Search Results (72)

Vegetation is a fundamental component of terrestrial ecosystems. Understanding how vegetation changes and what drives these evolutions is crucial for developing a high-quality ecological environment and addressing global climate change. Extensive evidence has shown that China has undergone substantial vegetation changes, characterized primarily by greening. To quantify vegetation dynamics in China and assess the contributions of various drivers, we explored the spatiotemporal variations in the kernel Normalized Difference Vegetation Index (kNDVI) from 2001 to 2020, and quantitatively separated the influences of climate and human factors. The kNDVI time series were generated from the MCD19A1 v061 dataset based on the Google Earth Engine (GEE) platform. We employed the Theil-Sen trend analysis, the Mann-Kendall test, and the Hurst index to analyze the historical patterns and future trajectories of kNDVI. Residual analysis was then applied to determine the relative contributions of climate change and human activities to vegetation dynamics across China. The results show that from 2001 to 2020, vegetation in China showed a fluctuating but predominantly increasing trend, with a significant annual kNDVI growth rate of 0.002. The significant greening pattern was observed in over 48% of vegetated areas, exhibiting a clear spatial gradient with lower increases in the northwest and higher amplitudes in the southeast. Moreover, more than 60% of vegetation areas are projected to experience a sustained increase in the future. Residual analysis reveals that climate change contributed 21.89% to vegetation changes, while human activities accounted for 78.11%, being the dominant drivers of vegetation variation. This finding is further supported by partial correlation analysis between kNDVI and temperature, precipitation, and the human footprint. Vegetation dynamics were found to respond more strongly to human influences than to climate drivers, underscoring the leading role of human activities. Further analysis of tree cover fraction and cropping intensity data indicates that the greening in forests and croplands is primarily attributable to large-scale afforestation efforts and improved agricultural management. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

The reconstruction of cropland across historical periods offers valuable insights into the relationship between climate change and human–environment interactions. By extracting key demographic and tax revenue data from historical documents, we estimated cropland data during the Tubo, Yuan, Ming, and Qing dynasties for the Rikaze area in China. Subsequently, according to the characteristics of cropland fragmentation in the Rikaze area, we employed geographically weighted regression (GWR) to reconstruct the 1 km × 1 km cropland cover datasets across the four dynasties for the Rikaze area. The findings are as follows. The amount of cropland showed that the change in cropland in the Rikaze area in the four periods was extremely high, which reflects the great instability of cropland in the Rikaze area. Under the combined action of social unification, cropland production policies, and a suitable climate, the Tubo dynasty was the most significant period of cropland development in the Rikaze area, with the area of cropland reaching 591,927 mu. However, under the influence of the nomadic regime and harsh climate in the Yuan dynasty, the cropland area was sharply reduced, reaching only 18,338 mu. During the Ming and Qing dynasties, the cropland area increased steadily, reaching 200,000 mu and 547,000 mu, respectively. The spatial distribution of cropland shows that the cropland in the Rikaze area is mainly distributed in the middle reaches of the Yarlung Zangbo River, the middle and lower reaches of the Nianchu River, and the Pengqu River Valley. Counties and districts with better agricultural conditions, such as Jiangzi, Bailang, and Renbu, are the main concentration areas of cropland in the Rikaze area. The overall spatial distribution pattern of cropland shows fragmented distribution along rivers, highlighting the characteristics of valley cropland. The research in this paper represents the active exploration of the reconstruction of cropland distribution under complex terrain conditions. Full article

Free trade zones are key regions experiencing rapid economic growth, urbanization, and a sharp increase in population density. During the development of free trade zones, these areas undergo drastic transformations in landscape types, large-scale urban construction, heightened resource consumption, and other associated challenges. These factors have led to severe landscape ecological risk (LER). Therefore, conducting comprehensive assessments and implementing effective management strategies for LER is crucial in advancing ecological civilization and ensuring high-quality development. This study takes Hainan Island (HI), China, as a case study and utilizes multi-source data to quantitatively evaluate land use and land cover change (LULCC) and the evolution of the LER in the study area from 2015 to 2023. Additionally, it examines the spatial patterns of LER under three future scenarios projected for 2033: a natural development scenario (NDS), an economic priority scenario (EPS), and an ecological conservation scenario (ECS). Adopting a spatiotemporal dynamic perspective framed by the “historical–present–future” approach, this research constructs a zoning framework for LER management to examine the temporal and spatial processes of risk evolution, its characteristics, future trends, and corresponding management strategies. The results indicate that, over an eight-year period, the area of built-up land expanded by 40.31% (504.85 km²). Specifically, between 2015 and 2018, built-up land increased by 95.85 km², while, from 2018 to 2023, the growth was significantly larger at 409.00 km², highlighting the widespread conversion of cropland into built-up land. From 2015 to 2023, the spatial distribution of LER in the study area exhibited a pattern of high-risk peripheries (central mountainous areas) and low-risk central regions (coastal areas). Compared to 2023, projections for 2033 under different scenarios indicate a decline in cropland (by approximately 17.8–19.45%) and grassland (by approximately 24.06–24.22%), alongside an increase in forestland (by approximately 4.5–5.35%) and built-up land (by approximately 23.5–41.35%). Under all three projected scenarios, high-risk areas expand notably, accounting for 4.52% (NDS), 3.33% (ECS), and 5.75% (EPS) of the total area. The LER maintenance area (65.25%) accounts for the largest proportion, primarily distributed in coastal economic development areas and urban–rural transition areas. In contrast, the LER mitigation area (7.57%) has the smallest proportion. Among the driving factors, the GDP (q = 0.1245) and year-end resident population (q = 0.123) were identified as the dominant factors regarding the spatial differentiation of LER. Furthermore, the interaction between economic factors and energy consumption further amplifies LER. This study proposes a policy-driven dynamic risk assessment framework, providing decision-making support and scientific guidance for LER management in tropical islands and the optimization of regional land spatial planning. Full article

One question about historical grassland ecosystems in the Great Plains region of central North America is the percentage of tree cover overall and near major rivers, compared to current tree cover. Here, I assessed tree cover in reconstructions of historical grasslands in the eastern Great Plains, isolating tree cover adjacent to major rivers, and then compared historical land cover to current (year 2019) land cover. As an extension to supply information for the entire Great Plains region, I modeled historical cover. For the 28 million ha extent of the eastern Great Plains, historical land cover was 86% grasslands and 14% trees, but 57% grasslands and 43% trees within 100 m of rivers. Tree cover near rivers ranged from 5.4% to 90% for 15 large river watersheds, indicating that any amount of tree cover could occur near rivers at landscape scales. Currently, the overall extent was 3.6% herbaceous vegetation and 6.6% forested, with 82% crops and pasture and 8% development. Within 100 m of rivers, crop and pasture decreased to 44% of cover, resulting in 14% herbaceous cover and 38% forested cover. Current tree cover ranged from 6.2% to 66% near rivers in 15 watersheds, which was relatively comparable to historical tree cover (ratios of 0.6 to 1.5). Results generally were similar for combined tree and shrub cover modeled for the entire Great Plains. Variability, even at landscape scales of large watersheds, was the normal condition for tree cover in grasslands and riparian ecosystems of the Great Plains. In answer to the question about tree cover in historical grassland ecosystems in the eastern Great Plains, tree cover typically was about three-fold greater near rivers than tree cover throughout grasslands. Combined tree and shrub cover near rivers was more than two-fold greater than tree and shrub cover throughout the Great Plains. Riparian forest restoration, as a management practice to reduce streambank erosion, overall has been effective, as indicated by current tree cover (38% near rivers in the eastern Great Plains) comparable to historical tree cover (43% near rivers in the eastern Great Plains), albeit as measured at coarse landscape scales with dynamics in vegetation and river locations. As a next step, restoration of grassland vegetation and non-riparian wetlands likely will help reestablish infiltrative watersheds, augmenting riparian forest restoration. Full article

Clarifying the spatio-temporal evolution characteristics of eco-environment quality (EEQ) under land use/cover change (LUCC) and its coordinated relationship is of great importance for formulating reliable environmental protection strategies and measures to promote regional sustainable development. Most studies have emphasized the importance of LUCC for regional ecological quality. However, deeply unraveling the complex interrelationships between them remains a significant challenge, particularly in ecologically fragile regions like the Li River Basin. Therefore, based on the historical land use data and the remote sensing ecological index (RSEI) of the Li River Basin from 1990 to 2020, we analyzed the spatio-temporal evolution characteristics of EEQ and LUCC, and explored the influences and non-linear effects between them by using the bivariate spatial autocorrelation and XGBoost model. The key findings are as follows: (1) Land use/cover (LUC) in the Li River Basin was predominantly characterized by forestland and cropland, which together accounted for approximately 97% of the region. The interconversion between forestland and cropland represented the primary form of regional LUCC, while built-up land demonstrated a growth trend by encroaching on cropland. (2) The EEQ exhibited a volatile upward trend within the research period, with an average RSEI value of 0.5891, indicating a generally favorable ecological condition. (3) A significant negative spatial correlation was observed between land use intensity (LUI) and the RSEI, characterized by H–L, L–H, and non-significant clusters. (4) There was a distinct non-linear relationship that existed between LUCC and the RSEI, underscoring that appropriately regulating regional land use scale can help maintain ecological balance. These findings provide a scientific basis for optimizing land spatial management models and formulating policies to improve ecological environment quality, while also offering a new framework and reference for further ecological research on EEQ influencing factors and driving mechanisms. Full article

Soil carbon sequestration (SCS) is a nature-based, low-cost climate mitigation strategy that also contributes to the climate adaptation of agricultural systems. Some land-use and land-management practices potentially lead to an enhancement of the soil organic carbon (SOC) sink, such as no-till, the use of cover crops, leaving residues on fields, improving the variety of legume species in grasslands and reducing grazing intensity. However, uncertainties remain both in estimating and measuring the impact of the application of certain practices, as these vary with the soil, climate and historic land use. IPCC (Intergovernmental Panel on Climate Change) guidelines are commonly used to estimate SOC and SOC sequestration potentials at different tiers. Here, the IPCC’s tier 1 methodology was applied to estimate (1) the sequestration potential of nine mitigation practices and (2) the emission or sequestration potential of four current land-change trends for n = 7092 unique agricultural sites in mainland Portugal. The conversion of irrigated crops to improved grasslands resulted in the highest average unit sequestration (1.05 tC ha⁻¹ yr⁻¹), while cropland conversion to poor degraded pasture (abandonment) resulted in the highest unit SOC loss (−0.08 tC ha⁻¹ yr⁻¹). The abandonment of cropland results in a national SOC loss of up to 0.09 MtC yr⁻¹, while the improvement of poor degraded pastures has the highest national sequestration potential, equal to 0.6 MtC yr⁻¹ (2.2 MtCO_2eq yr⁻¹), about 4% of Portugal’s emissions in 2021, if applied in all managed areas. The results enable a comparison between different practices and land uses; however, to enhance accuracy, a higher tier methodology tailored to the Portuguese context should be developed. Full article

Investigating the historical patterns of urban growth and their drivers is crucial to informing sustainable urban planning policies, especially in cities of the Global South. In Vietnam, most studies focus primarily on city extents, offering little insight into urban growth across various provinces. This study, therefore, combined categorical land use and land cover change detection, Random Forest classification and expert interviews to quantify the urban growth between 2000 and 2020, assess urban encroachment upon other land uses, and identify key drivers shaping this growth in Thua Thien Hue province. Findings show that the urban land areas were 27.94 km², 82.97 km², and 209.80 km² in 2000, 2010, and 2020, respectively. Urban encroachment upon other land use types, especially cropland, barren land, rice paddies, shrubs, and forests, was observed in these periods. Additionally, accessibility to built-up areas, DEM, proximity to rice paddies, slope, proximity to street roads, accessibility to social areas, and proximity to cropland are the major spatial drivers of urban growth in the province. The study concludes that rapid urban expansion is evident in the province at the expense of other land use types, especially agricultural land use types, which may impact food security and livelihoods in the province. Full article

Global climate fluctuations pose challenges not only to natural environments but also to the conservation and transmission of human cultural and historical heritage. World Heritage Sites are pivotal regions for studying climate change impacts and devising adaptation strategies, with remote sensing technology showcasing significant utility in monitoring these impacts, especially in the Mediterranean region’s diverse and sensitive climate context. Although existing work has begun to explore the role of remote sensing in monitoring the effects of climate change, detailed analysis of the spatial distribution and temporal trends of landscape stability remains limited. Leveraging remote sensing data and its derived products, this study assessed climate change impacts on the Causses and Cévennes Heritage Site, a typical Mediterranean heritage landscape. Specifically, this study utilized remote sensing data to analyze the trends in various climatic factors from 1985 to 2020. The landscape stability model was developed utilizing land cover information and landscape indicators to explore the landscape stability and its distribution features within the study area. Finally, we adopted the Geographical Detector to quantify the extent to which climatic factors influence the landscape stability’s spatial distribution across different periods. The results demonstrated that (1) the climate showed a warming and drying pattern during the study period, with distinct climate characteristics in different zones. (2) The dominance of woodland decreased (area proportion dropped from 76% to 66.5%); transitions primarily occurred among woodland, cropland, shrubland, and grasslands; landscape fragmentation intensified; and development towards diversification and uniformity was observed. (3) Significant spatiotemporal differences in landscape stability within the heritage site were noted, with an overall downward trend. (4) Precipitation had a high contribution rate in factor detection, with the interactive enhancement effects between temperature and precipitation being the most prominent. The present study delivers a thorough examination of how climate change affects the Causses and Cévennes Heritage Landscape, reveals its vulnerabilities, and offers crucial information for sustainable conservation efforts. Moreover, the results offer guidance for the preservation of similar Mediterranean heritage sites and contribute to the advancement and deepening of global heritage conservation initiatives. Full article

Land use/cover change (LUCC) significantly alters the carbon storage capacity of ecosystems with a profound impact on global climate change. The influence of land use changes on carbon storage capacity and the projection of future carbon stock changes under different scenarios are essential for achieving carbon peak and neutrality goals. This study applied the PLUS-InVEST model to predict the land use pattern in China’s arid Xinjiang Region in 2020–2050. The model assessed the carbon stock under four scenarios. Analysis of the historical LUCC data showed that the carbon storage in Xinjiang in 2000–2020 in five-year intervals was 85.69 × 10⁸, 85.79 × 10⁸, 85.87 × 10⁸, 86.01 × 10⁸, and 86.71 × 10⁸ t. The rise in carbon sequestration capacity in the study area, attributable to the expansion of cropland, water, and unused land areas, brought a concomitant increment in the regional carbon storage by 1.03 × 10⁸ t. However, prediction results for 2030–2050 showed that carbon storage capacity under the four scenarios would decrease by 0.11 × 10⁸ and increase by 1.2 × 10⁸, 0.98 × 10⁸ t, and 1.28 × 10⁸ t, respectively. The findings indicate that different land transfer modes will significantly affect Xinjiang’s carbon storage quantity, distribution, and trend. This research informs the past, present, and future of carbon storage in arid ecosystems of Xinjiang. It offers a reference for Xinjiang’s development planning and informs the efforts to achieve the carbon peak and neutrality goals. Full article

Climate change is expected to alter the environmental suitability of land use and land cover (LULC) classes globally. In this study, we investigated the potential impacts of climate change on the environmental suitability of the most representative LULC classes in the southern Brazilian semiarid region. We employed the Random Forest algorithm trained with climatic, soil, and topographic data to project future LULC suitability under the Representative Concentration Pathway RCP 2.6 (optimistic) and 8.5 (pessimistic) scenarios. The climate data included the mean annual air temperature and precipitation from the WorldClim2 platform for historical (1970–2000) and future (2061–2080) scenarios. Soil data were obtained from the SoilGrids 2.1 digital soil mapping platform, while topographic data were produced by NASA’s Shuttle Radar Topography Mission (SRTM). Our model achieved an overall accuracy of 60%. Under the worst-case scenario (RCP 8.5), croplands may lose approximately 8% of their suitable area, while pastures are expected to expand by up to 30%. Areas suitable for savannas are expected to increase under both RCP scenarios, potentially expanding into lands historically occupied by forests, grasslands, and eucalyptus plantations. These projected changes may lead to biodiversity loss and socioeconomic disruptions in the study area. Full article

Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land cover and their effects on carbon storage, land–atmosphere energy balance, and water cycle processes. However, current data products use different classification methods, resulting in significant classification inconsistency and triggering serious disagreements among related studies. Here, we compared four mainstream land cover products in China, namely GLC_FCS30, CLCD, Globeland30, and CNLUCC. The result shows that only 50.34% of the classification results were consistent across the four datasets. The differences between pairs of datasets ranged from 21.10% to 37.53%. Importantly, most inconsistency occurs in transitional zones among land cover types sensitive to climate change and human activities. Based on the accuracy evaluation, CLCD is the most accurate land cover product, with an overall accuracy reaching 86.98 ± 0.76%, followed by CNLUCC (81.38 ± 0.87%) and GLC_FCS30 (77.83 ± 0.80%). Globeland30 had the lowest accuracy (75.24 ± 0.91%), primarily due to misclassification between croplands and forests. Misclassification diagnoses revealed that vegetation-related spectral confusion among land cover types contributed significantly to misclassifications, followed by slope, cloud cover, and landscape fragmentation, which affected satellite observation angles, data availability, and mixed pixels. Automated classification methods using the random forest algorithm can perform better than those that depend on traditional human–machine interactive interpretation or object-based approaches. However, their classification accuracy depends more on selecting training samples and feature variables. Full article

The Maasai Mara and the wider Mau River Basin in East Africa provide fundamental ecosystem services that support people, wildlife, livestock and agriculture. The historical indigenous land use of the Mara and wider Mau basin was wildlife conservation and pastoralism with highland agriculture. However, land policy changes, the rise of community conservancies and the increase in human populations have mediated unprecedented land use shifts over time. We analyze land use and land cover change (LULCC) trends from 1990 to 2040 in the Mara and the wider Mau River Basin landscape. The study examines land use and land cover change trends, establishes factors driving the trends, and assesses the implications of these trends on biodiversity. Multi-temporal satellite images, together with physical and social economic data, were collated to generate future scenarios for transitions for forest, shrubland, grassland, cropland, wetlands and built-up areas between 1990 and 2040. Agricultural expansion is the chief driver of LULCC in the Mara and the wider Mau River Basin, particularly since 2015. There was insignificant change to the forest cover after 2015, which was in part due to government intervention on forest encroachment and boundaries. The anthropogenic choice of tilling the land in the basin caused a decline in grasslands, forests and expanded shrublands, particularly where there was clear tree cutting in the Mau forest. Land use and land cover trends have generated undesirable impacts on ecosystem services that support wildlife conservation. Full article

Understanding and managing land use/cover changes (LUCC) is crucial for ensuring the sustainability of the region. With the support of remote sensing technology, intensity analysis, the geodetic detector model, and the Mixed-Cell Cellular Automata (MCCA) model, this paper constructs an integrated framework linking historical evolutionary pattern-driving mechanisms for future simulation for LUCC in the Lower Liaohe Plain. From 1980 to 2018, the increasing trends were in built-up land and water bodies, and the decreasing trends were in grassland, cropland, forest land, unused land, and swamps. Overall, the changes in cropland, forest land, and built-up land are more active, while the changes in water bodies are more stable; the sources and directions of land use conversion are more fixed. Land use changes in the Lower Liaohe Plain are mainly influenced by socio-economic factors, of which population density, primary industry output value, and Gross Domestic Product (GDP) have a higher explanatory power. The interactive influence of each factor is greater than any single factor. The results of the MCCA model showed high accuracy, with an overall accuracy of 0.8242, relative entropy (RE) of 0.1846, and mixed-cell figure of merit (mcFoM) of 0.1204. By 2035, the built-up land and water bodies will increase, while the rest of the land use categories will decrease. The decrease is more pronounced in the central part of the plains. The findings of the study provide a scientific basis for strategically allocating regional land resources, which has significant implications for land use research in similar regions. Full article

This study assessed the impacts of the land use/cover (LULC) and climate changes on the runoff and sediment flows in the Megech watershed. The Geospatial Water Erosion Prediction Project (GeoWEPP) was used to assess LULC and climate changes’ impact on runoff, soil loss, and sediment yield. The QGIS 2.16.3 plugin module for land use change evaluation (MOLUSCE) tool with the cellular automata artificial neural network (CA-ANN) was used for LULC prediction based on historical data and exploratory maps. Two commonly used representative concentration pathways (RCPs)—4.5 and 8.5—were used for climate projection in the 2030s, 2050s, and 2070s. The LULC prediction analysis showed an expansion of cropland and settlement areas, with the reduction in the forest and rangelands. The climate projections indicated an increase in maximum temperatures and altered precipitation patterns, particularly with increased wet months and reduced dry periods. The average annual soil loss and sediment yield rates were estimated to increase under both the RCP4.5 and RCP8.5 climate scenarios, with a more noticeable increase under RCP8.5. By integrating DEM, soil, land use, and climate data, we evaluated runoff, soil loss, and sediment yield changes on only land use/cover, only climate, and the combined impacts in the watershed. The results revealed that, under all combined scenarios, the sediment yield in the Megech Reservoir was projected to substantially increase by 23.28–41.01%, showing a potential loss of reservoir capacity. This study recommends strong climate adaptation and mitigation measures to alleviate the impact on land and water resources. It is possible to lessen the combined impacts of climate and LULC change through implementing best-management practices and adaptation strategies for the identified scenarios. Full article