, changes in landscape pattern were simulated using a modeling platform that combines Geographic Information System (GIS) and Cellular Automaton (CA) modules. Experts in ecosystem conservation and management participated through surveys and workshops. Food, drinking water, service water, soil erosion control, water flow regulation, pollination/seed dispersal, regulation of macro climate, and landscape aesthetic/amenity were identified as the most relevant ES. Among the forest ecosystems, Páramo-related ecosystems were regarded to provide multiple ES with high capacities. Compared to the current status, the deforestation scenario showed to decrease most BES by 20–25 %, while increasing food provision by 5 %, as a trade-off. Regarding the climate change scenarios, the “Representative Concentration Pathways” (RCP) by 2070 were simulated with an increase in temperature of 2 °C (RCP 2.6) and of 4 °C (RCP 6.0). RCP 6.0 showed more noticeable impact than RCP 2.6, which caused a decrease in most BES whereas an increase in food provision due to the possible expansion of arable land into higher altitudes. The results of the spatial assessment also indicated high and low potential areas for BES provision. Such information can support decision-making for BES management e.g., priority areas for actions. Furthermore, the applied spatially explicit assessment could be a starting point for a regular assessment of BES, which has not yet been implemented in Ecuador.

Multi-scenario prediction and attribution analysis of carbon storage of ecological system in the Huaihe River Basin, China.

Evaluating the impact of large-scale human activities on carbon storage through land use changes is of growing interest in terrestrial ecosystem assessments. The Huaihe River Basin, a vital Chinese grain production area, has undergone marked land use changes amid socio-economic acceleration. Evaluating the impacts of land use change on carbon storage and future carbon sequestration is imperative for regional ecosystem sustainability and Chinese food security, simultaneously, furnishing data support to regional land use planning and decision-making processes. Nonetheless, the mechanisms linking land use changes to carbon storage and the future carbon reservoir responses remain unclear. We utilized a multi-source dataset and representative scenarios, integrating PLUS, InVEST models, and Geodetector to assess land use change impacts on carbon storage in the Huaihe River Basin (2000-2030). The data indicates the following: (1) from 2000 to 2020, cultivated land decreased by 28,344.69 km2, construction land increased by 26,914.56 km2, and other land types changed little. (2) Land use change resulted a carbon loss of 1.17 × 108 t, primarily due to the expansion of construction land. (3) All four simulation scenarios exhibited diminished carbon storage relative to 2020, with the economic development scenario recording the lowest at 4.98 × 109 t and the ecological protection scenario the highest at 5.06 × 109 t. (4) Elevation predominantly drives carbon storage changes, with its interaction with NPP having the greatest impact. The factors synergistically enhance their explanatory power. The research provides a scientific basis for strategies aimed at augmenting regional carbon sequestration and refining low-carbon land management, safeguarding ecosystem stability.

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.

Response of habitat quality to land use changes in The Johor River Estuary

ABSTRACT Rich resources and fragile ecosystems of estuaries are increasingly impacted by land use and land cover (LULC). However, previous studies of habitat quality (HQ) typically do not model the impact of LULC in Southeast Asian estuaries. Here, we used the Integrated Valuation of Environmental Services and Trade-offs (InVEST) model and the Patch-generating Land-Use Simulation (PLUS) model to assess and predict HQ and habitat degradation in the Johor River Estuary (JRE) from 1989 to 2047. Habitat quality contribution rate (HQCR) was used to quantify the impact of land-use changes on HQ. We analyzed the correlation and spatio-temporal changes in LULC and HQ, respectively. Our forecasts show (1) Construction land expanding more than 10 times from 1989 to 2047. (2) HQ increases slightly followed by a continuous decrease in the habitat quality index (HQI). (3) Degradation of HQI was from forests, agricultural land, residential land expansion and land reclamation. (4) Population growth, economic development, and national development policies may pose significant future threats to water, land, and marine traffic in the Johor River. Our results show that land-use changes affect HQ and future development of the JRE should integrate the impacts of natural and human factors on LULC changes.