Search Results (198)

The robotic exploration of space began only five decades ago, and yet in the intervening years, a wide and diverse ecosystem of robotic explorers has been developed for this purpose. Such devices have greatly benefited from miniaturization trends and the increased availability of high-quality commercial off-the-shelf (COTS) components. This review outlines the specific taxonomic distinction between planetary surface rovers and other robotic space exploration vehicles, such as orbiters and landers. Additionally, arguments are made to standardize the classification of planetary rovers by mass into categories similar to those used for orbital satellites. Discussions about recent noteworthy trends toward the miniaturization of planetary rovers are also included, as well as a compilation of previous planetary rovers. This analysis compiles relevant metrics such as the mass, the distance traveled, and the locomotion or actuation technique for previous planetary rovers. Additional details are also examined about archetypal rovers that were chosen as representatives of specific small-scale rover classes. Finally, potential future trends for miniature planetary surface rovers are examined by way of comparison to similar miniaturized orbital robotic explorers known as CubeSats. Based on the existing relationship between CubeSats and their Earth-based simulation equivalents, CanSats, the importance of a potential Earth-based analog for miniature rovers is identified. This research establishes such a device, coining the new term ‘CanBot’ to refer to pathfinding systems that are deployed terrestrially to help develop future planetary surface exploration robots. Establishing this explicit genre of robotic vehicle is intended to provide a unified means for categorizing and encouraging the development of future small-scale rovers. Full article

This study quantitatively evaluates the effects of human activities (HAs) and climate change (CC) on the terrestrial ecosystem carbon cycle, providing a scientific basis for ecosystem management and the formulation of sustainable development policies in urban agglomerations located in arid and ecotone regions. Using the LanXi urban agglomeration in China as a case study, we simulated the spatiotemporal variation of vegetation net primary productivity (NPP) from 2000 to 2023 based on MODIS remote sensing data and the CASA model. Trend analysis and the Hurst index were employed to identify the dynamic trends and persistence of NPP. Furthermore, the Geographical Detector model with optimized parameters, along with nonlinear residual analysis, was employed to investigate the driving mechanisms and relative contributions of HAs and CC to NPP variation. The results indicate that NPP in the LanXi urban agglomeration exhibited a fluctuating upward trend, with an average annual increase of 4.26 gC/m² per year. Spatially, this trend followed a pattern of “higher in the center, lower in the east and west,” with more than 95% of the region showing an increase in NPP. Precipitation, mean annual temperature, evapotranspiration, and land use types were identified as the primary driving factors of NPP change. The interaction among these factors demonstrated a stronger explanatory power through factor coupling. Compared with linear residual analysis, the nonlinear model showed clear advantages, indicating that vegetation NPP in the LanXi urban agglomeration was jointly influenced by HAs and CC. These findings can further act as a basis for resource and environmental research in similar ecotone regions globally, such as Central Asia, the Mediterranean Basin, the southwestern United States, and North Africa. Full article

Long-term sedimentation patterns influence the ecological succession of shallow lakes. However, human-induced impacts can disrupt these processes, leading to prolonged hysteresis. Using historical sedimentation data, we simulated the future terrestrialization of Lake Izunuma-Uchinuma, a Ramsar-listed wetland in Japan. The results indicated that ecotone recovery would take over 150 years, highlighting the strong legacy effects of shoreline vegetation loss. To accelerate restoration, we implemented an integrated approach that combined water-level management with sediment stabilization structures, including fences and coconut mat rolls. Over three years, these interventions successfully restored shoreline sediment accumulation, facilitated the re-establishment of Zizania latifolia (from 328 m² to 1537 m² in Ecotone 1), and improved water quality and waterbird use. Waterbird abundance significantly increased (p < 0.05) in the treated zones, and sediment exposure led to a reduction in COD release, indicating improved substrate conditions. Our results suggest that proactive ecotone restoration strategies, including hydrological regulation and sediment management, are essential in lakes where natural recovery is hindered by long-term sedimentation deficits and water-level changes. This study highlights the importance of integrating these measures to mitigate hysteresis and enhance ecosystem resilience in degraded shallow lakes. Full article

The Huaihe River Basin stands as a vital grain-producing base in China. Predicting the dynamic evolution of its carbon storage (CS) is of great theoretical value and practical significance for maintaining regional ecological security, guaranteeing food production capacity, and coping with climate change. This study established a multi-dimensional analysis framework of “scenario simulation–reservoir assessment–value quantification”. Using a sample of 195 cities, the PLUS-InVEST-GIS method was combined to explore the overall CS, spatial differentiation, and value changes in future scenarios. The results indicate that the following: (1) From 2000 to 2020, CS kept on declining, with cultivated land and forest land being the dominant carbon pools, accounting for over 86% of the total CS. (2) From a “city–grid–raster” perspective, the spatial pattern of high-value hot spots of CS remained stable, and the overall pattern remained unchanged under multi-scenario simulation, yet the overall carbon sink center of gravity shifted to the southwest. (3) The top five driving factors are elevation, slope, NDVI, GDP per capita, and population density, accounting for 77.2% of the total driving force. (4) The carbon sequestration capacity at the county scale continued to weaken, and the overall capacity presented the following order: 2035 Farmland protection scenario (FPS) > 2035 Natural development scenario (NDS) > 2035 Urban development scenario (UDS). The resulting carbon economic losses were USD 2.28 × 10⁸, 4.57 × 10⁸, and 6.90 × 10⁸, respectively. The research results will provide scientific land use decision-making support for the realization of the “double-carbon” goals in the Huaihe River grain-producing area. Full article

With accelerating climate change and urbanization, regional carbon balance faces increasing uncertainty. Terrestrial carbon sinks play a crucial role in advancing China’s sustainable development under the dual-carbon strategy. This study quantitatively modeled China’s terrestrial carbon sink capacity and analyzed the multidimensional relationships between impact factors and carbon sinks. After preprocessing multi-source raster data, we introduced kernel normalized the difference vegetation index (kNDVI) to the Carnegie–Ames–Stanford approach (CASA) model, together with a heterotrophic respiration (R_h) empirical equation, to simulate pixel-level net ecosystem productivity (NEP) across China. A light gradient-boosting machine (LightGBM) model, optimized via Bayesian algorithms, was trained to regress NEP drivers, categorized into atmospheric components (O₃, NO₂, and SO₂) and subsurface properties (a digital elevation model (DEM), enhanced vegetation index (EVI), soil moisture (SM)), and human activities (land use/cover change (LUCC), POP, gross domestic product (GDP)). Shapley Additive Explanation (SHAP) values were used for model interpretation. The results reveal significant spatial heterogeneity in NEP across geographic and climatic contexts. The pixel-level mean and total NEP in China were 268.588 gC/m²/yr and 2.541 PgC/yr, respectively. The north tropical zone (NRZ) exhibited the highest average NEP (828.631 gC/m²/yr), while the middle subtropical zone (MSZ) and south subtropical zone (SSZ) demonstrated the most stable NEP distributions. LightGBM achieved high simulation accuracy, further enhanced by Bayesian optimization. SHAP analysis identified EVI as the most influential factor, followed by SM, NO₂, DEM, and POP. Additionally, LightGBM effectively captured nonlinear relationships and variable interactions. Full article

Plantation forests (PFs) play a crucial role in China’s climate change mitigation strategy due to their significant capacity to sequestrate carbon (C). Understanding the long-term trend in PFs’ C uptake capacity and the key drivers influencing it is crucial for optimizing PF management and planning for climate mitigation. In this study, we quantified the long-term (1981–2019) C sequestration of PFs in Jiangsu Province, where PFs have expanded considerably in recent decades, particularly since 2015. Seasonal and interannual variations in gross primary productivity (GPP), net primary productivity (NPP), and net ecosystem productivity (NEP) were assessed using the boreal ecosystem productivity simulator (BEPS), a process-based terrestrial biogeochemical model. The model integrates multiple sources of remote-sensing datasets, such as leaf area index and land cover data, to simulate the critical biogeochemical processes governing land surface dynamics, enabling the quantification of vegetation and soil C stocks and nutrient cycling patterns. The results indicated a significant increasing trend in GPP, NPP, and NEP over the past four decades, suggesting enhanced C sequestration by PFs across the study region. The interannual variability in these indicators was associated with that of nitrogen (N) deposition in recent years, implying that nutrient availability could be a limiting factor for plantation productivity. Seasonal GPP and NPP exhibited peak values in spring (April to May) or late summer (August to September), with increases in growing season productivity in recent years. In contrast, NEP peaked in spring (April to May) but declined to negative values in early summer (July to August), indicating a seasonal C source–sink transition. All three indicators showed a general negative correlation with late-growing-season temperature (August to September), suggesting that summer droughts probably highly constrained the C sequestration of the existing PFs. These findings provide insights for the strategic implementation and management of PFs, particularly in regions with a warm temperate climate undergoing afforestation expansion. Full article

The carbon cycle of forest ecosystems is a component of the global terrestrial ecosystem carbon cycle, and the productivity of forest ecosystems is significantly influenced by vegetation phenology. In this investigation, we simulated the spatiotemporal trends of the carbon cycle in forest ecosystems in Hangzhou between 2001 and 2020 by means of the phenology-driven InTEC model and analyzed the mechanisms of carbon cycle changes in response to phenological changes. The results of this study suggested that the gross primary productivity (GPP), the net primary production (NPP), and the net ecosystem productivity (NEP) have obvious heterogeneity in spatiotemporal distribution, and the tendency of the start of the growing season (SOS) advancement, the end of the growing season (EOS) postponement, and the length of the growing season (LOS) lengthening is significant for a GPP increase with positive effects. Both phenology and climate have direct impacts on carbon cycle changes, while climate change indirectly affects carbon cycle changes through phenology changes. Full article

Human activities have increased the imbalance in atmospheric N and P deposition, which changes soil nutrient availability and subsequently affects the structure and function of terrestrial ecosystems. Dioecious plants are important parts of terrestrial ecosystems and are characterized by sex-related differences in their response to the external environment and always exhibit a skewed sex ratio, which makes them more vulnerable to climate change and increases their risk of extinction. However, little attention has been paid to the effects of unbalanced N and P deposition on these plants, especially on their defense traits. In this study, we used dioecious Populus cathayana to investigate the influence of gradient N and P deposition on the correlation between growth and defense traits. The results showed that although the different rates of N and P deposition enhanced biomass accumulation in both sexes to varying degrees, the most substantial biomass increment was noted under a lower-nitrogen and higher-phosphorus (LNHP) treatment regimen, with females showing an approximately 112% increase and males a 47% increase in total biomass. In response to varying levels of simulated N and P deposition, males and females adopt distinct strategies for biomass allocation. Although declines in root biomass were observed in both sexes as nutrient availability increased, the decrement was more marked in males; under the LNHP treatment, it dropped by about 11%, while under a high-nitrogen and high-phosphorus (HNHP) treatment, the decrease was about 35%. Conversely, females demonstrated a heightened propensity to allocate biomass towards leaf development. Furthermore, with increasing N and P deposition, there was a general reduction in the concentrations of physical and chemical defense substances within the leaves of both sexes. Nonetheless, the correlations between defense substances, nutrient element content, non-structural carbohydrate (NSC) content, and dry biomass were more pronounced in males, suggesting a greater sensitivity to defense substance responses in males than in females. Overall, these results indicate that there is sexual dimorphism in the accumulation of chemical substances in male and female P. cathayana under unbalanced N and P deposition and they provide a technical and theoretical basis for predicting the population dynamics of dioecious plants, maintaining the stability of poplar populations, and constructing high-productivity poplar plantations globally in the future. Full article

Context: The intensification of land use changes in coastal cities has been a result of the ongoing development of the social economy. A decrease in the ecosystem service value (ESV) and terrestrial carbon storage (TCS) of coastal cities has been observed as a result of the intensification of urbanization and climate change. However, it is unclear whether the influence of land use change on ESV and TCS in coastal towns would be facilitated or hampered under various growth scenarios. Aim: This study simulated the impact of land use change on the ESV and TCS of coastal cities under various future development scenarios and provided scientific policy references for the preservation of their ecological functions. Approaches: The InVEST model and PLUS model were employed to predict the land use changes in coastal cities in Liaoning Province from 2030 to 2060 under various development scenarios, based on the land use change data of three periods from 2000 to 2010 to 2020. The changes in ESV and TCS in coastal cities were also calculated. Results: The distribution pattern of ESV and TCS and future development scenarios are significantly influenced by the area changes and chief influencing factors of various land types in coastal cities of Liaoning Province. The dynamic changes in construction land, cultivated land, grassland, and unused land play a significant role in various development scenarios, given the variations in development patterns across different cities. Two of the primary factors that influence the variations in various land types are GDP, NDVI, DEM, rainfall, and population distribution. Three provisioning services, regulating services, supporting services, and cultural services, also experienced a gradual decline in the ESV variations of coastal cities, while the ESV of cultivated land, forest land, rivers, and grasslands exhibited a downward development trend. The spatial distribution of carbon storage in coastal cities exhibited the characteristics of “low coastal, high eastern, western, and inland forest distribution areas, and medium carbon storage in the central grassland distribution area.” Four coastal cities can effectively mitigate the impact of urbanization development on ecosystem services under the ecological protection scenario. Conclusions: The present study demonstrates the spatiotemporal variations and propelling forces of ecosystem services in coastal communities during land use change under various simulation scenarios. Important references for sustainable development and land use control in coastal cities are provided through recommendations for non-construction land management that enhance ESV and TCS. Full article

Hybrid satellite–terrestrial network access is gaining significant attention in the 5G ecosystem and is predicted to be an essential part of future 6G standards. Multipath support is required to utilize hybrid access features that are summarized as the ATSSS (Access Traffic Steering, Switching and Splitting) paradigm. Simulation frameworks can be very helpful to economically evaluate the performance of hybrid network access. In this paper, we propose a distributed AI (artificial intelligence)-driven simulation framework that can evaluate the performance of hybrid satellite–terrestrial network access. The AI part of the framework enables optimal session switching between satellite and terrestrial access networks based on SNR (Signal-to-Noise) predictions. The successful operation of the AI-based prediction model as well as the complete simulation framework is demonstrated in the UDP (User Datagram Protocol) streaming session scenario. The proposed framework is made to be flexible so it can be adjusted to various multipath scenarios of hybrid satellite–terrestrial network access. Full article

Accurately understanding the changes in global light-response parameters (i.e., maximum light use efficiency, LUEmax) is essential for improving the simulation of terrestrial ecosystem’s photosynthetic carbon cycling under climate change, but a comprehensive understanding and assessments are still lacking. In this study, LUEmax was quantified using data from 23 global flux stations, and the change patterns in LUEmax across various vegetation types and climate zones were analyzed. The extent of significant increases or decreases in LUEmax during different phenological stages of vegetation growth was evaluated using trend analysis methods. The contribution rates of environmental factors were determined using the Geodetector method. The results show that the LUEmax values of the same vegetation type varied across different climate types. More variable climates (e.g., polar and alpine climates) are associated with more significant fluctuations in LUEmax. Conversely, more stable climates (e.g., temperate climates) tend to show more consistent LUEmax values. Within the same climate type, evergreen needleleaf forests (ENF) and deciduous broadleaf forests (DBF) generally exhibited higher LUEmax values in temperate and continental climates, whereas the LUEmax values of wetlands (WET) were relatively high in polar and alpine climates. The mechanisms driving variations in LUEmax across different vegetation types exhibited significant disparities under diverse environmental conditions. For ENF and DBF, LUEmax is predominantly influenced by temperature and radiation. In contrast, the LUEmax of GRA, WET, and croplands is more closely associated with vegetation indices and temperature factors. The findings of this study play an important role in advancing the theoretical development of gross primary productivity (GPP) models and enhancing the accuracy of carbon sequestration simulations in terrestrial ecosystems. Full article

Tree structural information is essential for studying forest ecosystem functions, driving mechanisms, and global change response mechanisms. Although current terrestrial laser scanning (TLS) can acquire high-precision 3D structural information of forests, mutual occlusion between trees, the scanner’s field of view, and terrain changes make the point clouds captured by laser scanning sensors incomplete, further hindering downstream tasks. This study proposes a skeleton-embedded tree point cloud completion method, termed SK-TreePCN, which recovers complete individual tree point clouds from incomplete scanning data in the field. SK-TreePCN employs a transformer trained on simulated point clouds generated by a 3D radiative transfer model. Unlike existing point cloud completion algorithms designed for regular shapes and simple structures, the SK-TreePCN method addresses structurally heterogeneous trees. The 3D radiative transfer model LESS, which can simulate various TLS data over highly heterogeneous scenes, is employed to generate massive point clouds with training labels. Among the various point cloud completion methods evaluated, SK-TreePCN exhibits outstanding performance regarding the Chamfer distance (CD) and $F1$ Score. The generated point clouds display a more natural appearance and clearer branches. The accuracy of tree height and diameter at breast height extracted from the recovered point cloud achieved ${\mathit{R}}^2$ values of 0.929 and 0.904, respectively. SK-TreePCN demonstrates applicability and robustness in recovering individual tree point clouds. It demonstrated great potential for TLS-based field measurements of trees, refining point cloud 3D reconstruction and tree information extraction and reducing field data collection labor while retaining satisfactory data quality. Full article

The carbon stock in terrestrial ecosystems is closely linked to changes in land use. Understanding how land use alterations affect regional carbon stocks is essential for maintaining the carbon balance of ecosystems. This research leverages land use and driving factor data spanning from 2000 to 2020, utilizing the Patch-generating Land Use Simulation (PLUS) model alongside the InVEST ecosystem services model to examine the temporal and spatial changes in carbon storage across Beijing. Additionally, four future scenes for 2030—urban development, natural development, cropland protection, as well as eco-protection—are explored, with the PLUS and InVEST models employed to emulate dynamic land use changes and the corresponding carbon stock variations. The results show that the following: (1) Between 2000 and 2020, changes in land use resulted in a significant decline in carbon storage, with a total reduction of 1.04 × 10⁷ tons. (2) From 2000 to 2020, agricultural, forest, and grassland areas in Beijing all declined to varying extents, while built-up land expanded by 1292.04 km² (7.88%), with minimal changes observed in water bodies or barren lands. (3) Compared to the carbon storage distribution in 2020, carbon storage in the 2030 urban development scenario decreased by 6.99 × 10⁶ tons, highlighting the impact of rapid urbanization and the expansion of built-up areas on the decline in carbon storage. (4) In the ecological protection scenario, the optimization of land use structure resulted in an increase of 6.01 × 10⁵ tons in carbon storage, indicating that the land use allocation in this scenario contributes to the restoration of carbon storage and enhances the carbon sink capacity of the urban ecosystem. This study provides valuable insights for policymakers in optimizing ecosystem carbon storage from a land use perspective and offers essential guidance for the achievement of the “dual carbon” strategic objectives. Full article

As the basic unit of nature, basins concentrate most of the vegetation cover of terrestrial ecosystems and play an important role in forest carbon fixation and regulation of local climates. However, there are obvious differences between different basins in terms of topography, climate, population, economy, and other factors, so it is important to conduct a comparative study on the spatiotemporal patterns of factors affecting forest carbon storage in different basins. The province of Zhejiang is rich in vegetation resources, and there are obvious differences in the natural and economic factors within the province; GDP is higher in the eastern and northern regions, and natural resources are more abundant in the western and southern regions. Therefore, we used the BIOME-BGC model and the Optimal Parameters-based Geographical Detector (OPGD) model to simulate and analyze the spatiotemporal evolution and driving mechanism of forest aboveground carbon (AGC) storage in eight basins of Zhejiang Province over the past 30 years (1984–2014). The results showed that (1) the overall simulation accuracy of AGC in different basins based on the BIOME-BGC model is high, with the overall simulation accuracy ranging from 0.67 to 0.77. (2) The forest AGC of the eight basins showed an increasing trend over the past 30 years, with a growth rate ranging from 0.07 Tg C/10 yr to 3.45 Tg C/10 yr. (3) Climatic conditions (temperature and precipitation) play a dominant role in the variation in AGC, with an explanatory power above 16% in the southern and northern basins, and the explanatory power of human activities on the AGC is secondary, with more than 9% in the central basins. (4) The interaction between natural factors and socio-economic factors (especially the population density factor) has a more obvious effect on the changes in AGC in each basin, and the explanatory power of the interaction is much larger than that of the single factor. (5) The results of the risk detection showed that human activities were negatively correlated with AGC in all basins. Full article

This research explores the dynamics of vegetation patterns under changing environmental conditions, considering the United Nations Sustainable Development Goal 15: “Protect, restore, and promote the sustainable use of terrestrial ecosystems; combat desertification; halt and reverse land degradation; and prevent biodiversity loss”. In this context, this study presents a modeling and nonlinear analysis framework for plant–soil-moisture interactions, including Holling-II functional response and hyperbolic mortality models. The primary goal is to explore how nonlinear soil–water interactions influence vegetation patterns in semi-arid ecosystems. Moreover, the influence of nonlinear soil–water interaction on the establishment of population patterns is investigated. The formation and evolution of these patterns are explored using theoretical analysis and numerical simulations, as well as important factors and critical thresholds. These insights are crucial for addressing desertification, a key challenge in semi-arid regions that threatens biodiversity, ecosystem services, and sustainable land management. The model, which includes environmental parameters such as rainfall, plant growth rates, and soil moisture, was tested using both theoretical analysis and numerical simulations. These characteristics are carefully adjusted to find important thresholds influencing the danger of desertification. Simulation scenarios, run under set initial conditions and varying parameters, yield useful insights into the pattern of patch development under dynamically changing environmental conditions. The findings revealed that changes in environmental conditions, such as rainfall and plant growth rates, prompted Hopf bifurcation, resulting in the production of three distinct patterns: a dotted pattern, a striped pattern, and a combination of both. The creation of these patterns provides essential information about the sustainability of environmental equilibrium. The variation curve of the average plant biomass reveals that the biomass fluctuates around a constant period, with the amplitude initially increasing, then decreasing, and gradually stabilizing. This research provides a solid foundation for addressing desertification risks, using water resources responsibly, and contributing to a better understanding of ecosystem stability. Full article