Search Results (658)

Wildfire and drought are key drivers of shrubland expansion in southwestern US landscapes. Stand-replacing fires in conifer forests induce shrub-dominated stages, and changing climatic patterns may cause a long-term shift to deciduous shrubland. We assessed change in deciduous fractional shrub cover (DFSC) in the eastern Jemez Mountains from 2019 to 2023 using topographic and Sentinel-2 satellite data and evaluated the impact of spatial scale on model performance. First, we built a 10 m and a 20 m random forest model. The 20 m model outperformed the 10 m model, achieving an R-squared value of 0.82 and an RMSE of 7.85, compared to the 10 m model (0.76 and 9.99, respectively). We projected the 20 m model to the other years of the study using imagery from the respective years, yielding yearly DFSC predictions. DFSC decreased from 2019 to 2022, coinciding with severe drought and a 2022 fire, followed by an increase in 2023, particularly within the 2022 fire footprint. Overall, DFSC trends showed an increase, with elevation being a key variable influencing these trends. This framework revealed vegetation dynamics in a semi-arid system and provided a close look at post-fire regeneration in deciduous resprouting shrubs and could be applied to similar systems. Full article

Drought disturbances are becoming more frequent with global warming. Accurately assessing the regulatory effect of drought on vegetation phenology is key to understanding terrestrial ecosystem response mechanisms in the context of climate change. Previous studies on cumulative and lagged effects of drought on vegetation growth have mostly focused on a single vegetation type or the overall vegetation NDVI, overlooking the possible influence of different adaptation strategies of different vegetation types and differences in drought effects on different phenological nodes. This study investigates the cumulative and lagged effects of drought on vegetation phenology across a region of East Asia from 2001 to 2020 using NDVI data and the Standardized Precipitation Evapotranspiration Index (SPEI). We analyzed the start of growing season (SOS) and end of growing season (EOS) responses to drought across four vegetation types: deciduous needleleaf forests (DNFs), deciduous broadleaf forests (DBFs), shrublands, and grasslands. Results reveal contrasting phenological responses: drought delayed SOS in grasslands through a “drought escape” strategy but advanced SOS in forests and shrublands. All vegetation types showed earlier EOS under drought stress. Cumulative drought effects were strongest on DNFs, SOS, and shrubland SOS, while lagged effects dominated DBFs and grassland SOS. Drought impacts varied with moisture conditions: they were stronger in dry regions for SOS but more pronounced in humid areas for EOS. By confirming that drought effects vary by vegetation type and phenology node, these findings enhance our understanding of vegetation adaptation strategies and ecosystem responses to climate stress. Full article

The Mayanghe National Nature Reserve, a key habitat for the endangered François’ langur (Trachypithecus francoisi), faces significant anthropogenic disturbances, including extensive distribution of croplands, roads, and settlements. These human-modified features are predominantly concentrated at elevations between 500 and 800 m and on slopes of 10–20°, which notably overlap with the core elevation range utilized by François’ langur. Spatial analysis revealed that langurs primarily occupy areas within the 500–800 m elevation band, which comprises only 33% of the reserve but hosts a high density of human infrastructure—including approximately 4468 residential buildings and the majority of cropland and road networks. Despite slopes >60° representing just 18.52% of the area, langur habitat utilization peaked in these steep regions (exceeding 85.71%), indicating a strong preference for rugged karst terrain, likely due to reduced human interference. Habitat type analysis showed a clear preference for evergreen broadleaf forests (covering 37.19% of utilized areas), followed by shrublands. Landscape pattern metrics revealed high habitat fragmentation, with 457 discrete habitat patches and broadleaf forests displaying the highest edge density and total edge length. Connectivity analyses indicated that distribution areas exhibit a more continuous and aggregated habitat configuration than control areas. These results underscore François’ langur’s reliance on steep, forested karst habitats and highlight the urgent need to mitigate human-induced fragmentation in key elevation and slope zones to ensure the species’ long-term survival. Full article

Seeds in soil are often exposed to cycles of hydration and dehydration, which can prime them by triggering physiological activation without leading to germination. While this phenomenon has been scarcely studied in wild species, it may play a critical role in enhancing drought resilience and maintaining seed viability under the warmer conditions predicted by climate change. In this study, I investigated the effects of hydration–dehydration cycles on germination response under water stress in eight Cistus species typical of Mediterranean shrublands. First, seeds were exposed to a heat shock to break physical dormancy, simulating fire conditions. Subsequently, they underwent one of two hydration–dehydration treatments (24 or 48 h) and were germinated under a range of water potentials (0, –0.2, –0.4, –0.6, and –0.8 MPa). Six out of eight species showed enhanced germination responses following hydration–dehydration treatments, including higher final germination percentages, earlier germination onset (T₀), or increased tolerance to water stress. These findings highlight the role of water availability as a key factor regulating germination in Cistus species and evidence a hydration memory mechanism that may contribute in different ways to post-fire regeneration in Mediterranean ecosystems. Full article

Groundwater systems are intrinsically linked to climate, with changing conditions significantly altering recharge, storage, and discharge processes, thereby impacting water availability and ecosystem integrity. Critical knowledge gaps persist regarding groundwater equilibrium timescales, water table dynamics, and their governing factors. This study develops a novel remote sensing framework to quantify factor controls on groundwater–climate interaction characteristics in the Heihe River Basin (HRB). High-resolution (0.005° × 0.005°) maps of groundwater response time (GRT) and water table ratio (WTR) were generated using multi-source geospatial data. Employing Geographical Convergent Cross Mapping (GCCM), we established causal relationships between GRT/WTR and their drivers, identifying key influences on groundwater dynamics. Generalized Additive Models (GAM) further quantified the relative contributions of climatic (precipitation, temperature), topographic (DEM, TWI), geologic (hydraulic conductivity, porosity, vadose zone thickness), and vegetative (NDVI, root depth, soil water) factors to GRT/WTR variability. Results indicate an average GRT of ~6.5 × 10⁸ years, with 7.36% of HRB exhibiting sub-century response times and 85.23% exceeding 1000 years. Recharge control dominates shrublands, wetlands, and croplands (WTR < 1), while topography control prevails in forests and barelands (WTR > 1). Key factors collectively explain 86.7% (GRT) and 75.9% (WTR) of observed variance, with spatial GRT variability driven primarily by hydraulic conductivity (34.3%), vadose zone thickness (13.5%), and precipitation (10.8%), while WTR variation is controlled by vadose zone thickness (19.2%), topographic wetness index (16.0%), and temperature (9.6%). These findings provide a scientifically rigorous basis for prioritizing groundwater conservation zones and designing climate-resilient water management policies in arid endorheic basins, with our high-resolution causal attribution framework offering transferable methodologies for global groundwater vulnerability assessments. Full article

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies. Full article

Land cover changes have significant implications for ecosystem services, influencing agricultural productivity, soil stability, hydrological processes, and biodiversity. This study assesses the impacts of land use and land cover (LULC) change on soil erosion in the Upper Guder River catchment, Ethiopia, from 1986 to 2020. We analyzed Landsat imagery for three periods (1986, 2002, and 2020), achieving a classification accuracy of 89.21% and a kappa coefficient of 0.839. Using the Revised Universal Soil Loss Equation (RUSLE) model, we quantified spatial and temporal variations in soil erosion. Over the study period, cultivated land expanded from 51.89% to 78.40%, primarily at the expense of shrubland and grassland, which declined to 6.61% and 2.98%, respectively. Forest cover showed a modest decline, from 13.60% to 11.24%, suggesting a partial offset by reforestation efforts. Built-up areas nearly tripled, reflecting increasing anthropogenic pressure. Mean annual soil loss increased markedly from 107.63 to 172.85 t ha⁻¹ yr⁻¹, with cultivated land exhibiting the highest erosion rates (199.5 t ha⁻¹ yr⁻¹ in 2020). Severe erosion (>50 t ha⁻¹ yr⁻¹) was concentrated on steep slopes under intensive cultivation. These findings emphasize the urgent need for integrated land management strategies that stabilize erosion-prone landscapes while improving agricultural productivity and ecological resilience. Full article

In ecological restoration of arid/semi-arid sandy lands, micro-topographic variations and artificial shrub arrangement synergistically drive vegetation recovery and soil quality improvement. As a typical fragile ecosystem in northern China, the Mu Us Sandy Land has long suffered wind erosion, desertification, soil infertility, and vegetation degradation, demanding precise vegetation configuration for ecological rehabilitation. This study analyzed soil nutrients, plant diversity, and their correlations under various micro-topographic conditions across different types of artificial shrub plantations in the Mu Us Sandy Land. Employing one-way and two-way ANOVA, we compared the significant differences in soil nutrients and plant diversity indices among different micro-topographic conditions and shrub species. Additionally, redundancy analysis (RDA) was conducted to explore the direct and indirect relationships between micro-topography, shrub species, soil nutrients, and plant diversity. The results show the following: 1. The interdune depressions have the highest plant diversity and optimal soil nutrients, with relatively suitable pH values; the windward slopes and slope tops, due to severe wind erosion, have poor soil nutrients, high pH values, and the lowest plant diversity. Both micro-topography and vegetation can significantly affect soil nutrients and plant diversity (p < 0.05), and vegetation has a greater impact on soil nutrients. 2. The correlation between surface soil nutrients and plant diversity is the strongest, and the correlation weakens with increasing soil depth; under different micro-topographic conditions, the influence of soil nutrients on plant diversity varies. 3. In sandy land ecological restoration, a “vegetation type + terrain matching” strategy should be implemented, combining the characteristics of micro-topography and the ecological functions of shrubs for precise configuration, such as planting Corethrodendron fruticosum on windward slopes and slope tops to rapidly replenish nutrients, promoting Salix psammophila and mixed plantation in interdune depressions and leeward slopes to accumulate organic matter, and prioritizing Amorpha fruticosa in areas requiring soil pH adjustment. This study provides a scientific basis and management insights for the ecological restoration and vegetation configuration of the Mu Us Sandy Land. Full article

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

In recent years, remote sensing and geographic information systems (GISs) have become essential tools for effective landscape management. This study utilizes these technologies to analyze land use and land cover (LULC) changes in Dibru-Saikhowa National Park, a riverine ecosystem in Assam, India, from its designation as a national park in 2000 through 2024. The satellite imagery was used to classify LULC types and track landscape changes over time. In 2000, grasslands were the dominant land cover (28.78%), followed by semi-evergreen forests (25.58%). By 2013, shrubland became the most prominent class (81.31 km²), and degraded forest expanded to 75.56 km². During this period, substantial areas of grassland (29.94 km²), degraded forest (10.87 km²), semi-evergreen forest (12.33 km²), and bareland (10.50 km²) were converted to shrubland. In 2024, degraded forest further increased, covering 80.52 km² (23.47%). This change resulted since numerous areas of shrubland (11.46 km²) and semi-evergreen forest (27.48 km²) were converted into degraded forest. Furthermore, significant shifts were observed in grassland, shrubland, and degraded forest, indicating a substantial and consistent decline in grassland. These changes are largely attributed to recurring Brahmaputra River floods and increasing anthropogenic pressures. This study recommends a targeted Grassland Recovery Project, control of invasive species, improved surveillance, increased staffing, and the relocation of forest villages to reduce human impact and support community-based conservation efforts. Hence, protecting the landscape through informed LULC-based management can help maintain critical habitat patches, mitigate anthropogenic degradation, and enhance the survival prospects of native floral and faunal assemblages in DSNP. Full article

To explore the interactions within the vegetation–soil–microorganism continuum on the Jingpo Lake lava platform, five vegetation types—grassland (GL), shrubland (SL), deciduous broad-leaved forest (DB), coniferous and broad-leaved mixed forest (CB), and coniferous forest (CF)—were examined. Significant differences in the soil physical and chemical properties were identified among these types (p < 0.05). The soil bacterial community structures also varied significantly (p < 0.05), with Actinobacteriota, Proteobacteria, and Acidobacteria as the dominant phyla, exhibiting notable genus-level differences (p < 0.05). The soil organic matter (SOM), available nitrogen (AN), total nitrogen (TN), and soil water content (SWC) were significantly correlated with the bacterial community structure (p < 0.05 or p < 0.01), acting as key determinants of the microbial community structure and function. PICRUSt2 functional predictions revealed significant variations in the metabolic functions of the soil bacterial communities across vegetation types, indicating distinct functional specializations. In conclusion, the Jingpo Lake lava plateau harbors abundant bacterial resources. When devising vegetation adaptation strategies, it is essential to take into account variations in the rhizosphere soil bacteria across different vegetation types. Furthermore, prioritizing the implementation of forest vegetation is crucial in the adaptive management of the lava plateau. This approach holds significant implications for studying the bacterial diversity in the lava plateau and exploring the cultivation and application of functional bacteria in extreme environments. Full article

Land surface models (LSMs) play a crucial role in climate prediction and carbon cycle assessment. To ensure their reliability, it is crucial to evaluate their performance in simulating key processes, such as evapotranspiration (ET) and gross primary productivity (GPP), across various temporal scales and vegetation types. This study systematically evaluates the performance of the newly modernized Noah-MP LSM version 5.0 in simulating water and carbon fluxes, specifically ET and GPP, across temporal scales ranging from half-hourly (capturing diurnal cycles) to annual using observational data from 105 sites within the globally FLUXNET2015 dataset. The results reveal that Noah-MP effectively captured the overall variability of both ET and GPP, particularly at short temporal scales. The model successfully simulated the diurnal and seasonal cycles of both fluxes, though cumulative errors increased at the annual scale. Diurnally, the largest simulation biases typically occurred around noon; while, seasonally, biases were smallest in winter. Performance varied significantly across vegetation types. For ET, the simulations were most accurate for open shrublands and deciduous broadleaf forests, while showing the largest deviation for woody savannas. Conversely, GPP simulations were most accurate for wetlands and closed shrublands, showing the largest deviation for evergreen broadleaf forests. Furthermore, an in-depth analysis stratified by the climate background revealed that ET simulations failed to capture inter-annual variability in the temperate and continental zones, while GPP was severely overestimated in arid and temperate climates. This study identifies the strengths and weaknesses of Noah-MP in simulating water and carbon fluxes, providing valuable insights for future model improvements. Full article

Wildfires threaten ecosystems, biodiversity, and human livelihood while exacerbating climate change. Accurate identification and monitoring of burned areas (BA) are critical for effective post-fire recovery and management. Although satellite multi-spectral imagery offers a practical solution for BA monitoring, existing methods often prioritize specific spectral bands while neglecting full spectral shape information, which encapsulates overall spectral characteristics. This limitation compromises adaptability to diverse vegetation types and environmental conditions, particularly across varying spatial scales. To address these challenges, we propose the time-series spectral-angle-normalized burn index (TSSA-NBR). This unsupervised BA extraction method integrates normalized spectral angle and normalized burn ratio (NBR) to leverage full spectral shape and temporal features derived from Sentinel-2 time-series data. Seven globally distributed study areas with diverse climatic conditions and vegetation types were selected to evaluate the method’s adaptability and scalability. Evaluations compared Sentinel-2-derived BA with moderate-resolution products and high-resolution PlanetScope-derived BA, focusing on spatial scale and methodological performance. TSSA-NBR achieved a Dice Coefficient (DC) of 87.81%, with commission (CE) and omission errors (OE) of 8.52% and 15.58%, respectively, demonstrating robust performance across all regions. Across diverse land cover types, including forests, grasslands, and shrublands, TSSA-NBR exhibited high adaptability, with DC values ranging from 0.53 to 0.97, CE from 0.03 to 0.27, and OE from 0.02 to 0.61. The method effectively captured fire scars and outperformed band-specific and threshold-dependent approaches by integrating spectral shape features with fire indices, establishing a data-driven framework for BA detection. These results underscore its potential for fire monitoring and broader applications in detecting surface anomalies and environmental disturbances, advancing global ecological monitoring and management strategies. Full article

The accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. This study employed high-resolution imagery captured by unmanned aircraft systems (UASs) throughout the shrub growing season and explored the effectiveness of transfer learning for both semantic segmentation (Attention U-Net) and instance segmentation (Mask R-CNN). It utilized pre-trained model weights from two previous studies that originally focused on tree crown delineation to improve shrub crown segmentation in non-forested areas. Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. However, fine-tuning the pre-trained models by unfreezing additional layers improved segmentation accuracy by around 30%. Fine-tuned pre-trained models show limited sensitivity to shrubs in the early growing season (April to June) and improved performance when shrub crowns become more spectrally unique in late summer (July to September). These findings highlight the value of combining pre-trained models with targeted fine-tuning to enhance model adaptability in complex remote sensing environments. The proposed framework demonstrates a scalable solution for ecological monitoring in data-scarce regions, supporting informed land management decisions and advancing the use of deep learning for long-term environmental monitoring. Full article

As the ecological security barrier in northwestern China, understanding how natural grassland (NG) utilization pattern transformation in the northern Qilian foothills affects soil quality and ecosystem multifunctionality supports regional ecosystem management. The study compared soil chemical and biological properties, soil quality index (SQI), and soil ecosystem multifunctionality (SMF) among four grassland utilization patterns in the northern foothills of the Qilian Mountains, Gansu Province, China. Soil samples were collected in early October 2024 following crop harvest from the following systems: traditionally grazed NG, monocropping Hordeum vulgare (barley; MHV), monocropping Avena sativa (oat; MAS), and Hippophae rhamnoides shrubland (sea buckthorn; HRS). The results showed that compared with NG, SQI was decreased by 52.69% (p = 0.000059) under MHV treatment and by 18.78% (p = 0.03) under MAS treatment, while HRS did not have a significant reduction in SQI. Under the three patterns of transformative utilization of NG, SMF followed the order of HRS (0.11) > MAS (−0.06) > MHV (−0.51). Overall, the establishment of restoration vegetation (sea buckthorn shrubland) retained SQI under different grassland utilization patterns in the study area, whereas long-term monocropping resulted in significant reductions in SQI and SMF due to compromised chemical and biological properties. Full article