Search Results (1,544)

Livestock farming is a key socioeconomic activity on Santa Cruz Island, Galápagos (Ecuador); however, its development within a fragile ecological context poses significant challenges for biodiversity conservation. This study analyzes the perceptions of livestock producers regarding the effects of livestock production on key components of biodiversity on Santa Cruz Island. It also examines the relationship between these perceptions and sociodemographic and productive variables, while identifying mitigation strategies from the perspective of local stakeholders. A structured survey was conducted with 92 producers, covering sociodemographic profiles, environmental perceptions, perceived effects on soil, water, and fauna, as well as mitigation strategies. Descriptive statistics, chi-square tests, and logistic regression were used to examine relationships between production characteristics and perceived impacts. The results indicate that producers primarily associate biodiversity decline with livestock expansion, intensification, and land-use change driven by deforestation. Producers with greater experience more frequently reported changes in native species, with significant associations between these perceptions and reported effects on water quality and soil degradation. Nevertheless, some cumulative environmental effects were inconsistently recognized, highlighting gaps in the understanding of island ecological dynamics. Regarding mitigation, 80% of respondents supported subsidies for sustainable practices, and 60% expressed willingness to receive training; in contrast, the implementation of these strategies could be limited by technical, economic, and institutional constraints. Overall, the results reflect heterogeneous perceptions of livestock-related impacts, highlighting the importance of incorporating local knowledge into the design of management strategies, while not replacing the need for direct ecological assessments. Full article

Using accurate land cover data is essential to monitor land use and cover changes and assess the effectiveness of various environmental policies. This study evaluates the accuracy of contemporary global land cover products with 10 m spatial resolution, including Google’s Dynamic World (GDW), European Space Agency’s World Cover (ESA WC) and Esri Land Cover (ELC) in mapping forested areas in Poland, aiming to test an assumption if the combination of these products may improve forest mapping accuracy compared to any individual product. Three global datasets and their combinations were assessed with the 2022 EU Land Use/Cover Area Frame Survey (LUCAS). A land cover map of Poland (S2GLC PL) for 2021 served as an auxiliary reference data set. Forest cover classification accuracy was evaluated using precision, recall, and F1-score metrics, and spatial agreement of binary forest maps in the thematic global products was measured with the Intersection over Union (IoU) at two various scale levels (country and province). Our results showed that forest mapping accuracy of three global products varies for Poland, with F1-score equal to 72.2% for ELC, 76.9% for ESA WC, and 68.8% for GDW. IoU against S2GLC PL was equal to 82.6%, 82.3% and 75.2%, for ELC, ESA WC and GDW, respectively, and slightly exceeded 70.5% for three global products. A specific combination of binary forest maps from global products, where the output forest area consisted of forests mapped at the same time by all three products and forests mapped at the same time only by GDW and ESA WC yielded better accuracy indicators than any single product and other tested combinations (F1-score equal to 80.4%, and IoU against S2GLC PL equal to 87.1%). Full article

Multi-source remote sensing is transforming landslide susceptibility assessment from static terrain-based zonation toward observation-driven spatiotemporal inference and dynamic map updating. Satellite precipitation products, interferometric synthetic aperture radar (InSAR) deformation time series, optical image sequences, land-cover products, and multi-temporal terrain observations provide complementary evidence of hydrometeorological forcing, slope kinematics, land-system regulation, and geomorphic reorganization. However, these observation streams differ substantially in spatial support, temporal resolution, physical meaning, and uncertainty structure and therefore cannot be reliably integrated as generic predictors without process-aware interpretation. This review synthesizes recent progress in remote sensing-enabled dynamic landslide susceptibility assessment by linking four key components: dynamic factor construction from Earth observation data, spatiotemporal representation and learning, susceptibility map updating, and validation under temporal and spatial independence. The reviewed literature is organized around four process roles: rainfall- and soil moisture-related forcing, kinematic state and response captured by InSAR, land-system and ecological regulation derived from optical time series, and geomorphic memory represented by multi-temporal digital elevation models (DEMs). We further examine how these signals are encoded and integrated through temporal models, graph-based representations, attention mechanisms, and hybrid frameworks, with particular emphasis on consistency among process role, data structure, mapping unit, inference target, and validation design. Current progress remains constrained by temporally coarse landslide inventories, cross-scale incompatibility among remote sensing products, uneven and insufficiently process-aware multimodal fusion, and limited physical interpretability. Future advances require event-resolved inventories, uncertainty-aware multimodal fusion, process-consistent spatiotemporal learning, and validation designs that explicitly test whether susceptibility maps can be updated in a scientifically defensible manner as new Earth observation data become available. Full article

Vegetation net primary productivity (NPP) is a key indicator of terrestrial carbon sequestration and ecological restoration effectiveness. The karst mountainous region of Southwest China is characterized by fragmented terrain and high ecological vulnerability, making quantification of NPP dynamics and drivers essential for regional management. Using MOD17A3 NPP data (2000–2020), this study applied trend analysis, Hurst exponent analysis, partial correlation analysis, residual trend analysis, and Geodetector to investigate NPP spatiotemporal patterns and driving mechanisms in Guizhou Province. Results show a significant increasing trend in NPP (3.653 gC·m⁻²·a⁻¹, p < 0.01), with 78.61% of the area exhibiting growth and a spatial pattern of higher values in the south and lower values in the north. NPP shows persistence, indicating a continued increasing tendency. Along elevation gradients, NPP exhibits a unimodal pattern, peaking at 1000–1200 m, while growth rates increase with elevation and slope, with greater variability at higher altitudes. Temperature exerts a stronger and more extensive influence on NPP than precipitation, with significant correlations over 34.35% and 10.16% of the study area, respectively (p < 0.05). Residual trend analysis indicates that non-climatic factors accounted for a larger share of NPP variation (64.49%) than climatic factors (35.51%), with ecological restoration likely the leading non-climatic driver. Geomorphological type is the primary driver of spatial heterogeneity (q = 0.220), followed by precipitation, temperature, and land use, with interaction effects mainly showing nonlinear enhancement. These findings provide insights for ecological restoration and vegetation management in karst regions. Full article

Saline–alkaline land serves as a potential arable land reserve for augmenting agricultural productivity and safeguarding food security. However, long-term monitoring of saline–alkaline land conversion remains challenging because of vegetation recovery, surface changes, hydrological modification, and agricultural phenology. Compared with CCDC and LandTrendr, the proposed MK-based framework detects conversion occurrence and timing while reducing dependence on dense observations, parameter tuning, and annual classification. This study examines the spatiotemporal dynamics of saline–alkaline land converted into paddies in Da’an City, utilizing Landsat time-series data (2007–2021) from the Google Earth Engine (GEE) platform. The analysis employed Mann–Kendall (MK) trend and mutation tests to monitor conversion processes and analyze spatiotemporal dynamics. Point-biserial correlation analysis was applied to evaluate the sensitivity of various remote sensing indices in detecting land conversion. The top fifteen indices, including the Land Surface Water Index (LSWI), Salinity Index 4 (SI4), and Salinity Index 5 (SI5), demonstrated strong correlations (|r| = 0.788–0.885) and significant pre- and post-conversion spectral differences (p < 0.01). Validation via confusion matrix confirmed that the June SI5 index attained the highest detection accuracy (overall accuracy: 94.15%; Kappa coefficient: 0.86), supporting the MK trend test’s efficacy in monitoring conversion processes. The MK mutation test achieved 80.36% temporal accuracy in determining conversion timing. The spatiotemporal analyses identified heterogeneity in saline–alkaline land conversion patterns. Spatially, large contiguous paddy fields dominated the eastern region, whereas fragmented conversion characterized the west, with minimal activity in the central zone. Temporally, the conversion area expanded rapidly before 2015 and then gradually declined, reaching a cumulative converted area of 276.29 km² by 2021. This study elucidates spatiotemporal conversion dynamics to guide sustainable land use. Full article

Gross primary productivity (GPP) is central to terrestrial carbon cycling and forest carbon sink assessment. Using Google Earth Engine, MODIS GPP, ERA5-Land meteorological data, and forest extent masks, this study examined GPP dynamics and climatic controls in China’s northeast, southern, and southwest forest regions from 2005 to 2025. GPP increased overall in all three regions, with higher values in the south and lower values in the north. Climatic drivers differed regionally: in the northeast, GPP responded positively to temperature, while VPD slightly exceeded temperature in the dominant-control area; in the southern region, temperature was the main driver but VPD remained important; in the southwest, temperature dominated larger areas, whereas moisture-related controls showed stronger spatial heterogeneity. Piecewise analysis identified temperature–VPD turning points of 11.74 °C, 10.43 °C, and 25.64 °C for the northeast, southwest, and southern regions, respectively. Two-dimensional temperature–VPD binning further revealed nonlinear GPP distributions and distinct optimal hydrothermal combinations across regions. These results show that warming effects on forest productivity are region-specific and constrained by atmospheric dryness, providing evidence for assessing China’s forest carbon sink responses to climate change. Full article

Extreme wildfire events are increasingly shaping Mediterranean fire regimes under the combined influence of climatic variability, vegetation stress, and growing anthropogenic pressure, posing critical risks to ecosystem stability and landscape resilience. Understanding the spatial determinants of wildfire susceptibility is therefore essential for advancing evidence-based fire risk assessment in fire-prone Mediterranean environments. This ongoing doctoral research investigates the environmental controls of wildfire occurrence in Muğla Province (Türkiye) through the integration of multi-source remote sensing data, geospatial analysis, and interpretable machine learning techniques. Burned-area reference data for 2021–2024 were derived from the MODIS MCD64A1 product within the Google Earth Engine environment and represented using a binary burned/non-burned classification. Predictor variables include ERA5-Land meteorological indicators, SRTM-derived topographic parameters, MODIS-based NDVI vegetation condition, and WorldPop population density as a proxy for human exposure, harmonized at a common 1 km spatial resolution. A Random Forest model was implemented to examine wildfire susceptibility patterns with emphasis on model interpretability and variable contribution rather than predictive optimization. Preliminary results indicate that vegetation condition, wind-related dynamics, and population density are dominant contributors to wildfire occurrence, reflecting coupled ecological vulnerability and human influence. Extreme fire conditions observed during 2021 are intentionally reserved for subsequent validation and stress-testing analyses. The proposed framework provides a transparent and transferable methodological basis for analyzing extreme wildfire susceptibility in Mediterranean landscapes and supports future development of interpretable, data-driven wildfire risk assessment approaches. Full article

Net primary productivity (NPP) is a key indicator of the carbon sequestration capacity of terrestrial ecosystems, and its dynamics are jointly influenced by climate change and human activities. However, quantitatively disentangling their respective contributions and clarifying their non-linear interactions remains challenging. In this study, remote sensing, meteorological, and anthropogenic data were integrated to investigate the spatiotemporal dynamics of vegetation NPP in the Yellow River Basin (YRB) from 2000 to 2024. Six scenarios were constructed to quantify the relative contributions of climate change and human activities. Furthermore, an XGBoost-SHAP framework was employed to elucidate the underlying non-linear driving mechanisms. The results indicate that vegetation NPP exhibited a significant increasing trend over the study period, with a rapid recovery phase after 2012 and a peak in 2024 (351.75 gC·m⁻²·a⁻¹), representing a 71.43% increase compared with the baseline period. Spatially, the upper reaches were primarily climate-driven (58.74%), the middle reaches showed a strong synergistic effect between climate and human factors (97.41%), while the lower reaches were dominated by human activities (73.02%). The XGBoost-SHAP analysis identifies land surface temperature (LST) as the primary moderator of carbon sequestration across river basins (mean SHAP > 12.0). The driving mechanisms exhibit a clear longitudinal shift, transitioning from a heat-dominated regime in the upper reaches to a complex interplay of precipitation and intense urbanization in the middle and lower reaches. These non-linear interactions reveal critical feedback loops between natural hydrological constraints and urban expansion pressures. These findings clarify the drivers of regional carbon sequestration, providing a scientific basis for targeted ecological management and carbon neutrality strategies in the YRB. Full article

Actual evapotranspiration (ETa) is a key component of the hydrological cycle, and its partitioning into soil evaporation (Es) and vegetation transpiration (Ec) is essential for understanding hydrological processes. Focusing on the Yiluo River Basin during 1960–2020, this study developed a hybrid framework combining the physically based Penman–Monteith–Leuning (PML) model with machine learning to dynamically parameterize the soil evaporation coefficient f. ERA5-Land reanalysis data were used to drive the model, while the Pettitt change-point test and ridge regression were applied to identify potential change points and quantify driving factors. The results show that the framework improved the agreement of Es simulations with the GLEAM-derived reference product (R² and NSE > 0.8) and reduced the difference in ETa estimates by approximately 10% within the product-constrained modeling framework. ETa exhibited a significant upward trend (0.28 mm·yr⁻¹) with a potential change point around 2004, while its components responded earlier, with Ec and Es changing in 1994 and 2002. Ec dominated ETa, accounting for about 70% of the total. Net radiation, temperature and leaf area index were primary controls, while increasing vapor pressure deficit, together with changes in relative humidity and precipitation, jointly regulated the identified shifts. These findings provide a process-based understanding of ETa dynamics and improve the representation of ETa components in hydrological modeling. Full article

Mongolian rangelands face interacting ecological and livelihood pressures, including livestock pressure, vegetation change, soil-carbon dynamics, household income variability, and inefficiencies in livestock by-product recovery. This paper examines whether observed administrative and household data, field-observed pilot-area audit evidence, satellite-derived/backcast vegetation indicators, model-reconstructed ecological trajectories, econometric associations, machine-learning diagnostics, Monte Carlo uncertainty outputs, and scenario-based carbon-finance calculations are consistent with a study-specific ecological–economic feedback framework in Mongolian pastoral rangelands. The analysis combines observed livestock and household data, satellite-derived vegetation indicators, field-anchored soil organic carbon (SOC) information, climate controls, and pilot-area by-product audit evidence in a dual-scale framework comprising nine pasture-user groups in Öndörshireet Soum, Töv Aimag, and a national soum-level panel for 2002–2024. SOC, above-ground biomass (AGB), and below-ground biomass (BGB) trajectories are treated as model-reconstructed series rather than independently observed annual field measurements. Fixed-effects panel models are used to estimate conditional associations, while machine-learning models assess predictive consistency within reconstructed data structures. Under the fitted full specification, the best-performing national-panel model reports an out-of-sample R² of 0.942 for model-reconstructed SOC; this value is interpreted as high internal predictive consistency within the reconstructed SOC panel, not as independent validation of observed annual SOC change. Because the SU/SOC ratio mechanically contains SOC, the full-specification predictive results are subject to leakage risk, and leakage-free validation is needed for a more conservative assessment of predictive performance. Panel estimates suggest that vegetation condition is positively associated with ln(household income), while the by-product waste ratio is negatively associated with ln(income), conditional on fixed effects and model specification. Scenario-based carbon-finance outputs, framed with reference to Verra’s VM0042 Improved Agricultural Land Management methodology, vary materially with compliance, carbon price, weighted average cost of capital, and revenue-sharing assumptions; these outputs are illustrative sensitivity calculations and do not demonstrate VM0042 compliance, project eligibility, project-registration readiness, verified emission reductions, or credit-issuance readiness. The findings are associational, reconstruction-dependent, and scenario-based. They support an analytical framework rather than establish a closed causal loop. Full article

Accurate estimation of daily net radiation (R_{n_daily}) at high spatial resolution (1 km) over the Tibetan Plateau (TP) is crucial for understanding land surface energy budgets and climate dynamics. This study proposes a densely connected multilayer perceptron (DenseMLP)-based transfer learning framework, with a two-stage strategy (coarse pre-training on GLASS R_{n_daily}, followed by fine-tuning on limited TP ground observations) using MODIS land surface parameters and auxiliary data to generate 1 km R_{n_daily}. When evaluated on the training set, the proposed model achieves an overall R² of 0.87, MAE of 16.06 W m⁻², RMSE of 21.94 W m⁻², and a near-zero bias of 0.07 W m⁻². On an independent test set, the model maintains robust performance with R² = 0.83, MAE = 17.43 W m⁻², RMSE = 22.55 W m⁻², and bias = −1.12 W m⁻². The method exhibits consistently low bias across individual sites (mostly within ±3.7 W m⁻²) and accurately captures seasonal variability. When applied to the entire TP for 2018, the 1 km R_{n_daily} product reveals clear aspect-related terrain effects and a distinct annual cycle. This framework effectively mitigates site-dependent errors, providing a useful reference for long-term R_n product development over the TP. Full article

Drought is one of the most complex and impactful natural hazards under global climate change, exerting profound effects on water resources, agricultural productivity, ecosystem stability, and socio-economic systems. Despite extensive research, current drought studies remain fragmented due to inconsistent definitions, index-specific monitoring approaches, and limited understanding of cross-variable and cross-scale interactions. The objective of this review is to synthesize recent advances in drought monitoring and to establish an integrated understanding of drought as a coupled, multiscale process. We revisit traditional drought typologies, including meteorological, agricultural, hydrological, groundwater drought, and socio-economic drought, and critically evaluate their commonly used monitoring indices and data sources. We highlight that no single indicator can adequately capture the full dynamics of drought evolution, emphasizing the need for multi-index integration and process-based monitoring frameworks. Moreover, we examine the mechanisms of drought propagation, demonstrating that drought evolves through nonlinear and scale-dependent pathways linking atmospheric conditions, soil moisture, hydrological processes, and human water use. In particular, the emergence of flash drought reveals a shift from conventional water-deficit-driven processes to multi-process coupled dynamics, posing new challenges for early warning and prediction. Furthermore, we discuss how climate change and human activities jointly reshape drought characteristics by altering hydrological cycles, land–atmosphere interactions, and water resource management systems. The review reveals three major findings. First, drought monitoring is progressively shifting from single-index assessments toward integrated, multi-source monitoring frameworks. Second, drought propagation is inherently nonlinear and scale-dependent, involving complex interactions among climatic, hydrological, ecological, and human systems. Third, flash drought and groundwater drought have emerged as critical research frontiers due to their rapid evolution, monitoring challenges, and increasing impacts under climate change. Finally, we identify key challenges in drought research, including methodological uncertainties, data limitations, and the lack of a unified theoretical framework. These findings support a paradigm shift from traditional drought classification toward an integrated process-based perspective and provide guidance for the development of next-generation drought monitoring and early-warning systems. Full article

Industrial land bias is a persistent outcome of China’s land allocation system, but why its efficiency penalty differs across cities remains insufficiently explained. This study examines this unevenness by linking land allocation, population density, and city type heterogeneity within a unified framework. Using panel data for 281 prefecture-level and above cities in China from 2010 to 2022, we combine two-way fixed effects estimation with robustness checks, dynamic panel analysis, transmission channel tests, subsample comparison, and interaction models. Results show that industrial land bias significantly reduces urban land economic efficiency, with the strongest penalty after a one-year lag. Population density is an important spatial transmission channel: industrial land bias lowers density mainly by expanding built-up land faster than population concentration. The penalty is the largest in border cities, smaller in coastal cities, and statistically insignificant in general cities. The negative effect weakens as the secondary industry share increases, suggesting that local production capacity helps absorb industrial land expansion. The contribution of this study is to explain why the same industrial land bias generates uneven efficiency penalties across coastal, general, and border cities, providing evidence for place-sensitive land supply policies. Full article

Soil salinization represents one of the most severe abiotic constraints on global agricultural productivity, threatening crop yields and food security across increasingly large areas of cultivated land. Among the molecular mechanisms underlying plant physiological adaptation to salinity, histone methylation has emerged as a central epigenetic regulatory layer governing salt-responsive transcriptional reprogramming through the coordinated and opposing actions of histone methyltransferases, demethylases, and reader proteins at specific chromatin loci. Recent advances reveal how dynamic changes in activating marks, principally H3K4me3 and H3K36me3, and repressive marks, H3K9me2 and H3K27me3, orchestrate the activation of stress-responsive gene networks and the silencing of growth-incompatible programs under salt stress. How these modifications establish and sustain stress memory across somatic and transgenerational timescales is discussed. Recent technological advances, including single-cell epigenomics, CUT&RUN, CUT&Tag, and spatial transcriptomics, are assessed as future research priorities. The application of CRISPR/dCas9-based epigenome editing and epigenetic breeding strategies for improving crop salt tolerance is further explored. Full article

Banana production plays a vital role in food security and livelihood in developing countries, yet scholarly attention has highlighted the growing attention on climate change, CO₂ emissions, and land dynamic impacts on agricultural production; however, empirical evidence on short- and long-term effects of climate change and CO₂ emissions on banana production in China remains limited. This study employed the autoregressive distributed lag error correction model (ARDL-ECM) framework using time-series data from China over three decades spanning 1991–2023, to investigate the long-run, short-run effects of CO₂ emissions, precipitation, temperature, and production inputs of land and labor on banana production. The empirical results indicate that the CO₂ emissions exert a significant and negative long-run effect on banana production. Precipitation exhibited a positive influence on banana production in China. Banana-harvested area presents a positive and significant impact on banana production, underscoring the importance of land management for long-run growth of banana production. Findings demonstrate that greater resilience, supported by advanced technology, a mechanized production system, and stronger institutional capacity, reduces climate impact on banana production. Study findings contribute to the empirical evidence to the climate–agriculture nexus in China and offer actionable policy for enhancing banana resilience in developing countries. Full article